**2. Exploiting population variation and molecular techniques**

Although environmental variation is not necessarily reflected in transformed vital rates, such as growth rate, interplay between environmental variation and population dynamics has been shown in a variety of species Stenseth et al., 2002). Understanding the consequences of demographic stochasticity in populations requires information of local fluctuations in population size, extinction probability and colonisation potential as well as reproductive success, which can be gained from population dynamics analyses. DNA analyses are progressively used to estimate the extent and organization of genetic diversity in populations in order to infer the causes of spatio-temporal dynamics (Schwartz et al.,

Possible Evolutionary Response to Global Change – Evolutionary Rescue? 89

aim is to determine how much a response of a given trait to environmental change is due to plastic and/or evolutionary response. Such information is becoming very relevant for evolutionary biology as there is a need for detailed studies on how variation at the level of genes translates, through developmental and physiological processes, into phenotypic

Quantitative genetic investigations have thus far often been limited to laboratory conditions and the neutral molecular markers in natural populations are not necessarily relevant to understand the evolution of functional genes subject to selection, which point to the potential adaptability of a population to environmental changes. In natural populations it is difficult to show selection (let alone to quantify). However, genome scans and association studies are increasingly promising due to new statistical methods with improved power (Stephens et al., 2009). Although identifying selected and functionally important genes is no easy task, genome scans offer the possibility of finding genomic domains with selective value, which in turn is a first step in separating selection from the background of random genetic drift. This would make way for describing how changing environments (and fragmentation) can affect different domains of the genome. Hence, finding genomic domains under selection may be at least as useful as gene finding per se. A combination of ecological genomics and quantitative genetics will therefore lead to a greatly increased understanding of ecological responses, starting from genetic variation in natural populations to the description of shifts in phenotypes as a result of evolutionary responses

The development of theoretical models and the use of computer simulations have also contributed significantly to the understanding of the consequences of CIEC. These models include stochastic environmental effects, allowing us to make probabilistic predictions that can be reasonably precise when we consider averages over large scales. Considerable progress has been achieved in incorporating age- or stage-structure into population genetic models, mostly in the context of life history evolution and estimation of the effective population size (NE) of large and stable populations (Engen et al., 2010). However, knowledge on the interaction between age- or stage-structure and other factors, such as variance in reproductive success, temporal fluctuations in population size, is still fairly limited. Although attempts have been made to combine ecological and genetics theory,

Deterministic simulations are based on algebraic equations that predict the likely outcome of sampling, while stochastic (Monte Carlo) simulation models mimic random processes. Although being transparent and analytically tractable, deterministic predictions cannot deal with the same level of complexity over many generations as stochastic simulations. The benefit of combining these approaches is evident from simulations used to verify the accuracy when prediction equations are developed. Stochastic simulations are relevant for the design of risk estimates and there are no inherent limitations excluding representation of

The study objects, such as populations or individuals, do not necessarily comply with the mean field assumptions that all units are organised as uniform masses and interactions are unconditioned and can be averaged. In such cases the individual-based models (IBM) or agent-based approaches can be appropriate ways to allow variation in many aspects of the

there is still insight to be gained from integrating the disciplines further.

variation for ecologically significant traits (Coulson et al., 2006).

to environmental changes (Luikart et al., 2003).

**3. Theoretical approaches** 

the genetic level.

2007). Such assessment is performed by investigating the degree of neutral genetic variation, which is informative in inferring ancient or recent historical dynamics of populations. Information on the genetic composition of a populations prior to environmental perturbation is now accessible thanks to the recent progress in biostatistics and mathematics (e.g. theory of coalescence, Bayesian statistics, individual-based population dynamics, algorithms for efficient simulation and sampling of complex processes), which have greatly improved the possibility to infer population genetic processes through the development of theoretical models (Stephens & Balding, 2009). Going beyond plain parameter estimation is possible in applying a Bayesian approach, which can integrate both genetic and non-genetic data and hence test hypotheses about the factors that control demographic and genetic changes. In particular, the development of Bayesian models aimed to infer historical population dynamics and population parameters are particularly promising (Riebler et al., 2008).

The causal relationship between molecular genetic variation and phenotype-based measures of success are associated with some debate. Part of this incongruity stems from confusing the levels of organization at which genetic variation and phenotypic accomplishment have been conceptualized (Coulson et al., 2006). Further, molecular markers cannot identify the likelihood of loss of genetic variance in traits of ecological significance, as the correlation between molecular diversity (which is per definition neutral) and ecologically relevant traits (which are per definition non-neutral) is weak and becomes even weaker in expanding or declining populations. However, the attempt to correlate neutral and non-neutral variability can be made by using a promising new tool in conservation genetics consisting of the single nucleotide polymorphisms (SNPs). It is at present viewed as the richest polymorphic genetic marker in many genomes and may get round some of the problems related to microsatellites because of the enhanced resolution of genetic variation. In natural populations SNPs hold the potential to expand our ability to survey both neutral (non-coding region) variation as well as genes under selection (coding region), while also providing wider genome coverage compared to microsatellites (Morin et al., 2004). Further, moving the genomic methodology from lab-model organisms to non-model organisms is now becoming achievable, allowing genomic analysis in a population- and species wide fashion (Mitchell-Olds et al., 2008). Until recently, the genomic tools and resources have unfortunately been limited when it came to key ecological species as opposed to models species with plenty of genomic approaches readily available.

Recent identification of functional genes and genes linked to quantitative traits are opening the way to the analysis of functional genes and components of genetic control of physiological processes and are therefore expected to contribute to the understanding of local adaptation (Marsano et al., 2010). Population genomics will very soon add important contributions to these issues, delivering substantial amounts of data on regulatory polymorphisms on a genomic scale. Moreover, we may address the question of whether the regulatory variation per se cause adaptation to local conditions and whether it is able to significantly alter life-time reproductive success.

Quantitative genetic analyses are important in the assessment of the extinction risk since this approach can give information on the amount of non-neutral genetic variability present for a given trait. This information enables us to scrutinize fitness components on various genetic and environmental backgrounds, producing information on the fate of genetic diversity and the force of selection acting on the populations. Note however, that in practice we are thus limited to manageable organisms with short generation times. Nevertheless our ultimate aim is to determine how much a response of a given trait to environmental change is due to plastic and/or evolutionary response. Such information is becoming very relevant for evolutionary biology as there is a need for detailed studies on how variation at the level of genes translates, through developmental and physiological processes, into phenotypic variation for ecologically significant traits (Coulson et al., 2006).

Quantitative genetic investigations have thus far often been limited to laboratory conditions and the neutral molecular markers in natural populations are not necessarily relevant to understand the evolution of functional genes subject to selection, which point to the potential adaptability of a population to environmental changes. In natural populations it is difficult to show selection (let alone to quantify). However, genome scans and association studies are increasingly promising due to new statistical methods with improved power (Stephens et al., 2009). Although identifying selected and functionally important genes is no easy task, genome scans offer the possibility of finding genomic domains with selective value, which in turn is a first step in separating selection from the background of random genetic drift. This would make way for describing how changing environments (and fragmentation) can affect different domains of the genome. Hence, finding genomic domains under selection may be at least as useful as gene finding per se. A combination of ecological genomics and quantitative genetics will therefore lead to a greatly increased understanding of ecological responses, starting from genetic variation in natural populations to the description of shifts in phenotypes as a result of evolutionary responses to environmental changes (Luikart et al., 2003).

## **3. Theoretical approaches**

88 International Perspectives on Global Environmental Change

2007). Such assessment is performed by investigating the degree of neutral genetic variation, which is informative in inferring ancient or recent historical dynamics of populations. Information on the genetic composition of a populations prior to environmental perturbation is now accessible thanks to the recent progress in biostatistics and mathematics (e.g. theory of coalescence, Bayesian statistics, individual-based population dynamics, algorithms for efficient simulation and sampling of complex processes), which have greatly improved the possibility to infer population genetic processes through the development of theoretical models (Stephens & Balding, 2009). Going beyond plain parameter estimation is possible in applying a Bayesian approach, which can integrate both genetic and non-genetic data and hence test hypotheses about the factors that control demographic and genetic changes. In particular, the development of Bayesian models aimed to infer historical population dynamics and population parameters are particularly promising (Riebler et al.,

The causal relationship between molecular genetic variation and phenotype-based measures of success are associated with some debate. Part of this incongruity stems from confusing the levels of organization at which genetic variation and phenotypic accomplishment have been conceptualized (Coulson et al., 2006). Further, molecular markers cannot identify the likelihood of loss of genetic variance in traits of ecological significance, as the correlation between molecular diversity (which is per definition neutral) and ecologically relevant traits (which are per definition non-neutral) is weak and becomes even weaker in expanding or declining populations. However, the attempt to correlate neutral and non-neutral variability can be made by using a promising new tool in conservation genetics consisting of the single nucleotide polymorphisms (SNPs). It is at present viewed as the richest polymorphic genetic marker in many genomes and may get round some of the problems related to microsatellites because of the enhanced resolution of genetic variation. In natural populations SNPs hold the potential to expand our ability to survey both neutral (non-coding region) variation as well as genes under selection (coding region), while also providing wider genome coverage compared to microsatellites (Morin et al., 2004). Further, moving the genomic methodology from lab-model organisms to non-model organisms is now becoming achievable, allowing genomic analysis in a population- and species wide fashion (Mitchell-Olds et al., 2008). Until recently, the genomic tools and resources have unfortunately been limited when it came to key ecological species as opposed to models species with plenty of genomic approaches

Recent identification of functional genes and genes linked to quantitative traits are opening the way to the analysis of functional genes and components of genetic control of physiological processes and are therefore expected to contribute to the understanding of local adaptation (Marsano et al., 2010). Population genomics will very soon add important contributions to these issues, delivering substantial amounts of data on regulatory polymorphisms on a genomic scale. Moreover, we may address the question of whether the regulatory variation per se cause adaptation to local conditions and whether it is able to

Quantitative genetic analyses are important in the assessment of the extinction risk since this approach can give information on the amount of non-neutral genetic variability present for a given trait. This information enables us to scrutinize fitness components on various genetic and environmental backgrounds, producing information on the fate of genetic diversity and the force of selection acting on the populations. Note however, that in practice we are thus limited to manageable organisms with short generation times. Nevertheless our ultimate

2008).

readily available.

significantly alter life-time reproductive success.

The development of theoretical models and the use of computer simulations have also contributed significantly to the understanding of the consequences of CIEC. These models include stochastic environmental effects, allowing us to make probabilistic predictions that can be reasonably precise when we consider averages over large scales. Considerable progress has been achieved in incorporating age- or stage-structure into population genetic models, mostly in the context of life history evolution and estimation of the effective population size (NE) of large and stable populations (Engen et al., 2010). However, knowledge on the interaction between age- or stage-structure and other factors, such as variance in reproductive success, temporal fluctuations in population size, is still fairly limited. Although attempts have been made to combine ecological and genetics theory, there is still insight to be gained from integrating the disciplines further.

Deterministic simulations are based on algebraic equations that predict the likely outcome of sampling, while stochastic (Monte Carlo) simulation models mimic random processes. Although being transparent and analytically tractable, deterministic predictions cannot deal with the same level of complexity over many generations as stochastic simulations. The benefit of combining these approaches is evident from simulations used to verify the accuracy when prediction equations are developed. Stochastic simulations are relevant for the design of risk estimates and there are no inherent limitations excluding representation of the genetic level.

The study objects, such as populations or individuals, do not necessarily comply with the mean field assumptions that all units are organised as uniform masses and interactions are unconditioned and can be averaged. In such cases the individual-based models (IBM) or agent-based approaches can be appropriate ways to allow variation in many aspects of the

Possible Evolutionary Response to Global Change – Evolutionary Rescue? 91

populations may lack the genetic diversity that would allow adaptation to a new environment, and thus might risk extinction (Spielman et al., 2004). Further, genetic drift in small populations (Gilpin and Soulè, 1986) leads to loss of genetic diversity, further depressing the evolutionary potential and thereby the ability to respond to changing environments (see Lynch 1996). Additionally, in small populations the chance of mating among relatives is increased due to the limited number of individuals, which causes inbreeding and further decreases mean fitness (Spielman et al., 2004). The increased probability of mating among relatives and the accelerated rate of loss of genetic variability in populations are strongly associated with a reduction of NE which is the size of an "ideal" (stable, random mating) population that results in the same degree of genetic drift as observed in the actual population (Wright, 1931). Due to the numerous ways in which natural populations can deviate from the "ideal" population, NE may be only a fraction of the population census size (N) size (Lande and Barrowclough, 1987). The NE of a population can predict its capacity to survive in a changing environment more reliably than the census

Global scale environmental change may affect the local NE in several ways that may not be entirely independent. Firstly, as environmental changes accelerate, the demand for rapid adaptation becomes more pronounced, as in the simplest case where an optimum mean trait value shifts as a result of e.g. a rise in mean temperature. This requires a certain 'standing crop' of genetic variation in order for the population to track the moving optimum. Failing to do so, the populations may suffer demographically from the load of being maladapted. Secondly, the variance of environmental conditions may increase putting its toll on genetic variance by lowering the harmonic mean (HM) through the population dynamic response to environmental fluctuations. Theoretical models predict that fluctuation in population size is one of the dominant causes of reduction of NE and the low NE/N ratios (Kalinowski and Waples, 2002). If generations are non-overlapping, NE can be approximated as the HM of the

The expected heterozygosity (He), a measure of genetic variability, can provide an indication of the immediate evolutionary potential of a population, but it has no necessary relationship to longer term potential (Nunney, 2000). This is particularly true when the environment of the population is changing. The notion of NE can therefore be viewed as a bridging point between ecology and genetics, with the ecological characteristics including life history traits, social structure and population dynamics determining NE and hence the rate of loss of genetic variation (Caballero, 1994). Likewise, environmental factors and changes thereof are mirrored in the genetic composition of affected populations. Moreover, recent work points to the impact of altered environmental variability on the variation of vital rates, which in turn obviously affects the demography and therefore NE (see Boyce et al., 2006 and references therein). The effective population size is therefore related to the temporal variability of the population, which is a fundamental property of the ecological system. Theoretical studies have established that both statistical and biological mechanisms have the potential to influence the temporal variability of populations (Tilman, 1999). Statistical averaging and mean variance rescaling are predominantly statistical mechanisms, while species interactions and contrasting responses of different species to environmental fluctuations are primarily biological mechanisms. These mechanisms may very well be interdependent, and some have both

size and/or the amount of genetic variability (Nunney, 2000).

population census size N (Caballero, 1994).

statistical and biological elements (Tilman, 1999).

individual's characteristics as well as variable and conditional interactions (Travis et al., 2009). Likewise the geospatial implementations of IBM can account for specific spatial effects. This approach can be especially relevant for heterogeneous populations of higher animals in spatiotemporally heterogeneous environments with behaviour depending on its own state, the state of conspecifics, or the specific states of the environment (Bach et al., 2006; Bach et al. 2007). In other words the individual in an IBM does not perceive and interact with 'the average individual' of an abstract averaged population according to an average encounter rate and it does not experience the average environment. However, as entities, interactions and environment can be freely defined it follows that the extreme flexibility can become a challenge when designing simulations to address simple questions. In terms of genetics, another advantage of IBM is the straightforward implementation of genotypes, representing either neutral or selected genes where the latter permit the agents to adapt to changing environments. Such models are often referred to as complex adaptive systems (CAS) (DeAngelis & Mooij, 2005). Also the fact that events in IBM simulations are inherently stochastic may prove an advantage when the goal is to obtain probabilities. Much depends on the specific question and available data.

### **4. Developments in geographical ecology for understanding the consequences of climate-induced environmental changes and its interactions with other biotic and abiotic factors**

Given that human impacts in terms of both anthropogenic climate warming, habitat loss and fragmentation, are likely to increase over the 21st century (Smith et al., 2009), the consideration of geographical ecology research is an important new avenue of research. Therefore, the inclusion of new developments in geographical ecology towards much improved quantification of the determinants of species distributions and diversity patterns will be interesting (Guisan & Zimmermann, 2000). Notably the role of geographic variation in environmental factors such as climate creates an important basis for predicting responses to future climate change (e.g. Thomas et al., 2004). Furthermore, climatically-driven global geographical variation in metabolic rates may both be of fundamental importance to biodiversity and ecosystems and a determining factor in organism sensitivity to stressors (Dillon et al., 2010). Another motivation to look towards geographical ecology is the question of ascertaining effects of habitat destruction and fragmentation on species distribution changes from the separate effects of stressors, as well as their interactions (as fragmentation may affect exposure and susceptibility to environmental stressors (Gandhi et al., 2011).
