Best practices for genetic and genomic data archiving.

Genetic and genomic data are collected for a vast array of scientific and applied purposes. Despite mandates for public archiving, data are typically used only by the generating authors. The reuse of genetic and genomic datasets remains uncommon because it is difficult, if not impossible, due to non-standard archiving practices and lack of contextual metadata. But as the new field of macrogenetics is demonstrating, if genetic data and their metadata were more accessible and FAIR (findable, accessible, interoperable and reusable) compliant, they could be reused for many additional purposes. We discuss the main challenges with existing genetic and genomic data archives, and suggest best practices for archiving genetic and genomic data. Recognizing that this is a longstanding issue due to little formal data management training within the fields of ecology and evolution, we highlight steps that research institutions and publishers could take to improve data archiving.

The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population.

Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO = 0.65) was higher in the recipient population than predicted had no augmentation occurred (HO = 0.58) but less than what was predicted by our model (HO = 0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype-environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.

Influencia potencial de la divergencia adaptativa a nivel genoma sobre el resultado de la reubicación para conservación en una población aislada de urogallo mayor Resumen Las reubicaciones para conservación son una herramienta importante que se usa con frecuencia para aumentar las poblaciones en declinación o reestablecer las poblaciones erradicadas. Una de las metas de este aumento es incrementar la diversidad genética y reducir el riesgo de depresión endogámica (es decir, rescate genético). Sin embargo, la introducción de individuos de una población con divergencia significativa puede perturbar los rasgos coadaptados y reducir la aptitud local (es decir, depresión exogámica). La información genética es cada vez más accesible para las especies silvestres y puede proporcionar conocimiento único con respecto a la presencia y retención de la variación genética introducida a partir del aumento como un indicador de eficiencia y las similitudes adaptativas como un indicador de la idoneidad de la población de origen y la receptora. Usamos dos conjuntos de datos genéticos para evaluar el aumento de las poblaciones aisladas del urogallo mayor (Centrocercus urophasianus) en la región noroeste de la distribución de la especie (Washington, EUA) y para evaluar de forma retrospectiva la divergencia adaptativa entre la población de origen y la receptora. Desarrollamos dos modelos estadísticos para los datos microsatelitales para así evaluar los resultados del aumento. Usamos un modelo para predecir la diversidad genética después del aumento y comparamos estas predicciones con observaciones del cambio genético. Usamos el segundo modelo para cuantificar el aumento de la reproducción observada atribuida a las reubicaciones (evidencia de la integración poblacional). También caracterizamos la divergencia adaptativa a nivel genoma entre la población de origen y la población receptora. La diversidad genética observada (HO = 0.65) fue mayor de lo que se predijo en la población receptora de no haber ocurrido el aumento (HO = 0.58) pero menor de lo que se predijo en nuestro modelo (HO = 0.75). El aumento de la variación genética compartida entre las dos poblaciones residentes geográficamente aisladas incrementó, lo cual es evidencia de un flujo génico periódico que antes se supuso casi no ocurría. Entre los genes adaptativos candidatos asociados a una FST elevada (143 genes) o a variables ambientales locales (97 y 157 genes para cada método de asociación entre el ambiente y el genotipo, respectivamente) encontramos grupos de genes con funciones relacionadas que pueden influir sobre la habilidad de cada reubicación para usar recursos locales y navegar ambientes desconocidos y su potencial reproductivo, todas posibles razones para la baja retención genética en el aumento.

Comparing modern identification methods for wild bees: Metabarcoding and image-based morphological taxonomic assignment.

With the decline of bee populations worldwide, studies determining current wild bee distributions and diversity are increasingly important. Wild bee identification is often completed by experienced taxonomists or by genetic analysis. The current study was designed to compare two methods of identification including: (1) morphological identification by experienced taxonomists using images of field-collected wild bees and (2) genetic analysis of composite bee legs (multiple taxa) using metabarcoding. Bees were collected from conservation grasslands in eastern Iowa in summer 2019 and identified to the lowest taxonomic unit using both methods. Sanger sequencing of individual wild bee legs was used as a positive control for metabarcoding. Morphological identification of bees using images resulted in 36 unique taxa among 22 genera, and >80% of Bombus specimens were identified to species. Metabarcoding was limited to genus-level assignments among 18 genera but resolved some morphologically similar genera. Metabarcoding did not consistently detect all genera in the composite samples, including kleptoparasitic bees. Sanger sequencing showed similar presence or absence detection results as metabarcoding but provided species-level identifications for cryptic species (i.e., Lasioglossum). Genus-specific detections were more frequent with morphological identification than metabarcoding, but certain genera such as Ceratina and Halictus were identified equally well with metabarcoding and morphology. Genera with proportionately less tissue in a composite sample were less likely to be detected using metabarcoding. Image-based methods were limited by image quality and visible morphological features, while genetic methods were limited by databases, primers, and amplification at target loci. This study shows how an image-based identification method compares with genetic techniques, and how in combination, the methods provide valuable genus- and species-level information for wild bees while preserving tissue for other analyses. These methods could be improved and transferred to a field setting to advance our understanding of wild bee distributions and to expedite conservation research.

The ties that bind the sagebrush biome: integrating genetic connectivity into range-wide conservation of greater sage-grouse.

Conserving genetic connectivity is fundamental to species persistence, yet rarely is made actionable into spatial planning for imperilled species. Climate change and habitat degradation have added urgency to embrace connectivity into networks of protected areas. Our two-step process integrates a network model with a functional connectivity model, to identify population centres important to maintaining genetic connectivity then to delineate those pathways most likely to facilitate connectivity thereamong for the greater sage-grouse (Centrocercus urophasianus), a species of conservation concern ranging across eleven western US states and into two Canadian provinces. This replicable process yielded spatial action maps, able to be prioritized by importance to maintaining range-wide genetic connectivity. We used these maps to investigate the efficacy of 3.2 million ha designated as priority areas for conservation (PACs) to encompass functional connectivity. We discovered that PACs encompassed 41.1% of cumulative functional connectivity-twice the amount of connectivity as random-and disproportionately encompassed the highest-connectivity landscapes. Comparing spatial action maps to impedances to connectivity such as cultivation and woodland expansion allows both planning for future management and tracking outcomes from past efforts.

A genetic warning system for a hierarchically structured wildlife monitoring framework.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species' conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., neighborhood-scale) with low genetic diversity, further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity and developed a statistical model for microsatellite data to robustly estimate these values for hierarchically nested populations. We found that 41 of 224 neighborhood-scale clusters had low genetic diversity, 23 of which were coupled with documented local population trend decline. We also found evidence of cross-scale low genetic diversity in the small and isolated Washington population, unlikely to be reversed through typical local management actions alone. The combination of low genetic diversity and a declining population suggests relatively high conservation concern. Our findings could further facilitate conservation action prioritization in combination with population trend assessments and (or) local information, and act as a base-line of genetic diversity for future comparison. Importantly, the approach we used is broadly applicable across taxa.

New strategies for characterizing genetic structure in wide-ranging, continuously distributed species: A Greater Sage-grouse case study.

Characterizing genetic structure across a species' range is relevant for management and conservation as it can be used to define population boundaries and quantify connectivity. Wide-ranging species residing in continuously distributed habitat pose substantial challenges for the characterization of genetic structure as many analytical methods used are less effective when isolation by distance is an underlying biological pattern. Here, we illustrate strategies for overcoming these challenges using a species of significant conservation concern, the Greater Sage-grouse (Centrocercus urophasianus), providing a new method to identify centers of genetic differentiation and combining multiple methods to help inform management and conservation strategies for this and other such species. Our objectives were to (1) describe large-scale patterns of population genetic structure and gene flow and (2) to characterize genetic subpopulation centers across the range of Greater Sage-grouse. Samples from 2,134 individuals were genotyped at 15 microsatellite loci. Using standard STRUCTURE and spatial principal components analyses, we found evidence for four or six areas of large-scale genetic differentiation and, following our novel method, 12 subpopulation centers of differentiation. Gene flow was greater, and differentiation reduced in areas of contiguous habitat (eastern Montana, most of Wyoming, much of Oregon, Nevada, and parts of Idaho). As expected, areas of fragmented habitat such as in Utah (with 6 subpopulation centers) exhibited the greatest genetic differentiation and lowest effective migration. The subpopulation centers defined here could be monitored to maintain genetic diversity and connectivity with other subpopulation centers. Many areas outside subpopulation centers are contact zones where different genetic groups converge and could be priorities for maintaining overall connectivity. Our novel method and process of leveraging multiple different analyses to find common genetic patterns provides a path forward to characterizing genetic structure in wide-ranging, continuously distributed species.

Scale-dependent influence of the sagebrush community on genetic connectivity of the sagebrush obligate Gunnison sage-grouse.

Habitat fragmentation and degradation impacts an organism's ability to navigate the landscape, ultimately resulting in decreased gene flow and increased extinction risk. Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse (Centrocercus minimus). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.

Historical effective population size of North American hoary bat (Lasiurus cinereus) and challenges to estimating trends in contemporary effective breeding population size from archived samples.

BACKGROUND: Hoary bats (Lasiurus cinereus) are among the bat species most commonly killed by wind turbine strikes in the midwestern United States. The impact of this mortality on species census size is not understood, due in part to the difficulty of estimating population size for this highly migratory and elusive species. Genetic effective population size (Ne) could provide an index of changing census population size if other factors affecting Ne are stable. METHODS: We used the NeEstimator package to derive effective breeding population size (Nb) estimates for two temporally spaced cohorts: 93 hoary bats collected in 2009-2010 and an additional 93 collected in 2017-2018. We sequenced restriction-site associated polymorphisms and generated a de novo genome assembly to guide the removal of sex-linked and multi-copy loci, as well as identify physically linked markers. RESULTS: Analysis of the reference genome with psmc suggested at least a doubling of Ne in the last 100,000 years, likely exceeding Ne = 10,000 in the Holocene. Allele and genotype frequency analyses confirmed that the two cohorts were comparable, although some samples had unusually high or low observed heterozygosities. Additionally, the older cohort had lower mean coverage and greater variability in coverage, and batch effects of sampling locality were observed that were consistent with sample degradation. We therefore excluded samples with low coverage or outlier heterozygosity, as well as loci with sequence coverage far from the mode value, from the final data set. Prior to excluding these outliers, contemporary Nb estimates were significantly higher in the more recent cohort, but this finding was driven by high values for the 2018 sample year and low values for all other years. In the reduced data set, Nb did not differ significantly between cohorts. We found base substitutions to be strongly biased toward cytosine to thymine or the complement, and further partitioning loci by substitution type had a strong effect on Nb estimates. Minor allele frequency and base quality bias thresholds also had strong effects on Nb estimates. Instability of Nb with respect to common data filtering parameters and empirically identified factors prevented robust comparison of the two cohorts. Given that confidence intervals frequently included infinity as the stringency of data filtering increased, contemporary trends in Nb of North American hoary bats may not be tractable with the linkage disequilibrium method, at least using the protocol employed here.

Environmental gradients of selection for an alpine-obligate bird, the white-tailed ptarmigan (Lagopus leucura).

The warming climate will expose alpine species adapted to a highly seasonal, harsh environment to novel environmental conditions. A species can shift their distribution, acclimate, or adapt in response to a new climate. Alpine species have little suitable habitat to shift their distribution, and the limits of acclimation will likely be tested by climate change in the long-term. Adaptive genetic variation may provide the raw material for species to adapt to this changing environment. Here, we use a genomic approach to describe adaptive divergence in an alpine-obligate species, the white-tailed ptarmigan (Lagopus leucura), a species distributed from Alaska to New Mexico, across an environmentally variable geographic range. Previous work has identified genetic structure and morphological, behavioral, and physiological differences across the species' range; however, those studies were unable to determine the degree to which adaptive divergence is correlated with local variation in environmental conditions. We used a genome-wide dataset generated from 95 white-tailed ptarmigan distributed throughout the species' range and genotype-environment association analyses to identify the genetic signature and environmental drivers of local adaptation. We detected associations between multiple environmental gradients and candidate adaptive loci, suggesting ptarmigan populations may be locally adapted to the plant community composition, elevation, local climate, and to the seasonality of the environment. Overall, our results suggest there may be groups within the species' range with genetic variation that could be essential for adapting to a changing climate and helpful in guiding conservation action.

An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern.

BACKGROUND: Use of genomic tools to characterize wildlife populations has increased in recent years. In the past, genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed with microsatellites and SNPs have been conducted. RESULTS: Here we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus) samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic diversity (HO, HE, FIS, AR) and differentiation (FST, GST, DJost). Additionally, we evaluated clustering of individuals using putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and adaptive loci. We took particular interest in the conservation implications of any differences. Generally, we found high concordance between microsatellites and SNPs for HE, FIS, AR, and all differentiation estimates. Although there was strong correlation between metrics from SNPs and microsatellites, the magnitude of the diversity and differentiation metrics were quite different in some cases. Clustering analyses also showed similar patterns, though SNP data was able to cluster individuals into more distinct groups. Importantly, clustering analyses with SNP data suggest strong demographic independence among the six distinct populations of Gunnison sage-grouse with some indication of evolutionary independence in two or three populations; a finding that was not revealed by microsatellite data. CONCLUSION: We demonstrate that SNPs have three main advantages over microsatellites: more precise estimates of population-level diversity, higher power to identify groups in clustering methods, and the ability to consider local adaptation. This study adds to a growing body of work comparing the use of SNPs and microsatellites to evaluate genetic diversity and differentiation for a species of conservation concern with relatively high population structure and using the most common method of obtaining SNP genotypes for non-model organisms.

Signatures of adaptive divergence among populations of an avian species of conservation concern.

Understanding the genetic underpinning of adaptive divergence among populations is a key goal of evolutionary biology and conservation. Gunnison sage-grouse (Centrocercus minimus) is a sagebrush obligate species with a constricted range consisting of seven discrete populations, each with distinctly different habitat and climatic conditions. Though geographically close, populations have low levels of natural gene flow resulting in relatively high levels of differentiation. Here, we use 15,033 SNP loci in genomic outlier analyses, genotype-environment association analyses, and gene ontology enrichment tests to examine patterns of putatively adaptive genetic differentiation in an avian species of conservation concern. We found 411 loci within 5 kbp of 289 putative genes associated with biological functions or pathways that were overrepresented in the assemblage of outlier SNPs. The identified gene set was enriched for cytochrome P450 gene family members (CYP4V2, CYP2R1, CYP2C23B, CYP4B1) and could impact metabolism of plant secondary metabolites, a critical challenge for sagebrush obligates. Additionally, the gene set was also enriched with members potentially involved in antiviral response (DEAD box helicase gene family and SETX). Our results provide a first look at local adaption for isolated populations of a single species and suggest adaptive divergence in multiple metabolic and biochemical pathways may be occurring. This information can be useful in managing this species of conservation concern, for example, to identify unique populations to conserve, avoid translocation or release of individuals that may swamp locally adapted genetic diversity, or guide habitat restoration efforts.

Mismatches between breeding phenology and resource abundance of resident alpine ptarmigan negatively affect chick survival.

Phenological mismatches-defined here as the difference in reproductive timing of an individual relative to the availability of its food resources-occur in many avian species. Mistiming breeding activities in environments with constrained breeding windows may have severe fitness costs due to reduced opportunities for repeated breeding attempts. Therefore, species occurring in alpine environments may be particularly vulnerable.We studied fitness consequences of timing of breeding in an alpine-endemic species, the white-tailed ptarmigan (Lagopus leucura), to investigate its influence on chick survival. We estimated phenological mismatch by measuring plant and arthropods used by ptarmigan in relation to their timing of breeding.We monitored 120 nests and 67 broods over a three-year period (2013-2015) at three alpine study sites in the Rocky Mountains of Colorado. During this same period, we actively monitored food resource abundance in brood-use areas to develop year and site-specific resource phenology curves. We developed several mismatch indices from these curves that were then fit as covariates in mark-recapture chick survival models.A correlation analysis between seasonal changes in arthropod and food plant abundance indicated that a normalized difference vegetation index (NDVI) was likely the best predictor for food available to hens and chicks. A survival model that included an interaction between NDVI mismatch and chick age received strong support and indicated young chicks were more susceptible to mismatch than older chicks.We provide evidence that individual females of a resident alpine species can be negatively affected by phenological mismatch. Our study focused on individual females and did not examine if phenological mismatch was present at the population level. Future work in animal populations occurring in mountain systems focusing on a combination of both individual- and population-level metrics of mismatch will be beneficial.

Development of microsatellite loci for two New World vultures (Cathartidae).

OBJECTIVE: Use next-generation sequencing to develop microsatellite loci that will provide the variability necessary for studies of genetic diversity and population connectivity of two New World vulture species. RESULTS: We characterized 11 microsatellite loci for black vultures (Coragyps atratus) and 14 loci for turkey vultures (Cathartes aura). These microsatellite loci were grouped into 3 multiplex panels for each species. The number of alleles among black vulture samples ranged from 2 to 11, and 3 to 48 among turkey vulture samples.

Conservation Genomics in the Sagebrush Sea: Population Divergence, Demographic History, and Local Adaptation in Sage-Grouse (Centrocercus spp.).

Sage-grouse are two closely related iconic species of the North American West, with historically broad distributions across sagebrush-steppe habitat. Both species are dietary specialists on sagebrush during winter, with presumed adaptations to tolerate the high concentrations of toxic secondary metabolites that function as plant chemical defenses. Marked range contraction and declining population sizes since European settlement have motivated efforts to identify distinct population genetic variation, particularly that which might be associated with local genetic adaptation and dietary specialization of sage-grouse. We assembled a reference genome and performed whole-genome sequencing across sage-grouse from six populations, encompassing both species and including several populations on the periphery of the species ranges. Population genomic analyses reaffirmed genome-wide differentiation between greater and Gunnison sage-grouse, revealed pronounced intraspecific population structure, and highlighted important differentiation of a small isolated population of greater sage-grouse in the northwest of the range. Patterns of genome-wide differentiation were largely consistent with a hypothesized role of genetic drift due to limited gene flow among populations. Inferred ancient population demography suggested persistent declines in effective population sizes that have likely contributed to differentiation within and among species. Several genomic regions with single-nucleotide polymorphisms exhibiting extreme population differentiation were associated with candidate genes linked to metabolism of xenobiotic compounds. In vitro activity of enzymes isolated from sage-grouse livers supported a role for these genes in detoxification of sagebrush, suggesting that the observed interpopulation variation may underlie important local dietary adaptations, warranting close consideration for conservation strategies that link sage-grouse to the chemistry of local sagebrush.

Evaluation of a Chicken 600K SNP genotyping array in non-model species of grouse.

The use of single nucleotide polymorphism (SNP) arrays to generate large SNP datasets for comparison purposes have recently become an attractive alternative to other genotyping methods. Although most SNP arrays were originally developed for domestic organisms, they can be effectively applied to wild relatives to obtain large panels of SNPs. In this study, we tested the cross-species application of the Affymetrix 600K Chicken SNP array in five species of North American prairie grouse (Centrocercus and Tympanuchus genera). Two individuals were genotyped per species for a total of ten samples. A high proportion (91%) of the total 580 961 SNPs were genotyped in at least one individual (73-76% SNPs genotyped per species). Principal component analysis with autosomal SNPs separated the two genera, but failed to clearly distinguish species within genera. Gene ontology analysis identified a set of genes related to morphogenesis and development (including genes involved in feather development), which may be primarily responsible for large phenotypic differences between Centrocercus and Tympanuchus grouse. Our study provided evidence for successful cross-species application of the chicken SNP array in grouse which diverged ca. 37 mya from the chicken lineage. As far as we are aware, this is the first reported application of a SNP array in non-passerine birds, and it demonstrates the feasibility of using commercial SNP arrays in research on non-model bird species.

An experimental comparison of composite and grab sampling of stream water for metagenetic analysis of environmental DNA.

Use of environmental DNA (eDNA) to assess distributions of aquatic and semi-aquatic macroorganisms is promising, but sampling schemes may need to be tailored to specific objectives. Given the potentially high variance in aquatic eDNA among replicate grab samples, compositing smaller water volumes collected over a period of time may be more effective for some applications. In this study, we compared eDNA profiles from composite water samples aggregated over three hours with grab water samples. Both sampling patterns were performed with identical autosamplers paired at two different sites in a headwater stream environment, augmented with exogenous fish eDNA from an upstream rearing facility. Samples were filtered through 0.8 µm cellulose nitrate filters and DNA was extracted with a cetyl trimethylammonium bromide procedure. Eukaryotic and bacterial community profiles were derived by amplicon sequencing of 12S ribosomal, 16S ribosomal, and cytochrome oxidase I loci. Operational taxa were assigned to genus with a lowest common ancestor approach for eukaryotes and to family with the RDP Classifier software for prokaryotes. Eukaryotic community profiles were more consistent with composite sampling than grab sampling. Downstream, rarefaction curves suggested faster taxon accumulation for composite samples, and estimated richness was higher for composite samples as a set than for grab samples. Upstream, composite sampling produced lower estimated richness than grab samples, but with overlapping standard errors. Furthermore, a bimodal pattern of richness as a function of sequence counts suggested the impact of clumped particles on upstream samples. Bacterial profiles were insensitive to sample method, consistent with the more even dispersion expected for bacteria compared with eukaryotic eDNA. Overall, samples composited over 3 h performed equal to or better than triplicate grab sampling for quantitative community metrics, despite the higher total sequencing effort provided to grab replicates. On the other hand, taxon-specific detection rates did not differ appreciably and the two methods gave similar estimates of the ratio of the common fish genera Salmo and Coregonus at each site. Unexpectedly, Salmo eDNA dropped out substantially faster than Coregonus eDNA between the two sites regardless of sampling method, suggesting that differential settling affects the estimation of relative abundance. We identified bacterial patterns that were associated with eukaryotic diversity, suggesting potential roles as biomarkers of sample representativeness.

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range-wide gene flow in sage-grouse.

Functional connectivity, quantified using landscape genetics, can inform conservation through the identification of factors linking genetic structure to landscape mechanisms. We used breeding habitat metrics, landscape attributes, and indices of grouse abundance, to compare fit between structural connectivity and genetic differentiation within five long-established Sage-Grouse Management Zones (MZ) I-V using microsatellite genotypes from 6,844 greater sage-grouse (Centrocercus urophasianus) collected across their 10.7 million-km2 range. We estimated structural connectivity using a circuit theory-based approach where we built resistance surfaces using thresholds dividing the landscape into "habitat" and "nonhabitat" and nodes were clusters of sage-grouse leks (where feather samples were collected using noninvasive techniques). As hypothesized, MZ-specific habitat metrics were the best predictors of differentiation. To our surprise, inclusion of grouse abundance-corrected indices did not greatly improve model fit in most MZs. Functional connectivity of breeding habitat was reduced when probability of lek occurrence dropped below 0.25 (MZs I, IV) and 0.5 (II), thresholds lower than those previously identified as required for the formation of breeding leks, which suggests that individuals are willing to travel through undesirable habitat. The individual MZ landscape results suggested terrain roughness and steepness shaped functional connectivity across all MZs. Across respective MZs, sagebrush availability (<10%-30%; II, IV, V), tree canopy cover (>10%; I, II, IV), and cultivation (>25%; I, II, IV, V) each reduced movement beyond their respective thresholds. Model validations confirmed variation in predictive ability across MZs with top resistance surfaces better predicting gene flow than geographic distance alone, especially in cases of low and high differentiation among lek groups. The resultant resistance maps we produced spatially depict the strength and redundancy of range-wide gene flow and can help direct conservation actions to maintain and restore functional connectivity for sage-grouse.

The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity.

Genetic networks can characterize complex genetic relationships among groups of individuals, which can be used to rank nodes most important to the overall connectivity of the system. Ranking allows scarce resources to be guided toward nodes integral to connectivity. The greater sage-grouse (Centrocercus urophasianus) is a species of conservation concern that breeds on spatially discrete leks that must remain connected by genetic exchange for population persistence. We genotyped 5,950 individuals from 1,200 greater sage-grouse leks distributed across the entire species' geographic range. We found a small-world network composed of 458 nodes connected by 14,481 edges. This network was composed of hubs-that is, nodes facilitating gene flow across the network-and spokes-that is, nodes where connectivity is served by hubs. It is within these hubs that the greatest genetic diversity was housed. Using indices of network centrality, we identified hub nodes of greatest conservation importance. We also identified keystone nodes with elevated centrality despite low local population size. Hub and keystone nodes were found across the entire species' contiguous range, although nodes with elevated importance to network-wide connectivity were found more central: especially in northeastern, central, and southwestern Wyoming and eastern Idaho. Nodes among which genes are most readily exchanged were mostly located in Montana and northern Wyoming, as well as Utah and eastern Nevada. The loss of hub or keystone nodes could lead to the disintegration of the network into smaller, isolated subnetworks. Protecting both hub nodes and keystone nodes will conserve genetic diversity and should maintain network connections to ensure a resilient and viable population over time. Our analysis shows that network models can be used to model gene flow, offering insights into its pattern and process, with application to prioritizing landscapes for conservation.

Long-term persistence of horse fecal DNA in the environment makes equids particularly good candidates for noninvasive sampling.

Fecal DNA collected noninvasively can provide valuable information about genetic and ecological characteristics. This approach has rarely been used for equids, despite the need for conservation of endangered species and management of abundant feral populations. We examined factors affecting the efficacy of using equid fecal samples for conservation genetics. First, we evaluated two fecal collection methods (paper bag vs. ethanol). Then, we investigated how time since deposition and month of collection impacted microsatellite amplification success and genotyping errors. Between May and November 2014, we collected feral horse fecal samples of known age each month in a feral horse Herd Management Area in western Colorado and documented deterioration in the field with photographs. Samples collected and dried in paper bags had significantly higher amplification rates than those collected and stored in ethanol. There was little difference in the number of loci that amplified per sample between fresh fecal piles and those that had been exposed to the environment for up to 2 months (in samples collected in paper bags). After 2 months of exposure, amplification success declined. When comparing fresh (0-2 months) and old (3-6 months) fecal piles, samples from fresh piles had more matching genotypes across samples, better amplification success and less allelic dropout. Samples defecated during the summer and collected within 2 months of deposition had highest number of genotypes matching among samples, and lowest rates of amplification failure and allelic dropout. Due to the digestive system and amount of fecal material produced by equids, as well as their occurrence in arid ecosystems, we suggest that they are particularly good candidates for noninvasive sampling using fecal DNA.

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations.

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.