Evolutionary simulations clarify and reconcile biodiversity-disturbance models

Abstract

There is significant geographic variation in species richness. However, the nature of the underlying relationships, such as that between species richness and environmental stability, remains unclear. The stability-time hypothesis suggests that environmental instability reduces species richness by suppressing speciation and increasing extinction risk. By contrast, the patch-mosaic hypothesis suggests that small-scale environmental instability can increase species richness by providing a steady supply of non-equilibrium environments. Although these hypotheses are often applied to different time scales, their core mechanisms are in conflict. Reconciling these apparently competing hypotheses is key to understanding how environmental conditions shape the distribution of biodiversity. Here, we use REvoSim, an individual-based, eco-evolutionary system, to model the evolution of sessile organisms in environments with varying magnitudes and scales of environmental instability. We demonstrate that when environments have substantial permanent heterogeneity, a high level of localized environmental instability reduces biodiversity, whereas in environments lacking permanent heterogeneity, high levels of localized instability increase biodiversity. By contrast, broad-scale environmental instability, acting on the same time scale, invariably reduces biodiversity. Our results provide a new view of the biodiversity–disturbance relationship that reconciles contrasting hypotheses within a single model and implies constraints on the environmental conditions under which those hypotheses apply. These constraints can inform attempts to conserve adaptive potential in different environments during the current biodiversity crisis.

Perturbation responses in co‐evolved model meta‐communities

“…Range (Furness et al, 2021), this model would therefore be most suited to describing communities of sessile species in aquatic food webs that slowly disperse across neighboring environments, or communities of low-range vegetation and insect populations.…”

“…Therefore, this model currently could not represent far‐ranging top predator species or migratory birds. Like other meta‐population and individual‐based studies (Furness et al, 2021 ), this model would therefore be most suited to describing communities of sessile species in aquatic food webs that slowly disperse across neighboring environments, or communities of low‐range vegetation and insect populations. Allometric size–structure, as implemented in this model, is also observed in both aquatic and soil food webs (Berg et al, 2015 ).…”

“…To complement these, simulation models have been developed to investigate the impact of habitat loss on target species (Michael Reed et al, 2002), and have successfully generated qualitative predictions of the impact of habitat loss-for example, on white-footed mice (Burns & Grear, 2008), provided that accurate assumptions about species re-settlement behavior are incorporated. However, studies of habitat loss on model metacommunities usually feature only a few (2-4) species in pre-defined relationships (Bascompte & Solé, 1998;Liao et al, 2017;Melián & Bascompte, 2002;Moilanen & Hanski, 1995;Nee & May, 1992), or use alternative individual-based approaches (Furness et al, 2021). A practical application of spatial stability modeling for conservation and management is the question of optimal placement of reserves, but reserve decision models typically lack the multi-species population dynamics that may influence optimal site selection (Williams et al, 2005).…”

palaeoverse: a community-driven R package to support palaeobiological analysis

“…The growth and increasing availability of such datasets has made coding an integral part of palaeobiological research. Today, palaeobiologists commonly use code to clean (Zizka et al, 2019;, analyse (Guillerme, 2018;Kocsis et al, 2019), and visualise data (Bell and Lloyd, 2015), as well as build models (Silvestro, Salamin and Schnitzler, 2014;Starrfelt and Liow, 2016) and implement simulations (Fraser, 2017;Barido-Sottani et al, 2019;Furness et al, 2021;Jones et al, 2021). Whilst software has been developed in languages such as C++ (Garwood, Spencer and Sutton, 2019) and Python (Silvestro et al, 2014), the programming language R is currently the most popular in palaeobiology.…”

Productivity, niche availability, species richness, and extinction risk: Untangling relationships using individual‐based simulations

“…Here, we present a series of experiments that evaluate the predictions of the MIH as presented in Storch et al (2018) within a digital, individual-based eco-evolutionary system, REvoSim (Garwood et al, 2019). Simulation studies are becoming increasingly influential in ecology because of their ability to disentangle tightly correlated variables in a way that observational studies cannot (Furness et al, 2021;Pontarp et al, 2019;Pontarp & Wiens, 2016;Saupe et al, 2019;Zurell et al, 2010). However, unlike many previous simulation studies (Gotelli et al, 2009;Münkemüller & Gallien, 2015;van der Plas et al, 2015;Rangel et al, 2018;Ruffley et al, 2019), REvoSim works at the level of the individual, which has the benefit of removing otherwise necessary assumptions about species-level processes (Pontarp & Wiens, 2016).…”