Using indicator species to predict species richness of multiple taxonomic groups
Using indicator species to predict species richness of multiple taxonomic groups
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DOI：
10.1111/j.1523-1739.2005.00168.x
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发表时间：
2005-08-01
影响因子：
6.3
通讯作者：
Fay, JP
中科院分区：
文献类型：
--
作者：
Fleishman, E;Thomson, JR;Fay, JP
物种丰富度的价值被广泛用于确定保护和管理的优先事项。由于库存数据，资金和时间有限，使用替代品，如“指示”物种来估计物种丰富度已变得普遍。确定可能可靠地预测物种丰富度的指标物种，特别是跨分类群体的指标物种，仍然是一个相当大的挑战。我们使用遗传算法和贝叶斯方法来解释个人和两个分类组的组合物种丰富度作为一个功能的指示物种从两个组或一组的发生模式。遗传算法在模仿自然选择的过程中反复筛选大量潜在的模型和预测变量。鸟类物种丰富度和蝴蝶物种丰富度的最佳拟合模型解释了约80%的偏差，仅包括来自同一分类群的指示物种。使用两个分类组的物种作为潜在的预测因子并没有提高模型拟合，但略有改善的简约性（较少的预测因子）的模型的鸟类物种丰富度。最好的组合物种丰富度模型包括五只蝴蝶和一只鸟，解释了83%的偏差，而基于六只蝴蝶作为指标的组合物种丰富度模型解释了82%的偏差。一个基于鸟类的物种丰富度模型解释了72%的偏差。我们发现，一个小的，共同的物种集可以用来分别预测多个分类组的物种丰富度。我们建立的模型解释了大约70%的鸟类和蝴蝶的物种丰富度的偏差的基础上，一组常见的三种鸟类和三种蝴蝶。我们还确定了一组六种蝴蝶，预测了鸟类物种丰富度和蝴蝶物种丰富度的66%以上。我们的方法是适用于任何组合或生态系统，并可能是有用的估计物种丰富度和深入了解影响多样性模式的机制。
Values of species richness are used widely to establish conservation and management priorities. Because inventory data, money, and time are limited, use of surrogates such as "indicator" species to estimate species richness has become common. Identifying sets of indicator species that might reliably predict species richness, especially across taxonomic groups, remains a considerable challenge. We used genetic algorithms and a Bayesian approach to explain individual and combined species richness of two taxonomic groups as a function of occurrence patterns of indicator species drawn from either both groups or one group. Genetic algorithms iteratively screen large numbers of potential models and predictor variables in a process that emulates natural selection. The best fitting models of bird species richness and butterfly species richness explained approximately 80% of deviances and included only indicator species from the same taxonomic group. Using species from both taxonomic groups as potential predictors did not improve model fit but slightly improved the parsimony (fewer predictors) of the model of bird species richness. The best model of combined species richness included five butterflies and one bird and explained 83% of deviance, whereas a model of combined species richness based on six butterflies as indicators explained 82% of deviance. A model of combined species richness based on birds alone explained 72% of deviance. We found that a small, common set of species could be used to predict separately the species richness of multiple taxonomic groups. We built models explaining approximately 70% of the deviance in species richness of birds and butterflies based on a common set of three bird species and three butterfly species. We also identified a set of six species of butterflies that predicted >66% of both bird species richness and butterfly species richness. Our approach is applicable to any assemblage or ecosystem, and may be useful both for estimating species richness and for gaining insight into mechanisms that influence diversity patterns.