Professor University of Wyoming, Wyoming, United States
Abstract: Eco-evolutionary interactions affecting dispersal are thought to cause accelerated invasion speeds in many invasive species. However little work has been done on eco-evolutionary dynamics that affect other traits besides dispersal. In particular, the effects of evolution in traits involved in biotic interactions on invasion speeds are understudied. We know from theoretical work that the presence of a competitor can sometimes decrease invasion speed, however there have been few studies on how allowing evolution in traits related to competition might affect invasion speed. In this paper, we extend to a spatial extent a partial-differential-equation based model of eco-evolutionary interactions between toxic invasive species and native species that can evolve resistance. We ask how allowing trait evolution changes the invasion speed versus a model without evolution. We also compare the coexistence and extinction status of the native and invasive species after invasion with a non-spatial model.
We found that on average, invasion speed increased when evolution was allowed, though there were some exceptions where invasion speed decreased with evolution. In addition, we compared these results to the effect of allowing trait evolution in a non-spatial model on final species dominance. Surprisingly, in most cases, allowing the evolution of competition increases invasion speed even though equilibrium density of the invasive species decreases, indicating there may be a trade-off between growing at high density and invading quickly. We also found that the spatial model with evolution heavily favored coexistence (57.5% of cases) when compared to the non-spatial model without evolution (37.5% of cases), the non-spatial model with evolution (25.0% of cases) or the spatial model without evolution (36.2% of cases). It is therefore important to consider both spatial and eco-evolutionary effects when predicting impact of invasive species on native species, since ignoring either one could lead to biased and overly pessimistic predictions.
