Dataset Information

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.

SUBMITTER: Yates KL

PROVIDER: S-EPMC4917103 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Yates Katherine L KL Mellin Camille C Caley M Julian MJ Radford Ben T BT Meeuwig Jessica J JJ

PloS one 20160622 6

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 biodiversit ...[more]

PMID: 27333202

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Project description:Statistical models built using different data sources and methods can exhibit conflicting patterns. We used the northern stock of black sea bass (Centropristis striata) as a case study to assess the impacts of using different fisheries data sources and laboratory-derived physiological metrics in the development of thermal habitat models for marine fishes. We constructed thermal habitat models using generalized additive models (GAMs) based on various fisheries datasets as input, including the NOAA Northeast Fisheries Science Center (NEFSC) bottom trawl surveys, various inshore fisheries-independent trawl surveys (state waters), NEFSC fisheries-dependent observer data, and laboratory-based physiological metrics. We compared each model's GAM response curve and coupled them to historical ocean conditions in the U.S. Northeast Shelf using bias-corrected ocean temperature output from a regional ocean model. Thermal habitat models based on shelf-wide data (NEFSC fisheries-dependent observer data and fisheries-independent spring and fall surveys) explained the most variation in black sea bass presence/absence data at ~15% deviance explained. Models based on a narrower range of sampled thermal habitat from inshore survey data in the Northeast Area Monitoring and Assessment Program (NEAMAP) and the geographically isolated Long Island Sound data performed poorly. All models had similar lower thermal limits around 8.5℃, but thermal optima, when present, ranged from 16.7 to 24.8℃. The GAMs could reliably predict habitat from years excluded from model training, but due to strong seasonal temperature fluctuations in the region, could not be used to predict habitat in seasons excluded from training. We conclude that survey data source can greatly impact development and interpretation of thermal habitat models for marine fishes. We suggest that model development be based on data sources that sample the widest range of ocean temperature and physical habitat throughout multiple seasons when possible, and encourage thorough consideration of how data gaps may influence model uncertainty.

Project description:As species distribution models, and similar techniques, have emerged in marine ecology, a vast array of predictor variables have been created and diverse methodologies have been applied. Marine fish are vital food resources worldwide, yet identifying the most suitable methodology and predictors to characterize spatial habitat associations, and the subsequent distributions, often remains ambiguous. Our objectives were to identify knowledge gaps in fish guilds, identify research themes, and to determine how data sources, statistics, and predictor variables differ among fish guilds. Data were obtained from an international literature search of peer-reviewed articles (2007-2018; n = 225) and research themes were determined based on abstracts. We tested for differences in data sources and modeling techniques using multinomial regressions and used a linear discriminant analysis to distinguish differences in predictors among fish guilds. Our results show predictive studies increased over time, but studies of forage fish, sharks, coral reef fish, and other fish guilds remain sparse. Research themes emphasized habitat suitability and distribution shifts, but also addressed abundance, occurrence, stock assessment, and biomass. Methodologies differed by fish guilds based on data limitations and research theme. The most frequent predictors overall were depth and temperature, but most fish guilds were distinguished by their own set of predictors that focused on their specific life history and ecology. A one-size-fits-all approach is not suitable for predicting marine fish distributions. However, given the paucity of studies for some fish guilds, researchers would benefit from utilizing predictors and methods derived from more commonly studied fish when similar habitat requirements are expected. Overall, the findings provide a guide for determining predictor variables to test and identifies novel opportunities to apply non-spatial knowledge and mechanisms to models.

Dataset Information

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

Publications

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

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