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Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis
- Source :
- PLoS ONE, PLoS ONE, Vol 6, Iss 6, p e20141 (2011)
- Publication Year :
- 2011
-
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 (R(2)). 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 R(2). 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 R(2), with particularly low predictability in tropical reefs. Surrogate effectiveness was greatest for higher-taxa surrogates at a
- Subjects :
- 0106 biological sciences
Marine conservation
Multivariate statistics
Aquatic Organisms
Internationality
Bayesian probability
Biodiversity
lcsh:Medicine
Biology
010603 evolutionary biology
01 natural sciences
Models, Biological
Bayes' theorem
Econometrics
Marine ecosystem
Seawater
14. Life underwater
Community Assembly
Predictability
lcsh:Science
Community Structure
Conservation Science
Multidisciplinary
Ecology
010604 marine biology & hydrobiology
lcsh:R
Marine Ecology
Bayes Theorem
15. Life on land
Community Ecology
Spatial ecology
Bioindicators
lcsh:Q
Biomarkers
Coastal Ecology
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 6
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....091e424c2a3f176c71f4c5dead08c845