Assessing seasonal demographic covariation to understand environmental‐change impacts on a hibernating mammal. (23rd January 2020)

• Record Type: Journal Article
• Authors: Paniw, Maria; Childs, Dylan Z.; Armitage, Kenneth B.; Blumstein, Daniel T.; Martin, Julien G. A.; Oli, Madan K.; Ozgul, Arpat
• Editors: Munch, Stephan
• Is Part Of: Ecology letters. Volume 23:Number 4(2020)
• Page Start: 588
• Page End: 597
• Publication Date: 2020-01-23
• Subjects: Bayesian population model -- carry‐over effects -- demography -- factor‐analytic models -- seasonal structured population models -- viability; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.13459
• Languages: English
• ISSNs: 1461-023X

Abstract

Abstract: Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development and reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor‐analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual‐based demography from yellow‐bellied marmots ( Marmota flaviventer ) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneously, negative responses of winter survival and reproductive‐status change to declining environmental quality result in a higher risk of population quasi‐extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments. Abstract : The importance of seasonal processes determining the persistence of natural populations is increasingly recognised, but accounting for the numerous drivers of these processes is a significant challenge. Here, we apply a novel latent‐variable approach to circumvent this challenge in seasonal population models for yellow‐bellied marmots. The latent‐variable approach allows us to capture complex, partially unobserved drivers of marmot population dynamics into a univariate measure of environmental quality. We show that even in a winter‐adapted mammal, demographic responses to environmental quality in the inactive winter season determine population‐level effects of environmental change.