Jean P. Gibert

How fast is fast? Eco-evolutionary dynamics and rates of change in populations and phenotypes.

Abstract It is increasingly recognized that evolution may occur in ecological time. It is not clear, however, how fast evolution – or phenotypic change more generally – may be in comparison with the associated ecology, or whether systems with fast ecological dynamics generally have relatively fast rates of phenotypic change. We developed a new dataset on standardized rates of change in population size and phenotypic traits for a wide range of species and taxonomic groups. We show that rates of change in phenotypes are generally no more than 2/3, and on average about 1/4, the concurrent rates of change in population size. There was no relationship between rates of population change and rates of phenotypic change across systems. We also found that the variance of both phenotypic and ecological rates increased with the mean across studies following a power law with an exponent of two, while temporal variation in phenotypic rates was lower than in ecological rates. Our results are consistent with the view that ecology and evolution may occur at similar time scales, but clarify that only rarely do populations change as fast in traits as they do in abundance.

Individual phenotypic variation reduces interaction strengths in a consumer-resource system.

Natural populations often show variation in traits that can affect the strength of interspecific interactions. Interaction strengths in turn influence the fate of pairwise interacting populations and the stability of food webs. Understanding the mechanisms relating individual phenotypic variation to interaction strengths is thus central to assess how trait variation affects population and community dynamics. We incorporated nonheritable variation in attack rates and handling times into a classical consumer–resource model to investigate how variation may alter interaction strengths, population dynamics, species persistence, and invasiveness. We found that individual variation influences species persistence through its effect on interaction strengths. In many scenarios, interaction strengths decrease with variation, which in turn affects species coexistence and stability. Because environmental change alters the direction and strength of selection acting upon phenotypic traits, our results have implications for species coexistence in a context of habitat fragmentation, climate change, and the arrival of exotic species to native ecosystems.

Temperature alters food web body-size structure.

The increased temperature associated with climate change may have important effects on body size and predator–prey interactions. The consequences of these effects for food web structure are unclear because the relationships between temperature and aspects of food web structure such as predator–prey body-size relationships are unknown. Here, we use the largest reported dataset for marine predator–prey interactions to assess how temperature affects predator–prey body-size relationships among different habitats ranging from the tropics to the poles. We found that prey size selection depends on predator body size, temperature and the interaction between the two. Our results indicate that (i) predator–prey body-size ratios decrease with predator size at below-average temperatures and increase with predator size at above-average temperatures, and (ii) that the effect of temperature on predator–prey body-size structure will be stronger at small and large body sizes and relatively weak at intermediate sizes. This systematic interaction may help to simplify forecasting the potentially complex consequences of warming on interaction strengths and food web stability.

Conflicting Selection in the Course of Adaptive Diversification: The Interplay between Mutualism and Intraspecific Competition

Adaptive speciation can occur when a population undergoes assortative mating and disruptive selection caused by frequency-dependent intraspecific competition. However, other interactions, such as mutualisms based on trait matching, may generate conflicting selective pressures that constrain species diversification. We used individual-based simulations to explore how different types of mutualism affect adaptive diversification. A magic trait was assumed to simultaneously mediate mate choice, intraspecific competition, and mutualisms. In scenarios of intimate, specialized mutualisms, individuals interact with one or few individual mutualistic partners, and diversification is constrained only if the mutualism is obligate. In other scenarios, increasing numbers of different partners per individual limit diversification by generating stabilizing selection. Stabilizing selection emerges from the greater likelihood of trait mismatches for rare, extreme phenotypes than for common intermediate phenotypes. Constraints on diversification imposed by increased numbers of partners decrease if the trait matching degree has smaller positive effects on fitness. These results hold after the relaxation of various assumptions. When trait matching matters, mutualism-generated stabilizing selection would thus often constrain diversification in obligate mutualisms, such as ant-myrmecophyte associations, and in low-intimacy mutualisms, including plant-seed disperser systems. Hence, different processes, such as trait convergence favoring the incorporation of nonrelated species, are needed to explain the higher richness of low-intimacy assemblages—shown here to be up to 1 order of magnitude richer than high-intimacy systems.

The Spatial Structure of Antagonistic Species Affects Coevolution in Predictable Ways

A current challenge in evolutionary ecology is to assess how the spatial structure of interacting species shapes coevolution. Previous work on the geographic mosaic of coevolution has shown that coevolution depends on the spatial structure, the strength of selection, and gene flow across populations. We used spatial subgraphs and coevolutionary models to evaluate how spatial structure and the location of coevolutionary hotspots (sites in which reciprocal selection occurs) and coldspots (sites in which unidirectional selection occurs) contribute to the dynamics of coevolution and the maintenance of polymorphisms. Specifically, we developed a new approach based on the Laplacian matrices of spatial subgraphs to explore the tendency of interacting species to evolve toward stable polymorphisms. Despite the complex interplay between gene flow and the strength of reciprocal selection, simple rules drive coevolution in small groups of spatially structured interacting populations. Hotspot location and the spatial organization of coldspots are crucial for understanding patterns in the maintenance of polymorphisms. Moreover, the degree of spatial variation in the outcomes of the coevolutionary process can be predicted from the network pattern of gene flow among sites. Our work provides us with novel tools that can be used in the field or the laboratory to predict the effects of spatial structure on coevolutionary trajectories.

The combined effects of reactant kinetics and enzyme stability explain the temperature dependence of metabolic rates

Abstract A mechanistic understanding of the response of metabolic rate to temperature is essential for understanding thermal ecology and metabolic adaptation. Although the Arrhenius equation has been used to describe the effects of temperature on reaction rates and metabolic traits, it does not adequately describe two aspects of the thermal performance curve (TPC) for metabolic rate—that metabolic rate is a unimodal function of temperature often with maximal values in the biologically relevant temperature range and that activation energies are temperature dependent. We show that the temperature dependence of metabolic rate in ectotherms is well described by an enzyme‐assisted Arrhenius (EAAR) model that accounts for the temperature‐dependent contribution of enzymes to decreasing the activation energy required for reactions to occur. The model is mechanistically derived using the thermodynamic rules that govern protein stability. We contrast our model with other unimodal functions that also can be used to describe the temperature dependence of metabolic rate to show how the EAAR model provides an important advance over previous work. We fit the EAAR model to metabolic rate data for a variety of taxa to demonstrate the models utility in describing metabolic rate TPCs while revealing significant differences in thermodynamic properties across species and acclimation temperatures. Our model advances our ability to understand the metabolic and ecological consequences of increases in the mean and variance of temperature associated with global climate change. In addition, the model suggests avenues by which organisms can acclimate and adapt to changing thermal environments. Furthermore, the parameters in the EAAR model generate links between organismal level performance and underlying molecular processes that can be tested for in future work.

Crossing regimes of temperature dependence in animal movement

A pressing challenge in ecology is to understand the effects of changing global temperatures on food web structure and dynamics. The stability of these complex ecological networks largely depends on how predator-prey interactions may respond to temperature changes. Because predators and prey rely on their velocities to catch food or avoid being eaten, understanding how temperatures may affect animal movement is central to this quest. Despite our efforts, we still lack a mechanistic understanding of how the effect of temperature on metabolic processes scales up to animal movement and beyond. Here, we merge a biomechanical approach, the Metabolic Theory of Ecology and empirical data to show that animal movement displays multiple regimes of temperature dependence. We also show that crossing these regimes has important consequences for population dynamics and stability, which depend on the parameters controlling predator-prey interactions. We argue that this dependence upon interaction parameters may help explain why experimental work on the temperature dependence of interaction strengths has so far yielded conflicting results. More importantly, these changes in the temperature dependence of animal movement can have consequences that go well beyond ecological interactions and affect, for example, animal communication, mating, sensory detection, and any behavioral modality dependent on the movement of limbs. Finally, by not taking into account the changes in temperature dependence reported here we might not be able to properly forecast the impact of global warming on ecological processes and propose appropriate mitigation action when needed.

Gillespie eco-evolutionary models (GEMs) reveal the role of heritable trait variation in eco-evolutionary dynamics

Abstract Heritable trait variation is a central and necessary ingredient of evolution. Trait variation also directly affects ecological processes, generating a clear link between evolutionary and ecological dynamics. Despite the changes in variation that occur through selection, drift, mutation, and recombination, current eco‐evolutionary models usually fail to track how variation changes through time. Moreover, eco‐evolutionary models assume fitness functions for each trait and each ecological context, which often do not have empirical validation. We introduce a new type of model, Gillespie eco‐evolutionary models (GEMs), that resolves these concerns by tracking distributions of traits through time as eco‐evolutionary dynamics progress. This is done by allowing change to be driven by the direct fitness consequences of model parameters within the context of the underlying ecological model, without having to assume a particular fitness function. GEMs work by adding a trait distribution component to the standard Gillespie algorithm – an approach that models stochastic systems in nature that are typically approximated through ordinary differential equations. We illustrate GEMs with the Rosenzweig–MacArthur consumer–resource model. We show not only how heritable trait variation fuels trait evolution and influences eco‐evolutionary dynamics, but also how the erosion of variation through time may hinder eco‐evolutionary dynamics in the long run. GEMs can be developed for any parameter in any ordinary differential equation model and, furthermore, can enable modeling of multiple interacting traits at the same time. We expect GEMs will open the door to a new direction in eco‐evolutionary and evolutionary modeling by removing long‐standing modeling barriers, simplifying the link between traits, fitness, and dynamics, and expanding eco‐evolutionary treatment of a greater diversity of ecological interactions. These factors make GEMs much more than a modeling advance, but an important conceptual advance that bridges ecology and evolution through the central concept of heritable trait variation.

Phenotypic variation explains food web structural patterns

Significance Understanding food web structure—networks of species and their feeding interactions—is central to predicting how ecosystems may respond to climate change. Feeding interactions are often determined by the traits individuals have, but how the effects of such traits and their intraspecific variation scale up from individuals to food webs is not known. Here, we use mathematical models to show that trait variation influences predator connectivity and trophic level within food webs. By doing so, we predict the occurrence of important structural features in food webs, and we confirm these predictions using a large global dataset of the best-resolved available food webs. Food webs (i.e., networks of species and their feeding interactions) share multiple structural features across ecosystems. The factors explaining such similarities are still debated, and the role played by most organismal traits and their intraspecific variation is unknown. Here, we assess how variation in traits controlling predator–prey interactions (e.g., body size) affects food web structure. We show that larger phenotypic variation increases connectivity among predators and their prey as well as total food intake rate. For predators able to eat only a few species (i.e., specialists), low phenotypic variation maximizes intake rates, while the opposite is true for consumers with broader diets (i.e., generalists). We also show that variation sets predator trophic level by determining interaction strengths with prey at different trophic levels. Merging these results, we make two general predictions about the structure of food webs: (i) trophic level should increase with predator connectivity, and (ii) interaction strengths should decrease with prey trophic level. We confirm these predictions empirically using a global dataset of well-resolved food webs. Our results provide understanding of the processes structuring food webs that include functional traits and their naturally occurring variation.

Life history traits and functional processes generate multiple pathways to ecological stability

Stability contributes to the persistence of ecological communities, yet the interactions among different stabilizing forces are poorly understood. We assembled mesocosms with an algal resource and one to eight different clones of the consumer Daphnia ambigua and tracked algal and Daphnia abundances through time. We then fitted coupled ordinary differential equations (ODEs) to the consumer-resource time series. We show that variation in different components of stability (local stability and the magnitude of population fluctuations) across mesocosms arises through variation in life history traits and the functional processes represented by ODE model parameters. Local stability was enhanced by increased algal growth rate and Daphnia mortality and foraging rate. Population fluctuations were dampened by high Daphnia conversion efficiency and lower interaction strengths, low algal growth rate, high Daphnia death rate, and low Daphnia foraging. These results indicate that (1) stability in consumer-resource systems may arise through the net effect of multiple related stabilizing pathways and (2) different aspects of stability can vary independently and may respond in opposite directions to the same forces.