Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Systems Ecology Group, Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany
School of Science, Constructor University, Bremen, Germany
Esteban Acevedo-Trejos
Earth Surface Process Modelling, GFZ German Research Centre for Geosciences, Potsdam, Germany
Andrew D. Barton
Scripps Institution of Oceanography and Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, United States
Agostino Merico
Systems Ecology Group, Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany
School of Science, Constructor University, Bremen, Germany
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With the likely emergence of satellite-based phytoplankton pigment data, it is increasingly important to examine relationships between phytoplankton pigments and other metrics of phytoplankton community composition. By using quantitative approaches, we show that phytoplankton pigments correlate with DNA- and RNA-based abundances, and examine how integration of these data addresses ecological questions relating to diversity patterns, harmful algal blooms, and inferring cellular activity.
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Cited articles
Acevedo-Trejos, E., Brandt, G., Bruggeman, J., and Merico, A.: Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean, Sci. Rep., 5, 8918, https://doi.org/10.1038/srep08918, 2015. a
Acevedo-Trejos, E., Brandt, G., Smith, S. L., and Merico, A.: PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model, Geosci. Model Dev., 9, 4071–4085, https://doi.org/10.5194/gmd-9-4071-2016, 2016. a
Anderson, T. R.: A spectrally averaged model of light penetration and photosynthesis, Limnology and Oceanography, 38, 1403–1419, https://doi.org/10.4319/lo.1993.38.7.1403, 1993. a, b, c
Anderson, T. R.: Plankton functional type modelling: Running before we can walk?, J. Plankton Res., 27, 1073–1081, https://doi.org/10.1093/plankt/fbi076, 2005. a
Argent, R. M.: An overview of model integration for environmental applications – components, frameworks and semantics, Environ. Model. Softw., 19, 219–234, https://doi.org/10.1016/S1364-8152(03)00150-6, 2004. a
Baird, M. E. and Suthers, I. M.: Increasing model structural complexity inhibits the growth of initial condition errors, Ecol. Complex., 7, 478–486, https://doi.org/10.1016/j.ecocom.2009.12.001, 2010. a
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J., and Follows, M. J.: Patterns of diversity in marine phytoplankton, Science, 327, 1509–1511, https://doi.org/10.1126/science.1184961, 2010. a
Belete, G. F., Voinov, A., and Laniak, G. F.: An overview of the model integration process: From pre-integration assessment to testing, Environ. Modell. Softw., 87, 49–63, https://doi.org/10.1016/j.envsoft.2016.10.013, 2017. a
Bovy, B.: fastscape-lem/fastscape: Release v0.1.0beta3, Zenodo [code], https://doi.org/10.5281/ZENODO.4435110, 2021. a, b
Bovy, B. and Braun, J.: Xarray-simlab: a Python package to build, customize and run computational models interactively, AGUFM, 2018, NS53A–0548, Bibcode: 2018AGUFMNS53A0548B, 2018. a
Bovy, B., McBain, G. D., Gailleton, B., and Lange, R.: benbovy/xarray-simlab: 0.5.0, Zenodo [code], https://doi.org/10.5281/ZENODO.4469813, 2021. a
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical models, Environ. Modell. Softw., 61, 249–265, https://doi.org/10.1016/j.envsoft.2014.04.002, 2014. a
De Boyer Montégut, C.: Mixed layer depth climatology computed with a density threshold criterion of 0.03kg/m3 from 10 m depth value, SEANOE [data set], https://doi.org/10.17882/91774, 2023. a, b
De Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology, J. Geophys. Res.-Oceans, 109, C12, https://doi.org/10.1029/2004JC002378, 2004. a, b
Dormand, J. R. and Prince, P. J.: A family of embedded Runge-Kutta formulae, J. Comput. Appl. Math., 6, 19–26, https://doi.org/10.1016/0771-050X(80)90013-3, 1980. a
Dutkiewicz, S., Follows, M. J., and Bragg, J. G.: Modeling the coupling of ocean ecology and biogeochemistry, Global Biogeochem. Cy., 23, 1–15, https://doi.org/10.1029/2008GB003405, 2009. a
Dutkiewicz, S., Cermeno, P., Jahn, O., Follows, M. J., Hickman, A. E., Taniguchi, D. A. A., and Ward, B. A.: Dimensions of marine phytoplankton diversity, Biogeosciences, 17, 609–634, https://doi.org/10.5194/bg-17-609-2020, 2020. a
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. Bull., 70, 1063–1085, 1972. a
Eppley, R. W., Rogers, J. N., and McCarthy, J. J.: Half‐Saturation Constants for Uptake of Nitrate and Ammonium By Marine Phytoplankton, Limnol. Oceanogr., 14, 912–920, https://doi.org/10.4319/lo.1969.14.6.0912, 1969. a
Evans, G. T. and Parslow, J. S.: A model of annual plankton cycles, Deep-Sea Res. Pt. II, 32, 759, https://doi.org/10.1016/0198-0254(85)92902-4, 1985. a, b, c
Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W.: Emergent biogeography of microbial communities in a model ocean, Science, 315, 1843–1846, https://doi.org/10.1126/science.1138544, 2007. a, b
Franks, P. J. S.: Planktonic ecosystem models: Perplexing parameterizations and a failure to fail, J. Plankton Res., 31, 1299–1306, https://doi.org/10.1093/plankt/fbp069, 2009. a
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., and Seidov, D.: WORLD OCEAN ATLAS 2018 Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate and nitrate+ nitrite, silicate), NOAA Atlas NESDIS [data set], 84, https://www.nodc.noaa.gov/OC5/woa18/pubwoa18.htm (last access: May 2022), 2019. a, b, c
Gentleman, W.: A chronology of plankton dynamics in silico: How computer models have been used to study marine ecosystems, Hydrobiologia, 480, 69–85, https://doi.org/10.1023/A:1021289119442, 2002. a
Haefner, J. W.: Modeling biological systems: Principles and applications, Springer Science & Business Media, ISBN 0387250115, https://doi.org/10.1007/0-387-25012-3{_}1, 2005. a
Häfner, D., Jacobsen, R. L., Eden, C., Kristensen, M. R. B., Jochum, M., Nuterman, R., and Vinter, B.: Veros v0.1 – a fast and versatile ocean simulator in pure Python, Geosci. Model Dev., 11, 3299–3312, https://doi.org/10.5194/gmd-11-3299-2018, 2018. a
Hansen, B., Bjornsen, P. K., and Hansen, P. J.: The size ratio between planktonic predators and their prey, Limnol. Oceanogr., 39, 395–403, https://doi.org/10.4319/lo.1994.39.2.0395, 1994. a
Hansen, P. J., Bjørnsen, P. K., and Hansen, B. W.: Zooplankton grazing and growth: Scaling within the 2-2,000-µm body size range, Limnol. Oceanogr., 42, 687–704, https://doi.org/10.4319/lo.1997.42.4.0687, 1997. a
Hoyer, S. and Hamman, J. J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Softw., 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Hu, F., Bolding, K., Bruggeman, J., Jeppesen, E., Flindt, M. R., van Gerven, L., Janse, J. H., Janssen, A. B. G., Kuiper, J. J., Mooij, W. M., and Trolle, D.: FABM-PCLake – linking aquatic ecology with hydrodynamics, Geosci. Model Dev., 9, 2271–2278, https://doi.org/10.5194/gmd-9-2271-2016, 2016. a
Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Abdollahi, B., Aerts, J., Albers, T., Alidoost, F., Andela, B., Camphuijsen, J., Dzigan, Y., van Haren, R., Hutton, E., Kalverla, P., van Meersbergen, M., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., de Vos, M., and Weel, B.: The eWaterCycle platform for open and FAIR hydrological collaboration, Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, 2022. a
Janssen, A. B., Arhonditsis, G. B., Beusen, A., Bolding, K., Bruce, L., Bruggeman, J., Couture, R. M., Downing, A. S., Alex Elliott, J., Frassl, M. A., Gal, G., Gerla, D. J., Hipsey, M. R., Hu, F., Ives, S. C., Janse, J. H., Jeppesen, E., Jöhnk, K. D., Kneis, D., Kong, X., Kuiper, J. J., Lehmann, M. K., Lemmen, C., Özkundakci, D., Petzoldt, T., Rinke, K., Robson, B. J., Sachse, R., Schep, S. A., Schmid, M., Scholten, H., Teurlincx, S., Trolle, D., Troost, T. A., Van Dam, A. A., Van Gerven, L. P., Weijerman, M., Wells, S. A., and Mooij, W. M.: Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective, Aqua. Ecol., 49, 513–548, https://doi.org/10.1007/s10452-015-9544-1, 2015. a, b
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., and Willing, C.: Jupyter Notebooks – a publishing format for reproducible computational workflows, vol. 2016, ISBN 9781614996484, https://doi.org/10.3233/978-1-61499-649-1-87, 2016. a
Koralewski, T. E., Westbrook, J. K., Grant, W. E., and Wang, H.-H.: Coupling general physical environmental process models with specific question-driven ecological simulation models, Ecol. Model., 405, 102–105, https://doi.org/10.1016/j.ecolmodel.2019.02.004, 2019. a
Lin, J. W. B.: Why python is the next wave in earth sciences computing, B. Am. Meteorol. Soc., 93, 1823–1824, https://doi.org/10.1175/BAMS-D-12-00148.1, 2012. a
Litchman, E. and Klausmeier, C. A.: Trait-Based Community Ecology of Phytoplankton, Annu. Rev. Ecol. Evol. Syst., 39, 615–639, https://doi.org/10.1146/annurev.ecolsys.39.110707.173549, 2008. a
Locarnini, M., Mishonov, A., Baranova, O., Boyer, T., Zweng, M., Garcia, H., Seidov, D., Weathers, K., Paver, C., and Smolyar, I.: World Ocean Atlas 2018, Volume 1: Temperature [data set], https://archimer.ifremer.fr/doc/00651/76338/ (last access: May 2022), 2019. a
Long, M. C., Moore, J. K., Lindsay, K., Levy, M., Doney, S. C., Luo, J. Y., Krumhardt, K. M., Letscher, R. T., Grover, M., and Sylvester, Z. T.: Simulations With the Marine Biogeochemistry Library (MARBL), J. Adv. Model. Earth Sy., 13, e2021MS002647, https://doi.org/10.1029/2021MS002647, 2021. a
Merico, A., Tyrrell, T., and Cokacar, T.: Is there any relationship between phytoplankton seasonal dynamics and the carbonate system?, J. Marine Syst., 59, 120–142, https://doi.org/10.1016/j.jmarsys.2005.11.004, 2006. a
Monod, J.: The growth of bacterial cultures, Annu. Rev. Microbiol., 3, 371–394, https://doi.org/10.1146/annurev.mi.03.100149.002103, 1949. a
NASA Goddard Space Flight Center: Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data, NASA Ocean Biology Distributed Active Archive Center [data set], https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018, 2018. a
Norling, M. D., Jackson-Blake, L. A., Calidonio, J.-L. G., and Sample, J. E.: Rapid development of fast and flexible environmental models: the Mobius framework v1.0, Geosci. Model Dev., 14, 1885–1897, https://doi.org/10.5194/gmd-14-1885-2021, 2021. a
Sarmiento, J. L., Hughes, T., Stouffer, R. J., and Manabe, S.: Simulated response of the ocean carbon cycle to anthropogenic climate warming, Nature, 393, 245–249, https://doi.org/10.1038/30455, 1998. a
Sathyendranath, S., Stuart, V., Nair, A., Oka, K., Nakane, T., Bouman, H., Forget, M. H., Maass, H., and Platt, T.: Carbon-to-chlorophyll ratio and growth rate of phytoplankton in the sea, Mar. Ecol. Prog. Ser., 383, 73–84, https://doi.org/10.3354/meps07998, 2009. a
Smith, S. L., Merico, A., Wirtz, K. W., and Pahlow, M.: Leaving misleading legacies behind in plankton ecosystem modelling, J. Plankton Res., 36, 613–620, https://doi.org/10.1093/plankt/fbu011, 2014. a
Steenbeek, J., Buszowski, J., Chagaris, D., Christensen, V., Coll, M., Fulton, E. A., Katsanevakis, S., Lewis, K. A., Mazaris, A. D., Macias, D., de Mutsert, K., Oldford, G., Pennino, M. G., Piroddi, C., Romagnoni, G., Serpetti, N., Shin, Y. J., Spence, M. A., and Stelzenmüller, V.: Making spatial-temporal marine ecosystem modelling better – A perspective, Environ. Model. Softw., 145, 105209, https://doi.org/10.1016/j.envsoft.2021.105209, 2021. a
Straile, D.: Gross growth efficiencies of protozoan and metazoan zooplankton and their dependence on food concentration, predator-prey weight ratio, and taxonomic group, Limnol. Oceanogr., 42, 1375–1385, https://doi.org/10.4319/lo.1997.42.6.1375, 1997. a, b
Sverdrup, H. U.: On conditions for the vernal blooming of phytoplankton, J. Cons. Int. Explor. Mer., 18, 287–295, 1953. a
Tang, E. P. Y.: The allometry of algal growth rates, J. Plankton Res., 17, 1325–1335, https://doi.org/10.1093/plankt/17.6.1325, 1995. a
Taylor, A. H., Harbour, D. S., Harris, R. P., Burkill, P. H., and Edwards, E. S.: Seasonal succession in the pelagic ecosystem of the North Atlantic and the utilization of nitrogen, J. Plankton Res., 15, 875–891, https://doi.org/10.1093/plankt/15.8.875, 1993. a
Vaillant, J., Grechi, I., Normand, F., and Boudon, F.: Towards virtual modelling environments for functional-structural plant models based on Jupyter notebooks: Application to the modelling of mango tree growth and development, In Silico Plants, 4, diab040, https://doi.org/10.1093/insilicoplants/diab040, 2022. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., and Bright, J.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Meth., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Yool, A., Popova, E. E., and Anderson, T. R.: Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain, Geosci. Model Dev., 4, 381–417, https://doi.org/10.5194/gmd-4-381-2011, 2011. a
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Creating computational models of how phytoplankton grows in the ocean is a technical challenge....