The transition from evolutionary stability to branching: A catastrophic evolutionary shift

Evolutionary coexistence in a metacommunity: Competition-colonization trade-off, ownership effects, environmental fluctuations

We study the adaptive dynamics of the colonization rate of species living in a patchy habitat when there is a trade-off with the competitive strength for individual patches. To that end, we formulate a continuous-time competition-colonization model that also includes ownership effects as well as random disturbance affecting the mortality rate. We find that intermediate disturbance (as measured by the fluctuation intensity of the mortality rate), a strong competition-colonization trade-off, and a weak ownership effect are necessary conditions for evolutionary branching and hence for the emergence of polymorphisms (i.e., coexistence) by small evolutionary steps. Specifically, concerning ownership we find that with low-intermediate disturbance, a weak ownership advantage favours evolutionary branching while ownership disadvantage does not. This asymmetry disappears at the higher-intermediate disturbance. Moreover, at a low-intermediate disturbance, the effect of the strength of the competition-colonization trade-off on evolutionary branching is non-monotonic disappears because the possibility of branching disappears again when the trade-off is too strong. We also find that there can be multiple evolutionary attractors for polymorphic populations, each with its own basin of attraction. With small but non-zero random evolutionary steps and depending on the initial polymorphic condition just after branching, a coevolutionary trajectory may come arbitrarily close to the shared boundary of two such basins and may even jump from one side to the other, which can lead to various kinds of long-term evolutionary dynamics, including evolutionary branching-extinction cycles.

Trait positions for elevated invasiveness in adaptive ecological networks

Our ability to predict the outcome of invasion declines rapidly as non-native species progress through intertwined ecological barriers to establish and spread in recipient ecosystems. This is largely due to the lack of systemic knowledge on key processes at play as species establish self-sustaining populations within the invaded range. To address this knowledge gap, we present a mathematical model that captures the eco-evolutionary dynamics of native and non-native species interacting within an ecological network. The model is derived from continuous-trait evolutionary game theory (i.e., Adaptive Dynamics) and its associated concept of invasion fitness which depicts dynamic demographic performance that is both trait mediated and density dependent. Our approach allows us to explore how multiple resident and non-native species coevolve to reshape invasion performance, or more precisely invasiveness, over trait space. The model clarifies the role of specific traits in enabling non-native species to occupy realised opportunistic niches. It also elucidates the direction and speed of both ecological and evolutionary dynamics of residing species (natives or non-natives) in the recipient network under different levels of propagule pressure. The versatility of the model is demonstrated using four examples that correspond to the invasion of (i) a horizontal competitive community; (ii) a bipartite mutualistic network; (iii) a bipartite antagonistic network; and (iv) a multi-trophic food web. We identified a cohesive trait strategy that enables the success and establishment of non-native species to possess high invasiveness. Specifically, we find that a non-native species can achieve high levels of invasiveness by possessing traits that overlap with those of its facilitators (and mutualists), which enhances the benefits accrued from positive interactions, and by possessing traits outside the range of those of antagonists, which mitigates the costs accrued from negative interactions. This ‘central-to-reap, edge-to-elude’ trait strategy therefore describes the strategic trait positions of non-native species to invade an ecological network. This model provides a theoretical platform for exploring invasion strategies in complex adaptive ecological networks.

Dominance-discovery and discovery-exploitation trade-offs promote diversity in ant communities

In ant communities, species coexist by using different foraging strategies. We developed an adaptive dynamics model to gain a better understanding of the factors that promote the emergence and maintenance of strategy diversity. We analysed the consequences of both interspecific competition and resource distribution for the evolutionary dynamics of social foraging in ants. The evolution of social foraging behaviour was represented using a stochastic mutation-selection process involving interactions among colonies. In our theoretical community, ant colonies inhabit an environment where resources are limited, and only one resource type is present. Colony interactions depend on colony-specific foraging strategies (defined as the degree of collective foraging), resource distribution patterns, and the degree of competition asymmetry. At the ecological timescale, we have created a model of foraging processes that reflects trade-offs between resource discovery and resource exploitation and between resource discovery and ant behavioural dominance. At the evolutionary timescale, we have identified the conditions of competition and resource distribution that can lead to the emergence and coexistence of both collective and individual foraging strategies. We suggest that asymmetric competition is an essential component in the emergence of diverse foraging strategies in a sympatric ant community.

Variability in life-history switch points across and within populations explained by Adaptive Dynamics

Understanding the factors that shape the timing of life-history switch points (SPs; e.g. hatching, metamorphosis and maturation) is a fundamental question in evolutionary ecology. Previous studies examining this question from a fitness optimization perspective have advanced our understanding of why the timing of life-history transitions may vary across populations and environments. However, in nature we also often observe variability among individuals within populations. Optimization theory, which typically predicts a single optimal SP under physiological and environmental constraints for a given environment, cannot explain this variability. Here, we re-examine the evolution of a single life-history SP between juvenile and adult stages from an Adaptive Dynamics (AD) perspective, which explicitly considers the feedback between the dynamics of population and the evolution of life-history strategy. The AD model, although simple in structure, exhibits a diverse range of evolutionary scenarios depending upon demographic and environmental conditions, including the loss of the juvenile stage, a single optimal SP, alternative optimal SPs depending on the initial phenotype, and sympatric coexistence of two SP phenotypes under disruptive selection. Such predictions are consistent with previous optimization approaches in predicting life-history SP variability across environments and between populations, and in addition they also explain within-population variability by sympatric disruptive selection. Thus, our model can be used as a theoretical tool for understanding life-history variability across environments and, especially, within species in the same environment.

Heterogeneity in local density allows a positive evolutionary relationship between self-fertilisation and dispersal

Despite empirical evidence for a positive relationship between dispersal and self-fertilization (selfing), theoretical work predicts that these traits should always be negatively correlated, and the Good Coloniser Syndrome of high dispersal and selfing (Cf. Baker's Law) should not evolve. Critically, previous work assumes that adult density is spatiotemporally homogeneous, so selfing results in identical offspring production for all patches, eliminating the benefit of dispersal for escaping from local resource competition. We investigate the joint evolution of dispersal and selfing in a demographically structured metapopulation model where local density is spatiotemporally heterogeneous due to extinction-recolonization dynamics. Selfing alleviates outcrossing failure due to low local density (an Allee effect) while dispersal alleviates competition through dispersal of propagules from high- to low-density patches. Because local density is spatiotemporally heterogeneous in our model, selfing does not eliminate heterogeneity in competition, so dispersal remains beneficial even under full selfing. Hence the Good Coloniser Syndrome is evolutionarily stable under a broad range of conditions, and both negative and positive relationships between dispersal and selfing are possible, depending on the environment. Our model thus accommodates positive empirical relationships between dispersal and selfing not predicted by previous theoretical work and provides additional explanations for negative relationships.

Evolutionary fields can explain patterns of high-dimensional complexity in ecology

One of the properties that make ecological systems so unique is the range of complex behavioral patterns that can be exhibited by even the simplest communities with only a few species. Much of this complexity is commonly attributed to stochastic factors that have very high-degrees of freedom. Orthodox study of the evolution of these simple networks has generally been limited in its ability to explain complexity, since it restricts evolutionary adaptation to an inertia-free process with few degrees of freedom in which only gradual, moderately complex behaviors are possible. We propose a model inspired by particle-mediated field phenomena in classical physics in combination with fundamental concepts in adaptation, which suggests that small but high-dimensional chaotic dynamics near to the adaptive trait optimum could help explain complex properties shared by most ecological datasets, such as aperiodicity and pink, fractal noise spectra. By examining a simple predator-prey model and appealing to real ecological data, we show that this type of complexity could be easily confused for or confounded by stochasticity, especially when spurred on or amplified by stochastic factors that share variational and spectral properties with the underlying dynamics.