Publication Repository

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

Show simple item record

dc.contributor Australian Institute Of Marine Science
dc.contributor Univ Adelaide
dc.contributor University Of Manchester
dc.contributor Sch Biol Sci
dc.contributor University Of Western Australia
dc.contributor Sch Environm & Life Sci
dc.contributor Univ Western Australia
dc.contributor University Of Adelaide
dc.contributor Ctr Marine Futures
dc.contributor Australian Inst Marine Sci
dc.contributor Univ Salford
dc.contributor Oceans Inst
dc.contributor Sch Anim Biol
dc.contributor University Of Salford
dc.contributor Global Change Ecol Lab
dc.contributor Uwa Oceans Inst
dc.contributor.author MEEUWIG, JESSICA J.
dc.contributor.author YATES, KATHERINE L.
dc.contributor.author MELLIN, CAMILLE
dc.contributor.author CALEY, M. JULIAN
dc.contributor.author RADFORD, BEN T.
dc.date.accessioned 2017-03-21T01:06:20Z
dc.date.accessioned 2017-03-21T01:06:20Z
dc.date.accessioned 2016-07-05T04:46:35Z
dc.date.accessioned 2018-11-01T03:06:50Z
dc.date.available 2016-07-05T04:46:35Z
dc.date.available 2016-07-05T04:46:35Z
dc.date.available 2017-03-21T01:06:20Z
dc.date.available 2018-11-01T03:06:50Z
dc.date.issued 2016-06-22
dc.identifier.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 en_US
dc.identifier.issn 1932-6203
dc.identifier.uri http://epubs.aims.gov.au/11068/12804
dc.description.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.
dc.description.sponsorship 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.
dc.description.sponsorship No specific funding was obtained for this piece of research. However, the original data collection was funded by the Natural Heritage Trust II (as part of a different project) and CM is funded by an Australian Research Council grant (DE140100701). en_US
dc.description.uri http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0155634 en_US
dc.language English
dc.language.iso en en_US
dc.publisher PLoS en_US
dc.relation.ispartof Null
dc.rights Attribution 3.0 Australia *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/au/ *
dc.subject Ecosystems
dc.subject Species Richness
dc.subject Ocean
dc.subject Science & Technology - Other Topics
dc.subject Fisheries
dc.subject Conservation
dc.subject Multidisciplinary Sciences
dc.subject Regression Trees
dc.subject Abundance
dc.subject Patterns
dc.subject Western-australia
dc.subject Assemblages
dc.title Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
dc.type journal article en_US
dc.identifier.doi 10.1371/journal.pone.0155634
dc.identifier.wos WOS:000378212800005


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Attribution 3.0 Australia Except where otherwise noted, this item's license is described as Attribution 3.0 Australia

Search Publication


Browse

My Account