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Microorganisms grow despite imbalances in the availability of nutrients and energy. The biochemical and elemental adjustments that bacteria employ to sustain growth when these resources are suboptimal are not well understood. We assessed how Pseudomonas putida KT2440 adjusts its physiology at differing dilution rates (to approximate growth rates) in response to carbon (C), nitrogen (N), and phosphorus (P) stress using chemostats. Cellular elemental and biomolecular pools were variable in response to different limiting resources at a slow dilution rate of 0.12 h -1 , but these pools were more similar across treatments at a faster rate of 0.48 h -1 . At slow dilution rates, limitation by P and C appeared to alter cell growth efficiencies as reflected by changes in cellular C quotas and rates of oxygen consumption, both of which were highest under P- and lowest under C- stress. Underlying these phenotypic changes was differential gene expression of terminal oxidases used for ATP generation that allows for increased energy generation efficiency. In all treatments under fast dilution rates, KT2440 formed aggregates and biofilms, a physiological response that hindered an accurate assessment of growth rate, but which could serve as a mechanism that allows cells to remain in conditions where growth is favorable. Our findings highlight the ways that microorganisms dynamically adjust their physiology under different resource supply conditions, with distinct mechanisms depending on the limiting resource at slow growth and convergence toward an aggregative phenotype with similar compositions under conditions that attempt to force fast growth., Importance: All organisms experience suboptimal growth conditions due to low nutrient and energy availability. Their ability to survive and reproduce under such conditions determines their evolutionary fitness. By imposing suboptimal resource ratios under different dilution rates on the model organism Pseudomonas putida KT2440, we show that this bacterium dynamically adjusts its elemental composition, morphology, pools of biomolecules, and levels of gene expression. By examining the ability of bacteria to respond to C:N:P imbalance, we can begin to understand how stoichiometric flexibility manifests at the cellular level and impacts the flow of energy and elements through ecosystems., Competing Interests: The authors declare no conflict of interest.

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here., (© 2023 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.)

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (N e ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally., (© 2022 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behlaf of Cambridge Philosophical Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner., (© 2022 Cambridge Philosophical Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Understanding how environmental variation influences population genetic structure can help predict how environmental change influences population connectivity, genetic diversity, and evolutionary potential. We used riverscape genomics modeling to investigate how climatic and habitat variables relate to patterns of genetic variation in 2 stonefly species, one from mainstem river habitats (Sweltsa coloradensis) and one from tributaries (Sweltsa fidelis) in 40 sites in northwest Montana, USA. We produced a draft genome assembly for S. coloradensis (N50 = 0.251 Mbp, BUSCO > 95% using "insecta_ob9" reference genes). We genotyped 1930 SNPs in 372 individuals for S. coloradensis and 520 SNPs in 153 individuals for S. fidelis. We found higher genetic diversity for S. coloradensis compared to S. fidelis, but nearly identical genetic differentiation among sites within each species (both had global loci median FST = 0.000), despite differences in stream network location. For landscape genomics and testing for selection, we produced a less stringently filtered data set (3454 and 1070 SNPs for S. coloradensis and S. fidelis, respectively). Environmental variables (mean summer precipitation, slope, aspect, mean June stream temperature, land cover type) were correlated with 19 putative adaptive loci for S. coloradensis, but there was only one putative adaptive locus for S. fidelis (correlated with aspect). Interestingly, we also detected potential hybridization between multiple Sweltsa species which has never been previously detected. Studies like ours, that test for adaptive variation in multiple related species are needed to help assess landscape connectivity and the vulnerability of populations and communities to environmental change., (© The Author(s) 2022. Published by Oxford University Press on behalf of The American Genetic Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The rapidly emerging field of macrogenetics focuses on analysing publicly accessible genetic datasets from thousands of species to explore large-scale patterns and predictors of intraspecific genetic variation. Facilitated by advances in evolutionary biology, technology, data infrastructure, statistics and open science, macrogenetics addresses core evolutionary hypotheses (such as disentangling environmental and life-history effects on genetic variation) with a global focus. Yet, there are important, often overlooked, limitations to this approach and best practices need to be considered and adopted if macrogenetics is to continue its exciting trajectory and reach its full potential in fields such as biodiversity monitoring and conservation. Here, we review the history of this rapidly growing field, highlight knowledge gaps and future directions, and provide guidelines for further research., (© 2021. Springer Nature Limited.)

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss ), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii ), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Carter, van Rees, Hand, Muhlfeld, Luikart and Kimball.)

A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis., (© The American Genetic Association. 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Millette et al. (Ecology Letters, 2020, 23:55-67) reported no consistent worldwide anthropogenic effects on animal genetic diversity using repurposed mitochondrial DNA sequences. We reexamine data from this study, describe genetic marker and scale limitations which might lead to misinterpretations with conservation implications, and provide advice to improve future macrogenetic studies., (© 2021 John Wiley & Sons Ltd.)

Estimating the effective population size and effective number of breeders per year (N b ) can facilitate early detection of population declines. We used computer simulations to quantify bias and precision of the one-sample LDNe estimator of N b in age-structured populations using a range of published species life history types, sample sizes, and DNA markers. N b estimates were biased by ~5%-10% when using SNPs or microsatellites in species ranging from fishes to mosquitoes, frogs, and seaweed. The bias (high or low) was similar for different life history types within a species suggesting that life history variation in populations will not influence N b estimation. Precision was higher for 100 SNPs (H ≈ 0.30) than for 15 microsatellites (H ≈ 0.70). Confidence intervals (CIs) were occasionally too narrow, and biased high when N b was small (N b  < 50); however, the magnitude of bias would unlikely influence management decisions. The CIs (from LDNe) were sufficiently narrow to achieve high statistical power (≥0.80) to reject the null hypothesis that N b  = 50 when the true N b  = 30 and when sampling 50 individuals and 200 SNPs. Similarly, CIs were sufficiently narrow to reject N b  = 500 when the true N b  = 400 and when sampling 200 individuals and 5,000 loci. Finally, we present a linear regression method that provides high power to detect a decline in N b when sampling at least five consecutive cohorts. This study provides guidelines and tools to simulate and estimate N b for age structured populations (https://github.com/popgengui/agestrucnb/), which should help biologists develop sensitive monitoring programmes for early detection of changes in N b and population declines., (© 2020 John Wiley & Sons Ltd.)

Alluvial aquifers are key components of river floodplains and biodiversity worldwide, but they contain extreme environmental conditions and have limited sources of carbon for sustaining food webs. Despite this, they support abundant populations of aquifer stoneflies that have large proportions of their biomass carbon derived from methane. Methane is typically produced in freshwater ecosystems in anoxic conditions, while stoneflies (Order: Plecoptera) are thought to require highly oxygenated water. The potential importance of methane-derived food resources raises the possibility that stonefly consumers have evolved anoxia-resistant behaviors and physiologies. Here we tested the anoxic and hypoxic responses of 2,445 stonefly individuals in three aquifer species and nine benthic species. We conducted experimental trials in which we reduced oxygen levels, documented locomotor activity, and measured survival rates. Compared to surface-dwelling benthic relatives, stoneflies from the alluvial aquifer on the Flathead River (Montana) performed better in hypoxic and anoxic conditions. Aquifer species sustained the ability to walk after 4-76 h of anoxia vs. 1 h for benthic species and survived on average three times longer than their benthic counterparts. Aquifer stoneflies also sustained aerobic respiration down to much lower levels of ambient oxygen. We show that aquifer taxa have gene sequences for hemocyanin, an oxygen transport respiratory protein, representing a possible mechanism for surviving low oxygen. This remarkable ability to perform well in low-oxygen conditions is unique within the entire order of stoneflies (Plecoptera) and uncommon in other freshwater invertebrates. These results show that aquifer stoneflies can exploit rich carbon resources available in anoxic zones, which may explain their extraordinarily high abundance in gravel-bed floodplain aquifers. These stoneflies are part of a novel food web contributing biodiversity to river floodplains., (© 2020 by the Ecological Society of America.)

Aquatic insects cope with hypoxia and anoxia using a variety of behavioral and physiological responses. Most stoneflies (Plecoptera) occur in highly oxygenated surface waters, but some species live underground in alluvial aquifers containing heterogeneous oxygen concentrations. Aquifer stoneflies appear to be supported by methane-derived food resources, which they may exploit using anoxia-resistant behaviors. We documented dissolved oxygen dynamics and collected stoneflies over 5 years in floodplain wells of the Flathead River, Montana. Hypoxia regularly occurred in two wells, and nymphs of Paraperla frontalis were collected during hypoxic periods. We measured mass-specific metabolic rates (MSMRs) at different oxygen concentrations (12, 8, 6, 4, 2, 0.5 mg l -1 , and during recovery) for 111 stonefly nymphs to determine whether aquifer and benthic taxa differed in hypoxia tolerance. Metabolic rates of aquifer taxa were similar across oxygen concentrations spanning 2 to 12 mg l -1 ( P >0.437), but the MSMRs of benthic taxa dropped significantly with declining oxygen ( P <0.0001; 2.9-times lower at 2 vs. 12 mg l -1 ). Aquifer taxa tolerated short-term repeated exposure to extreme hypoxia surprisingly well (100% survival), but repeated longer-term (>12 h) exposures resulted in lower survival (38-91%) and lower MSMRs during recovery. Our work suggests that aquifer stoneflies have evolved a remarkable set of behavioral and physiological adaptations that allow them to exploit the unique food resources available in hypoxic zones. These adaptations help to explain how large-bodied consumers might thrive in the underground aquifers of diverse and productive river floodplains., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

The increasing availability and complexity of next-generation sequencing (NGS) data sets make ongoing training an essential component of conservation and population genetics research. A workshop entitled "ConGen 2018" was recently held to train researchers in conceptual and practical aspects of NGS data production and analysis for conservation and ecological applications. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic (e.g., Bayesian) genotype calling to the detection of local adaptation signatures from genomic, transcriptomic, and epigenomic data. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists., (© The American Genetic Association 2020.)

Aquatic species represent a vast diversity of metazoans, provide humans with the most abundant animal protein source, and are of increasing conservation concern, yet landscape genomics is dominated by research in terrestrial systems. We provide researchers with a roadmap to plan aquatic landscape genomics projects by aggregating spatial and software resources and offering recommendations from sampling to data production and analyses, while cautioning against analytical pitfalls. Given the unique properties of water, we discuss the importance of considering freshwater system structure and marine abiotic properties when assessing genetic diversity, population connectivity, and signals of natural selection. When possible, genomic datasets should be parsed into neutral, adaptive, and sex-linked datasets to generate the most accurate inferences of eco-evolutionary processes., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Genetic monitoring estimates temporal changes in population parameters from molecular marker information. Most populations are complex in structure and change through time by expanding or contracting their geographic range, becoming fragmented or coalescing, or increasing or decreasing density. Traditional approaches to genetic monitoring rely on quantifying temporal shifts of specific population metrics-heterozygosity, numbers of alleles, effective population size-or measures of geographic differentiation such as F ST . However, the accuracy and precision of the results can be heavily influenced by the type of genetic marker used and how closely they adhere to analytical assumptions. Care must be taken to ensure that inferences reflect actual population processes rather than changing molecular techniques or incorrect assumptions of an underlying model of population structure. In many species of conservation concern, true population structure is unknown, or structure might shift over time. In these cases, metrics based on inappropriate assumptions of population structure may not provide quality information regarding the monitored population. Thus, we need an inference model that decouples the complex elements that define population structure from estimation of population parameters of interest and reveals, rather than assumes, fine details of population structure. Encompassing a broad range of possible population structures would enable comparable inferences across biological systems, even in the face of range expansion or contraction, fragmentation, or changes in density. Currently, the best candidate is the spatial Λ-Fleming-Viot (SLFV) model, a spatially explicit individually based coalescent model that allows independent inference of two of the most important elements of population structure: local population density and local dispersal. We support increased use of the SLFV model for genetic monitoring by highlighting its benefits over traditional approaches. We also discuss necessary future directions for model development to support large genomic datasets informing real-world management and conservation issues.

Climate-change vulnerability assessments (CCVAs) are valuable tools for assessing species' vulnerability to climatic changes, yet failure to include measures of adaptive capacity and to account for sources of uncertainty may limit their effectiveness. We took a more comprehensive approach that incorporates exposure, sensitivity, and capacity to adapt to climate change. We applied our approach to anadromous steelhead trout (Oncorhynchus mykiss) and nonanadromous bull trout (Salvelinus confluentus), threatened salmonids within the Columbia River Basin (U.S.A.). We quantified exposure on the basis of scenarios of future stream temperature and flow, and we represented sensitivity and capacity to adapt to climate change with metrics of habitat quality, demographic condition, and genetic diversity. Both species were found to be highly vulnerable to climate change at low elevations and in their southernmost habitats. However, vulnerability rankings varied widely depending on the factors (climate, habitat, demographic, and genetic) included in the CCVA and often differed for the 2 species at locations where they were sympatric. Our findings illustrate that CCVA results are highly sensitive to data inputs and that spatial differences can complicate multispecies conservation. Based on our results, we suggest that CCVAs be considered within a broader conceptual and computational framework and be used to refine hypotheses, guide research, and compare plausible scenarios of species' vulnerability to climate change., (© 2016 Society for Conservation Biology.)

Evolutionary and ecological consequences of hybridization between native and invasive species are notoriously complicated because patterns of selection acting on non-native alleles can vary throughout the genome and across environments. Rapid advances in genomics now make it feasible to assess locus-specific and genome-wide patterns of natural selection acting on invasive introgression within and among natural populations occupying diverse environments. We quantified genome-wide patterns of admixture across multiple independent hybrid zones of native westslope cutthroat trout and invasive rainbow trout, the world's most widely introduced fish, by genotyping 339 individuals from 21 populations using 9380 species-diagnostic loci. A significantly greater proportion of the genome appeared to be under selection favouring native cutthroat trout (rather than rainbow trout), and this pattern was pervasive across the genome (detected on most chromosomes). Furthermore, selection against invasive alleles was consistent across populations and environments, even in those where rainbow trout were predicted to have a selective advantage (warm environments). These data corroborate field studies showing that hybrids between these species have lower fitness than the native taxa, and show that these fitness differences are due to selection favouring many native genes distributed widely throughout the genome., (© 2016 The Author(s).)

Effective population size ( N e ) is among the most important metrics in evolutionary biology. In natural populations, it is often difficult to collect adequate demographic data to calculate N e directly. Consequently, genetic methods to estimate N e have been developed. Two N e estimators based on sibship reconstruction using multilocus genotype data have been developed in recent years: sibship assignment and parentage analysis without parents. In this study, we evaluated the accuracy of sibship reconstruction using a large empirical dataset from five hatchery steelhead populations with known pedigrees and using 95 single nucleotide polymorphism (SNP) markers. We challenged the software COLONY with 2,599,961 known relationships and demonstrated that reconstruction of full-sib and unrelated pairs was greater than 95% and 99% accurate, respectively. However, reconstruction of half-sib pairs was poor (<5% accurate). Despite poor half-sib reconstruction, both estimators provided accurate estimates of the effective number of breeders ( N b ) when sample sizes were near or greater than the true N b and when assuming a monogamous mating system. We further demonstrated that both methods provide roughly equivalent estimates of N b . Our results indicate that sibship reconstruction and current SNP panels provide promise for estimating N b in steelhead populations in the region.

Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST ) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics., (© 2015 John Wiley & Sons Ltd.)

Understanding how climatic variation influences ecological and evolutionary processes is crucial for informed conservation decision-making. Nevertheless, few studies have measured how climatic variation influences genetic diversity within populations or how genetic diversity is distributed across space relative to future climatic stress. Here, we tested whether patterns of genetic diversity (allelic richness) were related to climatic variation and habitat features in 130 bull trout (Salvelinus confluentus) populations from 24 watersheds (i.e., ~4-7th order river subbasins) across the Columbia River Basin, USA. We then determined whether bull trout genetic diversity was related to climate vulnerability at the watershed scale, which we quantified on the basis of exposure to future climatic conditions (projected scenarios for the 2040s) and existing habitat complexity. We found a strong gradient in genetic diversity in bull trout populations across the Columbia River Basin, where populations located in the most upstream headwater areas had the greatest genetic diversity. After accounting for spatial patterns with linear mixed models, allelic richness in bull trout populations was positively related to habitat patch size and complexity, and negatively related to maximum summer temperature and the frequency of winter flooding. These relationships strongly suggest that climatic variation influences evolutionary processes in this threatened species and that genetic diversity will likely decrease due to future climate change. Vulnerability at a watershed scale was negatively correlated with average genetic diversity (r = -0.77; P < 0.001); watersheds containing populations with lower average genetic diversity generally had the lowest habitat complexity, warmest stream temperatures, and greatest frequency of winter flooding. Together, these findings have important conservation implications for bull trout and other imperiled species. Genetic diversity is already depressed where climatic vulnerability is highest; it will likely erode further in the very places where diversity may be most needed for future persistence., (Published 2015. This article is a U.S. Government work and is in the public domain in the USA.)

Extrinsic factors influencing evolutionary processes are often categorically lumped into interactions that are environmentally (e.g., climate, landscape) or community-driven, with little consideration of the overlap or influence of one on the other. However, genomic variation is strongly influenced by complex and dynamic interactions between environmental and community effects. Failure to consider both effects on evolutionary dynamics simultaneously can lead to incomplete, spurious, or erroneous conclusions about the mechanisms driving genomic variation. We highlight the need for a landscape community genomics (LCG) framework to help to motivate and challenge scientists in diverse fields to consider a more holistic, interdisciplinary perspective on the genomic evolution of multi-species communities in complex environments., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Puritz et al. provide a review of several RADseq methodological approaches in response to our 'Population Genomic Data Analysis' workshop (Sept 2013) review (Andrews & Luikart 2014). We agree with Puritz et al. on the importance for researchers to thoroughly understand RADseq library preparation and data analysis when choosing an approach for answering their research questions. Some of us are currently using multiple RADseq protocols, and we agree that the different methods may offer advantages in different cases. Our workshop review did not intend to provide a thorough review of RADseq because the workshop covered a broad range of topics within the field of population genomics. Similarly, neither the response of Puritz et al. nor our comments here provide sufficient space to thoroughly review RADseq. Nonetheless, here we address some key points that we find unclear or potentially misleading in their evaluation of techniques., (© 2014 John Wiley & Sons Ltd.)