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An approach for regional threat assessment under IUCN Red List criteria that is robust to uncertainty: The Fiordland bottlenose dolphins are critically endangered

机译:根据世界自然保护联盟红色名录标准进行区域威胁评估的方法,该方法可应对不确定性:峡湾宽吻海豚极度濒危

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Numerous globally abundant species are exposed to human impacts that threaten the viability of regional populations. Assessing and characterising the risks faced by these populations can have significant implications for biodiversity conservation, given the ecological importance of many such species. To address these risks, the IUCN is starting to conduct assessments of regional populations in addition to species-level assessments of conservation status. Here, we demonstrate a threat assessment process that is robust to uncertainty, applying the IUCN criteria to a regional population of bottlenose dolphins in Fiordland, New Zealand. We compiled available population-specific information to assess the population under the five Red List criteria. We estimated there were 205 Fiordland bottlenose dolphins (CV=3.5%), using current estimates of abundance for two sub-populations and stochastic modelling of an earlier estimate for the third sub-population. Population trajectory and extinction risk were assessed using stochastic age-structured Leslie matrix population models. The majority of model runs met the criteria for classification as critically endangered (C1: 67.6% of runs) given the number of mature individuals (123; CV=6.7%) and the predicted rate of population decline (average decline: 31.4% over one generation). The evidence of isolation of the population confirms this was an appropriate regional classification. This approach provided an assessment that was robust to uncertainty.
机译:许多全球丰富的物种都受到人类的威胁,威胁到区域人口的生存能力。鉴于许多此类物种的生态重要性,评估和表征这些种群面临的风险可能会对生物多样性保护产生重大影响。为了应对这些风险,自然保护联盟除了物种一级的保护状况评估之外,还开始对区域人口进行评估。在这里,我们展示了对不确定性具有鲁棒性的威胁评估过程,将IUCN标准应用于新西兰Fiordland的区域宽吻海豚种群。我们汇总了可用的特定人群信息,以根据五项红色清单标准评估了人群。我们使用两个亚种群的当前丰度估计和第三亚种群的较早估计的随机模型,估计有205个Fiordland宽吻海豚(CV = 3.5%)。使用随机年龄结构的莱斯利矩阵人口模型评估了人口轨迹和灭绝风险。考虑到成年个体的数量(123; CV = 6.7%)和预测的人口下降速度(平均下降:31.4%以上),大多数模型运行符合分类为极度濒危的标准(C1:运行的67.6%)。代)。人口隔离的证据证实这是一个适当的区域分类。这种方法提供了一种对不确定性具有鲁棒性的评估。

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