A unified framework to identify demographic buffering in natural populations

Abstract

The Demographic Buffering Hypothesis (DBH) predicts that natural selection reduces the temporal fluctuations in demographic processes (such as survival, development, and reproduction), due to their negative impacts on population dynamics. However, a comprehensive approach that allows for the examination of demographic buffering patterns across multiple species is still lacking. Here, we propose a three-step framework aimed at identifying and quantifying demographic buffering. Firstly, we categorize species along a continuum of variance based on their stochastic elasticities. Secondly, we examine the linear selection gradients, followed by the examination of nonlinear selection gradients as the third step. With these three steps, our framework overcomes existing limitations of conventional approaches to identify and quantify demographic buffering, allows for multi-species comparisons, and offers insight into the evolutionary forces that shape demographic buffering. We apply this framework to mammal species and discuss both the advantages and potential of our framework.

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

“…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].…”

Population responses to environmental stochasticity are primarily driven by survival-reproduction trade-offs and mediated by aridity

“…As such, we argue that these life history axes reflect how species have evolved to cope with past environmental stochasticity rather than current or future pressures populations -are likely to -face. On the other hand, these complex responses exemplify the numerous evolutionary processes that can influence temporal variation in vital rates, upon which the demographic buffering hypothesis was developed 22,48 . The existence of a demographic buffering continuum that quantifies the temporal variance in vital rates, as we report here, is a promising tool to generate new insights regarding which and how populations respond to environmental stochasticity.…”

“…with respect to vital rate temporal variance, ∑𝐸 " # , has two areas for improvement. First, the theoretical aspect related to the buffering hypothesis and its alternative hypothesis -the demographic lability hypothesis 19,21,48 -remains poorly understood.…”

“…This gap of knowledge hampers a direct link between to the demographic buffering continuum of variance explored in this study. The demographic buffering and lability hypotheses have been understood in terms of underlying evolutionary mechanisms such as canalising by stabilising selection resulting in buffering, or boosting the temporal variance by disruptive selection in lability 19,21,48 . Ultimately, these evolutionary processes might result in the observed patterns of vital rate variance, from constraints (if buffered) to boosting (if labile), which have been the analytical focus of the attempts to test these hypotheses in recent years 19,27,43 .…”

“…Ultimately, these evolutionary processes might result in the observed patterns of vital rate variance, from constraints (if buffered) to boosting (if labile), which have been the analytical focus of the attempts to test these hypotheses in recent years 19,27,43 . However, such underlying evolutionary mechanisms of buffering and lability have only been inferred, instead of being directly assessed 48 . So far, the only two attempts to explicitly identify and quantify the underlying evolutionary mechanisms behind demographic buffering and lability (nonlinear indexes 22 and second-order derivatives 48 ) have not considered a critical component: the observed temporal variation and its impact on population growth rate.…”