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

Mellin, Camille, Delean, Steve, Caley, Julian, Edgar, Graham, Meekan, Mark G., Pitcher, Roland, Przeslawski, Rachel, Williams, Alan and Bradshaw, Corey J. A. (2011). Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis. PLoS ONE,6(6):e20141.

Document type: Journal Article
Citation counts: Altmetric Score Altmetric Score is 11
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Title Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis
Author Mellin, Camille
Delean, Steve
Caley, Julian
Edgar, Graham
Meekan, Mark G.
Pitcher, Roland
Przeslawski, Rachel
Williams, Alan
Bradshaw, Corey J. A.
Journal Name PLoS ONE
Publication Date 2011
Volume Number 6
Issue Number 6
ISSN 1932-6203   (check CDU catalogue open catalogue search in new window)
Start Page e20141
Total Pages 11
Place of Publication United States
Publisher Public Library of Science
HERDC Category C1 - Journal Article (DEST)
Abstract The 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.
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