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.   

Saltmarsh Sparrows (n = 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 (n = 6), New Hampshire (n = 4), Massachusetts (n = 2), Rhode Island (n = 2), Connecticut (n = 3), New York (n = 5), and New Jersey (n = 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: Aca01, Aca04, Aca05, Aca08, Aca11, Aca12, Aca17 (Hill et al. 2008), Escm1 (Hanotte et al. 1994), Asm15 (Bulgin et al. 2003), Ammo006, Ammo011, Ammo015, Ammo020, Ammo027, Ammo034, and Ammo037 (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).