Evolution, Ecology and Organismal Biology Department, University of California-Riverside, 900 University Avenue, Riverside, CA 92521, U.S.A.
College of Biological Sciences, University of Minnesota, 315 Ecology Building, 1987 Upper Buford Circle, St. Paul, MN 55108, U.S.A.
Conserv Biol. 2018 Aug;32(4):916-925. doi: 10.1111/cobi.13081. Epub 2018 Apr 16.
The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.
世界自然保护联盟 (IUCN) 红色名录类别和标准是一种根据灭绝风险对物种进行分类的定量框架。可以使用种群模型来估计灭绝风险或种群下降。在威胁分类中，通过经验数据的测量和过程误差以及用于估计灭绝风险和种群下降的模型的不确定性，会出现不确定性和可变性。此外，物种特征已知会影响灭绝风险。我们研究了测量和过程误差、模型类型、种群增长率和首次繁殖年龄对基于预测种群下降的 IUCN 红色名录分类的可靠性的影响。我们使用一个年龄结构的种群模型来模拟具有不同增长率、繁殖年龄和变异水平的真实种群轨迹，并对其进行测量误差。我们评估了这些模拟时间序列参数化的标量和矩阵模型准确捕捉基于真实种群下降的 IUCN 红色名录分类的能力。在测试的所有测量误差水平和低过程误差下，分类都相当准确；标量和矩阵模型产生的错误分类大致相同，但误差分布不同；矩阵模型导致对灭绝风险的高估大于低估；过程误差比测量误差更倾向于导致错误分类；对于快速而不是缓慢的生活史，错误分类更多。这些结果表明，当使用种群模型评估时，根据标准 A 对受威胁程度高的类群（即增长率低的类群）的分类比对受威胁程度低的类群的分类更可靠。需要更仔细地审查用于对增长率较高的物种进行种群模型参数化的数据，特别是在有证据表明可能向更高风险类别过渡时。
