Transferability of predictive models of coral reef fish species richness

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dc.contributor.authorSequeira, A.M.M.
dc.contributor.authorMellin, C.
dc.contributor.authorLozano-Montes, H.M.
dc.contributor.authorVanderklift, M.A.
dc.contributor.authorBabcock, R.C.
dc.contributor.authorHaywood, M.D.E.
dc.contributor.authorMeeuwig, J.J.
dc.contributor.authorCaley, M.J.
dc.contributor.editorHeino, J.
dc.date.issued2016
dc.description.abstract1. Understanding biodiversity patterns depends on data collection, which in marine environments can be prohibitively expensive. Transferable predictive models could therefore provide time- and cost-effective tools for understanding biodiversity–environment relationships. 2. We used fish species counts and spatial and environmental predictors to develop predictive models of fish species richness (S) for two major coral reefs located in separate ocean basins: Australia’s Great Barrier Reef (GBR; Queensland) and Ningaloo Reef (NR; Western Australia). We tested the ability of the GBR model to predict S at NR (its transferability) under various scenarios using different sampling durations, years sampled and transect sizes. 3. Based on R², the GBR model poorly predicted S at NR (R² < 16%) with few predicted values strongly correlated with observations. However, comparable spatial patterns in S across NR were predicted by both the NR and the GBR models when calibrated at similar spatio-temporal scales. 4. This result suggests that poor validation of the transferred models may indicate low deviance explained by the predictors in the new system (where other predictors not included might have a more direct effect on the response) and that in some situations, model transferability may be considerably improved by using data sets of similar spatio-temporal scales. Therefore, data filtering by time and space may be required prior to transferring models. 5. Policy implications. Transferable models can provide initial estimates of fish species richness patterns in poorly sampled systems, and thereby guide the design of better and more efficient sampling programs. Further improvements in model transferability will increase their predictive power and utility in conservation planning and management.
dc.description.statementofresponsibilityAna M.M. Sequeira, Camille Mellin, Hector M. Lozano-Montes, Mathew A. Vanderklift, Russ C. Babcock, Michael D.E. Haywood, Jessica J. Meeuwig, and M. Julian Caley
dc.identifier.citationJournal of Applied Ecology, 2016; 53(1):64-72
dc.identifier.doi10.1111/1365-2664.12578
dc.identifier.issn0021-8901
dc.identifier.issn1365-2664
dc.identifier.orcidMellin, C. [0000-0002-7369-2349]
dc.identifier.urihttp://hdl.handle.net/2440/129154
dc.language.isoen
dc.publisherWiley; British Ecological Society
dc.relation.granthttp://purl.org/au-research/grants/arc/DE140100701
dc.rights© 2015 The Authors. Journal of Applied Ecology © 2015 British Ecological Society
dc.source.urihttps://doi.org/10.1111/1365-2664.12578
dc.subjectBiodiversity distribution models; generalized linear mixed-effects modelling; Great Barrier Reef; macroecology; Ningaloo Reef; prediction; spatial autocorrelation
dc.titleTransferability of predictive models of coral reef fish species richness
dc.typeJournal article
pubs.publication-statusPublished

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