Project: Collaborative research: The genomic underpinnings of local adaptation despite gene flow along a coastal environmental cline

Acronym/Short Name:GenomAdapt
Project Duration:2018-03 - 2021-02
Geolocation:Eastern coastline of North America

Description

NSF Abstract:

Oceans are large, open habitats, and it was previously believed that their lack of obvious barriers to dispersal would result in extensive mixing, preventing organisms from adapting genetically to particular habitats. It has recently become clear, however, that many marine species are subdivided into multiple populations that have evolved to thrive best under contrasting local environmental conditions. Nevertheless, we still know very little about the genomic mechanisms that enable divergent adaptations in the face of ongoing intermixing. This project focuses on the Atlantic silverside (Menidia menidia), a small estuarine fish that exhibits a remarkable degree of local adaptation in growth rates and a suite of other traits tightly associated with a climatic gradient across latitudes. Decades of prior lab and field studies have made Atlantic silverside one of the marine species for which we have the best understanding of evolutionary tradeoffs among traits and drivers of selection causing adaptive divergence. Yet, the underlying genomic basis is so far completely unknown. The investigators will integrate whole genome sequencing data from wild fish sampled across the distribution range with breeding experiments in the laboratory to decipher these genomic underpinnings. This will provide one of the most comprehensive assessments of the genomic basis for local adaptation in the oceans to date, thereby generating insights that are urgently needed for better predictions about how species can respond to rapid environmental change. The project will provide interdisciplinary training for a postdoc as well as two graduate and several undergraduate students from underrepresented minorities. The findings will also be leveraged to develop engaging teaching and outreach materials (e.g. a video documentary and popular science articles) to promote a better understanding of ecology, evolution, and local adaptation among science students and the general public.

The goal of the project is to characterize the genomic basis and architecture underlying local adaptation in M. menidia and examine how the adaptive divergence is shaped by varying levels of gene flow and maintained over ecological time scales. The project is organized into four interconnected components. Part 1 examines fine-scale spatial patterns of genomic differentiation along the adaptive cline to a) characterize the connectivity landscape, b) identify genomic regions under divergent selection, and c) deduce potential drivers and targets of selection by examining how allele frequencies vary in relation to environmental factors and biogeographic features. Part 2 maps key locally adapted traits to the genome to dissect their underlying genomic basis. Part 3 integrates patterns of variation in the wild (part 1) and the mapping of traits under controlled conditions (part 2) to a) examine how genomic architectures underlying local adaptation vary across gene flow regimes and b) elucidating the potential role of chromosomal rearrangements and other tight linkage among adaptive alleles in facilitating adaptation. Finally, part 4 examines dispersal - selection dynamics over seasonal time scales to a) infer how selection against migrants and their offspring maintains local adaptation despite homogenizing connectivity and b) validate candidate loci for local adaptation. Varying levels of gene flow across the species range create a natural experiment for testing general predictions about the genomic mechanisms that enable adaptive divergence in the face of gene flow. The findings will therefore have broad implications and will significantly advance our understanding of the role genomic architecture plays in modifying the gene flow - selection balance within coastal environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.


DatasetLatest Version DateCurrent State
Adult Black Sea Bass (Centropristis striata) winter survival and lipid accumulation in wild-caught fish in Long Island Sound in Sept of 2022 to Apr of 20232024-09-23Final no updates expected
Adult Black Sea Bass (Centropristis striata) winter survival and lipid accumulation under varying diet and temperature conditions from a laboratory mesocosm experiment (Oct 2022 to Apr 2023) with individuals collected in Long Island Sound2024-09-23Final no updates expected
Hatching success, survival and growth in northern stock black sea bass reared at contrasting pCO2 conditions in laboratory experiments conduced with embryos from adults collected in Long Island Sound in 20222024-09-16Final no updates expected
Morphometrics of black sea bass reared at contrasting pCO2 conditions in laboratory experiments conduced with embryos from adults collected in Long Island Sound in 20222024-09-16Final no updates expected
RADseq data from Atlantic silversides used for linkage and QTL mapping.2024-04-24Final no updates expected
2024-01-02Data not available
Sample information and genetic accession information for raw low-coverage genomic sequence reads from 248 different Atlantic silverside (Menidia menidia) collected along the east coast of North America between 2005 to 20072021-06-30Final no updates expected
Sample and genetic accession information for RNA-seq data from whole Atlantic silverside (Menidia menidia) larvae from two populations and their F1 hybrids reared under different temperatures in 20172021-06-29Final no updates expected
Methodology information and links to data access for allele frequencies and FST estimates for 1,904,119 SNPs analyzed in five population samples of Atlantic silverside (Menidia menidia) collected along the east coast of North America between 2005 to 20072021-06-29Final no updates expected

Project Home Page


People

Principal Investigator: Hannes Baumann
University of Connecticut (UConn)

Principal Investigator: Nina Overgaard Therkildsen
Cornell University (Cornell)

Contact: Nina Overgaard Therkildsen
Cornell University (Cornell)


Data Management Plan

DMP_Therkildsen_Baumann_OCE-1756316_1756751.pdf (92.21 KB)
02/09/2025