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

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 o

SUBMITTER: Yates KL 

PROVIDER: S-EPMC4917103 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature