The integrity of regional and local biological diversity is under siege as a result of a multiple anthropogenic threats. The conversion of habitats, such as rain forests, into agricultural ecosystems reduces the area available to support species populations. Rising temperatures and altered rainfall patterns lead to additional challenges for species. The ability of conservation biologists to ascertain the threats to a species requires data on changes in distribution, abundance, life history and ecology. The IUCN uses these data to appraise the extinction risk for a species. However, many species remain data deficient or unassessed. Here, I use 14 morphological traits related to locomotor function, habitat and feeding to predict the threat status of over 400 species of lizards in the infraorder Iguania. Morphological traits are an ideal proxy for making inferences about a species' risk of extinction. Patterns of morphological covariation have a known association with habitat use, foraging behavior and physiological performance across multiple taxa. Results from phylogenetic general linear models revealed that limb lengths as well as head characters predicted extinction risk. In addition, I used an artificial neural network technique to generate a classification function based on the morphological traits of species with an assigned IUCN threat status. The network approach identified six morphological traits as predictors of extinction risk, which included head and limb characters. The best supported model had a classification accuracy of 87.4%. Moreover, the ANN model predicted over 18% of data deficient/not assessed species were at risk of extinction. The predicted assessments were supported by other sources of threat status, e.g., CITES appendices. Because of the functional link between morphology, performance, and ecology, an ecomorphological approach may be a useful tool for rapid assessment of data deficient or poorly known species.

中文翻译：

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形态学可以预测鬣蜥蜥蜴的保护状况吗？

由于多重人为威胁，区域和地方生物多样性的完整性受到围攻。栖息地（例如雨林）转变为农业生态系统会减少可用于支持物种种群的面积。气温上升和降雨模式的改变给物种带来了额外的挑战。保护生物学家确定物种面临威胁的能力需要有关分布、丰度、生活史和生态变化的数据。IUCN 使用这些数据来评估一个物种的灭绝风险。然而，许多物种仍然缺乏数据或未经评估。在这里，我使用与运动功能、栖息地和摄食相关的 14 种形态特征来预测亚纲鬣蜥中 400 多种蜥蜴的威胁状况。形态特征是推断物种灭绝风险的理想代表。形态协变的模式与栖息地使用、觅食行为和多个分类群的生理表现有已知的关联。系统发育一般线性模型的结果表明，肢体长度和头部特征预测了灭绝风险。此外，我使用人工神经网络技术根据具有指定 IUCN 威胁状态的物种的形态特征生成分类函数。网络方法确定了六个形态特征作为灭绝风险的预测因子，其中包括头部和四肢特征。最佳支持模型的分类准确率为 87.4%。而且，ANN 模型预测，超过 18% 的数据不足/未评估物种面临灭绝风险。预测的评估得到了其他威胁状态来源的支持，例如 CITES 附录。由于形态、性能和生态之间的功能联系，生态形态方法可能是快速评估数据不足或鲜为人知的物种的有用工具。