Demography-Dispersal Trait Correlations Modify the Eco-Evolutionary Dynamics of Range Expansion.

Spreading populations are subject to evolutionary processes acting on dispersal and reproduction that can increase invasion speed and variability. It is typically assumed that dispersal and demography traits evolve independently, but abundant evidence points to correlations between them that may be positive or negative and genetic, maternal, or environmental. We sought to understand how demography-dispersal correlations modify the eco-evolutionary dynamics of range expansion. We first explored this question with the beetle Callosobruchus maculatus, a laboratory model in which evolutionary acceleration of invasion has been demonstrated. We then built a simulation model to explore the role of trait correlations in this system and more generally. We found that positive correlations amplify the positive influence of evolution on speed and variability while negative correlations (such as we found empirically) constrain that influence. Strong negative genetic correlations can even cause evolution to decelerate invasion. Genetic and nongenetic (maternal and environmental) correlations had similar effects on some measures of invasion but different effects on others. Model results enabled us to retrospectively explain invasion dynamics and trait evolution in C. maculatus and may similarly aid the interpretation of other field and laboratory studies. Nonindependence of demography and dispersal is an important consideration for understanding and predicting outcomes of range expansion.

Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity.

Mathematical models of cholera and waterborne disease vary widely in their structures, in terms of transmission pathways, loss of immunity, and a range of other features. These differences can affect model dynamics, with different models potentially yielding different predictions and parameter estimates from the same data. Given the increasing use of mathematical models to inform public health decision-making, it is important to assess model distinguishability (whether models can be distinguished based on fit to data) and inference robustness (whether inferences from the model are robust to realistic variations in model structure). In this paper, we examined the effects of uncertainty in model structure in the context of epidemic cholera, testing a range of models with differences in transmission and loss of immunity structure, based on known features of cholera epidemiology. We fit these models to simulated epidemic and long-term data, as well as data from the 2006 Angola epidemic. We evaluated model distinguishability based on fit to data, and whether the parameter values, model behavior, and forecasting ability can accurately be inferred from incidence data. In general, all models were able to successfully fit to all data sets, both real and simulated, regardless of whether the model generating the simulated data matched the fitted model. However, in the long-term data, the best model fits were achieved when the loss of immunity structures matched those of the model that simulated the data. Two parameters, one representing person-to-person transmission and the other representing the reporting rate, were accurately estimated across all models, while the remaining parameters showed broad variation across the different models and data sets. The basic reproduction number (R0) was often poorly estimated even using the correct model, due to practical unidentifiability issues in the waterborne transmission pathway which were consistent across all models. Forecasting efforts using noisy data were not successful early in the outbreaks, but once the epidemic peak had been achieved, most models were able to capture the downward incidence trajectory with similar accuracy. Forecasting from noise-free data was generally successful for all outbreak stages using any model. Our results suggest that we are unlikely to be able to infer mechanistic details from epidemic case data alone, underscoring the need for broader data collection, such as immunity/serology status, pathogen dose response curves, and environmental pathogen data. Nonetheless, with sufficient data, conclusions from forecasting and some parameter estimates were robust to variations in the model structure, and comparative modeling can help to determine how realistic variations in model structure may affect the conclusions drawn from models and data.

Genetic mixture of multiple source populations accelerates invasive range expansion.

A wealth of population genetic studies have documented that many successful biological invasions stem from multiple introductions from genetically distinct source populations. Yet, mechanistic understanding of whether and how genetic mixture promotes invasiveness has lagged behind documentation that such mixture commonly occurs. We conducted a laboratory experiment to test the influence of genetic mixture on the velocity of invasive range expansion. The mechanistic basis for effects of genetic mixture could include evolutionary responses (mixed invasions may harbour greater genetic diversity and thus elevated evolutionary potential) and/or fitness advantages of between-population mating (heterosis). If driven by evolution, positive effects of source population mixture should increase through time, as selection sculpts genetic variation. If driven by heterosis, effects of mixture should peak following first reproductive contact and then dissipate. Using a laboratory model system (beetles spreading through artificial landscapes), we quantified the velocity of range expansion for invasions initiated with one, two, four or six genetic sources over six generations. Our experiment was designed to test predictions corresponding to the evolutionary and heterosis mechanisms, asking whether any effects of genetic mixture occurred in early or later generations of range expansion. We also quantified demography and dispersal for each experimental treatment, since any effects of mixture should be manifest in one or both of these traits. Over six generations, invasions with any amount of genetic mixture (two, four and six sources) spread farther than single-source invasions. Our data suggest that heterosis provided a 'catapult effect', leaving a lasting signature on range expansion even though the benefits of outcrossing were transient. Individual-level trait data indicated that genetic mixture had positive effects on local demography (reduced extinction risk and enhanced population growth) during the initial stages of invasion but no consistent effects on dispersal ability. Our work is the first to demonstrate that genetic mixture can alter the course of spatial expansion, the stage of invasion typically associated with the greatest ecological and economic impacts. We suggest that similar effects of genetic mixture may be a common feature of biological invasions in nature, but that these effects can easily go undetected.

Rapid evolution of dispersal ability makes biological invasions faster and more variable.

Genetic variation in dispersal ability may result in the spatial sorting of alleles during range expansion. Recent theory suggests that spatial sorting can favour the rapid evolution of life history traits at expanding fronts, and therefore modify the ecological dynamics of range expansion. Here we test this prediction by disrupting spatial sorting in replicated invasions of the bean beetle Callosobruchus maculatus across homogeneous experimental landscapes. We show that spatial sorting promotes rapid evolution of dispersal distance, which increases the speed and variability of replicated invasions: after 10 generations of range expansion, invasions subject to spatial sorting spread 8.9% farther and exhibit 41-fold more variable spread dynamics relative to invasions in which spatial sorting is suppressed. Correspondingly, descendants from spatially evolving invasions exhibit greater mean and variance in dispersal distance. Our results reveal an important role for rapid evolution during invasion, even in the absence of environmental filters, and argue for evolutionarily informed forecasts of invasive spread by exotic species or climate change migration by native species.