From 8d4491d1e40bb3e99bcfef167c53cac6165cc554 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 25 Oct 2023 13:17:36 +0200 Subject: [PATCH 01/97] Update README.md remove allometric scaling for Kn --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index ef82fa5..390b372 100644 --- a/README.md +++ b/README.md @@ -25,15 +25,13 @@ The allometric relationships considered in the model are: $$\mu_{max}(S_i^P) = \beta_{\mu_{max}}\cdot (S_i^P)^{\alpha_{\mu_{max}}}$$ -$$K_n(S_i^P) = \beta_{K_n}\cdot (S_i^P)^{\alpha_{K_n}}$$ - $$T_{opt}(S_i^P) = \beta_{T_{opt}}\cdot (S_i^P)^{\alpha_{T_{opt}}}$$ $$I_{max}(S_j^Z) = \beta_{I_{max}}\cdot (S_j^Z)^{\alpha_{I_{max}}}$$ $$P_{opt}(S_i^P, S_j^Z) = \beta_{P_{opt}}\cdot (S_j^Z)^{\alpha_{P_{opt}}}$$ -representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, half-saturation for nutrient uptake, $K_n(S_i^P)$, and thermal optima, $T_{opt}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. +representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, and thermal optima, $T_{opt}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. From 8d58332e0174555b573f8b75618b09c50e7059fb Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 27 Oct 2023 13:58:17 +0200 Subject: [PATCH 02/97] Update README.md added Shurin et al. (2012) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 390b372..aad58c8 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ ## Background Over half of the global freshwater bodies are reporting threats of anthropogenic eutrophication (Ho et al. 2019). Anthropogenic eutrophication has become a major environmental problem since the mid-20th century. It is characterized by excessive input of nutrient in lakes that facilitates an overgrowth of algae in the aquatic ecosystem (Carpenter 2005). Such overabundance of phytoplantkon population can cause multiple harmful effects to the ecosystem like degradation in water quality, harmful algae blooms, and trophic disorganization such as mass fish kills due to the deprivation of oxygen by large-scale degradations of dead algae (Huisman et al. 2018). -Climate warming is another factor increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Phytoplankton is one of the groups at the forefront of rising water temperautures. Temperature change has led to alterations in the temporal dynamics of phytoplankton (i.e. phenological shifts), the community compositions and even the trophic structures. It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Striebel et al. 2016). +Climate warming is another factor increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Phytoplankton is one of the groups at the forefront of rising water temperautures. Temperature change has led to alterations in the temporal dynamics of phytoplankton (i.e. phenological shifts), the community compositions and even the trophic structures. It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Shurin et al. 2012; Striebel et al. 2016). In the current realm of global environmental change, understanding the effects of temperature and eutrophication on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like nutrient and zooplankton grazer (Pomati et al. 2020), thus contributing to various nonlinear feedbacks to the phytoplankton community and making predictions on community dynamics difficult. For example, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Reinl et al. 2021, 2023). From 92ba3040564ae70ad71dac271634561196f64adc Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 27 Oct 2023 14:02:31 +0200 Subject: [PATCH 03/97] Update README.md added Sterner et al. (2020) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index aad58c8..9e81f83 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Over half of the global freshwater bodies are reporting threats of anthropogenic Climate warming is another factor increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Phytoplankton is one of the groups at the forefront of rising water temperautures. Temperature change has led to alterations in the temporal dynamics of phytoplankton (i.e. phenological shifts), the community compositions and even the trophic structures. It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Shurin et al. 2012; Striebel et al. 2016). -In the current realm of global environmental change, understanding the effects of temperature and eutrophication on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like nutrient and zooplankton grazer (Pomati et al. 2020), thus contributing to various nonlinear feedbacks to the phytoplankton community and making predictions on community dynamics difficult. For example, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Reinl et al. 2021, 2023). +In the current realm of global environmental change, understanding the effects of temperature and eutrophication on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like nutrient and zooplankton grazer (Pomati et al. 2020), thus contributing to various nonlinear feedbacks to the phytoplankton community and making predictions on community dynamics difficult. For example, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Sterner et al. 2020; Reinl et al. 2021, 2023). Trait-based models have been applied to investigate the effects of environmental conditions on phytoplankton population dynamics (Litchman 2023). Phytoplantkon cell size is a master trait and has been used widely in modelling works (Litchman and Klausmeier 2008). Size-based models allow explorations on aggregated community properties such as total biomass, community mean cell size, and size variances (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions (Merico et al. 2009). From 2fc8a68e84e28a410ff0aa3427f68414c6c4c1e8 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 12:49:12 +0100 Subject: [PATCH 04/97] Update README.md Added description for temperature dependence --- README.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 9e81f83..f2cff7c 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Trait-based models have been applied to investigate the effects of environmental ## Model description -The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990) incorporated to a size-based framework (e.g. Moloney and Field 1991; Armstrong 1994; Stock et al. 2008). The model (differential equation-based) includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different sizes ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes. +The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990) incorporated to a size-based framework (e.g. Moloney and Field 1991; Armstrong 1994; Stock et al. 2008). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes. Figure1_v2 @@ -18,20 +18,28 @@ The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankto The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing (Hansen et al. 1994, 1997; Edwards et al. 2012). The model aims at studying changes in the size compositions of lake phytoplankton communities. +## Temperature dependence in the model +The temperature dependence for phytoplankton growth follows a bell-shaped thermal tolerance curve, given by, + +$$E(T) = e^{0.063T} \left[1- \left(\frac{T-T_{opt}}{\sigma_T}\right)^2 \right] $$ + +, where $T$ is the ambient lake water surface temperature (LWST), $T_{opt}$ is the thermal optima that determines the median of the curve, and $\sigma_T$ is the thermal tolerance that determines the width of the curve. In this study, we assume a community mean thermal tolerance curve to all phytoplankton size classes. + +Zooplankton grazing follows a Q10 dependence such that, zooplankton graze stronger in higher temperature. + + ## Allometric relationships in the model -Based on multiple allometric equations, the model produce an ecological trade-off that favour a certain phytoplantkon size classes at different environmental conditions. +The model comprises of three allometric equations. These allometries allow an ecological trade-off to arise in the model based on water temperature throughout the year. The small phytoplantkon can grow faster than the large phytoplankton, but are subject to stronger grazing from the smaller zooplankton, who will selectively graze on the small cells. The allometric relationships considered in the model are: $$\mu_{max}(S_i^P) = \beta_{\mu_{max}}\cdot (S_i^P)^{\alpha_{\mu_{max}}}$$ -$$T_{opt}(S_i^P) = \beta_{T_{opt}}\cdot (S_i^P)^{\alpha_{T_{opt}}}$$ - $$I_{max}(S_j^Z) = \beta_{I_{max}}\cdot (S_j^Z)^{\alpha_{I_{max}}}$$ $$P_{opt}(S_i^P, S_j^Z) = \beta_{P_{opt}}\cdot (S_j^Z)^{\alpha_{P_{opt}}}$$ -representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, and thermal optima, $T_{opt}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. +representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. 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-2.123253264033476917e+00 -2.046230829426653131e+00 -1.968329916278390268e+00 -1.889550524588688551e+00 -1.809892654357548425e+00 -1.729356305584969222e+00 -1.650967614024394070e+00 -1.577752715429267649e+00 -1.509711609799588627e+00 -1.446844297135358115e+00 From 8dff5d345fc41ab7a6079b00e36c040b87791762 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 13:55:04 +0100 Subject: [PATCH 08/97] Delete LakeData/nSSI_13p_syn.csv --- LakeData/nSSI_13p_syn.csv | 13 ------------- 1 file changed, 13 deletions(-) delete mode 100644 LakeData/nSSI_13p_syn.csv diff --git a/LakeData/nSSI_13p_syn.csv b/LakeData/nSSI_13p_syn.csv deleted file mode 100644 index d09c079..0000000 --- a/LakeData/nSSI_13p_syn.csv +++ /dev/null @@ -1,13 +0,0 @@ -0,297.9206049,293.3837429,227.221172,158.8679245,97.73584906,55.09433962,24.1509434,9.433962264,228.160 -30.68584071,301.6377537,306.9307593,240.7681884,171.6981132,113.5849057,67.9245283,34.33962264,17.35849057,228.162 -61.77544248,310.6470716,329.9287351,281.1574686,210.1886792,160.754717,110.5660377,67.54716981,41.88679245,228.164 -92.46128319,304.1562808,338.9388895,318.1449387,258.1132075,223.7735849,175.8490566,120.754717,84.90566038,228.166 -123.550885,294.640079,332.0691905,336.6060525,295.4716981,282.6415094,253.2075472,191.3207547,144.5283019,228.168 -155.0442478,286.635328,311.588069,333.8943072,318.1132075,328.3018868,309.4339623,265.6603774,225.6603774,228.170 -185.7300885,285.059471,298.670057,323.2447262,322.6415094,350.1886792,327.5471698,310.5660377,298.8679245,228.172 -216.8196903,294.068789,303.142513,320.1557455,313.9622642,336.9811321,310.9433962,286.7924528,273.9622642,228.170 -247.909292,306.1026816,306.1026816,311.7737591,289.0566038,297.7358491,256.6037736,215.0943396,188.3018868,228.168 -279.4026549,310.952373,308.683942,292.4268531,256.9811321,235.0943396,183.7735849,132.4528302,103.0188679,228.166 -310.0884956,306.3519414,301.4370075,261.3613932,212.4528302,166.4150943,114.7169811,65.66037736,40,228.164 -341.1780973,299.8603142,293.0550212,236.3442461,173.9622642,115.0943396,67.54716981,29.81132075,14.33962264,228.162 -364,296.4083176,285.8223062,221.5500945,153.9622642,92.83018868,50.18867925,20.37735849,7.547169811,228.160 From 076bc3b50df16d16a09c95b0a1ef69a1b80bbc14 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 15:09:15 +0100 Subject: [PATCH 09/97] Update and rename note to README --- LakeData/README | 15 +++++++++++++++ LakeData/note | 4 ---- 2 files changed, 15 insertions(+), 4 deletions(-) create mode 100644 LakeData/README delete mode 100644 LakeData/note diff --git a/LakeData/README b/LakeData/README new file mode 100644 index 0000000..4b60612 --- /dev/null +++ b/LakeData/README @@ -0,0 +1,15 @@ +This model adapts data from a specific Swiss lake close to Zurich, Greifensee. +There are three environmental forcing to the model, respectively, Lake Water Surface Temperature (LWST), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). +These data are collected from different sources. LWST is obtained from the in-situ CTD probe (documented in Pomati et al. 2011) averaged from depths range from 0 to 8m. PAR is obtained from the nearby weather station by MeteoSwiss. Mixed layer depth is adopted from the published data for Greifensee in Pomati et al. (2020), https://zenodo.org/record/3582838#.YzQzuexBwc8. + +The original dataset contains an approximately three-year time-series from 13/3/2019 to 31/12/2022, containing 1390 data points. To generate an annual forcing profile, LWST was taken from a three-year mean, PAR was taken from a 75 percentile due to high daily variations, and MLD was derived by month to preserve the key mixing of the epilimnion in the water column. + +The steps deriving these forcing profile is shown in the python script 'TSM_TempForcing.py', 'TSM_PARForcing.py', and 'TSM_MLDForcing.py' respectively. + + + + +Reference +Pomati, F., J. Jokela, M. Simona, M. Veronesi, and B. W. Ibelings. 2011. An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring. Environ. Sci. Technol. 45: 9658–9665. doi:10.1021/es201934n + +Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 diff --git a/LakeData/note b/LakeData/note deleted file mode 100644 index d94ed02..0000000 --- a/LakeData/note +++ /dev/null @@ -1,4 +0,0 @@ -This is the supplementary lake data (https://zenodo.org/record/3582838#.YzQzuexBwc8) from Pomati et al. (2020) and is used for comparison in this theoretical temperature size-based model. - -There are in total 8 lakes: Walensee (WA); Upper Lake Zurich (UZ); Lake Lucerne (LU); Lower Lake Zurich (LZ); Sempachersee (SE); Hallwilersee (HA); Baldeggersee (BA); Greifensee (GR). -Further information on data format please refer to the readme.txt From 56dbe089e23422d6fdc7d99690e18dc1de15d1c3 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 15:11:03 +0100 Subject: [PATCH 10/97] Delete LakeData/PO4_final.csv --- LakeData/PO4_final.csv | 12 ------------ 1 file changed, 12 deletions(-) delete mode 100644 LakeData/PO4_final.csv diff --git a/LakeData/PO4_final.csv b/LakeData/PO4_final.csv deleted file mode 100644 index e0ea1b8..0000000 --- a/LakeData/PO4_final.csv +++ /dev/null @@ -1,12 +0,0 @@ -0.000000000000000000e+00,4.700000000000000000e+01 -4.700000000000000000e+01,3.091626053692527876e+01 -7.500000000000000000e+01,1.562859146644477804e+01 -1.060000000000000000e+02,4.189183767541666548e+00 -1.360000000000000000e+02,2.028841349777585457e+00 -1.670000000000000000e+02,3.003733603664022311e+00 -1.970000000000000000e+02,2.391293779641272099e+00 -2.280000000000000000e+02,2.446676674405127816e+00 -2.590000000000000000e+02,2.053143339012125423e+00 -2.890000000000000000e+02,2.100300062691874636e+00 -3.200000000000000000e+02,6.049784503957676307e+00 -3.640000000000000000e+02,4.500000000000000000e+01 From f1a1eadfb8e925c1b918c3ff97b07056ae056349 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:13:23 +0100 Subject: [PATCH 11/97] Update and rename README to README.md --- LakeData/{README => README.md} | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename LakeData/{README => README.md} (93%) diff --git a/LakeData/README b/LakeData/README.md similarity index 93% rename from LakeData/README rename to LakeData/README.md index 4b60612..619f552 100644 --- a/LakeData/README +++ b/LakeData/README.md @@ -1,5 +1,5 @@ This model adapts data from a specific Swiss lake close to Zurich, Greifensee. -There are three environmental forcing to the model, respectively, Lake Water Surface Temperature (LWST), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). +There are three environmental forcing to the model, respectively, Water Temperature from photic zone (WT), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). These data are collected from different sources. LWST is obtained from the in-situ CTD probe (documented in Pomati et al. 2011) averaged from depths range from 0 to 8m. PAR is obtained from the nearby weather station by MeteoSwiss. Mixed layer depth is adopted from the published data for Greifensee in Pomati et al. (2020), https://zenodo.org/record/3582838#.YzQzuexBwc8. The original dataset contains an approximately three-year time-series from 13/3/2019 to 31/12/2022, containing 1390 data points. To generate an annual forcing profile, LWST was taken from a three-year mean, PAR was taken from a 75 percentile due to high daily variations, and MLD was derived by month to preserve the key mixing of the epilimnion in the water column. @@ -8,8 +8,8 @@ The steps deriving these forcing profile is shown in the python script 'TSM_Temp + Reference Pomati, F., J. Jokela, M. Simona, M. Veronesi, and B. W. Ibelings. 2011. An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring. Environ. Sci. Technol. 45: 9658–9665. doi:10.1021/es201934n - Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 From 6f80017bf3d7a9e40e2dced34d3100a3aef01d23 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:17:43 +0100 Subject: [PATCH 12/97] Update README.md Added forcing plots --- LakeData/README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/LakeData/README.md b/LakeData/README.md index 619f552..7598d46 100644 --- a/LakeData/README.md +++ b/LakeData/README.md @@ -6,10 +6,13 @@ The original dataset contains an approximately three-year time-series from 13/3/ The steps deriving these forcing profile is shown in the python script 'TSM_TempForcing.py', 'TSM_PARForcing.py', and 'TSM_MLDForcing.py' respectively. +![Temp_final](https://github.com/Debbcwing/TempSizeMod/assets/51200142/143b28e3-91aa-4873-8128-206084cb0d92) +![PAR_final](https://github.com/Debbcwing/TempSizeMod/assets/51200142/c73307e7-6112-4a46-98ff-70498ce8ea82) +![MLD_final](https://github.com/Debbcwing/TempSizeMod/assets/51200142/320f93d5-26a6-42d7-a63a-247817c84df5) Reference -Pomati, F., J. Jokela, M. Simona, M. Veronesi, and B. W. Ibelings. 2011. An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring. Environ. Sci. Technol. 45: 9658–9665. doi:10.1021/es201934n -Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 ++ Pomati, F., J. Jokela, M. Simona, M. Veronesi, and B. W. Ibelings. 2011. An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring. Environ. Sci. Technol. 45: 9658–9665. doi:10.1021/es201934n ++ Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 From a84db93e9ca91eb92da23f4f88055f3326fbff0f Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:18:44 +0100 Subject: [PATCH 13/97] Add forcing python scripts --- LakeData/TSM_MLDForcing.py | 82 +++++++++++++++++++++++++++++++ LakeData/TSM_PARForcing.py | 97 +++++++++++++++++++++++++++++++++++++ LakeData/TSM_TempForcing.py | 85 ++++++++++++++++++++++++++++++++ 3 files changed, 264 insertions(+) create mode 100644 LakeData/TSM_MLDForcing.py create mode 100644 LakeData/TSM_PARForcing.py create mode 100644 LakeData/TSM_TempForcing.py diff --git a/LakeData/TSM_MLDForcing.py b/LakeData/TSM_MLDForcing.py new file mode 100644 index 0000000..ef6fe8f --- /dev/null +++ b/LakeData/TSM_MLDForcing.py @@ -0,0 +1,82 @@ +""" +Generate MLD forcing from observation data of Greifensee +""" +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +wd = 'your wd' + +data_mld = pd.read_csv(wd+'/Original_MLD.csv') + +# convert real data to julian day (0-365) +import datetime + +def datestdtojd(stddate): + """ + source: https://rafatieppo.github.io/post/2018_12_01_juliandate/ + :param stddate: standard date for input + :return: + """ + fmt = '%Y-%m-%d' + sdtdate = datetime.datetime.strptime(stddate, fmt) + sdtdate = sdtdate.timetuple() + jdate = sdtdate.tm_yday + return jdate + +jday = np.array([]) +for i in range(len(data_mld)): + jday = np.append(jday, datestdtojd(data_mld['date'][i])) +data_mld.insert(1, 'Julian day', pd.DataFrame(jday)) # phychem data + +#--------------------------------------------------------------------- +""" +Mixed Layer Depth (MLD) +1. taking the mean for each julian day +2. taking the mean for each month +3. interpolate a function based on 12-month data points +""" +mld_GR = data_mld.groupby('Julian day', as_index=False)['thermocl'].median().to_numpy() + +condlist = [0., 32., 70., 90., 121., 152., 182., 213., 244., 274., 305., 330., 345, 365] + +# there are noise in MLD data, so we did some manipulation from visual inspection +extrct_GR_mld = np.zeros((len(condlist), 2)) +extrct_GR_mld[:, 0] = condlist +extrct_GR_mld[0:2, 1] = np.repeat(30, 2) +extrct_GR_mld[2:, 1] = np.array([12, 3, 4, 5, 6, 7, 8, 9, 12, 18, 30, 30]) + + +## Quick plot +fig, axs = plt.subplots(1, 1, figsize=(7, 4), sharex='all') +fig.subplots_adjust(wspace=0.3, hspace=0.12, left=0.1, right=0.96, top=0.9, bottom=0.2) +axs.scatter(mld_GR[:, 0], mld_GR[:, 1], s=15, color='gray') +axs.scatter(extrct_GR_mld[:, 0], extrct_GR_mld[:, 1], s=25, color='r') +axs.axis([0, 365, 35, -1]) +axs.set_xlabel('day', labelpad=10) +axs.set_ylabel('MLD', labelpad=10) + +from scipy.interpolate import PchipInterpolator +MLD_pchip_GR = PchipInterpolator(extrct_GR_mld[:, 0], extrct_GR_mld[:, 1], axis=0) +MLD_pchip_GR_spl = MLD_pchip_GR(np.arange(365)) + +# Detailed plot +fig, axs = plt.subplots(figsize=(8, 4)) +fig.subplots_adjust(wspace=0.2, hspace=0.1, left=0.1, right=0.95, top=0.95, bottom=0.15) + +axs.plot(np.arange(365), MLD_pchip_GR_spl, color='r', lw=2.5, zorder=2, label='forcing') +axs.scatter(mld_GR[:, 0], mld_GR[:, 1], s=15, facecolor='steelblue', edgecolor='k', lw=0.25, zorder=1, label='data') +# axs.scatter(extrct_GR_mld[:, 0], extrct_GR_mld[:, 1], s=50, color='r', lw=1, edgecolor='k', zorder=3, +# label='Extracted lake median') +axs.axis([0, 365, 35, -1]) +axs.set_ylabel('Mixed layer depth, MLD (m)\nGreifen', fontsize=12, labelpad=10) +axs.set_xlabel('Month', fontsize=12, labelpad=10) +axs.legend(loc=4, fontsize=9, ncol=1, bbox_to_anchor=(0.9, 0)) +axs.set_xticks([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) +axs.set_xticklabels(['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D'], fontsize=12) + +plt.savefig(fname=wd + '/MLD_final.png', dpi=800) + +## save csv, save points and interpolate again in the run script +MLD_pt = np.vstack((extrct_GR_mld[:, 0], extrct_GR_mld[:, 1])).T +np.savetxt(wd+"/MLD_final.csv", MLD_pt, delimiter=",") diff --git a/LakeData/TSM_PARForcing.py b/LakeData/TSM_PARForcing.py new file mode 100644 index 0000000..e755593 --- /dev/null +++ b/LakeData/TSM_PARForcing.py @@ -0,0 +1,97 @@ +""" +Generate PAR (irradiance) forcing from observation data of Greifensee +irradiance is measured by the unit of W m-2 +""" +import seaborn as sns +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +wd = 'your wd' + +data = pd.read_csv(wd+'/Original_Temp+PAR.csv') +data_nSSI = data[['date', 'global_radiation']] + + +# convert real data to julian day (0-365) +import datetime + +def datestdtojd(stddate): + """ + source: https://rafatieppo.github.io/post/2018_12_01_juliandate/ + :param stddate: standard date for input + :return: + """ + fmt = '%Y-%m-%d' + sdtdate = datetime.datetime.strptime(stddate, fmt) + sdtdate = sdtdate.timetuple() + jdate = sdtdate.tm_yday + return jdate + +jday = np.array([]) +for i in range(len(data_nSSI)): + jday = np.append(jday, datestdtojd(data_nSSI['date'][i])) +data_nSSI.insert(1, 'Julian day', pd.DataFrame(jday)) # phychem data + + +#--------------------------------------------------------------------- +data['month'] = pd.DatetimeIndex(data['date']).month +dayno = np.array([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) +dayno_used = np.array([16, 47, 75, 106, 136, 167, 197, 228, 259, 289, 320, 350]) + + +""" +Irradiance +1. taking the interquartile range at 75% for each julian day +2. taking the monthly mean of the daily interquantile +3. calculating PAR +5. interpolate a function based on 12-month data points +""" +nSSI_quart = data.groupby(['Julian day'], as_index=False)['global_radiation'].quantile(0.75) + +# adding the column month from julian day +nSSI_quart.insert(1, 'year_fool', np.repeat(2000, len(nSSI_quart))) +nSSI_quart['date_fool'] = pd.to_datetime(nSSI_quart['year_fool'], format='%Y') + pd.to_timedelta(nSSI_quart['Julian day'], unit='D') - pd.Timedelta('1D') +nSSI_quart['month'] = pd.DatetimeIndex(nSSI_quart['date_fool']).month +nSSI_quart_month = nSSI_quart.groupby(['month'], as_index=False)['global_radiation'].mean().to_numpy() + + +def nSSI2PAR(netSSI): + """ + This function is to convert nSSI (W m-2) into PAR (E m-2 d-1) + Procedure: + 1. extr = extraction factor (only about 45% of the nSSI is in the 400-700nm range, Howell et al. 1983) + 2. conv = conversion factor from W m-2 to Ein m-2 d-1 (Lampert and Sommer, 2007, pp.20)** + ** further details please see PAR_UnitConversion.docx + """ + extr = 0.45 + conv = 0.432 + return netSSI * extr * conv + +#### Interpolation #### +from scipy.interpolate import make_interp_spline +PAR_pchip = make_interp_spline(dayno+15, nSSI2PAR(nSSI_quart_month[:, 1]), k=3) +PAR_pchip_spl = PAR_pchip(np.arange(365)) +################### + +fig, axs = plt.subplots(figsize=(8, 4)) +fig.subplots_adjust(wspace=0.2, hspace=0.1, left=0.1, right=0.95, top=0.95, bottom=0.15) + +axs.scatter(x=data_nSSI['Julian day'], y=nSSI2PAR(data_nSSI['global_radiation']), s=15, color='steelblue', lw=0.25, ec='k') +axs.set_ylabel('PAR (Ein m$^{-2}$ d$^{-1}$)', fontsize=12) +axs.plot(np.arange(365), PAR_pchip_spl, color='r', lw=2.5, label='forcing') +axs.set_xlim(0, 365) +axs.set_ylim(0, 80) +axs.set_xticks([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) +axs.set_xticklabels(['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']) +axs.set_xlabel('Month', fontsize=12, labelpad=10) +axs.legend(loc=1, fontsize=12) + +plt.savefig(fname=wd + '/PAR_final.png', dpi=800) + +## save csv, save points and interpolate again in the run script +PAR_pt = np.vstack((dayno+15, nSSI2PAR(nSSI_quart_month[:, 1]))).T +np.savetxt(wd+"TempSizeMod_TSM/Forcing/Final/PAR_final.csv", PAR_pt, delimiter=",") + + + diff --git a/LakeData/TSM_TempForcing.py b/LakeData/TSM_TempForcing.py new file mode 100644 index 0000000..d47f652 --- /dev/null +++ b/LakeData/TSM_TempForcing.py @@ -0,0 +1,85 @@ +""" +Generate temperature forcing from observation data of Greifensee +""" +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +wd = 'your wd' + +data = pd.read_csv(wd+'/Original_forc_data.csv') +data_temp = data[['date', 'water_temp']] + + +# convert real data to julian day (0-365) +import datetime + +def datestdtojd(stddate): + """ + source: https://rafatieppo.github.io/post/2018_12_01_juliandate/ + :param stddate: standard date for input + :return: + """ + fmt = '%Y-%m-%d' + sdtdate = datetime.datetime.strptime(stddate, fmt) + sdtdate = sdtdate.timetuple() + jdate = sdtdate.tm_yday + return jdate + +jday = np.array([]) +for i in range(len(data_temp)): + jday = np.append(jday, datestdtojd(data_temp['date'][i])) +data_temp.insert(1, 'Julian day', pd.DataFrame(jday)) + +#--------------------------------------------------------------------- +data['month'] = pd.DatetimeIndex(data['date']).month +dayno = np.array([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) +dayno_used = np.array([16, 47, 75, 106, 136, 167, 197, 228, 259, 289, 320, 350]) + +""" +Temperature +1. taking the mean from each julian day +2. taking the mean from each month +3. interpolate a function based on 12-month data points +""" + +temp = data_temp.groupby(['Julian day'], as_index=False)['water_temp'].mean() + +# adding the column month from julian day +temp.insert(1, 'year_fool', np.repeat(2000, len(temp))) +temp['date_fool'] = pd.to_datetime(temp['year_fool'], format='%Y') + pd.to_timedelta(temp['Julian day'], unit='D') - pd.Timedelta('1D') +temp['month'] = pd.DatetimeIndex(temp['date_fool']).month + +temp_mean = temp.groupby(['month'], as_index=False)['water_temp'].mean().to_numpy() + +#### Interpolation #### +from scipy.interpolate import make_interp_spline +ext_time = np.append(np.append(dayno+15, 365+dayno+15), 365*2+dayno+15) +Temp_pchip = make_interp_spline(ext_time, np.tile(temp_mean[:, 1], 3), k=3) +Temp_pchip_spl = Temp_pchip(np.arange(365*3)) +Temp_pchip_spl = Temp_pchip_spl[365:730] # we select the middle year for a smoother forcing + +temp_val = [] +for i in range(len(dayno_used)): + temp_val = np.append(temp_val, Temp_pchip_spl[dayno_used[i]]) + +################### +fig, axs = plt.subplots(figsize=(8, 4)) +fig.subplots_adjust(wspace=0.2, hspace=0.1, left=0.1, right=0.95, top=0.95, bottom=0.15) + +axs.scatter(x=data_temp['Julian day'], y=data_temp['water_temp'], s=15, color='steelblue', lw=0.25, ec='k', label='data') +axs.plot(np.arange(365), Temp_pchip_spl, color='r', lw=2.5, label='forcing') +# axs.plot(dayno+15, temp_mean[:, 1], color='r', lw=2) +axs.set_ylabel('Photic zone water temperature', fontsize=12) +axs.set_xlim(0, 365) +axs.set_ylim(0, 25) +axs.set_xticks([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) +axs.set_xticklabels(['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']) +axs.set_xlabel('Month', fontsize=12, labelpad=10) +axs.legend(loc=2) + +plt.savefig(fname=wd + '/Temp_final.png', dpi=800) + +## save csv, save points and interpolate again in the run script +Temp_pt = np.vstack((dayno_used, temp_val)).T +np.savetxt(wd+"/Temp_final.csv", Temp_pt, delimiter=",") From 69842a63eeda2ba27aef97c9c819e86feb1df3d3 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:20:07 +0100 Subject: [PATCH 14/97] Update README.md Organize references --- README.md | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index f2cff7c..aab91a6 100644 --- a/README.md +++ b/README.md @@ -45,27 +45,27 @@ representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, for phytopl Reference: -Acevedo-Trejos, E., E. Marañón, and A. Merico. 2018. Phytoplankton size diversity and ecosystem function relationships across oceanic regions. Proc. R. Soc. B. 285: 20180621. doi:10.1098/rspb.2018.0621 -Armstrong, R. A. 1994. Grazing limitation and nutrient limitation in marine ecosystems: Steady state solutions of an ecosystem model with multiple food chains. Limnol. Oceanogr. 39: 597–608. doi:10.4319/lo.1994.39.3.0597 -Carpenter, S. R. 2005. Eutrophication of aquatic ecosystems: Bistability and soil phosphorus. Proc. Natl. Acad. Sci. U.S.A. 102: 10002–10005. doi:10.1073/pnas.0503959102 ++ Acevedo-Trejos, E., E. Marañón, and A. Merico. 2018. Phytoplankton size diversity and ecosystem function relationships across oceanic regions. Proc. R. Soc. B. 285: 20180621. doi:10.1098/rspb.2018.0621 ++ Armstrong, R. A. 1994. Grazing limitation and nutrient limitation in marine ecosystems: Steady state solutions of an ecosystem model with multiple food chains. Limnol. Oceanogr. 39: 597–608. doi:10.4319/lo.1994.39.3.0597 ++ Carpenter, S. R. 2005. Eutrophication of aquatic ecosystems: Bistability and soil phosphorus. Proc. Natl. Acad. Sci. U.S.A. 102: 10002–10005. doi:10.1073/pnas.0503959102 Dudgeon, D. 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology 29: R960–R967. doi:10.1016/j.cub.2019.08.002 -Edwards, K. F., M. K. Thomas, C. A. Klausmeier, and E. Litchman. 2012. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57: 554–566. doi:10.4319/lo.2012.57.2.0554 -Fasham, M. J. R., H. W. Ducklow, and S. M. McKelvie. 1990. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. j mar res 48: 591–639. doi:10.1357/002224090784984678 -Hansen, B., P. K. Bjørnsen, and P. J. Hansen. 1994. The size ratio between planktonic predators and their prey. Limnology and Oceanography 39: 395–403. doi:10.4319/lo.1994.39.2.0395 -Hansen, P. J., P. K. Bjørnsen, and B. Hansen. 1997. Zooplankton grazing and growth: Scaling within the 2-2,000um body size range. Limnology and Oceanography 42: 687–704. -Ho, J. C., A. M. Michalak, and N. Pahlevan. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574: 667–670. doi:10.1038/s41586-019-1648-7 -Huisman, J., G. A. Codd, H. W. Paerl, B. W. Ibelings, J. M. H. Verspagen, and P. M. Visser. 2018. Cyanobacterial blooms. Nature Reviews Microbiology 16: 471–483. doi:10.1038/s41579-018-0040-1 -Litchman, E. 2023. Understanding and predicting harmful algal blooms in a changing climate: A trait‐based framework. Limnol Oceanogr Letters 8: 229–246. doi:10.1002/lol2.10294 -Litchman, E., and C. A. Klausmeier. 2008. Trait-Based Community Ecology of Phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39: 615–639. doi:10.1146/annurev.ecolsys.39.110707.173549 -Merico, A., J. Bruggeman, and K. Wirtz. 2009. A trait-based approach for downscaling complexity in plankton ecosystem models. Ecological Modelling 220: 3001–3010. doi:10.1016/j.ecolmodel.2009.05.005 -Moloney, C. L., and J. G. Field. 1991. The size-based dynamics of plankton food webs. I. A simulation model of carbon and nitrogen flows. J Plankton Res 13: 1003–1038. doi:10.1093/plankt/13.5.1003 -Paerl, H. W., W. S. Gardner, K. E. Havens, A. R. Joyner, M. J. McCarthy, S. E. Newell, B. Qin, and J. T. Scott. 2016. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae 54: 213–222. doi:10.1016/j.hal.2015.09.009 -Petchey, O. L., P. T. McPhearson, T. M. Casey, and P. J. Morin. 1999. Environmental warming alters food-web structure and ecosystem function. Nature 402: 69–72. doi:10.1038/47023 -Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 -Reid, A. J., A. K. Carlson, I. F. Creed, and others. 2019. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews 94: 849–873. doi:10.1111/brv.12480 -Reinl, K. L., J. D. Brookes, C. C. Carey, and others. 2021. Cyanobacterial blooms in oligotrophic lakes: Shifting the high‐nutrient paradigm. Freshwater Biology 66: 1846–1859. doi:10.1111/fwb.13791 -Reinl, K. L., T. D. Harris, R. L. North, and others. 2023. Blooms also like it cold. Limnol Oceanogr Letters 8: 546–564. doi:10.1002/lol2.10316 -Stock, C. A., T. M. Powell, and S. A. Levin. 2008. Bottom–up and top–down forcing in a simple size-structured plankton dynamics model. Journal of Marine Systems 74: 134–152. doi:10.1016/j.jmarsys.2007.12.004 -Striebel, M., S. Schabhüttl, D. Hodapp, P. Hingsamer, and H. Hillebrand. 2016. Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composition. Oecologia 182: 815–827. doi:10.1007/s00442-016-3693-3 -Ward, B. A., S. Dutkiewicz, O. Jahn, and M. J. Follows. 2012. A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57: 1877–1891. doi:10.4319/lo.2012.57.6.1877 -Yvon-Durocher, G., J. M. Montoya, M. Trimmer, and G. Woodward. 2011. Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems: WARMING ALTERS COMMUNITY SIZE STRUCTURE. Global Change Biology 17: 1681–1694. doi:10.1111/j.1365-2486.2010.02321.x ++ Edwards, K. F., M. K. Thomas, C. A. Klausmeier, and E. Litchman. 2012. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57: 554–566. doi:10.4319/lo.2012.57.2.0554 ++ Fasham, M. J. R., H. W. Ducklow, and S. M. McKelvie. 1990. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. j mar res 48: 591–639. doi:10.1357/002224090784984678 ++ Hansen, B., P. K. Bjørnsen, and P. J. Hansen. 1994. The size ratio between planktonic predators and their prey. Limnology and Oceanography 39: 395–403. doi:10.4319/lo.1994.39.2.0395 ++ Hansen, P. J., P. K. Bjørnsen, and B. Hansen. 1997. Zooplankton grazing and growth: Scaling within the 2-2,000um body size range. Limnology and Oceanography 42: 687–704. ++ Ho, J. C., A. M. Michalak, and N. Pahlevan. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574: 667–670. doi:10.1038/s41586-019-1648-7 ++ Huisman, J., G. A. Codd, H. W. Paerl, B. W. Ibelings, J. M. H. Verspagen, and P. M. Visser. 2018. Cyanobacterial blooms. Nature Reviews Microbiology 16: 471–483. doi:10.1038/s41579-018-0040-1 ++ Litchman, E. 2023. Understanding and predicting harmful algal blooms in a changing climate: A trait‐based framework. Limnol Oceanogr Letters 8: 229–246. doi:10.1002/lol2.10294 ++ Litchman, E., and C. A. Klausmeier. 2008. Trait-Based Community Ecology of Phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39: 615–639. doi:10.1146/annurev.ecolsys.39.110707.173549 ++ Merico, A., J. Bruggeman, and K. Wirtz. 2009. A trait-based approach for downscaling complexity in plankton ecosystem models. Ecological Modelling 220: 3001–3010. doi:10.1016/j.ecolmodel.2009.05.005 ++ Moloney, C. L., and J. G. Field. 1991. The size-based dynamics of plankton food webs. I. A simulation model of carbon and nitrogen flows. J Plankton Res 13: 1003–1038. doi:10.1093/plankt/13.5.1003 ++ Paerl, H. W., W. S. Gardner, K. E. Havens, A. R. Joyner, M. J. McCarthy, S. E. Newell, B. Qin, and J. T. Scott. 2016. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae 54: 213–222. doi:10.1016/j.hal.2015.09.009 ++ Petchey, O. L., P. T. McPhearson, T. M. Casey, and P. J. Morin. 1999. Environmental warming alters food-web structure and ecosystem function. Nature 402: 69–72. doi:10.1038/47023 ++ Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 ++ Reid, A. J., A. K. Carlson, I. F. Creed, and others. 2019. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews 94: 849–873. doi:10.1111/brv.12480 ++ Reinl, K. L., J. D. Brookes, C. C. Carey, and others. 2021. Cyanobacterial blooms in oligotrophic lakes: Shifting the high‐nutrient paradigm. Freshwater Biology 66: 1846–1859. doi:10.1111/fwb.13791 ++ Reinl, K. L., T. D. Harris, R. L. North, and others. 2023. Blooms also like it cold. Limnol Oceanogr Letters 8: 546–564. doi:10.1002/lol2.10316 ++ Stock, C. A., T. M. Powell, and S. A. Levin. 2008. Bottom–up and top–down forcing in a simple size-structured plankton dynamics model. Journal of Marine Systems 74: 134–152. doi:10.1016/j.jmarsys.2007.12.004 ++ Striebel, M., S. Schabhüttl, D. Hodapp, P. Hingsamer, and H. Hillebrand. 2016. Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composition. Oecologia 182: 815–827. doi:10.1007/s00442-016-3693-3 ++ Ward, B. A., S. Dutkiewicz, O. Jahn, and M. J. Follows. 2012. A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57: 1877–1891. doi:10.4319/lo.2012.57.6.1877 ++ Yvon-Durocher, G., J. M. Montoya, M. Trimmer, and G. Woodward. 2011. Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems: WARMING ALTERS COMMUNITY SIZE STRUCTURE. Global Change Biology 17: 1681–1694. doi:10.1111/j.1365-2486.2010.02321.x From c2b0303fe21bf8df77a797ae593ba4f2b5c07e25 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:24:01 +0100 Subject: [PATCH 15/97] Rename MLD_final.csv to data_MLD.csv --- LakeData/{MLD_final.csv => data_MLD.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{MLD_final.csv => data_MLD.csv} (100%) diff --git a/LakeData/MLD_final.csv b/LakeData/data_MLD.csv similarity index 100% rename from LakeData/MLD_final.csv rename to LakeData/data_MLD.csv From 719a025976beda9d34c8ecab2518b5db0476f742 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:24:17 +0100 Subject: [PATCH 16/97] Rename Temp_final.csv to data_Temp.csv --- LakeData/{Temp_final.csv => data_Temp.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{Temp_final.csv => data_Temp.csv} (100%) diff --git a/LakeData/Temp_final.csv b/LakeData/data_Temp.csv similarity index 100% rename from LakeData/Temp_final.csv rename to LakeData/data_Temp.csv From 5aa8f2fda71f66ef5e219490d7f606038792e646 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:24:31 +0100 Subject: [PATCH 17/97] Rename PAR_final.csv to data_PAR.csv --- LakeData/{PAR_final.csv => data_PAR.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{PAR_final.csv => data_PAR.csv} (100%) diff --git a/LakeData/PAR_final.csv b/LakeData/data_PAR.csv similarity index 100% rename from LakeData/PAR_final.csv rename to LakeData/data_PAR.csv From 6568e3187ac2f3e79be1aae8eaf570e37c0e246e Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:25:00 +0100 Subject: [PATCH 18/97] Rename data_MLD.csv to ForcData_MLD.csv --- LakeData/{data_MLD.csv => ForcData_MLD.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{data_MLD.csv => ForcData_MLD.csv} (100%) diff --git a/LakeData/data_MLD.csv b/LakeData/ForcData_MLD.csv similarity index 100% rename from LakeData/data_MLD.csv rename to LakeData/ForcData_MLD.csv From c7d05f30616ad46aceb6ec7fe29b500bd78e4102 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:25:15 +0100 Subject: [PATCH 19/97] Rename data_PAR.csv to ForcData_PAR.csv --- LakeData/{data_PAR.csv => ForcData_PAR.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{data_PAR.csv => ForcData_PAR.csv} (100%) diff --git a/LakeData/data_PAR.csv b/LakeData/ForcData_PAR.csv similarity index 100% rename from LakeData/data_PAR.csv rename to LakeData/ForcData_PAR.csv From 27857ff55a1ec7d41c99d007491393df4d8ccd17 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:25:30 +0100 Subject: [PATCH 20/97] Rename data_Temp.csv to ForcData_Temp.csv --- LakeData/{data_Temp.csv => ForcData_Temp.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{data_Temp.csv => ForcData_Temp.csv} (100%) diff --git a/LakeData/data_Temp.csv b/LakeData/ForcData_Temp.csv similarity index 100% rename from LakeData/data_Temp.csv rename to LakeData/ForcData_Temp.csv From f0b97e6fba0f39dd2d8f86d29dc1287b148455c1 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:26:13 +0100 Subject: [PATCH 21/97] Upload raw files --- LakeData/Original_MLD.csv | 520 ++++++++++++ LakeData/Original_Temp+PAR.csv | 1391 ++++++++++++++++++++++++++++++++ 2 files changed, 1911 insertions(+) create mode 100644 LakeData/Original_MLD.csv create mode 100644 LakeData/Original_Temp+PAR.csv diff --git a/LakeData/Original_MLD.csv b/LakeData/Original_MLD.csv new file mode 100644 index 0000000..d229924 --- /dev/null +++ b/LakeData/Original_MLD.csv @@ -0,0 +1,520 @@ +,date,thermocl +0,1972-01-18,10.6760152218561 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+1388,2022-12-30,43.6954829742877,6.6562605 +1389,2022-12-31,54.9155797101449,6.668514 From a9b76f3c91e07d3cc415b2941608c5dbfa6593e5 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:26:34 +0100 Subject: [PATCH 22/97] Rename Original_MLD.csv to RawData_MLD.csv --- LakeData/{Original_MLD.csv => RawData_MLD.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{Original_MLD.csv => RawData_MLD.csv} (100%) diff --git a/LakeData/Original_MLD.csv b/LakeData/RawData_MLD.csv similarity index 100% rename from LakeData/Original_MLD.csv rename to LakeData/RawData_MLD.csv From 0ddea5c8b5fe3eff0ffb7629efbba0139cf12fc0 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 2 Feb 2024 19:26:49 +0100 Subject: [PATCH 23/97] Rename Original_Temp+PAR.csv to RawData_Temp+PAR.csv --- LakeData/{Original_Temp+PAR.csv => RawData_Temp+PAR.csv} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename LakeData/{Original_Temp+PAR.csv => RawData_Temp+PAR.csv} (100%) diff --git a/LakeData/Original_Temp+PAR.csv b/LakeData/RawData_Temp+PAR.csv similarity index 100% rename from LakeData/Original_Temp+PAR.csv rename to LakeData/RawData_Temp+PAR.csv From 217242d4d9dc9d6d689139b1f4acb9866e82b784 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 14 Feb 2024 13:15:25 +0100 Subject: [PATCH 24/97] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index aab91a6..5df602f 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,8 @@ Trait-based models have been applied to investigate the effects of environmental ## Model description The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990) incorporated to a size-based framework (e.g. Moloney and Field 1991; Armstrong 1994; Stock et al. 2008). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes. -Figure1_v2 +

+ Figure1_v3 The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing (Hansen et al. 1994, 1997; Edwards et al. 2012). The model aims at studying changes in the size compositions of lake phytoplankton communities. @@ -25,8 +26,11 @@ $$E(T) = e^{0.063T} \left[1- \left(\frac{T-T_{opt}}{\sigma_T}\right)^2 \right] , where $T$ is the ambient lake water surface temperature (LWST), $T_{opt}$ is the thermal optima that determines the median of the curve, and $\sigma_T$ is the thermal tolerance that determines the width of the curve. In this study, we assume a community mean thermal tolerance curve to all phytoplankton size classes. -Zooplankton grazing follows a Q10 dependence such that, zooplankton graze stronger in higher temperature. +The maximum ingestion rates of zooplankton follows a Q10 model such that, the maximum grazing increases with temperature. The equation for the dependence is, +$$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$ + +The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10$^{\circ}$C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). ## Allometric relationships in the model The model comprises of three allometric equations. These allometries allow an ecological trade-off to arise in the model based on water temperature throughout the year. The small phytoplantkon can grow faster than the large phytoplankton, but are subject to stronger grazing from the smaller zooplankton, who will selectively graze on the small cells. From d48f90a74a704415670904318629554ae81b7caa Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 14 Feb 2024 13:16:51 +0100 Subject: [PATCH 25/97] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5df602f..583d932 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ The maximum ingestion rates of zooplankton follows a Q10 model such that, the ma $$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$ -The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10$^{\circ}$C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). +The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10 $^{\circ}$ C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). ## Allometric relationships in the model The model comprises of three allometric equations. These allometries allow an ecological trade-off to arise in the model based on water temperature throughout the year. The small phytoplantkon can grow faster than the large phytoplankton, but are subject to stronger grazing from the smaller zooplankton, who will selectively graze on the small cells. From 963b08195bc50a6c1962a69842844a560fd5c918 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 11:49:57 +0200 Subject: [PATCH 26/97] Create README.md --- Models/FinalMod/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Models/FinalMod/README.md diff --git a/Models/FinalMod/README.md b/Models/FinalMod/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Models/FinalMod/README.md @@ -0,0 +1 @@ + From 868aefdee681e1597a6aa3d1b31641b2cc15b40b Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 12:44:07 +0200 Subject: [PATCH 27/97] Add files via upload # TSM1_v0_PO4_Q10_flux_final.py --- FinalMod.py | 294 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 294 insertions(+) create mode 100644 FinalMod.py diff --git a/FinalMod.py b/FinalMod.py new file mode 100644 index 0000000..a3c1bc2 --- /dev/null +++ b/FinalMod.py @@ -0,0 +1,294 @@ +import pdb + +import numpy as np +from scipy.integrate import odeint + + +class SizeModLogspace: + """ + Size-based model + Model has multiple groups of phytoplankton, two groups of zooplankton in different sizes, one nutrient source, + and a detritus community. + + Model assumption: + - thermal response of the phytoplankton follows a unimodal curve adapted from Norberg (2004) + - the maximum growth of phytoplankton follows an allometric relationship taken from data compilations of Edwards et al. (2012) + - phytoplankton nutrient uptake follows Michelis-Menton kinetics + - the half-saturation constant of the uptake kinetics derived from a community mean value and is not size-dependent + - phytoplankton are grazed by size-selective zooplankton, small phytoplankton are grazed more intensively + - zooplankton graze on a wide size-feeding range; small zooplankton has a narrower range than large zooplankton in absolute values + - zooplankton grazing is scaled by a Q10 factor + ------- + Flexible parameters: + - n0 + - prey tolerance + - P size range + - Z size range + - no. of P size classes + - no. of years for model running + + Fixed parameters: + - Community mean Kn + - Allometric parameters for Mu + - Allometric parameters for Topt + - Allometric parameters for Imax + - Allometric parameters for grazing size preference + - Model params + """ + + def __init__(self, mld, par, sst, dmdt, N0, minP=0, maxP=np.log10(200), numP=15, minZ=20, maxZ=600, numZ=2, numYears=5, + Zsmall0=6.25e-4, Zlarge0=6.25e-4, D0=6.25e-4, Mz2S0=0, Mz2L0=0, + Pmax=1.1, Kpar=0.1, alpha=0.02, Mz=0.1, Mp=0.1, Mz2=0.34, + Ieff=0.75, beta=0.69, Kp=0.19, Rem=0.15, k=0.1, + mu_alpha=-0.36, mu_beta=10 ** 0.69, Kn=0.06, + sig_T=12.5, topt=13, graz_Q10=2.8, #sig_T=12.5, topt=16.5 + graz_alpha=-0.28, graz_beta=23, ops_alpha=0.7, ops_beta=0.45, + SizeTol=[0.435, 0.6]): + self.mld = mld + self.par = par # photosynethically activated radiation (E*m^-2*d^-1) + self.sst = sst # sea surface temperature/lake surface water temperature (LSWT) + self.dmdt = dmdt # dmdt + self.N0 = N0 # Initial nutrient concentrations (µmol P*L^-1) + self.minP = minP # min. size of P size range + self.maxP = maxP # max. size of P size range + self.numP = numP # number of phytoplankton size class + self.minZ = minZ # min. size of Z size range + self.maxZ = maxZ # max. size of Z size range + self.numZ = numZ # number of zooplankton size class + self.numYears = numYears # Number of years for model run + self.Zsmall0 = Zsmall0 # Initial Zsmall concentration (µmol P*L^-1) + self.Zlarge0 = Zlarge0 # Initial Zlarge concentration (µmol P*L^-1) + self.D0 = D0 # Initial detritus concentration (µmol P*L^-1) + self.Ps0 = np.repeat(6.25e-4, self.numP, axis=0) # Initial P concentration (µmol P*L^-1) + self.muP0 = np.repeat(0, self.numP, axis=0) # Initial concentration for growth fluxes (µmol P*L^-1) + self._2ZS0 = np.repeat(0, self.numP, axis=0) # Initial concentration for Z1 grazing fluxes (µmol P*L^-1) + self._2ZL0 = np.repeat(0, self.numP, axis=0) # Initial concentration for Z2 grazing fluxes (µmol P*L^-1) + self.Mz2S0 = Mz2S0 + self.Mz2L0 = Mz2L0 + self.Pmax = Pmax # Max photosynthesis rate + self.Kpar = Kpar # Water attenuation coefficient + self.alpha = alpha # Initial slope of P-I curve + self.Mz = Mz # Natural mortality - Zooplankton + self.Mp = Mp # Natural mortality - Phytoplankton + self.Mz2 = Mz2 # Mortality from upper predators + self.Ieff = Ieff # Ingestion efficiency + self.beta = beta # Assimlation efficiency + self.Kp = Kp # Phytoplankton half-saturation constant (µmol P*L^-1) + self.Rem = Rem # Remineralization rate (day^-1) + self.k = k # Cross thermocline mixing factor (m/day) + self.mu_alpha = mu_alpha # Slope of allometric max. uptk rate + self.mu_beta = mu_beta # Intercept of allometric max. uptk rate + self.Kn = Kn # nutrient half-saturation constant for phytoplankton + self.sig_T = sig_T # community mean thermal tolerance breadth width as per Thomas et al. (2015) + self.topt = topt # optimal growth temperature + self.graz_alpha = graz_alpha # Slope of allometric max. grazing rate (day^-1) + self.graz_beta = graz_beta # Intercept of allometric max.grazing rate (day^-1) + self.ops_alpha = ops_alpha # Slope of allometric optimum prey size + self.ops_beta = ops_beta # Intercept of allometric optimum prey size + self.SizeTol = SizeTol # Prey size tolerance (should be an array) + self.graz_Q10 = graz_Q10 # Q10 temperature for grazing + self.init_con = np.concatenate(([self.N0[0]], # N0 + [self.Zsmall0], # Zsmall0 + [self.Zlarge0], # Zlarge0 + [self.D0], # D0 + [self.Ps0], # Ps0 + [self.muP0], + [self._2ZS0], + [self._2ZL0], + [self.Mz2S0], + [self.Mz2L0]), axis=None) + self.t_array = np.arange(0, 365 * self.numYears) # Time array + self.Psize_array = np.logspace(self.minP, self.maxP, num=self.numP, base=10) # Size array for phytoplankton + self.Zsize_array = np.linspace(self.minZ, self.maxZ, self.numZ) # Size array for zooplankton + self.solution = odeint(self.AlloNPsZD, self.init_con, self.t_array, rtol=1.e-10, atol=1.e-10) #, hmax=1, hmin=1) + + def Vol2ESD(self, v): # Vol2ESD(1) = 1.2407 + esd = ((v * 6) / np.pi) ** (1 / 3) + return esd + + def Allo_mumax(self, PhySize): + return self.mu_beta * ((PhySize / self.Vol2ESD(1)) ** self.mu_alpha) + + def NU(self, nut): + """ + Michelis-Menton uptake + :param nut: + :return: + """ + return nut / (nut + self.Kn) + + def temp_dependence(self, time): + """ + Eppley-Norberg formulation + :param time: timestep to derive temperature + :return: + """ + b_eppley = 0.0633 + thermtol = (1 - ((time - self.topt) / self.sig_T) ** 2) * np.exp(b_eppley * time) + return thermtol + + def LightLim(self, I, Z): + """ + Light limitation function on photosynthesis based on Smith's function, + illustrated by a P-I curve + """ + I0 = I + Iz = I0 * np.exp(-self.Kpar * Z) + LL = (((self.Pmax / (self.Kpar * Z)) * np.log((self.alpha * I0 + np.sqrt((self.Pmax ** 2) + + (self.alpha * I0) ** 2)) / ((self.alpha * Iz) + + np.sqrt(self.Pmax ** 2 + (self.alpha * Iz) ** 2))))) + return LL + + def Imax_Q10(self, time): + """ + Q10 method based on Sherman et al. (2016) Global Biogeochemical Cycles, 30(4) + ----------------------------------------------------------------------------------- + T0: reference temperature, adopted as 303.15K (30 degree Celcius) + Q10: the factor change in growth rate for a 10 degree change in temperature + :param time: timestep + :return: size- and temperature- dependent grazing rates + """ + ref_temp = 15 # reference temperature + # G0 = 0.57 # (marine zooplankton: Sherman et al., 2016):: we use size-dependent grazing rates + # Q10 = 1.47 # (marine zooplankton: Sherman et al., 2016) + # Q10 = 2.4 # Durbin and Durbin (1992) + # Q10 = 3.9 # Kiørboe et al. (1982) + # Q10 = 2.8 # Hansen et al. (1997) & Gosselain et al. (1996) + return self.graz_Q10 ** ((time - ref_temp) / 10) + + def AlloImax(self, ZooSize, time): + """ + Max. ingestion rate also depends on the size of the zooplankton + ---------- + graz_alpha = -0.4 # Hansen et al. 1994, Banas, 2011 + graz_beta = 26 # Hansen et al. 1994, Banas, 2011 [day^-1] + """ + imax = self.graz_beta * ((ZooSize / 1) ** self.graz_alpha) + return self.Imax_Q10(time) * imax + + def AllogpP(self, PhySize, ZooSize, SizeTol): + """ + Selected zooplankton size class of 10 & 200[µm] + """ + OPS = self.ops_beta * ((ZooSize / 1) ** self.ops_alpha) # µm ESD + gpP = np.exp(-((np.log10(PhySize) - np.log10(OPS)) / SizeTol) ** 2) + return gpP + + def AlloGraz(self, PhySize, ZooSize, Pi, SizeTol, denom, time): + """ + PhySize = Size class/array of phytoplankton population; + ZooSize = Size class/array of zooplankton population; + Pi = Phytoplankton biomass + """ + graz = self.AlloImax(ZooSize, time) * ((self.AllogpP(PhySize, ZooSize, SizeTol) * Pi) / (self.Kp + denom)) + return graz + + def mixing(self, time): + """k refers to the cross thermocline mixing factor""" + return (self.k + max(self.dmdt[time], 0.)) / self.mld[time] + + def AlloNPsZD(self, y, t): + """ + A size-based trophic model with multiple phytoplankton and zooplankton size class + ---------- + y: array of initial conditions of state variables (var) + t: time array + """ + # Initialization + N = y[0] + Zsmall = y[1] + Zlarge = y[2] + D = y[3] + Ps = y[4:4+self.numP*1] + flux_muP = y[4+self.numP*1:4+self.numP*2] + flux_2ZS = y[4+self.numP*2:4+self.numP*3] + flux_2ZL = y[4 + self.numP * 3:4 + self.numP * 4] + Mz2_ZS = y[4 + self.numP * 4 + 0] + Mz2_ZL = y[4 + self.numP * 4 + 1] + var = np.zeros(len(y)) + t = int(t) + + # non-P dependent equations + """ + Zsmall = specialist, narrower size tolerance factor; + Zlarge = generalist, wider size tolerance factor. + """ + Graz_denomZsmall = np.sum(self.AllogpP(self.Psize_array, self.Zsize_array[0], self.SizeTol[0]) * Ps) + Graz_denomZlarge = np.sum(self.AllogpP(self.Psize_array, self.Zsize_array[1], self.SizeTol[1]) * Ps) + Mz2_ZS = self.Mz2 * (Zsmall ** 2) + Mz2_ZL = self.Mz2 * (Zlarge ** 2) + + # [dNdt] + var[0] = (self.Rem * D # Remineration + + self.mixing(t) * (self.N0[t] - N)) # MixingN + + # [dZ1dt] + var[1] = (- self.Mz * Zsmall # Natural mortality Z1 + - Mz2_ZS # density-dependent top predator grazing Z1 + - (self.dmdt[t] / self.mld[t]) * Zsmall) # Mixing loss Z1 + + # [dZ2dt] + var[2] = (- self.Mz * Zlarge # Natural mortality Z2 + - Mz2_ZL # Top predator grazing Z2 + - (self.dmdt[t] / self.mld[t]) * Zlarge) # Mixing loss Z2 + + # [dDdt] + var[3] = (self.Mz * (Zsmall + Zlarge) # MortalityZsmall+Zlarge + - self.Rem * D # Remineration + - self.mixing(t) * D) # Mixing loss D + + # P-dependent (loop) + for i in range(len(Ps)): + """ + Psize = Size class/array of phytoplankton population; + """ + P = Ps[i] # define P in the loop + Psize = self.Psize_array[i] # define Psize in the loop + flux_muP = (self.Allo_mumax(Psize) * self.temp_dependence(self.sst[t])) * self.NU(N) * self.LightLim(self.par[t], self.mld[t]) * P + # flux_mu_temp = self.temp_dependence(self.sst[t]) + # flux_mu_nut = self.NU(N) + flux_2ZS = self.beta * self.Ieff * self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], Graz_denomZsmall, self.sst[t]) * Zsmall + flux_2ZL = self.beta * self.Ieff * self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], Graz_denomZlarge, self.sst[t]) * Zlarge + + # [dNdt] + var[0] = (var[0] + - flux_muP + + (self.beta * (1. - self.Ieff) * self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], Graz_denomZsmall, self.sst[t])) * Zsmall + # Excretion from grazing(Zsmall)) + + (self.beta * (1. - self.Ieff) * self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], Graz_denomZlarge, self.sst[t])) * Zlarge) + # Excretion from grazing(Zlarge)) + + # [dZ1dt] + var[1] = (var[1] + + flux_2ZS) # GrazingP from Zsmall + + # [dZ2dt] + var[2] = (var[2] + + flux_2ZL) # GrazingP from Zlarge + + # [dDdt] + var[3] = (var[3] + + ((1. - self.beta) * self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], Graz_denomZsmall, self.sst[t])) * Zsmall # SloppyfeedP - Zsmall + + ((1. - self.beta) * self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], Graz_denomZlarge, self.sst[t])) * Zlarge # SloppyfeedP - Zlarge + + self.Mp * P) + + # [dPsdt] + var[4 + i] = (var[4 + i] + + flux_muP # Uptk + - self.Mp * P # MortalityP + - self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], Graz_denomZsmall, self.sst[t]) * Zsmall # GrazingP from Zsmall + - self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], Graz_denomZlarge, self.sst[t]) * Zlarge # GrazingP from Zlarge + - self.mixing(t) * P) # Mixing + + # growth fluxes + var[4 + self.numP*1 + i] = var[4 + self.numP*1 + i] + flux_muP + + # grazing fluxes + var[4 + self.numP*2 + i] = var[4 + self.numP*2 + i] + flux_2ZS + var[4 + self.numP*3 + i] = var[4 + self.numP*3 + i] + flux_2ZL + + # predatory fluxes + var[4 + self.numP*4 + 0] = Mz2_ZS + var[4 + self.numP * 4 + 1] = Mz2_ZL + #pdb.set_trace() + return var From 270d91fc80c7e5e5f459d80783ee4373388955c1 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 13:07:53 +0200 Subject: [PATCH 28/97] Rename FinalMod.py to Models/FinalMod/FinalMod_Sbm.py --- FinalMod.py => Models/FinalMod/FinalMod_Sbm.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename FinalMod.py => Models/FinalMod/FinalMod_Sbm.py (100%) diff --git a/FinalMod.py b/Models/FinalMod/FinalMod_Sbm.py similarity index 100% rename from FinalMod.py rename to Models/FinalMod/FinalMod_Sbm.py From 20ea28695e9b0da7725746951b78e602db70157c Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 13:56:34 +0200 Subject: [PATCH 29/97] Add files via upload --- Models/FinalMod/FinalMod_proj.py | 110 +++++++++++++++++++++++++++++++ Models/FinalMod/FinalMod_run.py | 87 ++++++++++++++++++++++++ 2 files changed, 197 insertions(+) create mode 100644 Models/FinalMod/FinalMod_proj.py create mode 100644 Models/FinalMod/FinalMod_run.py diff --git a/Models/FinalMod/FinalMod_proj.py b/Models/FinalMod/FinalMod_proj.py new file mode 100644 index 0000000..7e64724 --- /dev/null +++ b/Models/FinalMod/FinalMod_proj.py @@ -0,0 +1,110 @@ +import numpy as np +import time + +import pandas as pd +import xarray as xr +from scipy.interpolate import make_interp_spline + +Pnum = 10 # specify the nos of phytoplankton size groups +Ynum = 10 # no. of modeling year +mod_name = 'Mod_proj' + +# Import forcing data +wd = 'wd' + +LWST = np.genfromtxt(wd+'Final/Temp_final.csv', delimiter=',') +PAR = np.genfromtxt(wd+'Final/PAR_final.csv', delimiter=',') +MLD = np.genfromtxt(wd+'Final/MLD_final.csv', delimiter=',') +PO4 = np.genfromtxt(wd+'Final/PO4_final.csv', delimiter=',') +PO4[:, 1] = PO4[:, 1]*0.010529 # convert from µg L-1 to µmol L-1 + +""" +Interpolation +""" +LWST_spl = make_interp_spline(LWST[:, 0], LWST[:, 1], k=3) +LWST_spl = LWST_spl(np.arange(365)) + +PAR_spl = make_interp_spline(PAR[:, 0], PAR[:, 1], k=3) +PAR_spl = PAR_spl(np.arange(365)) + +from scipy.interpolate import PchipInterpolator +MLD_pchip_spl = PchipInterpolator(MLD[:, 0], MLD[:, 1], axis=0) +MLD_pchip_spl = MLD_pchip_spl(np.arange(365)) +MLD_pchip_spl[364] = MLD_pchip_spl[0] + +PO4_spl = make_interp_spline(PO4[:, 0], PO4[:, 1], k=3) +PO4_spl = PO4_spl(np.arange(365)) + + +""" +dmdt +""" +dmdt = np.zeros((365)) +dmdt[0:364] = np.diff(MLD_pchip_spl) # dmdt (364,) +dmdt[364] = dmdt[0] + +####### +## Setting up temperature increase for different RCP scenarios +temp_incr = [0, 1.4, 2.5, 4.2] +LWST_rcp = np.zeros((365, len(temp_incr))) + +for i in range(len(temp_incr)): + LWST_rcp[:, i] = LWST_spl + temp_incr[i] + + +####### + +LWST_run = np.tile(LWST_rcp, (Ynum, 1)) # change this line for RCP scenarios +PAR_run = np.tile(PAR_spl, Ynum) +mld_run = np.tile(MLD_pchip_spl, Ynum) # multiplicating physical forcing arrays +dmdt_run = np.tile(dmdt, Ynum) +PO4_run = np.tile(PO4_spl, Ynum) +NO3_run = np.tile(NO3_spl, Ynum) + +N0 = np.repeat(0.65, Ynum*365) + +sol = np.zeros((Ynum*365, Pnum + 4, len(temp_incr))) # time; no. var; RCP scenarios + + +from TSM1_v0_PO4_Q10_final import SizeModLogspace + +# Run solution +starttime = time.ctime() # save it for documentation purposes +print(mod_name, str(Pnum), "start:", starttime) +start = time.time() # start the timer + +for i in range(len(temp_incr)): + sol[:, :, i] = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run[:, i], dmdt=dmdt_run, N0=N0, numP=Pnum, + numYears=Ynum, minZ=15, maxZ=200).solution + +# Complete +end = time.time() # stop the timer +endtime = time.ctime() # save it for documentation purposes +print(mod_name, str(Pnum), "complete:", endtime) +runtime = (end - start) / 3600 # calculate run time (hour) +print(mod_name, str(Pnum), "run time(hour):", runtime) +print(mod_name, str(Pnum), "run time(day):", runtime/24) + + +# Storing output +out = xr.Dataset( + {'Nut': (['time', 'RCP'], sol[:, 0, :]), + 'Zoo': (['time', 'ZooSize', 'RCP'], sol[:, 1:3, :]), + 'Phy': (['time', 'PhySize', 'RCP'], sol[:, 4:4+Pnum*1, :])}, + coords={'time': np.arange(Ynum*365), + 'ZooSize': [20, 400], + 'PhySize': np.logspace(0, np.log10(150), Pnum), + 'RCP': [0, 1.4, 2.5, 4.2]}, + attrs={'Title': 'This file contains sol output with different nutrient and mixing regimes', + 'No. phytoplankton size group': Pnum, + 'LWST units': 'Degree celcius', + 'PAR units': 'Ein m$^{-2}$ d$^{-1}$', + 'MLD units': 'Meters', + 'Variable units': 'µmol P L$^{-1}$', + 'Size units': 'µm ESD', + 'Phyto size range': '1 - 150', + 'Zoo size range': '20 - 400'}) +out.to_netcdf(wd + 'output_proj.nc') # export + + + diff --git a/Models/FinalMod/FinalMod_run.py b/Models/FinalMod/FinalMod_run.py new file mode 100644 index 0000000..498c021 --- /dev/null +++ b/Models/FinalMod/FinalMod_run.py @@ -0,0 +1,87 @@ +import numpy as np +import time + +import pandas as pd +import xarray as xr +from scipy.interpolate import make_interp_spline + +Pnum = 10 # specify the nos of phytoplankton size groups +Ynum = 10 # no. of modelling year +mod_name = 'Mod' + +# Import forcing data +wd = 'wd' + +LWST = np.genfromtxt(wd+'/ForcData_Temp.csv', delimiter=',') +PAR = np.genfromtxt(wd+'/ForcData_PAR.csv', delimiter=',') +MLD = np.genfromtxt(wd+'/ForcData_MLD.csv', delimiter=',') + +""" +Interpolation +""" +LWST_spl = make_interp_spline(LWST[:, 0], LWST[:, 1], k=3) +LWST_spl = LWST_spl(np.arange(365)) + +PAR_spl = make_interp_spline(PAR[:, 0], PAR[:, 1], k=3) +PAR_spl = PAR_spl(np.arange(365)) + +from scipy.interpolate import PchipInterpolator +MLD_pchip_spl = PchipInterpolator(MLD[:, 0], MLD[:, 1], axis=0) +MLD_pchip_spl = MLD_pchip_spl(np.arange(365)) +MLD_pchip_spl[364] = MLD_pchip_spl[0] + + +""" +dmdt +""" +dmdt = np.zeros((365)) +dmdt[0:364] = np.diff(MLD_pchip_spl) # dmdt (364,) +dmdt[364] = dmdt[0] + + +LWST_run = np.tile(LWST_spl, Ynum) # change this line for RCP scenarios +PAR_run = np.tile(PAR_spl, Ynum) +mld_run = np.tile(MLD_pchip_spl, Ynum) # multiplicating physical forcing arrays +dmdt_run = np.tile(dmdt, Ynum) + +N0 = np.repeat(0.75, Ynum*365) + +from FinalMod_Sbm import SizeModLogspace + +# Run solution +starttime = time.ctime() # save it for documentation purposes +print(mod_name, str(Pnum), "start:", starttime) +start = time.time() # start the timer + +sol = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, numYears=Ynum).solution #, graz_beta=23, + +# Complete +end = time.time() # stop the timer +endtime = time.ctime() # save it for documentation purposes +print(mod_name, str(Pnum), "complete:", endtime) +runtime = (end - start) / 3600 # calculate run time (hour) +print(mod_name, str(Pnum), "run time(hour):", runtime) +print(mod_name, str(Pnum), "run time(day):", runtime/24) + + +# Storing output +out = xr.Dataset( + {'Nut': (['time'], sol[:, 0]), + 'Zoo': (['time', 'ZooSize'], sol[:, 1:3]), + 'Phy': (['time', 'PhySize'], sol[:, 4+Pnum*0:4+Pnum*1]), + 'mu': (['time', 'PhySize'], sol[:, 4+Pnum*1:4+Pnum*2]), + 'grazS': (['time', 'PhySize'], sol[:, 4+Pnum*2:4+Pnum*3]), + 'grazL': (['time', 'PhySize'], sol[:, 4 + Pnum * 3:4 + Pnum * 4])}, + coords={'time': np.arange(Ynum*365), + 'ZooSize': [20, 600], + 'PhySize': np.logspace(0, np.log10(200), Pnum)}, + attrs={'Title': 'This file contains sol output with different nutrient and mixing regimes', + 'No. phytoplankton size group': Pnum, + 'LWST units': 'Degree celcius', + 'PAR units': 'Ein m$^{-2}$ d$^{-1}$', + 'MLD units': 'Meters', + 'Variable units': 'µmol P L$^{-1}$', + 'Size units': 'µm ESD', + 'Phyto size range': '1 - 200', + 'Zoo size range': '20 - 600'}) +out.to_netcdf(wd + 'output.nc') # export From ad29ea703479f39b6b3818baf79d7733e305468e Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 19:59:48 +0200 Subject: [PATCH 30/97] Create Readme.md --- Results/Readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Results/Readme.md diff --git a/Results/Readme.md b/Results/Readme.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Results/Readme.md @@ -0,0 +1 @@ + From d18dfa1c8a4a07ba4b6dbd10c14e5d717a2cdc62 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 20:00:56 +0200 Subject: [PATCH 31/97] Create Readme.md --- Sensit_Test/Readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Sensit_Test/Readme.md diff --git a/Sensit_Test/Readme.md b/Sensit_Test/Readme.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Sensit_Test/Readme.md @@ -0,0 +1 @@ + From e9716ee025bf2458739ee3bb2933a1692076857a Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 20:03:23 +0200 Subject: [PATCH 32/97] Delete Sensit_Test directory --- Sensit_Test/Readme.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Sensit_Test/Readme.md diff --git a/Sensit_Test/Readme.md b/Sensit_Test/Readme.md deleted file mode 100644 index 8b13789..0000000 --- a/Sensit_Test/Readme.md +++ /dev/null @@ -1 +0,0 @@ - From 0ec7f524ab50ca0c0fde7232dd723decd1c46f02 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 20:03:59 +0200 Subject: [PATCH 33/97] Create Readme.md --- Results/Sensit_Test/Readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Results/Sensit_Test/Readme.md diff --git a/Results/Sensit_Test/Readme.md b/Results/Sensit_Test/Readme.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Results/Sensit_Test/Readme.md @@ -0,0 +1 @@ + From 2e5091868fd752309eb7d391a55be1f64755737b Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 23 May 2024 20:04:12 +0200 Subject: [PATCH 34/97] Create Readme.md --- Results/Projection/Readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 Results/Projection/Readme.md diff --git a/Results/Projection/Readme.md b/Results/Projection/Readme.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Results/Projection/Readme.md @@ -0,0 +1 @@ + From 88676f56dcee55ab4fd6f57ac33014b724171baf Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 29 May 2024 19:08:15 +0200 Subject: [PATCH 35/97] Delete Models/FinalMod/FinalMod_proj.py --- Models/FinalMod/FinalMod_proj.py | 110 ------------------------------- 1 file changed, 110 deletions(-) delete mode 100644 Models/FinalMod/FinalMod_proj.py diff --git a/Models/FinalMod/FinalMod_proj.py b/Models/FinalMod/FinalMod_proj.py deleted file mode 100644 index 7e64724..0000000 --- a/Models/FinalMod/FinalMod_proj.py +++ /dev/null @@ -1,110 +0,0 @@ -import numpy as np -import time - -import pandas as pd -import xarray as xr -from scipy.interpolate import make_interp_spline - -Pnum = 10 # specify the nos of phytoplankton size groups -Ynum = 10 # no. of modeling year -mod_name = 'Mod_proj' - -# Import forcing data -wd = 'wd' - -LWST = np.genfromtxt(wd+'Final/Temp_final.csv', delimiter=',') -PAR = np.genfromtxt(wd+'Final/PAR_final.csv', delimiter=',') -MLD = np.genfromtxt(wd+'Final/MLD_final.csv', delimiter=',') -PO4 = np.genfromtxt(wd+'Final/PO4_final.csv', delimiter=',') -PO4[:, 1] = PO4[:, 1]*0.010529 # convert from µg L-1 to µmol L-1 - -""" -Interpolation -""" -LWST_spl = make_interp_spline(LWST[:, 0], LWST[:, 1], k=3) -LWST_spl = LWST_spl(np.arange(365)) - -PAR_spl = make_interp_spline(PAR[:, 0], PAR[:, 1], k=3) -PAR_spl = PAR_spl(np.arange(365)) - -from scipy.interpolate import PchipInterpolator -MLD_pchip_spl = PchipInterpolator(MLD[:, 0], MLD[:, 1], axis=0) -MLD_pchip_spl = MLD_pchip_spl(np.arange(365)) -MLD_pchip_spl[364] = MLD_pchip_spl[0] - -PO4_spl = make_interp_spline(PO4[:, 0], PO4[:, 1], k=3) -PO4_spl = PO4_spl(np.arange(365)) - - -""" -dmdt -""" -dmdt = np.zeros((365)) -dmdt[0:364] = np.diff(MLD_pchip_spl) # dmdt (364,) -dmdt[364] = dmdt[0] - -####### -## Setting up temperature increase for different RCP scenarios -temp_incr = [0, 1.4, 2.5, 4.2] -LWST_rcp = np.zeros((365, len(temp_incr))) - -for i in range(len(temp_incr)): - LWST_rcp[:, i] = LWST_spl + temp_incr[i] - - -####### - -LWST_run = np.tile(LWST_rcp, (Ynum, 1)) # change this line for RCP scenarios -PAR_run = np.tile(PAR_spl, Ynum) -mld_run = np.tile(MLD_pchip_spl, Ynum) # multiplicating physical forcing arrays -dmdt_run = np.tile(dmdt, Ynum) -PO4_run = np.tile(PO4_spl, Ynum) -NO3_run = np.tile(NO3_spl, Ynum) - -N0 = np.repeat(0.65, Ynum*365) - -sol = np.zeros((Ynum*365, Pnum + 4, len(temp_incr))) # time; no. var; RCP scenarios - - -from TSM1_v0_PO4_Q10_final import SizeModLogspace - -# Run solution -starttime = time.ctime() # save it for documentation purposes -print(mod_name, str(Pnum), "start:", starttime) -start = time.time() # start the timer - -for i in range(len(temp_incr)): - sol[:, :, i] = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run[:, i], dmdt=dmdt_run, N0=N0, numP=Pnum, - numYears=Ynum, minZ=15, maxZ=200).solution - -# Complete -end = time.time() # stop the timer -endtime = time.ctime() # save it for documentation purposes -print(mod_name, str(Pnum), "complete:", endtime) -runtime = (end - start) / 3600 # calculate run time (hour) -print(mod_name, str(Pnum), "run time(hour):", runtime) -print(mod_name, str(Pnum), "run time(day):", runtime/24) - - -# Storing output -out = xr.Dataset( - {'Nut': (['time', 'RCP'], sol[:, 0, :]), - 'Zoo': (['time', 'ZooSize', 'RCP'], sol[:, 1:3, :]), - 'Phy': (['time', 'PhySize', 'RCP'], sol[:, 4:4+Pnum*1, :])}, - coords={'time': np.arange(Ynum*365), - 'ZooSize': [20, 400], - 'PhySize': np.logspace(0, np.log10(150), Pnum), - 'RCP': [0, 1.4, 2.5, 4.2]}, - attrs={'Title': 'This file contains sol output with different nutrient and mixing regimes', - 'No. phytoplankton size group': Pnum, - 'LWST units': 'Degree celcius', - 'PAR units': 'Ein m$^{-2}$ d$^{-1}$', - 'MLD units': 'Meters', - 'Variable units': 'µmol P L$^{-1}$', - 'Size units': 'µm ESD', - 'Phyto size range': '1 - 150', - 'Zoo size range': '20 - 400'}) -out.to_netcdf(wd + 'output_proj.nc') # export - - - From bc73573843ba3c3d796eeb57f3ac47892ba83cb2 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 29 May 2024 19:08:30 +0200 Subject: [PATCH 36/97] Add files via upload --- Models/FinalMod/FinalMod_proj.py | 103 +++++++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 Models/FinalMod/FinalMod_proj.py diff --git a/Models/FinalMod/FinalMod_proj.py b/Models/FinalMod/FinalMod_proj.py new file mode 100644 index 0000000..bda8489 --- /dev/null +++ b/Models/FinalMod/FinalMod_proj.py @@ -0,0 +1,103 @@ +import numpy as np +import time + +import pandas as pd +import xarray as xr +from scipy.interpolate import make_interp_spline + +Pnum = 10 # specify the nos of phytoplankton size groups +Ynum = 10 # no. of modeling year +mod_name = 'Mod_proj' + +# Import forcing data +wd = 'wd' + +LWST = np.genfromtxt(wd+'ForcData_Temp.csv', delimiter=',') +PAR = np.genfromtxt(wd+'ForcData_PAR.csv', delimiter=',') +MLD = np.genfromtxt(wd+'ForcData_MLD.csv', delimiter=',') + +""" +Interpolation +""" +LWST_spl = make_interp_spline(LWST[:, 0], LWST[:, 1], k=3) +LWST_spl = LWST_spl(np.arange(365)) + +PAR_spl = make_interp_spline(PAR[:, 0], PAR[:, 1], k=3) +PAR_spl = PAR_spl(np.arange(365)) + +from scipy.interpolate import PchipInterpolator +MLD_pchip_spl = PchipInterpolator(MLD[:, 0], MLD[:, 1], axis=0) +MLD_pchip_spl = MLD_pchip_spl(np.arange(365)) +MLD_pchip_spl[364] = MLD_pchip_spl[0] + + +""" +dmdt +""" +dmdt = np.zeros((365)) +dmdt[0:364] = np.diff(MLD_pchip_spl) # dmdt (364,) +dmdt[364] = dmdt[0] + +####### +## Setting up temperature increase for different RCP scenarios +temp_incr = [0, 1.4, 2.5, 4.2] +LWST_rcp = np.zeros((365, len(temp_incr))) + +for i in range(len(temp_incr)): + LWST_rcp[:, i] = LWST_spl + temp_incr[i] + + +####### + +LWST_run = np.tile(LWST_rcp, (Ynum, 1)) # change this line for RCP scenarios +PAR_run = np.tile(PAR_spl, Ynum) +mld_run = np.tile(MLD_pchip_spl, Ynum) # multiplicating physical forcing arrays +dmdt_run = np.tile(dmdt, Ynum) + +N0 = np.repeat(0.75, Ynum*365) + +sol = np.zeros((Ynum*365, Pnum + 4, len(temp_incr))) # time; no. var; RCP scenarios + + +from FinalMod_Sbm import SizeModLogspace + +# Run solution +starttime = time.ctime() # save it for documentation purposes +print(mod_name, str(Pnum), "start:", starttime) +start = time.time() # start the timer + +for i in range(len(temp_incr)): + sol[:, :, i] = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run[:, i], dmdt=dmdt_run, N0=N0, numP=Pnum, + numYears=Ynum).solution + +# Complete +end = time.time() # stop the timer +endtime = time.ctime() # save it for documentation purposes +print(mod_name, str(Pnum), "complete:", endtime) +runtime = (end - start) / 3600 # calculate run time (hour) +print(mod_name, str(Pnum), "run time(hour):", runtime) +print(mod_name, str(Pnum), "run time(day):", runtime/24) + + +# Storing output +out = xr.Dataset( + {'Nut': (['time', 'RCP'], sol[:, 0, :]), + 'Zoo': (['time', 'ZooSize', 'RCP'], sol[:, 1:3, :]), + 'Phy': (['time', 'PhySize', 'RCP'], sol[:, 4:4+Pnum*1, :])}, + coords={'time': np.arange(Ynum*365), + 'ZooSize': [20, 600], + 'PhySize': np.logspace(0, np.log10(150), Pnum), + 'RCP': [0, 1.4, 2.5, 4.2]}, + attrs={'Title': 'This file contains sol output with different nutrient and mixing regimes', + 'No. phytoplankton size group': Pnum, + 'LWST units': 'Degree celcius', + 'PAR units': 'Ein m$^{-2}$ d$^{-1}$', + 'MLD units': 'Meters', + 'Variable units': 'µmol P L$^{-1}$', + 'Size units': 'µm ESD', + 'Phyto size range': '1 - 200', + 'Zoo size range': '20 - 600'}) +out.to_netcdf(wd + 'output_proj.nc') # export + + + From 863d4a57adfa2cad147f35442ad529f7dd770685 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 13:35:17 +0200 Subject: [PATCH 37/97] Update Readme.md --- Results/Readme.md | 60 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) diff --git a/Results/Readme.md b/Results/Readme.md index 8b13789..c95854d 100644 --- a/Results/Readme.md +++ b/Results/Readme.md @@ -1 +1,61 @@ +Here contains two sets of model results, one for the projection of future RCP scenarios ('Proj.nc') and one for sensitivity test ('Sensit.nc') over thermal traits (i.e. thermal optima and thermal tolerance) in all scenarios. + +----------------------- Proj.nc -----------------------\ +This file stores the outpur data from the projection, and is saved as a multi-dimensional dataset in a netCDF file. +The dataset contains phytoplankton biomass ('Phy'; $P_i$, $i$=1,2,..,10) and zooplankton biomass ('Zoo'; $Z_j$, $j$=1,2) data of each size class from day 0 to day 3650. For analyses, we used data from the last year, after the model is spinned-up, hence, day 3285 to 3650. + +To extract the data, one could use the netCDF4 library in python (https://unidata.github.io/netcdf4-python/) + +```` +wd = 'YourWorkingDirectory' + +import netCDF4 +out = netCDF4.Dataset(wd+'/Proj.nc') # output file +sol_phy = out.variables['Phy'][3285:] +sol_zoo = out.variables['Zoo'][3285:] +sol_nut = out.variables['Nut'][3285:] +```` + +The coordinates (dimensions) of the 'Phy' dataset are as below:\ +'time': '365 days',\ +'PhySize': '10 phytoplankton size classes ranged from 1 to 200µm ESD',\ +'RCP': '4 different climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5'\ +\ +The coordinates (dimensions) of the 'Zoo' dataset are as below:\ +'time': '365 days',\ +'ZooSize': '2 zooplankton size classes at 20 and 600µm ESD',\ +'RCP': '4 different climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5'\ +\ +The coordinates (dimensions) of the 'Nut' dataset are as below:\ +'time': '365 days',\ +'RCP': '4 different climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5'\ + +\ +\ +----------------------- Sensit.nc -----------------------\ +```` +wd = 'YourWorkingDirectory' + +import netCDF4 +out = netCDF4.Dataset(wd+'/Sensit.nc') # output file +sol_phy = out.variables['Phy'][3285:] +sol_zoo = out.variables['Zoo'][3285:] +sol_nut = out.variables['Nut'][3285:] +```` +The coordinates (dimensions) of the 'Phy' dataset are as below:\ +'time': '365 days',\ +'PhySize': '10 phytoplankton size classes ranged from 1 to 200µm ESD',\ +'RCP': '4 different climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5',\ +'Sensit': '4 different configurations on thermal traits to test their sensitivities in climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5'\ +\ +The coordinates (dimensions) of the 'Zoo' dataset are as below:\ +'time': '365 days',\ +'ZooSize': '2 zooplankton size classes at 20 and 600µm ESD',\ +'RCP': '4 different climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5',\ +'Sensit': '4 different configurations on thermal traits to test their sensitivities in climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5'\ +\ +The coordinates (dimensions) of the 'Nut' dataset are as below:\ +'time': '365 days',\ +'RCP': '4 different climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5',\ +'Sensit': '4 different configurations on thermal traits to test their sensitivities in climate change scenarios considered by IPCC: RCP 2.6; RCP 6.0; and RCP 8.5'\ From 547a3298da2288db774917cdc7681b779907ed8f Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 19:16:09 +0200 Subject: [PATCH 38/97] Update Readme.md --- Results/Readme.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/Results/Readme.md b/Results/Readme.md index c95854d..5d47c94 100644 --- a/Results/Readme.md +++ b/Results/Readme.md @@ -1,20 +1,20 @@ -Here contains two sets of model results, one for the projection of future RCP scenarios ('Proj.nc') and one for sensitivity test ('Sensit.nc') over thermal traits (i.e. thermal optima and thermal tolerance) in all scenarios. +Here contains two sets of model results, one for the projection of future RCP scenarios ('Proj.nc') and one for sensitivity test ('Sensit.nc') over thermal traits (i.e. thermal optima and thermal tolerance) in the standard and RCP scenarios. ----------------------- Proj.nc -----------------------\ -This file stores the outpur data from the projection, and is saved as a multi-dimensional dataset in a netCDF file. -The dataset contains phytoplankton biomass ('Phy'; $P_i$, $i$=1,2,..,10) and zooplankton biomass ('Zoo'; $Z_j$, $j$=1,2) data of each size class from day 0 to day 3650. For analyses, we used data from the last year, after the model is spinned-up, hence, day 3285 to 3650. +This file stores the output data from the projection, and is saved as a multi-dimensional dataset in a netCDF file. +The dataset contains nutrient fluxes ('Nut'), phytoplankton biomass ('Phy'; $P_i$, $i$=1,2,..,10) and zooplankton biomass ('Zoo'; $Z_j$, $j$=1,2) data of each size class from day 0 to day 3650. For analyses, we used data from the last year, after the model is spinned-up, hence, day 3285 to 3650. To extract the data, one could use the netCDF4 library in python (https://unidata.github.io/netcdf4-python/) ```` -wd = 'YourWorkingDirectory' +wd = '/YourWorkingDirectory' import netCDF4 out = netCDF4.Dataset(wd+'/Proj.nc') # output file -sol_phy = out.variables['Phy'][3285:] -sol_zoo = out.variables['Zoo'][3285:] -sol_nut = out.variables['Nut'][3285:] +phy = out.variables['Phy'][3285:] +zoo = out.variables['Zoo'][3285:] +nut = out.variables['Nut'][3285:] ```` The coordinates (dimensions) of the 'Phy' dataset are as below:\ @@ -34,8 +34,10 @@ The coordinates (dimensions) of the 'Nut' dataset are as below:\ \ \ ----------------------- Sensit.nc -----------------------\ +This file stores the output data from the sensitivity test, and is saved as a multi-dimensional dataset in a netCDF file. +The dataset contains nutrient fluxes ('Nut'), phytoplankton biomass ('Phy'; $P_i$, $i$=1,2,..,10) and zooplankton biomass ('Zoo'; $Z_j$, $j$=1,2) data of each size class from day 0 to day 3650. For analyses, we used data from the last year, after the model is spinned-up, hence, day 3285 to 3650. ```` -wd = 'YourWorkingDirectory' +wd = '/YourWorkingDirectory' import netCDF4 out = netCDF4.Dataset(wd+'/Sensit.nc') # output file From 24d01aee5bb679f9ab0ac4459db5ff8750ac916a Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 19:45:14 +0200 Subject: [PATCH 39/97] Update Readme.md --- Results/Readme.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/Results/Readme.md b/Results/Readme.md index 5d47c94..4d72ddc 100644 --- a/Results/Readme.md +++ b/Results/Readme.md @@ -3,7 +3,7 @@ Here contains two sets of model results, one for the projection of future RCP sc ----------------------- Proj.nc -----------------------\ This file stores the output data from the projection, and is saved as a multi-dimensional dataset in a netCDF file. -The dataset contains nutrient fluxes ('Nut'), phytoplankton biomass ('Phy'; $P_i$, $i$=1,2,..,10) and zooplankton biomass ('Zoo'; $Z_j$, $j$=1,2) data of each size class from day 0 to day 3650. For analyses, we used data from the last year, after the model is spinned-up, hence, day 3285 to 3650. +The dataset contains nutrient fluxes ('Nut'), phytoplankton biomass ('Phy'; $P_i$, $i$=1,2,..,10) and zooplankton biomass ('Zoo'; $Z_j$, $j$=1,2) data of each size class from day 3285 to day 3650, on the 10th year of the simulation. This is a time range after the spin-up stage of the model. To extract the data, one could use the netCDF4 library in python (https://unidata.github.io/netcdf4-python/) @@ -12,9 +12,9 @@ wd = '/YourWorkingDirectory' import netCDF4 out = netCDF4.Dataset(wd+'/Proj.nc') # output file -phy = out.variables['Phy'][3285:] -zoo = out.variables['Zoo'][3285:] -nut = out.variables['Nut'][3285:] +Phy = out.variables['Phy'][:] +Zoo = out.variables['Zoo'][:] +Nut = out.variables['Nut'][:] ```` The coordinates (dimensions) of the 'Phy' dataset are as below:\ @@ -41,9 +41,9 @@ wd = '/YourWorkingDirectory' import netCDF4 out = netCDF4.Dataset(wd+'/Sensit.nc') # output file -sol_phy = out.variables['Phy'][3285:] -sol_zoo = out.variables['Zoo'][3285:] -sol_nut = out.variables['Nut'][3285:] +Phy = out.variables['Phy'][:] +Zoo = out.variables['Zoo'][:] +Nut = out.variables['Nut'][:] ```` The coordinates (dimensions) of the 'Phy' dataset are as below:\ 'time': '365 days',\ From 0eb5f2febe551bd33cf51f0f375e14a377ee0dd6 Mon Sep 17 00:00:00 2001 From: Debbie 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z#z|(Rp{Ze{aYRkST3^T5gp7W;9HI%UJv~WxZrH3*=5l|$Q5&G{xDE{}%aLfW_g|b0WPeuYCtuiT2O7M4pbMa z2i1ojh8}?)g&IH&p+-<+DE?{o@9h}$IMfts1~rGCfLcILLM@?IP;2NZs14K>dKzj6 zJp(-pwTC)D9iiu-PEcp43zYDjcY|Yhs0Y*&>IEfK#Rrakp?*+*XaMv)G!PmD4Tgq5 zL!n{NaA*WH5*h`KhQ>f+p>fc7=mls3^dj^U^fELNngmUTUV)}SuR>Fy*Pv<8bZ7=N z6Pg9hhUP$Xp?T1JXaTekS_CbImOx9PWzcfyb!Y{&5?TeVhSor9p>@!D=nd#iXalqn z+5~Ne-h#G3TcNk1ZP0e;9cTx%6WRs63+;yXK<`0&p?%PP=m2yOdLKFj9fpoTA3z^M zN1>9Gwb>12B#EcJK&fC zx)VwXrGipJX`r-FIw(Dq0g8Ykp^Q)_C^M7=$_izJqM(H7ItLtcLU%#ApxjV2ln2TS z<%9A=1)zdZAt(llg$hGOprTMQs5o>tbPsedR01jqm4f1+($IZS8K^8&4k`~N93C8i zV+APTZ43wDSP7~O1yB{JD)bOk4XO^+fNDaupxRI!s4i3wst-L3Jpw%nHGmpI33Ga5 zI5vSEgC2*PLd~G&&=XJ#=t-z0)Cy`1Jq5La+Coo5?Vx9%XQB2`2dE?T9MlQw3}yT8 zX+~zY1ODb;mch{lGtNunTfpOfYe4l^T9qtYZ^zSi94c|4bBsYOSe5)>SM$J4X%z}Z zzRCDqsw#zNBkuQ)98uZtTCy{%(*pkUIjRuu_3Xd5FW|rNPL<)Ie{M`FnHm4w7QnuL zZ)@P!G2#3F`gaff-2;F3z~4RacMtsC1ONZ+f&cq8#{cg7TPOqm5N;>g|EsSt!s!cs z`~JHTUPB~|AIRc<{4b9Ue|Sdq|LkL Date: Thu, 6 Jun 2024 19:45:56 +0200 Subject: [PATCH 41/97] Delete Results/Projection directory --- Results/Projection/Readme.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Results/Projection/Readme.md diff --git a/Results/Projection/Readme.md b/Results/Projection/Readme.md deleted file mode 100644 index 8b13789..0000000 --- a/Results/Projection/Readme.md +++ /dev/null @@ -1 +0,0 @@ - From 60a1a63ac955d33db43833d0c12d69c0a0d538e2 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 19:46:04 +0200 Subject: [PATCH 42/97] Delete Results/Sensit_Test directory --- Results/Sensit_Test/Readme.md | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Results/Sensit_Test/Readme.md diff --git a/Results/Sensit_Test/Readme.md b/Results/Sensit_Test/Readme.md deleted file mode 100644 index 8b13789..0000000 --- a/Results/Sensit_Test/Readme.md +++ /dev/null @@ -1 +0,0 @@ - From a9b505a305555e8e2cb6b162a2747c83d608a2b1 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 20:05:54 +0200 Subject: [PATCH 43/97] Update README.md --- README.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 583d932..6cf9f2b 100644 --- a/README.md +++ b/README.md @@ -1,24 +1,22 @@ # TempSizeMod - Temperature-size Model ## Background -Over half of the global freshwater bodies are reporting threats of anthropogenic eutrophication (Ho et al. 2019). Anthropogenic eutrophication has become a major environmental problem since the mid-20th century. It is characterized by excessive input of nutrient in lakes that facilitates an overgrowth of algae in the aquatic ecosystem (Carpenter 2005). Such overabundance of phytoplantkon population can cause multiple harmful effects to the ecosystem like degradation in water quality, harmful algae blooms, and trophic disorganization such as mass fish kills due to the deprivation of oxygen by large-scale degradations of dead algae (Huisman et al. 2018). +Over half of the global lakes are recording increases in lake temperature and reporting rising bloom events (O'Reilley et al. 2015; Ho et al. 2019; Dokulil et al. 2021). Climate warming increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Temperature change leads to alterations in the temporal dynamics (i.e. phenological shifts) and community compositions of phytoplankton (Winder and Sommer 2012; Zohary et al. 2021). It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Shurin et al. 2012; Striebel et al. 2016). -Climate warming is another factor increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Phytoplankton is one of the groups at the forefront of rising water temperautures. Temperature change has led to alterations in the temporal dynamics of phytoplankton (i.e. phenological shifts), the community compositions and even the trophic structures. It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Shurin et al. 2012; Striebel et al. 2016). - -In the current realm of global environmental change, understanding the effects of temperature and eutrophication on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like nutrient and zooplankton grazer (Pomati et al. 2020), thus contributing to various nonlinear feedbacks to the phytoplankton community and making predictions on community dynamics difficult. For example, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Sterner et al. 2020; Reinl et al. 2021, 2023). - -Trait-based models have been applied to investigate the effects of environmental conditions on phytoplankton population dynamics (Litchman 2023). Phytoplantkon cell size is a master trait and has been used widely in modelling works (Litchman and Klausmeier 2008). Size-based models allow explorations on aggregated community properties such as total biomass, community mean cell size, and size variances (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions (Merico et al. 2009). +In the current realm of global environmental change, understanding the effects of temperature on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like lake mixing and stratifications, lights, and zooplankton grazer (Pomati et al. 2020), thus it affects phytoplankton communities both directly and indirectly (Zohary et al. 2021). This makes predictions on community dynamics difficult. For instance, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Sterner et al. 2020; Reinl et al. 2021, 2023). +Trait-based models are valueable tools to disentangle possible effects and test possible mechanisms temperature has on phytoplankton (Litchman 2023). Phytoplantkon cell size is a master trait and has been used widely in modelling works (Litchman and Klausmeier 2008). Size-based models allow explorations on aggregated community properties such as total biomass and community mean cell size of phytoplankton (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018; To et al. 2024). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions in response to changes in environmental conditions. ## Model description -The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990) incorporated to a size-based framework (e.g. Moloney and Field 1991; Armstrong 1994; Stock et al. 2008). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes. +The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated to a size-based framework (e.g. Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes.

- Figure1_v3 + Figure1_v2 1 The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing (Hansen et al. 1994, 1997; Edwards et al. 2012). The model aims at studying changes in the size compositions of lake phytoplankton communities. + ## Temperature dependence in the model The temperature dependence for phytoplankton growth follows a bell-shaped thermal tolerance curve, given by, @@ -32,6 +30,7 @@ $$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$ The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10 $^{\circ}$ C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). + ## Allometric relationships in the model The model comprises of three allometric equations. These allometries allow an ecological trade-off to arise in the model based on water temperature throughout the year. The small phytoplantkon can grow faster than the large phytoplankton, but are subject to stronger grazing from the smaller zooplankton, who will selectively graze on the small cells. @@ -52,24 +51,25 @@ Reference: + Acevedo-Trejos, E., E. Marañón, and A. Merico. 2018. Phytoplankton size diversity and ecosystem function relationships across oceanic regions. Proc. R. Soc. B. 285: 20180621. doi:10.1098/rspb.2018.0621 + Armstrong, R. A. 1994. Grazing limitation and nutrient limitation in marine ecosystems: Steady state solutions of an ecosystem model with multiple food chains. Limnol. Oceanogr. 39: 597–608. doi:10.4319/lo.1994.39.3.0597 + Carpenter, S. R. 2005. Eutrophication of aquatic ecosystems: Bistability and soil phosphorus. Proc. Natl. Acad. Sci. U.S.A. 102: 10002–10005. doi:10.1073/pnas.0503959102 -Dudgeon, D. 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology 29: R960–R967. doi:10.1016/j.cub.2019.08.002 ++ Dokulil, M. T., De Eyto, E., Maberly, S. C., May, L., Weyhenmeyer, G. A., & Woolway, R. I. (2021). Increasing maximum lake surface temperature under climate change. Climatic Change, 165(3–4), 56. https://doi.org/10.1007/s10584-021-03085-1 ++ Dudgeon, D. 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology 29: R960–R967. doi:10.1016/j.cub.2019.08.002 + Edwards, K. F., M. K. Thomas, C. A. Klausmeier, and E. Litchman. 2012. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57: 554–566. doi:10.4319/lo.2012.57.2.0554 + Fasham, M. J. R., H. W. Ducklow, and S. M. McKelvie. 1990. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. j mar res 48: 591–639. doi:10.1357/002224090784984678 + Hansen, B., P. K. Bjørnsen, and P. J. Hansen. 1994. The size ratio between planktonic predators and their prey. Limnology and Oceanography 39: 395–403. doi:10.4319/lo.1994.39.2.0395 + Hansen, P. J., P. K. Bjørnsen, and B. Hansen. 1997. Zooplankton grazing and growth: Scaling within the 2-2,000um body size range. Limnology and Oceanography 42: 687–704. + Ho, J. C., A. M. Michalak, and N. Pahlevan. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574: 667–670. doi:10.1038/s41586-019-1648-7 -+ Huisman, J., G. A. Codd, H. W. Paerl, B. W. Ibelings, J. M. H. Verspagen, and P. M. Visser. 2018. Cyanobacterial blooms. Nature Reviews Microbiology 16: 471–483. doi:10.1038/s41579-018-0040-1 + Litchman, E. 2023. Understanding and predicting harmful algal blooms in a changing climate: A trait‐based framework. Limnol Oceanogr Letters 8: 229–246. doi:10.1002/lol2.10294 + Litchman, E., and C. A. Klausmeier. 2008. Trait-Based Community Ecology of Phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39: 615–639. doi:10.1146/annurev.ecolsys.39.110707.173549 -+ Merico, A., J. Bruggeman, and K. Wirtz. 2009. A trait-based approach for downscaling complexity in plankton ecosystem models. Ecological Modelling 220: 3001–3010. doi:10.1016/j.ecolmodel.2009.05.005 -+ Moloney, C. L., and J. G. Field. 1991. The size-based dynamics of plankton food webs. I. A simulation model of carbon and nitrogen flows. J Plankton Res 13: 1003–1038. doi:10.1093/plankt/13.5.1003 -+ Paerl, H. W., W. S. Gardner, K. E. Havens, A. R. Joyner, M. J. McCarthy, S. E. Newell, B. Qin, and J. T. Scott. 2016. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae 54: 213–222. doi:10.1016/j.hal.2015.09.009 ++ O’Reilly, C. M., Sharma, S., Gray, D. K., Hampton, S. E., Read, J. S., Rowley, R. J., Schneider, P., Lenters, J. D., McIntyre, P. B., Kraemer, B. M., Weyhenmeyer, G. A., Straile, D., Dong, B., Adrian, R., Allan, M. G., Anneville, O., Arvola, L., Austin, J., Bailey, J. L., … Zhang, G. (2015). Rapid and highly variable warming of lake surface waters around the globe. Geophysical Research Letters, 42(24). https://doi.org/10.1002/2015GL066235 + Petchey, O. L., P. T. McPhearson, T. M. Casey, and P. J. Morin. 1999. Environmental warming alters food-web structure and ecosystem function. Nature 402: 69–72. doi:10.1038/47023 + Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 ++ Post, B., Acevedo-Trejos, E., Barton, A. D., & Merico, A. (2024). The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python. Geoscientific Model Development, 17(3), 1175–1195. https://doi.org/10.5194/gmd-17-1175-2024 + Reid, A. J., A. K. Carlson, I. F. Creed, and others. 2019. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews 94: 849–873. doi:10.1111/brv.12480 + Reinl, K. L., J. D. Brookes, C. C. Carey, and others. 2021. Cyanobacterial blooms in oligotrophic lakes: Shifting the high‐nutrient paradigm. Freshwater Biology 66: 1846–1859. doi:10.1111/fwb.13791 + Reinl, K. L., T. D. Harris, R. L. North, and others. 2023. Blooms also like it cold. Limnol Oceanogr Letters 8: 546–564. doi:10.1002/lol2.10316 -+ Stock, C. A., T. M. Powell, and S. A. Levin. 2008. Bottom–up and top–down forcing in a simple size-structured plankton dynamics model. Journal of Marine Systems 74: 134–152. doi:10.1016/j.jmarsys.2007.12.004 + Striebel, M., S. Schabhüttl, D. Hodapp, P. Hingsamer, and H. Hillebrand. 2016. Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composition. Oecologia 182: 815–827. doi:10.1007/s00442-016-3693-3 ++ To, S., Acevedo‐Trejos, E., Chakraborty, S., Pomati, F., & Merico, A. (2024). Grazing strategies determine the size composition of phytoplankton in eutrophic lakes. Limnology and Oceanography, lno.12538. https://doi.org/10.1002/lno.12538 + Ward, B. A., S. Dutkiewicz, O. Jahn, and M. J. Follows. 2012. A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57: 1877–1891. doi:10.4319/lo.2012.57.6.1877 -+ Yvon-Durocher, G., J. M. Montoya, M. Trimmer, and G. Woodward. 2011. Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems: WARMING ALTERS COMMUNITY SIZE STRUCTURE. Global Change Biology 17: 1681–1694. doi:10.1111/j.1365-2486.2010.02321.x ++ Winder, M., & Sommer, U. (2012). Phytoplankton response to a changing climate. Hydrobiologia, 698(1), 5–16. https://doi.org/10.1007/s10750-012-1149-2 ++ Yvon-Durocher, G., J. M. Montoya, M. Trimmer, and G. Woodward. 2011. Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems. Global Change Biology 17: 1681–1694. doi:10.1111/j.1365-2486.2010.02321.x ++ Zohary, T., Flaim, G., & Sommer, U. (2021). Temperature and the size of freshwater phytoplankton. Hydrobiologia, 848(1), 143–155. https://doi.org/10.1007/s10750-020-04246-6 From d11d7647206a4537a4b7ded32746b47ce34de5b6 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 20:35:23 +0200 Subject: [PATCH 44/97] Update README.md --- README.md | 43 ++++++++++++++++++++++++++++++++----------- 1 file changed, 32 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 6cf9f2b..8d10b88 100644 --- a/README.md +++ b/README.md @@ -5,32 +5,31 @@ Over half of the global lakes are recording increases in lake temperature and re In the current realm of global environmental change, understanding the effects of temperature on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like lake mixing and stratifications, lights, and zooplankton grazer (Pomati et al. 2020), thus it affects phytoplankton communities both directly and indirectly (Zohary et al. 2021). This makes predictions on community dynamics difficult. For instance, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Sterner et al. 2020; Reinl et al. 2021, 2023). Trait-based models are valueable tools to disentangle possible effects and test possible mechanisms temperature has on phytoplankton (Litchman 2023). Phytoplantkon cell size is a master trait and has been used widely in modelling works (Litchman and Klausmeier 2008). Size-based models allow explorations on aggregated community properties such as total biomass and community mean cell size of phytoplankton (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018; To et al. 2024). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions in response to changes in environmental conditions. - +

## Model description The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated to a size-based framework (e.g. Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes.

- Figure1_v2 1 + Figure1_v2 1 The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing (Hansen et al. 1994, 1997; Edwards et al. 2012). The model aims at studying changes in the size compositions of lake phytoplankton communities. - - +
## Temperature dependence in the model The temperature dependence for phytoplankton growth follows a bell-shaped thermal tolerance curve, given by, $$E(T) = e^{0.063T} \left[1- \left(\frac{T-T_{opt}}{\sigma_T}\right)^2 \right] $$ - +
, where $T$ is the ambient lake water surface temperature (LWST), $T_{opt}$ is the thermal optima that determines the median of the curve, and $\sigma_T$ is the thermal tolerance that determines the width of the curve. In this study, we assume a community mean thermal tolerance curve to all phytoplankton size classes. The maximum ingestion rates of zooplankton follows a Q10 model such that, the maximum grazing increases with temperature. The equation for the dependence is, $$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$ - +
The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10 $^{\circ}$ C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). - +

## Allometric relationships in the model The model comprises of three allometric equations. These allometries allow an ecological trade-off to arise in the model based on water temperature throughout the year. The small phytoplantkon can grow faster than the large phytoplankton, but are subject to stronger grazing from the smaller zooplankton, who will selectively graze on the small cells. @@ -41,15 +40,34 @@ $$\mu_{max}(S_i^P) = \beta_{\mu_{max}}\cdot (S_i^P)^{\alpha_{\mu_{max}}}$$ $$I_{max}(S_j^Z) = \beta_{I_{max}}\cdot (S_j^Z)^{\alpha_{I_{max}}}$$ $$P_{opt}(S_i^P, S_j^Z) = \beta_{P_{opt}}\cdot (S_j^Z)^{\alpha_{P_{opt}}}$$ - +
representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. - - - +

+## Experiments +Using the standard model, we conduct two numerical experiments:
+1. a projection based on Representative Climate Pathways (RCPs) issued by IPCC +2. a sensitivity test for the two thermal traits, $T_{opt}$ and $\sigma_T$ in different RCP scenarios + +For details, please refer to related publication of this model (under review). + +

+## Suggestions for future works +1. Numerical explorations, e.g. how different assumptions on the temperature dependence of phytoplankton growth would change the predictions on communities compositions of phytoplankton + - Community Eppley / Q10 model (Ho et al. 2013) + - Size-dependent Eppley (e.g. Taherzadeh et al. 2017; Anderson et al. 2023; To et al. 2024) + - Community unimodal curve (Cagle and Roelke 2021; This study) + - Size-dependent unimodal curve +
+2. Ecological explorations over altered stratification phenology in lakes (Woolway et al. 2021), for example, + Figure1_v2 1 + +





Reference: + Acevedo-Trejos, E., E. Marañón, and A. Merico. 2018. Phytoplankton size diversity and ecosystem function relationships across oceanic regions. Proc. R. Soc. B. 285: 20180621. doi:10.1098/rspb.2018.0621 ++ Anderson, S. I., Fronda, C., Barton, A. D., Clayton, S., Rynearson, T. A., & Dutkiewicz, S. (2024). Phytoplankton thermal trait parameterization alters community structure and biogeochemical processes in a modeled ocean. Global Change Biology, 30(1), e17093. https://doi.org/10.1111/gcb.17093 + Armstrong, R. A. 1994. Grazing limitation and nutrient limitation in marine ecosystems: Steady state solutions of an ecosystem model with multiple food chains. Limnol. Oceanogr. 39: 597–608. doi:10.4319/lo.1994.39.3.0597 ++ Cagle, S. E., & Roelke, D. L. (2021). Relative roles of fundamental processes underpinning PEG dynamics in dimictic lakes as revealed by a self-organizing, multi-population plankton model. Ecological Modelling, 462, 109793. https://doi.org/10.1016/j.ecolmodel.2021.109793 + Carpenter, S. R. 2005. Eutrophication of aquatic ecosystems: Bistability and soil phosphorus. Proc. Natl. Acad. Sci. U.S.A. 102: 10002–10005. doi:10.1073/pnas.0503959102 + Dokulil, M. T., De Eyto, E., Maberly, S. C., May, L., Weyhenmeyer, G. A., & Woolway, R. I. (2021). Increasing maximum lake surface temperature under climate change. Climatic Change, 165(3–4), 56. https://doi.org/10.1007/s10584-021-03085-1 + Dudgeon, D. 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology 29: R960–R967. doi:10.1016/j.cub.2019.08.002 @@ -57,6 +75,7 @@ Reference: + Fasham, M. J. R., H. W. Ducklow, and S. M. McKelvie. 1990. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. j mar res 48: 591–639. doi:10.1357/002224090784984678 + Hansen, B., P. K. Bjørnsen, and P. J. Hansen. 1994. The size ratio between planktonic predators and their prey. Limnology and Oceanography 39: 395–403. doi:10.4319/lo.1994.39.2.0395 + Hansen, P. J., P. K. Bjørnsen, and B. Hansen. 1997. Zooplankton grazing and growth: Scaling within the 2-2,000um body size range. Limnology and Oceanography 42: 687–704. +Ho, P.-C., Chang, C.-W., Hsieh, C. H., Shiah, F.-K., & Miki, T. (2013). Effects of increasing nutrient supply and omnivorous feeding on the size spectrum slope: A size-based nutrient-phytoplankton-zooplankton model. Population Ecology, 55(2), 247–259. https://doi.org/10.1007/s10144-013-0368-3 + Ho, J. C., A. M. Michalak, and N. Pahlevan. 2019. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574: 667–670. doi:10.1038/s41586-019-1648-7 + Litchman, E. 2023. Understanding and predicting harmful algal blooms in a changing climate: A trait‐based framework. Limnol Oceanogr Letters 8: 229–246. doi:10.1002/lol2.10294 + Litchman, E., and C. A. Klausmeier. 2008. Trait-Based Community Ecology of Phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39: 615–639. doi:10.1146/annurev.ecolsys.39.110707.173549 @@ -68,8 +87,10 @@ Reference: + Reinl, K. L., J. D. Brookes, C. C. Carey, and others. 2021. Cyanobacterial blooms in oligotrophic lakes: Shifting the high‐nutrient paradigm. Freshwater Biology 66: 1846–1859. doi:10.1111/fwb.13791 + Reinl, K. L., T. D. Harris, R. L. North, and others. 2023. Blooms also like it cold. Limnol Oceanogr Letters 8: 546–564. doi:10.1002/lol2.10316 + Striebel, M., S. Schabhüttl, D. Hodapp, P. Hingsamer, and H. Hillebrand. 2016. Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composition. Oecologia 182: 815–827. doi:10.1007/s00442-016-3693-3 ++ Taherzadeh, N., Kerimoglu, O., & Wirtz, K. W. (2017). Can we predict phytoplankton community size structure using size scalings of eco-physiological traits? Ecological Modelling, 360, 279–289. https://doi.org/10.1016/j.ecolmodel.2017.07.008 + To, S., Acevedo‐Trejos, E., Chakraborty, S., Pomati, F., & Merico, A. (2024). Grazing strategies determine the size composition of phytoplankton in eutrophic lakes. Limnology and Oceanography, lno.12538. https://doi.org/10.1002/lno.12538 + Ward, B. A., S. Dutkiewicz, O. Jahn, and M. J. Follows. 2012. A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57: 1877–1891. doi:10.4319/lo.2012.57.6.1877 + Winder, M., & Sommer, U. (2012). Phytoplankton response to a changing climate. Hydrobiologia, 698(1), 5–16. https://doi.org/10.1007/s10750-012-1149-2 ++ Woolway, R. I., Sharma, S., Weyhenmeyer, G. A., Debolskiy, A., Golub, M., Mercado-Bettín, D., Perroud, M., Stepanenko, V., Tan, Z., Grant, L., Ladwig, R., Mesman, J., Moore, T. N., Shatwell, T., Vanderkelen, I., Austin, J. A., DeGasperi, C. L., Dokulil, M., La Fuente, S., … Jennings, E. (2021). Phenological shifts in lake stratification under climate change. Nature Communications, 12(1), 2318. https://doi.org/10.1038/s41467-021-22657-4 + Yvon-Durocher, G., J. M. Montoya, M. Trimmer, and G. Woodward. 2011. Warming alters the size spectrum and shifts the distribution of biomass in freshwater ecosystems. Global Change Biology 17: 1681–1694. doi:10.1111/j.1365-2486.2010.02321.x + Zohary, T., Flaim, G., & Sommer, U. (2021). Temperature and the size of freshwater phytoplankton. Hydrobiologia, 848(1), 143–155. https://doi.org/10.1007/s10750-020-04246-6 From 98eaf2aae3a77eae131337b86c0e50ea18d06936 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 20:46:28 +0200 Subject: [PATCH 45/97] Update README.md --- LakeData/README.md | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/LakeData/README.md b/LakeData/README.md index 7598d46..1621449 100644 --- a/LakeData/README.md +++ b/LakeData/README.md @@ -1,18 +1,12 @@ -This model adapts data from a specific Swiss lake close to Zurich, Greifensee. -There are three environmental forcing to the model, respectively, Water Temperature from photic zone (WT), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). -These data are collected from different sources. LWST is obtained from the in-situ CTD probe (documented in Pomati et al. 2011) averaged from depths range from 0 to 8m. PAR is obtained from the nearby weather station by MeteoSwiss. Mixed layer depth is adopted from the published data for Greifensee in Pomati et al. (2020), https://zenodo.org/record/3582838#.YzQzuexBwc8. +This model forcing adopts in-situ data from Greifensee, Switzerland. +There are three environmental forcing functions to the model, respectively, Water Temperature from photic zone (LWST), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). +These forcing are derived from various time-series data sources, and the data cover different time periods. The LWST is obtained from the CTD profiles as daily epilimnetic temperature from 13/3/2019 to 31/12/2022 (n=1390), measured by the Swiss Federal Institute of Aquatic Science and Technology, Eawag. The PAR data is converted from the solar radiance data collected from a weather station next to Greifensee, measured daily by MeteoSwiss for the same period as the LWST. The PAR conversion is detailed in the related publication of this model. Finally, we retrieve MLD data from Pomati et al. (2020), which documented monthly MLD of Greifensee from 1972 to 2015 (n=270). -The original dataset contains an approximately three-year time-series from 13/3/2019 to 31/12/2022, containing 1390 data points. To generate an annual forcing profile, LWST was taken from a three-year mean, PAR was taken from a 75 percentile due to high daily variations, and MLD was derived by month to preserve the key mixing of the epilimnion in the water column. +To generate an annual forcing profile, LWST was taken from a three-year mean, PAR was taken from a 75 percentile due to high daily variations, and MLD was derived by month to preserve the key mixing of the epilimnion in the water column. The steps deriving these forcing profile is shown in the python script 'TSM_TempForcing.py', 'TSM_PARForcing.py', and 'TSM_MLDForcing.py' respectively. -The steps deriving these forcing profile is shown in the python script 'TSM_TempForcing.py', 'TSM_PARForcing.py', and 'TSM_MLDForcing.py' respectively. +

+ Figure1_v2 1 -![Temp_final](https://github.com/Debbcwing/TempSizeMod/assets/51200142/143b28e3-91aa-4873-8128-206084cb0d92) - -![PAR_final](https://github.com/Debbcwing/TempSizeMod/assets/51200142/c73307e7-6112-4a46-98ff-70498ce8ea82) - -![MLD_final](https://github.com/Debbcwing/TempSizeMod/assets/51200142/320f93d5-26a6-42d7-a63a-247817c84df5) - - +

Reference -+ Pomati, F., J. Jokela, M. Simona, M. Veronesi, and B. W. Ibelings. 2011. An Automated Platform for Phytoplankton Ecology and Aquatic Ecosystem Monitoring. Environ. Sci. Technol. 45: 9658–9665. doi:10.1021/es201934n -+ Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes. Front. Microbiol. 10: 3155. doi:10.3389/fmicb.2019.03155 ++ Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting temperature, nutrients and zooplankton grazing control phytoplankton size-abundance relationships in eight Swiss lakes [Data set]. In Frontiers in Microbiology, Aquatic Microbiology. Zenodo. https://doi.org/10.5281/zenodo.3582838 From bb1d78d346bf977a093f93a24604692685588416 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 6 Jun 2024 21:09:55 +0200 Subject: [PATCH 46/97] Update README.md --- Models/FinalMod/README.md | 46 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/Models/FinalMod/README.md b/Models/FinalMod/README.md index 8b13789..17dfa96 100644 --- a/Models/FinalMod/README.md +++ b/Models/FinalMod/README.md @@ -1 +1,47 @@ +To run the model, one needs the model script 'FinalMod_Sbm.py' and the model run script 'FinalMod_run.py'. +

+ +#### FinalMod_Sbm.py +This is the file containing the size-based NPZD model. The default parameter values are coded in the model. + + +

+#### FinalMod_run.py +In the run script, one could adjust the number of year and of phytoplankton size classes according to their own preferences. +```` +Pnum = 10 # specify the nos of phytoplankton size groups +Ynum = 10 # no. of modelling year +```` + +To change model parameter values from the default values, one enters their preferred values in below lines. +```` +sol = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, numYears=Ynum).solution +```` +For example, if one wants to change remineralization rate to 0.5, please input it as +```` +sol = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, numYears=Ynum, Rem=0.5).solution +```` +or if one wants to change the parameters that determine the allometric relationships for $\mu_{max}$ i.e. $\alpha_{\mu_{max}}$ to 0.25, +```` +sol = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, numYears=Ynum, mu_alpha=0.25).solution +```` + +The code for the parameters can be found in FinalMod_Sbm.py. + + + +

+#### FinalMod_proj.py +In this script one can run a number of projection sceanrios, i.e. linear increase in temperature, automatically. +If one wants to configure the increase in temperature by their own preferred values, or adjust the number of scenarios, please change at the below line: +```` +temp_incr = [0, 1.4, 2.5, 4.2] +```` + +




+* Shall there be any queries or difficulties in conducting the runs, please feel free to reach out at: sto@constructor.university + + + + From f6a9af0340f567e8fb158f4fa40dbf9d5c078cea Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 20:45:51 +0200 Subject: [PATCH 47/97] Update README.md --- LakeData/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakeData/README.md b/LakeData/README.md index 1621449..2fde9e7 100644 --- a/LakeData/README.md +++ b/LakeData/README.md @@ -1,6 +1,6 @@ This model forcing adopts in-situ data from Greifensee, Switzerland. There are three environmental forcing functions to the model, respectively, Water Temperature from photic zone (LWST), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). -These forcing are derived from various time-series data sources, and the data cover different time periods. The LWST is obtained from the CTD profiles as daily epilimnetic temperature from 13/3/2019 to 31/12/2022 (n=1390), measured by the Swiss Federal Institute of Aquatic Science and Technology, Eawag. The PAR data is converted from the solar radiance data collected from a weather station next to Greifensee, measured daily by MeteoSwiss for the same period as the LWST. The PAR conversion is detailed in the related publication of this model. Finally, we retrieve MLD data from Pomati et al. (2020), which documented monthly MLD of Greifensee from 1972 to 2015 (n=270). +These forcing are derived from various time-series data sources, and the data cover different time periods. The LWST is obtained from the CTD profiles as daily epilimnetic temperature from 13/3/2019 to 31/12/2022 (n=1390), measured by the Swiss Federal Institute of Aquatic Science and Technology, Eawag. The PAR data is converted from the solar radiance data collected from a weather station next to Greifensee, measured daily by MeteoSwiss for the same period as the LWST. The PAR conversion is detailed in the related publication of this model. Finally, we retrieve MLD data from Pomati et al. (2020), under lake 'Greifensee', that documented the monthly MLD of Greifensee from 1972 to 2015 (n=270). To generate an annual forcing profile, LWST was taken from a three-year mean, PAR was taken from a 75 percentile due to high daily variations, and MLD was derived by month to preserve the key mixing of the epilimnion in the water column. The steps deriving these forcing profile is shown in the python script 'TSM_TempForcing.py', 'TSM_PARForcing.py', and 'TSM_MLDForcing.py' respectively. From 65c8787277bcbe2cef214a9db4a8bb43a73b6ad2 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 20:46:33 +0200 Subject: [PATCH 48/97] Update TSM_MLDForcing.py --- LakeData/TSM_MLDForcing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakeData/TSM_MLDForcing.py b/LakeData/TSM_MLDForcing.py index ef6fe8f..5bf6fac 100644 --- a/LakeData/TSM_MLDForcing.py +++ b/LakeData/TSM_MLDForcing.py @@ -7,7 +7,7 @@ wd = 'your wd' -data_mld = pd.read_csv(wd+'/Original_MLD.csv') +data_mld = pd.read_csv(wd+'/RawData_MLD.csv') # convert real data to julian day (0-365) import datetime From 85e3afaf46d5e625953d2b1242fff4e357fbf30d Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 20:46:51 +0200 Subject: [PATCH 49/97] Update TSM_PARForcing.py --- LakeData/TSM_PARForcing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakeData/TSM_PARForcing.py b/LakeData/TSM_PARForcing.py index e755593..dee785a 100644 --- a/LakeData/TSM_PARForcing.py +++ b/LakeData/TSM_PARForcing.py @@ -9,7 +9,7 @@ wd = 'your wd' -data = pd.read_csv(wd+'/Original_Temp+PAR.csv') +data = pd.read_csv(wd+'/RawData_Temp+PAR.csv') data_nSSI = data[['date', 'global_radiation']] From b192a77d0d741a3a569a6602f0b24a7e9264c2d8 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 20:48:25 +0200 Subject: [PATCH 50/97] Update TSM_TempForcing.py --- LakeData/TSM_TempForcing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakeData/TSM_TempForcing.py b/LakeData/TSM_TempForcing.py index d47f652..1d44ce4 100644 --- a/LakeData/TSM_TempForcing.py +++ b/LakeData/TSM_TempForcing.py @@ -7,7 +7,7 @@ wd = 'your wd' -data = pd.read_csv(wd+'/Original_forc_data.csv') +data = pd.read_csv(wd+'/RawData_Temp+PAR.csv') data_temp = data[['date', 'water_temp']] From 051aedfa3f34bc721197b196deaf10df9cd3b678 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 20:52:06 +0200 Subject: [PATCH 51/97] Rename README.md to README.md --- {LakeData => LakeEnvData}/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {LakeData => LakeEnvData}/README.md (100%) diff --git a/LakeData/README.md b/LakeEnvData/README.md similarity index 100% rename from LakeData/README.md rename to LakeEnvData/README.md From 0643396de8561b88cfa52d7ddc6a1fe1b8e915b9 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 20:53:46 +0200 Subject: [PATCH 52/97] Rename README.md to README.md --- {LakeEnvData => LakePlankData}/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {LakeEnvData => LakePlankData}/README.md (100%) diff --git a/LakeEnvData/README.md b/LakePlankData/README.md similarity index 100% rename from LakeEnvData/README.md rename to LakePlankData/README.md From 18c1aae7b7cc9fb1fcd3dde7f4156e22ead20abc Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:02:55 +0200 Subject: [PATCH 53/97] Add files via upload --- LakeData/ForcData_futMLD.csv | 14 ++++++ LakeData/TSM_futureMLDForcing.py | 83 ++++++++++++++++++++++++++++++++ 2 files changed, 97 insertions(+) create mode 100644 LakeData/ForcData_futMLD.csv create mode 100644 LakeData/TSM_futureMLDForcing.py diff --git a/LakeData/ForcData_futMLD.csv b/LakeData/ForcData_futMLD.csv new file mode 100644 index 0000000..6665e29 --- /dev/null +++ b/LakeData/ForcData_futMLD.csv @@ -0,0 +1,14 @@ +0.000000000000000000e+00,2.500000000000000000e+01 +3.200000000000000000e+01,2.500000000000000000e+01 +6.500000000000000000e+01,2.500000000000000000e+01 +7.800000000000000000e+01,5.000000000000000000e+00 +1.210000000000000000e+02,6.000000000000000000e+00 +1.520000000000000000e+02,7.000000000000000000e+00 +1.820000000000000000e+02,8.000000000000000000e+00 +2.130000000000000000e+02,9.000000000000000000e+00 +2.440000000000000000e+02,1.000000000000000000e+01 +2.740000000000000000e+02,1.100000000000000000e+01 +3.050000000000000000e+02,1.200000000000000000e+01 +3.410000000000000000e+02,2.000000000000000000e+01 +3.450000000000000000e+02,2.500000000000000000e+01 +3.650000000000000000e+02,2.500000000000000000e+01 diff --git a/LakeData/TSM_futureMLDForcing.py b/LakeData/TSM_futureMLDForcing.py new file mode 100644 index 0000000..6aa461f --- /dev/null +++ b/LakeData/TSM_futureMLDForcing.py @@ -0,0 +1,83 @@ +""" +Produce environmental forcing for a hypothesized future MLD +""" +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +wd = '/Users/szewing/Desktop/PhD_work/3. ThermalTrait/' + +data_mld = pd.read_csv(wd + '/TempSizeMod_TSM/Forcing/Final/Original_MLD.csv', index_col=0) + +#--------------------------------------------------------------------- +# convert real data to julian day (0-365) + +import datetime + +def datestdtojd(stddate): + """ + source: https://rafatieppo.github.io/post/2018_12_01_juliandate/ + :param stddate: standard date for input + :return: + """ + fmt = '%Y-%m-%d' + sdtdate = datetime.datetime.strptime(stddate, fmt) + sdtdate = sdtdate.timetuple() + jdate = sdtdate.tm_yday + return jdate + +jday = np.array([]) +for i in range(len(data_mld)): + jday = np.append(jday, datestdtojd(data_mld['date'][i])) +data_mld.insert(1, 'Julian day', pd.DataFrame(jday)) # phychem data + + +""" +Mixed Layer Depth (MLD) +1. taking the mean for each julian day +2. taking the mean for each month +3. interpolate a function based on 12-month data points +""" +mld_GR = data_mld.groupby('Julian day', as_index=False)['thermocl'].median().to_numpy() + +condlist = [0., 32., 75., 100., 121., 152., 182., 213., 244., 274., 305., 330., 345, 365] +condlist_future = [0., 32., (75.-22.), (100.-22.), 121., 152., 182., 213., 244., 274., 305, (330.+11.), (345.+11.), 365] +# approximated julian day of the first day of each month* + +# there are more noise in MLD, so we did some manipulation from visual inspection +extrct_GR_mld = np.zeros((len(condlist), 2)) +extrct_GR_mld[:, 0] = condlist +extrct_GR_mld[0:3, 1] = np.repeat(30, 3) +extrct_GR_mld[3:, 1] = np.array([3, 4, 5, 6, 7, 8, 9, 12, 18, 30, 30]) + +extrct_GR_futmld = np.zeros((len(condlist_future), 2)) +extrct_GR_futmld[:, 0] = condlist_future +extrct_GR_futmld[0:3, 1] = np.repeat(30, 3) +extrct_GR_futmld[3:, 1] = np.array([3, 4, 5, 6, 7, 8, 9, 10, 18, 30, 30]) + + +from scipy.interpolate import PchipInterpolator +MLD_pchip_GR = PchipInterpolator(extrct_GR_mld[:, 0], extrct_GR_mld[:, 1], axis=0) +MLD_pchip_GR_spl = MLD_pchip_GR(np.arange(365)) + +futMLD_pchip_GR = PchipInterpolator(extrct_GR_futmld[:, 0], extrct_GR_futmld[:, 1], axis=0) +futMLD_pchip_GR_spl = futMLD_pchip_GR(np.arange(365)) + + +# Detailed plot +fig, axs = plt.subplots(figsize=(8, 4)) +fig.subplots_adjust(wspace=0.2, hspace=0.1, left=0.1, right=0.95, top=0.95, bottom=0.15) + +axs.plot(np.arange(365), MLD_pchip_GR_spl, color='k', lw=2.5, zorder=2, label='Standard MLD') +axs.plot(np.arange(365), futMLD_pchip_GR_spl, color='r', lw=2.5, zorder=2, label='Hypothesized MLD') +axs.axis([0, 365, 35, 0]) +axs.set_ylabel('Mixed layer depth, MLD (m)', fontsize=12, labelpad=10) +axs.set_xlabel('Month', fontsize=12, labelpad=10) +axs.legend(loc=4, fontsize=12, ncol=1, bbox_to_anchor=(0.9, 0)) +axs.set_xticks([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) +axs.set_xticklabels(['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D'], fontsize=12) + + +## save to csv +futMLD_pt = np.vstack((extrct_GR_futmld[:, 0], extrct_GR_futmld[:, 1])).T +np.savetxt(wd+"TempSizeMod_TSM/Forcing/Final/futMLD_final.csv", futMLD_pt, delimiter=",") From f04939450db0eb1b9136cab41665208f9b0d878d Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:16:09 +0200 Subject: [PATCH 54/97] Update TSM_futureMLDForcing.py --- LakeData/TSM_futureMLDForcing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/LakeData/TSM_futureMLDForcing.py b/LakeData/TSM_futureMLDForcing.py index 6aa461f..c50d1fc 100644 --- a/LakeData/TSM_futureMLDForcing.py +++ b/LakeData/TSM_futureMLDForcing.py @@ -5,9 +5,9 @@ import numpy as np import pandas as pd -wd = '/Users/szewing/Desktop/PhD_work/3. ThermalTrait/' +wd = 'your wd' -data_mld = pd.read_csv(wd + '/TempSizeMod_TSM/Forcing/Final/Original_MLD.csv', index_col=0) +data_mld = pd.read_csv(wd + '/RawData_MLD.csv', index_col=0) #--------------------------------------------------------------------- # convert real data to julian day (0-365) From 2250584a0df06d5bc352598dd005b8651a5a206d Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:16:38 +0200 Subject: [PATCH 55/97] Update TSM_futureMLDForcing.py --- LakeData/TSM_futureMLDForcing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakeData/TSM_futureMLDForcing.py b/LakeData/TSM_futureMLDForcing.py index c50d1fc..afb38ff 100644 --- a/LakeData/TSM_futureMLDForcing.py +++ b/LakeData/TSM_futureMLDForcing.py @@ -7,7 +7,7 @@ wd = 'your wd' -data_mld = pd.read_csv(wd + '/RawData_MLD.csv', index_col=0) +data_mld = pd.read_csv(wd+'/RawData_MLD.csv') #--------------------------------------------------------------------- # convert real data to julian day (0-365) From a9fc10c49ca6323ea6b3ca6d07a1d3852072fd03 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:34:17 +0200 Subject: [PATCH 56/97] Add files via upload --- LakeData/TSM_PlanktonData.py | 292 +++++++++++++++++++++++++++++++++++ 1 file changed, 292 insertions(+) create mode 100644 LakeData/TSM_PlanktonData.py diff --git a/LakeData/TSM_PlanktonData.py b/LakeData/TSM_PlanktonData.py new file mode 100644 index 0000000..eb997c7 --- /dev/null +++ b/LakeData/TSM_PlanktonData.py @@ -0,0 +1,292 @@ +""" +[Include colonial large species] +Plotting selected plankton data in Greifensee (data source: Eawag) +- annual composite +""" +import seaborn as sns +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib import cm + +wd = 'your wd' + +#### Selection of zooplantkon data #### +# We chose the more abundant zooplantkon species that are +# 1. mostly herbivores (non-carnivores and consume autotrophs) +# 2. non-predatory species +# p.s. The model does not consider intra-guild predation; there are certain trophic links unable to capture. + +phydata = pd.read_csv(wd + '/GRE_raw_plankton_ab_size.csv', sep=',', + usecols=['date', + 'dinobryon_ROIs_0p5x', 'dinobryon_area_log10_mm2_5p0x', + 'uroglena_ROIs_0p5x', 'uroglena_area_log10_mm2_0p5x', + 'centric_diatom_ROIs_5p0x', 'centric_diatom_area_log10_mm2_5p0x', + 'cryptophyceae_ROIs_5p0x', + 'cryptophyceae_area_log10_mm2_5p0x_weightedmean', 'cryptophyceae_area_log10_mm2_5p0x_mean', + 'rhodomonas_ROIs_5p0x', 'rhodomonas_area_log10_mm2_5p0x', + 'chlorophytes_ROIs_5p0x', 'chlorophytes_area_log10_mm2_5p0x']) +# Wherever weighted mean bio-area and major axis length is available for the phytoplankton, we used the weighted mean. +# The mean would underestaimte the size in most days, except in time with high abundance +phydata = phydata.drop(columns=['cryptophyceae_area_log10_mm2_5p0x_mean', 'cryptophyceae_maj_axis_len_mm_5p0x_mean', + 'cryptophyceae_min_axis_len_mm_5p0x_mean']) + +# rename +phydata.columns = ['date', + 'dinobryon_ROIs', 'dinobryon_area_log10_mm2', + 'uroglena_ROIs', 'uroglena_area_log10_mm2', + 'centric_diatom_ROIs', 'centric_diatom_area_log10_mm2', + 'cryptophyceae_ROIs', 'cryptophyceae_area_log10_mm2', + 'rhodomonas_ROIs', 'rhodomonas_area_log10_mm2', + 'chlorophytes_ROIs', 'chlorophytes_area_log10_mm2'] + + +zoodata = pd.read_csv(r'/Users/szewing/Desktop/PhD_work/Data/Steffi_eawag/GRE_plankton_ab_size_20240416.csv', sep=',', + usecols=['date', + 'daphnia_ROIs_0p5x', 'daphnia_area_log10_mm2_0p5x', + 'eudiaptomus_ROIs_0p5x', 'eudiaptomus_area_log10_mm2_0p5x', + 'cyclops_ROIs_0p5x', 'cyclops_area_log10_mm2_0p5x', + 'nauplius_ROIs_0p5x', 'nauplius_area_log10_mm2_0p5x', + 'ciliates_ROIs_5p0x', + 'ciliates_area_log10_mm2_5p0x_weightedmean', 'ciliates_area_log10_mm2_5p0x_mean', + 'rotifers_ROIs_0p5x', + 'rotifers_area_log10_mm2_0p5x_weightedmean', 'rotifers_area_log10_mm2_0p5x_mean']) +# Wherever weighted mean bio-area and major axis length is available for the phytoplankton, we used the weighted mean. +# The mean would underestaimte the size in most days, except in time with high abundance +zoodata = zoodata.drop(columns=['ciliates_area_log10_mm2_5p0x_mean', 'ciliates_maj_axis_len_mm_5p0x_mean', 'ciliates_min_axis_len_mm_5p0x_mean', + 'rotifers_area_log10_mm2_0p5x_mean', 'rotifers_maj_axis_len_mm_0p5x_mean', 'rotifers_min_axis_len_mm_0p5x_mean']) + +zoodata.columns = ['date', + 'daphnia_ROIs', 'daphnia_area_log10_mm2', + 'eudiaptomus_ROIs', 'eudiaptomus_area_log10_mm2', + 'cyclops_ROIs', 'cyclops_area_log10_mm2', + 'nauplius_ROIs', 'nauplius_area_log10_mm2', + 'ciliates_ROIs', 'ciliates_area_log10_mm2', + 'rotifers_ROIs', 'rotifers_area_log10_mm2'] + +zoodata_used = zoodata[['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', 'ciliates_ROIs', 'ciliates_area_log10_mm2']] + +## set name +physpecies = ['dinobryon', 'uroglena', 'centric_diatom', 'cryptophyceae', 'rhodomonas', 'chlorophytes'] +zoospecies_used = ['daphnia', 'ciliates'] + + +## bio-area unit conversion +def logArea2μmArea(LnArea): + """ + The conversion of raw data from the underwater camera to μm ESD + :param LnArea: log10 cell area (mm2) + :return: μm2 bio-area + """ + mm_sq2μm_sq = 1e6 + return (10 ** LnArea) * mm_sq2μm_sq + + +for i in range(len(physpecies)): + phydata.insert(1 + i, physpecies[i] + '_area_μm2', logArea2μmArea(phydata[physpecies[i] + '_area_log10_mm2'])) + +for i in range(len(zoospecies_used)): + zoodata_used.insert(1 + i, zoospecies_used[i] + '_area_μm2', logArea2μmArea(zoodata_used[zoospecies_used[i] + '_area_log10_mm2'])) + + + +# The ROIs data refers to region of interest (ROI), it is taken by the underwater imaging camera, and the unit is objects capture or +# detected per second [Detailed refers to Merz et al. (2020)] + +def ROI_0p5x(roi): + """ + 0p5x is for cells or individuals that are in large size (100μm-7mm). + All phytoplankton >100μm ESD, dinobryon and uroglena, and large zooplankton e.g. daphnia are measured in this magnification. + This linear log-log relationship of transforming ROI to count/mL is verified by field measurement data in Merz et al. (2021). + :param roi: region of interest; basically refer to counts/identification by the camera + :return: count per mL per day + """ + x = 10 ** (0.91 / 1.3) * roi ** (1 / 1.3) + return x + +def ROI_5p0x(roi): + """ + 5p0x is for cells or individuals that are in sMaLler size (~10-150μm). + All phytoplankton <100μm ESD and ciliates are measured in this magnification. + This linear log-log relationship of transforming ROI to count/mL is verified by field measurement data in Merz et al. (2021). + :param roi: region of interest; basically refer to counts/identification by the camera + :return: count per mL per day + """ + x = 10 ** (2.9 / 0.71) * roi ** (1 / 0.71) + return x + + +### to phydata +phydata.insert(2, 'dinobryon_count/mL', ROI_0p5x(phydata['dinobryon_ROIs'])) # large phyto +phydata.insert(3, 'uroglena_count/mL', ROI_0p5x(phydata['uroglena_ROIs'])) # large phyto +phydata.insert(4, 'centric_diatom_count/mL', ROI_5p0x(phydata['centric_diatom_ROIs'])) # small cells +phydata.insert(5, 'cryptophyceae_count/mL', ROI_5p0x(phydata['cryptophyceae_ROIs'])) # small cells +phydata.insert(6, 'rhodomonas_count/mL', ROI_5p0x(phydata['rhodomonas_ROIs'])) # small cells +phydata.insert(7, 'chlorophytes_count/mL', ROI_5p0x(phydata['chlorophytes_ROIs'])) # small cells + +zoodata_used.insert(2, 'daphnia_count/mL', ROI_0p5x(zoodata_used['daphnia_ROIs'])) +zoodata_used.insert(4, 'ciliates_count/mL', ROI_5p0x(zoodata_used['ciliates_ROIs'])) + + +def bioArea2Vol(Area): + """ + Based on the scaling provided in Ryabov et al. (2021) + :param Area: + :return: Biovolume (μm3) + """ + v = ((1 / (10**0.25)) * Area) ** (1/0.68) # calibrated + return v + + +# calculate biovolume from area using the scaling function from Ryabov et al. (2021) +for i in range(len(physpecies)): + phydata.insert(1 + i, physpecies[i] + '_biov', bioArea2Vol(phydata[physpecies[i] + '_area_μm2'])) + +for i in range(len(zoospecies_used)): + zoodata_used.insert(1 + i, zoospecies_used[i] + '_biov', bioArea2Vol(zoodata_used[zoospecies_used[i] + '_area_μm2'])) + + +def mwP_Ind(biov): + """ + This function uses the allometric relationship on P content for each size class (Hillebrand et al. 2022) Fig. 5C + :param biov: input ESD (µm) + :return: µg P count-1 + """ + slope, intercept = 0.88, -2.95 # slope and intercept regressed from P content data of Hillebrand et al. (2022) + # 1. picogram phosphate per cell at a certain cell size + pgPcount = (10 ** intercept) * (biov ** slope) + # 2. microgram phosphorus per cell at a certain cell size + µgP_count = pgPcount * 1e-6 + return µgP_count + + +def Phy_abun2biomass(biov, abun): + """ + Converting phytoplankton abundance of specific size classes to biomass (count mL-1 to µmol P L-1) + :param biov: input biovolume (µm3) + :param abun: abundance (count mL-1) + :return: µmol P L-1 + """ + mwP = 30.973762 # µg/L 1 µg P/l = 1/MW P = 0.032285 µmol/l (https://www.ices.dk/data/tools/Pages/Unit-conversions.aspx) + mL2L = 1e3 + µgP_ind = mwP_Ind(biov) + µmolP_ind = µgP_ind / mwP # from g to mol + µmolP_L = µmolP_ind * (abun * mL2L) + return µmolP_L + + +def Daph_L_W_Schalau(MaL_mm): + """ + Length-weight ratio for daphnia used in the model of Schalau et al. (2008) and sourced from Urabe and Watanabe (1991) & Lynch et al. (1986) + :param MaL_mm: Maximum Axis Length (mm) + :return: µmol P ind-1 + """ + mwC = 12.011 + L = MaL_mm * 0.75 # ref: Ranta et al. (1993); Karpowicz et al. (2020) + µgC_ind = 1.6 * (L**3) + µmolC_ind = µgC_ind / mwC # convert to molar + C2P_ratio = 350 # the mean threshold elemental ratio (to maintain Daphnia growth - good reference for C:P) + # a mean from Hessen (2006) - assuming redfield ratio for carbon to phosphate transformation - + # but the range can vary between 80-385 for daphnia + µmolP_ind = µmolC_ind / C2P_ratio + return µmolP_ind + + +def CiliatePcontent_Putt(biov): + """ + Scaling function for ciliate body size and carbon content (Putt and Stoecker 1989) + #(10**0.12) * (v**0.19) + :param biov: input biovolume (µm3) + :return: µmol P ind-1 + """ + Cfactor = 0.19 # pg C um-3 + pg2µg = 1e-6 + mwC = 12.011 + µgC_ind = ((10**0.12) * biov * Cfactor) * pg2µg + µmolC_ind = µgC_ind / mwC + C2P_ratio = 100 # a mean from 80-120 of Golz et al. (2015) - assuming redfield ratio for carbon to phosphate transformation - but the range can vary between 80-300 for daphnia + µmolP_ind = µmolC_ind / C2P_ratio + return µmolP_ind + + +## phytoplankton total biomasses +for i in range(len(physpecies)): + phydata.insert(1 + i, physpecies[i] + '_biomass', Phy_abun2biomass(phydata[physpecies[i] + '_biov'], phydata[physpecies[i] + '_count/mL'])) +phydata.insert(1, 'Phy_TB', phydata.loc[:, ['dinobryon_biomass', 'uroglena_biomass', 'centric_diatom_biomass', 'cryptophyceae_biomass', + 'rhodomonas_biomass', 'chlorophytes_biomass']].sum(axis=1)) + +## zooplankton total biomasses +zoodata_used.insert(1, 'daphnia_biomass', Daph_L_W_Schalau(zoodata_used['daphnia_MaL_mm']) * zoodata_used['daphnia_count/mL'] * 1e3) +zoodata_used.insert(2, 'ciliates_biomass', CiliatePcontent_Putt(zoodata_used['ciliates_biov2']) * zoodata_used['ciliates_count/mL'] * 1e3) + +zoodata_used.insert(1, 'Zoo_TB', zoodata_used.loc[:, ['daphnia_biomass', 'ciliates_biomass']].sum(axis=1)) + + + +### biomass weighted-mean biovolume +def WMeanSize(biom, biov): + """ + ab = abundance of a specific size class; s = that size class (i.e. size array) + axis=1 because the result data array is in the size of array[days, size groups, nut, mix] + """ + return np.nansum(biom * biov, axis=1) / np.nansum(biom, axis=1) + # return np.nansum(biom * np.log10(biov), axis=1) / np.nansum(biom, axis=1) + +npBiom = phydata.loc[:, ['dinobryon_biomass', 'uroglena_biomass', 'centric_diatom_biomass', 'cryptophyceae_biomass', + 'rhodomonas_biomass', 'chlorophytes_biomass']].to_numpy() +npBiov = phydata.loc[:, ['dinobryon_biov', 'uroglena_biov', 'centric_diatom_biov', 'cryptophyceae_biov', 'rhodomonas_biov', + 'chlorophytes_biov']].to_numpy() + +Sw_df = pd.DataFrame(WMeanSize(npBiom, npBiov), columns=['Sw']) + + +### Add julian date stamps +import datetime + +def datestdtojd(stddate): + """ + source: https://rafatieppo.github.io/post/2018_12_01_juliandate/ + :param stddate: standard date for input + :return: + """ + fmt = '%Y-%m-%d' + sdtdate = datetime.datetime.strptime(stddate, fmt) + sdtdate = sdtdate.timetuple() + jdate = sdtdate.tm_yday + return jdate + + +jday = np.array([]) +for i in range(len(phydata)): + jday = np.append(jday, datestdtojd(phydata['date'][i])) + +phydata.insert(1, 'Julian day', pd.DataFrame(jday)) +zoodata_used.insert(1, 'Julian day', pd.DataFrame(jday)) +Sw_df.insert(1, 'Julian day', pd.DataFrame(jday)) + + +phydata['year'] = pd.DatetimeIndex(phydata['date']).year +phy_median = phydata.groupby(['Julian day'], as_index=False)['Phy_TB'].median().to_numpy() +phy_mean = phydata.groupby(['Julian day'], as_index=False)['Phy_TB'].mean().to_numpy() + + +Sw_df['year'] = pd.DatetimeIndex(phydata['date']).year +Sw_median = Sw_df.groupby(['Julian day'], as_index=False)['Sw'].median().to_numpy() +Sw_mean = Sw_df.groupby(['Julian day'], as_index=False)['Sw'].mean().to_numpy() + + +## export to csv +PhyTB_arr = phydata.loc[:, ['dinobryon_biomass', 'uroglena_biomass', 'centric_diatom_biomass', 'cryptophyceae_biomass', + 'rhodomonas_biomass', 'chlorophytes_biomass']].to_numpy() +PhyBiov_arr = phydata.loc[:, ['dinobryon_biov', 'uroglena_biov', 'centric_diatom_biov', 'cryptophyceae_biov', 'rhodomonas_biov', + 'chlorophytes_biov']].to_numpy() + + +plankdata = np.column_stack((Sw_df['Julian day'].to_numpy(), phydata['Phy_TB'].to_numpy(), Sw_df['Sw'].to_numpy(), + zoodata_used['Zoo_TB'].to_numpy())) +plankdata = pd.DataFrame(plankdata) +plankdata.columns = ['Julian day', 'PhyBiom', 'Sw', 'ZooBiom'] + + +plankdata.to_csv(wd + "/PlankData_comp.csv") + From e05b2aa5ef95302b2edaedac9740f4803cf1301a Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:37:22 +0200 Subject: [PATCH 57/97] Rename TSM_PlanktonData.py to TSM_PlanktonData.py --- {LakeData => LakeEnvData}/TSM_PlanktonData.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {LakeData => LakeEnvData}/TSM_PlanktonData.py (100%) diff --git a/LakeData/TSM_PlanktonData.py b/LakeEnvData/TSM_PlanktonData.py similarity index 100% rename from LakeData/TSM_PlanktonData.py rename to LakeEnvData/TSM_PlanktonData.py From 640d4b8fc6ad748cc5f206e086b685faecbc0610 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:37:45 +0200 Subject: [PATCH 58/97] Rename TSM_PlanktonData.py to TSM_PlanktonData.py --- {LakeEnvData => LakePlankData}/TSM_PlanktonData.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {LakeEnvData => LakePlankData}/TSM_PlanktonData.py (100%) diff --git a/LakeEnvData/TSM_PlanktonData.py b/LakePlankData/TSM_PlanktonData.py similarity index 100% rename from LakeEnvData/TSM_PlanktonData.py rename to LakePlankData/TSM_PlanktonData.py From 32fc956099d0cf5c63b6c92705d46f7dfd08e71e Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:38:43 +0200 Subject: [PATCH 59/97] Update README.md --- LakePlankData/README.md | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/LakePlankData/README.md b/LakePlankData/README.md index 2fde9e7..a6828e0 100644 --- a/LakePlankData/README.md +++ b/LakePlankData/README.md @@ -1,12 +1,2 @@ -This model forcing adopts in-situ data from Greifensee, Switzerland. -There are three environmental forcing functions to the model, respectively, Water Temperature from photic zone (LWST), Photosynthetically-active Radiance (PAR), and Mixed Layer Depth (MLD). -These forcing are derived from various time-series data sources, and the data cover different time periods. The LWST is obtained from the CTD profiles as daily epilimnetic temperature from 13/3/2019 to 31/12/2022 (n=1390), measured by the Swiss Federal Institute of Aquatic Science and Technology, Eawag. The PAR data is converted from the solar radiance data collected from a weather station next to Greifensee, measured daily by MeteoSwiss for the same period as the LWST. The PAR conversion is detailed in the related publication of this model. Finally, we retrieve MLD data from Pomati et al. (2020), under lake 'Greifensee', that documented the monthly MLD of Greifensee from 1972 to 2015 (n=270). +Here stores the plankton data used for the model. -To generate an annual forcing profile, LWST was taken from a three-year mean, PAR was taken from a 75 percentile due to high daily variations, and MLD was derived by month to preserve the key mixing of the epilimnion in the water column. The steps deriving these forcing profile is shown in the python script 'TSM_TempForcing.py', 'TSM_PARForcing.py', and 'TSM_MLDForcing.py' respectively. - -

- Figure1_v2 1 - -

-Reference -+ Pomati, F., J. B. Shurin, K. H. Andersen, C. Tellenbach, and A. D. Barton. 2020. Interacting temperature, nutrients and zooplankton grazing control phytoplankton size-abundance relationships in eight Swiss lakes [Data set]. In Frontiers in Microbiology, Aquatic Microbiology. Zenodo. https://doi.org/10.5281/zenodo.3582838 From 343e59f68c5be6fe0aa1e764f76f8671ff7cf200 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sun, 16 Jun 2024 21:39:03 +0200 Subject: [PATCH 60/97] Add files via upload --- LakePlankData/PlanktData_calib.csv | 1391 ++++++++++++++++++++++++++++ 1 file changed, 1391 insertions(+) create mode 100644 LakePlankData/PlanktData_calib.csv diff --git a/LakePlankData/PlanktData_calib.csv b/LakePlankData/PlanktData_calib.csv new file mode 100644 index 0000000..5db7164 --- /dev/null +++ b/LakePlankData/PlanktData_calib.csv @@ -0,0 +1,1391 @@ +,Julian day,PhyBiom,Sw,ZooBiom +0,72.0,0.0,,0.0 +1,73.0,0.0,,0.0 +2,74.0,0.0,,0.0 +3,75.0,0.0,,0.0 +4,76.0,0.0,,0.0 +5,77.0,0.0,,0.0 +6,78.0,0.0,,0.0 +7,79.0,0.0,,0.0 +8,80.0,0.0,,0.0 +9,81.0,0.0,,0.0 +10,82.0,0.0,,0.0 +11,83.0,0.0,,0.0 +12,84.0,0.0,,0.0 +13,85.0,0.08829921885768073,5388.677645798787,0.10717580038692659 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Trait-based models are valueable tools to disentangle possible effects and test The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated to a size-based framework (e.g. Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes.

- Figure1_v2 1 + Figure1_v3 -The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing (Hansen et al. 1994, 1997; Edwards et al. 2012). The model aims at studying changes in the size compositions of lake phytoplankton communities. +The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing. In these study, the allometric parameters are calibrated against lake-specific data within the reported ranges (Hansen et al. 1994, 1997; Edwards et al. 2012). For this study, the model aims at capturing nutrient and plankton dynamics in a Swiss lake, Greifensee, followed by projecting changes to these dynamics based on IPCC reported climate change scenarios (RCPs). Alternaitvely, the model can be used to study changes in the size compositions of lake phytoplankton communities under varying temperature and nutrient conditions.
## Temperature dependence in the model @@ -43,6 +43,11 @@ $$P_{opt}(S_i^P, S_j^Z) = \beta_{P_{opt}}\cdot (S_j^Z)^{\alpha_{P_{opt}}}$$
representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. +

+## Model calibration +We fine-tuned selected parameters against the time-averaged plankton data collected from lake Greifensee, consisting plankton size (bio-area) and abundance (ROI/sec). For details, please refer to the related publication of this model (under review). After calibrating the parameter values, we obtained a best-fit model that reflect the current average state of the lake. + +

## Experiments Using the standard model, we conduct two numerical experiments:
@@ -53,25 +58,38 @@ For details, please refer to related publication of this model (under review).

## Suggestions for future works -1. Numerical explorations, e.g. how different assumptions on the temperature dependence of phytoplankton growth would change the predictions on communities compositions of phytoplankton - - Community Eppley / Q10 model (Ho et al. 2013) - - Size-dependent Eppley (e.g. Taherzadeh et al. 2017; Anderson et al. 2023; To et al. 2024) - - Community unimodal curve (Cagle and Roelke 2021; This study) - - Size-dependent unimodal curve +The current model are structured flexibly and can be extended for future studies. Below are some options.
-2. Ecological explorations over altered stratification phenology in lakes (Woolway et al. 2021), for example, - Figure1_v2 1 +1. Experiments on model hypothesis - How different assumptions on the temperature dependence of phytoplankton growth and zooplankton grazing would change the predictions on communities compositions of phytoplankton community? + e.g. Comparisons between: + - Eppley curve or Q10 model (Ho et al. 2013) versus thermal tolerance curve (Chen, 2022) + - Size-dependent Eppley curves (e.g. To et al. 2024) + - Community-averaged unimodal curve (Cagle and Roelke 2021; the present study) + - Size-dependent unimodal curve (due to be examined) + +2. Experiments on model hypothesis - mechanisms between temperature dependence and resource levels + - optimal growth temperature, $T_{opt}$, with nutrient levels (Bestion et al. 2018) + - optimal growth temperature, $T_{opt}$, with irradiance levels (Edwards et al. 2016) + +3. Numerical explorations on the relationships between phytoplankton size and thermal traits, e.g. $T_{opt}$, $\sigma_{T}$ + - What are the probable/observable range of parameters for such allometric relationships? + +4. Ecological explorations of altered stratification phenology in lakes (Woolway et al. 2021), for example, + - Figure1_v2 1 +





Reference: + Acevedo-Trejos, E., E. Marañón, and A. Merico. 2018. Phytoplankton size diversity and ecosystem function relationships across oceanic regions. Proc. R. Soc. B. 285: 20180621. doi:10.1098/rspb.2018.0621 -+ Anderson, S. I., Fronda, C., Barton, A. D., Clayton, S., Rynearson, T. A., & Dutkiewicz, S. (2024). Phytoplankton thermal trait parameterization alters community structure and biogeochemical processes in a modeled ocean. Global Change Biology, 30(1), e17093. https://doi.org/10.1111/gcb.17093 + Armstrong, R. A. 1994. Grazing limitation and nutrient limitation in marine ecosystems: Steady state solutions of an ecosystem model with multiple food chains. Limnol. Oceanogr. 39: 597–608. doi:10.4319/lo.1994.39.3.0597 ++ Bestion, E., Schaum, C., & Yvon‐Durocher, G. (2018). Nutrient limitation constrains thermal tolerance in freshwater phytoplankton. Limnology and Oceanography Letters, 3(6), 436–443. https://doi.org/10.1002/lol2.10096 + Cagle, S. E., & Roelke, D. L. (2021). Relative roles of fundamental processes underpinning PEG dynamics in dimictic lakes as revealed by a self-organizing, multi-population plankton model. Ecological Modelling, 462, 109793. https://doi.org/10.1016/j.ecolmodel.2021.109793 + Carpenter, S. R. 2005. Eutrophication of aquatic ecosystems: Bistability and soil phosphorus. Proc. Natl. Acad. Sci. U.S.A. 102: 10002–10005. doi:10.1073/pnas.0503959102 ++ Chen, B. (2022). Thermal diversity affects community responses to warming. Ecological Modelling, 464, 109846. https://doi.org/10.1016/j.ecolmodel.2021.109846 + Dokulil, M. T., De Eyto, E., Maberly, S. C., May, L., Weyhenmeyer, G. A., & Woolway, R. I. (2021). Increasing maximum lake surface temperature under climate change. Climatic Change, 165(3–4), 56. https://doi.org/10.1007/s10584-021-03085-1 + Dudgeon, D. 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology 29: R960–R967. doi:10.1016/j.cub.2019.08.002 + Edwards, K. F., M. K. Thomas, C. A. Klausmeier, and E. Litchman. 2012. Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton. Limnol. Oceanogr. 57: 554–566. doi:10.4319/lo.2012.57.2.0554 ++ Edwards, K. F., Thomas, M. K., Klausmeier, C. A., & Litchman, E. (2016). Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level: Light-Temperature Interactions. Limnology and Oceanography, 61(4), 1232–1244. https://doi.org/10.1002/lno.10282 + Fasham, M. J. R., H. W. Ducklow, and S. M. McKelvie. 1990. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. j mar res 48: 591–639. doi:10.1357/002224090784984678 + Hansen, B., P. K. Bjørnsen, and P. J. Hansen. 1994. The size ratio between planktonic predators and their prey. Limnology and Oceanography 39: 395–403. doi:10.4319/lo.1994.39.2.0395 + Hansen, P. J., P. K. Bjørnsen, and B. Hansen. 1997. Zooplankton grazing and growth: Scaling within the 2-2,000um body size range. Limnology and Oceanography 42: 687–704. @@ -87,7 +105,6 @@ Ho, P.-C., Chang, C.-W., Hsieh, C. H., Shiah, F.-K., & Miki, T. (2013). Effects + Reinl, K. L., J. D. Brookes, C. C. Carey, and others. 2021. Cyanobacterial blooms in oligotrophic lakes: Shifting the high‐nutrient paradigm. Freshwater Biology 66: 1846–1859. doi:10.1111/fwb.13791 + Reinl, K. L., T. D. Harris, R. L. North, and others. 2023. Blooms also like it cold. Limnol Oceanogr Letters 8: 546–564. doi:10.1002/lol2.10316 + Striebel, M., S. Schabhüttl, D. Hodapp, P. Hingsamer, and H. Hillebrand. 2016. Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composition. Oecologia 182: 815–827. doi:10.1007/s00442-016-3693-3 -+ Taherzadeh, N., Kerimoglu, O., & Wirtz, K. W. (2017). Can we predict phytoplankton community size structure using size scalings of eco-physiological traits? Ecological Modelling, 360, 279–289. https://doi.org/10.1016/j.ecolmodel.2017.07.008 + To, S., Acevedo‐Trejos, E., Chakraborty, S., Pomati, F., & Merico, A. (2024). Grazing strategies determine the size composition of phytoplankton in eutrophic lakes. Limnology and Oceanography, lno.12538. https://doi.org/10.1002/lno.12538 + Ward, B. A., S. Dutkiewicz, O. Jahn, and M. J. Follows. 2012. A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57: 1877–1891. doi:10.4319/lo.2012.57.6.1877 + Winder, M., & Sommer, U. (2012). Phytoplankton response to a changing climate. Hydrobiologia, 698(1), 5–16. https://doi.org/10.1007/s10750-012-1149-2 From d8bfae7e91cf8a014d200d146ae5e1312fb6eebb Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 22:44:27 +0200 Subject: [PATCH 62/97] Add files via upload --- LakePlankData/GRE_raw_plankton_ab_size.csv | 1391 ++++++++++++++++++++ 1 file changed, 1391 insertions(+) create mode 100644 LakePlankData/GRE_raw_plankton_ab_size.csv diff --git a/LakePlankData/GRE_raw_plankton_ab_size.csv b/LakePlankData/GRE_raw_plankton_ab_size.csv new file mode 100644 index 0000000..5cf4e49 --- /dev/null +++ b/LakePlankData/GRE_raw_plankton_ab_size.csv @@ -0,0 +1,1391 @@ 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From 740c528cdf141990799c6e0d6ef71a3dc760227c Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 22:46:49 +0200 Subject: [PATCH 63/97] Add files via upload --- LakePlankData/PlanktData4Calibr.csv | 1391 +++++++++++++++++++++++++++ 1 file changed, 1391 insertions(+) create mode 100644 LakePlankData/PlanktData4Calibr.csv diff --git a/LakePlankData/PlanktData4Calibr.csv b/LakePlankData/PlanktData4Calibr.csv new file mode 100644 index 0000000..5db7164 --- /dev/null +++ b/LakePlankData/PlanktData4Calibr.csv @@ -0,0 +1,1391 @@ +,Julian day,PhyBiom,Sw,ZooBiom +0,72.0,0.0,,0.0 +1,73.0,0.0,,0.0 +2,74.0,0.0,,0.0 +3,75.0,0.0,,0.0 +4,76.0,0.0,,0.0 +5,77.0,0.0,,0.0 +6,78.0,0.0,,0.0 +7,79.0,0.0,,0.0 +8,80.0,0.0,,0.0 +9,81.0,0.0,,0.0 +10,82.0,0.0,,0.0 +11,83.0,0.0,,0.0 +12,84.0,0.0,,0.0 +13,85.0,0.08829921885768073,5388.677645798787,0.10717580038692659 +14,86.0,0.12613880966830002,6194.996465048912,0.09709739400494749 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-1387,363.0,0.01640781573593223,513369.5016607739,0.08986243768071941 -1388,364.0,0.005679607526982159,1276585.1712535569,0.07721532206974116 -1389,365.0,0.010205032570554272,328611.04756265285,0.12868221021592263 From 4c8a4724734cd5c9d15564e6cc5495b45e074b5e Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 22:47:42 +0200 Subject: [PATCH 65/97] Update TSM_PlanktonData.py --- LakePlankData/TSM_PlanktonData.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakePlankData/TSM_PlanktonData.py b/LakePlankData/TSM_PlanktonData.py index eb997c7..481c550 100644 --- a/LakePlankData/TSM_PlanktonData.py +++ b/LakePlankData/TSM_PlanktonData.py @@ -288,5 +288,5 @@ def datestdtojd(stddate): plankdata.columns = ['Julian day', 'PhyBiom', 'Sw', 'ZooBiom'] -plankdata.to_csv(wd + "/PlankData_comp.csv") +plankdata.to_csv(wd + "/PlankData4Calibr.csv") From 1f50362138ee75223abfdc7aa9870d4a2ffcb4de Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 23:04:40 +0200 Subject: [PATCH 66/97] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e3cd5ab..f51b19c 100644 --- a/README.md +++ b/README.md @@ -54,7 +54,7 @@ Using the standard model, we conduct two numerical experiments:
1. a projection based on Representative Climate Pathways (RCPs) issued by IPCC 2. a sensitivity test for the two thermal traits, $T_{opt}$ and $\sigma_T$ in different RCP scenarios -For details, please refer to related publication of this model (under review). +For details, please refer to the related publication of this model (under review).

## Suggestions for future works From a6c7327a9736b4734bb12e58ff973b9bf097081b Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 23:20:47 +0200 Subject: [PATCH 67/97] Update README.md --- LakePlankData/README.md | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/LakePlankData/README.md b/LakePlankData/README.md index a6828e0..6270ba2 100644 --- a/LakePlankData/README.md +++ b/LakePlankData/README.md @@ -1,2 +1,22 @@ -Here stores the plankton data used for the model. +Here stores: +1. The raw plankton data ('GRE_raw_plankton_ab_size.csv') +2. The script used to produce a time-averaged plankton data for calibrating the model ('TSM_PlanktonData.py') +3. The time-averaged plankton data ('PlanktData4Calibr.csv') + +

+A graphical illustration of the transformation of time-averaged phytoplankton data*: +SuppFigS2_v3 + +A graphical illustration of the transformation of time-averaged phytoplankton data*: +SuppFigS3_v1 + +

+### A brief description: +This model outputs phytoplankton and zooplankton biomasses for different size classes. This information allow us to produce 1.) total phytoplankton biomass(µM P); 2.) total zooplankton biomass(µM P) ; and 3.) community mean phytoplankton size(µm ESD). +After the transformations, the common unit considered for comparing model outputs and data are: biomass(µM P); and biovolume(µm$^3$) + + +


+*For more detailed descriptions and indications of the symbols herein, please refer to the related publication of this model. + From da14af3b6812ae9d1f10d0446284e28a057f8f76 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 23:30:01 +0200 Subject: [PATCH 68/97] Create README.md --- LakeData/README.md | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 LakeData/README.md diff --git a/LakeData/README.md b/LakeData/README.md new file mode 100644 index 0000000..d37ec42 --- /dev/null +++ b/LakeData/README.md @@ -0,0 +1,7 @@ +Here contains all lake environmental data used for the model, they are: mixed layer depth (MLD), photosynthetically active radiation (PAR), and lake water surface temperature (Temp). + +'RawData_XXX.csv' : raw, untreated data collected from lake Greifensee (Note: the temporal length and frequence do not overlap completely between MLD, PAR, and Temp) +'ForcData_XXX.csv' : data used to force the model (input data) +'TSM_XXXX.py' : scripts used to obtain ForcData from RawData + +The plots of these data are provided in the related publication (See front page). From a1cb2879b4c8115a2a93c6bf07c44426d1d63e9b Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Mon, 19 Aug 2024 23:30:18 +0200 Subject: [PATCH 69/97] Delete LakeData/TSM_futureMLDForcing.py --- LakeData/TSM_futureMLDForcing.py | 83 -------------------------------- 1 file changed, 83 deletions(-) delete mode 100644 LakeData/TSM_futureMLDForcing.py diff --git a/LakeData/TSM_futureMLDForcing.py b/LakeData/TSM_futureMLDForcing.py deleted file mode 100644 index afb38ff..0000000 --- a/LakeData/TSM_futureMLDForcing.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -Produce environmental forcing for a hypothesized future MLD -""" -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -wd = 'your wd' - -data_mld = pd.read_csv(wd+'/RawData_MLD.csv') - -#--------------------------------------------------------------------- -# convert real data to julian day (0-365) - -import datetime - -def datestdtojd(stddate): - """ - source: https://rafatieppo.github.io/post/2018_12_01_juliandate/ - :param stddate: standard date for input - :return: - """ - fmt = '%Y-%m-%d' - sdtdate = datetime.datetime.strptime(stddate, fmt) - sdtdate = sdtdate.timetuple() - jdate = sdtdate.tm_yday - return jdate - -jday = np.array([]) -for i in range(len(data_mld)): - jday = np.append(jday, datestdtojd(data_mld['date'][i])) -data_mld.insert(1, 'Julian day', pd.DataFrame(jday)) # phychem data - - -""" -Mixed Layer Depth (MLD) -1. taking the mean for each julian day -2. taking the mean for each month -3. interpolate a function based on 12-month data points -""" -mld_GR = data_mld.groupby('Julian day', as_index=False)['thermocl'].median().to_numpy() - -condlist = [0., 32., 75., 100., 121., 152., 182., 213., 244., 274., 305., 330., 345, 365] -condlist_future = [0., 32., (75.-22.), (100.-22.), 121., 152., 182., 213., 244., 274., 305, (330.+11.), (345.+11.), 365] -# approximated julian day of the first day of each month* - -# there are more noise in MLD, so we did some manipulation from visual inspection -extrct_GR_mld = np.zeros((len(condlist), 2)) -extrct_GR_mld[:, 0] = condlist -extrct_GR_mld[0:3, 1] = np.repeat(30, 3) -extrct_GR_mld[3:, 1] = np.array([3, 4, 5, 6, 7, 8, 9, 12, 18, 30, 30]) - -extrct_GR_futmld = np.zeros((len(condlist_future), 2)) -extrct_GR_futmld[:, 0] = condlist_future -extrct_GR_futmld[0:3, 1] = np.repeat(30, 3) -extrct_GR_futmld[3:, 1] = np.array([3, 4, 5, 6, 7, 8, 9, 10, 18, 30, 30]) - - -from scipy.interpolate import PchipInterpolator -MLD_pchip_GR = PchipInterpolator(extrct_GR_mld[:, 0], extrct_GR_mld[:, 1], axis=0) -MLD_pchip_GR_spl = MLD_pchip_GR(np.arange(365)) - -futMLD_pchip_GR = PchipInterpolator(extrct_GR_futmld[:, 0], extrct_GR_futmld[:, 1], axis=0) -futMLD_pchip_GR_spl = futMLD_pchip_GR(np.arange(365)) - - -# Detailed plot -fig, axs = plt.subplots(figsize=(8, 4)) -fig.subplots_adjust(wspace=0.2, hspace=0.1, left=0.1, right=0.95, top=0.95, bottom=0.15) - -axs.plot(np.arange(365), MLD_pchip_GR_spl, color='k', lw=2.5, zorder=2, label='Standard MLD') -axs.plot(np.arange(365), futMLD_pchip_GR_spl, color='r', lw=2.5, zorder=2, label='Hypothesized MLD') -axs.axis([0, 365, 35, 0]) -axs.set_ylabel('Mixed layer depth, MLD (m)', fontsize=12, labelpad=10) -axs.set_xlabel('Month', fontsize=12, labelpad=10) -axs.legend(loc=4, fontsize=12, ncol=1, bbox_to_anchor=(0.9, 0)) -axs.set_xticks([1., 32., 60., 91., 121., 152., 182., 213., 244., 274., 305., 335]) -axs.set_xticklabels(['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D'], fontsize=12) - - -## save to csv -futMLD_pt = np.vstack((extrct_GR_futmld[:, 0], extrct_GR_futmld[:, 1])).T -np.savetxt(wd+"TempSizeMod_TSM/Forcing/Final/futMLD_final.csv", futMLD_pt, delimiter=",") From 6f0e70f6e6ac3bf1eac8b22b9ab43893e0645b4d Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 21 Aug 2024 11:53:19 +0200 Subject: [PATCH 70/97] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index f51b19c..8c0c6c8 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,19 @@ # TempSizeMod - Temperature-size Model ## Background -Over half of the global lakes are recording increases in lake temperature and reporting rising bloom events (O'Reilley et al. 2015; Ho et al. 2019; Dokulil et al. 2021). Climate warming increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Temperature change leads to alterations in the temporal dynamics (i.e. phenological shifts) and community compositions of phytoplankton (Winder and Sommer 2012; Zohary et al. 2021). It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Shurin et al. 2012; Striebel et al. 2016). +Over half of the global lakes are recording increases in lake temperature and reporting rising bloom events (O'Reilley et al. 2015; Ho et al. 2019; Dokulil et al. 2021). Climate warming is increasingly threatening freshwater ecosystem biodiversity (Paerl et al. 2016; Dudgeon 2019; Reid et al. 2019). Temperature change leads to alterations in the temporal dynamics (also known as phenological shifts) and community compositions of phytoplankton (Winder and Sommer 2012; Zohary et al. 2021). It is crucial to understand how temperature drives changes to the assembly and the dynamics of the phytoplankton community (Petchey et al. 1999; Yvon-Durocher et al. 2011; Shurin et al. 2012; Striebel et al. 2016). -In the current realm of global environmental change, understanding the effects of temperature on phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankotn is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like lake mixing and stratifications, lights, and zooplankton grazer (Pomati et al. 2020), thus it affects phytoplankton communities both directly and indirectly (Zohary et al. 2021). This makes predictions on community dynamics difficult. For instance, the occurrence of phytoplankton blooms is becoming more unpredictable (e.g. Sterner et al. 2020; Reinl et al. 2021, 2023). +In the current realm of global environmental change, understanding the effects of temperature on the phytoplankton community is highly important for ecosystem management (Paerl et al. 2016). Nevertheless, in a multi-stressor system, disentangling the effects of temperature on phytoplankton is not easy (Dudgeon 2019). Temperature often interacts with other environmental factors like lake mixing and stratification, lights, and zooplankton grazer (Pomati et al. 2020), thus affecting phytoplankton communities both directly and indirectly (Zohary et al. 2021). This makes predicting plankton dynamics difficult. For instance, the occurrence of phytoplankton blooms is becoming more unpredictable as blooms are found in both cold and warm waters (e.g. Sterner et al. 2020; Reinl et al. 2021, 2023). -Trait-based models are valueable tools to disentangle possible effects and test possible mechanisms temperature has on phytoplankton (Litchman 2023). Phytoplantkon cell size is a master trait and has been used widely in modelling works (Litchman and Klausmeier 2008). Size-based models allow explorations on aggregated community properties such as total biomass and community mean cell size of phytoplankton (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018; To et al. 2024). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions in response to changes in environmental conditions. +Trait-based models are valuable tools to disentangle possible effects and test the possible mechanisms that water temperature has on phytoplankton (Litchman 2023). Phytoplankton cell size is a master trait and has been widely used in modelling work (Litchman and Klausmeier 2008). Size-based models allow explorations of aggregated community properties such as total biomass and community mean cell size of phytoplankton (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018; To et al. 2024). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions in response to changes in environmental conditions.

## Model description -The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated to a size-based framework (e.g. Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The phytoplankton growth is limited by light and nutrient, and is scaled by a temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, follows by recharging the nutrient pool through remineralization processes. +The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated into a size-based framework (e.g., Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The growth of phytoplankton is limited by light and nutrients, and is scaled by temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, followed by recharging the nutrient pool through remineralization processes.

Figure1_v3 -The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships of phytoplankton growth and zooaplankton grazing. In these study, the allometric parameters are calibrated against lake-specific data within the reported ranges (Hansen et al. 1994, 1997; Edwards et al. 2012). For this study, the model aims at capturing nutrient and plankton dynamics in a Swiss lake, Greifensee, followed by projecting changes to these dynamics based on IPCC reported climate change scenarios (RCPs). Alternaitvely, the model can be used to study changes in the size compositions of lake phytoplankton communities under varying temperature and nutrient conditions. +The model focuses on capturing size-dependent bottom-up and top-down interactions through data-driven allometric relationships between phytoplankton growth and zooplankton grazing. In this study, the allometric parameters are calibrated against lake-specific data within the reported ranges (Hansen et al. 1994, 1997; Edwards et al. 2012). For this study, the model aims at capturing nutrient and plankton dynamics in a Swiss lake, Greifensee, followed by projecting changes to these dynamics based on IPCC reported climate change scenarios (RCPs). Alternatively, the model can be used to study changes in the size compositions of lake phytoplankton communities under varying temperature and nutrient conditions.
## Temperature dependence in the model From e2e3127712c4b7ca3fbf36648bff65476e6f0e31 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 21 Aug 2024 11:57:26 +0200 Subject: [PATCH 71/97] Update README.md --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 8c0c6c8..68f4959 100644 --- a/README.md +++ b/README.md @@ -19,13 +19,16 @@ The model focuses on capturing size-dependent bottom-up and top-down interaction ## Temperature dependence in the model The temperature dependence for phytoplankton growth follows a bell-shaped thermal tolerance curve, given by, -$$E(T) = e^{0.063T} \left[1- \left(\frac{T-T_{opt}}{\sigma_T}\right)^2 \right] $$ + +$$E(T) = e^{0.063T} \left[1- \left(\frac{T-T_{opt}}{\sigma_T}\right)^2 \right] $$, +
-, where $T$ is the ambient lake water surface temperature (LWST), $T_{opt}$ is the thermal optima that determines the median of the curve, and $\sigma_T$ is the thermal tolerance that determines the width of the curve. In this study, we assume a community mean thermal tolerance curve to all phytoplankton size classes. +where $T$ is the ambient lake water surface temperature (LWST), $T_{opt}$ is the thermal optima that determines the median of the curve, and $\sigma_T$ is the thermal tolerance that determines the width of the curve. In this study, we assume a community mean thermal tolerance curve to all phytoplankton size classes. The maximum ingestion rates of zooplankton follows a Q10 model such that, the maximum grazing increases with temperature. The equation for the dependence is, -$$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$ +$$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$. +
The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10 $^{\circ}$ C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). From 640cc5caac2098fd7e764907a0458c5e973cdf99 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 22 Aug 2024 20:43:47 +0200 Subject: [PATCH 72/97] Delete LakePlankData/GRE_raw_plankton_ab_size.csv --- LakePlankData/GRE_raw_plankton_ab_size.csv | 1391 -------------------- 1 file changed, 1391 deletions(-) delete mode 100644 LakePlankData/GRE_raw_plankton_ab_size.csv diff --git a/LakePlankData/GRE_raw_plankton_ab_size.csv b/LakePlankData/GRE_raw_plankton_ab_size.csv deleted file mode 100644 index 5cf4e49..0000000 --- a/LakePlankData/GRE_raw_plankton_ab_size.csv +++ /dev/null @@ -1,1391 +0,0 @@ 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From 114fc929077fcb390ca423ba2ae91cf9f286c988 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 22 Aug 2024 20:44:06 +0200 Subject: [PATCH 73/97] Add files via upload --- LakePlankData/raw_NPdata.csv | 1391 ++++++++++++++++++++++++++++++++++ 1 file changed, 1391 insertions(+) create mode 100644 LakePlankData/raw_NPdata.csv diff --git a/LakePlankData/raw_NPdata.csv b/LakePlankData/raw_NPdata.csv new file mode 100644 index 0000000..77efc7d --- /dev/null +++ b/LakePlankData/raw_NPdata.csv @@ -0,0 +1,1391 @@ +,date,Julian day,phosphate_ug,dinobryon_ROIs,dinobryon_area_log10_mm2,uroglena_ROIs,uroglena_area_log10_mm2,centric_diatom_ROIs,centric_diatom_area_log10_mm2,cryptophyceae_ROIs,cryptophyceae_area_log10_mm2,rhodomonas_ROIs,rhodomonas_area_log10_mm2,chlorophytes_ROIs,chlorophytes_area_log10_mm2,daphnia_ROIs,daphnia_area_log10_mm2,ciliates_ROIs,ciliates_area_log10_mm2 +0,2019-03-13,72.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA 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+67,2019-05-19,139.0,NA,0.0120833333333333,-2.06692387812481,0.0029166666666666,-1.50757496708242,0.0060416666666666,-3.17822210234299,0.0544444444444445,-3.31242261813056,0.0438888888888889,-3.82757491168126,0.0211805555555556,-3.37653523366454,0.04125,-0.354616445901517,0.0010416666666666,-3.00363793749132 +68,2019-05-20,140.0,1.30669125,0.00625,-2.8342460406398,0.0025,-1.49266415424212,0.0060416666666666,-3.19521981068513,0.0879166666666666,-3.32776379915314,0.0290972222222222,-3.82954056777237,0.0245138888888889,-3.32688976115172,0.03,-0.545307252660594,0.0007638888888888,-2.87815806574357 +69,2019-05-21,141.0,1.27273041666667,0.0095833333333333,-2.92896331518152,0.0008333333333333,-1.10654551428807,0.0067361111111111,-3.12340165046053,0.0782638888888889,-3.33854051914606,0.0345833333333333,-3.82129698058186,0.021875,-3.34413805184196,0.0591666666666667,-0.405175989963925,0.0009027777777777,-2.63543518489825 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+115,2019-07-06,187.0,2.05442666666667,0.0158333333333333,-2.94267952968185,0.0370833333333333,-1.36103408958141,0.0002083333333333,-3.40336435099665,0.0164583333333333,-3.39627281700968,0.0048611111111111,-3.87228454455778,0.0273611111111111,-3.78557162422063,0.156666666666667,-0.464849435838413,0.0034722222222222,-3.08514297088422 +116,2019-07-07,188.0,1.51064,0.01125,-2.58847562323983,0.0166666666666667,-1.33564649364292,0.0003472222222222,-3.35053405477498,0.0142361111111111,-3.41721001394885,0.0061805555555555,-3.85418227155617,0.0246527777777778,-3.77936205851201,0.109166666666667,-0.533421784539939,0.0044444444444444,-3.06425801636246 +117,2019-07-08,189.0,1.68910458333333,0.01375,-3.38705220157902,0.02,-1.34204835873839,0.0005555555555555,-3.28738320836822,0.0172916666666666,-3.42126404914999,0.0051388888888888,-3.84779281857211,0.0224305555555556,-3.78488007263835,0.0829166666666667,-0.567852330343883,0.0032638888888888,-3.09006372663765 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+121,2019-07-12,193.0,1.47065958333333,0.0595833333333333,-2.94413038336348,0.113333333333333,-1.32512072983084,0.0008333333333333,-3.27759880540345,0.0222916666666667,-3.47021109284605,0.0066666666666666,-3.83017911174798,0.01875,-3.75775352020772,0.0679166666666667,-0.532319299261166,0.0048611111111111,-3.07949959694665 +122,2019-07-13,194.0,1.59405666666667,0.0666666666666667,-2.97627900214989,0.215,-1.32438667341924,0.0005555555555555,-3.37928683154677,0.0233333333333333,-3.45519018035218,0.0044444444444444,-3.83801214799243,0.0183333333333333,-3.74364760254433,0.05,-0.553114573466703,0.0048611111111111,-3.13494990581922 +123,2019-07-14,195.0,1.29147958333333,0.109166666666667,-3.00469251426916,0.285833333333333,-1.33484470356954,0.0004166666666666,-3.27918343796389,0.0199305555555555,-3.4539406404057,0.0056944444444444,-3.84379186461173,0.0172916666666667,-3.76917920356019,0.0625,-0.454740769594699,0.0054861111111111,-3.14978575932251 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+154,2019-08-14,226.0,2.03331958333333,3.67791666666667,-2.2580307519734,0.779166666666667,-1.50276778165532,0.0002777777777777,-3.29234569813906,0.0414583333333333,-3.45816575841011,0.010625,-3.81190947066654,0.0118055555555556,-3.61735868795832,0.04625,-0.482098401746368,0.0073611111111111,-3.15303332698811 +155,2019-08-15,227.0,NA,6.06875,-2.36564338490345,1.32041666666667,-1.42449888678071,0.0009027777777777,-3.05492914342054,0.0461805555555555,-3.46063163393694,0.0100694444444444,-3.81648647378696,0.0159027777777778,-3.58830195285547,0.04875,-0.520895350301157,0.0077083333333333,-3.11215489648303 +156,2019-08-16,228.0,1.63845583333333,5.73333333333333,-2.35430392630009,0.82625,-1.35473860686073,0.0004166666666666,-3.2306198180285,0.0363888888888889,-3.46745624762631,0.0096527777777777,-3.8115427443504,0.0161111111111111,-3.61853465320695,0.0291666666666667,-0.543519452310772,0.0056944444444444,-3.16159395264278 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+167,2019-08-27,239.0,5.26116125000001,6.02166666666667,-2.51455514771122,4.10375,-1.55143873060697,0.0009722222222222,-3.30280314956327,0.0502083333333334,-3.46024586241725,0.0122222222222222,-3.81724111848534,0.0478472222222222,-3.59228409042944,0.03625,-0.570847294911261,0.0203472222222222,-3.1247271996947 +168,2019-08-28,240.0,2.986925,3.87416666666667,-2.53923138976755,3.51916666666667,-1.61432977554737,0.0017361111111111,-3.30320180293128,0.0575694444444445,-3.47354471020935,0.0170833333333333,-3.81751697695762,0.0552777777777778,-3.6162659579949,0.0520833333333333,-0.600851808225285,0.0260416666666667,-3.15070086633019 +169,2019-08-29,241.0,2.56957208333333,9.00708333333333,-2.48059884941187,2.1075,-1.65113109900242,0.0023611111111111,-3.21715819278181,0.0777083333333333,-3.49500548016137,0.02125,-3.81539417494818,0.0417361111111111,-3.64920114774516,0.0658333333333333,-0.567597796646602,0.0197222222222222,-3.16148984513916 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+369,2020-03-16,76.0,27.9798533333333,0.0020833333333333,-2.25160805757244,0.0004166666666666,-1.61449954272111,0.00875,-2.98338842803664,0.0344444444444444,-3.2378413396113,0.0185416666666667,-3.81472095187799,0.0045833333333333,-3.74078695627069,0.0266666666666667,-0.433544511032168,0.0027777777777777,-2.81890121520837 +370,2020-03-17,77.0,16.39114875,0.0004166666666666,NA,0.0016666666666666,-1.66259205837673,0.0027777777777777,-3.03123080797467,0.0313194444444444,-3.24825594071366,0.0182638888888889,-3.81152490215559,0.0025,-3.74677893171023,0.0191666666666667,-0.456422222188624,0.0027083333333333,-3.02204910395169 +371,2020-03-18,78.0,10.0202404166667,0.0020833333333333,-2.95236966300843,0.0004166666666666,-1.7421957138746,0.0011805555555555,-3.0206310304128,0.0408333333333333,-3.23177881630957,0.02,-3.81947169413014,0.0027777777777777,-3.81055864895562,0.0141666666666667,-0.601059153470724,0.0020833333333333,-2.91872210487099 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+375,2020-03-22,82.0,26.5213979166667,0.0008333333333333,-2.87419939317285,NA,NA,0.0033333333333333,-2.98029858385474,0.0104166666666667,-3.23896875483485,0.0042361111111111,-3.8193952608408,0.0049305555555555,-3.77727611547189,0.0120833333333333,-0.508200219234348,0.0055555555555555,-2.77378024665561 +376,2020-03-23,83.0,19.97051,0.0020833333333333,-2.99534346733716,0.0004166666666666,-1.77872872465013,0.0013888888888888,-3.05393275241496,0.0248611111111111,-3.27776723859335,0.0157638888888889,-3.81944370582,0.0045833333333333,-3.81720583218191,0.0208333333333333,-0.476624852948601,0.0052083333333333,-2.79685786817918 +377,2020-03-24,84.0,22.5855508333333,0.0025,-2.7717639445173,0.00125,-1.81424372531674,0.0048611111111111,-3.0030393766495,0.0186111111111111,-3.27786425916911,0.0136111111111111,-3.80947743147018,0.0047222222222222,-3.78770016119744,0.0120833333333333,-0.27674078413123,0.0046527777777777,-2.82383939669815 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+402,2020-04-18,109.0,3.21521708333334,0.0025,NA,NA,NA,0.0005555555555555,-2.9079680983007,0.138888888888889,-3.32292627736874,0.0294444444444444,-3.80417753945158,0.0006944444444444,-3.76940150270685,0.14,-0.325340724122466,0.0005555555555555,-2.93046142702793 +403,2020-04-19,110.0,2.74235833333334,0.0033333333333333,-2.68316078558178,0.0008333333333333,-1.80776159778882,0.0005555555555555,-2.96703222157233,0.155486111111112,-3.33526032532406,0.0366666666666667,-3.81084327455424,0.0016666666666666,-3.76047871603873,0.13375,-0.2838752284531,0.0004166666666666,-3.07584422514281 +404,2020-04-20,111.0,2.75778333333333,0.0008333333333333,-3.07566581017782,0.0008333333333333,-1.72192946399784,0.0010416666666666,-2.91081823195479,0.174444444444444,-3.32837869164277,0.0351388888888889,-3.81367620549485,0.0020138888888888,-3.73861760486981,0.17875,-0.317014112917925,0.0011805555555555,-3.01203977148998 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+408,2020-04-24,115.0,3.32515708333334,0.0025,-3.81861952537703,NA,NA,0.0004166666666666,-2.87853195932704,0.0859027777777777,-3.33761178871638,0.0284722222222222,-3.81727006855783,0.0018055555555555,-3.68057062744116,0.200416666666667,-0.266471244376732,0.0006944444444444,-2.94415571957396 +409,2020-04-25,116.0,3.06976083333334,0.0004166666666666,-2.54364368545761,0.0004166666666666,-1.61794908017278,0.0001388888888888,-2.97786241061298,0.0740277777777778,-3.35917470582528,0.0242361111111111,-3.81629761872076,0.0014583333333333,-3.71229396611222,0.0825,-0.281742229380315,0.0006249999999999,-3.04248239516759 +410,2020-04-26,117.0,2.99864166666667,0.0008333333333333,-3.40746884300275,NA,NA,0.0003472222222222,-3.26865450784594,0.075,-3.33299839387423,0.0295138888888889,-3.81628714165206,0.0020833333333333,-3.72166553540758,0.124166666666667,-0.255633582757454,0.000625,-2.75283594745591 +411,2020-04-27,118.0,2.89535791666667,0.0020833333333333,-2.46500488454838,0.0016666666666666,-1.38219517632058,0.0003472222222222,-2.83708189805566,0.0424305555555556,-3.34101794340673,0.0309722222222222,-3.81432426886683,0.0015972222222222,-3.69642619649397,0.129166666666667,-0.2971405786603,0.0008333333333333,-3.0320307557807 +412,2020-04-28,119.0,4.23368708333335,0.0025,-3.07351836817562,0.0004166666666666,-1.78174816542391,0.0001388888888888,-3.04944803033479,0.05125,-3.35547022513254,0.03,-3.81868315733593,0.0020138888888888,-3.74184610433463,0.0725,-0.265672979724925,0.0003472222222222,-3.08631593385787 +413,2020-04-29,120.0,3.28742041666667,0.0020833333333333,-2.87746056997846,0.0025,-1.69450618239425,0.0001388888888888,-2.86315711850701,0.0539583333333333,-3.35771169093133,0.0240972222222222,-3.8103248202716,0.00125,-3.7411723993533,0.11625,-0.260641703577387,0.0005555555555555,-2.65820668382214 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+447,2020-06-02,154.0,1.76582041666667,21.125,-2.29936343256162,0.0270833333333333,-1.35903913302562,0.0001388888888888,-2.80620943752095,0.0388194444444444,-3.33734975176641,0.0075694444444444,-3.82964206775383,0.0326388888888889,-3.67849979493825,0.06875,-0.351772487352183,0.0072916666666666,-3.0543258814366 +448,2020-06-03,155.0,1.71005541666667,15.135,-2.31637839288344,0.01125,-1.11528178370259,0.0001388888888888,-3.02667140090754,0.0295833333333333,-3.33233895258789,0.0067361111111111,-3.81101284342909,0.0319444444444444,-3.66575254313278,0.0358333333333333,-0.467361895275898,0.0054861111111111,-3.05910812859393 +449,2020-06-04,156.0,1.86792125,5.4325,-2.45457361334063,0.0066666666666666,-1.28163779769534,0.0004166666666666,-3.04325639828051,0.0342361111111111,-3.34677878647116,0.0074305555555555,-3.82493325988276,0.0183333333333333,-3.66385002291628,0.0708333333333333,-0.509080990925817,0.0075,-3.08565386384929 +450,2020-06-05,157.0,1.80179125,2.26958333333333,-2.56357294884345,0.0041666666666666,-1.37224754761031,0.0001388888888888,-3.12977172098821,0.0426388888888889,-3.34000967905358,0.0069444444444444,-3.82658641897956,0.0090277777777777,-3.55784701421361,0.0875,-0.525045865393107,0.0061111111111111,-3.03610989796549 +451,2020-06-06,158.0,1.667345,1.71708333333333,-2.56728872465267,0.0045833333333333,-1.24158012806742,0.0002777777777777,-3.07840676480723,0.0390972222222222,-3.33289297544457,0.0052083333333333,-3.8265490927441,0.0078472222222222,-3.53859011487349,0.0983333333333333,-0.469197359100994,0.0061111111111111,-3.07988602554552 +452,2020-06-07,159.0,1.87113958333333,1.47541666666667,-2.65422744375159,0.005,-1.07952475244965,6.94444444444444e-05,-2.95537205699187,0.0338194444444444,-3.34541226484048,0.0047916666666666,-3.82508907322787,0.0077083333333333,-3.48063174985098,0.0683333333333333,-0.513095608273931,0.0039583333333333,-2.91612005497388 +453,2020-06-08,160.0,1.63015916666667,1.57166666666667,-2.51669794720983,0.00375,-1.10527905540699,6.94444444444444e-05,-3.38502183564674,0.0284027777777778,-3.36193096380638,0.0054861111111111,-3.82658330046239,0.0072916666666666,-3.66556863052766,0.0633333333333333,-0.501832955445641,0.004375,-3.02733076215337 +454,2020-06-09,161.0,1.85174333333333,1.57041666666667,-2.68147019125736,0.00875,-1.06280437822033,0.0001388888888888,-3.13852509215845,0.02875,-3.36110307408722,0.0079166666666666,-3.82205691116228,0.0065277777777777,-3.69652062878354,0.06625,-0.516283874133892,0.0044444444444444,-3.07685301284602 +455,2020-06-10,162.0,1.6294025,1.89875,-2.76040175530406,0.0070833333333333,-1.0424345361124,0.0001388888888888,-3.1095427635556,0.0378472222222223,-3.37677608205034,0.006875,-3.83636911631589,0.0075694444444444,-3.66062985075041,0.0375,-0.508711582651771,0.0051388888888888,-3.09075114250217 +456,2020-06-11,163.0,1.46394583333333,4.50041666666667,-2.44672179337588,0.0116666666666667,-1.00550685826636,0.0002083333333333,-3.23912536194781,0.0616666666666667,-3.32864035133418,0.0147222222222222,-3.8116892703734,0.0113888888888889,-3.6762283229476,0.0620833333333333,-0.481650187495227,0.0042361111111111,-3.14489301805683 +457,2020-06-12,164.0,1.59850125,9.37291666666667,-2.33814956830378,0.0283333333333333,-0.952886899916186,0.0004166666666666,-3.16556992803684,0.0639583333333333,-3.30347489791242,0.0141666666666667,-3.80445732953183,0.0134027777777778,-3.67590257652051,0.05125,-0.391200095403035,0.0065972222222222,-3.11410482487819 +458,2020-06-13,165.0,1.56820875,13.5320833333333,-2.36965109489289,0.0391666666666667,-0.959940760552245,0.0007638888888888,-3.09295970708148,0.0690972222222222,-3.31033786664053,0.0140277777777778,-3.8135061408384,0.0159027777777778,-3.64999394878284,0.0616666666666667,-0.443935129182482,0.0066666666666666,-3.11962537645736 +459,2020-06-14,166.0,1.96222208333333,11.2120833333333,-2.35224778867735,0.03625,-0.960832660643155,6.94444444444444e-05,-2.79748253115198,0.0698611111111111,-3.33981946788106,0.0111805555555556,-3.82185486595178,0.0105555555555556,-3.71650384087539,0.08375,-0.378999202276245,0.0048611111111111,-3.2602897035292 +460,2020-06-15,167.0,2.01922333333333,9.51333333333333,-2.42996467729563,0.0558333333333333,-1.03140795136311,0.0003472222222222,-2.73637060563451,0.0858333333333334,-3.34621309611615,0.0136805555555556,-3.81482978797833,0.0111111111111111,-3.68236568786714,0.0591666666666667,-0.407674989221749,0.0046527777777777,-3.2209280673385 +461,2020-06-16,168.0,1.86031458333333,3.52125,-2.45771205992427,0.095,-1.07407464036411,0.0001388888888888,-3.35057147621156,0.098125,-3.34309871980403,0.0209027777777778,-3.81589945227731,0.0109027777777778,-3.64459440597289,0.122916666666667,-0.41938473354614,0.0061111111111111,-3.20969478950047 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+542,2020-09-05,249.0,2.98914333333334,11.5308333333333,-2.44980394185664,0.24625,-1.65084069670939,0.0013888888888888,-3.17314751513496,0.154097222222222,-3.48817085225894,0.0297916666666667,-3.80376071284637,0.0488888888888889,-3.71615532571857,0.0270833333333333,-0.597229416311127,0.0195138888888889,-3.19605027290318 +543,2020-09-06,250.0,1.90053125,2.29166666666667,-2.59419308105056,0.05625,-1.63535836012709,0.00125,-3.14680769860486,0.124652777777778,-3.47172098762154,0.0224305555555556,-3.79296088322807,0.0317361111111111,-3.6980415844169,0.0370833333333333,-0.539649463451045,0.0180555555555556,-3.16825171615058 +544,2020-09-07,251.0,2.16822875,6.87916666666667,-2.52465034476608,0.185416666666667,-1.66413309566179,0.0010416666666666,-3.22810971104341,0.144722222222222,-3.47285141092651,0.0283333333333333,-3.79623845017888,0.0346527777777778,-3.66977412834952,0.03375,-0.599027712913352,0.0236805555555556,-3.22095821969023 +545,2020-09-08,252.0,2.2690475,8.51958333333333,-2.49794090565455,0.206666666666667,-1.67240758014384,0.0014583333333333,-3.2164025075318,0.190208333333333,-3.46976109261663,0.0323611111111111,-3.80108011782813,0.0439583333333333,-3.70318696950862,0.04,-0.59710005729505,0.0243055555555556,-3.20253652570267 +546,2020-09-09,253.0,2.3258875,9.50666666666667,-2.50345076343049,0.217083333333333,-1.67035477401033,0.0011805555555555,-2.97153956920957,0.163611111111111,-3.46648825393456,0.02625,-3.80998000271645,0.0415972222222222,-3.71490448235735,0.0416666666666667,-0.551302808720851,0.0218055555555556,-3.15742517688598 +547,2020-09-10,254.0,2.53173666666667,5.67583333333333,-2.53397011382099,0.0954166666666667,-1.65096868282161,0.00125,-3.18303964864232,0.127361111111111,-3.46117437517567,0.0195833333333333,-3.79794109220664,0.04375,-3.70085492314368,0.0733333333333333,-0.579624764020391,0.0253472222222222,-3.16256924522561 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+554,2020-09-17,261.0,2.30606916666667,1.72458333333333,-2.51756284253577,0.06125,-1.63280250343596,0.0010416666666666,-3.34493241605566,0.0590972222222222,-3.44989013568238,0.0070138888888888,-3.80313600734795,0.0655555555555556,-3.66112836055458,0.12625,-0.517984324341595,0.0252777777777778,-3.16943017939365 +555,2020-09-18,262.0,1.99819375,1.24333333333333,-2.56355568448538,0.0370833333333333,-1.63422815906076,0.0006944444444444,-3.31343518967552,0.0561111111111111,-3.44737851138683,0.006875,-3.79928781351292,0.0569444444444444,-3.63215882957514,0.10875,-0.510888954460305,0.0275,-3.12355310657741 +556,2020-09-19,263.0,2.3097,1.03625,-2.52808390023458,0.04,-1.5590039759522,0.00125,-3.20670062691777,0.0697916666666667,-3.43505953674676,0.0095833333333333,-3.80597804504841,0.0644444444444444,-3.64711300891958,0.102916666666667,-0.476678867782333,0.0346527777777778,-3.15065509368503 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a/LakePlankData/TSM_PlanktonData.py +++ b/LakePlankData/TSM_PlanktonData.py @@ -17,54 +17,18 @@ # 2. non-predatory species # p.s. The model does not consider intra-guild predation; there are certain trophic links unable to capture. -phydata = pd.read_csv(wd + '/GRE_raw_plankton_ab_size.csv', sep=',', +phydata = pd.read_csv(wd + '/raw_NPdata.csv', sep=',', usecols=['date', - 'dinobryon_ROIs_0p5x', 'dinobryon_area_log10_mm2_5p0x', - 'uroglena_ROIs_0p5x', 'uroglena_area_log10_mm2_0p5x', - 'centric_diatom_ROIs_5p0x', 'centric_diatom_area_log10_mm2_5p0x', - 'cryptophyceae_ROIs_5p0x', - 'cryptophyceae_area_log10_mm2_5p0x_weightedmean', 'cryptophyceae_area_log10_mm2_5p0x_mean', - 'rhodomonas_ROIs_5p0x', 'rhodomonas_area_log10_mm2_5p0x', - 'chlorophytes_ROIs_5p0x', 'chlorophytes_area_log10_mm2_5p0x']) -# Wherever weighted mean bio-area and major axis length is available for the phytoplankton, we used the weighted mean. -# The mean would underestaimte the size in most days, except in time with high abundance -phydata = phydata.drop(columns=['cryptophyceae_area_log10_mm2_5p0x_mean', 'cryptophyceae_maj_axis_len_mm_5p0x_mean', - 'cryptophyceae_min_axis_len_mm_5p0x_mean']) - -# rename -phydata.columns = ['date', 'dinobryon_ROIs', 'dinobryon_area_log10_mm2', 'uroglena_ROIs', 'uroglena_area_log10_mm2', 'centric_diatom_ROIs', 'centric_diatom_area_log10_mm2', 'cryptophyceae_ROIs', 'cryptophyceae_area_log10_mm2', 'rhodomonas_ROIs', 'rhodomonas_area_log10_mm2', - 'chlorophytes_ROIs', 'chlorophytes_area_log10_mm2'] + 'chlorophytes_ROIs', 'chlorophytes_area_log10_mm2']) - -zoodata = pd.read_csv(r'/Users/szewing/Desktop/PhD_work/Data/Steffi_eawag/GRE_plankton_ab_size_20240416.csv', sep=',', - usecols=['date', - 'daphnia_ROIs_0p5x', 'daphnia_area_log10_mm2_0p5x', - 'eudiaptomus_ROIs_0p5x', 'eudiaptomus_area_log10_mm2_0p5x', - 'cyclops_ROIs_0p5x', 'cyclops_area_log10_mm2_0p5x', - 'nauplius_ROIs_0p5x', 'nauplius_area_log10_mm2_0p5x', - 'ciliates_ROIs_5p0x', - 'ciliates_area_log10_mm2_5p0x_weightedmean', 'ciliates_area_log10_mm2_5p0x_mean', - 'rotifers_ROIs_0p5x', - 'rotifers_area_log10_mm2_0p5x_weightedmean', 'rotifers_area_log10_mm2_0p5x_mean']) -# Wherever weighted mean bio-area and major axis length is available for the phytoplankton, we used the weighted mean. -# The mean would underestaimte the size in most days, except in time with high abundance -zoodata = zoodata.drop(columns=['ciliates_area_log10_mm2_5p0x_mean', 'ciliates_maj_axis_len_mm_5p0x_mean', 'ciliates_min_axis_len_mm_5p0x_mean', - 'rotifers_area_log10_mm2_0p5x_mean', 'rotifers_maj_axis_len_mm_0p5x_mean', 'rotifers_min_axis_len_mm_0p5x_mean']) - -zoodata.columns = ['date', - 'daphnia_ROIs', 'daphnia_area_log10_mm2', - 'eudiaptomus_ROIs', 'eudiaptomus_area_log10_mm2', - 'cyclops_ROIs', 'cyclops_area_log10_mm2', - 'nauplius_ROIs', 'nauplius_area_log10_mm2', - 'ciliates_ROIs', 'ciliates_area_log10_mm2', - 'rotifers_ROIs', 'rotifers_area_log10_mm2'] - -zoodata_used = zoodata[['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', 'ciliates_ROIs', 'ciliates_area_log10_mm2']] +zoodata = pd.read_csv(wd + '/raw_NPdata.csv', sep=',', + usecols=['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', + 'ciliates_ROIs', 'ciliates_area_log10_mm2']) ## set name physpecies = ['dinobryon', 'uroglena', 'centric_diatom', 'cryptophyceae', 'rhodomonas', 'chlorophytes'] From c0fdd2914009673b874a0ceda75e70c85543818f Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Thu, 22 Aug 2024 20:48:56 +0200 Subject: [PATCH 75/97] Update README.md --- LakePlankData/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LakePlankData/README.md b/LakePlankData/README.md index 6270ba2..1eaec1d 100644 --- a/LakePlankData/README.md +++ b/LakePlankData/README.md @@ -13,7 +13,7 @@ A graphical illustration of the transformation of time-averaged phytoplankton da

### A brief description: This model outputs phytoplankton and zooplankton biomasses for different size classes. This information allow us to produce 1.) total phytoplankton biomass(µM P); 2.) total zooplankton biomass(µM P) ; and 3.) community mean phytoplankton size(µm ESD). -After the transformations, the common unit considered for comparing model outputs and data are: biomass(µM P); and biovolume(µm$^3$) +After the transformations, the common unit considered for comparing model outputs and data are: biomass(µM P); and biovolume(µm3)


From 084f507327c3af8ca35ae7f5d0367ac5aae34260 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 27 Aug 2024 12:54:45 +0200 Subject: [PATCH 76/97] Update TSM_PlanktonData.py --- LakePlankData/TSM_PlanktonData.py | 74 ++++++++++++++++++++----------- 1 file changed, 48 insertions(+), 26 deletions(-) diff --git a/LakePlankData/TSM_PlanktonData.py b/LakePlankData/TSM_PlanktonData.py index a5d0b4a..c7f8dd4 100644 --- a/LakePlankData/TSM_PlanktonData.py +++ b/LakePlankData/TSM_PlanktonData.py @@ -17,6 +17,7 @@ # 2. non-predatory species # p.s. The model does not consider intra-guild predation; there are certain trophic links unable to capture. +## For phytoplankton, we use only the ROIs and the bio-area data (see flow chart) phydata = pd.read_csv(wd + '/raw_NPdata.csv', sep=',', usecols=['date', 'dinobryon_ROIs', 'dinobryon_area_log10_mm2', @@ -26,9 +27,10 @@ 'rhodomonas_ROIs', 'rhodomonas_area_log10_mm2', 'chlorophytes_ROIs', 'chlorophytes_area_log10_mm2']) +## For zooplankton, we use major axis length and minor axis length for ciliates and major axis length for daphnia zoodata = pd.read_csv(wd + '/raw_NPdata.csv', sep=',', - usecols=['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', - 'ciliates_ROIs', 'ciliates_area_log10_mm2']) + usecols=['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', 'daphnia_MaL_mm', 'daphnia_MiL_mm', + 'ciliates_ROIs', 'ciliates_area_log10_mm2', 'ciliates_MaL_mm', 'ciliates_MiL_mm']) ## set name physpecies = ['dinobryon', 'uroglena', 'centric_diatom', 'cryptophyceae', 'rhodomonas', 'chlorophytes'] @@ -46,17 +48,23 @@ def logArea2μmArea(LnArea): return (10 ** LnArea) * mm_sq2μm_sq +# adjusting the data units for i in range(len(physpecies)): phydata.insert(1 + i, physpecies[i] + '_area_μm2', logArea2μmArea(phydata[physpecies[i] + '_area_log10_mm2'])) for i in range(len(zoospecies_used)): zoodata_used.insert(1 + i, zoospecies_used[i] + '_area_μm2', logArea2μmArea(zoodata_used[zoospecies_used[i] + '_area_log10_mm2'])) +for i in range(len(zoospecies_used)): + zoodata_used.insert(1+len(zoospecies_used) + i, zoospecies_used[i] + '_MaL_μm', zoodata_used[zoospecies_used[i] + '_MaL_mm'] * 1e3) +for i in range(len(zoospecies_used)): + zoodata_used.insert(1+len(zoospecies_used)*2 + i, zoospecies_used[i] + '_MiL_μm', zoodata_used[zoospecies_used[i] + '_MiL_mm'] * 1e3) -# The ROIs data refers to region of interest (ROI), it is taken by the underwater imaging camera, and the unit is objects capture or -# detected per second [Detailed refers to Merz et al. (2020)] + +# The ROIs data refers to region of interest (ROI), it is taken by the underwater imaging camera, and the unit is objects capture or +# detected per second [For details please refer to Merz et al. (2021)] def ROI_0p5x(roi): """ 0p5x is for cells or individuals that are in large size (100μm-7mm). @@ -80,7 +88,7 @@ def ROI_5p0x(roi): return x -### to phydata +### converting to phyto abundance phydata.insert(2, 'dinobryon_count/mL', ROI_0p5x(phydata['dinobryon_ROIs'])) # large phyto phydata.insert(3, 'uroglena_count/mL', ROI_0p5x(phydata['uroglena_ROIs'])) # large phyto phydata.insert(4, 'centric_diatom_count/mL', ROI_5p0x(phydata['centric_diatom_ROIs'])) # small cells @@ -92,6 +100,7 @@ def ROI_5p0x(roi): zoodata_used.insert(4, 'ciliates_count/mL', ROI_5p0x(zoodata_used['ciliates_ROIs'])) +### Bio-area to biovolume def bioArea2Vol(Area): """ Based on the scaling provided in Ryabov et al. (2021) @@ -101,15 +110,24 @@ def bioArea2Vol(Area): v = ((1 / (10**0.25)) * Area) ** (1/0.68) # calibrated return v +### Ellipsoidal ciliates +def Length2Vol(aa, bb, cc): + """ + Calculting biovolume assuming ellipsoidal ciliates, following Hillebrand et al. (1999) + :param aa: minor axis length (μm) + :param bb: depth (μm) + :param cc: major axis length (μm) + :return: biovolume (μm3) + """ + v = (np.pi/6) * aa * bb * cc + return v + -# calculate biovolume from area using the scaling function from Ryabov et al. (2021) +### Deriving phytoplankton biomass +# calculating the biovolume from area using the scaling function from Ryabov et al. (2021) for i in range(len(physpecies)): phydata.insert(1 + i, physpecies[i] + '_biov', bioArea2Vol(phydata[physpecies[i] + '_area_μm2'])) -for i in range(len(zoospecies_used)): - zoodata_used.insert(1 + i, zoospecies_used[i] + '_biov', bioArea2Vol(zoodata_used[zoospecies_used[i] + '_area_μm2'])) - - def mwP_Ind(biov): """ This function uses the allometric relationship on P content for each size class (Hillebrand et al. 2022) Fig. 5C @@ -138,23 +156,10 @@ def Phy_abun2biomass(biov, abun): µmolP_L = µmolP_ind * (abun * mL2L) return µmolP_L +### Deriving zooplankton biomass -def Daph_L_W_Schalau(MaL_mm): - """ - Length-weight ratio for daphnia used in the model of Schalau et al. (2008) and sourced from Urabe and Watanabe (1991) & Lynch et al. (1986) - :param MaL_mm: Maximum Axis Length (mm) - :return: µmol P ind-1 - """ - mwC = 12.011 - L = MaL_mm * 0.75 # ref: Ranta et al. (1993); Karpowicz et al. (2020) - µgC_ind = 1.6 * (L**3) - µmolC_ind = µgC_ind / mwC # convert to molar - C2P_ratio = 350 # the mean threshold elemental ratio (to maintain Daphnia growth - good reference for C:P) - # a mean from Hessen (2006) - assuming redfield ratio for carbon to phosphate transformation - - # but the range can vary between 80-385 for daphnia - µmolP_ind = µmolC_ind / C2P_ratio - return µmolP_ind - +# calculating the biovolume of ciliates +zoodata_used.insert(1, 'ciliates_biov2', Length2Vol(zoodata_used['ciliates_MiL_μm'], zoodata_used['ciliates_MiL_μm']*(2/3), zoodata_used['ciliates_MaL_μm'])) def CiliatePcontent_Putt(biov): """ @@ -171,6 +176,23 @@ def CiliatePcontent_Putt(biov): C2P_ratio = 100 # a mean from 80-120 of Golz et al. (2015) - assuming redfield ratio for carbon to phosphate transformation - but the range can vary between 80-300 for daphnia µmolP_ind = µmolC_ind / C2P_ratio return µmolP_ind + +def Daph_L_W_Schalau(MaL_mm): + """ + Length-weight ratio for daphnia used in the model of Schalau et al. (2008) and sourced from Urabe and Watanabe (1991) & Lynch et al. (1986) + :param MaL_mm: Maximum Axis Length (mm) + :return: µmol P ind-1 + """ + mwC = 12.011 + L = MaL_mm * 0.75 # ref: Ranta et al. (1993); Karpowicz et al. (2020) + µgC_ind = 1.6 * (L**3) + µmolC_ind = µgC_ind / mwC # convert to molar + C2P_ratio = 350 # the mean threshold elemental ratio (to maintain Daphnia growth - good reference for C:P) + # a mean from Hessen (2006) - assuming redfield ratio for carbon to phosphate transformation - + # but the range can vary between 80-385 for daphnia + µmolP_ind = µmolC_ind / C2P_ratio + return µmolP_ind + ## phytoplankton total biomasses From 287aca451562ec8249e99406a671013077d2a7ac Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 27 Aug 2024 13:16:13 +0200 Subject: [PATCH 77/97] Update README.md --- LakePlankData/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/LakePlankData/README.md b/LakePlankData/README.md index 1eaec1d..130b639 100644 --- a/LakePlankData/README.md +++ b/LakePlankData/README.md @@ -5,10 +5,10 @@ Here stores:

A graphical illustration of the transformation of time-averaged phytoplankton data*: -SuppFigS2_v3 +SuppFigS1_v2 -A graphical illustration of the transformation of time-averaged phytoplankton data*: -SuppFigS3_v1 +A graphical illustration of the transformation of time-averaged zooplankton data*: +SuppFigS4_v2

### A brief description: From ef25c1315ea6ddf3e46415f3c552f1025b322a67 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 28 Aug 2024 15:39:24 +0200 Subject: [PATCH 78/97] Update TSM_PlanktonData.py --- LakePlankData/TSM_PlanktonData.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/LakePlankData/TSM_PlanktonData.py b/LakePlankData/TSM_PlanktonData.py index c7f8dd4..cf256f1 100644 --- a/LakePlankData/TSM_PlanktonData.py +++ b/LakePlankData/TSM_PlanktonData.py @@ -28,9 +28,9 @@ 'chlorophytes_ROIs', 'chlorophytes_area_log10_mm2']) ## For zooplankton, we use major axis length and minor axis length for ciliates and major axis length for daphnia -zoodata = pd.read_csv(wd + '/raw_NPdata.csv', sep=',', - usecols=['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', 'daphnia_MaL_mm', 'daphnia_MiL_mm', - 'ciliates_ROIs', 'ciliates_area_log10_mm2', 'ciliates_MaL_mm', 'ciliates_MiL_mm']) +zoodata_used = pd.read_csv(wd + '/raw_NPdata.csv', sep=',', + usecols=['date', 'daphnia_ROIs', 'daphnia_area_log10_mm2', 'daphnia_MaL_mm', 'daphnia_MiL_mm', + 'ciliates_ROIs', 'ciliates_area_log10_mm2', 'ciliates_MaL_mm', 'ciliates_MiL_mm']) ## set name physpecies = ['dinobryon', 'uroglena', 'centric_diatom', 'cryptophyceae', 'rhodomonas', 'chlorophytes'] From 456698c69ca70c482a46cfc9590651e2d8394b70 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 28 Aug 2024 15:40:11 +0200 Subject: [PATCH 79/97] Add files via upload --- LakePlankData/raw_NPdata.csv | 2782 +++++++++++++++++----------------- 1 file changed, 1391 insertions(+), 1391 deletions(-) diff --git a/LakePlankData/raw_NPdata.csv b/LakePlankData/raw_NPdata.csv index 77efc7d..811ffe1 100644 --- a/LakePlankData/raw_NPdata.csv +++ b/LakePlankData/raw_NPdata.csv @@ -1,1391 +1,1391 @@ -,date,Julian day,phosphate_ug,dinobryon_ROIs,dinobryon_area_log10_mm2,uroglena_ROIs,uroglena_area_log10_mm2,centric_diatom_ROIs,centric_diatom_area_log10_mm2,cryptophyceae_ROIs,cryptophyceae_area_log10_mm2,rhodomonas_ROIs,rhodomonas_area_log10_mm2,chlorophytes_ROIs,chlorophytes_area_log10_mm2,daphnia_ROIs,daphnia_area_log10_mm2,ciliates_ROIs,ciliates_area_log10_mm2 -0,2019-03-13,72.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -1,2019-03-14,73.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -2,2019-03-15,74.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -3,2019-03-16,75.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -4,2019-03-17,76.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -5,2019-03-18,77.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -6,2019-03-19,78.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -7,2019-03-20,79.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -8,2019-03-21,80.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -9,2019-03-22,81.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -10,2019-03-23,82.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -11,2019-03-24,83.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -12,2019-03-25,84.0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -13,2019-03-26,85.0,NA,0.0095833333333333,-2.87632313315923,0.0008333333333333,-1.73126949025198,0.0175,-2.99365773963833,0.225208333333334,-3.30512708724267,0.0474305555555556,-3.82518366259581,0.0384027777777778,-3.66780902785654,0.0195833333333333,-0.430922053421442,0.0081249999999999,-2.98548840525407 -14,2019-03-27,86.0,NA,0.0116666666666667,-2.92384091789576,0.0016666666666666,-1.6570619990361,0.0079861111111111,-3.02625559361662,0.291736111111111,-3.29232888020795,0.0545833333333333,-3.83646808360172,0.0616666666666667,-3.66072193326168,0.0129166666666667,-0.416476499725435,0.0099305555555555,-3.09606718605246 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-152,2019-08-12,224.0,1.88354458333333,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -153,2019-08-13,225.0,2.13286,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -154,2019-08-14,226.0,2.03331958333333,3.67791666666667,-2.2580307519734,0.779166666666667,-1.50276778165532,0.0002777777777777,-3.29234569813906,0.0414583333333333,-3.45816575841011,0.010625,-3.81190947066654,0.0118055555555556,-3.61735868795832,0.04625,-0.482098401746368,0.0073611111111111,-3.15303332698811 -155,2019-08-15,227.0,NA,6.06875,-2.36564338490345,1.32041666666667,-1.42449888678071,0.0009027777777777,-3.05492914342054,0.0461805555555555,-3.46063163393694,0.0100694444444444,-3.81648647378696,0.0159027777777778,-3.58830195285547,0.04875,-0.520895350301157,0.0077083333333333,-3.11215489648303 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-427,2020-05-13,134.0,3.24431166666667,0.0052173913043478,-2.44326849554196,0.0026086956521739,-1.70878334210429,0.0004347826086956,-3.06746386916051,0.086304347826087,-3.3114295080206,0.0343478260869565,-3.82184408140844,0.0013043478260869,-3.75762965094444,0.0630434782608696,-0.384824067704996,0.0028985507246376,-2.84919342530238 -428,2020-05-14,135.0,3.73318000000001,0.00375,-2.2186536038184,0.0033333333333333,-1.77324702330143,0.0004166666666666,-3.09296282395748,0.109444444444444,-3.29672940002639,0.0170138888888889,-3.81394616751163,0.0019444444444444,-3.73455741786544,0.0541666666666667,-0.389408361604788,0.0028472222222222,-2.95714111604514 -429,2020-05-15,136.0,3.67435541666667,0.005,NA,0.005,-1.67742782929382,0.0002083333333333,-3.36833143016578,0.0986805555555555,-3.32405001657226,0.0202777777777778,-3.82341660926606,0.0011805555555555,-3.81823164698099,0.0504166666666667,-0.282639153814196,0.0023611111111111,-2.96083076533872 -430,2020-05-16,137.0,3.78731541666667,0.0075,-2.96136334868264,0.00625,-1.70617291399574,0.0004861111111111,-3.25620833773809,0.0786805555555555,-3.30216175045093,0.0173611111111111,-3.82786491108264,0.0020833333333333,-3.78952698480624,0.05375,-0.343958171861167,0.0032638888888888,-2.99602279702542 -431,2020-05-17,138.0,3.45604208333334,0.0091666666666666,-2.7647471987152,0.0045833333333333,-1.70625521739901,0.0003472222222222,-3.16504067291957,0.0439583333333333,-3.32525041846655,0.0197222222222222,-3.81360415290606,0.0020138888888888,-3.68204703824272,0.0470833333333333,-0.318123704769352,0.0035416666666666,-2.9260787093365 -432,2020-05-18,139.0,3.38250166666667,0.0141666666666667,-3.12305902747612,0.0054166666666666,-1.73950876451692,NA,NA,0.0295138888888889,-3.34015385105257,0.0161111111111111,-3.83152935972503,0.0017361111111111,-3.7825164348069,0.0604166666666667,-0.353545801596885,0.0030555555555555,-3.01940662628658 -433,2020-05-19,140.0,2.75017708333333,0.02375,-2.35769924387676,0.0079166666666666,-1.77290833116604,0.0004166666666666,-3.09593701744289,0.0403472222222222,-3.31429066194303,0.0195833333333333,-3.8227603023367,0.0024305555555555,-3.66488455750039,0.0475,-0.341196500834301,0.0047916666666666,-3.01543209460586 -434,2020-05-20,141.0,2.5254125,0.0220833333333333,-2.61058316081917,0.0054166666666666,-1.23407691405867,0.0001388888888888,-3.22153559146221,0.0654861111111111,-3.31201230724845,0.0279166666666667,-3.82117057561862,0.0024305555555555,-3.67330003456276,0.0379166666666667,-0.345172661927619,0.00625,-2.93811527440133 -435,2020-05-21,142.0,2.31407291666666,0.0491666666666667,-2.61565790192337,0.0041666666666666,-1.70892566374473,0.0004861111111111,-3.12476859268267,0.0634722222222222,-3.3139493575645,0.0284722222222222,-3.82291255496161,0.0029166666666666,-3.70604767271939,0.0383333333333333,-0.261082199063688,0.0083333333333333,-2.95276024405779 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-442,2020-05-28,149.0,2.41010208333333,3.74583333333333,-2.24559610218726,0.0120833333333333,-1.32532368507768,0.0002777777777777,-2.98805523920315,0.0286805555555556,-3.34501652327636,0.0125,-3.82279310537263,0.0111805555555556,-3.64873450440719,0.0279166666666667,-0.326304443579247,0.0038194444444444,-3.14948886932496 -443,2020-05-29,150.0,2.32126791666667,6.2375,-2.2250087562388,0.0204166666666667,-1.25057247160896,0.0003472222222222,-2.93066058193437,0.0436805555555555,-3.33825524513503,0.0132638888888889,-3.82273757180136,0.0140277777777778,-3.63999201333075,0.0545833333333333,-0.308023486809154,0.0042361111111111,-3.07495391333477 -444,2020-05-30,151.0,2.34180166666667,13.0795833333333,-2.27733065554593,0.0245833333333333,-1.37148146083219,0.0002777777777777,-3.03836605887619,0.0470833333333333,-3.35236640455927,0.0111111111111111,-3.81872948726695,0.0155555555555556,-3.66328829692861,0.0483333333333333,-0.394240599335312,0.0043055555555555,-3.05148506213892 -445,2020-05-31,152.0,2.09384625,17.835,-2.25622475957788,0.02375,-1.29194970322238,0.0002083333333333,-3.12488515762538,0.0411111111111111,-3.35445624358857,0.0102777777777778,-3.82358055399938,0.0234722222222222,-3.65744112181753,0.0429166666666667,-0.419038312426917,0.0048611111111111,-3.13273187038564 -446,2020-06-01,153.0,2.02080458333333,19.97875,-2.33944108458493,0.0204166666666667,-1.30852628745866,0.0002083333333333,-2.9936126656034,0.040625,-3.35684760998972,0.0097916666666666,-3.83036433778738,0.0261805555555556,-3.67098384288856,0.0416666666666667,-0.387742247948653,0.0065277777777777,-3.10028488335418 -447,2020-06-02,154.0,1.76582041666667,21.125,-2.29936343256162,0.0270833333333333,-1.35903913302562,0.0001388888888888,-2.80620943752095,0.0388194444444444,-3.33734975176641,0.0075694444444444,-3.82964206775383,0.0326388888888889,-3.67849979493825,0.06875,-0.351772487352183,0.0072916666666666,-3.0543258814366 -448,2020-06-03,155.0,1.71005541666667,15.135,-2.31637839288344,0.01125,-1.11528178370259,0.0001388888888888,-3.02667140090754,0.0295833333333333,-3.33233895258789,0.0067361111111111,-3.81101284342909,0.0319444444444444,-3.66575254313278,0.0358333333333333,-0.467361895275898,0.0054861111111111,-3.05910812859393 -449,2020-06-04,156.0,1.86792125,5.4325,-2.45457361334063,0.0066666666666666,-1.28163779769534,0.0004166666666666,-3.04325639828051,0.0342361111111111,-3.34677878647116,0.0074305555555555,-3.82493325988276,0.0183333333333333,-3.66385002291628,0.0708333333333333,-0.509080990925817,0.0075,-3.08565386384929 -450,2020-06-05,157.0,1.80179125,2.26958333333333,-2.56357294884345,0.0041666666666666,-1.37224754761031,0.0001388888888888,-3.12977172098821,0.0426388888888889,-3.34000967905358,0.0069444444444444,-3.82658641897956,0.0090277777777777,-3.55784701421361,0.0875,-0.525045865393107,0.0061111111111111,-3.03610989796549 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-475,2020-06-30,182.0,2.0845775,0.127083333333333,-2.78414507058811,1.25958333333333,-1.41741563177581,0.0002083333333333,-3.29103856552882,0.0303472222222222,-3.33918327381864,0.0038888888888888,-3.85377448058379,0.0074305555555555,-3.73604353722177,0.04375,-0.515461759982456,0.0067361111111111,-3.17869183819721 -476,2020-07-01,183.0,2.25750791666667,0.26625,-2.80330929154149,0.9175,-1.38889696366433,NA,NA,0.0281944444444444,-3.38727729004629,0.0027777777777777,-3.8357760238637,0.0074305555555555,-3.74963562998554,0.0258333333333333,-0.611837710073495,0.0080555555555555,-3.15501913438567 -477,2020-07-02,184.0,1.8779475,0.926666666666667,-2.54960647676171,1.65416666666667,-1.34639711009492,6.94444444444444e-05,-3.50140276861978,0.0524305555555556,-3.34349831976364,0.0033333333333333,-3.80404670954125,0.00875,-3.71791115118611,0.0358333333333333,-0.539673524457266,0.0094444444444444,-3.13703129290423 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-484,2020-07-09,191.0,2.18065166666667,7.47083333333333,-2.48570245675849,2.06083333333333,-1.44219340136915,0.0020138888888888,-3.23018274450515,0.0173611111111111,-3.39108685833055,0.0036111111111111,-3.83785740992685,0.0140972222222222,-3.72269963027768,0.0366666666666667,-0.519591943512354,0.0071527777777777,-3.11018978634449 -485,2020-07-10,192.0,2.0800225,9.69625,-2.39896120820909,2.16666666666667,-1.47239571899611,0.0021527777777777,-3.18554303421473,0.0179166666666667,-3.37508689557842,0.0031944444444444,-3.84967677621021,0.018125,-3.7245419942635,0.0766666666666667,-0.478355802394242,0.0072222222222222,-3.09835007832928 -486,2020-07-11,193.0,2.39148708333333,13.24875,-2.39028896010768,2.4125,-1.49412576443203,0.0022916666666666,-3.21207655494842,0.0180555555555556,-3.40047165565932,0.0031944444444444,-3.83258518151386,0.0184027777777778,-3.74404028395897,0.0483333333333333,-0.47938459836968,0.0075,-3.10786770948216 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-497,2020-07-22,204.0,1.88359708333333,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -498,2020-07-23,205.0,1.76913333333333,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA -499,2020-07-24,206.0,1.80787083333333,0.239583333333333,-2.72028369161407,0.0608333333333333,-1.48747019823721,0.0026388888888888,-3.19806528196855,0.0434027777777778,-3.38406815911633,0.0159722222222222,-3.82396323132594,0.0395138888888889,-3.67322223815854,0.0229166666666667,-0.494132002297574,0.0103472222222222,-3.17508295175992 -500,2020-07-25,207.0,1.78019041666667,0.244583333333333,-2.52698097352211,0.0420833333333333,-1.54970220439018,0.0016666666666666,-3.21744230496945,0.050625,-3.39713402160624,0.0172916666666667,-3.82875133391842,0.035,-3.67404625605116,0.02875,-0.547662031901369,0.0093055555555555,-3.17492012090539 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-531,2020-08-25,238.0,1.94245708333333,1.63708333333333,-2.60166596087257,0.0379166666666667,-1.56179923334438,0.0010416666666666,-3.1213681129304,0.041875,-3.52923026772361,0.0136111111111111,-3.81600771606618,0.0123611111111111,-3.73849732943652,0.112916666666667,-0.434514389904686,0.0176388888888889,-3.24967046663297 -532,2020-08-26,239.0,2.08577583333333,3.19291666666667,-2.68966011101599,0.0545833333333333,-1.56816227494582,0.0020138888888888,-3.20873504281234,0.0329861111111111,-3.55806106449325,0.01,-3.81166630242893,0.0120138888888889,-3.68882865670463,0.05375,-0.514247728417503,0.0120138888888889,-3.25665551373617 -533,2020-08-27,240.0,1.73392791666667,4.29291666666667,-2.36876861859755,0.113333333333333,-1.60224922655408,0.0006944444444444,-3.22600713142119,0.0588194444444444,-3.51336767652048,0.0118055555555556,-3.81549404801952,0.0130555555555556,-3.70669627688369,0.0425,-0.546398218498836,0.0177083333333333,-3.23106530053057 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+417,2020-05-03,124.0,5.25953541666669,0.0025,-2.58552704699608,0.0016666666666666,-1.81072056823242,0.0003472222222222,-3.10883448885329,0.0634027777777778,-3.30593172044596,0.0231944444444444,-3.80808598698352,0.0017361111111111,-3.80754718431525,0.09375,-0.295358036671466,1.2290556934867,0.683955250144354,0.0005555555555555,-3.03159340306086,0.04469328625,0.03376006625 +418,2020-05-04,125.0,4.88164791666669,0.00125,NA,0.0008333333333333,-1.47845162339355,0.0002083333333333,-3.10120883844465,0.0654166666666666,-3.28950874509508,0.0200694444444444,-3.81303642638611,0.0024305555555555,-3.79591454512788,0.13,-0.33615208407876,1.15680961678801,0.641258150404032,0.0005555555555555,-2.97011020890848,0.04429934875,0.03417199875 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+499,2020-07-24,206.0,1.80787083333333,0.239583333333333,-2.72028369161407,0.0608333333333333,-1.48747019823721,0.0026388888888888,-3.19806528196855,0.0434027777777778,-3.38406815911633,0.0159722222222222,-3.82396323132594,0.0395138888888889,-3.67322223815854,0.0229166666666667,-0.494132002297574,1.02532486489566,0.554674181857264,0.0103472222222222,-3.17508295175992,0.0331971071140939,0.026075656442953 +500,2020-07-25,207.0,1.78019041666667,0.244583333333333,-2.52698097352211,0.0420833333333333,-1.54970220439018,0.0016666666666666,-3.21744230496945,0.050625,-3.39713402160624,0.0172916666666667,-3.82875133391842,0.035,-3.67404625605116,0.02875,-0.547662031901369,0.897657726052273,0.510263966656703,0.0093055555555555,-3.17492012090539,0.0325909953731343,0.0269764943283582 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+1389,2022-12-31,365.0,10.7195225,0.0025,-3.04686775886764,0.00625,-1.42746100426497,0.01784722,-2.95776520569826,0.020277774,-3.3634594558677,0.01229167,-3.81793674889522,0.001736111,-3.72487876289943,0.13875,-0.656255437076182,0.870542839781374,0.446985506933019,0.0024305556,-2.93733927712455,0.0528326871140166,0.0358469266686687 From f599781303aa52e3f8d482257a96021020c3b1fb Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Wed, 8 Jan 2025 19:20:14 +0100 Subject: [PATCH 80/97] Delete Models/TheoMod directory --- Models/TheoMod/README.md | 17 -- Models/TheoMod/TheoMod_run.py | 81 --------- Models/TheoMod/TheoMod_v1.0.py | 317 --------------------------------- 3 files changed, 415 deletions(-) delete mode 100644 Models/TheoMod/README.md delete mode 100644 Models/TheoMod/TheoMod_run.py delete mode 100644 Models/TheoMod/TheoMod_v1.0.py diff --git a/Models/TheoMod/README.md b/Models/TheoMod/README.md deleted file mode 100644 index 8e672fb..0000000 --- a/Models/TheoMod/README.md +++ /dev/null @@ -1,17 +0,0 @@ -Theoretical model is set up with constant nutrient input, similar to a chemostat type model with other environmental forcing like the irradiance and the temperature. Nutrient is required to input with a forcing array in the model. - -The model output is a multi-dimensional array, containing the solutions for every time step. The model solution includes the below variables: -1. One nutrient; -2. Two zooplankton of different sizes; -3. One detritus; -4. _N_ classes of phytoplankton in different sizes; -5. Total growth from phytoplankton; -6. Grazing from small zooplankton; -7. Grazing from large zooplankton; -8. Temperature dependence for phytoplankton growth; and -9. Nutrient limitation for phytoplankton growth. - -The model solution contains all variable fluxes for environmental conditions as below: -- Nutrient levels (oligotrophic, eutrophic, and hypertrophic) -- Grazing regimes (nanoflagellate+daphnia, ciliates+daphnia, and copepods+daphnia) -- Grazing strategies (non-daphnia zooplankton as a specialist or as a generalist) diff --git a/Models/TheoMod/TheoMod_run.py b/Models/TheoMod/TheoMod_run.py deleted file mode 100644 index ffb61ab..0000000 --- a/Models/TheoMod/TheoMod_run.py +++ /dev/null @@ -1,81 +0,0 @@ -import os -import numpy as np -import time -import xarray as xr -from scipy.interpolate import make_interp_spline - -Pnum = 30 # specify the nos of phytoplankton size groups -Ynum = 10 # no. of modeling year - -# Import forcing data -wd = '/Users/szewing/Desktop/PhD_work/3. ThermalTrait/TempSizeMod_TSM' # '/people/home/sto/SizeMod' -os.chdir(wd) - -# Temperature and PAR data -LWST_CTD = np.genfromtxt('Forcing/TempForc_CTD.csv', delimiter=',') -# LWST_8 = np.genfromtxt('Forcing/TempForc.csv', delimiter=',') -nSSI = np.genfromtxt('Forcing/nSSI_13p_syn.csv', delimiter=',') -MLD = np.genfromtxt('Forcing/MLDForc_Theoretical.csv', delimiter=',') - -for a in range(8): # To synchonized the initial and end forcing value - nSSI[0, a + 1] = nSSI[12, a + 1] - - -# Converting nSSI into PAR -def nSSI2PAR(netSSI): - """ - This function is to convert nSSI (W m-2) into PAR (Ein m-2 d-1) - PAR is a term used to describle radiation in wavelengths usedful for photosynthesis [1] - PAR is generally accepted to be wavelengths between 400 and 700 nm [1] - Procedure: - 1. extr = extraction factor (only about 45% of the nSSI is in the 400-700nm range, Howell et al. 1983) - 2. conv = conversion factor from W m-2 to Ein m-2 d-1 (Lampert and Sommer, 2007, pp.20)** - ** see PAR_UnitConversion.docx - """ - extr = 0.45 - conv = 0.432 - return netSSI * extr * conv - - -PAR = nSSI2PAR(nSSI) -PAR[:, 0] = nSSI[:, 0] - -# Creating forcing curves -PAR_spl = make_interp_spline(PAR[:, 0], PAR[:, 5], k=2) -PAR_spl = PAR_spl(np.arange(365)) -PAR_spl[364] = PAR_spl[0] - -# generate dmdt arrays -dmdt = np.zeros(365) -dmdt[0:364] = np.diff(MLD[:]) -dmdt[364] = dmdt[0] - -MLD_run = np.tile(MLD, Ynum) -dmdt_run = np.tile(dmdt, Ynum) -LWST_CTD_spl = np.tile(LWST_CTD, Ynum) -PAR_spl = np.tile(PAR_spl, Ynum) - -# Runs - loop -N0_list = np.array([0.01, 0.1, 1]) # equivalent to 0.03, 0.3, 3 µmol PO4 L-1 ( x 95 to convert to µg) -Z1_size = np.array([5, 20, 150]) -Z1_GrazStrtgy = np.array([0.2, 0.5]) # specialist or generalist -######################################################## -sol = np.zeros((Ynum*365, 4 + Pnum + (5*Pnum), len(N0_list), len(Z1_size), len(Z1_GrazStrtgy))) - -mod_name = 'TSM_flux_theor' + str(Ynum) + 'yr' - -from TheoMod import SizeModLogspace - -# Run solution -starttime = time.ctime() # save it for documentation purposes -print(mod_name, "start:", starttime) -start = time.time() # start the timer -for i in range(len(N0_list)): # nutrient level - for j in range(len(Z1_size)): - for k in range(len(Z1_GrazStrtgy)): - sol[:, :, i, j, k] = SizeModLogspace(mld=MLD_run, par=PAR_spl, sst=LWST_CTD_spl, dmdt=dmdt_run, N0=N0_list[i], - numP=Pnum, numYears=Ynum, minZ=Z1_size[j], SizeTol=[Z1_GrazStrtgy[k], 0.5]).solution - -end = time.time() # stop the timer -endtime = time.ctime() # save it for documentation purposes -print(mod_name, "complete:", endtime) diff --git a/Models/TheoMod/TheoMod_v1.0.py b/Models/TheoMod/TheoMod_v1.0.py deleted file mode 100644 index edd3902..0000000 --- a/Models/TheoMod/TheoMod_v1.0.py +++ /dev/null @@ -1,317 +0,0 @@ -import numpy as np -from scipy.integrate import odeint - - -class SizeModLogspace: - """ - Size-based model under the assumption of allometric relationships on growth and grazing. - ------- - Model has multiple groups of phytoplankton, 2 groups of zooplankton in different sizes, - one nutrient source and a detritus state variable. - ------- - Flexible parameters: - - n0 - - prey tolerance - - P size range - - Z size range - - no. of P size classes - - no. of years for model running - - Fixed parameters: - - Allometric parameters for Kn - - Allometric parameters for Mu - - Allometric parameters for Topt - - Allometric parameters for Imax - - Allometric parameters for Popt - - Model params - """ - - def __init__(self, mld, par, sst, dmdt, minP=0., maxP=np.log10(200), numP=3, minZ=5, maxZ=400, numZ=2, numYears=5, - N0=0.1, Zsmall0=0.01, Zlarge0=0.01, D0=0.01, - Pmax=1.1, Kpar=0.1, alpha=0.15*0.432, Mz=0.1, Mp=0.15, Mz2=0.34, immigration=1e-4, - Ieff=0.75, beta=0.69, Kp=3, Rem=0.6, k=0.1, - mu_alpha=-0.36, mu_beta=10 ** 0.69, Kpo4_alpha=0.53, Kpo4_beta=10 ** -1.5, - graz_alpha=-0.4, graz_beta=26, ops_alpha=0.56, ops_beta=0.65, topt_alpha=-0.11, topt_beta=34.5, - SizeTol=[0.2, 0.5], graz_Q10=3, sig_T=15, vectorize=True): # graz_Q10=2, - self.mld = mld - self.par = par # photosynethically activated radiation (E*m^-2*d^-1) - self.sst = sst # sea surface temperature/lake surface water temperature (LSWT) - self.dmdt = dmdt # dmdt - self.minP = minP # min. size of P size range - self.maxP = maxP # max. size of P size range - self.numP = numP # number of phytoplankton size class - self.minZ = minZ # min. size of Z size range - self.maxZ = maxZ # max. size of Z size range - self.numZ = numZ # number of zooplankton size class - self.numYears = numYears # Number of years for model run - self.N0 = N0 # Initial nutrient concentrations (mmol N*m^-3) - self.Zsmall0 = Zsmall0 # Initial Zsmall concentration (mmol N*m^-3) - self.Zlarge0 = Zlarge0 # Initial Zlarge concentration (mmol N*m^-3) - self.D0 = D0 # Initial detritus concentration (mmol N*m^-3) - self.Ps0 = np.repeat(0.01, self.numP, axis=0) # Initial P concentration (mmol N*m^-3) - self.Pmax = Pmax # Max photosynthesis rate - self.Kpar = Kpar # Water attenuation coefficient - self.alpha = alpha # Initial slope of P-I curve - self.Mz = Mz # Natural mortality - Zooplankton - self.Mp = Mp # Natural mortality - Phytoplankton - self.Mz2 = Mz2 # Mortality from upper predators - self.immigration = immigration # exogeneous - self.Ieff = Ieff # Ingestion efficiency - self.beta = beta # Assimlation efficiency - self.Kp = Kp # Phytoplankton half-saturation constant (mmol*m^-3) - self.Rem = Rem # Remineralization rate (day^-1) - self.k = k # Cross thermocline mixing factor (m/day) - self.mu_alpha = mu_alpha # Slope of allometric max. uptk rate - self.mu_beta = mu_beta # Intercept of allometric max. uptk rate - self.Kpo4_alpha = Kpo4_alpha # Slope of allometric half-saturation constant - self.Kpo4_beta = Kpo4_beta # Intercept of allometric half-saturation constant - self.graz_alpha = graz_alpha # Slope of allometric max. grazing rate (day^-1) - self.graz_beta = graz_beta # Intercept of allometric max.grazing rate (day^-1) - self.ops_alpha = ops_alpha # Slope of allometric optimum prey size - self.ops_beta = ops_beta # Intercept of allometric optimum prey size - self.SizeTol = SizeTol # Prey size tolerance (should be an array) - self.sig_T = sig_T # community mean thermal tolerance breadth width as per Thomas et al. (2015) - self.topt_alpha = topt_alpha # Slope of allometric optimal growth temperature - self.topt_beta = topt_beta # Intercept of allometric optimal growth temperature - self.graz_Q10 = graz_Q10 # Q10 temperature for grazing - self._vectorize = vectorize - self.Fluxes0 = np.zeros(self.numP * 5) - self.init_con = np.concatenate(([self.N0], # N0 - [self.Zsmall0], # Zsmall0 - [self.Zlarge0], # Zlarge0 - [self.D0], # D0 - [self.Ps0], # Ps0 - [self.Fluxes0]), # Growth and grazing fluxes - axis=None) # Ps0 - self.t_array = np.arange(0, 365 * self.numYears) # Time array - self.Psize_array = np.logspace(self.minP, self.maxP, num=self.numP, base=10) # Size array for phytoplankton - self.Zsize_array = np.linspace(self.minZ, self.maxZ, self.numZ) # Size array for zooplankton - self.solution = odeint(self.AlloNPsZD, self.init_con, self.t_array, rtol=1.e-10, atol=1.e-10, hmax=1.e16, hmin=1.e-16) - - def Vol2ESD(self, v): # Vol2ESD(1) = 1.2407 - esd = ((v * 6) / np.pi) ** (1 / 3) - return esd - - def Allo_mumax(self, PhySize): - return self.mu_beta * ((PhySize / self.Vol2ESD(1)) ** self.mu_alpha) - - def Allo_NU(self, nut, PhySize): - Kpo4_scal = self.Kpo4_beta * ((PhySize / self.Vol2ESD(1)) ** self.Kpo4_alpha) # allometric function for half-saturation - return nut / (nut + Kpo4_scal) - - def Allo_Topt(self, PhySize): - return self.topt_beta * ((PhySize / 1) ** self.topt_alpha) - - def temp_dependence(self, time, topt): - """ - Eppley-Norberg formulation - :param topt: optimal growth temperature obtained from Allo_Topt - :param time: timestep to derive temperature - :return: - """ - thermtol = 1 - ((time - topt) / self.sig_T) ** 2 - return thermtol - - def LightLim(self, I, Z): - """ - Light limitation function on photosynthesis based on Smith's function, - illustrated by a P-I curve - """ - I0 = I - Iz = I0 * np.exp(-self.Kpar * Z) - LL = (((self.Pmax / (self.Kpar * Z)) * - np.log((self.alpha * I0 + np.sqrt((self.Pmax ** 2) + (self.alpha * I0) ** 2)) / - ((self.alpha * Iz) + np.sqrt(self.Pmax ** 2 + (self.alpha * Iz) ** 2))))) - return LL - - def Imax_Q10(self, time): - """ - Q10 method based on Sherman et al. (2016) Global Biogeochemical Cycles, 30(4) - ----------------------------------------------------------------------------------- - T0: reference temperature, adopted as 303.15K (30 degree Celcius) - Q10: the factor change in growth rate for a 10 degree change in temperature - :param time: timestep - :return: size- and temperature- dependent grazing rates - """ - ref_temp = 10 # reference temperature - # G0 = 0.57 # (marine zooplankton: Sherman et al., 2016):: we use size-dependent grazing rates - # Q10 = 1.47 # (marine zooplankton: Sherman et al., 2016) - # Q10 = 2.4 # Durbin and Durbin (1992) - # Q10 = 3.9 # Kiørboe et al. (1982) - # Q10 = 2.8 # Hansen et al. (1997) & Gosselain et al. (1996) - return self.graz_Q10 ** ((time - ref_temp) / 10) - - def AlloImax(self, ZooSize, time): - """ - Max. ingestion rate also depends on the size of the zooplankton - ---------- - graz_alpha = -0.4 # Hansen et al. 1994, Banas, 2011 - graz_beta = 26 # Hansen et al. 1994, Banas, 2011 [day^-1] - """ - return self.graz_beta * ((ZooSize / 1) ** self.graz_alpha) * self.Imax_Q10(time) - - def AllogpP(self, PhySize, ZooSize, SizeTol): - """ - Selected zooplankton size class of 10 & 200[µm] - """ - OPS = self.ops_beta * ((ZooSize / 1) ** self.ops_alpha) # µm ESD - gpP = np.exp(-((np.log10(PhySize) - np.log10(OPS)) / SizeTol) ** 2) - return gpP - - def AlloGraz(self, PhySize, ZooSize, Pi, SizeTol, denom, time): - """ - PhySize = Size class/array of phytoplankton population; - ZooSize = Size class/array of zooplankton population; - Pi = Phytoplankton biomass - """ - graz = self.AlloImax(ZooSize, time) * ((self.AllogpP(PhySize, ZooSize, SizeTol) * Pi) / (self.Kp + denom)) - return graz - - def mixing(self, time): - """k refers to the cross thermocline mixing factor""" - return (self.k + max(self.dmdt[time], 0.)) / self.mld[time] - - def AlloNPsZD(self, y, t): - """ - A size-based trophic model with multiple phytoplankton and zooplankton size class - ---------- - y: array of initial conditions of state variables (var) - t: time array - """ - # Initialization - N = y[0] - Zsmall = y[1] # copepod nauplii or - Zlarge = y[2] # cladocerans or rotifera - D = y[3] - Ps = y[4:4 + self.numP] - var = np.zeros(len(y)) - t = int(t) - - # non-P dependent equations - """ - Zsmall = specialist, narrower size tolerance factor; - Zlarge = generalist, wider size tolerance factor. - """ - Graz_denomZsmall = np.sum(self.AllogpP(self.Psize_array, self.Zsize_array[0], self.SizeTol[0]) * Ps) - Graz_denomZlarge = np.sum(self.AllogpP(self.Psize_array, self.Zsize_array[1], self.SizeTol[1]) * Ps) - - if self._vectorize: - Pgrowth = self.Allo_mumax(self.Psize_array) * (self.temp_dependence(self.sst[t], self.Allo_Topt(self.Psize_array)) * - self.Allo_NU(N, self.Psize_array) * self.LightLim(self.par[t], self.mld[t])) * Ps - Pmortality = self.Mp * Ps - grossGraz_S = (self.AlloGraz(self.Psize_array, self.Zsize_array[0], Ps, self.SizeTol[0], Graz_denomZsmall, self.sst[t]) * Zsmall) - grossGraz_L = (self.AlloGraz(self.Psize_array, self.Zsize_array[1], Ps, self.SizeTol[1], Graz_denomZlarge, self.sst[t]) * Zlarge) - PgrowthTemp = self.temp_dependence(self.sst[t], self.Allo_Topt(self.Psize_array)) * Ps - PgrowthNut = self.Allo_NU(N, self.Psize_array) * Ps - - # [dNdt] - var[0] = (self.Rem * D # Remineralization - + self.mixing(t) * (self.N0 - N) # MixingN - - np.sum(Pgrowth) # Phytoplankton growth - + self.beta * (1. - self.Ieff) * np.sum(grossGraz_S) # Excretion from grazing(Zsmall)) - + self.beta * (1. - self.Ieff) * np.sum(grossGraz_L) # Excretion from grazing(Zlarge)) - ) - - # [dZ1dt] - var[1] = (- self.Mz * Zsmall # Natural mortality Z1 - - self.Mz2 * (Zsmall ** 2) # density-dependent top predator grazing Z1 - - (self.dmdt[t] / self.mld[t]) * Zsmall # Mixing loss Z1 - + self.beta * self.Ieff * np.sum(grossGraz_S) - ) - - # [dZ2dt] - var[1] = (- self.Mz * Zsmall # Natural mortality Z1 - - self.Mz2 * (Zsmall ** 2) # density-dependent top predator grazing Z1 - - (self.dmdt[t] / self.mld[t]) * Zsmall # Mixing loss Z1 - + self.beta * self.Ieff * np.sum(grossGraz_L) - ) - - # [dDdt] - var[3] = (self.Mz * (Zsmall + Zlarge) # MortalityZsmall+Zlarge - + np.sum(Pmortality) - - self.Rem * D # Remineration - - self.mixing(t) * D # Mixing loss D - + (1. - self.beta) * np.sum(grossGraz_S) - + (1. - self.beta) * np.sum(grossGraz_L) - ) - - # [dPsdt] - var[4:4 + self.numP * 1] = (Pgrowth - Pmortality - grossGraz_S - grossGraz_L - self.mixing(t) * Ps) + self.immigration - - # [growth] - var[4 + self.numP * 1: 4 + self.numP * 2] = Pgrowth - - # [grazingS] - var[4 + self.numP * 2: 4 + self.numP * 3] = grossGraz_S - - # [grazingL] - var[4 + self.numP * 3: 4 + self.numP * 4] = grossGraz_L - - var[4 + self.numP * 4: 4 + self.numP * 5] = PgrowthTemp - var[4 + self.numP * 5: 4 + self.numP * 6] = PgrowthNut - - else: - # [dNdt] - var[0] = (self.Rem * D # Remineration - + self.mixing(t) * (self.N0 - N)) # MixingN - - # [dZ1dt] - var[1] = (- self.Mz * Zsmall # Natural mortality Z1 - - self.Mz2 * (Zsmall ** 2) # density-dependent top predator grazing Z1 - - (self.dmdt[t] / self.mld[t]) * Zsmall) # Mixing loss Z1 - - # [dZ2dt] - var[2] = (- self.Mz * Zlarge # Natural mortality Z2 - - self.Mz2 * (Zlarge ** 2) # Top predator grazing Z2 - - (self.dmdt[t] / self.mld[t]) * Zlarge) # Mixing loss Z2 - - # [dDdt] - var[3] = (self.Mz * (Zsmall + Zlarge) # MortalityZsmall+Zlarge - - self.Rem * D # Remineration - - self.mixing(t) * D) # Mixing loss D - - # P-dependent (loop) - for i in range(len(Ps)): # for each P size class - P = Ps[i] # define P in the loop - Psize = self.Psize_array[i] # Size class/array of phytoplankton population - - # [dNdt] - var[0] = (var[0] - - self.Allo_mumax(Psize) * (self.temp_dependence(self.sst[t], self.Allo_Topt(Psize)) * self.Allo_NU(N, Psize) * - self.LightLim(self.par[t], self.mld[t])) * P - + (self.beta * (1. - self.Ieff) * - self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], Graz_denomZsmall, self.sst[t])) * Zsmall # Excretion from grazing(Zsmall)) - + (self.beta * (1. - self.Ieff) * - self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], Graz_denomZlarge, self.sst[t])) * Zlarge) # Excretion from grazing(Zlarge)) - - # [dZ1dt] - var[1] = (var[1] - + (self.beta * self.Ieff * self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], - Graz_denomZsmall, self.sst[t])) * Zsmall) # GrazingP from Zsmall - - # [dZ2dt] - var[2] = (var[2] - + (self.beta * self.Ieff * self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], - Graz_denomZlarge, self.sst[t])) * Zlarge) # GrazingP from Zlarge - - # [dDdt] - var[3] = (var[3] - + ((1. - self.beta) * self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], - Graz_denomZsmall, self.sst[t])) * Zsmall # SloppyfeedP - Zsmall - + ((1. - self.beta) * self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], - Graz_denomZlarge, self.sst[t])) * Zlarge # SloppyfeedP - Zlarge - + self.Mp * P) - - # [dPsdt] - var[4 + i] = (var[4 + i] - + self.Allo_mumax(Psize) * (self.temp_dependence(self.sst[t], self.Allo_Topt(Psize)) * self.Allo_NU(N, Psize) - * self.LightLim(self.par[t], self.mld[t])) * P # Uptk - - self.Mp * P # MortalityP - - self.AlloGraz(Psize, self.Zsize_array[0], P, self.SizeTol[0], - Graz_denomZsmall, self.sst[t]) * Zsmall # GrazingP from Zsmall - - self.AlloGraz(Psize, self.Zsize_array[1], P, self.SizeTol[1], - Graz_denomZlarge, self.sst[t]) * Zlarge # GrazingP from Zlarge - - self.mixing(t) * P # Mixing - + self.immigration) # exogeneous - - return var From b6f83e4e33e4e7f575502652efad3386ea435251 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:18:48 +0100 Subject: [PATCH 81/97] Create README.md --- SensitivityTest/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 SensitivityTest/README.md diff --git a/SensitivityTest/README.md b/SensitivityTest/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/SensitivityTest/README.md @@ -0,0 +1 @@ + From ef319205c790ee0b4f54f2793150e76a6ca34afb Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:19:19 +0100 Subject: [PATCH 82/97] Create README.md --- SensitivityRun/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 SensitivityRun/README.md diff --git a/SensitivityRun/README.md b/SensitivityRun/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/SensitivityRun/README.md @@ -0,0 +1 @@ + From 036bcfb21d6961bc6f4d036e8e24919b8d93e0b0 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:24:24 +0100 Subject: [PATCH 83/97] Update README.md --- LakePlankData/README.md | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/LakePlankData/README.md b/LakePlankData/README.md index 130b639..d9813d0 100644 --- a/LakePlankData/README.md +++ b/LakePlankData/README.md @@ -3,17 +3,10 @@ Here stores: 2. The script used to produce a time-averaged plankton data for calibrating the model ('TSM_PlanktonData.py') 3. The time-averaged plankton data ('PlanktData4Calibr.csv') -

-A graphical illustration of the transformation of time-averaged phytoplankton data*: -SuppFigS1_v2 - -A graphical illustration of the transformation of time-averaged zooplankton data*: -SuppFigS4_v2 -

### A brief description: -This model outputs phytoplankton and zooplankton biomasses for different size classes. This information allow us to produce 1.) total phytoplankton biomass(µM P); 2.) total zooplankton biomass(µM P) ; and 3.) community mean phytoplankton size(µm ESD). -After the transformations, the common unit considered for comparing model outputs and data are: biomass(µM P); and biovolume(µm3) +We used the averaged (3-year) lake nutrient and plankton data to calibrate the model, based on four results: 1.) total phytoplankton biomass(µM P); 2.) total zooplankton biomass(µM P) ; and 3.) community mean phytoplankton size(µm ESD). For the data transformation, please refer to the supporting information of the related publication of this work. +The common unit considered for the model outputs and the data are: biomass concentration(µM P) and biovolume(µm3)


From 7d76cc653bbdcab67a4c3e34913e16afa888ae37 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:26:47 +0100 Subject: [PATCH 84/97] Update README.md --- Models/FinalMod/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Models/FinalMod/README.md b/Models/FinalMod/README.md index 17dfa96..c9f9476 100644 --- a/Models/FinalMod/README.md +++ b/Models/FinalMod/README.md @@ -26,13 +26,13 @@ or if one wants to change the parameters that determine the allometric relations sol = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, numYears=Ynum, mu_alpha=0.25).solution ```` -The code for the parameters can be found in FinalMod_Sbm.py. +The coding name for the parameters can be found in FinalMod_Sbm.py.

#### FinalMod_proj.py -In this script one can run a number of projection sceanrios, i.e. linear increase in temperature, automatically. +In this script one can run a number of projection sceanrios, i.e. linear (or, non-linear depending on the input forcing) increase in temperature. If one wants to configure the increase in temperature by their own preferred values, or adjust the number of scenarios, please change at the below line: ```` temp_incr = [0, 1.4, 2.5, 4.2] From 97357783256c11830e3e7f995325bd293543076e Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:28:43 +0100 Subject: [PATCH 85/97] Update README.md --- README.md | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/README.md b/README.md index 68f4959..b870c35 100644 --- a/README.md +++ b/README.md @@ -59,27 +59,6 @@ Using the standard model, we conduct two numerical experiments:
For details, please refer to the related publication of this model (under review). -

-## Suggestions for future works -The current model are structured flexibly and can be extended for future studies. Below are some options. -
-1. Experiments on model hypothesis - How different assumptions on the temperature dependence of phytoplankton growth and zooplankton grazing would change the predictions on communities compositions of phytoplankton community? - e.g. Comparisons between: - - Eppley curve or Q10 model (Ho et al. 2013) versus thermal tolerance curve (Chen, 2022) - - Size-dependent Eppley curves (e.g. To et al. 2024) - - Community-averaged unimodal curve (Cagle and Roelke 2021; the present study) - - Size-dependent unimodal curve (due to be examined) - -2. Experiments on model hypothesis - mechanisms between temperature dependence and resource levels - - optimal growth temperature, $T_{opt}$, with nutrient levels (Bestion et al. 2018) - - optimal growth temperature, $T_{opt}$, with irradiance levels (Edwards et al. 2016) - -3. Numerical explorations on the relationships between phytoplankton size and thermal traits, e.g. $T_{opt}$, $\sigma_{T}$ - - What are the probable/observable range of parameters for such allometric relationships? - -4. Ecological explorations of altered stratification phenology in lakes (Woolway et al. 2021), for example, - - Figure1_v2 1 -





Reference: From 92cc77f50b9817d4bdd6d96a7e08ea83f7352c6b Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:29:31 +0100 Subject: [PATCH 86/97] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b870c35..c6cdc0f 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ We fine-tuned selected parameters against the time-averaged plankton data collec ## Experiments Using the standard model, we conduct two numerical experiments:
1. a projection based on Representative Climate Pathways (RCPs) issued by IPCC -2. a sensitivity test for the two thermal traits, $T_{opt}$ and $\sigma_T$ in different RCP scenarios +2. a sensitivity analysis for the two thermal traits, $T_{opt}$ and $\sigma_T$ in different warming (i.e., RCP by IPCC) scenarios For details, please refer to the related publication of this model (under review). From 4a3d8a087424c314331ea08e262640d9808bf082 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:56:03 +0100 Subject: [PATCH 87/97] Update README.md --- SensitivityRun/README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/SensitivityRun/README.md b/SensitivityRun/README.md index 8b13789..dfe6532 100644 --- a/SensitivityRun/README.md +++ b/SensitivityRun/README.md @@ -1 +1,8 @@ +This is a sensitivity run for examining how sensitive is the model variables to the changes in lake stratification. This model has a simplified lake stratification dyanmics between the epilimnion and the hypolimnion. It is regulated by two components, the temporal change in mixed layer depth (a forcing function for one year) and the vertical diffusive mixing coefficient (a parameter with a unit of day-1). + +We vary the lake mixing dynamics for the worst climate change scenario of our projection, RCP 8.5, in which the lake water temperature increases by 4.2 ℃ throughout the year. Based on the estimation of the median change in stratification period by Woolway et al. (2021), we created a prolonged lake stratification that starts 22 days earlier and ends 11 days later (Fig. S9A). This leads to a longer stratified period of the lake by 33 days. In another case, we reduced the diffusive mixing coefficient by 50%. This parameter determines the strength of nutrient mixing from the hypolimnion into the epilimnion, which is a realistic approximation of nutrient transportations in a lake water column. + + +![Uploading Sensit_mix.png…]() + From c0282db98a34b56b768bb78cf206bb4325956e04 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Sat, 1 Feb 2025 10:11:33 +0100 Subject: [PATCH 88/97] Update README.md --- SensitivityRun/README.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/SensitivityRun/README.md b/SensitivityRun/README.md index dfe6532..b617c43 100644 --- a/SensitivityRun/README.md +++ b/SensitivityRun/README.md @@ -1,8 +1,13 @@ -This is a sensitivity run for examining how sensitive is the model variables to the changes in lake stratification. This model has a simplified lake stratification dyanmics between the epilimnion and the hypolimnion. It is regulated by two components, the temporal change in mixed layer depth (a forcing function for one year) and the vertical diffusive mixing coefficient (a parameter with a unit of day-1). +This is a sensitivity run for examining how sensitive are the model variables to the changes in lake stratification. This model has a simplified lake stratification dyanmics between the epilimnion and the hypolimnion. It is regulated by two components, the temporal change in mixed layer depth (a forcing function for one year) and the vertical diffusive mixing coefficient (a parameter with a unit of day-1). -We vary the lake mixing dynamics for the worst climate change scenario of our projection, RCP 8.5, in which the lake water temperature increases by 4.2 ℃ throughout the year. Based on the estimation of the median change in stratification period by Woolway et al. (2021), we created a prolonged lake stratification that starts 22 days earlier and ends 11 days later (Fig. S9A). This leads to a longer stratified period of the lake by 33 days. In another case, we reduced the diffusive mixing coefficient by 50%. This parameter determines the strength of nutrient mixing from the hypolimnion into the epilimnion, which is a realistic approximation of nutrient transportations in a lake water column. +We vary the lake mixing dynamics for the worst climate change scenario of our projection, RCP 8.5, in which the lake water temperature increases by 4.2 ℃ throughout the year. This number is based on literature data that are supplemented in the related publication of this work. Based on the estimation of the median change in stratification period by Woolway et al. (2021), we created a prolonged lake stratification that starts 22 days earlier and ends 11 days later (see below figure). This leads to a longer stratified period of the lake by 33 days. In another case, we reduced the diffusive mixing coefficient by 50%. This parameter determines the strength of nutrient mixing from the hypolimnion into the epilimnion, which is a realistic approximation of nutrient transportations in a lake water column (Peeters et al. 2007). -![Uploading Sensit_mix.png…]() +Sensit_mix + +


++ Peeters, F., D. Straile, A. Lorke, and D. M. Livingstone. 2007. Earlier onset of the spring phytoplankton bloom in lakes of the temperate zone in a warmer climate. Global Change Biology 13: 1898–1909. doi:10.1111/j.1365-2486.2007.01412.x + ++ Woolway, R. I., S. Sharma, G. A. Weyhenmeyer, and others. 2021. Phenological shifts in lake stratification under climate change. Nat Commun 12: 2318. doi:10.1038/s41467-021-22657-4 From 9801364b79df8e6ef85f1d6ca4d332305b607d03 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 4 Feb 2025 11:57:04 +0100 Subject: [PATCH 89/97] Update README.md --- SensitivityTest/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/SensitivityTest/README.md b/SensitivityTest/README.md index 8b13789..37fb608 100644 --- a/SensitivityTest/README.md +++ b/SensitivityTest/README.md @@ -1 +1,4 @@ +The sensitivity test is conducted to estimate the uncertainty of different model parameters on the model results. I selected 14 model parameters, which are neither examined by the sensitivty analysis (i.e., the thermal traits) nor the sensitivty run (i.e., the lake mixing parameters). +For these 14 parameters, I tested ±25% the default parameter values and examined the changes in the model results, i.e., total nutrient concentration (μM P), community biomass of phytoplankton and zooplankton (μM P), and community-weighted mean phytoplankton biovolume (μm3). +The results are attached in this folder. More details can be found in the related publication of this work (See front page). From 1b03fd902f934d1fdf7ceed5e1a9892ae3f457c7 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 4 Feb 2025 11:58:36 +0100 Subject: [PATCH 90/97] Update README.md --- SensitivityRun/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/SensitivityRun/README.md b/SensitivityRun/README.md index b617c43..4e9977a 100644 --- a/SensitivityRun/README.md +++ b/SensitivityRun/README.md @@ -6,6 +6,9 @@ We vary the lake mixing dynamics for the worst climate change scenario of our pr Sensit_mix +


+The results are attached in this folder. More details can be found in the related publication of this work (See front page). +


+ Peeters, F., D. Straile, A. Lorke, and D. M. Livingstone. 2007. Earlier onset of the spring phytoplankton bloom in lakes of the temperate zone in a warmer climate. Global Change Biology 13: 1898–1909. doi:10.1111/j.1365-2486.2007.01412.x From 8dd9eb29e576cc6525c28524b8f5fb88736e9848 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 4 Feb 2025 12:08:19 +0100 Subject: [PATCH 91/97] Update README.md --- README.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index c6cdc0f..a0fa34e 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,9 @@ In the current realm of global environmental change, understanding the effects o Trait-based models are valuable tools to disentangle possible effects and test the possible mechanisms that water temperature has on phytoplankton (Litchman 2023). Phytoplankton cell size is a master trait and has been widely used in modelling work (Litchman and Klausmeier 2008). Size-based models allow explorations of aggregated community properties such as total biomass and community mean cell size of phytoplankton (e.g. Ward et al. 2012; Acevedo-Trejos et al. 2018; To et al. 2024). They are useful for understanding changes in the macroecological patterns of the phytoplankton community size compositions in response to changes in environmental conditions.

## Model description -The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated into a size-based framework (e.g., Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $PO_4^{3-}$, available for uptake by different phytoplankton size classes ($P_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The growth of phytoplankton is limited by light and nutrients, and is scaled by temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, followed by recharging the nutrient pool through remineralization processes. +The model is adapted from the well-established Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model (_sensu_ Fasham et al., 1990; Post et al. 2024) incorporated into a size-based framework (e.g., Armstrong 1994; To et al. 2024). The model is differential equation-based and includes one nutrient source, phosphorus $N$, available for uptake by different phytoplankton size classes ($A_i$). The phytoplankton are subject to grazing by two zooplankton of different size groups ($Z_1$, $Z_2$). The growth of phytoplankton is limited by light and nutrients, and is scaled by temperature dependence. The detritus pool, $D$, collects the dead and ungrazed matters, followed by recharging the nutrient pool through remineralization processes. +Phytoplankton growth and zooplankton grazing are size-dependent. The depth of the epilimnion is known as the mixed layer depth (MLD) and is seasonally varying. The blue arrows indicate ecological and biogeochemical processes (solid lines) and physical processes (dashed lines). Environmental forcing to this model is provided by in-situ observations of photosynthetically active radiations (PAR), mixed layer depth (MLD – an indicator of lake mixing), and water temperature (LWST) from Greifensee, Switzerland. To approximate real-world grazing, the grazed material is split into (1) a portion that is not ingested (due to sloppy feeding) and is immediately lost into detritus, (2) a portion that is ingested and assimilated by zooplankton, and (3) an additional portion that is not assimilated after being ingested and quickly excreted into nutrient. The loss of zooplankton to higher order predation is density-dependent and formulated as a quadratic function. We assume that the loss of nutrients from the epilimnion is recharged through changes in MLD by a constant nutrient source, $N_0$, from the hypolimnion. The value of $N_0$ is determined by adjusting the modelled nutrient dynamics with observations and is also prescribed as forcing to the model. +

Figure1_v3 @@ -27,7 +29,7 @@ where $T$ is the ambient lake water surface temperature (LWST), $T_{opt}$ is the The maximum ingestion rates of zooplankton follows a Q10 model such that, the maximum grazing increases with temperature. The equation for the dependence is, -$$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}} $$. +$$I_{max}(S_j^Z) \cdot Q_{10}^{\frac{T-T_{ref}}{10}}$$.
The Q10 temperature coefficient here specifies the amount of maximum ingestion rate increases with a 10 $^{\circ}$ C temperature increase. It describes the sensitivity of zooplankton response to a higher temperature. $T$ refers to the lake temperature, while $T_{ref}$ refers to the reference temperature when the rate is equal to the baseline rate (i.e. no effects from temperature). @@ -40,9 +42,10 @@ The allometric relationships considered in the model are: $$\mu_{max}(S_i^P) = \beta_{\mu_{max}}\cdot (S_i^P)^{\alpha_{\mu_{max}}}$$ -$$I_{max}(S_j^Z) = \beta_{I_{max}}\cdot (S_j^Z)^{\alpha_{I_{max}}}$$ +$$\psi_{max}(S_j^Z) = \beta_{I_{max}}\cdot (S_j^Z)^{\alpha_{\psi_{max}}}$$ $$P_{opt}(S_i^P, S_j^Z) = \beta_{P_{opt}}\cdot (S_j^Z)^{\alpha_{P_{opt}}}$$ +
representing, respectively, maximum growth rate, $\mu_{max}(S_i^P)$, for phytoplankton size class $i$, and maximum ingestion rate, $I_{max}(S_j^Z)$, and optimal prey size, $P_{opt}(S_i^P, S_j^Z)$, for zooplankton size class $j$. @@ -57,6 +60,10 @@ Using the standard model, we conduct two numerical experiments:
1. a projection based on Representative Climate Pathways (RCPs) issued by IPCC 2. a sensitivity analysis for the two thermal traits, $T_{opt}$ and $\sigma_T$ in different warming (i.e., RCP by IPCC) scenarios +## Supplementary tests +- a sensitivity test for the model parameters for the standard model run +- a sensitivity run for varying lake mixing parameters for the worst warming scenario + For details, please refer to the related publication of this model (under review). From 171e7ec4ad190a13022a2002962fd96a06ea66f2 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 4 Feb 2025 12:46:38 +0100 Subject: [PATCH 92/97] Update README.md --- SensitivityTest/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SensitivityTest/README.md b/SensitivityTest/README.md index 37fb608..75ec6ea 100644 --- a/SensitivityTest/README.md +++ b/SensitivityTest/README.md @@ -1,4 +1,4 @@ The sensitivity test is conducted to estimate the uncertainty of different model parameters on the model results. I selected 14 model parameters, which are neither examined by the sensitivty analysis (i.e., the thermal traits) nor the sensitivty run (i.e., the lake mixing parameters). For these 14 parameters, I tested ±25% the default parameter values and examined the changes in the model results, i.e., total nutrient concentration (μM P), community biomass of phytoplankton and zooplankton (μM P), and community-weighted mean phytoplankton biovolume (μm3). -The results are attached in this folder. More details can be found in the related publication of this work (See front page). +The script and results are attached in this folder. More details can be found in the related publication of this work (See front page). From 323dc4e5b73d4c6ab3ee110a0c0562bf90670bbd Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 4 Feb 2025 14:03:32 +0100 Subject: [PATCH 93/97] Update README.md --- SensitivityRun/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/SensitivityRun/README.md b/SensitivityRun/README.md index 4e9977a..31e6bf2 100644 --- a/SensitivityRun/README.md +++ b/SensitivityRun/README.md @@ -2,6 +2,8 @@ This is a sensitivity run for examining how sensitive are the model variables to We vary the lake mixing dynamics for the worst climate change scenario of our projection, RCP 8.5, in which the lake water temperature increases by 4.2 ℃ throughout the year. This number is based on literature data that are supplemented in the related publication of this work. Based on the estimation of the median change in stratification period by Woolway et al. (2021), we created a prolonged lake stratification that starts 22 days earlier and ends 11 days later (see below figure). This leads to a longer stratified period of the lake by 33 days. In another case, we reduced the diffusive mixing coefficient by 50%. This parameter determines the strength of nutrient mixing from the hypolimnion into the epilimnion, which is a realistic approximation of nutrient transportations in a lake water column (Peeters et al. 2007). +The future MLD profile can be found in the 'LakeData' named under 'ForcData_futMLD.csv'. + Sensit_mix From 288fe31ac7fa33c915a4a760df08ffc3c97d1327 Mon Sep 17 00:00:00 2001 From: Debbie <51200142+Debbcwing@users.noreply.github.com> Date: Tue, 4 Feb 2025 14:11:41 +0100 Subject: [PATCH 94/97] Update README.md --- SensitivityRun/README.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/SensitivityRun/README.md b/SensitivityRun/README.md index 31e6bf2..ef2c928 100644 --- a/SensitivityRun/README.md +++ b/SensitivityRun/README.md @@ -2,17 +2,27 @@ This is a sensitivity run for examining how sensitive are the model variables to We vary the lake mixing dynamics for the worst climate change scenario of our projection, RCP 8.5, in which the lake water temperature increases by 4.2 ℃ throughout the year. This number is based on literature data that are supplemented in the related publication of this work. Based on the estimation of the median change in stratification period by Woolway et al. (2021), we created a prolonged lake stratification that starts 22 days earlier and ends 11 days later (see below figure). This leads to a longer stratified period of the lake by 33 days. In another case, we reduced the diffusive mixing coefficient by 50%. This parameter determines the strength of nutrient mixing from the hypolimnion into the epilimnion, which is a realistic approximation of nutrient transportations in a lake water column (Peeters et al. 2007). +Sensit_mix + +


The future MLD profile can be found in the 'LakeData' named under 'ForcData_futMLD.csv'. +To run this experiment, please import the future MLD profile (1): +``` +futMLD = np.genfromtxt(wd+'/ForcData_futMLD.csv', delimiter=',') +``` -Sensit_mix +or modify the diffusive mixing parameter, $k$ (2) in the line of the model run: +``` +sol = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, numYears=Ynum, k=0.05).solution +``` -


+
The results are attached in this folder. More details can be found in the related publication of this work (See front page). -


+







+ Peeters, F., D. Straile, A. Lorke, and D. M. Livingstone. 2007. Earlier onset of the spring phytoplankton bloom in lakes of the temperate zone in a warmer climate. Global Change Biology 13: 1898–1909. doi:10.1111/j.1365-2486.2007.01412.x + Woolway, R. I., S. Sharma, G. A. Weyhenmeyer, and others. 2021. Phenological shifts in lake stratification under climate change. 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z2mj<>?8LwM4?D99yRsX*vj=;!7kjf0`?4SVa{vc&5C?Mz<2aPVIGiIG&ygI((Hz6E z9LMpTz=@p1$xPrBPUSRC=M2u|EY9W}&gDGL=K?O|A};0?WG&Wa9oA(%)@KYGupt{UmH`_x^5Hpz{QsBbVN8u}y?s^NmMLkmy)vh6yVpE4 zNQ}6 Date: Tue, 4 Feb 2025 14:18:37 +0100 Subject: [PATCH 96/97] Update README.md --- SensitivityTest/README.md | 48 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 47 insertions(+), 1 deletion(-) diff --git a/SensitivityTest/README.md b/SensitivityTest/README.md index 75ec6ea..ad13f9c 100644 --- a/SensitivityTest/README.md +++ b/SensitivityTest/README.md @@ -1,4 +1,50 @@ The sensitivity test is conducted to estimate the uncertainty of different model parameters on the model results. I selected 14 model parameters, which are neither examined by the sensitivty analysis (i.e., the thermal traits) nor the sensitivty run (i.e., the lake mixing parameters). For these 14 parameters, I tested ±25% the default parameter values and examined the changes in the model results, i.e., total nutrient concentration (μM P), community biomass of phytoplankton and zooplankton (μM P), and community-weighted mean phytoplankton biovolume (μm3). -The script and results are attached in this folder. More details can be found in the related publication of this work (See front page). +To run this test, we first define a dictionary of the parameters with their default values. +``` +Param = { # these are all default parameter values + 'alpha': 0.02, # Initial slope of P-I curve + 'Pmax': 1.1, # Max photosynthesis rate + 'Kpar': 0.1, # Attenuation-phytoplankton self-shading + 'Mz': 0.1, # Mortality - zooplankton + 'Mp': 0.1, # Mortality - phytoplankton + 'Mz2': 0.34, # Mortality - zooplankton from higher predators + 'Ieff': 0.75, # Ingestion efficiency (sloppy feeding) + 'beta': 0.69, # Assimilation effiency + 'Kp': 0.19, # Phytoplankton half-saturation constant (mmol*m^-3) + 'Rem': 0.15, # Remineralization rate [time lagged for nutrient recharge] + 'k': 0.1, # Cross thermocline mixing factor + 'mu_alpha': -0.36, + 'mu_beta': 10 ** 0.69, + 'Kn': 0.06, + 'graz_Q10': 2.8, + 'graz_alpha': -0.28, + 'graz_beta': 23, + 'ops_alpha': 0.7, + 'ops_beta': 0.45} +``` + +We followed by creating the output arrays + +``` +numParam = len(Param.keys()) +outM25 = np.zeros((365*Ynum, Pnum*4 + 2 + 4, numParam)) #-25% +outP25 = np.zeros((365*Ynum, Pnum*4 + 2 + 4, numParam)) #+25% +``` + +To run the solution, we loop over the parameter values via the dictionary. + +``` +for k, v in enumerate(Param.keys()): + Param[v] = Param[v]*0.75 # change value (+/- 25%) + print('test value', k, v, Param[v]) # check value before run + solM25 = SizeModLogspace(mld=mld_run, par=PAR_run, sst=LWST_run, dmdt=dmdt_run, N0=N0, numP=Pnum, + numYears=Ynum, **Param) + outM25[:, :, k] = solM25.solution[:, :] # store results + Param[v] = Param[v]/0.75 # recover the value in the dictionary + print('default value', k, v, Param[v]) # check value after restoring the value +print(Param.items()) # check the whole dictionary after run +``` + +The results are attached in this folder. More details can be found in the related publication of this work (See front page). 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