Clade diversification dynamics and the biotic and abiotic controls of speciation and extinction rates

Abstract

How ecological interactions, genetic processes, and environmental variability jointly shape the evolution of species diversity remains a challenging problem in biology. We developed an individual-based model of clade diversification to predict macroevolutionary dynamics when resource competition, genetic differentiation, and landscape fluctuations interact. Diversification begins with a phase of geographic adaptive radiation. Extinction rates rise sharply at the onset of the next phase. In this phase of niche self-structuring, speciation and extinction processes, albeit driven by biotic mechanisms (competition and hybridization), have essentially constant rates, determined primarily by the abiotic pace of landscape dynamics. The final phase of diversification begins when intense competition prevents dispersing individuals from establishing new populations. Species’ ranges shrink, causing negative diversity-dependence of speciation rates. These results show how ecological and microevolutionary processes shape macroevolutionary dynamics and rates; they caution against the notion of ecological limits to diversity, and suggest new directions for the phylogenetic analysis of diversification.

Understanding the effect of competition during evolutionary radiations: an integrated model of phenotypic and species diversification

“…Our results suggest that increasing extinction rate could be another mark of adaptive radiations (as proposed in previous studies, e.g. McPeek ; Rabosky & Lovette ; Aguilée et al ), but that this signal is not picked up by common likelihood‐based models of diversification.…”

“…As with any modelling approach, we made a series of simplifying assumptions that may impact our results. In particular, population‐level dynamics or landscape features may affect macroevolutionary outcomes (Aguilée et al ). Nonetheless, we were able to investigate in a formal framework some fundamental ideas on adaptive radiations that clarify the macroevolutionary consequences of competition and will hopefully help design more precise and powerful tools to investigate them.…”

“…Slatkin, ) or adaptive dynamics (e.g. Doebeli & Ispolatov ), which have the advantage of being based on first principles of drift, fitness, and response to selection, but generally do not make predictions that can be confronted to comparative phylogenetic data (but see Aguilée et al ).…”

Macroecology in the age of Big Data – Where to go from here?

“…Brown, 1999), and the continuing diminution of computational limitations has made it possible to include various processes into macroecological analyses. These processes include, for example, physiology-related mechanisms (Kearney & Porter, 2004), microevolutionary dynamics of populations via explicit simulation of the genetic architecture of phenotypes (Schiffers et al, 2014), metapopulation dynamics via explicit simulation of dispersal and local demography across changing environment in distribution models (Juliano S Cabral & Schurr, 2010;Zurell et al, 2016), metacommunity dynamics via inclusion of resource competition and other biotic interactions (Juliano Sarmento Cabral & Kreft, 2012;Münkemüller et al, 2012), macroevolutionary processes (Aguilée, Gascuel, Lambert, & Ferriere, 2018;Cabral, Wiegand, & Kreft, 2019;Jõks & Pärtel, 2018;Rangel et al, 2018) and plate tectonics (Descombes et al, 2018;Leprieur et al, 2016). The current trend in mechanistic macroecology is to include the manifold processes into an integrative modelling framework (Cabral, Valente, & Hartig, 2017;Leidinger & Cabral, 2017;Methorst, Böhning-Gaese, Khaliq, & Hof, 2017;Pontarp et al, 2018;Thuiller et al, 2013;Urban et al, 2016).…”

Temporal environmental variation imposes differential selection on genomic and ecological traits of virtual plant communities

“…Confounding effects, such as land-255 scape configuration, different temporal dynamics, complex dispersal behavior 256 and macro-evolutionary processes (e.g. clade diversification) have been studied 257 elsewhere and were thus not included in the present study (Münkemüller et al 258 2012, Kubisch et al 2014, Aguilée et al 2018. Table 1 contains the parameters 259 which were varied for the scenarios, their meaning and their values.…”