1 Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow , Room 312, Graham Kerr Building, Glasgow G12 8QQ , UK.
2 School of Natural Sciences and Psychology, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
Proc Biol Sci. 2019 Apr 24;286(1901):20182911. doi: 10.1098/rspb.2018.2911.
The need to understand the impacts of land management for conservation, agriculture and disease prevention are driving demand for new predictive ecology approaches that can reliably forecast future changes in population size. Currently, although the link between habitat composition and animal population dynamics is undisputed, its function has not been quantified in a way that enables accurate prediction of population change in nature. Here, using 12 house sparrow colonies as a proof-of-concept, we apply recent theoretical advances to predict population growth or decline from detailed data on habitat composition and habitat selection. We show, for the first time, that statistical population models using derived covariates constructed from parametric descriptions of habitat composition and habitat selection can explain an impressive 92% of observed population variation. More importantly, they provide excellent predictive power under cross-validation, anticipating 81% of variability in population change. These models may be embedded in readily available generalized linear modelling frameworks, allowing their rapid application to field systems. Furthermore, we use optimization on our sample of sparrow colonies to demonstrate how such models, linking populations to their habitats, permit the design of practical and environmentally sound habitat manipulations for managing populations.
为了理解土地管理对保护、农业和疾病预防的影响，人们对新的预测生态学方法的需求不断增长，这些方法可以可靠地预测未来种群规模的变化。目前，尽管栖息地组成与动物种群动态之间的联系是无可争议的，但它的功能尚未以一种能够准确预测自然界中种群变化的方式进行量化。在这里，我们使用 12 个麻雀种群作为概念验证，应用最近的理论进展，根据栖息地组成和栖息地选择的详细数据来预测种群的增长或减少。我们首次表明，使用参数化描述的衍生协变量构建的统计种群模型可以解释令人印象深刻的 92%的观察到的种群变化。更重要的是，它们在交叉验证下提供了出色的预测能力，预测了 81%的种群变化的可变性。这些模型可以嵌入到现成的广义线性建模框架中，允许它们快速应用于野外系统。此外，我们使用麻雀种群的样本进行优化，以证明这些将种群与其栖息地联系起来的模型如何允许设计实用且对环境无害的栖息地操作，以管理种群。
