Eco-Evolutionary modeling and inference

Our lab develops eco-evolutionary models to advance our understand of evolution and to evaluate methods. 

We also develop new methods for the statistical analysis of data.

Advancing our understanding of evolution with eco-evolutionary modeling

Research in the lab advances our understanding of how genomes evolve through eco-evolutionary modeling. Two areas where we've made important advancements include the evolution of inversions in genomes and the evolution of non-intuitive genomic patterns on landscapes and seascapes.

Inversion Invasions: how genomes evolve with multiple, old, and overlapping inversions

Published in Philosophical Transactions of the Royal Society B

Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs had yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g. multiple large, old, and sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. This research was supported by funding from the National Science Foundation.

Non-intuitive genomic patterns on landscapes and seascapes

Population geneticists have historically modeled adaptation in meta-populations to a single environmental gradient, which evolves monotonic clinal patterns in allele frequency at the loci under selection. This study shows that under complex multivariate adaptation, trait clines can evolve despite nonmonotonic allele frequency patterns across environmental gradients. These patterns are not discovered by genotype–environment association methods, which are widely used to discover adaptation. This result challenges widely held conceptual models of adaptation via subtle shifts in allele frequencies across environmental gradients and can explain why genes that underlie environmental traits do not always evolve clines. 

Published in PNAS. This research was supported by a CAREER award from the National Science Foundation to PI Lotterhos.

The video below visualizes the evolution and adaptation of the population to 6 environments at once.

Evolution of phenotypic plasticity, seasonal adaptive tracking, and local adaptation

In 2025 we will be starting a new collaborative project with Joaquín Nuñez (University of Vermont) and Nick Keets (University of Kentucky). We will develop eco-evolutionary simulations to study how seasonal adaptive tracking, local adaptation, and phenotypic plasticity interact to determine species' responses to climate change. This research is funded by the National Science Foundation.

New methods for the statistical analysis of data

The lab develops and evaluates new methods for data analysis. In the past we have developed composite measures for selection and novel applications of network theory to study adaptation in genomes. Currently, we are advancing methods for understanding adaptation to multivariate environments and for quantifying phenotypic plasticity across landscapes and seascapes.

Adaptation to multivariate environments

Our reasearch has shown that some genetic patterns that evolve on landscapes and seascape are not discovered by widely used methods. We have developed statistical tools to accurately estimate multivariate traits from genetic and environmental/phenotypic data. We showed that this method can produce accurate inference even when the genomic basis of the traits is unknown and cannot be accurately estimated.

Published in PNAS. This research was supported by a CAREER award from the National Science Foundation to PI Lotterhos.

Quantifying phenotypic plasticity across landscapes and seascapes

For many decades, evolutionary biologists have studied how both genetics and the environment influences species traits. Over 50 years ago, biologists began to observe that spatial covariance can evolve between these genotypic and environmental influences on traits, but were unable to quantitatively measure it due to lack of a statistical method. This kind of covariance is called gradient variation.

We recently developed a novel quantitative/analytical approach to estimate and test the significance of gradient variation from reciprocal transplant or common garden experiments. The metric provides a way to measure how phenotypic plasticity covaries with genetic differentiation and highlights the importance of understanding the influences of gradient variation in studies of local adaptation and species’ responses to environmental change.

Published in Ecology Letters

This research was supported by the Research Coordination Network for Evolution in Changing Seas, which was funded by the National Science Foundation.

Experimental design and methods evaluations

Given the decreasing cost of genomic data, it is now becoming possible to realize that goal. But the large number of statistical tests that are employed while scanning a whole genome create a number of issues.  What are the best statistical tests to employ?  How does sampling design effect power?  What are the best ways to correct for non-independence in biological data sets?

We use eco-evolutionary simulations to evaluate methods

Research in our lab includes:

We recently published a review of using simulations to evaluate methos in ecology, evolution, and systematics. 

Check out this video that summarizes the paper: