DOI: 10.5061/dryad.7d7wm3816
title: Data for: Surrounding landscape, habitat and hybridization dynamics drive population structure and genetic diversity in the Saltmarsh Sparrow (Ammospiza caudacuta)

description: Determining factors that shape a species’ population genetic structure is
 beneficial for identifying effective conservation practices. We assessed
 population structure and genetic diversity for Saltmarsh Sparrow
 (Ammospiza caudacuta), an imperiled tidal marsh specialist, using 13
 microsatellite markers and 964 individuals sampled from 24 marshes across
 the breeding range. We show that Saltmarsh Sparrow populations are
 structured regionally by isolation-by-distance, with gene flow occurring
 among marshes within ~110-135 km of one another. Isolation-by-resistance
 and isolation-by-environment also shape genetic variation; several habitat
 and landscape features are associated with genetic diversity and genetic
 divergence among populations. Human development in the surrounding
 landscape isolates breeding marshes, reducing genetic diversity and
 increasing population genetic divergence, while surrounding marshland and
 patch habitat quality (proportion high marsh and sea-level-rise trend)
 have the opposite effect. The distance of the breeding marsh to the
 Atlantic Ocean also influences genetic variation, with marshes farther
 inland being more divergent than coastal marshes. In northern marshes,
 hybridization with Nelson’s Sparrow (A. nelsoni) strongly influences
 Saltmarsh Sparrow genetic variation, by increasing genetic diversity in
 the population; this has a concomitant effect of increasing genetic
 differentiation of marshes with high levels of introgression. From a
 conservation perspective, we found that the majority of population
 clusters have low effective population sizes, suggesting a lack of
 resiliency. To conserve the representative breadth of genetic and
 ecological diversity and to ensure redundancy of populations, it will be
 important to protect a diversity of marsh types across the latitudinal
 gradient of the species range, including multiple inland, coastal and
 urban populations, which we have shown to exhibit signals of genetic
 differentiation. It will also require maintaining connectivity at a
 regional level, by promoting high marsh habitat at the scale of gene flow
 (~130 km), while also ensuring “stepping stone” populations across the
 range.   
description: Saltmarsh Sparrows (<em>n </em>= 964) were sampled
 from 24 marshes along the northeastern coastline of the United States over
 a nine-year period, between 2007-2015 (Figure 1; Table S1). Sampling
 covered approximately 60% of the global breeding range of Saltmarsh
 Sparrows, with sites in Maine (<em>n</em> = 6), New Hampshire
 (<em>n</em> = 4), Massachusetts (<em>n </em>= 2),
 Rhode Island (<em>n</em> = 2), Connecticut
 (<em>n</em> = 3), New York (<em>n</em> = 5), and
 New Jersey (<em>n</em> = 2). A portion of the samples analyzed
 in this study were collected in 2007 – 2008 for a study evaluating
 fine-scale genetic structure in Saltmarsh Sparrows in the northern portion
 of their range (Walsh et al. 2012) and in 2012 – 2013 while studying
 patterns of introgression between Saltmarsh Sparrows and their sister
 species, the Nelson’s Sparrow (Walsh et al. 2015). The majority of the
 remaining samples were collected during a three-year (2011 – 2013) study
 investigating survival and fecundity of Saltmarsh Sparrows (Ruskin et al.,
 2017a,b, Field et al. 2017b). We captured adult individuals using mist
 nets and banded each individual with uniquely numbered aluminum USGS
 bands. Whenever possible, blood samples (10 µl) were drawn from the
 cutaneous ulnar vein using a non-heparinized capillary tube and stored at
 room temperature on filter cards (Whatman or Nobuto) for later genetic
 analysis. When blood sampling was not possible, we pulled the two outer
 tail feathers (R6) and stored feathers in a -20ºC freezer.
 DNA was extracted from blood samples using a DNeasy Blood Kit
 (Qiagen, Valencia, CA) following manufacturer’s instructions. For tail
 feathers, we isolated the calamus and followed the same protocol as with a
 standard tissue extraction, except with the addition of 10 μl of
 dithiothreitol to the lysis buffer and a 48-hour incubation. We amplified
 DNA using fluorescent dye-labeled primers for 16 microsatellite loci in
 three multiplexes: <em>Aca</em>01,<em> Aca</em>04,
 <em>Aca</em>05, <em>Aca</em>08,
 <em>Aca</em>11, <em>Aca</em>12,
 <em>Aca</em>17 (Hill et al. 2008), <em>Escm</em>1
 (Hanotte et al. 1994), <em>Asm</em>15 (Bulgin et al. 2003),
 <em>Ammo</em>006, <em>Ammo</em>011,
 <em>Ammo</em>015, <em>Ammo</em>020,<em>
 Ammo</em>027, <em>Ammo</em>034, and
 <em>Ammo</em>037 (Kovach et al. 2015). Amplification of the
 Hill et al. (2008), Hanotte et al. (1994), and Bulgin et al. (2003)
 primers followed conditions of Walsh et al. (2012). Amplification of the
 Kovach et al. (2015) primers followed conditions in Walsh et al. (2015)
 and Kovach et al. (2015). We conducted two replicates for feather samples
 to reduce genotyping error associated with lower quality samples.
 Amplified products were electrophoresed on an automated DNA sequencer (ABI
 3130 genetic analyzer, Applied Biosystems, Foster City, CA) and individual
 genotypes were scored manually using peakscanner software (Applied
 Biosystems). We compiled data for each breeding marsh
 for a suite of landscape variables that were expected to represent
 important patch-level (within a marsh) and between-marsh connectivity
 variables. Patch delineation was based on an existing marsh patch layer
 (Wiest et al. 2019) created in ArcGIS v 9.3 (ESRI 2009). Using this GIS
 layer, we obtained landscape variables for each sampling location. These
 included four patch-level variables (patch size (ha), patch perimeter (m),
 annual sea-level trend (changes in mean sea level over 30+ consecutive
 years, in mm/year), and proportion of high marsh vegetation) and six
 connectivity variables measured within a 1000-m buffer surrounding the
 marsh patch (proportion of surrounding natural lands, of developed lands,
 of agricultural lands, of surrounding roads, of open water, and of
 neighboring marsh). We calculated three additional patch-level
 characteristics, which we hypothesized to be important in classifying
 marsh habitat. These included distance of the marsh to the Atlantic
 coastline (m), average mean high water (average of high water heights over
 the National Tidal Datum Epoch, from NOAA), and a proximity index to
 quantify the connectivity of a patch to neighboring marshes (essentially a
 function of the size of the marsh and the distance of the marsh to the
 next nearest marshland; see Gustafson and Parker, 1994). We also included
 a binary dummy categorical variable in our analyses to code for whether a
 site fell within or outside of the Saltmarsh-Nelson’s sparrow hybrid zone,
 between South Thomaston, ME and Newburyport, MA, as defined by Hodgman et
 al. (2002) (within hybrid zone = 1; outside of hybrid zone = 0).