Cumulative weather effects can impact across the whole life cycle

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.

Lagged and dormant season climate better predict plant vital rates than climate during the growing season

“…This topic is increasingly salient because climate change is expected to alter dramatically population dynamics of many species, which is key for predicting local extinction risk and species' range shifts (Bellard et al, 2012;Kelly & Goulden, 2008;Urban, 2015). In the last decades, ecologists have been working toward understanding (Harper & White, 1971;Hindle et al, 2019;Sarukhan, 1974) and, more recently, forecasting the effects of climate on population dynamics (Iler et al, 2019;Urban et al, 2016).…”

“…This topic is increasingly salient because climate change is expected to alter dramatically population dynamics of many species, which is key for predicting local extinction risk and species' range shifts (Bellard et al, 2012; Kelly & Goulden, 2008; Urban, 2015). In the last decades, ecologists have been working toward understanding (Harper & White, 1971; Hindle et al, 2019; Sarukhan, 1974) and, more recently, forecasting the effects of climate on population dynamics (Iler et al, 2019; Urban et al, 2016). Models that link climate to biological processes such as population dynamics (Merow et al, 2014; Pagel & Schurr, 2012) have higher predictive ability in novel climates than those based on species occupancy, such as species distribution models (Zurell et al, 2016).…”

Assessing seasonal demographic covariation to understand environmental‐change impacts on a hibernating mammal

“…One important pathway through which environmental change can act on population dynamics is through seasonal direct and carry‐over effects on survival, development and reproduction (Harrison et al, ; Paniw et al, ). These effects, however, are often cryptic and therefore difficult to quantify in ecological models (Hindle et al, ). We use a novel, factor‐analytic approach to efficiently quantify partially unobserved environment–demography relationships.…”

“…Predation is however cryptic in the system (Van Vuren, ). Capturing the effects of unobserved environmental variation, including predation, the latent‐variable approach appears to be a promising alternative to modelling seasonal demographic processes under limited knowledge of their drivers (Evans & Holsinger, ; Hindle et al, ; Hindle et al, ). We note that this approach may find limited applications in cases where environment–demography relationships are more complex than in the yellow‐bellied marmots and include negative demographic covariation (e.g.…”

Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal

“…Predation is however cryptic in the system (Van Vuren, 2001). Capturing the effects of unobserved environmental variation, including predation, the latent-variable approach appears to be a promising alternative to modeling seasonal demographic processes under limited knowledge of their drivers (Evans & Holsinger, 2012; Hindle et al, 2019; Hindle et al, 2018). We note that this approach may find limited applications in cases where environment-demography relationships are more complex than in the yellow-bellied marmots and include negative demographic covariation (e.g., due to opposing environmental effects on demographic rates or tradeoffs between these rates).…”