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Climate change vulnerability for species-Assessing the assessments

机译:物种的气候变化脆弱性 - 评估评估

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摘要

Climate change vulnerability assessments are commonly used to identify species at risk from global climate change, but the wide range of methodologies available makes it difficult for end users, such as conservation practitioners or policymakers, to decide which method to use as a basis for decision-making. In this study, we evaluate whether different assessments consistently assign species to the same risk categories and whether any of the existing methodologies perform well at identifying climate-threatened species. We compare the outputs of 12 climate change vulnerability assessment methodologies, using both real and simulated species, and validate the methods using historic data for British birds and butterflies (i.e. using historical data to assign risks and more recent data for validation). Our results show that the different vulnerability assessment methods are not consistent with one another; different risk categories are assigned for both the real and simulated sets of species. Validation of the different vulnerability assessments suggests that methods incorporating historic trend data into the assessment perform best at predicting distribution trends in subsequent time periods. This study demonstrates that climate change vulnerability assessments should not be used interchangeably due to the poor overall agreement between methods when considering the same species. The results of our validation provide more support for the use of trend-based rather than purely trait-based approaches, although further validation will be required as data become available.
机译:气候变化脆弱性评估通常用于识别有可能从全球气候变化的风险的物种,但可用的各种方法使得最终用户(例如保护从业者或政策制定者)​​难以确定哪种方法作为决策的基础 - 制作。在这项研究中,我们评估不同的评估是否一致地将物种分配到相同的风险类别以及任何现有方法是否在识别威胁到气候威胁的物种时表现良好。我们使用真实和模拟物种比较12个气候变化漏洞评估方法的输出,并使用英国鸟类和蝴蝶的历史数据验证方法(即使用历史数据来分配风险和更新的验证数据)。我们的研究结果表明,不同的漏洞评估方法彼此不一致;为真实和模拟的物种集分配不同的风险类别。对不同漏洞评估的验证表明,将历史趋势数据纳入评估的方法最佳地在后续时间段预测分布趋势。本研究表明,由于在考虑相同物种时,由于方法之间的总体协议差,气候变化脆弱性评估不应互换。我们的验证结果提供了更多支持对基于趋势而不是基于特质的方法,尽管将需要进一步验证,因为数据可用。

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