Models of marine fish biodiversity: Assessing predictors from three habitat classification schemes
01
Marine futures
Rottnest Island was surveyed in 2007 as part of the Marine Futures Program. Surveys included multibeam mapping, towed video and 349 Baited Remote Underwater Video (BRUV) deployments (dark circles circles). Figure: Yates et al. 2016.
02
HABITAT MATTERS
Multivariate regression tress (MRT) of fish functional groups based on A) multibeam data and B) predicted habitats data. Figure: Yates et al. 2016.
CITATION
Yates KL, Mellin C, Caley MJ, Radford BT, Meeuwig JJ. 2016. Models of marine fish biodiversity: Assessing predictors from three habitat classification schemes. PloS ONE, 11(6): e0155634.
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.
FUNDING & ACKNOWLEDGEMENTS
This work was done under the auspices of the Marine Biodiversity Hub. Our thanks to Dr K van Niel, for her contribution to the Marine Futures Project, and to three anonymous reviewers for their constructive and insightful comments.