Interactions between ecological, evolutionary and environmental processes unveil complex dynamics of insular plant diversity

Abstract

Aims Understanding how biodiversity emerges and how it varies in space and time requires integration of the underlying processes that affect biodiversity at different levels of ecological organization. We present BioGEEM (BioGeographical Eco‐Evolutionary Model), a spatially explicit model that integrates theories and processes understood to drive biodiversity dynamics. We investigated the necessary degree of mechanistic complexity by exploring simulation experiments to evaluate the relative roles of the underlying processes across spatio‐temporal scales and ecological levels (e.g. populations, species, communities). Location Hypothetical oceanic islands. Methods BioGEEM is stochastic and grid‐based, and it integrates ecological (metabolic constraints, demography, dispersal and competition), evolutionary (mutation and speciation) and environmental (geo‐climatic dynamics) processes. Plants on oceanic islands served as a model system. We ran the simulations both with all processes on and with selected processes switched off to assess the role of each process from the emergent patterns. Results The full model was able to generate patterns matching empirical evidence and theoretical expectations. Population sizes were largest on young islands, and species, particularly endemics, better filled their potential range on young and old islands due to limited area and reduced competition. Richness peaked at mid‐elevations. The proportion of endemics was highest in old, large and isolated environments within the islands. Species and trait richness showed unimodal temporal trends. Switching off selected processes led to several unrealistic patterns, including the evolution of super‐dominant species, extremely high richness and weakened spatial diversity gradients. Main conclusions The main predictions derived from BioGEEM are: Competition has cross‐scale effects on diversity. Hump‐shaped temporal dynamics can be obtained without speciation. Endemic species seem less susceptible to extinction than native non‐endemic species. Endemism reflects stronger geographical and environmental isolation. Finally, only the integration of all implemented processes generates realistic spatio‐temporal dynamics at population, species, community and assemblage levels.

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

“…Tackling these confounding effects in empirical settings is challenging (Essl et al 2015a), especially due to the experimental complexity required. evolution, environmental change and metacommunity dynamics) should be more easily disentangled in theoretical studies utilizing mechanistic models that can integrate all these mechanisms simultaneously (Schiffers et al 2013, Cabral et al 2019, see also Cabral et al 2017 for a review of such integrative biodiversity models). evolution, environmental change and metacommunity dynamics) should be more easily disentangled in theoretical studies utilizing mechanistic models that can integrate all these mechanisms simultaneously (Schiffers et al 2013, Cabral et al 2019, see also Cabral et al 2017 for a review of such integrative biodiversity models).…”

“…The changes in species richness will then emerge from the interactions of eco-evolutionary processes over time at multiple levels of ecological organization (Cabral et al 2017(Cabral et al , 2019. Quantifying such extinctions and investigating long-term consequences of perturbations has proven challenging, because perturbations are not isolated and occur across various spatial and temporal scales, from local habitat losses to global warming.…”

“…The changes in species richness will then emerge from the interactions of eco-evolutionary processes over time at multiple levels of ecological organization (Cabral et al 2017(Cabral et al , 2019. This new equilibrium is also a community state that depends on how much the perturbation changes environmental conditions and community properties.…”

Assessing predicted isolation effects from the general dynamic model of island biogeography with an eco‐evolutionary model for plants

“…This enables the exploration of how temporal and spatial patterns at population, species, community and entire island assemblage levels emerge from population‐level processes at evolutionary time‐scales. Cabral et al, report that these patterns cannot be realistically generated if any of the integrated processes is switched off and that the emergent temporal trends at the island assemblage level are consistent with most GDM temporal predictions of species richness and biogeographical rates. However, they investigated only one isolation scenario, and it is thus the main aim herein to investigate isolation effects in general and to evaluate GDM predictions related to isolation in particular.…”

“…Borregaard et al, ). In a companion paper to the present paper, Cabral, Wiegand, and Kreft () present a BioGeographical Eco‐Evolutionary Model (BioGEEM) that builds on previous population‐ and niche‐based models for species’ range dynamics (Cabral et al, ; Sarmento Cabral et al, ; Zurell et al, ) and for metacommunity dynamics (Cabral & Kreft, ). BioGEEM goes beyond these precursor models by adding evolutionary and environmental processes.…”