Range-wide population genetic analysis of Seaside Sparrows (Ammospiza maritima) supports at least five distinct population segments that do not align with current subspecies descriptions

Kathryn E.C. Davis; Amie E. Settlecowski; Mackenzie R. Roeder; Carolyn Enloe; Thomas Virzi; Margaret Hunter; Stefan Woltmann; Sabrina S. Taylor

doi:10.1093/ornithapp/duab019

USGS · 70249824

Range-wide population genetic analysis of Seaside Sparrows (Ammospiza maritima) supports at least five distinct population segments that do not align with current subspecies descriptions

Kathryn E.C. Davis·Amie E. Settlecowski·Mackenzie R. Roeder·Carolyn Enloe·Thomas Virzi·Margaret Hunter·Stefan Woltmann·Sabrina S. Taylor

View original source ↗Export metadata ↓

Abstract

As an obligate salt marsh species, Seaside Sparrows ( Ammospiza maritima ) are vulnerable to numerous threats including climate change, coastal erosion, sea-level rise, and both natural and anthropogenic disasters. Of the 9 recognized subspecies, 2 are extinct and 1 is endangered. Previous genetic analyses of mitochondrial DNA (mtDNA) and microsatellite loci showed that current taxonomy does not accurately reflect underlying genetic diversity, with possible consequences for the distribution of conservation resources. To further inform Seaside Sparrow management, we comprehensively describe genetic structure among 24 range-wide sampling locations that include all extant subspecies. We inferred population structure from several thousand single-nucleotide polymorphisms collected from 272 individuals via restriction-site-associated DNA sequencing. Principal components, pairwise F ST values, and clustering approaches suggest that Seaside Sparrows on the Atlantic and Gulf Coasts are distinct and consist of at least 5 genetic clusters: 1 in southern Texas, 1 ranging from Aransas County, Texas, to Mississippi; 1 in western Florida; and 2 or 3 genetic groups intermixed along a gradient on the Atlantic Coast. These genetic clusters are not consistent with current subspecies taxonomy and could be used as distinct population segments (DPSs) to inform the most efficient allocation of resources to Seaside Sparrow conservation. Our results regarding the endangered subspecies, A. m. mirabilis , from southern Florida are inconclusive due to low sample size, but indicate that it is distinct and may represent a sixth DPS. Based on our genetic results, we recommend additional song and morphometric analyses in western Florida and a closer study of the boundary between the breeding distributions of A. m. maritima and A. m. macgillivraii to ensure the proper identification of DPSs.

Explore related subjects

Ornithological Applications Article

Source-reported bounding extent: 24.346311273292073° to 42.32675607314309° latitude; -98.44639168487419° to -68.7702480749089° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Kathryn E.C. Davis, Amie E. Settlecowski, Mackenzie R. Roeder, Carolyn Enloe, Thomas Virzi, Margaret Hunter, Stefan Woltmann, Sabrina S. Taylor. 2021-05-10. Range-wide population genetic analysis of Seaside Sparrows (Ammospiza maritima) supports at least five distinct population segments that do not align with current subspecies descriptions. https://doi.org/10.1093/ornithapp%2Fduab019

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗

USGS2026

Tringa flavipes (Lesser Yellowlegs) from separate breeding sites subdivides the Prairie Pothole Region in space and time during southbound migration

Some staging regions support multiple groups of the same migratory species, each of which may use the region differently. Characterizing the ways, in which separate groups use such regions can therefore help to identify vulnerabilities during this sensitive period of the annual cycle. The Prairie Pothole Region (PPR) is a massive wetland complex in the northern Great Plains of North America used by ∼11 million shorebirds during migration. The PPR has been heavily modified by agriculture and is experiencing varied effects of global climate change, threatening the health of the shorebirds that rely on it. Here, we used 6 seasons of southbound tracking data of Tringa flavipes (Lesser Yellowlegs)—a long-distance migratory shorebird species with an estimated population decline of 63% over the last 4 decades—from 9 sites across their breeding range to explore differences in migratory behavior within this important staging region. We found that 75% of tracked individuals used the region during southbound migration, and T. flavipes from different breeding sites detoured 110–875 km from their most direct migratory route to access the PPR. Individuals that arrived later stayed longer and made more stops within the region than those that arrived early. Individuals originating from different breeding sites also displayed spatial and temporal segregation within the region: T. flavipes from southwest and central Alaska relied heavily on the northwestern PPR, while those from Canada used the central and southeastern portions of the PPR. Finally, timing of use varied among groups, but the southeastern PPR became increasingly important over the course of the southbound migratory window, as other wetlands likely dried out. Our study highlights the portions of the PPR of critical importance to migrating T. flavipes and the diversity of ways, in which different groups from within the same species can use a single staging region.

Alaska, Manitoba, Northwest Territories, Ontario, ↗

USGS2026

Birds, breakpoints, and baselines: How citizen science data can reveal ecological boundaries in Kenya’s Upper Tana watershed

Tropical watersheds are increasingly threatened by climate change, land-use conversion, and resource extraction, yet conventional biodiversity monitoring in these systems is often spatially and temporally limited. Citizen science offers a complementary approach, enabling biodiversity data collection over large areas that can supplement professional scientific surveys. We analyzed 10 years (2012–2022) of Upper Tana Watershed bird data from the Kenya Bird Map project, covering 114 pentads (9 × 9 km) within a 17,000 km 2 watershed to assess patterns of bird community composition and distribution across this watershed and to also evaluate the effects of environmental variables, seasonality, and sampling effort to help inform improvements in future citizen science projects. Citizen (or community) scientists recorded 575 species (>50% of Kenya’s total avifauna) in 74 families. Asymptotic species accumulation indicates that most probable species present in the watershed were detected. Threshold indicator taxa analysis revealed distinct ecological boundaries along elevation (∼1,500 m), precipitation (∼1,100 mm), and mean temperature (∼19°C) gradients, corresponding to a turnover from xeric savanna to mesic montane forest assemblages. Notably, bird communities showed little seasonal differentiation between wet and dry periods, consistent with dominance by resident year-round species. Data limitations including uneven survey distribution and frequency, absence of abundance metrics, and coarse representation of local environmental conditions that likely reduced our ability to detect fine-scale species–habitat relationships. Addressing these gaps through spatially balanced sampling at greater resolution, greater survey frequency in underrepresented areas, and improved capture of habitat metrics could strengthen the use of citizen-science bird data for watershed bioassessment. Our findings demonstrate that structured citizen-science initiatives can identify ecological boundaries and inform adaptive management of tropical socio-ecological systems under rapid environmental change.

Upper Tana watershed↗