Spatial prediction of demersal fish diversity in the Baltic Sea: comparison of machine learning and regression-based techniques
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Marine spatial planning (MSP) is considered a valuable tool in the ecosystem-based management of marine areas. Predictive modelling may be applied in the MSP framework to obtain spatially explicit information about biodiversity patterns. The growing number of statistical approaches used for this purpose implies the urgent need for comparisons between different predictive techniques. In this study, we evaluated the performance of selected machine learning and regression-based methods that were applied for modelling fish community indices. We hypothesized that habitat features can influence fish assemblage and investigated the effect of environmental gradients on demersal fish diversity (species richness and Shannon–Weaver Index). We used fish data from the Baltic International Trawl Surveys (2001–2014) and maps of six potential predictors: bottom salinity, depth, seabed slope, growth season bottom temperature, seabed sediments and annual mean bottom current velocity. We compared the performance of six alternative modelling approaches: generalized linear models, generalized additive models, multivariate adaptive regression splines, support vector machines, boosted regression trees and random forests. We applied repeated 10-fold cross-validation, using accuracy as the measure of model quality. Finally, we selected random forest as the best performing algorithm and implemented it for the spatial prediction of fish diversity from the Baltic Proper to the Kattegat. To obtain information on the data reliability and confidence of the developed models, which are essential for MSP, we estimated the uncertainty of predictions with standard deviation of predictions obtained from all the trees in the ensemble random forest method. We showed how state-of-the-art predictive techniques, based on easily available data and simple Geographic Information System tools, can be used to obtain reliable spatial information about fish diversity. Our comparative work highlighted the potential of machine learning method to reduce prediction error in modelling of demersal fish diversity in the framework of MSP.
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Non-parametric ML techniques like Random Forest have been successfully used to predict species richness and have shown significant error reduction with respect to traditional counterparts used in ecology, for example in fishes, spiders, and small mammals.
“Non-parametric ML techniques like Random Forest126 have been successfully used to predict species richness and have shown significant error reduction with respect to the traditional counterparts used in ecology, for example in the estimation of richness distributions of fishes127,128, spiders 129, and small mammals130.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [128]
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Spatial Change of Dominant Baltic Sea Demersal Fish Across Two Decades
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- Authors
- Smoliński, S., & Radtke, K.
- Affiliations
- National Marine Fisheries Research Institute (PL)
- Corresponding author
- Smoliński, Szymon
- Venue
- ICES Journal of Marine Science
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-08-05
- Volume
- 74
- Issue
- 1
- Pages
- 102-111
- ISSN
- 1054-3139 (ISSN-L)
- 1095-9289
- OpenAlex
- W2519505014
- Cited by
- 69
- References
- 50
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 3 · 2023: 11 · 2022: 9 · 2021: 15 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 2
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- green
- oceanrep.geomar.de
- Licence
- Locations
- ICES Journal of Marine Science (publishedVersion)
- Helmholtz Centre for Ocean Research Kiel (GEOMAR) (acceptedVersion, other-oa)
- Funders
- Narodowe Centrum Badań i Rozwoju
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Marine and fisheries research
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine Biology and Ecology Research
- Keywords
- spatial prediction, marine spatial planning, machine learning, random forest, boosted regression trees, support vector machine, generalized additive models, multivariate adaptive regression splines, species richness, bottom salinity, bottom water temperature, marine sediment, bottom current velocity, forecast uncertainty, Baltic Sea, Kattegat
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