Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Abstract

Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of these strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environment factors and demographic characteristics are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to quantify the relative effects of three key drivers of demographic buffering: environment components (e.g., temporal autocorrelation and variance), population structure, and demographic rates (e.g., progression and fertility). Using Integral Projection Models, we explore how these drivers impact the demographic buffering abilities of three plant species with different life histories and demonstrate how our approach successfully characterises a population's capacity to demographically buffer against environmental stochasticity in a changing world.

More social species live longer, have higher generation times, and longer reproductive windows

“…Likewise, the reactivity of social species was lower than in gregarious species, suggesting that, when a population is hit by a disturbance, it is less likely to take advantage by increasing its population size [70]. The past decades have witnessed the development of indexes of demographic buffering based on structured population models [120][121][122][123]. Future research should evaluate whether the sociality continuum is intrinsically linked to a potential demographic lability-buffering continuum [124].…”

Current perspectives and future directions in animal life history evolution

“…Over the course of the past 25 years, may studies have explored the impact of demographic rate variances (𝜎 2 : Doak et al, 2005;Engen et al, 1998;Foley, 1994;J. L. McDonald et al, 2017;Morris et al, 2008;Morris & Doak, 2004;Pfister, 1998;Saether, 1997;Saether et al, 1998) with 20 relatively less focus on demographic rate covariances (Compagnoni et al, 2016; and environment autocorrelation (Evers et al, 2023;Gascoigne, Kajin, et al, 2023;Tuljapurkar & Haridas, 2006). Similarly, despite a large body of literature, little focus is given to the relationship between the most important term in Tuljapurkar's approximation, log(𝜆 1 ), and the variance components.…”