| 11 Aug 2025
Prediction of present and future spatial occurrence of cyanobacteria and the toxin nodularin in the Baltic Sea
Abstract. Blooms of filamentous cyanobacteria are recurrent phenomena in the brackish Baltic Sea. These blooms often include toxin producing species, however, predicting and modeling the toxins spatial distribution poses great challenges. In addition, projected rising temperature due to climate change is expected to increase the occurrence of cyanobacterial blooms, making it vital to understand the distribution of the blooms and the associated cyanotoxins across ecosystems. Herein, we integrated measured concentration of the cyanotoxin nodularin, abundance of the toxin producer Nodularia spumigena, and environmental variables using Empirical Bayesian Kriging (EBK) regression prediction, ensemble learning, and stacked species distribution modeling (SSDM). This setup was used to predict and interpret the current and future area distribution of N. spumigena and nodularin across the Baltic Sea. Predictions were based on results from biogeochemical models describing current and projected future concentrations of near surface chlorophyll, nitrate, phosphate, salinity, and temperature along with nitrate-to-phosphate ratio and a geographical variable of distance to shore. Prediction for the future distribution was performed using projected climate change scenarios in the year 2100. Findings show that the predicted area distribution of nodularin is determined by concentrations and interaction effects of salinity, temperature, phosphate, nitrate to phosphate ratio, and distance to shore, and is associated with the predicted area distribution of N. spumigena. Predicted site distribution shows increased nodularin occurrences in the Eastern and Western Gotland Sea, the Northern Baltic Proper, southern parts of the Bothnian Sea, and in the Arkona basin. By the year 2100, area distribution of nodularin is predicted to increase in the northern part of the Eastern Gotland Sea, Northern Baltic Proper, Åland Sea, southern parts of the Bothnian Sea, Arkona Basin, and slightly into the Bothnia Bay in response to projected climate change scenarios. Our developed modeling approach is useful for risk assessment and management of cyanotoxins where toxicological data are insufficient.
Review of the manuscript “Prediction of present and future spatial occurrence of cyanobacteria and the toxin nodularin in the Baltic Sea” by Mohanad Abdelgadir, Bengt Karlson, Elin Dahlgren, Malin Olofsson
Summary: The authors use Empirical Bayesian Kriging (EBK) regression prediction, ensemble learning, and stacked species distribution modelling (SSDM) to predict and interpret the current and future area distribution of Nodularia sp. and the toxin nodularin across the Baltic Sea. The underlying data base consists of 139 observed samples, combined with numerical model data from various sources. Predictions for the future distribution of Nodularia sp. blooms and nodularin are based on projected climate change scenarios in the year 2100.
Major comments:
The subject of the study is of importance and general interest. The overall approach is interesting and all the work that went into this study is greatly appreciated. Unfortunately, however, I found it very difficult to rate methods and results of the presented study because I got lost in many details while I still lack a description of important key aspects.
For my feeling the authors try to do too much in just one publication and I lack a clear aim of all their experiments. I thus strongly recommend to rather focus on one specific aspect. Potential candidates I could imagine are: (1) a comparison of different techniques for enhancing the output of climate models to resolve local cyanobacteria blooms and toxins (here I don’t regard the Baltic Sea as a perfect candidate because many important processes are not resolved in climate models). (2) a comparison of different methods to interpolate nodularin measurements in space (if the available data do not suffice considerations could be added on how many extra data would be required and where) or (3) suggestions how to implement nodularin into a prognostic Baltic Sea model which already contains cyanobacteria. This list is certainly not comprehensive.
As the study is designed now, I have several major concerns which in my eyes need to be addressed:
(1) There seem to be very few measurements of the toxin nodularin available (as I understood 139 observed sample were investigated while most of them are clustered at some coasts). Relating these few measurements to a multitude of environmental factors and their interactions will most likely lead to overfitting. While the authors state that they used part of the data for testing, I did not find clear evidence which could rule out this concern. I would expect something like a direct comparison plot of the best prediction versus observed nodularin for some independent test data.
(2) For the occurrence of Nodularia Sp. biogeochemical model data of the ERGOM model are combined with the available observations. This approach might lead to inconsistencies and since this is the key aspect I would like to see at least some quality assessment of both, the numerically simulated and predicted Nodularia sp. blooms. Since Satellite data for Nodularia sp. are provided e.g. by SMHI, it would be fairly easy to show 2-dimensional plots of the extent of a particularly large and small bloom during the recent years as simulated (by the numerical model) and as predicted (by the methods of this study) in direct comparison to satellite observations.
Additionally, the in-situ samples for Nodularia sp. could be plotted against the respective model data to ensure that these data sources can be combined without problems.
(3) The authors then combine many different data sources from global to regional models which are almost for certain inconsistent and might make it very difficult to draw robust conclusions. E.g. global models do not resolve coastal upwelling and it is very unlikely that these models capture the complex salinity dynamics, nutrient inputs or sediment processes of the Baltic Sea. These aspects need at least attention.
(3) I did not find an independence test for the predictors. Then, I am surprised that distance to the shore has been used as predictor. I am well aware of a prominent study which uses this factor when investigating the onset of blooms – still blooms may then drift to the shore and frequently do so.
(5) I did not find convincing evidence for reliable predictions for any of the many methods. I would like to see clear visual comparisons to independent test data.
(6) Even if the authors revise and illustrate robust relations to predictors for Nodularia Sp. bloom occurrence and nodularin under present climate conditions, it is still not at all guaranteed that these could be extrapolated to a much warmer climate (e.g. species composition and competitive advantages might well change). I do not at all recommend to base predictions or even recommendations for politics on such uncertain ground.
Specific comments:
Ln 13: change “blooms often include toxin producing species” to “ blooms can contain toxin producing stains,”
Ln 14: change “climate change is expected to increase the occurrence of cyanobacterial blooms” to something like that “climate change may increase the occurrence of cyanobacterial blooms”
Ln 17-18: The choice of each method should be motivated.
Ln 29: I do not recommend any risk assessment or management decisions at current state.
Ln 64: Empirical Bayesian Kriging (EBK) regression prediction depends heavily on the density and distribution of the underlying samples. In Figure 1 it appears that most of the few samples are clustered at some coasts. Also, I doubt that all samples were taken at the same time ad it is not clear which state of the system the Kriging refers to. It would be good if the time aspect was clarified and I would like to see a comparison to independent test data.
Ln 84: It does not become clear how these approaches could overcome the above-mentioned problems.
Ln 87: Again, I do not at all recommend to base future predictions on such uncertain ground.
Ln 91: How many of the samples did contain nodularin? Where all samples taken during Nodularia bloom conditions? Where other variables, such as nutrients, salinity or temperature, measured as well?
Ln 105ff: Blending the nutrients from the SMHI forecast with chlorophyll_a simulated by the ERGOM-model needs some good motivation because the respective fields will not be consistent.
Ln 118: I could not find a meaningful comparison in the supplement.
Ln 120: It does not become clear how the predictions were “tested” based on the mentioned Copernicus data.
Ln 123: I would be very interested to see 2-dimensional plots on predicted Nodularia Sp. and nodularin when using the climate models under present climate conditions in comparison the predictions based on the combination of numerical Baltic Sea models.
Ln 185 Something went wrong here.
Ln 187ff: From here on I am somehow lost in many details, different methods (all with different design choices) and various feature selections while I am lacking a clear purpose of all the experiments.