Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

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 manageme...

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Bibliographic Details
Published in:	PloS one 2016-06, Vol.11 (6), p.e0155634-e0155634
Main Authors:	Yates, Katherine L, Mellin, Camille, Caley, M Julian, Radford, Ben T, Meeuwig, Jessica J
Format:	Article
Language:	eng
Subjects:	Abundance Analysis Animals Aquatic habitats Aquatic Organisms - physiology Biodiversity Biodiversity conservation Biological diversity conservation Biology and Life Sciences Biomass Classification Conservation Earth Sciences Ecology and Environmental Sciences Endemic species Engineering and Technology Environmental management Fish Fish habitat improvement Fishes - physiology Functional groups Geography Habitats Image classification Islands Marine biology Marine fish Marine fishes Modelling Models, Theoretical New technology Performance prediction Regression Analysis Regression models Remote sensing Research and Analysis Methods Science Sonar imagery Species richness Trees Western Australia Wildlife conservation
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Summary:	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.
ISSN:	1932-6203 1932-6203