What processes must we understand to forecast regional-scale population dynamics?

Abstract

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.

Climate biogeography ofArabidopsis thaliana:linking distribution models and individual variation

“…flowering time) changes (Lovell et al, 2013; Martínez-Berdeja et al, 2020), though the environmental mechanisms of selection are less clear. However, it is challenging to determine how environment shapes individual performance and life history variation across the ranges of broadly distributed species, as well as the consequences for distributions (Lasky et al, 2020).…”

“…Environment-distribution relationships fundamentally arise from processes acting on individuals (Clark, 2010). In general, transplant experiments show that individual performance tends to decline outside species’ natural geographic range (Hargreaves et al, 2014) and efforts to integrate information at the individual level into distribution models are emerging (Buckley et al, 2011; Elith et al, 2010; Lasky et al, 2020; Merow et al, 2014; Samis & Eckert, 2007). For example, in the perennial herb Lathyrus, Greiser et al (2020) compared demographic models of performance in dozens of common gardens to distribution model predictions across a landscape.…”

The interplay between hunting rate, hunting selectivity, and reproductive strategies shapes population dynamics of a large carnivore

“…As the human impact on wild species is increasing, it is critical to predict the consequence of those changes on population dynamics and evaluate the effects of novel selective pressures on the fate of populations (Lasky et al, 2020 ). Models that include both human‐driven extrinsic mortality, as well as phenotypic trait changes, are therefore useful to explore the effect of multi‐trait life‐history changes in natural populations.…”

Genomic reaction norms inform predictions of plastic and adaptive responses to climate change

“…As the effects of climate change are expected to vary at local spatial scales (IPCC, 2018), the capacity of populations for plastic and adaptive responses must also be assessed locally (Lasky et al, 2020).…”

“…Much attention is focused on predicting these outcomes (e.g. Bay et al, 2017; Brito‐Morales et al, 2018; Lasky et al, 2020; Waldvogel et al, 2020), with the intent of informing effective conservation and management strategies while also highlighting emerging resources for potential sustainable exploitation.…”