Processes of ecometric patterning: modelling functional traits, environments, and clade dynamics in deep time

Abstract

Ecometric patterning is community‐level sorting of functional traits along environmental gradients that arises historically by geographic sorting, trait evolution, and extinction. We developed a stochastic model to explore how ecometric patterns and clade dynamics emerge from microevolutionary processes. Strong selection, high probability of extirpation, and high heritability led to strong ecometric patterning, but high rates of dispersal and weak selection do not. Phylogenetic structuring arose only when selection intensity, dispersal, and extirpation are all high. Ancestry and environmental geography produced historical effects on patterns of trait evolution and local diversity of species, but ecometric patterns appeared to be largely deterministic. Phylogenetic trait correlations and clade sorting appear to arise more easily in changing environments than static ones. Microevolutionary parameters and historical factors both affect ecometric lag time and thus balance between extinction, adaptation, and geographic reorganization as responses to climate change.

Spatial processes and evolutionary models: a critical review

“…Each run was part of a larger computational experiment in which most parameters were held constant at a mid‐level value and one was systematically varied from low to high values (Polly et al . ).…”

“…The impact of spatial selection gradients and speciation are illustrated using runs from the computational model described by Polly et al . (). The model was set in a mid‐latitude island continent with a north–south trending mountain ranges, latitudinal and altitudinal temperature gradients, a rain shadow effect east of the mountains, and four different vegetation biomes (Fig.…”

“…C–F) are a subset of experiments in which key parameters (strength of selection, phenotypic variance, extirpation probability and ancestral starting location) were systematically varied (see Polly et al . for a full description). The runs were selected because they illustrate the range of effects that spatial processes can have on trait evolution.…”

“…This computational model is fully described in Polly et al . () and its Mathematica code and outputs are archived with that paper. The runs were performed on the Karst high‐throughput computing cluster at Indiana University.…”

“…Computational modelling allows control of the parameters for population size, selection, dispersal probability, gene flow and probability of extinction in the local populations of a species (metapopulation) over thousands or millions of generations, either as a single unbranching lineage or as a branching phylogenetic tree (Polly ; Polly et al . ). We will first look at how drift processes that are normally associated with Brownian motion can produce punctuated bursts of evolutionary change driven solely by changes in the geographical range of the evolving species.…”