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 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.

Challenges of transferring models of fish abundance between coral reefs

“…To assist model improvement, where available, inclusion of variables that better represent the biology of reef systems or their benthic communities and habitats ( Pittman, Costa & Battista, 2009 ; Yates et al, 2016 ) should be considered. As shown here for the NR models with the inclusion of rugosity (which in coral reefs is a largely biogenic variable), local, fine-scale topographic predictors might considerably improve model fit.…”

Explaining island‐wide geographical patterns of Caribbean fish diversity: A multi‐scale seascape ecology approach

“…Although cost effective and widely available for relatively broad geographical areas, remotely sensed data may not capture sufficient ecological variability to explain the complex patterns of biological distributions for coral reef fishes. Additional types of environmental variables could also be explored such as diver‐defined habitat classes, higher resolution terrain models and outputs from connectivity models (Yates, Mellin, Caley, Radford, & Meeuwig, ).…”

Transferable, predictive models of benthic communities informs marine spatial planning in a remote and data‐poor region

“…In such cases, the effectiveness of conservation actions depends on the ability of such surrogates to adequately represent biodiversity patterns. However, the effectiveness of these surrogates can vary considerably depending on a range of factors including the taxon or spatial scale examined (Grantham, Pressey, Wells, & Beattie, 2010;Lombard et al, 2003;Mellin et al, 2011;Sutcliffe, Pitcher, Caley, & Possinghan, 2012), the nature of the surrogate (e.g., richness, vs. abundance vs. biomass, Yates, Mellin, Caley, Radford, & Meeuwig, 2016), the predictors used and how they are weighted (Mellin, Mengersen, Bradshaw, & Caley, 2014), the method of data collection (e.g., remotely sensed vs. observer classified, Yates et al, 2016), and the nature and extent of the extrapolation required to predict biodiversity patterns in an unsampled location Yates et al, 2018).…”