A road map for integrating eco-evolutionary processes into biodiversity models

Abstract

The demand for projections of the future distribution of biodiversity has triggered an upsurge in modelling at the crossroads between ecology and evolution. Despite the enthusiasm around these so-called biodiversity models, most approaches are still criticised for not integrating key processes known to shape species ranges and community structure. Developing an integrative modelling framework for biodiversity distribution promises to improve the reliability of predictions and to give a better understanding of the eco-evolutionary dynamics of species and communities under changing environments. In this article, we briefly review some eco-evolutionary processes and interplays among them, which are essential to provide reliable projections of species distributions and community structure. We identify gaps in theory, quantitative knowledge and data availability hampering the development of an integrated modelling framework. We argue that model development relying on a strong theoretical foundation is essential to inspire new models, manage complexity and maintain tractability. We support our argument with an example of a novel integrated model for species distribution modelling, derived from metapopulation theory, which accounts for abiotic constraints, dispersal, biotic interactions and evolution under changing environmental conditions. We hope such a perspective will motivate exciting and novel research, and challenge others to improve on our proposed approach.

Figure 1

a) Conceptual representation of ecological filters selecting species from the global pool and shaping the realized local communities. Filters operate at different dimensions (geographic space and ecological space) and are not hierarchical. b) Main processes involved in shaping species range dynamics and community structure and their direct (A-E) and indirect (F-K) effects on the filtering process. Interactions between abiotic environment, physiology and dispersal can also be important but are omitted here to avoid the figure becoming too complex. See main text for more details.

Figure 2

Example of the effects of rapid evolution on a single-species response to climate change. The potential number of life-cycle completions per year of Aedes aegypti in the Northern Territory of Australia as a function of climate under different evolutionary and climate change scenarios. Prediction of levels of egg desiccation resistance under current conditions (A), under climate change (50 years) (B) and under climate change while accounting for evolution of egg desiccation (C). The dotted and solid lines represent the maximum possible range, if egg desiccation survival was 100% under current climate, and under the 2050 climate change scenario, respectively. Redrawn from Kearney et al. (2009)

Figure 3

Effects of dispersal limitation and plant interactions on the distribution of an alpine plant species, Bromus erectus, depicted in abiotic space (Boulangeat et al. 2012a). (a) Observed distribution in abiotic space. Left: density of predicted presences normalised by the number of sample plots within each grid cell. Right: third quartile of predicted abundance class within each grid cell. (b) Left / right: Proportion of sources / sinks among predicted presences. Middle: abundances in sources and sinks. (c) Effect of biotic interactions. Left: predicted presence density without biotic interactions. Right: negative and positive effects of biotic interactions on the abiotic niche of the species. Redrawn from Boulangeat et al. (2012a).

Figure 4

Species distribution in trophic metacommunities. The environment varies linearly along the X-axis. The colonization probability is maximal at the niche optimum (C_i = 0.4), the baseline extinction probability is 0.3 and increases to 0.4 with the presence of one predator. A) Local species richness per community (from 0 –red- to 15 – blue-; black lines denote dispersal between patches). B) Relationship between species richness and the environment. C) Interaction matrix for the whole network (top left) and 3 selected communities. The interactions between predators (columns) and preys (rows) are denoted by black squares. Some interactions do not occur locally owing to the absence of the predator or the prey (light grey). D) The distribution of a randomly selected species along the environmental gradient (dots) and the fundamental niche (line), as determined by the relationship between the colonization probability and the environment. The discrepancy between occurrence and the fundamental niche arises from the impact of biotic interactions.