Summary
Species detection using environmental DNA (eDNA) has tremendous potential for contributing to the understanding of the ecology and conservation of aquatic species. Detecting species using ...eDNA methods, rather than directly sampling the organisms, can reduce impacts on sensitive species and increase the power of field surveys for rare and elusive species. The sensitivity of eDNA methods, however, requires a heightened awareness and attention to quality assurance and quality control protocols. Additionally, the interpretation of eDNA data demands careful consideration of multiple factors. As eDNA methods have grown in application, diverse approaches have been implemented to address these issues. With interest in eDNA continuing to expand, supportive guidelines for undertaking eDNA studies are greatly needed.
Environmental DNA researchers from around the world have collaborated to produce this set of guidelines and considerations for implementing eDNA methods to detect aquatic macroorganisms.
Critical considerations for study design include preventing contamination in the field and the laboratory, choosing appropriate sample analysis methods, validating assays, testing for sample inhibition and following minimum reporting guidelines. Critical considerations for inference include temporal and spatial processes, limits of correlation of eDNA with abundance, uncertainty of positive and negative results, and potential sources of allochthonous DNA.
We present a synthesis of knowledge at this stage for application of this new and powerful detection method.
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.
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 (H
, H
, F
, A
) and differentiation (F
, G
, D
). 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 H
, F
, A
, 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.
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.
Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate ...occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models.
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.
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.
全基因组适应性分化对一个孤立的艾草松鸡种群的保护性迁移结果的潜在影响
【摘要】保护性迁移是一项重要的保护工具, 通常用于帮助已衰退或灭绝后重建的种群进行增殖。增殖的目的之一是增加遗传多样性, 降低近交衰退的风险(即遗传拯救)。然而, 从明显分化的种群中引入个体有可能破坏共同适应性状, 降低局部适合度(即远交衰退)。野生动物的遗传数据已越来越容易获取, 这些数据可以提供独特的见解, 如了解通过增殖引入的遗传变异的存在和保留情况以指示有效性, 了解适应性相似性以指示源种群和迁入种群之间是否合适。我们利用两组遗传数据来评估艾草松鸡(Centrocercus urophasianus)分布区西北部(美国华盛顿州)的孤立种群的增殖情况, 并回顾性地评估了源种群和迁入种群之间的适应性分化。我们为微卫星数据开发了两个统计模型来评估增殖结果。我们使用一个模型预测增殖后的遗传多样性, 并将预测结果与观测到的遗传变化进行比较。我们使用第二个模型来量化观测到的迁入带来的繁殖量(证明发生种群融合)。我们还描述了源种群和迁入种群之间的全基因组适应性分化情况。观测到的迁入种群遗传多样性(HO = 0.65)高于未发生增殖的预测值(HO = 0.58), 但低于我们的模型预测值(HO = 0.75)。两个地理上隔离的种群之间共享遗传变异增加, 证明存在以往被认为十分少见的周期性基因流。在与FST升高(143个基因)或当地环境变量(每种基因型‐环境关联方法分别有97个和157个基因)相关的候选适应性基因中, 我们发现了可能会影响迁入个体利用当地资源和适应陌生环境的能力及其繁殖潜力的相关基因, 这些都可能是迁入个体基因保留率低的原因。【翻译:胡怡思;审校:聂永刚】
Identification of microsatellites, or simple sequence repeats (SSRs), can be a time-consuming and costly investment requiring enrichment, cloning, and sequencing of candidate loci. Recently, however, ...high throughput sequencing (with or without prior enrichment for specific SSR loci) has been utilized to identify SSR loci. The direct "Seq-to-SSR" approach has an advantage over enrichment-based strategies in that it does not require a priori selection of particular motifs, or prior knowledge of genomic SSR content. It has been more expensive per SSR locus recovered, however, particularly for genomes with few SSR loci, such as bird genomes. The longer but relatively more expensive 454 reads have been preferred over less expensive Illumina reads. Here, we use Illumina paired-end sequence data to identify potentially amplifiable SSR loci (PALs) from a snake (the Burmese python, Python molurus bivittatus), and directly compare these results to those from 454 data. We also compare the python results to results from Illumina sequencing of two bird genomes (Gunnison Sage-grouse, Centrocercus minimus, and Clark's Nutcracker, Nucifraga columbiana), which have considerably fewer SSRs than the python. We show that direct Illumina Seq-to-SSR can identify and characterize thousands of potentially amplifiable SSR loci for as little as $10 per sample--a fraction of the cost of 454 sequencing. Given that Illumina Seq-to-SSR is effective, inexpensive, and reliable even for species such as birds that have few SSR loci, it seems that there are now few situations for which prior hybridization is justifiable.
Sex ratio, and the extent to which it varies over time, is an important factor in the demography, management, and conservation of wildlife populations. Greater sage‐grouse Centrocercus urophasianus ...populations in western North America are monitored using counts of males at leks in spring. Population estimates derived from lek‐count data typically assume a constant, female‐biased sex ratio, yet few rigorous, empirically derived estimates of sex ratio are available to test that assumption. We estimated pre‐breeding sex ratio of greater sage‐grouse in a peripheral, geographically isolated population in northwestern Colorado during two consecutive winters using closed‐population, robust‐design, multi‐state, genetic mark–recapture models in program MARK. Sex ratio varied markedly between years, with estimates of 3.29 (95% CI: 2.36–4.59) females per male in winter 2012–2013 and 1.54 (95% CI: 1.22–1.95) females per male in winter 2013–2014. Rather than assuming a constant sex ratio, biologists should consider the potential for large annual variation in sex ratio of greater sage‐grouse populations when estimating population size or trend from male lek‐count data.
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.
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.
Establishing which landscape factors influence population connectivity has ecological and evolutionary importance. In this study, we tested maximum‐likelihood population‐effects models (MLPE) and determined that AIC and BIC were the best model selection indices for correctly identifying the underlying dispersal model. Further, model coefficients for MLPE models that controlled for geographic distance best reflected the underlying dispersal model with true variables having nonzero confidence intervals across the entire model set.
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.
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