Articles | Volume 23, issue 12
https://doi.org/10.5194/bg-23-4321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-4321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Prediction of present and future spatial occurrence of cyanobacteria and the toxin nodularin in the Baltic Sea
Mohanad Abdelgadir
CORRESPONDING AUTHOR
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, 750 07, Uppsala, Sweden
Department of Ecology, Environment and Geoscience, Umeå University, 901 87, Umeå, Sweden
Bengt Karlson
Department of Research and Development, Oceanography, Swedish Meteorological and Hydrological Institute, SMHI, 426 71 Västra Frölunda, Sweden
Elin Dahlgren
Department of Aquatic Resources, Swedish University of Agricultural Sciences, Stångholmsvägen 2, 178 93 Drottningholm, Sweden
Malin Olofsson
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, 750 07, Uppsala, Sweden
Related authors
No articles found.
Matteo Masini, Agnes Karlsson, Lars Arneborg, Bengt Karlson, and Inga Monika Koszalka
EGUsphere, https://doi.org/10.5194/egusphere-2026-1344, https://doi.org/10.5194/egusphere-2026-1344, 2026
Short summary
Short summary
On the 8th of August, Storm Hans swept through the Baltic Sea disturbing the operation of desalination plants producing drinking water from sea water. In this paper, we build upon reports from the desalination plants to analyze the operational ocean model outputs and observations to identify the coastal processes behind the reported hazards. The results show a potential for future development of forecast framework.
Jenny Hieronymus, Kari Eilola, Malin Olofsson, Inga Hense, H. E. Markus Meier, and Elin Almroth-Rosell
Biogeosciences, 18, 6213–6227, https://doi.org/10.5194/bg-18-6213-2021, https://doi.org/10.5194/bg-18-6213-2021, 2021
Short summary
Short summary
Dense blooms of cyanobacteria occur every summer in the Baltic Proper and can add to eutrophication by their ability to turn nitrogen gas into dissolved inorganic nitrogen. Being able to correctly estimate the size of this nitrogen fixation is important for management purposes. In this work, we find that the life cycle of cyanobacteria plays an important role in capturing the seasonality of the blooms as well as the size of nitrogen fixation in our ocean model.
Cited articles
Abdelgadir, M., Alharbi, R., AlRashidi, M., Alatawi, A. S., Sjöling, S., and Dinnétz, P.: Distribution of denitrifiers predicted by correlative niche modeling of changing environmental conditions and future climatic scenarios across the Baltic Sea, Ecol. Inform., 78, 102346, https://doi.org/10.1016/j.ecoinf.2023.102346, 2023.
Abdelgadir, M., Broman, E., Dinnétz, P., Olofsson, M., and Sjöling, S.: Future increase of filamentous cyanobacteria in coastal Baltic Sea predicted by multiple realm models of marine, terrestrial, and climate change scenarios, Ecol. Inform., 92, 103439, https://doi.org/10.1016/j.ecoinf.2025.103439, 2025.
Allouche, O., Tsoar, A., and Kadmon, R.: Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS): Assessing the accuracy of distribution models, J. Appl. Ecol., 43, 1223–1232, https://doi.org/10.1111/j.1365-2664.2006.01214.x, 2006.
Almroth-Rosell, E., Edman, M., Eilola, K., Meier, H. E. M., and Sahlberg, J.: Modelling nutrient retention in the coastal zone of an eutrophic sea, Biogeosciences, 13, 5753–5769, https://doi.org/10.5194/bg-13-5753-2016, 2016.
Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S. (Eds.): Ocean prediction: present status and state of the art (OPSR), Copernicus Publications, State Planet, 5-opsr, https://doi.org/10.5194/sp-5-opsr, 2025.
Andersen, I. M., Williamson, T. J., González, M. J., and Vanni, M. J.: Nitrate, ammonium, and phosphorus drive seasonal nutrient limitation of chlorophytes, cyanobacteria, and diatoms in a hyper-eutrophic reservoir, Limnol. Oceanogr., 65, 962–978, https://doi.org/10.1002/lno.11363, 2020.
Andersson, A., Jurgensone, I., Rowe, O. F., Simonelli, P., Bignert, A., Lundberg, E., and Karlsson, J.: Can Humic Water Discharge Counteract Eutrophication in Coastal Waters?, PLoS ONE, 8, e61293, https://doi.org/10.1371/journal.pone.0061293, 2013.
Andersson, A., Höglander, H., Karlsson, C., and Huseby, S.: Key role of phosphorus and nitrogen in regulating cyanobacterial community composition in the northern Baltic Sea, Estuar. Coast. Shelf Sci., 164, 161–171, https://doi.org/10.1016/j.ecss.2015.07.013, 2015.
Antonakos, A. and Lambrakis, N.: Spatial Interpolation for the Distribution of Groundwater Level in an Area of Complex Geology Using Widely Available GIS Tools, Environ. Process., 8, 993–1026, https://doi.org/10.1007/s40710-021-00529-9, 2021.
Araújo, M. and New, M.: Ensemble forecasting of species distributions, Trend. Ecol. Evol., 22, 42–47, https://doi.org/10.1016/j.tree.2006.09.010, 2007.
Assis, J., Fernández Bejarano, S. J., Salazar, V. W., Schepers, L., Gouvêa, L., Fragkopoulou, E., Leclercq, F., Vanhoorne, B., Tyberghein, L., Serrão, E. A., Verbruggen, H., and De Clerck, O.: Bio-ORACLE v3.0. Pushing marine data layers to the CMIP6 Earth System Models of climate change research, Global Ecol. Biogeogr., 33, e13813, https://doi.org/10.1111/geb.13813, 2024.
Barbet-Massin, M., Jiguet, F., Albert, C. H., and Thuiller, W.: Selecting pseudo-absences for species distribution models: how, where and how many?: How to use pseudo-absences in niche modelling?, Method. Ecol. Evol., 3, 327–338, https://doi.org/10.1111/j.2041-210X.2011.00172.x, 2012.
Beery, S., Cole, E., Parker, J., Perona, P., and Winner, K.: Species Distribution Modeling for Machine Learning Practitioners: A Review, in: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), COMPASS '21: ACM SIGCAS Conference on Computing and Sustainable Societies, 329–348, https://doi.org/10.1145/3460112.3471966, 2021.
Benesty, J., Chen, J., Huang, Y., and Cohen, I.: Pearson Correlation Coefficient, in: Noise Reduction in Speech Processing, Vol. 2, Springer Berlin Heidelberg, Berlin, Heidelberg, 1–4, https://doi.org/10.1007/978-3-642-00296-0_5, 2009.
Bivand, R. S., Pebesma, E., and Gomez-Rubio, V.: Applied spatial data analysis with R, Second edition, Springer, NY, https://doi.org/10.1007/978-1-4614-7618-4, 2013.
Bosch, S. and Fernandez, S.: sdmpredictors: Species Distribution Modelling Predictor Datasets, R package, http://lifewatch.github.io/sdmpredictors/, 2022.
Brandt, L. A., Benscoter, A. M., Harvey, R., Speroterra, C., Bucklin, D., Romañach, S. S., Watling, J. I., and Mazzotti, F. J.: Comparison of climate envelope models developed using expert-selected variables versus statistical selection, Ecol. Model., 345, 10–20, https://doi.org/10.1016/j.ecolmodel.2016.11.016, 2017.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Budakoti, S.: Examining the impact of water quality and meteorological drivers on primary productivity in the Baltic Sea, Mar. Pollut. Bull., 209, 117266, https://doi.org/10.1016/j.marpolbul.2024.117266, 2024.
Burford, M. A., Willis, A., Xiao, M., Prentice, M. J., and Hamilton, D. P.: Understanding the relationship between nutrient availability and freshwater cyanobacterial growth and abundance, Inland Waters, 13, 143–152, https://doi.org/10.1080/20442041.2023.2204050, 2023.
Caille, C., Duhamel, S., Latifi, A., and Rabouille, S.: Adaptive Responses of Cyanobacteria to Phosphate Limitation: A Focus on Marine Diazotrophs, Environ. Microbiol., 26, e70023, https://doi.org/10.1111/1462-2920.70023, 2024.
Carlsson, P. and Rita, D.: Sedimentation of Nodularia spumigena and distribution of nodularin in the food web during transport of a cyanobacterial bloom from the Baltic Sea to the Kattegat, Harmful Algae, 86, 74–83, https://doi.org/10.1016/j.hal.2019.05.005, 2019.
Carpenter, S. R.: Eutrophication of aquatic ecosystems: Bistability and soil phosphorus, P. Natl. Acad. Sci. USA, 102, 10002–10005, https://doi.org/10.1073/pnas.0503959102, 2005.
Chilès, J. and Delfiner, P.: Geostatistics: Modeling Spatial Uncertainty, 1st Edn., Wiley, https://doi.org/10.1002/9780470316993, 1999.
Clyde, M. A., Ghosh, J., and Littman, M. L.: Bayesian Adaptive Sampling for Variable Selection and Model Averaging, J. Comput. Graph. Stat., 20, 80–101, https://doi.org/10.1198/jcgs.2010.09049, 2011.
Deng, L., Cheung, S., Kang, C., Liu, K., Xia, X., and Liu, H.: Elevated temperature relieves phosphorus limitation of marine unicellular diazotrophic cyanobacteria, Limnol. Oceanogr., 67, 122–134, https://doi.org/10.1002/lno.11980, 2022.
Deutsch, C. V. and Journel, A. G.: GSLIB: geostatistical software library and user's guide, Oxford University Press, New York, ISBN: 0195073924, 9780195073928, 1992.
Dietterich, T. G.: Ensemble Methods in Machine Learning, in: Multiple Classifier Systems, Vol. 1857, Springer Berlin Heidelberg, Berlin, Heidelberg, 1–15, https://doi.org/10.1007/3-540-45014-9_1, 2000.
Dietze, H. and Löptien, U.: Effects of surface current–wind interaction in an eddy-rich general ocean circulation simulation of the Baltic Sea, Ocean Sci., 12, 977–986, https://doi.org/10.5194/os-12-977-2016, 2016.
Dubrule, O.: Cross validation of kriging in a unique neighborhood, Mathemat. Geol., 15, 687–699, https://doi.org/10.1007/BF01033232, 1983.
Efron, B.: The Jackknife, the Bootstrap and Other Resampling Plans, Society for Industrial and Applied Mathematics, 49–59, https://doi.org/10.1137/1.9781611970319, 1982.
European Union-Copernicus Marine Service: Global Ocean 1/12° Physics Analysis and Forecast updated Daily, EU Copernicus Marine Service, https://doi.org/10.48670/MOI-00016, 2016.
European Union-Copernicus Marine Service: Baltic Sea Biogeochemistry Reanalysis, EU Copernicus Marine Service, https://doi.org/10.48670/MOI-00012, 2018a.
European Union-Copernicus Marine Service: Baltic Sea Physics Reanalysis, EU Copernicus Marine Service, https://doi.org/10.48670/MOI-00013, 2018b.
European Union-Copernicus Marine Service: Global Ocean Biogeochemistry Analysis and Forecast, EU Copernicus Marine Service, https://doi.org/10.48670/MOI-00015, 2019.
Eyring, V., Gentine, P., Camps-Valls, G., Lawrence, D. M., and Reichstein, M.: AI-empowered next-generation multiscale climate modelling for mitigation and adaptation, Nat. Geosci., 17, 963–971, https://doi.org/10.1038/s41561-024-01527-w, 2024a.
Eyring, V., Collins, W. D., Gentine, P., Barnes, E. A., Barreiro, M., Beucler, T., Bocquet, M., Bretherton, C. S., Christensen, H. M., Dagon, K., Gagne, D. J., Hall, D., Hammerling, D., Hoyer, S., Iglesias-Suarez, F., Lopez-Gomez, I., McGraw, M. C., Meehl, G. A., Molina, M. J., Monteleoni, C., Mueller, J., Pritchard, M. S., Rolnick, D., Runge, J., Stier, P., Watt-Meyer, O., Weigel, K., Yu, R., and Zanna, L.: Pushing the frontiers in climate modelling and analysis with machine learning, Nat. Clim. Change, 14, 916–928, https://doi.org/10.1038/s41558-024-02095-y, 2024b.
Fotheringham, A. S. and Rogerson, P.: The Sage handbook of spatial analysis, Sage, London, https://doi.org/10.4135/9780857020130, 2009.
Franklin, J.: Mapping Species Distributions: Spatial Inference and Prediction, 1st Edn., Cambridge University Press, https://doi.org/10.1017/CBO9780511810602, 2010.
Friedland, R., Neumann, T., and Schernewski, G.: Climate change and the Baltic Sea action plan: Model simulations on the future of the western Baltic Sea, J. Mar. Syst., 105–108, 175–186, https://doi.org/10.1016/j.jmarsys.2012.08.002, 2012.
Friedman, J. H.: Multivariate Adaptive Regression Splines, Ann. Statist., 19, https://doi.org/10.1214/aos/1176347963, 1991.
Friedman, J. H.: Stochastic gradient boosting, Computational Statistics and Data Analysis, 38, 367–378, https://doi.org/10.1016/S0167-9473(01)00065-2, 2002.
Ghannam, R. B. and Techtmann, S. M.: Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring, Computational and Structural Biotechnology Journal, 19, 1092–1107, https://doi.org/10.1016/j.csbj.2021.01.028, 2021.
Goovaerts, P.: Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY, https://doi.org/10.1093/oso/9780195115383.001.0001, 1997.
Goovaerts, P.: Ordinary Cokriging Revisited, Mathemat. Geol., 30, 21–42, https://doi.org/10.1023/A:1021757104135, 1998.
Gribov, A. and Krivoruchko, K.: Empirical Bayesian kriging implementation and usage, Sci. Total Environ., 722, 137290, https://doi.org/10.1016/j.scitotenv.2020.137290, 2020.
Grömping, U.: Relative Importance for Linear Regression in R: The Package relaimpo, J. Stat. Soft., 17, https://doi.org/10.18637/jss.v017.i01, 2006.
Guisan, A., Edwards, T. C., and Hastie, T.: Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecol. Model., 157, 89–100, https://doi.org/10.1016/S0304-3800(02)00204-1, 2002.
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29–36, https://doi.org/10.1148/radiology.143.1.7063747, 1982.
Hansson, M. and Hakansson, B.: The Baltic Algae Watch System - a remote sensing application for monitoring cyanobacterial blooms in the Baltic Sea, J. Appl. Remote Sens, 1, 011507, https://doi.org/10.1117/1.2834769, 2007.
Harrell, F. E., Lee, K. L., and Mark, D. B.: Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors, in: Tutorials in Biostatistics, edited by: D'Agostino, R. B. John Wiley and Sons, Ltd, Chichester, UK, 223–249, https://doi.org/10.1002/0470023678.ch2b(i), 2005.
Hense, I., Meier, H. E. M., and Sonntag, S.: Projected climate change impact on Baltic Sea cyanobacteria: Climate change impact on cyanobacteria, Climatic Change, 119, 391–406, https://doi.org/10.1007/s10584-013-0702-y, 2013.
Hieronymus, J., Eilola, K., Olofsson, M., Hense, I., Meier, H. E. M., and Almroth-Rosell, E.: Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper, Biogeosciences, 18, 6213–6227, https://doi.org/10.5194/bg-18-6213-2021, 2021.
Hijmans, R. J. and Van Etten, J.: Geographic analysis and modeling with raster data, R package version, https://doi.org/10.32614/CRAN.package.raster, 2012.
Huisman, J., Codd, G. A., Paerl, H. W., Ibelings, B. W., Verspagen, J. M. H., and Visser, P. M.: Cyanobacterial blooms, Nat. Rev. Microbiol., 16, 471–483, https://doi.org/10.1038/s41579-018-0040-1, 2018.
Hysen, L., Nayeri, D., Cushman, S., and Wan, H. Y.: Background sampling for multi-scale ensemble habitat selection modeling: Does the number of points matter?, Ecol. Inform., 72, 101914, https://doi.org/10.1016/j.ecoinf.2022.101914, 2022.
Ibelings, B. W., Kurmayer, R., Azevedo, S. M. F. O., Wood, S. A., Chorus, I., and Welker, M.: Understanding the occurrence of cyanobacteria and cyanotoxins, in: Toxic Cyanobacteria in Water, CRC Press, London, 213–294, https://doi.org/10.1201/9781003081449-4, 2021.
Jaccard, P.: The distribution of the flora in the alpine zone, New Phytol., 11, 37–50, https://doi.org/10.1111/j.1469-8137.1912.tb05611.x, 1912.
JASP Team: JASP (Version 0.19.3) [Computer software], Open software, https://jasp-stats.org, 2025.
Jiang, Y., Luo, J., Huang, D., Liu, Y., and Li, D.: Machine Learning Advances in Microbiology: A Review of Methods and Applications, Front. Microbiol., 13, 925454, https://doi.org/10.3389/fmicb.2022.925454, 2022.
Jonasson, S., Vintila, S., Sivonen, K., and El-Shehawy, R.: Expression of the nodularin synthetase genes in the Baltic Sea bloom-former cyanobacterium Nodularia spumigena strain AV1: Expression of nda genes in Nodularia spumigena, FEMS Microbiol. Ecol., 65, 31–39, https://doi.org/10.1111/j.1574-6941.2008.00499.x, 2008.
Kahru, M. and Elmgren, R.: Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea, Biogeosciences, 11, 3619–3633, https://doi.org/10.5194/bg-11-3619-2014, 2014.
Kahru, M., Elmgren, R., Kaiser, J., Wasmund, N., and Savchuk, O.: Cyanobacterial blooms in the Baltic Sea: Correlations with environmental factors, Harmful Algae, 92, 101739, https://doi.org/10.1016/j.hal.2019.101739, 2020.
Karjalainen, M., Engström-Öst, J., Korpinen, S., Peltonen, H., Pääkkönen, J.-P., Rönkkönen, S., Suikkanen, S., and Viitasalo, M.: Ecosystem Consequences of Cyanobacteria in the Northern Baltic Sea, AMBIO, 36, 195–202, https://doi.org/10.1579/0044-7447(2007)36[195:ECOCIT]2.0.CO;2, 2007.
Karlberg, M. and Wulff, A.: Impact of temperature and species interaction on filamentous cyanobacteria may be more important than salinity and increased pCO2 levels, Mar. Biol., 160, 2063–2072, https://doi.org/10.1007/s00227-012-2078-3, 2013.
Karlson, B., Eilola, K., and Hansson, M.: Cyanobacterial blooms in the Baltic Sea: Correlating bloom observations with environmental conditions, Proc. 13th Int. Conf. Harmful Algae, 247–252, https://www.researchgate.net/publication/288846529, 2010.
Karlson, B., Arneborg, L., Johansson, J., Linders, J., Liu, Y., and Olofsson, M.: A suggested climate service for cyanobacteria blooms in the Baltic Sea – Comparing three monitoring methods, Harmful Algae, 118, 102291, https://doi.org/10.1016/j.hal.2022.102291, 2022.
Khan, S. and Verma, S.: Ensemble modeling to predict the impact of future climate change on the global distribution of Olea europaea subsp, cuspidata, Front. For. Glob. Change, 5, 977691, https://doi.org/10.3389/ffgc.2022.977691, 2022.
Klawonn, I., Nahar, N., Walve, J., Andersson, B., Olofsson, M., Svedén, J. B., Littmann, S., Whitehouse, M. J., Kuypers, M. M. M., and Ploug, H.: Cell-specific nitrogen- and carbon-fixation of cyanobacteria in a temperate marine system (Baltic Sea), Environ. Microbiol., 18, 4596–4609, https://doi.org/10.1111/1462-2920.13557, 2016.
Krivoruchko, K.: Empirical bayesian kriging implemented in ArcGIS geostatistical analyst, ArcUser, 15, 6–10, 2012.
Krivoruchko, K. and Gribov, A.: Evaluation of empirical Bayesian kriging, Spat. Stat.-Neth., 32, 100368, https://doi.org/10.1016/j.spasta.2019.100368, 2019.
Kuosa, H., Fleming-Lehtinen, V., Lehtinen, S., Lehtiniemi, M., Nygård, H., Raateoja, M., Raitaniemi, J., Tuimala, J., Uusitalo, L., and Suikkanen, S.: A retrospective view of the development of the Gulf of Bothnia ecosystem, J. Mar. Syst., 167, 78–92, https://doi.org/10.1016/j.jmarsys.2016.11.020, 2017.
Lê, S., Josse, J., and Husson, F.: FactoMineR: An R Package for Multivariate Analysis, J. Stat. Soft., 25, https://doi.org/10.18637/jss.v025.i01, 2008.
Lehtimäki, J., Sivonen, K., Luukkainen, R., and Niemelä, S. I.: The effects of incubation time, temperature, light, salinity, and phosphorus on growth and hepatotoxin production by Nodularia strains, archiv_hydrobiologie, 130, 269–282, https://doi.org/10.1127/archiv-hydrobiol/130/1994/269, 1994.
Lehtimaki, J., Moisander, P., Sivonen, K., and Kononen, K.: Growth, nitrogen fixation, and nodularin production by two Baltic Sea cyanobacteria, Appl. Environ. Microbiol., 63, 1647–1656, 1997.
Lima, T. A., Beuchle, R., Langner, A., Grecchi, R. C., Griess, V. C., and Achard, F.: Comparing Sentinel-2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon, Remote Sens., 11, 961, https://doi.org/10.3390/rs11080961, 2019.
Lindeman, R. H., Merenda, P. F., Gold, R. Z., Lindemann, R. H., and Merenda, P. F.: Introduction to bivariate and multivariate analysis, Scott, Foresman and Co, Glenview, Ill, 444 pp., ISBN: 0673150992, 9780673150998, 1980.
Lips, I. and Lips, U.: Abiotic factors influencing cyanobacterial bloom development in the Gulf of Finland (Baltic Sea), Hydrobiologia, 614, 133–140, https://doi.org/10.1007/s10750-008-9449-2, 2008.
Löptien, U. and Dietze, H.: Retracing cyanobacteria blooms in the Baltic Sea, Sci. Rep., 12, 10873, https://doi.org/10.1038/s41598-022-14880-w, 2022.
Lu, J., Zhu, B., Struewing, I., Xu, N., and Duan, S.: Nitrogen–phosphorus-associated metabolic activities during the development of a cyanobacterial bloom revealed by metatranscriptomics, Sci. Rep., 9, 2480, https://doi.org/10.1038/s41598-019-38481-2, 2019.
Lundholm, N., Bernard, C., Churro, C., Escalera, L., Fraga, S., Hoppenrath, M., Iwataki, M., Larsen, J., Mertens, K., Moestrup, Ø., Murray, S., Salas, R., Tillmann, U., and Zingone, A.: IOC-UNESCO Taxonomic Reference List of Harmful Micro Algae, UNESCO intergovernmental oceanographic commission (IOC-UNESCO), https://doi.org/10.14284/362, 2009.
Lürling, M., Van Oosterhout, F., and Faassen, E.: Eutrophication and Warming Boost Cyanobacterial Biomass and Microcystins, Toxins, 9, 64, https://doi.org/10.3390/toxins9020064, 2017.
Mallissery, A., Radtke, H., Neumann, T., and Meier, H. E. M.: Temperature driven coastal processes and their far reaching effects on deep Baltic Sea, Biogeochem. Dynam., 2025–4568, https://doi.org/10.5194/egusphere-2025-4568, 2025.
Meier, H. E. M.: Baltic Sea climate in the late twenty-first century: a dynamical downscaling approach using two global models and two emission scenarios, Clim. Dyn., 27, 39–68, https://doi.org/10.1007/s00382-006-0124-x, 2006.
Meier, H. E. M. and Kauker, F.: Modeling decadal variability of the Baltic Sea: 2. Role of freshwater inflow and large-scale atmospheric circulation for salinity, J. Geophys. Res., 108, 2003JC001799, https://doi.org/10.1029/2003JC001799, 2003.
Meier, H. E. M., Höglund, A., Döscher, R., Andersson, H., Löptien, U., and Kjellström, E.: Quality assessment of atmospheric surface fields over the Baltic Sea from an ensemble of regional climate model simulations with respect to ocean dynamics, Oceanologia, 53, 193–227, https://doi.org/10.5697/oc.53-1-TI.193, 2011.
Meier, H. E. M., Müller-Karulis, B., Andersson, H. C., Dieterich, C., Eilola, K., Gustafsson, B. G., Höglund, A., Hordoir, R., Kuznetsov, I., Neumann, T., Ranjbar, Z., Savchuk, O. P., and Schimanke, S.: Impact of Climate Change on Ecological Quality Indicators and Biogeochemical Fluxes in the Baltic Sea: A Multi-Model Ensemble Study, AMBIO, 41, 558–573, https://doi.org/10.1007/s13280-012-0320-3, 2012.
Meier, H. E. M., Dieterich, C., Eilola, K., Gröger, M., Höglund, A., Radtke, H., Saraiva, S., and Wåhlström, I.: Future projections of record-breaking sea surface temperature and cyanobacteria bloom events in the Baltic Sea, Ambio, 48, 1362–1376, https://doi.org/10.1007/s13280-019-01235-5, 2019.
Meier, H. E. M., Kniebusch, M., Dieterich, C., Gröger, M., Zorita, E., Elmgren, R., Myrberg, K., Ahola, M. P., Bartosova, A., Bonsdorff, E., Börgel, F., Capell, R., Carlén, I., Carlund, T., Carstensen, J., Christensen, O. B., Dierschke, V., Frauen, C., Frederiksen, M., Gaget, E., Galatius, A., Haapala, J. J., Halkka, A., Hugelius, G., Hünicke, B., Jaagus, J., Jüssi, M., Käyhkö, J., Kirchner, N., Kjellström, E., Kulinski, K., Lehmann, A., Lindström, G., May, W., Miller, P. A., Mohrholz, V., Müller-Karulis, B., Pavón-Jordán, D., Quante, M., Reckermann, M., Rutgersson, A., Savchuk, O. P., Stendel, M., Tuomi, L., Viitasalo, M., Weisse, R., and Zhang, W.: Climate change in the Baltic Sea region: a summary, Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, 2022.
Moisander, P. H., McClinton, E., and Paerl, H. W.: Salinity Effects on Growth, Photosynthetic Parameters, and Nitrogenase Activity in Estuarine Planktonic Cyanobacteria, Microb. Ecol., 43, 432–442, https://doi.org/10.1007/s00248-001-1044-2, 2002.
Monserud, R. A. and Leemans, R.: Comparing global vegetation maps with the Kappa statistic, Ecol. Model., 62, 275–293, https://doi.org/10.1016/0304-3800(92)90003-W, 1992.
Moullec, F., Barrier, N., Drira, S., Guilhaumon, F., Hattab, T., Peck, M. A., and Shin, Y.-J.: Using species distribution models only may underestimate climate change impacts on future marine biodiversity, Ecol. Model., 464, 109826, https://doi.org/10.1016/j.ecolmodel.2021.109826, 2022.
Munkes, B., Löptien, U., and Dietze, H.: Cyanobacteria blooms in the Baltic Sea: a review of models and facts, Biogeosciences, 18, 2347–2378, https://doi.org/10.5194/bg-18-2347-2021, 2021.
Naimi, B. and Araújo, M. B.: sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39, 368–375, https://doi.org/10.1111/ecog.01881, 2016.
Neumann, T.: Climate-change effects on the Baltic Sea ecosystem: A model study, J. Mar. Syst., 81, 213–224, https://doi.org/10.1016/j.jmarsys.2009.12.001, 2010.
Neumann, T., Koponen, S., Attila, J., Brockmann, C., Kallio, K., Kervinen, M., Mazeran, C., Müller, D., Philipson, P., Thulin, S., Väkevä, S., and Ylöstalo, P.: Optical model for the Baltic Sea with an explicit CDOM state variable: a case study with Model ERGOM (version 1.2), Geosci. Model Dev., 14, 5049–5062, https://doi.org/10.5194/gmd-14-5049-2021, 2021.
Olea, R. A.: Geostatistics for engineers and earth scientists, Kluwer Academic, Boston, 303 pp., ISBN: 9780792385233, 1999.
Olenina, I., Hajdu, S., Edler, L., Andersson, A., Wasmund, N., Busch, S., Göbel, J., Gromisz, S., Huseby, S., Huttunen, M., Jaanus, A., Kokkonen, P., Jurgensone, I., and Niemkiewicz, E.: Biovolumes and size-classes of phytoplankton in the Baltic Sea, HELCOM Balt, Sea Environ. Proc., 106, ISSN 0357-2994, 2006.
Oliver, M. A. and Webster, R.: Kriging: a method of interpolation for geographical information systems, Int. J. Geogr. Informa. Syst., 4, 313–332, https://doi.org/10.1080/02693799008941549, 1990.
Olofsson, M., Egardt, J., Singh, A., and Ploug, H.: Inorganic phosphorus enrichments in Baltic Sea water have large effects on growth, carbon fixation, and N2 fixation by Nodularia spumigena, Aquat. Microb. Ecol., 77, 111–123, https://doi.org/10.3354/ame01795, 2016.
Olofsson, M., Suikkanen, S., Kobos, J., Wasmund, N., and Karlson, B.: Basin-specific changes in filamentous cyanobacteria community composition across four decades in the Baltic Sea, Harmful Algae, 91, 101685, https://doi.org/10.1016/j.hal.2019.101685, 2020a.
Olofsson, M., Hagan, J. G., Karlson, B., and Gamfeldt, L.: Large seasonal and spatial variation in nano- and microphytoplankton diversity along a Baltic Sea—North Sea salinity gradient, Sci. Rep., 10, 17666, https://doi.org/10.1038/s41598-020-74428-8, 2020b.
Olofsson, M., Klawonn, I., and Karlson, B.: Nitrogen fixation estimates for the Baltic Sea indicate high rates for the previously overlooked Bothnian Sea, Ambio, 50, 203–214, https://doi.org/10.1007/s13280-020-01331-x, 2021.
Paerl, H. W. and Otten, T. G.: Harmful Cyanobacterial Blooms: Causes, Consequences, and Controls, Microb. Ecol., 65, 995–1010, https://doi.org/10.1007/s00248-012-0159-y, 2013.
Paerl, H. W., Otten, T. G., and Kudela, R.: Mitigating the Expansion of Harmful Algal Blooms Across the Freshwater-to-Marine Continuum, Environ. Sci. Technol., 52, 5519–5529, https://doi.org/10.1021/acs.est.7b05950, 2018.
Parker, W. S.: Ensemble modeling, uncertainty and robust predictions, WIREs Climate Change, 4, 213–223, https://doi.org/10.1002/wcc.220, 2013.
Pebesma, E. J. and Bivand, R.: Classes and methods for spatial data in R, R News, 5, 9–13, 2005.
Phillips, S. J., Anderson, R. P., and Schapire, R. E.: Maximum entropy modeling of species geographic distributions, Ecol. Model., 190, 231–259, https://doi.org/10.1016/j.ecolmodel.2005.03.026, 2006.
Pliński, M., Mazur-Marzec, H., Jóźwiak, T., and Kobos, J.: The potential causes of cyanobacterial blooms in Baltic Sea estuaries, Ocean. Hydrobiol. St., 36, 134–137, https://doi.org/10.2478/v10009-007-0001-x, 2007.
Pyrcz, M. and Deutsch, C. V.: Geostatistical reservoir modeling, 2nd Edn., New York, New York, Oxford University Press, Oxford, 433 pp., ISBN: 978-0199731442, 2014.
QGIS Development Team: QGIS Geographic Information System, Open Source Geospatial Foundation [Computer software], https://qgis.org, 2022.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing [Computer software], Vienna, Austria, https://www.R-project.org/, 2021.
Rabouille, S., Tournier, L., Duhamel, S., Claquin, P., Crispi, O., Talec, A., Landolfi, A., and Oschlies, A.: Organic Phosphorus Scavenging Supports Efficient Growth of Diazotrophic Cyanobacteria Under Phosphate Depletion, Front. Microbiol., 13, 848647, https://doi.org/10.3389/fmicb.2022.848647, 2022.
Reissmann, J. H., Burchard, H., Feistel, R., Hagen, E., Lass, H. U., Mohrholz, V., Nausch, G., Umlauf, L., and Wieczorek, G.: Vertical mixing in the Baltic Sea and consequences for eutrophication – A review, Prog. Oceanogr., 82, 47–80, https://doi.org/10.1016/j.pocean.2007.10.004, 2009.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C. F.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure, Ecography, 40, 913–929, https://doi.org/10.1111/ecog.02881, 2017.
Ross, C., Santiago-Vázquez, L., and Paul, V.: Toxin release in response to oxidative stress and programmed cell death in the cyanobacterium Microcystis aeruginosa, Aquat. Toxicol., 78, 66–73, https://doi.org/10.1016/j.aquatox.2006.02.007, 2006.
RStudio Team: RStudio: Integrated Development Environment for R, RStudio, PBC, Boston, MA, [Computer software], https://posit.co/, 2020.
Saraiva, S., Markus Meier, H. E., Andersson, H., Höglund, A., Dieterich, C., Gröger, M., Hordoir, R., and Eilola, K.: Baltic Sea ecosystem response to various nutrient load scenarios in present and future climates, Clim. Dyn., 52, 3369–3387, https://doi.org/10.1007/s00382-018-4330-0, 2019.
Sathya, R. and Abraham, A.: Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification, International Journal of Advanced Research in Artificial Intelligence (IJARAI), 2, https://doi.org/10.14569/IJARAI.2013.020206, 2013.
Schmitt, S., Pouteau, R., Justeau, D., Boissieu, F., and Birnbaum, P.: ssdm: An r package to predict distribution of species richness and composition based on stacked species distribution models, Methods Ecol. Evol., 8, 1795–1803, https://doi.org/10.1111/2041-210X.12841, 2017.
Schrum, C. and Backhaus, J. O.: Sensitivity of atmosphere-ocean heat exchange and heat content in the North Sea and the Baltic Sea: Atmosphere-ocean heat exchange and heat content, Tellus A, 51, 526–549, https://doi.org/10.1034/j.1600-0870.1992.00006.x, 2002.
Silveira, S. B. and Odebrecht, C.: Effects of Salinity and Temperature on the Growth, Toxin Production, and Akinete Germination of the Cyanobacterium Nodularia spumigena, Front. Mar. Sci., 6, 339, https://doi.org/10.3389/fmars.2019.00339, 2019.
Stal, L. J.: Is the distribution of nitrogen-fixing cyanobacteria in the oceans related to temperature?, Environ. Microbiol., 11, 1632–1645, https://doi.org/10.1111/j.1758-2229.2009.00016.x, 2009.
Steinberg, D.: CART: Classification and regression trees, in: Data Mining and Knowledge Discovery Handbook, edited by: Maimon, O. and Rokach, L., 2nd Edn., 179–201, Springer, https://doi.org/10.1007/978-0-387-09823-4_10, 2009.
Stockmayer, V. and Lehmann, A.: Variations of temperature, salinity and oxygen of the Baltic Sea for the period 1950 to 2020, Oceanologia, 65, 466–483, https://doi.org/10.1016/j.oceano.2023.02.002, 2023.
Suikkanen, S., Laamanen, M., and Huttunen, M.: Long-term changes in summer phytoplankton communities of the open northern Baltic Sea, Estuar., Coast. Shelf Sci., 71, 580–592, https://doi.org/10.1016/j.ecss.2006.09.004, 2007.
The jamovi project: The jamovi project (2023), jamovi (Version 2.3) [Computer Software], https://www.jamovi.org (last access: 9 April 2025), 2023.
Thompson, J., Johansen, R., Dunbar, J., and Munsky, B.: Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition, PLoS ONE, 14, e0215502, https://doi.org/10.1371/journal.pone.0215502, 2019.
Thuiller, W.: Patterns and uncertainties of species' range shifts under climate change, Glob. Change Biol., 10, 2020–2027, https://doi.org/10.1111/j.1365-2486.2004.00859.x, 2004.
Thuiller, W., Lafourcade, B., Engler, R., and Araújo, M. B.: BIOMOD – a platform for ensemble forecasting of species distributions, Ecography, 32, 369–373, https://doi.org/10.1111/j.1600-0587.2008.05742.x, 2009.
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N., and Zimmermann, N. E.: Uncertainty in ensembles of global biodiversity scenarios, Nat. Commun., 10, 1446, https://doi.org/10.1038/s41467-019-09519-w, 2019.
Unger, J., Endres, S., Wannicke, N., Engel, A., Voss, M., Nausch, G., and Nausch, M.: Response of Nodularia spumigena to pCO2 – Part 3: Turnover of phosphorus compounds, Biogeosciences, 10, 1483–1499, https://doi.org/10.5194/bg-10-1483-2013, 2013.
Veronesi, F. and Schillaci, C.: Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation, Ecol. Ind., 101, 1032–1044, https://doi.org/10.1016/j.ecolind.2019.02.026, 2019.
Vigouroux, G., Kari, E., Beltrán-Abaunza, J. M., Uotila, P., Yuan, D., and Destouni, G.: Trend correlations for coastal eutrophication and its main local and whole-sea drivers – Application to the Baltic Sea, Sci. Total Environ., 779, 146367, https://doi.org/10.1016/j.scitotenv.2021.146367, 2021.
Visser, P. M., Verspagen, J. M. H., Sandrini, G., Stal, L. J., Matthijs, H. C. P., Davis, T. W., Paerl, H. W., and Huisman, J.: How rising CO2 and global warming may stimulate harmful cyanobacterial blooms, Harmful Algae, 54, 145–159, https://doi.org/10.1016/j.hal.2015.12.006, 2016.
Walls, J. T., Wyatt, K. H., Doll, J. C., Rubenstein, E. M., and Rober, A. R.: Hot and toxic: Temperature regulates microcystin release from cyanobacteria, Sci. Total Environ., 610–611, 786–795, https://doi.org/10.1016/j.scitotenv.2017.08.149, 2018.
Walve, J., Sandberg, M., Larsson, U., and Lännergren, C.: A Baltic Sea estuary as a phosphorus source and sink after drastic load reduction: seasonal and long-term mass balances for the Stockholm inner archipelago for 1968–2015, Biogeosciences, 15, 3003–3025, https://doi.org/10.5194/bg-15-3003-2018, 2018.
Wang, H., Guan, Y., and Reich, B.: Nearest-Neighbor Neural Networks for Geostatistics, in: 2019 International Conference on Data Mining Workshops (ICDMW), 2019 International Conference on Data Mining Workshops (ICDMW), 196–205, https://doi.org/10.1109/ICDMW.2019.00038, 2019.
Wang, X.: Remote Sensing Applications to Climate Change, Remote Sens., 15, 747, https://doi.org/10.3390/rs15030747, 2023.
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yutani, H.: Welcome to the Tidyverse, Journal of Open Source Software (JOSS), 4, 1686, https://doi.org/10.21105/joss.01686, 2019.
Wurtsbaugh, W. A., Paerl, H. W., and Dodds, W. K.: Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum, WIREs Water, 6, https://doi.org/10.1002/wat2.1373, 2019.
Yang, W., John, V., Zhao, X., Lu, H., and Knapp, K.: Satellite Climate Data Records: Development, Applications, and Societal Benefits, Remote Sens., 8, 331, https://doi.org/10.3390/rs8040331, 2016.
Yang, X., Wu, X., Hao, H., and He, Z.: Mechanisms and assessment of water eutrophication, J. Zhejiang Univ. Sci. B, 9, 197–209, https://doi.org/10.1631/jzus.B0710626, 2008.
Zhang, H. and Wang, Y.: Kriging and cross-validation for massive spatial data, Environmetrics, 21, 290–304, https://doi.org/10.1002/env.1023, 2010.
Short summary
The study integrated empirical Bayesian kriging, ensemble learning, and stacked species distribution modeling to study the distribution of cyanotoxin nodularin and toxin producer Nodularia spumigena. Area distribution of nodularin is determined by salinity, temperature, phosphate, nitrate-to-phosphate ratio, and distance from shore. Predictions show increased nodularin occurrences in the Eastern Gotland Sea, Northern Baltic Proper, southern parts of the Bothnian Sea, and Arkona Basin by 2100.
The study integrated empirical Bayesian kriging, ensemble learning, and stacked species...
Altmetrics
Final-revised paper
Preprint