When the suit does not fit biodiversity: Loose surrogates compromise the achievement of conservation goals
When the suit does not fit biodiversity: Loose surrogates compromise the achievement of conservation goals
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DOI：
10.1016/j.biocon.2012.11.026
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发表时间：
2013-03-01
影响因子：
5.9
通讯作者：
Pressey, Robert L.
中科院分区：
文献类型：
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作者：
Hermoso, Virgilio;Januchowski-Hartley, Stephanie R.;Pressey, Robert L.
由于经常缺乏关于生物多样性模式和过程的一致数据，在保护规划中使用生物多样性替代物是不可避免的。自上而下的环境分类（粗过滤代理）是定义代理的最常见方法。它们的使用依赖于这样一种假设，即使用替代物确定的优先领域将充分代表生物多样性。对于不同因素的组合如何影响这些分类的代孕价值，仍然没有明确的理解。在这里，我们评估了三个因素的作用，可能会影响粗过滤器替代品的有效性：（a）专题分辨率（类的数量），（B）物种的流行，（c）分类描绘同质社区（分类强度）的能力。我们探讨了这些因素的直接和间接影响的作用与模拟数据集的10,000个规划单位和96个物种和结构方程modeling（SEM）。的替代值的粗过滤器的替代依赖于类和物种的分布和能力的分类之间的相对匹配的程度来描绘模式的物种组成（分类强度）。两者都决定了在某一类别中错误选择某些物种不存在的区域的可能性。只有在高分类强度值（>0.5）时，常见物种才比随机物种表现得更好，而稀有物种从未表现出这种情况。更精细的分类往往是更好的替代品，尽管当稀有物种被纳入时，即使是最精细的分类，达到目标水平的物种比例也从未超过68%。在生物多样性呈斑块状分布或有许多稀有物种的地区，这就损害了粗过滤替代物的适用性。我们建议在无法对所有物种实现高效率时使用包含环境类别和生物数据的复合数据集。(C)2012爱思唯尔有限公司保留所有权利。
The use of biodiversity surrogates is inevitable in conservation planning due to the frequent lack of consistent data on biodiversity patterns and processes. Top-down environmental classifications (coarse-filter surrogates) are the most common approach to defining surrogates. Their use relies on the assumption that priority areas identified using surrogates will adequately represent biodiversity. There remains no clear understanding about how the combination of different factors might affect the surrogacy value of these classifications. Here, we evaluate the role of three factors that could affect the effectiveness of coarse-filter surrogates: (a) thematic resolution (number of classes), (b) species' prevalence, and (c) the ability of classifications to portray homogeneous communities (classification strength). We explore the role of direct and indirect effects of these factors with a simulated dataset of 10,000 planning units and 96 species and structural equation modelling (SEM).The surrogacy value of coarse-filter surrogates depended on the relative match between the extent of classes and species' distributions and the capacity of classifications to portray patterns in species composition (classification strength). Both determine the likelihood of erroneous selection of areas within a class where certain species do not occur. Common species were represented better than random only at high classification strength values (>0.5), while rare species never did. Finer classifications tended to be better surrogates although, when rare species were incorporated, the proportion of species that achieved the target level never exceeded 68%, even for the finest classification. This compromises the suitability of coarse-filter surrogates in areas where biodiversity is patchily distributed or with many rare species. We recommend using composite data sets containing environmental classes and biological data when a high effectiveness for all the species cannot be achieved. (C) 2012 Elsevier Ltd. All rights reserved.