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Article: Geographic range determinants of two commercially important marine molluscs

TitleGeographic range determinants of two commercially important marine molluscs
Authors
KeywordsAbalone
Boosted regression trees
Generalized linear models
Haliotis
Sea surface temperature
Species distribution modelling
Issue Date2012
Citation
Diversity and Distributions, 2012, v. 18, n. 2, p. 133-146 How to Cite?
AbstractAim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood-based generalized linear models and boosted regression trees. For each modelling technique, a two-step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c.1-km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine-resolution predictive range maps (c.1km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established. © 2011 Blackwell Publishing Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/213216
ISSN
2015 Impact Factor: 4.566
2015 SCImago Journal Rankings: 2.701

 

DC FieldValueLanguage
dc.contributor.authorMellin, Camille-
dc.contributor.authorRussell, Bayden D.-
dc.contributor.authorConnell, Sean D.-
dc.contributor.authorBrook, Barry W.-
dc.contributor.authorFordham, Damien A.-
dc.date.accessioned2015-07-28T04:06:33Z-
dc.date.available2015-07-28T04:06:33Z-
dc.date.issued2012-
dc.identifier.citationDiversity and Distributions, 2012, v. 18, n. 2, p. 133-146-
dc.identifier.issn1366-9516-
dc.identifier.urihttp://hdl.handle.net/10722/213216-
dc.description.abstractAim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood-based generalized linear models and boosted regression trees. For each modelling technique, a two-step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c.1-km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine-resolution predictive range maps (c.1km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established. © 2011 Blackwell Publishing Ltd.-
dc.languageeng-
dc.relation.ispartofDiversity and Distributions-
dc.subjectAbalone-
dc.subjectBoosted regression trees-
dc.subjectGeneralized linear models-
dc.subjectHaliotis-
dc.subjectSea surface temperature-
dc.subjectSpecies distribution modelling-
dc.titleGeographic range determinants of two commercially important marine molluscs-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1472-4642.2011.00822.x-
dc.identifier.scopuseid_2-s2.0-84855495748-
dc.identifier.volume18-
dc.identifier.issue2-
dc.identifier.spage133-
dc.identifier.epage146-
dc.identifier.eissn1472-4642-

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