Search for: All records

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. 

The land surface hydrology of the North American Great Lakes region regulates ecosystem water availability, lake levels, vegetation dynamics, and agricultural practices. In this study, we analyze the Great Lakes terrestrial water budget using the Noah‐MP land surface model to characterize the catchment hydrological regimes and identify the dominant quantities contributing to the variability in the land surface hydrology. We show that the Great Lakes domain is not hydrologically uniform and strong spatiotemporal differences exist in the regulators of the hydrological budget at daily, monthly, and annual timescales. Subseasonally, precipitation and soil moisture explain nearly all the terrestrial water budget variability in the southern basins, while the northern latitudes are snow‐dominated regimes. Seasonal assessments reveal greater differences among the basins. Precipitation, evaporation, and runoff are the dominant sources of variability at lower latitudes, while at higher latitudes, terrestrial water storage in the form of ground snowpack and soil moisture has the leading role. Differences in land cover categorizations, for example, croplands, forests, or urban zones, further induce spatial differences in the hydrological characteristics. This quantification of variability in the terrestrial water cycle embedded at different temporal scales is important to assess the impacts of changes in climate and land cover on catchment sensitivities across the diverse hydroclimate of the Great Lakes region.

 

Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

 

In the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real‐time subseasonal‐to‐seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k‐nearest neighbors (kNN) technique is employed using North American Multi‐Model Ensemble one‐ and three‐month temperature and precipitation forecasts, and preceding three‐month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin‐wide kNN uses the same ESP weights over the entire basin, and a disaggregated‐basin kNN applies ESP weights separately to four subbasins. We find in general that climate‐informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated‐basin use of climate forecasts slightly improves skill over the basin‐wide method at most lead times.