Bučas, M., Bergström, U., Downie, A-L., Sundblad, G., Gullström, M., von Numers, M., Šiaulys, A., and Lindegarth, M. 2013. Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: evaluating the scope for predictive mapping using different modelling approaches. – ICES Journal of Marine Science, 70: 1233–1243. The predictive performance of distribution models of common benthic species in the Baltic Sea was compared using four non-linear methods: generalized additive models (GAMs), multivariate adaptive regression splines, random forest (RF), and maximum entropy modelling (MAXENT). The effects of data traits were also tested. In total, 292 occurrence models and 204 quantitative (abundance and diversity) models were assessed. The main conclusions are that (i) the spatial distribution, abundance, and diversity of benthic species in the Baltic Sea can be successfully predicted using several non-linear predictive modelling techniques; (ii) RF was the most accurate method for both models, closely followed by GAM and MAXENT; (iii) correlation coefficients of predictive performance among the modelling techniques were relatively low, suggesting that the performance of methods is related to specific responses; (iv) the differences in predictive performance among the modelling methods could only partly be explained by data traits; (v) the response prevalence was the most important explanatory variable for predictive accuracy of GAM and MAXENT on occurrence data; (vi) RF on the occurrence data was the only method sensitive to sampling density; (vii) a higher predictive accuracy of abundance models could be achieved by reducing variance in the response data and increasing the sample size.
Marine spatial management requires accurate data on species and habitat distributions. For the deep sea, these data are lacking. Habitat suitability modelling offers a robust defensible means to fill data gaps, provided models are sufficiently reliable. We tested the performance of published models of 2 deep-sea habitat-forming taxa at low and high resolutions (~1 km and 200 m grid-cell size), across the extended exclusive economic zones of the UK and Ireland. We constructed new data-rich models and compared new and old estimates of the area of habitat protected, noting changes in the protected area network since 2015. Results of independent validation suggest that all published models perform worse than expected considering original cross-validation results, but model performance is still good or fair for Desmophyllum pertusum reef, with poorer performance for Pheronema carpenteri sponge models. High-resolution models using multibeam data out-perform low-resolution GEBCO-based models. Newly constructed models are good to excellent according to cross validation. New model spatial predictions reflect published models, but with a significant reduction in predicted extent. The current marine protected area network and the European Union ban on bottom trawling below 800 m protect 40 and 60% of D. pertusum reef-suitable habitat, respectively, and 11 and 100% of P. carpenteri-suitable habitat, respectively, within the model domain. We conclude that high-resolution models of D. pertusum reef distribution are a useful tool in spatial management. The poorer performing P. carpenteri model indicates areas for more detailed study. While low-resolution models can provide conservative estimates of percentage area-based conservation targets following the precautionary principle, high-resolution sea-floor mapping supports the development of better-performing models.
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