Standardized selection gradients (
Climate change is thought to alter the patterns of genetic diversity within species and populations. Yet, it is not well-understood how genetic diversity influences organism’s adaptation to changing climate. In this chapter, I explore how within-population genetic diversity may be affected by local environmental heterogeneity and to what extent this variation may promote adaption. I focus on mountain ecosystems since they are heterogeneous environments at a fine scale that offer a unique mosaic of highly localized environmental conditions. I start summarizing the drivers of genetic isolation at a local scale and the diversification and adaptation patterns that result from it. I continue discussing these processes in terms of populations' reactions to changing conditions using my own long-term ecological genomic studies. This allows me to demonstrate that local-scale variation, in the long term, may offer safe places for species in a warming world due to their fine-scale topographic variability, which may provide suitable habitats within only a few meters of species’ current locations. Yet, such fine-scale habitat variability can also lead to locally genetically adapted populations, so that individuals and populations adapted to a narrow range of conditions may respond poorly to future environments.
- genome-wide scans
- evolutionary responses
- genetic adaptation
1. Introduction: The mosaic of environmental heterogeneity at a fine scale
Understanding how organisms respond to climate change is a main research area in evolutionary genetics. Organisms, populations or species may respond to environmental change in three possible ways: by migrating, persisting in current locations or going extinct . Persistence in new environments may be mediated by phenotypic plasticity, which is the range of phenotypes that a single genotype can express as a function of its environment  or by adaptation from genetic variation by increasing the frequency of existing allelic variants that can cope with the new conditions . Local adaptation to heterogeneous habitats has been documented [4, 5], but the genetics of locally adapted populations are not always well understood .
Globally, some of the largest impacts of climate change are expected to occur in alpine environments, particularly near mountain summits, which are dominated by long-lived plant species. In these environments, snow cover and summer temperatures are the major drivers of vegetation composition . The increase in temperature over the last decades has already led to patterns of upward migration in several species . The alpine region is a highly heterogeneous environment that is characterized not only by strong elevational gradients in temperature but also by local topography. Microtopographical features include depressions in which the snow accumulates and disappears very late in the summer (i.e. snowbeds) and more exposed ridges with less snow and where the snow disappears several weeks to months earlier (Figure 1). Similar heterogeneities or gradients that may occur across the alpine zone are due to wind exposure, water availability, rockiness and neighbouring shrubs , among other factors. Some of these local-scale differences have been shown to cause local adaptation (e.g. in
Therefore, to understand processes involved in potential responses to changing conditions, it is important to consider not only climate differences between different altitudes but also differences between microhabitats [12–14], because environmental variation at a local-scale can be high . In this chapter, I explore the microhabitat-driven patterns (Section 2) and processes (Section 3) and its impact on genetic diversity. Although there is a focus on alpine ecosystems and sessile organisms throughout this work, the concepts are generalizable. I also summarize the methodologies (Section 4) used to study phenotypic and genetic variation in microhabitats. To understand the interaction among fine-scale environmental variation, genetic diversity and the evolutionary responses of populations and organisms in a changing climate, the following questions can be asked:
Are patterns of genetic differentiation and gene flow driven by small-scale environmental differences?
Do morphological and fitness-related traits show heritable variation and is selection currently acting on any of these traits so that they can evolve given changing conditions?
What is the microhabitat-driven pattern of genomic divergence?
What is the genomic architecture of ecologically relevant traits at the microhabitat level?
2. Drivers of genetic isolation at a fine scale
The transfer of alleles between populations is known as gene flow. When this allelic transfer is limited or interrupted, there is genetic isolation. Understanding patterns of genetic variation and gene flow across the fine-scale mosaic will help to predict the response of species to climate change. As an example, under climate warming in alpine ecosystems, snowmelt is expected to occur earlier in the season . Restricted gene flow between subpopulations growing in different microhabitats can be linked with local adaptation . In this scenario, genotypes of long-lived dominant species in late snowmelt habitats may have difficulties to persist under warming conditions. On the other hand, genotypes in early snowmelt habitats would need to migrate to new localities, and this might be difficult in long-lived species even if suitable localities are nearby. Alternatively, lack of differentiation between subpopulations in different microhabitats and rampant gene flow between them could lead to genotypes able to grow in both microhabitats and thus persist
2.1. Mismatch in flowering time and pollen flow
Variation in the timing of flowering between subpopulations in different snow microhabitats can be a major driver of small-scale genetic structuring  through the restriction of pollen-mediated gene flow (as compared to seed-mediated gene flow), regardless of whether flowering time is genetically or environmentally regulated [9, 10, 18]. Small-scale genetic differentiation (measured by the FST, which is the fixation index, a measure of population differentiation due to genetic structure) has been reported in the majority of studies on snowmelt-driven genetic differentiation [11, 19–21].
2.2. Asymmetry in seed dispersal
Although there may be differentiation in populations’ phenology (the timing at which periodic life cycle events happen, like bud breaking, flowering, fruiting and bud setting) between microhabitats due to snowmelt timing (Section 2.1, Figure 2), sub-populations growing in different microhabitats do not have to be genetically differentiated [23, 24]. This pattern has been observed in
Seed dispersal can counteract isolation driven by barriers to pollen flow, like snow, because seed dispersal occurs later in the season when all winter snow has melted . Gene flow via seed dispersal may result in asymmetric source/sink-like patterns driven by wind, topology and the success of seed establishment .
3. Genetic adaptation and diversification at a fine scale
Most research on the responses of species to changing snowmelt and temperature conditions has focused on species migration toward higher altitudes, where researchers can track the species’ climate requirements [29–32]. However, if migration potential is limited, the only way organisms can persist is by adjusting to the new environmental conditions [18, 33]. Adjustment though phenotypic plasticity might be particularly important in long-lived species, as it can occur within the lifetime of an individual . However, plasticity may be constrained or even maladaptive, if populations are exposed to novel conditions outside the range of conditions they encountered in their evolutionary history . Alternatively, adaptation from standing genetic variation may happen by increasing the frequency of existing variants that can cope with the new conditions . While adaptation is dependent on the genotypes, plasticity itself depends on the environment.
Genomic divergence, which is the genetic differentiation throughout the genome, has been studied mostly among species and well-differentiated populations [35, 36] but few of them have been studied at a very local scale from a genome-wide point of view. Genomes are regarded as porous since different regions present multiple signatures and levels of gene flow, drift, selection and ancestral variation . Genome-wide divergence is heterogeneous, with peak-like or plateau-like sections of high divergence surrounded by genomic regions with lower divergence, a landscape described metaphorically like ‘islands’ and ‘continents’ of divergence . High divergence in specific sections may be due to disrupting selection from novel or standing genetic variation  or random drift . On the other hand, regions with low divergence may be maintained by balancing or uniform selection, continuous gene flow or ancestral-shared polymorphism [39, 40].
An approach that combines between-microhabitat genomic divergence with selection gradients and association mapping of ecologically relevant traits is useful to understand which regions in the genome are likely to differ between populations in different microhabitats, and therefore harbour genetic variation unique to each, and how these genomic regions may relate to phenological, growth and fitness traits (Figure 4) [41–44]. Ultimately, this combined approach will allow differentiating plastic and adaptive variation.
3.1. Selection and evolutionary responses
Three essential components are necessary for evolution to occur: there must be trait variation, which must be heritable and selection should be acting on it . A multivariate form of the breeder’s equation  illustrates this paradigm well and allows for the prediction of the evolutionary response of a trait to selection over one generation (
|Proportion of flowering stems||Leaf size||−0.023||67||0.032||0.86|
|Interval snowmelt to leaf expansion||0.029||67||0.342||0.561|
|Thermal duration until leaf expansion||−0.041||67||0.426||0.516|
|Thermal duration until flowering||0.211||67||4.153|
|Leaf size × MH||0.054||67||0.076||0.784|
|Interval snowmelt to leaf expansion × MH||0.179||67||1.110||0.296|
|Thermal duration until leaf expansion × MH||−0.095||67||0.376||0.542|
|Thermal duration until flowering × MH||−0.075||67||0.163||0.688|
|Change in stem number||Leaf size||−5.232||116||−3.279|
|Interval snowmelt to leaf expansion||−3.655||116||−2.217||0.051|
|Thermal duration until leaf expansion||−3.646||116||−2.218|
|Thermal duration until flowering||2.467||116||1.418||0.85|
|Leaf size × MH||3.758||116||1.614||0.073|
|Interval snowmelt to leaf expansion × MH||4.058||116||1.308||0.184|
|Thermal duration until leaf expansion × MH||1.644||116||0.696||0.805|
|Thermal duration until flowering × MH||−4.821||116||−2.047|
Additionally, when using the multivariate form of the breeder’s equation (Eq. (1)) to estimate potential evolutionary responses of traits in the
3.2. Adaptation and plasticity
Persistence of populations and species given climate change may be mediated by phenotypic plasticity  or by adaptation from standing variation by increasing the frequency of existing variants that can cope with the new conditions . Epigenetic mechanisms, which are those modulated by environmental factors that switch genes on and off and affect how cells read those genes, may also affect how plants respond to climatic variability . In Section 4, I review ways to access the roles of plasticity and adaptation in explaining trait variation across the fine-scale mosaic. In this section, I discuss the utility of genome-wide analysis to infer microhabitat-driven divergent selection and the genetic basis of trait variation. Although the following results concern
In addition to the population genomics approach used to study microhabitat-driven divergence and identify traits that may have diverged between environments, association mapping is usually performed to explore genetically based variation in ecologically relevant traits across microhabitats [54, 55], part of what is known as reverse genetics or top-down inference, as it looks at the genetic basis of specific traits. In the
4. Heterogeneous microhabitats as a field laboratory to study genetic reactions to climate change
Regions in the world that are experiencing extremely high diversification rates, such as the alpine tundra ecosystems known as páramos [54, 56], and convergent adaptation, like in mountainous microhabitats [57, 58], are good candidates to serve as evolutionary experiments for today’s scientists. In this section, I explore the ecological and genetic sampling strategies and the analytical methods commonly used to cover biological variation across altitudes and microhabitats when addressing the main questions suggested at the beginning of this chapter.
4.1. Natural surveys across microhabitats
Plot-based and transect surveys are the two main sampling strategies that allow the study of microhabitat-driven variation. Three-to-five different transects spanning a range of north-east (sun/shade) exposure and covering the main elevational range are the best compromise between sampling effort and feasibility. These surveys at a microhabitat level follow an experimental approach known as space-for-time (SFT) substitution , in which current spatial heterogeneity is used as a proxy for predicting ecological time series (i.e. reactions to future conditions).
4.1.1. Plot surveys
In this type of sampling, few representative categorical altitudes (e.g. high and low) are chosen to cover the desired altitudinal distribution. Depressions and ridge-like microhabitats may be chosen at each altitude based on indicators such as topology and vegetation. In each altitude/microhabitat combination, one big plot (~10 × 10 m) is designated. Within each plot, several patches (~100) should be sampled randomly. This sampling is best for assessing isolation between microhabitats (e.g. ecological, trait-based or genetic isolation). Heritabilities and evolutionary responses for different traits are best computed in natural populations with this sampling strategy  because it allows including many individuals in close proximity with a high potential of being genetically related in different degrees.
4.1.2. Transect surveys
In this kind of sampling, the main elevational range is covered continuously. Around five to ten elevational bands along transects, with one or more small study plots (~3 × 3 m), must be set up in different microhabitat sites (e.g. early-season exposure from snow and late-season exposure) to grasp a complete overview of the environmental variation. In each plot, patches (at least two) must be selected randomly. When compared to the plot survey, the transect survey has many smaller plots with fewer total patches but covers the desired altitudinal and microhabitat variation at a better resolution by having more total plots. This is ideal to assess ecological responses  or genetic isolation-by-distance and to explore trait and microhabitat-driven genomic architecture [15, 22].
4.2. Transplant experiments
To rigorously test how organisms respond to microhabitat-driven changes through phenotypic plasticity, as well as whether populations experience local adaptation (home-site advantage), reciprocal transplant experiments are needed (Figure 6) . Transplant experiments are typically carried out across altitudinal gradients. However, reciprocal transplant studies explicitly examining the effects of local-scale variation (e.g. altered snowmelt timing) are scarce. Almost all reciprocal transplants have examined short-lived perennial herbs, and experiments with long-lived woody species are rare due to the difficulty in establishing clones of perennial, slow-growing species. Yet, it is important to understand how long-lived species will respond to changes in snowmelt timing, as they are a dominant functional vegetation type in alpine areas. Transplant experiments can be carried out in long-lived species using long-term monitoring  or clonal propagation .
Phenotyping is the systematic assessment of trait variation. It is an essential component that must be accounted for in ecological and genetic studies. Soil temperature data loggers, nutrient probes and field observations can be used to estimate drought severity , frost events , snowmelt timing , nutrient availability  and other soil properties . Monitoring of individuals carried out weekly during the growing season and across microhabitats during several growing seasons is the most exhaustive and informative survey method, although fewer snapshots can also be used.
Genotyping is the systematic assessment of genetic variation. Adaption and diversification are recognized as important processes that generate diversity [62, 63]. However, their effects on genetic divergence and on the generation of morphological and ecological variation are poorly understood. Low- and medium-throughput techniques (as in Section 4.4.1) together with newly developed high-throughput next-generation sequencing methods (as in Section 4.4.2) offer the promise of major advances in the study of these interactions.
4.4.1. Microsatellite genotyping
Microsatellites loci (also called single sequence repeats or SSRs, which are regions of repetitive DNA that vary in the number of repeated DNA motifs) are commonly used to assess population structure , as shown in Section 2.2, and may help to estimate relatedness , as shown in Section 3.1, due to their high polymorphism [63, 66]. The PCR (polymerase-chain-reaction, a procedure used in molecular biology to replicate DNA exponentially) reactions are usually multiplexed into several PCR runs. Two or more multiplexed PCR runs can be pooled afterward and separated by capillary electrophoresis. Allele sizes are estimated using software such as GeneMapper v.3.7 (Applied Biosystems).
Recent developments allow scientists to survey entire genomes to answer questions beyond the population genetics paradigm, in what is starting to be recognized as population genomics (as in Section 3.2). Genotyping-by-sequencing (GBS) is one of the cheapest and most used methods to generate massive amount of SNPs (single-nucleotide polymorphism, a type of molecular marker representing a change in a single base pair). GBS libraries are prepared according to Elshire et al.  using different enzymes for digestions. Raw Illumina DNA sequence data from the libraries can be aligned to reference genomes using BWA aligner  and processed through the GBS analysis pipeline as in TASSEL-GBS .
4.5. Common statistical approaches to compare microhabitats
4.5.1. Linear models
Linear models (e.g. ANOVA, ANCOVA, linear regression) are the first option to assess the effects of microhabitats and altitude on flowering time, and how temperature, humidity and snowmelt vary between microhabitats. In order to assess whether microhabitat and phenological differences trigger genetic isolation, pairwise FST (fixation index) values can be computed among plots or populations using, for instance, GENEPOP . The number of alleles or heterozygosity, which are standard measures of genetic diversity, may be compared between microhabitats as well (as in Section 2.2), using linear mixed models  with the microhabitat as the fixed effect and the transect as the random effect (covariate). These can be done in R (R Core Team), with the packages
4.5.2. Population structure
Population structure is usually examined using the software STRUCTURE . It is suggested that several independent runs are performed with different K (number of assumed populations) values using an admixture model with a minimum of 100,000 iterations for the burn-in and 100,000 subsequent iterations for the MCMC analysis. The optimal K is posteriorly determined using the rate at which the likelihood changes across different K values . Pairwise migration rates (Nem) and effective population size (Ne) are also meaningful statistics calculated across microhabitats, as in Section 2.2. They can be estimated following coalescent theory and a maximum-likelihood–based approach using software such as, for instance, MIGRATE .
4.5.3. Trait heritability and selection
When it comes to trait variation, narrow sense heritability (
4.5.4. Genetic mapping
To understand trait architecture in natural populations, trait-marker association studies are used . Standard trait-marker association analysis can be easily implemented in FaST-LMM  or BiForce , the latter detects epistatic interactions (i.e. second-order trait-marker associations) and dominance effects.
4.5.5. Scans for genome-wide selection
As a descriptive approach, genome-wide sliding window analysis can be used to determine FST, as it is in Section 3.2, and the proportion of variable SNPs that are fixed between microhabitats using, for instance, ARLEQUIN [81, 82]. Linkage-disequilibrium (LD) and Tajima’s D  are also usually computed in the same windows with the R package
Fast-evolving microhabitat-driven genomic divergence and, at the same time, genetically based trait variation at a larger scale may play a role in the ability of species and populations to persist in diverse and variable conditions in heterogeneous ecosystems. Populations from different microhabitats may be isolated, and they may act as sinks or sources of genetic diversity. From a genomic point of view, multiple genetic regions that diverge between microhabitats may arise at very local geographic scales even in the presence of gene flow, due to strong environmental differentiation. Additionally, regions of high genomic divergence are possibly related to traits under selection that may matter for the prediction of evolutionary responses. In this chapter, I have shown how small-scale environmental variability helps understanding the way organisms may react to changing conditions by looking at processes such as genetic adaptation and diversification at a very fine environmental scale. In the oncoming years, I expect to see an increase in the number of genetic studies aiming to resolve the genomic architecture of environmentally relevant trait variation at a fine scale, improving in that way our understanding on how species and populations may cope with rapidly changing climatic conditions.
I want to acknowledge my friends and colleagues J. Wheeler and J. Sedlacek (R.I.P.) for their professionalism and constancy. Thanks to S. Karrenberg for her advice and stimulating discussions. I very much appreciate C. Lexer’s enthusiasm for discussing some of the ideas drafted in this chapter and for welcoming me in Fribourg, Switzerland, on several occasions. I also wish to thank the rest of the SNFS-funded Sinergia Team devoted to the study of genetic adaptation in snowmelt microhabitats—O. Bossdorf, G. Hoch, M. van Kleunen, C. Rixen and S. Wipf—for insightful discussions during the project meetings and for making my stays in Konstanz and Davos very enjoyable. B. Moreno’s support while drafting this chapter is also deeply acknowledged. The invitation from the editor to contribute to this book on Genetic Diversity is highly appreciated. Writing time was possible thanks to a grant (4.1-2016-00418) from Vetenskapsråde to AJC.
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