Australian Institute of Marine Science, Townsville, Queensland, Australia.
PLoS One. 2011;6(6):e20141. doi: 10.1371/journal.pone.0020141. Epub 2011 Jun 14.
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 <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.
生物替代物作为生物多样性模式的代理越来越受欢迎，特别是在海洋系统中，实地调查可能很昂贵，物种丰富度很高。然而，由于定义不一致、缺乏估计有效性的标准方法以及考虑的空间尺度不同，它们的适用性仍然存在不确定性。我们提出了一种海洋生态系统中生物替代物有效性的贝叶斯荟萃分析。替代物的有效性定义为基于替代物的预测比随机预测更好的替代物测试比例（即，犯第一类错误的概率低；P），以及使用替代物预测目标的可预测性（R(2)）。总共 264 个已发表的替代物测试以及从八位国际专家那里得出的先验概率表明，生境、空间尺度、替代物类型和统计方法的使用都影响了替代物的有效性，至少根据 P 或 R(2)。使用的替代物类型（高分类群、跨分类群或子集分类群）是 P 的最佳预测因子，高分类群替代物优于所有其他替代物。海洋生境是 R(2)的最佳预测因子，热带珊瑚礁的可预测性特别低。在 <10 公里的空间尺度上，在低复杂性的海洋生境（如软底）中，使用基于多变量的方法，高分类群替代物的替代物有效性最大。与陆地研究在研究替代物时使用的方法进行比较表明，海洋应用仍然忽略了几种广泛使用的替代物统计方法存在的一些问题。我们的研究为在海洋生态系统中可靠使用生物替代物提供了基准，并强调了未来在预测生物多样性方面开发生物替代物的方向。
