Models of marine fish biodiversity: assessing predictors from three habitat classification schemes
Files
(Published version)
Date
2016
Authors
Yates, K.L.
Mellin, C.
Caley, M.J.
Radford, B.T.
Meeuwig, J.J.
Editors
Hiddink, J.G.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
PLoS ONE, 2016; 11(6):e0155634-1-e0155634-16
Statement of Responsibility
Katherine L. Yates, Camille Mellin, M. Julian Caley, Ben T. Radford, Jessica J. Meeuwig
Conference Name
Abstract
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 improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models 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) to model 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 habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 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.