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Selectin areas for species persistence using occurrence data

机译:使用发生数据选择物种持久性区域

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Quantitative area selection methods seek to maximise the amount of biodiversity represented in networks of areas for conservation. However, because criteria for persistence are usually not incorporated, there is no guarantee against choosing areas where species have low probabilities of persistence. Here, we propose one framework for introducing criteria for persistence into quantitative area-selection methods when dealing with atlas data for large numbers of species. The framework includes three steps: (1) fit models explaining current occurrence of species; (2) transform current probabilities of occurrence into estimatas of persistence using available information on expected threats and species' vulnerability; and (3) select complementary areasto ensure high estimates of persistence for each species. This paper provides an example using coarse-scale data for European trees, without threat data. Three approaches for modelling species probabilities of occurrence are compared:first, by considering occurrence in relation to environmental variation; second, by considering occurrence in relation to patterns of geographical aggregation or contagion among records;and third, by combining these two components. The third model fits the original data most closely, but field assessments of persistence estimates are needed. As expected, introducing additional constraints into area selection reduces the flexibility (fewer alternative sets of areas) and increases the cost (more areas needed to achieve thegoal). However, the proposed method increases the overall expected probability of persistenceo for the species. This benefit is greatest among the species with the most restricted ranges, which are the species of greatest conservation concern.
机译:定量区域选择方法力求使保护区网络中代表的生物多样性最大化。但是,由于通常不包含持久性标准,因此不能保证不能选择物种持久性较低的区域。在这里,我们提出了一个框架,用于在处理大量物种的地图集数据时将持久性标准引入定量区域选择方法。该框架包括三个步骤:(1)解释当前物种发生的拟合模型; (2)使用有关预期威胁和物种脆弱性的可用信息,将当前的发生概率转化为持久性估计; (3)选择互补区域,以确保对每个物种的持久性进行高估。本文提供了一个使用欧洲树木的粗尺度数据而没有威胁数据的示例。比较了三种对物种发生概率建模的方法:第一,考虑与环境变化有关的发生;第二,考虑与记录之间的地理聚集或传播模式有关的发生;第三,通过结合这两个组成部分。第三种模型最适合原始数据,但是需要对持久性估计值进行现场评估。不出所料,在区域选择中引入其他限制会降低灵活性(减少其他区域设置)并增加成本(需要更多的区域来实现目标)。但是,提出的方法增加了该物种持久性的总体预期概率。在保护范围最广的物种中,这种益处最大。

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