Our Research

We use a variety of approaches to understand the role of eco-evolutionary mechanisms in spatial population and community dynamics

Our Approach

We use a combination of theoretical models and experimental microcosms to establish a rigorous, mechanistic understanding of ecological and evolutionary mechanisms and then attempt to use that understanding to explain patterns of variation in real-world data from the field.

Themes in our work include:

  • Eco-Evolutionary Dynamics and Geographic Ranges

    Geographic range limits are ubiquitous, but we still lack a comprehensive understanding of how ecological and evolutionary mechanisms contribute to the formation of stable range limits. Further, instability in these range limits underlies some of the most pressing contemporary conservation challenges (namely range expansions and shifts). We strive to tease apart the mechanistic basis of stable range limits and better understand the consequences of their failures.

  • Analyzing Genomic Data to Isolate and Quantify Distinct Evolutionary Mechanisms

    Both selection and genetic drift play important roles in the evolution of spreading populations. However, it can be difficult to distinguish the effects of selection from drift using only a single realization (e.g., only a single range expansion). We combine models with analyses of genomic data from replicated experimental microcosms to disentangle the roles of selection and drift in range expansions and shifts.

  • Statistical Method for Mechanistic Predictions

    It can sometimes be difficult to link ecological and evolutionary mechanisms directly to real-world data due to the mismatch between relatively simplistic statistical approaches and the complex, non-linear mechanistic models developed by theoreticians. We strive to develop novel statistical approaches to directly link eco-evolutionary mechanisms and theoretical models to field data, thus improving our ability to predict the complexities and variation observed in nature.

Featured Project

Testing the Eco-evolutionary Drivers of Range Limits

Many models explain the occurrence of stable range limits, some from an evolutionary perspective and some from an ecological perspective. However, these ecological and evolutionary perspectives have rarely been competed against each other and even more rarely tested in controlled experimental frameworks.

With funding from an NSF DEB grant, our lab is directly testing multiple competing hypotheses on the formation of stable range limits using our flour beetle microcosms. We are also building novel theoretical models informed by our experiments to extend our findings beyond our microcosms.

Featured Project

Evolutionary Consequences of Range Shifts

Many species are shifting their ranges in response to climate change, moving upwards in either latitude or elevation to track warming temperatures. There is much we don’t know about the long-term consequences of these range shifts, and we are particularly interested in understanding their evolutionary consequences. During shifts, populations are expected to lose genetic diversity due to gene surfing. This could make it difficult for shifting populations to adapt to novel conditions.

To explore this possibility, our lab is conducting experimental range shifts in our beetle microcosms. Using these experiments, we can better understand the risk of extinction during range shifts and their evolutionary consequences by examining the ability of shifted populations to adapt to novel selection pressures.

Featured Project

Sparse Models for Ecology

Ecology is an extremely complex field in which a variety of environmental and biotic drivers can affect the dynamics in a system. Additionally, with the increasing prevalence of long-term datasets, environmental data loggers, and remote-sensing techniques, ecologists frequently find themselves confronted with an overabundance of covariates that complicate potential analyses.

Sparse modeling provides a potential solution to this conundrum by dynamically restricting all but a subset of model parameters to zero or near-zero.

This provides a more efficient and accurate workflow for dealing with so many covariates compared to traditional AIC-based approaches.

Our lab is working to develop sparse modeling approaches for use in several common ecological challenges, including quantifying species interactions in diverse communities and identifying environmental drivers and seasonal dynamics in ecological time series.

JOIN THE weiss-lehman LAB

We’re a collaborative group that’s passionate about ecology and evolution.

We strive to build a respectful, engaging, and inclusive environment for students to learn and thrive. Meet our team and learn about upcoming opportunities to get involved!