Effectiveness of biological surrogates for predicting patterns of marine biodiversity: A global meta-analysis

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dc.contributor.authorMellin, C.
dc.contributor.authorDelean, J.
dc.contributor.authorCaley, M.
dc.contributor.authorEdgar, G.
dc.contributor.authorMeekan, M.
dc.contributor.authorPitcher, R.
dc.contributor.authorPrzeslawski, R.
dc.contributor.authorWilliams, A.
dc.contributor.authorBradshaw, C.
dc.contributor.editorGilbert, J.A.
dc.date.issued2011
dc.description.abstractThe use of biological surrogates as proxies for biodiversity patterns is gaining popularity, particularly in marine systems where field surveys can be expensive and species richness high. Yet, uncertainty regarding their applicability remains because of inconsistency of definitions, a lack of standard methods for estimating effectiveness, and variable spatial scales considered. We present a Bayesian meta-analysis of the effectiveness of biological surrogates in marine ecosystems. Surrogate effectiveness was defined both as the proportion of surrogacy tests where predictions based on surrogates were better than random (i.e., low probability of making a Type I error; P) and as the predictability of targets using surrogates (R2). A total of 264 published surrogacy tests combined with prior probabilities elicited from eight international experts demonstrated that the habitat, spatial scale, type of surrogate and statistical method used all influenced surrogate effectiveness, at least according to either P or R2. The type of surrogate used (higher-taxa, cross-taxa or subset taxa) was the best predictor of P, with the higher-taxa surrogates outperforming all others. The marine habitat was the best predictor of R2, with particularly low predictability in tropical reefs. Surrogate effectiveness was greatest for higher-taxa surrogates at a ,10-km spatial scale, in low-complexity marine habitats such as soft bottoms, and using multivariate-based methods. Comparisons with terrestrial studies in terms of the methods used to study surrogates revealed that marine applications still ignore some problems with several widely used statistical approaches to surrogacy. Our study provides a benchmark for the reliable use of biological surrogates in marine ecosystems, and highlights directions for future development of biological surrogates in predicting biodiversity.
dc.description.statementofresponsibilityCamille Mellin, Steve Delean, Julian Caley, Graham Edgar, Mark Meekan, Roland Pitcher, Rachel Przeslawski, Alan Williams and Corey Bradshaw
dc.identifier.citationPLoS One, 2011; 6(6):e20141-1-e20141-11
dc.identifier.doi10.1371/journal.pone.0020141
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.orcidMellin, C. [0000-0002-7369-2349]
dc.identifier.orcidDelean, J. [0000-0003-1116-5014]
dc.identifier.orcidBradshaw, C. [0000-0002-5328-7741]
dc.identifier.urihttp://hdl.handle.net/2440/66426
dc.language.isoen
dc.publisherPublic Library of Science
dc.rightsCopyright 2011 Mellin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pone.0020141
dc.subjectBayes Theorem
dc.subjectBiodiversity
dc.subjectSeawater
dc.subjectModels, Biological
dc.subjectInternationality
dc.subjectAquatic Organisms
dc.subjectBiomarkers
dc.titleEffectiveness of biological surrogates for predicting patterns of marine biodiversity: A global meta-analysis
dc.typeJournal article
pubs.publication-statusPublished

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