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Use of population viability analysis and reserve selection algorithms in regional conservation plans

机译:人口生存力分析和保护区选择算法在区域保护计划中的使用

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Current reserve selection algorithms have difficulty evaluating connectivity and other factors necessary to conserve wide-ranging species in developing landscapes. Conversely, population viability analyses may incorporate detailed demographic data, but often lack sufficient spatial detail or are limited to too few taxa to be relevant to regional conservation plans. We developed a regional conservation plan for mammalian carnivores in the Rocky Mountain region using both a reserve selection algorithm (SITES) and, a spatially explicit population model (PATCH). The spatially explicit population model informed reserve selection and network design by producing data on the locations of population sources, the degree of threat to those areas from landscape change, the existence of thresholds to population viability as the size of the reserve network increased, and the effect of linkage areas on population persistence. A 15% regional decline in carrying capacity for large carnivores was predicted within 25 years if no addition to protected areas occurred. Increasing the percentage of the region in reserves from the current 17.2% to 36.4% would result in a 1-4% increase over current carrying capacity, despite the effects of landscape change. The population model identified linkage areas that were not chosen by the reserve selection algorithm, but whose protection strongly affected population viability. A reserve network based on carnivore conservation goals incidentally protected 76% of ecosystem types, but was poor at capturing localized rare species. Although it is unlikely that planning for focal species requirements alone will capture all facets of biodiversity, when used in combination with other planning foci, it may help to forestall the effects of loss of connectivity on a larger group of threatened species and ecosystems. A better integration of current reserve selection tools and spatial simulation models should produce reserve designs that are simultaneously biologically realistic and taxonomically inclusive. [References: 52]
机译:当前的保护区选择算法难以评估连通性和在发展中的景观中保护广泛物种所需的其他因素。相反,人口生存力分析可能会纳入详细的人口统计数据,但通常缺乏足够的空间细节,或者仅限于分类单元太少而无法与区域保护计划相关。我们使用保护区选择算法(SITES)和空间显性种群模型(PATCH)为落基山地区的哺乳动物肉食动物制定了区域保护计划。空间明确的人口模型通过产生以下数据来指导保护区选择和网络设计:人口源的位置,景观变化对这些地区的威胁程度,随着保护区网络规模的增大而存在的人口生存能力阈值,以及联系区域对人口持久性的影响。如果没有增加保护区,则预计大型食肉动物的区域承载能力将在25年内下降15%。尽管有景观变化的影响,但该地区的储量百分比从目前的17.2%增加到36.4%,将导致当前承载力增加1-4%。种群模型确定的联系区域未被储备金选择算法选择,但是其保护强烈影响了种群的生存能力。基于食肉动物保护目标的保护区网络偶然保护了76%的生态系统类型,但捕获本地稀有物种的能力较弱。尽管仅针对重点物种的需求进行规划不太可能涵盖生物多样性的所有方面,但与其他规划重点结合使用时,它可能有助于防止失去连通性对一大批受威胁物种和生态系统的影响。当前储量选择工具和空间模拟模型的更好集成应该产生同时具有生物学现实性和生物分类学意义的储量设计。 [参考:52]

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