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dc.contributor.authorGalaiduk, R.
dc.contributor.authorRadford, B.
dc.contributor.authorWilson, S.
dc.contributor.authorHarvey, Euan
dc.identifier.citationGalaiduk, R. and Radford, B. and Wilson, S. and Harvey, E. 2017. Comparing two remote video survey methods for spatial predictions of the distribution and environmental niche suitability of demersal fishes. Scientific Reports. 7 (1): Article ID 17633.

Information on habitat associations from survey data, combined with spatial modelling, allow the development of more refined species distribution modelling which may identify areas of high conservation/fisheries value and consequentially improve conservation efforts. Generalised additive models were used to model the probability of occurrence of six focal species after surveys that utilised two remote underwater video sampling methods (i.e. baited and towed video). Models developed for the towed video method had consistently better predictive performance for all but one study species although only three models had a good to fair fit, and the rest were poor fits, highlighting the challenges associated with modelling habitat associations of marine species in highly homogenous, low relief environments. Models based on baited video dataset regularly included large-scale measures of structural complexity, suggesting fish attraction to a single focus point by bait. Conversely, models based on the towed video data often incorporated small-scale measures of habitat complexity and were more likely to reflect true species-habitat relationships. The cost associated with use of the towed video systems for surveying low-relief seascapes was also relatively low providing additional support for considering this method for marine spatial ecological modelling.

dc.publisherNature Publishing Group
dc.titleComparing two remote video survey methods for spatial predictions of the distribution and environmental niche suitability of demersal fishes
dc.typeJournal Article
dcterms.source.titleScientific Reports
curtin.departmentSchool of Molecular and Life Sciences (MLS)
curtin.accessStatusOpen access

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