A framework for incorporating evolutionary genomics into biodiversity conservation and management
Climate Change Responses volume 2, Article number: 1 (2015)
Evolutionary adaptation drives biodiversity. So far, however, evolutionary thinking has had limited impact on plans to counter the effects of climate change on biodiversity and associated ecosystem services. This is despite habitat fragmentation diminishing the ability of populations to mount evolutionary responses, via reductions in population size, reductions in gene flow and reductions in the heterogeneity of environments that populations occupy. Research on evolutionary adaptation to other challenges has benefitted enormously in recent years from genomic tools, but these have so far only been applied to the climate change issue in a piecemeal manner. Here, we explore how new genomic knowledge might be combined with evolutionary thinking in a decision framework aimed at reducing the long-term impacts of climate change on biodiversity and ecosystem services. This framework highlights the need to rethink local conservation and management efforts in biodiversity conservation. We take a dynamic view of biodiversity based on the recognition of continuously evolving lineages, and we highlight when and where new genomic approaches are justified. In general, and despite challenges in developing genomic tools for non-model organisms, genomics can help management decide when resources should be redirected to increasing gene flow and hybridisation across climate zones and facilitating in situ evolutionary change in large heterogeneous areas. It can also help inform when conservation priorities need to shift from maintaining genetically distinct populations and species to supporting processes of evolutionary change. We illustrate our argument with particular reference to Australia’s biodiversity.
Climate change threats to biodiversity
The latest IPCC report  provides a very clear picture about current and accelerating climate change. Even if CO2 emissions can be completely curtailed by 2050, it is likely that there will be a further rise in temperature of at least 2°C above the current level of almost 1°C. Given the political challenges associated with emission reductions, it is doubtful whether such a timeframe for emission reduction will be realised. Therefore, the world is more likely facing an increase in mean temperature of 3°C–6°C, approaching the extent of change experienced in the last glacial maximum, coupled with an increase in temperature extremes. In addition, there will be a gamut of associated changes including ocean acidification, increases in fire incidence and severity, storm activity, the length and intensity of drought and flood conditions, as well as changes in the salinity of coastal areas .
The distributions of many species are expected to shift markedly during this period. Climate niche modelling predicts that many areas currently occupied by species and communities will no longer be suitable for them . Similarly, areas suitable for alpine and sub-alpine vegetation and fauna in Europe are expected to decline by more than 90%, e.g. [3,4]. At the same time, changes can be idiosyncratic  and some species are expected to benefit from the effects of climate change; groups of invasive species and even some native species are expected to benefit in this way [6,7].
Predictions based on species distribution models are relatively crude because they assume that current distributions are limited by climatic factors, whereas the climatic space a species can tolerate may be substantially greater than the area where it actually persist [8,9]. To resolve this issue, investigators have explored the limits of tolerance or growth of species [10-12] but controversy remains about the best approach and interpretations of patterns across different species. Tolerance limits often depend not only on the immediate conditions being experienced but also on those encountered during development and the rate at which stresses act, as well as a variety of other factors [13-15]. Species may respond via plasticity, altering growth rates, triggering phenological changes and increasing resistance to extremes, all in an adaptive direction . Behavioural adaptation might also allow species to find areas with suitable microclimates within their current distributions  or to track their niches as they move across space [18-20].
Biotic factors introduce another level of uncertainty into predictions, particularly when they generate an additional source of environmental stress, such as the widespread impact of mountain pine beetles on pine tree mortality in North America . Perhaps the most important biotic factor is human population growth and the impacts of more than 7 billion people on a natural environment that is increasingly under stress . The negative impacts of human activities on biodiversity are well documented and range from direct effects triggering species extinctions through overexploitation to indirect effects through removal of habitat for agriculture or resource extraction .
It is already clear that large-scale changes to natural communities are occurring and will accelerate over the coming decades . Many (perhaps the majority of) species face local extinction in at least part of their native range. A few species will track climate successfully, others will benefit from vacant space created by departing species, tropical and subtropical species may invade higher latitudes, but many communities will be lost, e.g. [3,5,25]. All of these changes will take place within the context of increased landscape fragmentation due to ongoing vegetation clearing and increasing calls to manipulate the environment to safeguard agriculture and property such as through fire suppression. There will also be flow-on effects of these changes on ecosystem services provided by the natural environment, directly impacting on the ability of species, including our own, to access the resources needed for persistence [26,27].
Opportunities and constraints for evolutionary adaptation
Given the scale and timeframe of climate change effects, what is the likelihood that species and communities can respond through evolutionary changes? Major life forms have persisted and adapted across geological epochs despite temperature changes that exceed those predicted under anthropogenic climate change. Whilst speciation and evolutionary divergence have occurred over millions of years, current species and populations have persisted through the more recent climatic oscillations of the Pleistocene [28,29]. In addition to these past evolutionary changes over geological time frames, there is also a growing (albeit still small) number of cases of rapid and contemporary evolutionary changes in natural animal and plant populations [30,31] that allow us to track the direct and indirect effects of climate change. Examples include genetic changes in the body colour of owls in response to predation linked to changing snow covers , changes in allozyme frequencies and inversions that preserve functional sets of genes in Drosophila known to be sensitive to temperature changes , and adaptive changes in the flowering time of Brassicas in response to drought . However, not all populations are expected to successfully adapt through evolutionary change. This may reflect a lack of genetic variation in base populations , interactions among traits that constrain evolutionary responses in one direction [32,36], and other factors such as the reduced effectiveness of selection in the presence of the plastic responses of individual genotypes (i.e. the extent to which they can be modified by the environment). These types of factors may help account for cases where adaptive evolutionary changes have not occurred, but were expected, as in the case of breeding time in birds .
Three interacting demographic factors are widely recognised to have major effects on the likelihood of successful adaptation to rapid climate change—generation time, population size and population structure (Figure 1). Selection responses are typically slower in long lived organisms, although such species can still evolve effectively if able to exploit existing variation within or among populations . The variation available within populations in turn depends on population size; in situ evolution will be maximised at larger population sizes , and this becomes a major challenge for threatened species living in fragmented landscapes . Population structure has more variable effects on evolutionary potential because gene flow across the landscape can assist evolution or retard it depending on selection gradients and rates of gene flow . Whilst often providing potentially useful new variation for the population in question, it can also swamp processes of local adaptation with an influx of genes that are poorly adapted for the local climate, which might be particularly important for marginal populations [42,43]. On the other hand, gene flow often seems to be related to environmental conditions: processes like flowering, propagule dispersal and mating time can mean that gene flow is higher among populations from similar environments, and this might increase rates of adaptive evolution .
Both population size and patterns of gene flow have been dramatically affected by human activities. Environments have become increasingly fragmented, leading to increasing levels of genetic distinctness and a loss of accessible genetic variation, e.g. [45,46]. Threatened environments may represent a series of islands surrounded by hostile conditions for the species. At its most extreme, species might be confined to zoos or botanical collections, living in a highly defined set of environmental conditions, at a small effective population size, with only limited scope to recruit new genes even with coordinated programmes to exchange material; see .
From an evolutionary perspective, natural populations are therefore threatened by three forces that interact to produce a downward spiral of evolutionary potential (Figure 1): (i) a reduction in genetic variation as a consequence of decreases in population size affecting in situ evolution, (ii) a reduction in gene flow preventing an influx of genetic variants from other populations and (iii) a reduction in environmental heterogeneity that can lead to a decrease in adaptive capacity of the species as a whole. The likelihood that evolutionary rescue (involving forced introgression from other populations or (sub)species) might mitigate some of the threats imposed by environmental change remains unknown , although it will likely depend on the availability of genetic variation within the populations/species concerned.
Against this backdrop of gloomy projections, the current revolution in genomics and other -omics technologies is providing unprecedented insights into evolutionary processes and offers an opportunity to significantly improve conservation planning and management decisions. Researchers can now identify parts of the genome that have been or could be involved in adaptive shifts, via new or existing variants in situ, or through hybridisation. At a functional level, genomics approaches can also identify the networks of genes/proteins and their expression profiles required for key adaptations. Whilst once limited in application to model organisms, the technology is now increasingly applicable to non-model species despite ongoing challenges around annotation [49,50]. Below, we briefly outline the various methods for generating and analysing genomic data bearing on biodiversity conservation, their strengths and weaknesses, and then describe how genomic information can explicitly be incorporated into a decision-making framework for biodiversity conservation in the face of climate change.
Methods for generating genomic data
The advent of next-generation sequencing has enabled population genetic and microevolutionary studies on a genome-wide scale. From hundreds of millions of dollars for the first draft of the human genome in 2001, the sequencing required to assemble a reference genome for a species now costs just a few thousand dollars. Economical sample preparation strategies reviewed in [51-53] now enable high-throughput genomics studies even without a reference genome . “Home-brew” methods for sequencing library preparation [55,56] have reduced per-sample cost and prices of commercial kits have followed a similar downward trend. It is now realistic to carry out whole genome sequencing (WGS) of 30 individuals of an insect species with a small (250 Mb) genome, for less than US$4000.
Although multi-individual WGS provides the highest accuracy and power in population genomics, it can still present a significant financial challenge when multiple populations are under investigation. There are several economical alternatives to WGS. First, the recently developed reduced-representation sequencing (RRS), including genotyping-by-sequencing (GBS) and RADSeq [57-59] technologies, can overcome this problem by targeting a subset (approximately 1%) of the genome. These approaches typically involve restriction enzyme digestion of genomic DNA, sample barcoding by attaching unique oligo-nucleotide sequences identifying individuals and selection of a subset of genomic fragments, followed by sequencing of multiple samples in the same lane on an Illumina sequencing platform. They provide data on hundreds to tens of thousands of nucleotide polymorphisms. In some cases, they tag the majority of the genes in the genome and, importantly, reduce per-sample costs substantially. These approaches can be used to answer a wide variety of questions on population structure and phylogeography; see [51,53,57]. RRS strategies typically aim for up to 10× coverage per site, which generally allows for accurate identification of heterozygous sites. Dual-end barcode sets of 384 or more now exist for the identification of individuals, which makes large sample sizes economical. One limitation of RRS approaches is that loci can suffer from “allelic dropout” due to polymorphisms in restriction sites [60,61], which may lead to an overestimate of divergence.
A second alternative to individual-based WGS is combining individuals into a pool, as in Poolseq; see [51,62] (PPS). The Poolseq approach does not allow the data for different individuals to be separated post-sequencing, but it is highly cost-effective for assessing population structure , genetic distance [64-66] and genome-wide patterns of heterozygosity. As little as 1× coverage of each diploid individual’s genome is needed, further reducing cost. However, a lower level of coverage will not adequately represent the pool of individuals, especially when the pool is small to begin with, and can therefore produce misleading population parameter estimates; see [60,61,67]. Guidelines [51,60,61,67] and software packages such as ngsTools  and npstat , which carry out likelihood-based estimation of allele frequencies, are now available to help tackle these challenges.
Another affordable strategy for population genomic studies is transcriptome sequencing (TS). This approach yields data on genes which are expressed at reasonable levels, representing perhaps 1%–10% of the genome. Variants can then be identified in the sequenced transcriptome. If coverage is deep enough, biases are addressed and appropriate experimental replicates are included; differences in gene expression can also be detected between samples . Transcriptome sequencing is usually performed at the individual level, but it is also possible to estimate allele frequencies from sequencing of pooled samples  and to compare different lines and populations . Variant identification from transcriptome data can suffer the same biases from low coverage sequences as Poolseq experiments and can also suffer from allelic dropout when only certain alleles are expressed in individual samples.
Whatever the sequencing platform used, most population genomics studies to date have based their analyses on single nucleotide polymorphisms (SNPs)—single-base variants in either functional or neutral regions of the genome. Whilst SNPs are informative and relatively easy to identify, studies on model species have shown that insertion-deletion polymorphisms (indels) also play an important role in genome evolution and adaptation [73-76]. Identifying indels from high-throughput sequencing data remains a difficult bioinformatic problem , and identifying large indels in RRS data is especially difficult because only a small percentage of the genome is sequenced. Indels are not just important for their own sake: if small indels are misaligned, then SNPs may be misidentified in the region . A few programs like the GATK2 best-practice pipeline attempt to resolve this by a local refinement of read alignments, but this can still suffer from discordance . Chromosomal rearrangements and other forms of structural variation are also involved in adaptation (reviewed in ). Such variation is still difficult to identify using short-read technology, but improved methodologies are under development [79,80].
RRS, Poolseq and transcriptome sequencing strategies can provide genomic insights into the majority of management questions described in Table 1 below. Some of the common population genetic parameters can be estimated reliably from samples of 30 non-related individuals per population, but if low-frequency alleles are of particular interest, 30 individuals may not be sufficient. Taking spatial structure and landscape features into account may also require additional sampling along transects and environmental clines. On the other hand, some other experimental questions, including identification of subspecies and long-range migrants, may require fewer samples.
Significantly, none of these applications absolutely requires individual-based whole-genome sequencing. However, high-quality whole-genome sequencing may be a viable option for species with small genomes and will always provide the most complete data set. Another reason to consider whole-genome sequencing is to assemble a reference genome from one individual or line of the species in question. This can greatly aid in SNP calling, mapping the variants that are identified and associating phenotypes to particular regions, either in genetic crosses or population surveys, involving quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS), respectively . A typical reference genome sequencing project aims for >30× coverage, which is now relatively affordable. Currently, the limiting factor in de novo genome sequencing is the bioinformatic expertise required to assemble and annotate genomes to a high quality. At a minimum, annotation involves predicting the location and structure of a gene [117,118]; functional annotation then involves predicting the function of an identified gene, generally by comparison to related annotated genomes . In the latter case, annotations remain challenging for non-model organisms , particularly when genome assemblies are of a low quality .
Sequencing costs may well continue to decrease in the near future with third-generation sequencing (single molecule sequencing), and innovations such as nanopore-enabled nucleic acid sequencing could further improve quality and reduce costs . With read lengths of 49 kb + projected by companies like Oxford Nanopore , it may soon become possible to sequence entire genomes of non-model organisms for less than a thousand (US) dollars. This, combined with re-usable sequencing chips and mini USB-powered sequencers, ensures an increasingly important role for sequencing technologies in population genetic and microevolutionary studies related to climate change adaptation.
A decision framework
A framework for management decisions and subsequent actions for biodiversity conservation under climate change is presented in Figure 2 and Table 1; the framework is modified from that of Shoo et al.  to consider the potential for adaptation and possible roles for genomic data. The aim of the framework is to guide thorough but practicable assessments of whether a species can adapt to climatic change through migration, physiological tolerance or adaptive evolution and to recommend appropriate management actions that will help it avoid extinction and retain genetic variation for long-term survival. Although the framework is designed to consider threatened species, we have interpreted it broadly to include an assessment of adaptation in species that might not be threatened but nevertheless perform a critical function within ecosystems. Each step in the framework requires answering a question relevant to climate change tolerance/adaptation, and those where genomic approaches are particularly relevant are shaded purple in Figure 2 (those best answered with non-genomic information like climatic or ecological data are shaded grey). Questions where genomics are relevant are broken down further in Table 1 into specific experimental approaches that may or may not be appropriate for the species of interest. The limitations of genomic approaches are also noted. The text below considers each step in the framework in turn, expanding particularly on those to which the genomics applies.
Assessing environment suitability and persistence
Assessing the likelihood that environmental suitability will decline
Species distribution models, often also referred to as ecological niche models or bioclimatic envelope models, and methods of modelling community-level turnover such as generalised dissimilarity modelling are currently the main tools used to obtain spatially explicit predictions of habitat (environmental) suitability for species under climate change [25,124,125] (D1 in Figure 2). These approaches use associations between climate and species’ distributions to enable projections of future potential distributions under climate change scenarios. Whilst uses of such models have been criticised in the past, models that thoroughly account for algorithmic uncertainties, followed by careful interpretation of results, remain useful and widely used tools for forecasting impacts of climate change on large numbers of species .
Assessing whether species can tolerate change in situ
If a substantive risk that environmental suitability will decline under climate change has been identified, then the next step is to determine which species and communities should become the focus of ongoing management (D2 in Figure 2). Whilst many species are expected to be at risk from climate change, others may not be threatened because the projected change will fall within their tolerance limits. This section briefly discusses how genomic approaches might be used to determine the extent to which species will be able to tolerate climatic changes in situ, without the need for evolutionary responses and management intervention.
The first approach is to screen biomarkers that are consistently linked to levels of physiological stress to determine whether physiological limits are being approached or exceeded. Transcriptome sequencing can provide a signal of physiological stress in natural populations and wild-caught individuals [49,127], indicating a population that may not be functioning at its peak; for instance, transcriptomic stress profiling on several fish species has demonstrated a link between changes in the expression of particular genes and the physical condition of the fish [81,128]. One current challenge with this approach is that key biomarker genes have not yet been identified for many groups of organisms, although transcriptomic data for a range of species across various stresses are rapidly accumulating and generalities about useful markers may emerge. A subsequent challenge is to interpret quantitative transcript changes in terms of the critical physiological limits for the species in question .
A related approach is to use transcriptomic profiling to determine whether there is a capacity to mitigate the detrimental effects of environmental change via phenotypic plasticity. Just as some aspects of a transcriptomic profile may indicate a species approaching a physiological limit, so can other changes in the profile highlight an underlying capacity to tolerate change through the physiological plasticity of individual genotypes, even when phenotypic responses are not outwardly evident [82,130]. Given the importance of phenotypic plasticity as an adaptive mechanism for organisms facing climate change, such transcriptomic approaches could be used to investigate their capacity to respond physiologically without necessarily involving any evolutionary change. As above, such an approach is currently constrained by the very limited understanding of how gene expression changes link to fitness/performance under stressful conditions, but the situation is expected to improve given the current proliferation of transcriptomic studies. The approach is illustrated by a transcriptomic comparison of populations of the sparrow Zonotrichia capensis from altitudinal extremes of its range, carried out on both individuals sampled directly from the field and on those then transferred to a low altitude “common garden” environment . There was no difference between the transcriptomes of the two populations under the latter conditions whereas samples obtained directly from the field differed in their expression of nearly 200 genes, pointing to the involvement of plastic changes in gene expression profiles rather than evolved differences among the populations.
Genomics can provide insights into the way populations of a species may have responded to climate change in the past. Estimates of historical demographic change over recent or long time scales can be obtained from analyses of the scale and structure of sequence variation in extant populations [132,133]. The time course of changes in population size and structure obtained can then be used to link past population expansions and contractions to historical climate change, giving a clue as to the vulnerability of a species (based on both plastic and evolved responses) to future climate change.
Finally, phylogenetic and phylogenomic studies may provide insights into the capacity of species and lineages to tolerate contemporary climate change (Table 1). The well-supported, well-dated phylogenetic trees that can be produced with genomic data provide an opportunity to assess whether certain taxonomic groups are more vulnerable to climate change than others. The potential insights that might emerge from such studies are illustrated by traditional multi-locus phylogenetic (rather than phylogenomic) studies carried out to date. Thus a continent-wide avian phylogeny showed that European birds whose niches evolved more slowly in the past exhibited greater levels of demographic decline in the twentieth century, both at the individual species and the overall family level . Similarly, Crisp et al.  used a phylogenetic framework to show that relatively few groups of southern hemisphere plants have speciated from the alpine biome to the sclerophyll biome, but many have speciated across sclerophyll/arid boundaries, suggesting that groups of alpine species are more at risk of extinction than sclerophyll species given an equivalent amount of climate change.
If a species is predicted to tolerate climatic changes and persist in situ, then no further action is required other than ongoing monitoring and assessment (action 1 in Figure 2). If, on the other hand, it is predicted that the species may not be able to persist in situ, then the next step in the decision framework is to identify whether there are any climatic refugia, internal to the species range, that might buffer it from change and facilitate persistence.
Identifying climate refugia within a species’ current range
Refugia are defined as habitats that species retreat to, persist in and potentially expand from under changing environmental conditions, and are usually places providing environmental heterogeneity and climatic stability as regional environments change . Genomic data can be used in combination with ecological data and species distribution models to identify places where populations of a species have persisted through periods of climatic instability and maintained genetic diversity (D3 in Figure 2). Such places become candidate refugia for the species under future climate change. Genetic signatures of refugia have often been detected using organelle markers [137,138], but as noted above, lineages that have undergone bottlenecks over relatively recent geological timescales (e.g. glacial cycles) can also be identified using high numbers of neutral loci, which can accurately reconstruct temporal changes in effective population size skyline plots . For example, in antbirds in the Brazilian Atlantic Forest, genetic studies showed that populations in areas with high last glacial maximum (LGM) stability exhibited long-term population growth, whilst populations in less climatically stable regions showed strong demographic fluctuations, supporting previously hypothesised refugial areas . Ongoing work incorporating these genomically estimated demographic effects of climatic changes with spatial modelling is likely to improve future estimations of extinction risk . Climate change in the last glacial maximum (approximately 21 kya) can also be used to project future refugia utilising spatial modelling approaches (see below). Once refugia have been identified, they should be secured from further threat (Action 2 in Figure 2).
Measuring genetic diversity across landscapes: landscape genomics and beyond
Do populations have enough genetic diversity for an evolutionary response?
Conservation geneticists working on threatened species and other key species maintaining ecosystem function have largely focussed on selectively neutral variation to this point, in part because of its ability to provide unbiased estimates of demographic factors like population size, random drift, mutation and migration. The level of neutral variation in threatened and non-threatened species can also provide an indirect but reasonable indication of adaptive diversity when this is dependent on factors like population size [142,143]. Adaptive variation is much more difficult to measure directly because it requires either linking variation in specific genes to adaptive responses or assessing the extent to which variation in traits under selection is genetically determined (heritability and evolvability). Therefore, overall genetic diversity has generally been taken as a reasonable proxy for the small fraction of the diversity that is functionally associated with higher adaptive potential under climate change. However, population and quantitative genomics also now offer some powerful new ways to probe for adaptive variation (D4 in Figure 2).
Population genomics can distinguish particular loci showing signatures of selection from the genomic background, identifying whether adaptive genetic variation is present in the organism. The data required usually involve genome-wide sequencing of multiple genomes from the species in question, although various sampling designs are appropriate depending on the precise nature of the organism and the question. For example, some sampling designs are better able to measure linkage disequilibrium than others, and some designs also allow timeframes for selection to be estimated. The bioinformatics approaches are well established and have been used successfully in many cases, e.g. [144-149]. A key finding of early studies has been that genomic landscapes appear as mosaics, with some regions providing signatures diagnostic of various forms of positive and balancing selection, and others comprised of apparently neutral or near-neutral diversity [150-152].
This population genomic approach does not itself elucidate the precise targets of selection (which may be in large tracts of the genome spanning coding or regulatory regions) or the specific nature of fitness differences. However, two major quantitative genomics techniques are available that enable genomic data to be linked to the phenotype. In both cases, data are required jointly on genomic and phenotypic variation, either from population samples (GWAS) or crossing experiments (for QTL mapping). A variety of experimental designs are used, mostly based on samples of individuals but in some cases on pooled samples, and either WGS or various RRS, DNA enrichment (DE) or other sequencing strategies can be deployed (Table 1). Issues including the level of linkage disequilibrium and population structure in field populations and ease of breeding and productivity in laboratory crosses will determine which approach is taken and the specifics of the design, but there is a large body of literature to guide such studies; see discussion and references in . Whilst laborious, these experiments are being used more frequently and becoming cheaper. Examples of climate-related phenotypes that have been mapped to particular genetic variants by these means include life history adaptations in various populations of Arabidopsis  and Parus major  and tolerance to desiccation resistance  and thermal stress  in Drosophila.
The welter of transcriptomic and other functional genomic studies now being conducted on a wide range of organisms is rapidly expanding our understanding of both the potential functions of particular sorts of genes and their networks of functional relationships . Comparative genomics is helping to identify syntenic blocks and gene families which have expanded or contracted in association with particular ecological niches or adaptations such as frugivory in bats  and sensitivity of honeybees to insecticides . A rapidly increasing number of studies are using such approaches to provide functional links between components of the genome and climate-related phenotypes, e.g. [131,161]. We anticipate a time when the results of genome-wide scans of sequence variation will be interpretable in these specific ways.
When assessing changes in genetic variation, museum and herbarium specimens can provide access to temporal series of collections or other material that for various reasons cannot be obtained otherwise. The technology for retrieving data of usable quality from such specimens has improved substantially, e.g. , and it has already enabled several studies showing progressive changes in gene frequencies in various organisms over time frames out to about 100 years, e.g. [112,163]. This may provide unique clues about recent genetic changes, be they losses of genetic variation or of positive selective processes already underway. In either case, they will be important inputs into decisions about interventions such as conservation translocations, both within (reinforcement translocation) and external to (assisted colonisation) species’ current ranges (“Defining translocations” section, actions 4 and 6 in Figure 2) and enforced hybridisation (action 7 in Figure 2) discussed further below.
If a species is assessed as harbouring adequate levels of genetic variation, then no direct management actions should be automatically triggered, although ongoing monitoring of genetic diversity can ensure the levels remain sufficient for adaptive responses (action 1 in Figure 2). If some populations harbour more genetic diversity than others, then understanding how that diversity is distributed across the species’ range, and the extent to which some populations have adapted to local climatic conditions, may lead to specific management actions (action 3 in Figure 2).
Although genomics provides powerful ways of assessing adaptive and neutral genetic variation, links between the different types of genetic diversity and adaptive capacity can only ultimately be established through phenotypic association studies. Whilst the presence of genetic variation in loci generally, and in those likely to be involved in adaptive changes, can highlight the potential for evolution, it does not necessarily indicate the extent to which phenotypes in populations can be changed by selection.
Do some populations have high genetic diversity?
Understanding how overall genetic diversity is partitioned among populations across a species’ range is critical in predicting the adaptive capacity of the species (D5a and D5b in Figure 2, Table 1). Additionally, it informs about the potential for migration to facilitate persistence under climate change . The approaches described in the sections below allow the identification of species that harbour low diversity in some populations and “hotspots” of genetic diversity in others. The latter are obvious targets for conservation and useful source populations for reinforcement translocations (“Defining translocations” section). Low-diversity populations, however, may have low adaptive potential under climate change and may be targets for improving connectivity (see “Gene flow” below, Figure 2 D6) or reinforcement translocations (action 4 in Figure 2, “Defining translocations” section) to increase diversity. The various sequencing strategies outlined above for population and quantitative genomics within populations are also applicable to samples from different populations and, as illustrated below, have often revealed significant differences in divergence levels across the genome.
Are some populations already adapted to local climate?
Where populations differ in their allelic composition, tests for genetic divergence deviating from theoretical neutral expectations can be applied to detect local adaptation (D5b in Figure 2). Quantifying local adaptation is important because this can indicate whether populations already possess genetic variation that could allow persistence under climate change. Measures of population differentiation such as Wright’s F ST are commonly used as a metric for local adaptation in methods for detecting adaptive divergence that can include explicit assumptions about demographic history [164-166], although this approach can indicate an excessive number of apparently adaptive loci if assumptions about demography are incorrect . Similarly, tests for consistent differences in the frequencies of alleles between replicated pairs of populations such as the Cochran-Mantel-Haenszel (CMH) test can be used to identify locally adapted loci where population pairs are compared for a common selective constraint . Relative rate tests such as the McDonald-Kreitman (MK) test permit comparisons of diversity within populations to divergences between them (or from related species), where departure from theoretical ratios for neutral loci can imply local adaptation [168,169]. Software is becoming available to allow many of these well-known tests to be carried out on genome-scale data [170-172].
For widespread species whose geographic ranges encompass environmental gradients, the association of allelic variation among populations (or individuals) with environmental factors can also be an indicator of local adaptation, as long as neutral patterns of genetic variation are taken into account [147,173-175]. Several statistical approaches have been developed to test for such associations, many of which incorporate information on demographic history utilising general linear models , logistic regression , generalised estimating equations  or other types of models [179-181]. Central to these analyses is that demographic history is explicitly accounted for, in order to avoid erroneous conclusions of adaptive divergence in allele frequencies . These approaches have identified genomic regions differentiated across climatic gradients, such as four regions repeatedly associated with minimum temperature in the alpine plant Arabis alpina  and five regions associated with precipitation in the alpine plant Campanula barbata . However, isolating the specific environmental factor responsible for spatial genetic variation can still be challenging because different factors will often be spatially correlated.
The above approaches highlight ways to identify genomic regions that are involved either in historic adaptation to longstanding ecological gradients  or adaptation to recent environmental change including from anthropogenic sources [93,183]. This can help distinguish populations that may be at risk due to a lack of adaptive diversity from those which already possess genetic variants that could allow persistence under climate change. However, unless the contribution of specific genes to the size of adaptive shifts is known and the nature of environmental variation linked to the genes has been clearly identified, these types of approaches cannot indicate the rate and extent of an adaptive response possible across a species’ range. As with the intra-population variation considered above, quantitative genomics is still needed, both to narrow down the genomic region specifically responsible for the adaptive phenotypic differences and to assess the size of phenotypic effects associated with particular regions.
Is gene flow high enough (or too high)?
If genetic variation has been identified in certain populations of a species that could help other populations adapt to climate change, then it is important to determine whether there is an appropriate level of gene flow between populations (D6 in Figure 2). Gene flow can aid adaptation by increasing genetic variation and/or by introducing better adapted genotypes. Interbreeding with divergent individuals migrating into a population can also generate entirely new genotypes that may be better suited to tolerating the novel conditions expected under climate change [184-186]. Whilst gene flow usually does improve adaptive capacity, high levels of gene flow can also result in a loss of local adaptation and reduction in population fitness , although empirical evidence for deleterious gene flow is still limited .
Natural or anthropogenic barriers as well as habitat fragmentation can disrupt gene flow by preventing the migration and dispersal of individuals. Topographically complex landscapes with sharp environmental gradients may drive local adaptation and produce regions containing genotypes adapted to different conditions. This in turn may lead to isolation by adaptation—i.e. the exclusion of immigrating individuals from the breeding pool due to higher fitness of local genotypes [188,189]. These landscape-scale processes can also lead to reproductive isolation—such as through mating or flowering phenology—resulting in little or no effective gene flow between geographically close populations [190-192].
Historical and contemporary gene flow between populations can be accurately estimated using genomic data. For example, high contemporary gene flow as well as local adaptation in red abalone has been identified utilising SNPs discovered through transcriptome sequencing , whilst historical gene flow between closely related species of Heliconius butterflies was identified using targeted enrichment sequencing . RAD sequencing has identified genetic isolation among populations of herring  and speciation in cichlid fishes . Gene flow estimates utilising RAD-seq-derived SNPs detected inbreeding in wild harbour seals, suggesting isolation between natural seal populations . Some of these genomic studies on gene flow, e.g. [99,193] are pointing to highly heterogeneous rates of gene flow across the genome; intra-population and quantitative analyses as outlined in previous sections are then invaluable in ascertaining the adaptive significance of such heterogeneity.
If key populations have been shown to be isolated from adaptive variation, or from high overall variation located elsewhere in the species’ range, then the next decision is to determine whether migration pathways can be restored (D7 in Figure 2).
Genetic opportunities—managing for diversity and adaptive capacity
The previous part of the framework deals with the importance of genetic diversity to evolutionary responses to environmental change, how to infer adaptive capacity from measures of genetic diversity and the potential importance of gene flow. The next part considers the potential of more active interventions for species for which the actions outlined to this point are unlikely to be sufficient.
Can degraded landscapes be restored to enhance gene flow and adaptive shifts?
Landscape revegetation is a major programme of activity to address climate change worldwide [195,196]. The aim is generally to restore fragmented and degraded landscapes, thus enhancing the scope, quality and accessibility of key refuge areas for both key species and whole communities . However, little effort is currently invested in assessing the adaptive potential of the trees and shrubs that have been planted and hence the likelihood that they will persist under climate change. At present, most revegetation efforts revolve around the notion of local provenancing, where germplasm is collected from neighbouring areas on the assumption that it is adapted to local conditions. If there is strong local adaptation, then this approach will facilitate short-term establishment, but it may not be the best approach in the longer term, given changing environmental conditions. Compounding the issue, local provenancing often results in seed collections from small local populations that are genetically depauperate [198,199], leading to low genetic variation with inadequate potential for adaptive response to future changed conditions [200,201].
Genomics, in conjunction with functional trait analysis, can play a major role in addressing these issues, as it helps characterise climatic adaptation potential (D7 in Figure 2). Whilst most landscape-scale revegetation programmes do not undertake breeding or selection for specific traits, assessment of genomic sequence variation in natural populations or provenances under consideration as seed sources can provide important information on standing genetic diversity and, in due course, on adaptive variation in particular regions . In revegetation programmes using foundation species where some selection may be feasible, identification of gene variants that have been targets of environmental selection may be used to guide selections for alleles that may be best suited to projected environments. Such genomic approaches are being undertaken in an increasing number of key species in revegetation programmes in Australia (Table 2).
Several provenancing strategies involving assisted gene flow have been suggested in the context of climate change. Predictive provenancing requires identification of the predicted climate at a certain point in the future and sourcing seed from sites where that climate currently occurs . Composite provenancing involves mixing seed collected from increasing distances away from the site to maximise genetic diversity and mimic natural gene flow . Admixture provenancing suggests collecting seed from a range of environments without regard to the local site conditions . Climate-adjusted provenancing involves sourcing seed from sites along the projected direction of climate change [202,207]. Climate-adjusted provenancing has the advantage of simultaneously mixing seed sources to increase genetic variation and recruiting from populations likely to be adapted to future climates without needing to target any particular population specifically. This approach is particularly suitable for species with long generation times, where the impacts of climate change will be felt within a generation, and maximising the adaptive diversity in the gene pool is essential to future population persistence.
Assisted migration approaches, such as the various provenancing strategies outlined above, may be critical to maintain ecosystems under climate change . However, it should also be noted that the genetic potential of seed sources is just one of the many issues that require management in revegetation programmes (e.g. soil symbionts, disease, weed risk), and there are decision frameworks available for managing these issues [199,208,209] that can be applied in a wider climatic context.
Potential for naturally occurring hybridisation and introgression
If no populations within a species harbour adequate genetic diversity, the next step is to consider whether ongoing evolutionary responses to climate change might be enhanced by naturally occurring hybridisation with closely related species (D8 in Figure 2). Hybridisation has been shown to play a role in moving adaptive gene sets between closely related species, and in such cases, it will alter predictions for future phylogenetic diversity as well as the adaptive capacity of species, e.g. [210-213]. If hybridisation is as common and evolutionarily significant in natural systems as many researchers now consider, it could help species modify their phenotypes rapidly enough to accommodate current rates of environmental change. In the past, statistical evidence for determining the extent of hybridisation in nature has been difficult to obtain because patterns of genetic variation caused by hybridisation look similar to the patterns of genetic variation caused by the incomplete sorting of alleles that can accompany species divergence [212,214,215]. However, analytical approaches that use genome sequence data for robust inferences of hybridisation have recently been developed [215-219], which should help to better understand the extent and adaptive significance of hybridisation in nature.
Importantly, next-generation sequencing (NGS) sequencing of species complexes is showing that adaptive differentiation and introgression do not necessarily involve much of the genome. With our own species, researchers estimate that whilst less than 7% of our genome is introgressed with the DNA of extinct hominid species, the captured DNA sequences have helped humans adapt to a variety of climates and resist pathogens [211,212,218]. Although “genomic extinction” resulting from hybridisation in nature has been suggested by some researchers to occur where endemic species are replaced by invasive species that acquire endemic adaptations , genetic rescue by interspecific hybridisation need not abolish local adaptations. The genetic basis for many of these is likely to be concentrated in islands of adaptive divergence, such as seen in Eurasian Ficedula flycatchers  and Heliconius butterflies . In humans, recent evidence has emerged that there is strong selection against regions of introgressed genomes that are not advantageous, including selection against genes that reduce the fertility of hybrids .
Several studies are also now capitalising on inexpensive NGS-based transcriptome analyses to dissect hybridisation and the impact that introgression events have on ecological diversification and reproductive compatibility of plant species (e.g. Helianthus sunflowers , tomatoes , alpine cress  and Senecio ). Biotic and abiotic stress response genes are commonly implicated in ecological diversification and adaptation. Whilst gene expression differences for such genes occur between parent species and hybrids, there is little evidence at this point for regulatory incompatibility between the respective genomes of closely related hybridising species. In an informative study, Moran and Fontdevilla  followed up full genome sequencing of two hybridising Drosophila species with a QTL analysis of the (incomplete) post-zygotic reproductive barriers between them. They successfully mapped several loci contributing to those barriers and showed they acted cumulatively according to a polygenic threshold model. That is, sterility was more a function of the extent of genetic divergence of the parent species’ genomes than the action of major hybrid sterility genes. Such findings could have important implications for genetic rescue efforts that consider breeding between genetically isolated populations and species, e.g. [226-228], but many more studies will be needed before generalisations will emerge.
Do climate refugia outside species’ current ranges exist and can species reach them?
From an ecological perspective, climatic refugia are often defined as those areas where the projected future environment is most similar to the current environment of a species or community  or where environmental and spatial heterogeneity maintains microclimatic variation as regional environments change  (D9 and D10 in Figure 2). The premise is that such sites are likely to serve as important refugia for species that are unable to adapt to the novel conditions projected under climate change. Identification of refugia is modelled for individual species using species distribution models as discussed previously or continent-wide for functional groups of organisms using community modelling . Such refugia can be identified using a range of pattern- and process-based characteristics, including climate projection models, combined with information about current environmental attributes, to estimate the scale of change expected across the landscape and the overall similarity between areas of current and future landscape. For instance, Dunlop et al.  modelled the scale of novel environments expected under climate change across the Australian continent, in order to estimate the areas likely to have the least amount of change from their current climate. These projections were then used to estimate how well current environments are represented in the National Reserve System of Australia under future environments and thus how well the reserve system protects biodiversity over the longer term. These methods estimate refugial areas that are inside as well as outside the current range of species, which are important to persistence as long as the species are able to disperse to them and there is vacant ecological space .
Once such refugia have been identified, the next management step is to ensure that they are secured against threatening processes (action 5 in Figure 2), such as by inclusion in protected areas, e.g. [2,229]. The key question then is whether the species of concern will be able to reach them and be able to establish there. Modelling approaches estimating the velocity of climate change  provide an estimate of the scale of effort required for a species to reach a refugium. Information about current levels of gene flow combined with information about movement pathways, and the extent of landscape fragmentation/revegetation, is likely to assist in understanding whether species are able to reach refugial areas. If it seems unlikely that the species in question will be able to colonise such refugia because of barriers to gene flow, low rates of migration or the absence of vacant space, assisted colonisation might be considered (Table 3, action 6 in Figure 2).
Last ditch efforts for critical species
Can assisted colonisation, enforced hybridisation and ex situ conservation help?
This section considers interventions for threatened species or populations that have failed or are likely to fail to persist with the management options above and are at, or approaching, endangered or critically endangered status (D11, D12 and D13 in Figure 2). It deals first with translocations aimed at restoring levels of genetic diversity and adaptive capacity within a species’ range (reinforcement translocations) (“Defining translocations” section, action 4 in Figure 2). Weeks et al.  define these types of translocations as genetic rescue (where the aim is to rescue populations from the genetic effects of inbreeding and associated loss of genetic diversity and inbreeding depression) or genetic restoration (where the aim is to restore levels of adaptive genetic diversity via ongoing translocations from the source population). Note that some of the provenancing strategies considered in the section on revegetation above also have elements of genetic rescue/restoration, the key difference being that the species in question for revegetation are not themselves endangered and the focus for conservation. This section then considers translocations aimed at hybridising evolutionary significant units [232,233] and sub- or sibling species (assisted colonisation) (action 7 in Figure 2). Finally it discusses the last resort option of ex situ conservation (e.g. captive breeding or seed nurseries), which Weeks et al.  term genetic capture (action 8 in Figure 2).
Genetic rescue and genetic restoration are appropriate where a key population of a species or subspecies has fallen to such low numbers <1,000 [199,234] that the exposure of genetic load through inbreeding becomes a significant fitness issue (inbreeding depression) compounding the challenges of adapting to a changing environment. Both genetic rescue and genetic restoration involve the translocation of individuals from another, larger population of the species, usually aiming for up to 20% gene flow from the source population [199,235] and, in the case of genetic restoration, also aiming to continue gene flow through ongoing translocation at a rate of at least one effective migrant per generation, which is thought to be enough to reduce the disruptive effects of genetic drift . The goal is to reduce genetic load, inbreeding depression and the detrimental effects of genetic drift whilst also, as with genetic adaptation above, enhancing the prospects for successful adaptation to the changing environment by boosting genetic variation and the opportunities it provides for generating novel recombinants. Hedrick  has shown that gene flow of up to 20% into a recipient population is not likely to swamp locally adapted alleles, particularly those under strong selection. Such genetic rescue/restoration has been successful in several recent cases, such as the Florida panthers, greater prairie chickens in North America, adders in Sweden, South Island robins in New Zealand and mountain pygmy possums in Australia [227,237,238]. However, as with translocations for genetic adaptation above, it is still contentious and has been underutilised as a tool in the conservation of endangered species.
Genetic rescue and restoration translocations have partly been underutilised due to concerns around preserving “unique” genetically distinct populations and avoiding outbreeding depression. But uniqueness in endangered populations and species is more likely to be a result of drift processes than mutation alone  and the risk of outbreeding depression has clearly been overstated . At any rate, the markers generated using NGS technologies will be more informative than neutral markers (e.g. microsatellites) for differentiating between populations that are adaptively unique, compared with those populations that have lost variation through drift processes by identifying loci under selection . Similarly, NGS might give greater insight into the likelihood of inbreeding and outbreeding depression by assessing the number of genomic regions that are adaptively unique within source and recipient populations, and that decrease fitness, e.g. .
Concerns about conserving genetic integrity, and problems with outbreeding depression, become more pronounced when the only option available for genetic rescue involves translocating individuals from a different subspecies or species. However, the increasing pressure from climate change and other drivers of widespread environmental change mean that the potential risks of such genetic rescue are increasingly outweighed by the opportunity to rescue species or subspecies that would otherwise disappear altogether. Whilst it is generally only enacted when population sizes have fallen to a few individuals, there have been some significant successes with this strategy. The classic case of genetic rescue involved the Florida panther (Puma concolor coryi) where the introduction of eight female pumas from a different subspecies (Puma concolor stanleyana) from Texas restored depleted genetic diversity, reversed inbreeding depression and increased population size . Similarly, the Norfolk Island boobook owl, Ninox novaeseelandiae undulata, was reduced to a single female in 1986, and the deliberate introduction of two males of its nearest relative (the New Zealand boobook, N. n. novaeseelandiae) saved this subspecies from extinction, albeit in hybrid form . Clearly, there are instances when such radical translocations can save endangered species (or at least some of their genetic history), and more thought needs to be given as to how NGS technologies might better inform about when these instances will lead to success (e.g. by examining patterns of adaptive diversity, developing better estimates of divergence at adaptive loci, etc.). NGS monitoring in the first few generations after the initial hybridisation might also suggest possible further interventions to maximise adaptation.
Another last resort option for endangered species involves ex situ conservation. Genetic issues are critical in this option but genetic input into the management of captive breeding/seed nursery programmes has generally been based on relatively few neutral markers . As already noted, genomics can now provide much more comprehensive coverage of neutral markers and give new insights into important adaptive processes. The data quality can also be improved by having access to a reference genome from the target species (or related species) and, as already noted, the costs of this continue to decrease, making it a viable option for conservation programmes.
More specifically, NGS resequencing will permit genetic relatedness among individuals to be accurately estimated across the genome. This in turn will enable better decisions, both in captive breeding strategies and in the selection of individuals for release back into particular field populations . This increases the chances of avoiding inbreeding depression and perhaps even increasing adaptive variation both in captive and natural populations. The prospects of avoiding some of the specific deleterious fitness effects that have plagued ex situ captive breeding programmes [245,246] are also improved, for example, by ensuring maximum diversity is retained in key genomic regions related to disease resistance and by reducing the frequencies of alleles that have been associated with mating incompatibility or specific recessive conditions, either in the species under study or in others . Changes in gene frequencies over generations of captive breeding can also alert managers to avoid alleles that may be associated with adaptation to captivity and to compare the genetic composition to natural populations, either overall or in specific localities targeted for reintroductions . As knowledge of gene function improves, it may be possible to identify and select for alleles associated with particular environments (e.g. desiccation resistance, drought tolerance or phenology that may be required under future climate scenarios ).
However, it should be emphasised that decisions around assisted colonisation and ex situ conservation will involve many considerations unrelated to genetic variation and evolutionary capacity. These include factors like evaluating the impact of removing individuals from source populations as well as the impact of introductions on existing biota in target sites, assessing the likely costs of such translocations within the context of other demands on conservation budgets, and social or cultural aspects such as the value placed by the public on a threatened species.
Our framework highlights the potential of genomic studies to contribute to strategies for conserving biodiversity. Both population and quantitative genomics are crucial, aided by, but not dependent on, a good reference genome sequence. However, these genomic approaches do not provide a panacea for the problems in biodiversity conservation under climate change. Their value will be easier to realise in some decision areas than others.
Population genomic data can now be used in relatively straightforward experiments to assess genetic diversity within and between species, to map levels of genetic diversity across landscapes, and to understand the relative importance of neutral evolutionary processes like genetic drift and migration in driving population dynamics. They can also now be used to understand the extent to which genetic changes have occurred as a consequence of natural selection driving local adaptation and to make inferences about the relative importance of evolutionary adaptation versus neutral process in driving patterns of biodiversity across landscapes. As such, population genomic data can provide unprecedented insights into the extent and evolutionary consequences of naturally occurring hybridisation in nature, and to assess and monitor the outcomes of management decisions that involve translocations, and efforts to restore degraded landscapes and communities through revegetation programmes.
However, the key limitations with population genomic approaches are that they do not of themselves identify the precise genetic variants that causally underpin adaptive responses to climate change, nor do they tell us about the size of the adaptive differences mediated by variation in particular genomic regions. Quantitative genomics, combined with appropriate ecological and quantitative evolutionary work, can address both these issues, although it is challenging in such studies to define complex physiological traits that are relevant to the ecology of species. One major issue with QTL mapping (but not GWAS) is its absolute dependence on managed breeding programmes, which may be not be feasible or affordable in many cases. Where population and quantitative genomics approaches can be undertaken, it may be possible to identify and implement substantively more effective and efficient management strategies for biodiversity under climate change.
Zoos and other breeding establishments will be important resources for the genomics work required for threatened fauna, as they have unique capabilities in rearing and breeding animals and are increasingly concerned with conservation issues. One of their major contributions to date has been in restoration programmes, breeding captive populations of animals for eventual release into the wild. This exercise has often suffered from a low success rate, due to factors such as ongoing inbreeding, genetic adaptation to captivity at the expense of adaptation to wild conditions and so on [247,248]. Whilst avoiding inbreeding and the exposure of deleterious recessive conditions is already a major goal in their breeding programmes, genomic approaches together with evolutionary thinking could provide data which are both more comprehensive and more precise on this point. Zoos could also play a larger role in the future in the quantitative and population genetics needed for evaluating other key management options, such as translocations and hybridisation by, for example, testing the viability and various adaptively important phenotypes of F1 and F2 offspring generated from crosses between populations, subspecies and other taxa.
Herbarium collections, seed banks and botanic gardens could fulfil the same sorts of functions for plants. In addition, where seed material can be maintained across years, there is an opportunity to capture the genetic variation present at a particular point in time and preserve it for later re-establishment of populations [249,250]. Such a resource could provide a valuable source of genetic variation and capture novel genotypes across regions as plant populations adapt to changing environmental conditions .
Finally, once genomic approaches become routine components of conservation programmes and restoration efforts, novel ways of thinking about the role of evolution in management programmes to maintain biodiversity and ecosystem functions are likely to emerge. New examples of phenomena like incomplete allele sorting and islands of adaptive divergence during speciation, and various introgression scenarios following hybridisation, have already become evident from the application of genomics to a range of non-model species. New levels of understanding of climate change adaptation and the role of hybridisation in adaptive processes are likely to emerge from this work. This understanding in turn will suggest novel approaches to biodiversity conservation and the maintenance of ecosystem function under a rapidly changing climate.
IPCC. Climate Change 2013: The physical science basis. UK: Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press; 2013.
Dunlop M, Hilbert DW, Ferrier S, House A, Liedloff A, Prober SM, et al. The implications of climate change for biodiversity conservation and the National Reserve System: final synthesis. Canberra: CSIRO; 2012.
Perez-Garcia N, Font X, Ferre A, Carreras J. Drastic reduction in the potential habitats for alpine and subalpine vegetation in the Pyrenees due to twenty-first-century climate change. Reg Envir Chang. 2013;13:1157–69.
Popescu VD, Rozylowicz L, Cogalniceanu D, Niculae IM, Cucu AL. Moving into protected areas? Setting conservation priorities for Romanian reptiles and amphibians at risk from climate change. PLoS One. 2013;8:e793305.
Staudinger MD, Carter SL, Cross MS, Dubois NS, Duffy JE, Enquist C, et al. Biodiversity in a changing climate: a synthesis of current and projected trends in the US. Front Ecol Environ. 2013;11:465–73.
Bellard C, Thuiller W, Leroy B, Genovesi P, Bakkenes M, Courchamp F. Will climate change promote future invasions? Glob Change Biol. 2013;19:3740–8.
Bertelsmeier C, Luque GM, Courchamp F. Increase in quantity and quality of suitable areas for invasive species as climate changes. Conserv Biol. 2013;27:1458–67.
Duncan RP, Cassey P, Blackburn TM. Do climate envelope models transfer? A manipulative test using dung beetle introductions. Proc R Soc B-Biol Sci. 2009;276:1449–57.
Kearney M, Porter W. Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges. Ecol Lett. 2009;12:334–50.
Overgaard J, Kearney MR, Hoffmann AA. Sensitivity to thermal extremes predicts the distribution limits of Australian Drosophila: similar implications of climate change for widespread and tropical species. Glob Change Biol. 2014;20:1738–50.
Deutsch CA, Tewksbury JJ, Huey RB, Sheldon KS, Ghalambor CK, Haak DC, et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc Natl Acad Sci U S A. 2008;105:6668–72.
Dillon ME, Wang G, Huey RB. Global metabolic impacts of recent climate warming. Nature. 2010;467:704–U788.
Zhao F, Zhang W, Hoffmann AA, Ma C-S. Night warming on hot days produces novel impacts on development, survival and reproduction in a small arthropod. J Anim Ecol. 2014;83:769–78.
Chown SL, Jumbam KR, Sorensen JG, Terblanche JS. Phenotypic variance, plasticity and heritability estimates of critical thermal limits depend on methodological context. Funct Ecol. 2009;23:133–40.
Terblanche JS, Deere JA, Clusella-Trullas S, Janion C, Chown SL. Critical thermal limits depend on methodological context. Proc R Soc B-Biol Sci. 2007;274:2935–42.
Chevin L-M, Lande R, Mace GM. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 2010;8:e1000357.
Huey RB, Kearney MR, Krockenberger A, Holtum JAM, Jess M, Williams SE. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos Trans R Soc Lond Ser B-Biol Sci. 2012;367:1665–79.
Chen IC, Hill JK, Ohlemuller R, Roy DB, Thomas CD. Rapid range shifts of species associated with high levels of climate warming. Science. 2011;333:1024–6.
Berg MP, Kiers ET, Driessen G, van der Heijden M, Kooi BW, Kuenen F, et al. Adapt or disperse: understanding species persistence in a changing world. Glob Change Biol. 2010;16:587–98.
Travis JMJ, Delgado M, Bocedi G, Baguette M, Barton K, Bonte D, et al. Dispersal and species' responses to climate change. Oikos. 2013;122:1532–40.
Macfarlane WW, Logan JA, Kern WR. An innovative aerial assessment of Greater Yellowstone Ecosystem mountain pine beetle-caused whitebark pine mortality. Ecol Appl. 2013;23:421–37.
Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, et al. Global consequences of land use. Science. 2005;309:570–4.
Battisti DS, Naylor RL. Historical warnings of future food insecurity with unprecedented seasonal heat. Science. 2009;323:240–4.
Parmesan C. Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Syst. 2006;37:637–69.
Araujo MB, Alagador D, Cabeza M, Nogues-Bravo D, Thuiller W. Climate change threatens European conservation areas. Ecol Lett. 2011;14:484–92.
Nelson EJ, Kareiva P, Ruckelshaus M, Arkema K, Geller G, Girvetz E, et al. Climate change's impact on key ecosystem services and the human well-being they support in the US. Front Ecol Environ. 2013;11:483–93.
Myers SS, Gaffikin L, Golden CD, Ostfeld RS, Redford KH, Ricketts TH, et al. Human health impacts of ecosystem alteration. Proc Natl Acad Sci U S A. 2013;110:18753–60.
Byrne M, Yeates DK, Joseph L, Kearney M, Bowler J, Williams MAJ, et al. Birth of a biome: insights into the assembly and maintenance of the Australian arid zone biota. Mol Ecol. 2008;17:4398–417.
Lawing AM, Polly PD. Pleistocene climate, phylogeny, and climate envelope models: an integrative approach to better understand species' response to climate change. PLoS One. 2011;6:e28554.
Hoffmann AA, Sgro CM. Climate change and evolutionary adaptation. Nature. 2011;470:479–85.
Merila J, Hendry AP. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol Appl. 2014;7:1–14.
Karell P, Ahola K, Karstinen T, Valkama J, Brommer JE. Climate change drives microevolution in a wild bird. Nat Commun. 2011;2:20810. 1038/ncomms1213.
Umina PA, Weeks AR, Kearney MR, McKechnie SW, Hoffmann AA. A rapid shift in a classic clinal pattern in Drosophila reflecting climate change. Science. 2005;308:691–3.
Franks SJ, Sim S, Weis AE. Rapid evolution of flowering time by an annual plant in response to a climate fluctuation. Proc Natl Acad Sci U S A. 2007;104:1278–82.
Kellermann V, van Heerwaarden B, Sgrò CM, Hoffmann AA. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science. 2009;325:1244–6.
Etterson JR, Shaw RG. Constraint to adaptive evolution in response to global warming. Science. 2001;294:151–4.
Merila J. Evolution in response to climate change: in pursuit of the missing evidence. BioEssays. 2012;34:811–8.
Kuparinen A, Savolainen O, Schurr FM. Increased mortality can promote evolutionary adaptation of forest trees to climate change. For Ecol Manage. 2009;259:1003–8.
Weber KE. Increased selection response in larger populations. I Selection for wing-tip height in Drosophila melanogaster at three population sizes. Genetics. 1990;125:579–84.
Willi Y, Hoffmann AA. Demographic factors and genetic variation influence population persistence under environmental change. J Evol Biol. 2009;22:124–33.
Holt RD, Gomulkiewicz R. How does immigration influence local adaptation? A reexamination of a familiar paradigm. Am Nat. 1997;149:563–72.
Fedorka KM, Winterhalter WE, Shaw KL, Brogan WR, Mousseau TA. The role of gene flow asymmetry along an environmental gradient in constraining local adaptation and range expansion. J Evol Biol. 2012;25:1676–85.
Magiafoglou A, Carew ME, Hoffmann AA. Shifting clinal patterns and microsatellite variation in Drosophila serrata populations: a comparison of populations near the southern border of the species range. J Evol Biol. 2002;15:763–74.
Sexton JP, Hangartner SB, Hoffmann AA. Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution. 2013;68:1–15.
Prinz K, Weising K, Hensen I. Habitat fragmentation and recent bottlenecks influence genetic diversity and differentiation of the central European halophyte Suaeda maritima (Chenopodiaceae). Am J Bot. 2013;100:2210–8.
Cosic N, Ricanova S, Bryja J, Penezic A, Cirovic D. Do rivers and human-induced habitat fragmentation affect genetic diversity and population structure of the European ground squirrel at the edge of its Pannonian range? Conserv Genet. 2013;14:345–54.
Cibrian-Jaramillo A, Hird A, Oleas N, Ma H, Meerow AW, Francisco-Ortega J, et al. What is the conservation value of a plant in a botanic garden? Using indicators to improve management of ex situ collections. Bot Rev. 2013;79:559–77.
Gonzalez A, Ronce O, Ferriere R, Hochberg ME. Evolutionary rescue: an emerging focus at the intersection between ecology and evolution. Philos Trans R Soc Lond Ser B-Biol Sci. 2013;368:20120404.
Champigny MJ, Sung WWL, Catana V, Salwan R, Summers PS, Dudley SA, et al. RNA-Seq effectively monitors gene expression in Eutrema salsugineum plants growing in an extreme natural habitat and in controlled growth cabinet conditions. BMC Genomics. 2013;14:23.
Schunter C, Vollmer SV, Macpherson E, Pascual M. Transcriptome analyses and differential gene expression in a non-model fish species with alternative mating tactics. BMC Genomics. 2014;15:13.
Davey JW, Cezard T, Fuentes-Utrilla P, Eland C, Gharbi K, Blaxter ML. Special features of RAD sequencing data: implications for genotyping. Mol Ecol. 2013;22:3151–64.
Ellegren H. Genome sequencing and population genomics in non-model organisms. Trends Ecol Evol. 2014;29:51–63.
McCormack JE, Hird SM, Zellmer AJ, Carstens BC, Brumfield RT. Applications of next-generation sequencing to phylogeography and phylogenetics. Mol Phylogenet Evol. 2013;66:526–38.
Grabowski PP, Morris GP, Casler MD, Borevitz JO. Population genomic variation reveals roles of history, adaptation and ploidy in switchgrass. Mol Ecol. 2014;23:4059–73.
Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22:939–46.
Wilkening S, Tekkedil MM, Lin G, Fritsch ES, Wei W, Gagneur J, et al. Genotyping 1000 yeast strains by next-generation sequencing. BMC Genomics. 2013;14:90.
Narum SR, Buerkle CA, Davey JW, Miller MR, Hohenlohe PA. Genotyping-by-sequencing in ecological and conservation genomics. Mol Ecol. 2013;22:2841–7.
Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Gen. 2011;12:499–510.
Davey JW, Blaxter ML. RADSeq: next-generation population genetics. Brief Funct Genomics. 2010;9:416–23.
Arnold B, Corbett-Detig RB, Hartl D, Bomblies K. RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling. Mol Ecol. 2013;22:3179–90.
Gautier M, Foucaud J, Gharbi K, Cézard T, Galan M, Loiseau A, et al. Estimation of population allele frequencies from next-generation sequencing data: pool-versus individual-based genotyping. Mol Ecol. 2013;22:3766–79.
Futschik A, Schlötterer C. The next generation of molecular markers from massively parallel sequencing of pooled DNA samples. Genetics. 2010;186:207–18.
Corander J, Majander KK, Cheng L, Merilä J. High degree of cryptic population differentiation in the Baltic Sea herring Clupea harengus. Mol Ecol. 2013;22:2931–40.
Cheng C, White BJ, Kamdem C, Mockaitis K, Costantini C, Hahn MW, et al. Ecological genomics of Anopheles gambiae along a latitudinal cline: a population-resequencing approach. Genetics. 2012;190:1417–32.
Fabian DK, Kapun M, Nolte V, Kofler R, Schmidt PS, Schlötterer C, et al. Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America. Mol Ecol. 2012;21:4748–69.
Kasowski M, Grubert F, Heffelfinger C, Hariharan M, Asabere A, Waszak SM, et al. Variation in transcription factor binding among humans. Science. 2010;328:232–5.
Anderson EC, Skaug HJ, Barshis DJ. Next-generation sequencing for molecular ecology: a caveat regarding pooled samples. Mol Ecol. 2014;23:502–12.
Fumagalli M, Vieira FG, Linderoth T, Nielsen R. ngsTools: methods for population genetics analyses from next-generation sequencing data. Bioinformatics. 2014;30:1486–7.
Ferretti L, Ramos-Onsins SE, Perez-Enciso M. Population genomics from pool sequencing. Mol Ecol. 2013;22:5561–76.
Jiang LC, Schlesinger F, Davis CA, Zhang Y, Li RH, Salit M, et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011;21:1543–51.
Konczal M, Koteja P, Stuglik MT, Radwan J, Babik W. Accuracy of allele frequency estimation using pooled RNA-Seq. Mol Ecol Res. 2014;14:381–92.
Salem M, Vallejo RL, Leeds TD, Palti Y, Liu SX, Sabbagh A, et al. RNA-Seq identifies SNP markers for growth traits in rainbow trout. PLoS One. 2012;7:13.
Lee SF, Eyre-Walker YC, Rane RV, Reuter C, Vinti G, Rako L, et al. Polymorphism in the neurofibromin gene, Nf1, is associated with antagonistic selection on wing size and development time in Drosophila melanogaster. Mol Ecol. 2013;22:2716–25.
Jovelin R, Cutter AD. Fine-scale signatures of molecular evolution reconcile models of indel-associated mutation. Genome Biol Evol. 2013;5:978–86.
Wetterbom A, Sevov M, Cavelier L, Bergstrom TF. Comparative genomic analysis of human and chimpanzee indicates a key role for indels in primate evolution. J Mol Evol. 2006;63:682–90.
Hoffmann AA, Blacket MJ, McKechnie SW, Rako L, Schiffer M, Rane RV, et al. A proline repeat polymorphism of the Frost gene of Drosophila melanogaster showing clinal variation but not associated with cold resistance. Insect Mol Biol. 2012;21:437–45.
O'Rawe J, Jiang T, Sun G, Wu Y, Wang W, Hu J, et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 2013;5:28.
Hoffmann AA, Rieseberg LH. Revisiting the impact of inversions in evolution: from population genetic markers to drivers of adaptive shifts and speciation? Annu Rev Ecol Evol Syst. 2008;39:21–42.
Corbett-Detig RB, Hartl DL. Population genomics of inversion polymorphisms in Drosophila melanogaster. PLoS Genet. 2012;8:e1003056.
Layer R, Chiang C, Quinlan A, Hall I. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 2014;15:R84.
Connon RE, D’Abronzo LS, Hostetter NJ, Javidmehr A, Roby DD, Evans AF, et al. Transcription profiling in environmental diagnostics: health assessments in Columbia River Basin Steelhead (Oncorhynchus mykiss). Environ Sci Technol. 2012;46:6081–7.
Johansson F, Veldhoen N, Lind MI, Helbing CC. Phenotypic plasticity in the hepatic transcriptome of the European common frog (Rana temporaria): the interplay between environmental induction and geographical lineage on developmental response. Mol Ecol. 2013;22:5608–23.
Yampolsky LY, Schaer TMM, Ebert D. Adaptive phenotypic plasticity and local adaptation for temperature tolerance in freshwater zooplankton. Proc R Soc B-Biol Sci. 2014;281:20132744.
Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 2009;5:e1000695.
Ruzzante DE, Walde SJ, Gosse JC, Cussac VE, Habit E, Zemlak TS, et al. Climate control on ancestral population dynamics: insight from Patagonian fish phylogeography. Mol Ecol. 2008;17:2234–44.
Zhao S, Zheng P, Dong S, Zhan X, Wu Q, Guo X, et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat Genet. 2012;45:67–71.
Rasić G, Filipović I, Weeks AR, Hoffmann AA. Genome-wide SNPs lead to strong signals of geographic structure and relatedness patterns in the major arbovirus vector. Aedes aegypti BMC Genomics. 2014;15:275.
Barrett RDH, Schluter D. Adaptation from standing genetic variation. Trends Ecol Evol. 2008;23:38–44.
Asthana S, Noble WS, Kryukov G, Grant CE, Sunyaev S, Stamatoyannopoulos JA. Widely distributed noncoding purifying selection in the human genome. Proc Natl Acad Sci U S A. 2007;104:12410–5.
Slotte T, Foxe JP, Hazzouri KM, Wright SI. Genome-wide evidence for efficient positive and purifying selection in Capsella grandiflora, a plant species with a large effective population size. Mol Biol Evol. 2010;27:1813–21.
Locke DP, Hillier LW, Warren WC, Worley KC, Nazareth LV, Muzny DM, et al. Comparative and demographic analysis of orang-utan genomes. Nature. 2011;469:529–33.
Prunier J, Laroche J, Beaulieu J, Bosquet J. Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce. Mol Ecol. 2011;20:1702–16.
Orsini L, Spanier KI, De Meester L. Genomic signature of natural and anthropogenic stress in wild populations of the waterflea Daphnia magna: validation in space, time and experimental evolution. Mol Ecol. 2012;21:2160–75.
Ellison CE, Hall C, Kowbel D, Welch J, Brem RB, Glass NL, et al. Population genomics and local adaptation in wild isolates of a model microbial eukaryote. Proc Natl Acad Sci U S A. 2011;108:2831–6.
Hamilton JA, Lexer C, Aitken SN. Differential introgression reveals candidate genes for selection across a spruce (Picea sitchensis × P. glauca) hybrid zone. New Phytol. 2012;197:927–38.
Orozco-terWengel P, Kapun M, Nolte V, Kofler R, Flatt T, Schlötterer C. Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Mol Ecol. 2012;21:4931–41.
Jump AS, Peñuelas J, Rico L, Ramallo E, Estiarte M, Martínez-Izquierdo J, et al. Simulated climate change provokes rapid genetic change in the Mediterranean shrub Fumana thymifolia. Glob Change Biol. 2008;14:637–43.
Lourenco JM, Glemin S, Galtier N. The rate of molecular adaptation in a changing environment. Mol Biol Evol. 2013;30:1292–301.
Nadeau NJ, Martin SH, Kozak KM, Salazar C, Dasmahapatra KK, Davey JW, et al. Genome-wide patterns of divergence and gene flow across a butterfly radiation. Mol Ecol. 2012;22:814–26.
Keller I, Wagner CE, Greuter L, Mwaiko S, Selz OM, Sivasundar A, et al. Population genomic signatures of divergent adaptation, gene flow and hybrid speciation in the rapid radiation of Lake Victoria cichlid fishes. Mol Ecol. 2013;22:2848–63.
Cheng J, Czypionka T, Nolte AW. The genomics of incompatibility factors and sex determination in hybridizing species of Cottus (Pisces). Heredity. 2013;111:520–9.
Larson EL, Andrés JA, Bogdanowicz SM, Harrison RG. Differential introgression in a mosaic hybrid zone reveals candidate barrier genes. Evolution. 2013;67:3653–61.
Parchman TL, Gompert Z, Braun MJ, Brumfield RT, McDonald DB, Uy JAC, et al. The genomic consequences of adaptive divergence and reproductive isolation between species of manakins. Mol Ecol. 2013;22:3304–17.
Fitzpatrick BM, Johnson JR, Kump DK, Smith JJ, Voss SR, Shaffer HB. Rapid spread of invasive genes into a threatened native species. Proc Natl Acad Sci U S A. 2010;107:3606–10.
Stölting KN, Nipper R, Lindtke D, Caseys C, Waeber S, Castiglione S, et al. Genomic scan for single nucleotide polymorphisms reveals patterns of divergence and gene flow between ecologically divergent species. Mol Ecol. 2012;22:842–55.
Landguth EL, Cushman SA, Murphy MA, Luikart G. Relationships between migration rates and landscape resistance assessed using individual-based simulations. Mol Ecol Res. 2010;10:854–62.
Cao J, Schneeberger K, Ossowski S, Günther T, Bender S, Fitz J, et al. Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat Genet. 2011;43:956–63.
Chen SL, Zhang GJ, Shao CW, Huang QF, Liu G, Zhang P, et al. Whole-genome sequence of a flatfish provides insights into ZW sex chromosome evolution and adaptation to a benthic lifestyle. Nat Genet. 2014;46:253.
Deschamps S, Llaca V, May GD. Genotyping-by-sequencing in plants. Biology. 2012;1:460–83.
Greminger MP, Stolting KN, Nater A, Goossens B, Arora N, Bruggmann R, et al. Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms. BMC Genomics. 2014;15:16.
Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. Double Digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One. 2012;7:e37135.
Bi K, Linderoth T, Vanderpool D, Good JM, Nielsen R, Moritz C. Unlocking the vault: next-generation museum population genomics. Mol Ecol. 2013;22:6018–32.
Lemmon AR, Emme SA, Lemmon EM. Anchored hybrid enrichment for massively high-throughput phylogenomics. Syst Biol. 2012;61:727–44.
Miller NJ, Sun J, Sappington TW. High-throughput transcriptome sequencing for SNP and gene discovery in a moth. Environ Entomol. 2012;41:997–1007.
Wittkopp PJ. Population genetics and a study of speciation using next-generation sequencing: an educational primer for use with "patterns of transcriptome divergence in the male accessory gland of two closely related species of field crickets". Genetics. 2013;193:671–5.
Zook J, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat Biotechnol. 2014;32:246–51.
Yandell M, Ence D. A beginner's guide to eukaryotic genome annotation. Nat Rev Gen. 2012;13:329–42.
Elsik C, Worley K, Bennett A, Beye M, Camara F, Childers C, et al. Finding the missing honey bee genes: lessons learned from a genome upgrade. BMC Genomics. 2014;15:86.
Primmer CR, Papakostas S, Leder EH, Davis MJ, Ragan MA. Annotated genes and nonannotated genomes: cross-species use of Gene Ontology in ecology and evolution research. Mol Ecol. 2013;22:3216–41.
Amin S, Prentis PJ, Gilding EK, Pavasovic A. Assembly and annotation of a non-model gastropod (Nerita melanotragus) transcriptome: a comparison of de novo assemblers. BMC Res Notes. 2014;7:488.
Schneider GF, Dekker C. DNA sequencing with nanopores. Nat Biotechnol. 2012;30:326–8.
Varshney RK, Terauchi R, McCouch SR. Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol. 2014;12:e1001883.
Shoo LP, Hoffmann AA, Garnett S, Pressey RL, Williams YM, Taylor M, et al. Making decisions to conserve species under climate change. Clim Change. 2013;119:239–46.
Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, et al. Predicting species distributions for conservation decisions. Ecol Lett. 2013;16:1424–35.
Ferrier S, Manion G, Elith J, Richardson K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers Distrib. 2007;13:252–64.
Fordham DA, Akcakaya HR, Araujo MB, Keith DA, Brook BW. Tools for integrating range change, extinction risk and climate change information into conservation management. Ecography. 2013;36:956–64.
Voelckel C, Gruenheit N, Biggs P, Deusch O. P.J. L. Chips and tags suggest plant-environment interactions differ for two alpine Pachycladon species. BMC Genomics. 2012;13:322.
Bozinovic G, Sit TL, Di Giulio R, Wills LF, Oleksiak MF. Genomic and physiological responses to strong selective pressure during late organogenesis: few gene expression changes found despite striking morphological differences. BMC Genomics. 2013;14:15.
Pujolar JM, Marino IAM, Milan M, Coppe A, Maes GE, Capoccioni F, et al. Surviving in a toxic world: transcriptomics and gene expression profiling in response to environmental pollution in the critically endangered European eel. BMC Genomics. 2012;13:21.
Barshis DJ, Ladner JT, Oliver TA, Seneca FO, Traylor-Knowles N, Palumbi SR. Genomic basis for coral resilience to climate change. Proc Natl Acad Sci U S A. 2013;110:1387–92.
Cheviron ZA. Transcriptomic variation and plasticity in rufous-collared sparrows (Zonotrichia capensis) along an altitudinal gradient. Mol Ecol. 2008;17:4556–69.
Gattepaille LM, Jakobsson M, Blum MGB. Inferring population size changes with sequence and SNP data: lessons from human bottlenecks. Heredity. 2013;110:409–19.
Sousa V, Hey J. Understanding the origin of species with genome-scale data: modelling gene flow. Nat Rev Gen. 2013;14:404–14.
Lavergne S, Evans MEK, Burfield IJ, Jiguet F, Thuiller W. Are species' responses to global change predicted by past niche evolution? Philos Trans R Soc Lond Ser B-Biol Sci. 2013;368:11.
Crisp MD, Arroyo MTK, Cook LG, Gandolfo MA, Jordan GJ, McGlone MS, et al. Phylogenetic biome conservatism on a global scale. Nature. 2009;458:754–U790.
Keppel G, Van Niel KP, Wardell-Johnson GW, Yates CJ, Byrne M, Mucina L, et al. Refugia: identifying and understanding safe havens for biodiversity under climate change. Global Ecol Biogeogr. 2012;21:393–404.
Byrne M. Evidence for multiple refugia at different time scales during Pleistocene climatic oscillations in southern Australia inferred from phylogeography. Quaternary Sci Rev. 2008;27:2576–85.
Hewitt GM. Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc Lond Ser B-Biol Sci. 2004;359:183–95.
Minin V, Bloomquist E, Suchard M. Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. Mol Biol Evol. 2008;25:1459–71.
Do Amaral FR, Albers PK, Edwards SV, Miyaki CY. Multilocus tests of Pleistocene refugia and ancient divergence in a pair of Atlantic Forest antbirds (Myrmeciza). Mol Ecol. 2013;22:3996–4013.
Fordham D, Brook B, Moritz C, Nogués-Bravo D. Better forecasts of range dynamics using genetic data. Trends Ecol Evol. 2014;29:436–43.
Willi Y, van Buskirk J, Hoffmann AA. Limits to the adaptive potential of small populations. Annu Rev Ecol Syst. 2006;37:433–78.
Markert JA, Champlin DM, Gutjahr-Gobell R, Grear JS, Kuhn A, McGreevy TJ, et al. Population genetic diversity and fitness in multiple environments. BMC Evol Biol. 2010;10:13.
Luikart G, England PR, Tallmon D, Jordan S, Taberlet P. The power and promise of population genomics: from genotyping to genome typing. Nat Rev Gen. 2003;4:981–94.
Nielsen R. Molecular signatures of natural selection. Ann Rev Gen. 2005;39:197–218.
Allendorf FW, Hohenlohe PA, Luikart G. Genomics and the future of conservation genetics. Nat Rev Gen. 2010;11:697–709.
Novembre J, Di Rienzo A. Spatial patterns of variation due to natural selection in humans. Nat Rev Gen. 2009;10:745–55.
Vitti JJ, Grossman SR, Sabeti PC. Detecting natural selection in genomic data. Ann Rev Gen. 2013;47:97–120.
Manel S, Holderegger R. Ten years of landscape genetics. Trends Ecol Evol. 2013;28:614–21.
Ellegren H. Comparative genomics and the study of evolution by natural selection. Mol Ecol. 2008;17:4586–96.
Ellegren H, Smeds L, Burri R, Olason PI, Backstrom N, Kawakami T, et al. The genomic landscape of species divergence in Ficedula flycatchers. Nature. 2012;491:756–60.
Via S. Divergence hitchhiking and the spread of genomic isolation during ecological speciation-with-gene-flow. Philos Trans R Soc Lond Ser B-Biol Sci. 2012;367:451–60.
Mott R, Flint J. Dissecting quantitative traits in mice. Annu Rev Genomics Hum Genet. 2013;14:421–39.
Li Y, Huang Y, Bergelson J, Nordborg M, Borevitz JO. Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana. Proc Natl Acad Sci U S A. 2010;107:21199–204.
Santure AW, De Cauwer I, Robinson MR, Poissant J, Sheldon BC, Slate J. Genomic dissection of variation in clutch size and egg mass in a wild great tit (Parus major) population. Mol Ecol. 2013;22:3949–62.
Telonis-Scott M, Gane M, DeGaris S, Sgrò CM, Hoffmann AA. High resolution mapping of candidate alleles for desiccation resistance in Drosophila melanogaster under selection. Mol Biol Evol. 2012;29:1335–51.
Morgan TJ, Mackay TFC. Quantitative trait loci for thermotolerance phenotypes in Drosophila melanogaster. Heredity. 2006;96:232–42.
De Coninck D, Asselman J, Glaholt S, Janssen CR, Colbourne JK, Shaw JR, et al. Genome-wide transcription profiles reveal genotype-dependent responses of biological pathways and gene-families in Daphnia exposed to single and mixed stressors. Environ Sci Technol. 2014;48:3513–22.
Hayden EJ, Ferrada E, Wagner A. Cryptic genetic variation promotes rapid evolutionary adaptation in an RNA enzyme. Nature. 2011;474:92–U120.
Nosil P, Feder J. Genome evolution and speciation: toward quantitative descriptions of pattern and process. Evolution. 2013;67:2461–7.
Moritz C, Agudo R. The future of species under climate change: resilience or decline? Science. 2013;341:504–8.
Prufer K, Racimo F, Patterson N, Jay F, Sankararaman S, Sawyer S, et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature. 2014;505:43–9.
Burg TM, Taylor SA, Lemmen KD, Gaston AJ, Friesen VL. Postglacial population genetic differentiation potentially facilitated by a flexible migratory strategy in Golden-crowned Kinglets (Regulus satrapa). Can J Zool-Rev Can Zool. 2014;92:163–72.
Foll M, Gaggiotti O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics. 2008;180:977–93.
Excoffier L, Hofer T, Foll M. Detecting loci under selection in a hierarchically structured population. Heredity. 2009;103:285–98.
Beaumont MA, Balding DJ. Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol. 2004;13:969–80.
Lotterhos KE, Whitlock MC. Evaluation of demographic history and neutral parameterization on the performance of F-ST outlier tests. Mol Ecol. 2014;23:2178–92.
McDonald JH, Kreitman M. Adaptive protein evolution at the Adh locus in Drosophila. Nature. 1991;351:652–4.
Sella G, Petrov DA, Przeworski M, Andolfatto P. Pervasive natural selection in the Drosophila genome? PLoS Genet. 2009;5:1–13.
Feng S, Liu D, Zhan X, Wing MK, Abecasis GR. RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics. 2014;30:2828–9.
Garrigan D. POPBAM: tools for evolutionary analysis of short read sequence alignments. Evol Bioinform Online. 2013;9:343–53.
Pfeifer B, Wittelsburger U, Ramos-Onsins SE, Lercher MJ. PopGenome: an efficient Swiss army knife for population genomic analyses in R. Mol Biol Evol. 2014;31:1929–36.
De Mita S, Thuillet AC, Gay L, Ahmadi N, Manel S, Ronfort J, et al. Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations. Mol Ecol. 2013;22:1383–99.
Guillot G, Leblois R, Coulon A, Frantz AC. Statistical methods in spatial genetics. Mol Ecol. 2009;18:4734–56.
Joost S, Vuilleumier S, Jensen JD, Schoville S, Leempoel K, Stucki S, et al. Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics. Mol Ecol. 2013;22:3659–65.
Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–5.
Joost S, Bonin A, Bruford MW, Despres L, Conord C, Erhardt G, et al. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol Ecol. 2007;16:3955–69.
Poncet BN, Herrmann D, Gugerli F, Taberlet P, Holderegger R, Gielly L, et al. Tracking genes of ecological relevance using a genome scan in two independent regional population samples of Arabis alpina. Mol Ecol. 2010;19:2896–907.
Coop G, Witonsky D, Di Rienzo A, Pritchard JK. Using environmental correlations to identify loci underlying local adaptation. Genetics. 2010;185:1411–23.
Guillot G, Vitalis R, Rouzic AL, Gautier M. Detecting correlation between allele frequencies and environmental variables as a signature of selection. A fast computational approach for genome-wide studies. Spatial Stats. 2014;8:145–55.
Cushman SA, Landguth EL. Spurious correlations and inference in landscape genetics. Mol Ecol. 2010;19:3592–602.
Jones MR, Forester BR, Teufel AI, Adams RV, Anstett DN, Goodrich BA, et al. Integrating landscape genomics and spatially explicit approaches to detect loci under selection in clinal populations. Evolution. 2013;67:3455–68.
Jump AS, Hunt JM, Martinez-Izquierdo JA, Penuelas J. Natural selection and climate change: temperature-linked spatial and temporal trends in gene frequency in Fagus sylvatica. Mol Ecol. 2006;15:3469–80.
Holliday J, Suren H, Aitken SN. Divergent selection and heterogeneous migration rates across the range of Sitka spruce (Picea sitchensis). Proc R Soc B-Biol Sci. 2012;279:1675–83.
Kremer A, Le Corre V. Decoupling of differentiation between traits and their underlying genes in response to divergent selection. Heredity. 2012;108:375–85.
Pereira R, Barreto F, Burton R. Ecological novelty by hybridization: experimental evidence for increased thermal tolerance by transgressive segregation in Tigriopus californicus. Evolution. 2014;68:204–15.
Bridle JR, Vines TH. Limits to evolution at range margins: when and why does adaptation fail? Trends Ecol Evol. 2007;22:140–7.
Nosil P, Funk DJ, Ortiz-Barrientos D. Divergent selection and heterogeneous genomic divergence. Mol Ecol. 2009;18:375–402.
Andrew RL, Ostevik KL, Ebert DP, Rieseberg LH. Adaptation with gene flow across the landscape in a dune sunflower. Mol Ecol. 2012;21:2078–91.
Lee CE. Global phylogeography of a cryptic copepod species complex and reproductive isolation between genetically proximate "populations". Evolution. 2000;54:2014–27.
Lowry DB, Rockwood RC, Willis JH. Ecological reproductive isolation of coast and inland races of Mimulus guttatus. Evolution. 2008;62:2196–214.
Powell T, Forbes A, Hood G, Feder J. Ecological adaptation and reproductive isolation in sympatry: genetic and phenotypic evidence for native host races of Rhagoletis pomonella. Mol Ecol. 2014;23:688–704.
De Wit P, Palumbi SR. Transcriptome-wide polymorphisms of red abalone (Haliotis rufescens) reveal patterns of gene flow and local adaptation. Mol Ecol. 2013;22:2884–97.
Hoffmann J, Simpson F, David P, Rijks J, Kuiken T, Thorne M, et al. High-throughput sequencing reveals inbreeding depression in a natural population. Proc Natl Acad Sci U S A. 2014;111:3775–80.
Chazdon RL. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science. 2008;320:1458–60.
Galatowitsch SM. Carbon offsets as ecological restorations. Restor Ecol. 2009;17:563–70.
Sgro CM, Lowe AJ, Hoffmann AA. Building evolutionary resilience for conserving biodiversity under climate change. Evol Appl. 2011;4:326–37.
Broadhurst LM, Lowe A, Coates DJ, Cunningham SA, McDonald M, Vesk PA, et al. Seed supply for broadscale restoration: maximizing evolutionary potential. Evol Appl. 2008;1:587–97.
Weeks AR, Sgro CM, Young AG, Frankham R, Mitchell NJ, Miller KA, et al. Assessing the benefits and risks of translocations in changing environments: a genetic perspective. Evol Appl. 2011;4:709–25.
Breed M, Stead M, Ottewell K, Gardner M, Lowe A. Which provenance and where? Seed sourcing strategies for revegetation in a changing environment. Conserv Genet. 2013;14:1–10.
Millar MA, Byrne M, Coates DJ. Seed collection for revegetation: guidelines for Western Australian flora. J Royal Soci WA. 2008;91:293–9.
Steane DA, Potts BM, McLean E, Prober SM, Stock WD, Vaillancourt RE, et al. Genome-wide scans detect adaptation to aridity in a widespread forest tree species. Mol Ecol. 2014;23:2500–13.
Dillon S, McEvoy R, Baldwin DS, Rees GNYP, Southerton SG. Characterisation of adaptive genetic diversity in environmentally contrasted populations of Eucalyptus camaldulensis Dehnh. (River Red Gum). PLoS One. 2014;9:e103515.
McLean EH, Prober SM, Stock WD, Steane DA, Potts BM, Vaillancourt RE, et al. Plasticity of functional traits varies clinally along a rainfall gradient in Eucalyptus tricarpa. Plant Cell Environ. 2014;37:1440–51.
Byrne M, Prober SM, McLean E, Steane DA, Stock WD, Vaillancourt RE. Adaptation to climate in widespread eucalypt species. Gold Coast: National Climate Change Adaptation Research Facility; 2013.
Rossetto M, Thurlby K, Offord C, Allen CPW. The impact of distance and a shifting temperature gradient on genetic connectivity across a heterogeneous landscape. BMC Evol Biol. 2011;11:126.
Lunt ID, Byrne M, Hellmann JJ, Mitchell NJ, Garnett ST, Hayward MW, et al. Using assisted colonisation to conserve biodiversity and restore ecosystem function under climate change. Biol Conserv. 2013;157:172–7.
Byrne M, Stone L, Millar MA. Assessing genetic risk in revegetation. J Appl Ecol. 2011;48:1365–73.
Stone LM, Byrne M. Comparing the outputs of five weed risk assessment models implemented in Australia: are there consistencies across models? Plant Prot Q. 2011;26:29–35.
Abbott R, Albach D, Ansell S, Arntzen JW, Baird SJE, Bierne N, et al. Hybridization and speciation. J Evol Biol. 2013;26:229–46.
Abi-Rached L, Jobin MJ, Kulkarni S, McWhinnie A, Dalva K, Gragert L, et al. The shaping of modern human immune systems by multiregional admixture with archaic humans. Science. 2011;334:89–94.
Huerta-Sánchez E, Jin X, Zhuoma Bianba A, Peter BM, Vinckenbosch N, Liang Y, et al. Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA. Nature. 2014;512:194–7.
Rieseberg LH. Evolution: replacing genes and traits through hybridization. Curr Biol. 2009;19:R119–22.
Joly S, McLenachan PA, Lockhart PJ. A statistical approach for distinguishing hybridization and incomplete lineage sorting. Am Nat. 2009;174:E54–70.
Pavlova A, Amos JN, Joseph L, Loynes K, Austin J, Keogh JS, et al. Perched at the mito-nuclear crossroads: divergent mitochondrial lineages correlate with environment in the face of ongoing nuclear gene flow in an Australian bird. Evolution. 2013;67:3412–28.
Joly S. JML: testing hybridization from species trees. Mol Ecol Res. 2012;12:179–84.
Fraïsse C, Roux C, Welch JJ, Bierne N. Gene flow in a mosaic hybrid zone: is local introgression adaptive? BMC Evol Biol. 2014;197:939–51.
Sankararaman S, Mallick S, Dannemann M, Prufer K, Kelso J, Paabo S, et al. The genomic landscape of Neanderthal ancestry in present-day humans. Nature. 2014;507:354–7.
Skoglund P, Jakobsson M. Archaic human ancestry in East Asia. Proc Natl Acad Sci U S A. 2011;108:18301–6.
Muhlfeld CC, Kovach RP, Jones LA, Al-chokhachy R, Boyer MC, Leary RF, et al. Invasive hybridization in a threatened species is accelerated by climate change. Nat Clim Chang. 2014;4:620–4.
Dasmahapatra KK, Walters JR, Briscoe AD, Davey JW, Whibley A, Nadeau NJ, et al. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature. 2012;487:94–8.
Rowe HC, Rieseberg LH. Genome-scale transcriptional analyses of first-generation interspecific sunflower hybrids reveals broad regulatory compatibility. BMC Genomics. 2013;14:13.
Koenig D, Jimenez-Gomez JM, Kimura S, Fulop D, Chitwood DH, Headland LR, et al. Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato. Proc Natl Acad Sci U S A. 2013;110:E2655–62.
Osborne OG, Batstone TE, Hiscock SJ, Filatov DA. Rapid speciation with gene flow following the formation of Mt. Etna Genome Biol Evol. 2013;5:1704–15.
Moran T, Fontdevila A. Genome-wide dissection of hybrid sterility in Drosophila confirms a polygenic threshold architecture. J Hered. 2014;105:381–96.
Heber S, Briskie JV, Apiolaza LA. A test of the 'genetic rescue' technique using bottlenecked donor populations of Drosophila melanogaster. PLoS One. 2012;7:e43113.
Heber S, Varsani A, Kuhn S, Girg A, Kempenaers B, Briskie J. The genetic rescue of two bottlenecked South Island robin populations using translocations of inbred donors. Proc R Soc B-Biol Sci. 2013;280:2012–228.
Hostetler JA, Onorato DP, Jansen D, Oli MK. A cat's tale: the impact of genetic restoration on Florida panther population dynamics and persistence. J Anim Ecol. 2013;82:608–20.
Game ET, Lipsett-Moore G, Saxon E, Peterson N, Sheppard S. Incorporating climate change adaptation into national conservation assessments. Glob Change Biol. 2011;17:3150–60.
Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD. The velocity of climate change. Nature. 2009;462:1052–U1111.
Burrows MT, Schoeman DS, Richardson AJ, Molinos JG, Hoffmann A, Buckley LB, et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature. 2014;507:492–505.
Moritz C. Defining evolutionary significant units for conservation. Trends Ecol Evol. 1994;9:373–5.
Ryder OA. Species conservation and systematics: the dilemma of subspecies. Trends Ecol Evol. 1986;1:9–10.
Hedrick PW, Fredrickson R. Genetic rescue guidelines with examples from Mexican wolves and Florida panthers. Conserv Genet. 2010;11:615–26.
Hedrick PW. Gene flow and genetic restoration: the Florida panther as a case study. Conserv Biol. 1995;5:996–1007.
Mills LS, Allendorf FW. The one-migrant-per-generation rule in conservation and management. Conserv Biol. 1996;10:1509–18.
Madsen T, Ujvari B, Olsson M. Novel genes continue to enhance population growth in adders (Vipera berus). Biol Conserv. 2004;120:145–7.
Westermeier RL, Brawn J, Simpson SA, Esker TL, Jansen RW, Walk JW, et al. Tracking the long-term decline and recovery of an isolated population. Science. 1998;282:1695–8.
Coleman RA, Weeks AR, Hoffmann AA. Balancing genetic uniqueness and genetic variation in determining conservation and translocation strategies: a comprehensive case study of threatened dwarf galaxias, Galaxiella pusilla (Mack) (Pisces: Galaxiidae). Mol Ecol. 2013;22:1820–35.
Frankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, et al. Predicting the probability of outbreeding depression. Conserv Biol. 2010;25:465–75.
Chancerel E, Lamy JB, Lesur I, Noirot C, Klopp C, Ehrenmann F, et al. High-density linkage mapping in a pine tree reveals a genomic region associated with inbreeding depression and provides clues to the extent and distribution of meiotic recombination. BMC Biol. 2013;11:19.
Johnson WE, Onorato DP, Roelke ME, Land ED, Cunningham M, Belden RC, et al. Genetic restoration of the Florida panther. Science. 2010;329:1641–5.
Garnett ST, Olsen P, Butchart SHM, Hoffmann AA. Did hybridization save the Norfolk Island boobook owl Ninox novaeseelandiae undulata? Oryx. 2011;45:500–4.
Roca AL, Schook LB. Genomics: captive breeding and wildlife conservation. In: Encyclopedia of Biotechnology in Agriculture and Food. Boca Raton: CRC Press; 2011.
Araki H, Cooper B, Blouin MS. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science. 2007;318:100–3.
Christie MR, Marine ML, French RA, Blouin MS. Genetic adaptation to captivity can occur in a single generation. Proc Natl Acad Sci U S A. 2012;109:238–42.
Snyder NFR, Derrickson SR, Beissinger SR, Wiley JW, Smith TB, Toone WD, et al. Limitations of captive breeding in endangered species recovery. Conserv Biol. 1996;10:338–48.
Conde DA, Colchero F, Gusset M, Pearce-Kelly P, Byers O, Flesness N, et al. Zoos through the lens of the IUCN Red List: a global metapopulation approach to support conservation breeding programs. PLoS One. 2013;8:9.
Keilwagen J, Kilian B, Ozkan H, Babben S, Perovic D, Mayer KFX, et al. Separating the wheat from the chaff - a strategy to utilize plant genetic resources from ex situ genebanks. Sci Rep. 2014;4:6.
Cochrane J, Crawford A, Monks L. The significance of ex situ seed conservation to reintroduction of threatened plants. Aust J Bot. 2007;55:356–61.
Guerrant EO, Havens K, Vitt P. Sampling for effective ex situ plant conservation. Int J Plant Sci. 2014;175:11–20.
This paper arose out of a workshop funded through the Office of the Chief Executive Science Team at CSIRO and the Science Industry Endowment Fund.
The authors declare that they have no competing interests.
All authors contributed to the development of the ideas presented in this review through discussions, as well as providing references and contributing written material. All authors read and approved the final manuscript.
About this article
Cite this article
Hoffmann, A., Griffin, P., Dillon, S. et al. A framework for incorporating evolutionary genomics into biodiversity conservation and management. Clim Chang Responses 2, 1 (2015). https://doi.org/10.1186/s40665-014-0009-x