Department of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy.
Departamento de Biología Vegetal y Ecología, Universidad de Sevilla, Sevilla, Spain.
Conserv Biol. 2024 Dec;38(6):e14316. doi: 10.1111/cobi.14316. Epub 2024 Jul 1.
Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land-use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species' range size and spatial configuration of the range, biological traits, climate change, and land-use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models' uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up-to-date IUCN RL.
基于国际自然保护联盟 (IUCN) 红色名录 (RL) 评估物种的灭绝风险是指导保护政策和减少生物多样性丧失的关键。然而，这个过程需要大量资源，并且需要不断更新，随着新物种被添加到 RL 中，更新变得越来越困难。自动方法，例如用于预测物种 RL 类别的比较分析，可以作为保持评估最新的有效替代方法。我们选择两栖动物作为研究群体，预测哪些物种更有可能改变其 RL 类别，从而应优先进行重新评估。我们使用物种的生物特征、环境变量以及气候和土地利用变化的代理作为 RL 类别的预测因子。我们通过结合 4 种不同的模型算法：累积链接模型、系统发育广义最小二乘法、随机森林和神经网络，为每个物种生成了 IUCN RL 类别的综合预测。通过将 RL 类别与综合预测进行比较，并考虑模型算法之间的不确定性，我们根据预测值和观察值之间的不匹配，确定了应优先进行未来重新评估的物种。跨模型最重要的预测变量是物种的范围大小和范围的空间配置、生物特征、气候变化和土地利用变化。我们将我们提出的优先级指数和 RL 类别变化的预测与独立的 IUCN RL 重新评估进行了比较，发现这两种方法的优先级和 RL 类别变化的预测方向都具有很高的性能。RL 类别综合建模是一种有前途的工具，可用于在考虑模型不确定性的情况下优先对物种进行重新评估。这种方法广泛适用于 IUCN RL 上的所有分类群以及区域和国家评估，并可能改善用于维持最新 IUCN RL 的有限人力和经济资源的分配。
