Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

The following is an excerpt of the methods used to generate these data. Full details can be found in the associated publication and supplemental material of Wilder et al. (2020).

Data generation:

Atlantic silverside muscle samples collected from Georgia (GA), New York (NY), Gulf of Maine (GoM) and Gulf of St. Lawrence (GSL) were sequenced by Therkildsen & Palumbi (2017) to a final, average depth of 1.5x across the reference transcriptome. All the samples were collected in the spring.  See Therkildsen & Palumbi (2017) and Therkildsen et al. (2019) for further detail. We added 47 additional samples from Oregon Inlet, North Carolina (NC) near Cape Hatteras. 

Individually barcoded sample libraries were prepared at Cornells Biotechnology Resource Center using similar methods to Therkildsen & Palumbi (2017), with a few minor modifications. Reagents from the Illumina Nextera kit (96 sample Nextera DNA Library Prep Kit) were used at 1/3 the recommended concentration in 1/10 the recommended volume (5ul instead of 50ul), with 2ng of input DNA. Individual libraries were pooled, and size-selected using a Pippin Prep to remove fragments <286 bp (150 bp insert plus 136 bp of Illumina adapters). Filtered mapped reads for the NC samples had a final average depth of 1.5x.

Sampling and analytical procedures:

Samples were collected directly from the wild as described in Hice et al. (2012).

Locations:
Five locations along the east coast of North America.

USA:Jekyll Island,Georgia 31.02 -81.43
USA:Oregon Inlet,NorthCarolina 35.77 -75.52
USA:Minas Basin,Gulf of Maine 45.2 -64.38
USA:Patchogue,NewYork 40.75 -73.00
Canada:Magdalen Island, Gulf of St. Lawrence 47.4 -61.85

Problem report:

The following samples should be excluded from analysis because of possible errors during library preparation:
JekyllIs_1223, JekyllIs_1224, MagdalenIs_911, MagdalenIs_912, MagdalenIs_913, MagdalenIs_914, MagdalenIs_915, MagdalenIs_916, MagdalenIs_917, MagdalenIs_918, Patchogue_1030

BCO-DMO Data Manager Processing Notes:
* Converted SampleLocationDetails.xlsx Sheet 1 to csv and imported into the BCO-DMO data system.
* Split latitude and longitude into respective columns from combined string column.
* replaced comma in geolocation name to semicolon for better support for csv exports.

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.