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The Kirtland's warbler (Setophaga kirtlandi) is a rare migratory passerine species and habitat specialist of the North American Jack Pine Forests. Their near extinction in the 1970s classified them as endangered and protected under the Endangered Species Act of 1973. After decades of intense conservation management, their population size recovered, and they were delisted from federal protection in 2019. We explore the genomic consequences of this harsh bottleneck and recovery by comparing the genomic architecture of two closely related species whose population sizes have remained large and stable, Hooded Warblers (Setophaga citrina) and American Redstarts (Setophaga ruticilla). We used whole‐genome sequencing to characterize the distribution of runs of homozygosity and deleterious genetic variation. We find evidence that Kirtland's warblers exhibit genetic patterns consistent with recent inbreeding. Our results also show that Kirtland's warblers carry an excess proportion of deleterious variation, which could complicate management for this conservation‐reliant species. This analysis provides a genetically informed perspective that should be thoroughly considered when delisting other species from federal protections. Through the increasing accessibility of genome sequencing technology, it will be more feasible to monitor the genetic landscape of recovering populations to ensure their long‐term survival independent of conservation intervention. 

Vertebrates host complex microbiomes that impact their physiology. In many taxa, including colourful wood-warblers, gut microbiome similarity decreases with evolutionary distance. This may suggest that as host populations diverge, so do their microbiomes, because of either tight coevolutionary dynamics, or differential environmental influences, or both. Hybridization is common in wood-warblers, but the effects of evolutionary divergence on the microbiome during secondary contact are unclear. Here, we analyse gut microbiomes in two geographically disjunct hybrid zones between blue-winged warblers (Vermivora cyanoptera) and golden-winged warblers (Vermivora chrysoptera). We performed 16S faecal metabarcoding to identify species-specific bacteria and test the hypothesis that host admixture is associated with gut microbiome disruption. Species identity explained a small amount of variation between microbiomes in only one hybrid zone. Co-occurrence of species-specific bacteria was rare for admixed individuals, yet microbiome richness was similar among admixed and parental individuals. Unexpectedly, we found several bacteria that were more abundant among admixed individuals with a broader deposition of carotenoid-based plumage pigments. These bacteria are predicted to encode carotenoid biosynthesis genes, suggesting birds may take advantage of pigments produced by their gut microbiomes. Thus, host admixture may facilitate beneficial symbiotic interactions which contribute to plumage ornaments that function in sexual selection. 

Bachman’s warbler (Vermivora bachmanii)—last sighted in 1988—is one of the only North American passerines to recently go extinct. Given extensive ongoing hybridization of its two extant congeners—the bluewinged warbler (V. cyanoptera) and golden-winged warbler (V. chrysoptera)—and shared patterns of plumage variation between Bachman’s warbler and hybrids between those extant species, it has been suggested that Bachman’s warbler might have also had a component of hybrid ancestry. Here, we use historic DNA (hDNA) and whole genomes of Bachman’s warblers collected at the turn of the 20th century to address this. We combine these data with the two extant Vermivora species to examine patterns of population differentiation, inbreeding, and gene flow. In contrast to the admixture hypothesis, the genomic evidence is consistent with V. bachmanii having been a highly divergent, reproductively isolated species, with no evidence of introgression. We show that these three species have similar levels of runs of homozygosity (ROH), consistent with effects of a small long-term effective population size or population bottlenecks, with one V. bachmanii outlier showing numerous long ROH and a FROH greater than 5%. We also found—using population branch statistic estimates—previously undocumented evidence of lineage-specific evolution in V. chrysoptera near a pigmentation gene candidate, CORIN, which is a known modifier of ASIP, which is in turn involved in melanic throat and mask coloration in this family of birds. Together, these genomic results also highlight how natural history collections are such invaluable repositories of information about extant and extinct species. 

Abstract Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.