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Mapping benthic biodiversity using georeferenced environmental data and predictive modeling

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Abstract

Biodiversity is critical for maintaining and stabilizing ecosystem processes. There is a need for high-resolution biodiversity maps that cover large sea areas in order to address ecological questions related to biodiversity-ecosystem functioning relationships and to provide data for marine environmental protection and management decisions. However, traditional sampling-point-wise field work is not suitable for covering extensive areas in high detail. Spatial predictive modeling using biodiversity data from sampling points and georeferenced environmental data layers covering the whole study area is a potential way to create biodiversity maps for large spatial extents. Random forest (RF), generalized additive models (GAM), and boosted regression trees (BRT) were used in this study to produce benthic (macroinvertebrates, macrophytes) biodiversity maps in the northern Baltic Sea. Environmental raster layers (wave exposure, salinity, temperature, etc.) were used as independent variables in the models to predict the spatial distribution of species richness. A validation dataset containing data that was not included in model calibration was used to compare the prediction accuracy of the models. Each model was also evaluated visually to check for possible modeling artifacts that are not revealed by mathematical validation. All three models proved to have high predictive ability. RF and BRT predictions had higher correlations with validation data and lower mean absolute error than those of GAM. Both mathematically and visually, the predictions by RF and BRT were very similar. Depth and seabed sediments were the most influential abiotic variables in predicting the spatial patterns of biodiversity.

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Acknowledgements

This work was supported by the institutional research funding IUT02-20 of the Estonian Ministry of Education and Research. The project has received funding from the BONUS project BIO-C3, the Joint Baltic Sea Research and Development Programme (Art 185), funded jointly from the European Union’s Seventh Programme for Research, Technological Development and Demonstration, and from the Estonian Research Council. The authors thank Kiran Liversage for the language revision.

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  1. Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618, Tallinn, Estonia

    Anneliis Peterson & Kristjan Herkül

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  1. Anneliis Peterson
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Correspondence to Anneliis Peterson.

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Communicated by D. M. Paterson

Appendices

Appendix 1

Table 4 List of macrobenthos taxa. Taxonomic level in brackets
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Appendix 2 Partial dependence plots of the random forest models that were used for making spatial predictions

Fig. 5
figure 5

Partial dependence plots for total benthic species diversity

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Fig. 6
figure 6

Partial dependence plots for faunal species diversity

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Fig. 7
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Partial dependence plots for floral species diversity

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Peterson, A., Herkül, K. Mapping benthic biodiversity using georeferenced environmental data and predictive modeling. Mar Biodiv 49, 131–146 (2019). https://doi.org/10.1007/s12526-017-0765-5

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