Models of marine fish biodiversity: Assessing predictors from three habitat classification schemes

K.L. Yates, C. Mellin, M.J. Caley, Ben Radford, Jessica Meeuwig

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


© 2016 Yates et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improvedmanagement outcomes. Here we examined the utility of environmental data, obtained using different methods, for developingmodels of both uni- And multivariate biodiversity metrics.We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: Acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) tomodel biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer
Original languageEnglish
Article numbere0155634
JournalPLoS One
Issue number6
Publication statusPublished - 22 Jun 2016


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