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Empirical modelling...
Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea : evaluating the scope for predictive mapping using different modelling approaches
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Bucas, M. (author)
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- Bergström, Ulf (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för akvatiska resurser,Department of Aquatic Resources
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Downie, A-L (author)
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- Sundblad, G. (author)
- AquaBiota Water Research
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- Gullström, Martin (author)
- Gothenburg University,Göteborgs universitet,Stockholms universitet,Institutionen för ekologi, miljö och botanik,Institutionen för biologi och miljövetenskap,Department of Biological and Environmental Sciences
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von Numers, M. (author)
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Siaulys, A. (author)
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- Lindegarth, Mats, 1965 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för biologi och miljövetenskap, Tjärnö marinbiologiska laboratorium,Department of Biological and Environmental Sciences, Tjärnö Marine Biological Laboratory
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(creator_code:org_t)
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- 2013-05-19
- 2013
- English.
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In: ICES Journal of Marine Science. - : Oxford University Press (OUP). - 1054-3139 .- 1095-9289. ; 70:6, s. 1233-1243
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://gup.ub.gu.se...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Biologi -- Ekologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Ecology (hsv//eng)
- NATURVETENSKAP -- Biologi -- Botanik (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Botany (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Miljövetenskap (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Environmental Sciences (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- art (subject category)
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