Predicting population change from models based on habitat availability and utilization. Issue 1901 (17th April 2019)

• Record Type: Journal Article
• Authors: Matthiopoulos, Jason; Field, Christopher; MacLeod, Ross
• Is Part Of: Proceedings. Volume 286:Issue 1901(2019)
• Publication Date: 2019-04-17
• Subjects: habitat engineering -- habitat selection -- habitats-to-populations -- house sparrow (Passer domesticus) -- population declines -- population modelling; Biology -- Periodicals; 570.5
• DOI: 10.1098/rspb.2018.2911
• Languages: English
• ISSNs: 0962-8452

Abstract

Abstract : 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.