Simulating the effects of using different types of species distribution data in reserve selection
Simulating the effects of using different types of species distribution data in reserve selection
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DOI：
10.1016/j.biocon.2009.11.010
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发表时间：
2010-02-01
影响因子：
5.9
通讯作者：
Possingham, Hugh P.
中科院分区：
文献类型：
--
作者：
Carvalho, Silvia B.;Brito, Jose C.;Possingham, Hugh P.
在完美的世界中，系统的保护规划将使用有关生物多样性分布的完整信息。然而，有关大多数物种的信息非常不完整。保护规划中经常使用两种主要类型的分布数据：观测分布数据和预测分布数据。规划者面临的一个基本问题是——在什么情况下哪种数据更好？我们使用模拟程序来分析在使用不同储备选择问题、已知物种分布数量的场景中，使用不同类型的分布数据对储备选择算法性能的影响。保护目标和成本。为了比较这些场景，我们使用了伊比利亚半岛 25 种两栖动物和 41 种爬行动物的发生数据，并假设可用数据代表了全部事实。然后，我们对这些数据的一部分进行采样，并按原样使用它们，或者将它们转换为建模的预测分布。这使我们能够构建保护规划中常用的其他三种类型的物种分布数据集：“预测”、“转换预测”和“混合”。我们的结果表明，保护区选择绩效对所使用的物种分布数据的类型敏感，并且最具成本效益的决策主要取决于保护区选择问题以及我们拥有多少物种分布数据。选择最合适的分布数据类型应该从评估场景环境开始。虽然没有适合每种情况的最佳方法，但我们发现使用混合方法通常可以在物种代表性和成本之间提供可接受的折衷方案。 (C) 2009 Elsevier Ltd. 保留所有权利。
In a perfect world, systematic conservation planning would use complete information on the distribution of biodiversity. However, information on most species is grossly incomplete. Two main types of distribution data are frequently used in conservation planning: observed and predicted distribution data. A fundamental question that planners face is - which kind of data is better under what circumstances? We used simulation procedures to analyse the effects of using different types of distribution data on the performance of reserve selection algorithms in scenarios using different reserve selection problems, amounts of species distribution known. conservation targets and costs. To compare these scenarios we used occurrence data from 25 amphibian and 41 reptile species of the Iberian Peninsula and assumed the available data represented the whole truth. We then sampled fractions of these data and either used them as they were, or converted them to modelled predicted distributions. This enabled us to build three other types of species distribution data sets commonly used in conservation planning: "predicted", "transformed predicted" and "mixed". Our results suggest that reserve selection performance is sensitive to the type of species distribution data used and that the most cost-efficient decision depends most on the reserve selection problem and on how much we have of the species distribution data. Choosing the most appropriate type of distribution data should start by evaluating the scenario circumstances. While there is no one best approach for every scenario, we discovered that using a mixed approach usually provides an acceptable compromise between species representation and cost. (C) 2009 Elsevier Ltd. All rights reserved.