About

Alan Bergland is a professor whose research focuses on ecological and evolutionary genetics, with particular emphasis on Drosophila and other model organisms. His work involves studying genetic variation, adaptation, and the impact of structural genetic features such as inversions on quantitative variation. Bergland's lab investigates how natural selection and genetic mechanisms drive evolutionary processes, including seasonal adaptation and gene family evolution. He has contributed to understanding the genetic basis of adaptation in natural populations, including the effects of balancing selection, plasticity, and shared polymorphisms. His research also encompasses citizen science projects and collaborations with educational institutions, aiming to explore and promote evolutionary biology through both fieldwork and genomic analysis.

Research topics

• Biology
• Ecology
• Evolutionary biology
• Genetics
• Demography

Selected publications

• Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila

  eLife · 2021 · 163 citations

  • Biology
  • Evolutionary biology
  • Genetics

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  DOI

• Host plants and <i>Wolbachia</i> shape the population genetics of sympatric herbivore populations

  Evolutionary Applications · 2020 · 17 citations

  • Biology
  • Ecology

  , while the putatively resident COI haplotype generally did not. Genetic intermediates between the two genetic populations of insects were rare, consistent with recent sympatry or reproductive isolation, although admixture patterns of apparent hybrids were consistent with introgression of genes from introduced into resident populations. Our results suggest that both host-plant associations and endosymbionts are shaping the population genetic structure of sympatric psyllid populations associated with different non-crop hosts. It is of future interest to explicitly examine vectorial capacity of the two populations and their potential hybrids, as population structure and hybridization might alter regional vector capacity and disease outbreaks.

  DOI

• Genomic Analysis of European Drosophila melanogaster Populations Reveals Longitudinal Structure, Continent-Wide Selection, and Previously Unknown DNA Viruses

  Molecular Biology and Evolution · 2020 · 165 citations

  • Biology
  • Evolutionary biology
  • Genetics

  Genetic variation is the fuel of evolution, with standing genetic variation especially important for short-term evolution and local adaptation. To date, studies of spatiotemporal patterns of genetic variation in natural populations have been challenging, as comprehensive sampling is logistically difficult, and sequencing of entire populations costly. Here, we address these issues using a collaborative approach, sequencing 48 pooled population samples from 32 locations, and perform the first continent-wide genomic analysis of genetic variation in European Drosophila melanogaster. Our analyses uncover longitudinal population structure, provide evidence for continent-wide selective sweeps, identify candidate genes for local climate adaptation, and document clines in chromosomal inversion and transposable element frequencies. We also characterize variation among populations in the composition of the fly microbiome, and identify five new DNA viruses in our samples.

  DOI

• Evolutionary genomics can improve prediction of species’ responses to climate change

  Evolution Letters · 2020 · 354 citations

  • Ecology
  • Biology

  Global climate change (GCC) increasingly threatens biodiversity through the loss of species, and the transformation of entire ecosystems. Many species are challenged by the pace of GCC because they might not be able to respond fast enough to changing biotic and abiotic conditions. Species can respond either by shifting their range, or by persisting in their local habitat. If populations persist, they can tolerate climatic changes through phenotypic plasticity, or genetically adapt to changing conditions depending on their genetic variability and census population size to allow for de novo mutations. Otherwise, populations will experience demographic collapses and species may go extinct. Current approaches to predicting species responses to GCC begin to combine ecological and evolutionary information for species distribution modelling. Including an evolutionary dimension will substantially improve species distribution projections which have not accounted for key processes such as dispersal, adaptive genetic change, demography, or species interactions. However, eco-evolutionary models require new data and methods for the estimation of a species' adaptive potential, which have so far only been available for a small number of model species. To represent global biodiversity, we need to devise large-scale data collection strategies to define the ecology and evolutionary potential of a broad range of species, especially of keystone species of ecosystems. We also need standardized and replicable modelling approaches that integrate these new data to account for eco-evolutionary processes when predicting the impact of GCC on species' survival. Here, we discuss different genomic approaches that can be used to investigate and predict species responses to GCC. This can serve as guidance for researchers looking for the appropriate experimental setup for their particular system. We furthermore highlight future directions for moving forward in the field and allocating available resources more effectively, to implement mitigation measures before species go extinct and ecosystems lose important functions.

  DOI

Recent grants

• CAREER: Backyard Evolution across a Seasonal Metapopulation in Drosophila

  NSF · $1.2M · 2022–2027

• NIH Grant F32GM097837

  NIH · $148k · 2014

• The genetic and physiological architecture of rapid and cyclic adaptation

  NIH · $1.9M · 2016–2022

Frequent coauthors

• Paul Schmidt

  University of Pennsylvania

  44 shared

• Dmitri A. Petrov

  Stanford University

  36 shared

• Josefa González

  Institut de Biologia Evolutiva

  31 shared

• Emily L. Behrman

  Dartmouth College

  27 shared

• Martin Kapun

  Natural History Museum Vienna

  24 shared

• Iryna Kozeretska

  State Institution National Antarctic Scientific Center

  22 shared

• Svitlana Serga

  Institut Agro Montpellier

  20 shared

• Thomas Flatt

  University of Fribourg

  18 shared

Labs

• Bergland LabPI

Similar researchers at University of Virginia

• Stephen S. Richh-170
• Gary Owensh-107
• Jarrod Martoh-101
• Jennifer L. Westh-93
• Edward H. Egelmanh-93