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Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: evaluating the scope for predictive mapping using different modelling approaches
Vise andre og tillknytning
2013 (engelsk)Inngår i: ICES Journal of Marine Science, ISSN 1054-3139, E-ISSN 1095-9289, Vol. 70, nr 6, s. 1233-1243Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
2013. Vol. 70, nr 6, s. 1233-1243
Emneord [en]
generalized additive models, habitat suitability models, marine benthic ecosystems, maximum entropy modelling, multivariate adaptive regression splines, niche modelling, prevalence and sampling density, random forest, species distribution modelling, variance in the response data and sample size
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Identifikatorer
URN: urn:nbn:se:su:diva-94026DOI: 10.1093/icesjms/fst036ISI: 000323635600019OAI: oai:DiVA.org:su-94026DiVA, id: diva2:651788
Merknad

AuthorCount:8;

Tilgjengelig fra: 2013-09-27 Laget: 2013-09-24 Sist oppdatert: 2017-12-06bibliografisk kontrollert

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