Cumulative weather effects can impact across the whole life cycle. (25th July 2019)

• Record Type: Journal Article
• Authors: Hindle, Bethan J.; Pilkington, Jill G.; Pemberton, Josephine M.; Childs, Dylan Z.
• Is Part Of: Global change biology. Volume 25:Number 10(2019)
• Page Start: 3282
• Page End: 3293
• Publication Date: 2019-07-25
• Subjects: climate -- covariation -- density dependence -- environmental variation -- functional linear model -- North Atlantic Oscillation -- structural equation model -- survival; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.14742
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Predicting how species will be affected by future climatic change requires the underlying environmental drivers to be identified. As vital rates vary over the lifecycle, structured population models derived from statistical environment–demography relationships are often used to inform such predictions. Environmental drivers are typically identified independently for different vital rates and demographic classes. However, these rates often exhibit positive temporal covariance, suggesting that vital rates respond to common environmental drivers. Additionally, models often only incorporate average weather conditions during a single, a priori chosen time window (e.g. monthly means). Mismatches between these windows and the period when the vital rates are sensitive to variation in climate decrease the predictive performance of such approaches. We used a demographic structural equation model (SEM) to demonstrate that a single axis of environmental variation drives the majority of the (co)variation in survival, reproduction, and twinning across six age–sex classes in a Soay sheep population. This axis provides a simple target for the complex task of identifying the drivers of vital rate variation. We used functional linear models (FLMs) to determine the critical windows of three local climatic drivers, allowing the magnitude and direction of the climate effects to differ over time. Previously unidentified lagged climatic effects were detected in this well‐studied population. The FLMs had a better predictive performance than selecting a critical window a priori, but not than a large‐scale climate index. Positive covariance amongst vital rates and temporal variation in the effects of environmental drivers are common, suggesting our SEM–FLM approach is a widely applicable tool for exploring the joint responses of vital rates to environmental change. Abstract : Many populations face large changes in seasonal climate, yet the demographic mechanisms that mediate the impact of these changes on population dynamics remain largely unknown. We demonstrate a widely applicable method to facilitate better understanding of the mechanisms through which climatic variables drive population responses. In a well‐studied mammal population we found that a single axis accounts for most of the (co)variation in survival and reproduction and when we attribute seasonal impacts of climatic variables to this axis we find that the direction and magnitude of their effects changes over the course of a year.