Biology subjects

Weiss-Lehman, C.

Publications and source records attributed to Weiss-Lehman, C..

3 recordsLinked to original sources

bioRxiv2023

Environmental gradients mediate dispersal evolution during biological invasions

Rapid dispersal evolution at the edge of a range expansion can accelerate invasions. However, expanding populations will often encounter environmental gradients that entail a fitness cost of dispersal. We used an eco-evolutionary model to explore how environmental heterogeneity influences adaptation and dispersal evolution during range expansion and, in turn, modulates the speed and predictability of invasion. Environmental gradients opposed the evolution of increased dispersal during invasion, even leading to the evolution of reduced dispersal along steep gradients. Counterintuitively, reduced dispersal allowed for faster expansion by minimizing maladaptation. While evolution across homogenous landscapes caused invasions to be highly unpredictable, even shallow environmental gradients greatly increased invasion predictability. We illustrate our models potential application to prediction and management of invasions by parameterizing it with data from a recent invertebrate range expansion. Overall, we find that environmental heterogeneity and local adaptation strongly modulate the effect of dispersal evolution on invasion trajectories.

ecology↗

bioRxiv2021

Increasing temporal variance leads to stable species range limits

What prevents populations of a species from adapting to the novel environments outside the species geographic distribution? Previous models highlighted how gene flow across spatial environmental gradients determines species expansion vs. extinction and the location of species range limits. However, space is only one of two axes of environmental variation -- environments also vary in time, and we know temporal environmental variation has important consequences for population demography and evolution. We used analytical and individual-based evolutionary models to explore how temporal variation in environmental conditions influences the spread of populations across a spatial environmental gradient. We find that temporal variation greatly alters our predictions for range dynamics compared to temporally static environments. When temporal variance is equal across the landscape, the fate of species (expansion vs. extinction) is determined by the interaction between the degree of temporal autocorrelation in environmental fluctuations and the steepness of the spatial environmental gradient. When the magnitude of temporal variance changes across the landscape, stable range limits form where this variance increases maladaptation sufficiently to prevent local persistence. These results illustrate the pivotal influence of temporal variation on the likelihood of populations colonizing novel habitats and the location of species range limits.

evolutionary biology↗

bioRxiv2021

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

1Modeling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species-interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighboring species from the diverse community that had non-generic interactions with our focal species. This sparse modeling approach facilitates exploration of species-interactions in diverse communities while maintaining a manageable number of parameters.

ecology↗