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Oikos 120: 1776–1783, 2011 doi: 10.1111/j.1600-0706.2011.19844.x © 2011 The Author. Oikos © 2011 Nordic Society Oikos Subject Editor: Ben Chapman. Accepted 5 September 2011 Evolutionary genetics of partial migration – the threshold model of migration revis(it)ed Francisco Pulido F. Pulido ([email protected]), Dept of Zoology and Physical Anthropology, Complutense Univ. Madrid, ES-28040 Madrid, Spain. Partial migration is a common and widespread phenomenon in animal populations. Even though the ecological causes for the evolution and maintenance of partial migration have been widely discussed, the consequences of the genetics underlying differences in migration patterns have been little acknowledged. Here, I revise current ideas on the genetics of partial migration and identify open questions, focussing on migration in birds. The threshold model of migration describing the inheritance and phenotypic expression of migratory behaviour is strongly supported by experimental results. As a consequence of migration being a threshold trait, high levels of genetic variation can be preserved, even under strong directional selection. This is partly due to strong environmental canalization. This cryptic genetic variation may explain rapid de novo evolution of migratory behaviour in resident populations and the high prevalence of partial migration in animal populations. To date the threshold model of migration has been tested only under laboratory conditions. For obtaining a more realistic representation of migratory behaviour in the wild, the simple threshold model needs to be extended by considering that the threshold of migration or the liability may be modified by environmental effects. This environmental threshold model is valid for both facultative and obligate migration movements, and identifies genetic accommodation as an important process underlying evolutionary change in migration status. Future research should aim at identifying the major environmental variables modifying migration propensity and at determining reaction norms of the threshold and liability across variation in these variables. Partial migration is the situation in which migratory and non-migratory individuals of one species breed in the same population. It is well documented and widespread phenomenon in a wide array of taxa, including insects, fish, birds and mammals (Baker 1978, Jonsson and Jonsson 1993, Dingle 1996, Berthold 1999, Roff and Fairbairn 2001, 2007, Chapman et al. 2011). In birds, partial migration is common, particularly in areas with seasonal fluctuation of food availability, where the carrying capacity during the reproductive season is larger than outside it (Newton 2008). For about thirty years, this phenomenon, particularly its control and evolutionary persistence (Berthold 1984, 1999, Lundberg 1987, 1988, Kaitala et al. 1993, Taylor and Norris 2007, Jahn et al. 2010, Kokko 2011), has attracted much attention as a key for understanding the proximate and ultimate causes of migration, especially in birds. Partial migration is a central element of theories of the evolution of avian migration as it is considered a transitional stage in the evolution from migrancy to residency and from residency to migrancy (Terrill 1990, Berthold 1999, Bell 2000, Salewski and Bruderer 2007). The interest in partial migration has grown in the last decade since Peter Berthold’s influential ‘comprehensive theory of bird migration’ (Berthold 1999). This theory aimed at describing the origin, evolution, maintenance and adaptive changes of bird migration and builds on the assumptions of the ubiquity of partial migration and the high evolutionary potential of partially migratory populations. 1776 According to Berthold (2001) ‘partial migration represents a form of evolutionary turntable between migratoriness and sedentariness and is an extremely important starting point for microevolutionary processes…(and) could well have helped partial migration to achieve its widespread, possibly fundamental occurrence in birds’. He based these assertions on the finding that the inheritance of migratory behaviour is best described by a polygenic threshold model, and that selection for lower or higher migratory activity in a partially migratory population would result in the gradual evolution of complete residency or migratoriness (Pulido et al. 1996). Since its original proposal, fifteen years ago, there has been no re-evaluation of the threshold model of migration. The aim of the present article is therefore to update and revise the genetic model on the control of partial migration, discuss its consequences and provide a framework for future research. The threshold model of migration The threshold model of migration was proposed as a (quantitative) genetic model to describe the inheritance and evolution of the incidence of migratory behaviour, i.e whether a bird is migratory or resident (Pulido et al. 1996). It is based on a model, which was originally developed by Wright (1934) and later popularized by Falconer (1960), that allows estimating genetic parameters and migration threshold Frequency Environmental effect on threshold position “obligate” residents “facultative” migrants increasing canalization “obligate” migrants increasing canalization Figure 1. The environmental threshold model of migration. The model describes the distribution of migration propensity (⫽ liability) in a partially migratory bird population accounting for environmental effects on the threshold. Birds with migration propensities below the threshold are sedentary, with propensities above the threshold are migratory. The environmental shift of the threshold to the right will render facultative migrants (grey) sedentary. A shift of the threshold to the left will elicit migratory behaviour in facultative migrants that are currently sedentary (Fig. 4a). “cryptic”variation expressed phenotypic and genetic variation F5 F4 F3 F 2 Frequency making predictions about the inheritance and response to selection of dichotomous traits using standard quantitative genetic approaches (reviewed by Roff 1996). The ‘trick’ of this model is to assume that there is a normally distributed trait, called liability, which underlies the expression of the dichotomous trait, and a threshold, through which a phenotype of the dichotomous trait is produced. If the liability of an individual is above the threshold, the trait will be expressed phenotypically. If liability lies below the threshold the individual will not express the trait (Fig. 1, 2). This model has been particularly important in human genetics for predicting the inheritance and prevalence of polygenic congenital diseases (Falconer 1965). Similarly, the threshold model of migration may be used to predict the inheritance of migratory behaviour, the incidence of migrants and residents in a population, and the response to selection. Well established is the application of the threshold model to study the distribution and inheritance of macroptery in insects, which is correlated with migration tendency, and is determined by the concentration of juvenile hormone at a particular stage of development (Kent and Rankin 2001, Roff and Fairbairn 2007). Similarly, migration tendency in salmonid fishes is determined by a migration threshold and a liability, which is correlated to body size at a particular age (Økland et al. 1993, Thériault and Dodson 2003, Páez et al. 2011). For migratory birds, it was shown experimentally in the blackcap Sylvia atricapilla that the incidence of migration is accurately described by the threshold model (Pulido et al. 1996), whereby it is assumed that there is some underlying normally distributed continuous variable, i.e. the ‘liability’, which may be the concentration of a P F1 -500 -300 -100 100 300 500 700 Migratory activity [1/2 h] 900 1100 Figure 2. Changes in the distribution of migratory restlessness in response to selection for lower incidence of migrants in southern French blackcaps (data from Berthold et al. 1990 and Pulido et al. 1996). The probability density functions were estimated from the frequency of migrants in the population and from the distribution of the amount of migratory activity in the migratory fraction of the population (details in Pulido et al. 1996). Note that birds with negative values, i.e. with values below the threshold, do not show migratory activity resulting in cryptic genetic variation. protein or hormone, which is tightly correlated with the amount of migratory restlessness (zugunruhe). Individuals showing no migratory activity are classified as nonmigrants, and it is assumed that their liability is below the migration threshold. The migration threshold model is supported by experimental results that are in line with four critical predictions of this model: 1) that a censored rather than a standard normal distribution fits best the actual distribution of migratory restlessness in the migratory fraction of partially migratory populations, 2) that the amount of migratory activity of parents is the best predictor of the frequency of migrants/residents in their offspring, 3) that the frequency of migrants/residents in a population is genetically correlated to the amount of migratory activity shown by the migrants in that population. This was demonstrated in a two-way selection experiment in partially migratory blackcaps (Berthold et al. 1990), in which selection on the frequency of migrants caused, as a correlated response, a change in the amount of migratory activity in the migratory fraction of the population (Pulido et al. 1996, Pulido 2007). Furthermore, it was demonstrated recently 4) that a completely migratory population may evolve residency, thereby becoming partially migratory, by selecting for lower amounts of migratory activity without the need to introduce ‘residency genes’ by mutation or gene flow (Pulido and Berthold 2010). Four generations of artificial selection for lower migratory activity not only reduced the amount of activity in that population, but also increased the frequency of non-migrants, from zero in the parental and F1 generation to 4.5–13%, in the F2 and F3 generation (Fig. 3). Further evidence supporting the threshold model comes from the distributions of migration distances in the house finch Carpodacus mexicanus populations that were newly founded in eastern North America. In these populations a correlation between migration distance and incidence of migration was found (Able and Belthoff 1998). We presently have strong evidence that migratory activity as 1777 Frequency of Non-migrants [%] 0.14 F2 0.12 0.10 0.08 0.06 0.04 F3 F4 0.02 0.00 250 F1 300 350 400 P 450 500 550 600 Amount of migratory restlessness [1/2 hours] Figure 3. Genetic changes in the frequency of non-migratory individuals in response to artificial selection for lower migratory activity in the completely migratory blackcap population from Southern Germany (data from Pulido and Berthold 2010). The line gives the fitted function describing the transition from a migratory to a partially migratory population. measured in the laboratory as ‘zugunruhe’ is tightly correlated to the liability in migratory populations, which enables us to determine the migration threshold and to predict the dynamics of the evolution of residency. However, we have not yet identified the liability variable, which would allow us to predict the propensity of a resident population to become migratory by measuring the expression of this variable in non-migratory individuals. In the last years, a great effort has been made to identify the elements (hormones, enzymes) involved in the physiological control of bird migration, but no clear patterns have been found so far (reviews by Wingfield et al. 1990, Ramenofsky and Wingfield 2007). Recent developments in genomics and proteomics and their use for identifying regulatory pathways of complex traits may help us to find the migration liability trait in the future. A first step in this direction is the recent identification of a genetic polymorphism correlated with the amount of avian migratory activity, though it explains only a small part of within and among population variance in migratoriness (Müller et al. 2011). Consequences of the threshold model: canalization and the maintenance of genetic variation For understanding the evolutionary potential of partially migratory populations and for predicting evolutionary trajectories, it is essential to know the particularities of threshold characters. Threshold traits, such as migration propensity, maintain high levels of genetic variation, even under persistent and strong directional selection. This is due to the facts 1) that selection intensity decreases as selection proceeds, i.e. as the frequency of the selected type (migrant or resident) approaches one (Roff 1994a or b, 1996). As a consequence of the very low selection intensities near fixation of one phenotype, in large populations the loss of genetic variation may partly be restored by mutation (Roff 1998a), particularly if the threshold trait 1778 is controlled by many genes (⬎ 50). For migratory activity we have now circumstantial evidence that this is the case (Müller et al. 2011). Moreover, frequency-dependent selection, as often postulated for the evolutionary maintenance of partial migration (Lundberg 1987, 1988, Kaitala et al. 1993), could maintain high levels of additive genetic variation and explain the persistence of both types of migration habits (migrant and resident) in the population (Roff 1998b). Another important mechanism explaining the maintenance of (hidden) genetic variation in migration is the canalization of the phenotype (Flatt 2005, Schlichting 2008), which results in robustness, i.e. the ‘invariance of the phenotype in the face of genetic or environmental variation’ (McGuigan and Sgró 2009). Below the migration threshold all individuals are phenotypically uniform with regard to migration, i.e. they are nonmigratory. As a consequence, genetic variation in migration propensity is not phenotypically expressed (Fig. 1, 2) and, therefore, cannot be eroded by natural selection, unless correlated with some other trait that is phenotypically expressed and is selected against (Roff 1996, Pulido 2007). Above the threshold, close to the extreme of the distribution, i.e. in birds with high migratory activity (long-distance migrants), the expression of migratory activity is largely insensitive to environmental variation, i.e. it is environmentally canalized (Pulido and Widmer 2005). This cryptic genetic variation, which may be particularly large in nonmigratory and partially migratory populations, may be released in novel environments, and may contribute to adaptation (Schlichting 2008, McGuigan and Sgrò 2009). An example for the importance of this mechanism in the evolution of migration may be the apparent ‘de novo’ evolution of migratory behaviour in resident populations as observed, for instance, in the North American house finch. Shortly after being introduced into eastern North America it extended its breeding range and started performing seasonal migration in the northernmost breeding areas (Able and Belthoff 1998). If we assume that this was an evolutionary change, the subsequent rapid increase in the frequency of migrants and migration distance in different populations in the newly established range suggests that a considerable amount of genetic variation for migration distance and propensity had been present in the original nonmigratory population. Resident populations can thus potentially maintain genetic variation for migratory activity and other traits of the migratory syndrome but do not express it. This genetic variation in resident populations, however, is not expected to be uniform. Populations may preserve different amounts of cryptic genetic variation in migration propensity depending on their effective population size, their current and historical selective regime and the time elapsed since they became completely sedentary. Under strong directional selection for residency, the population variance in migration propensity is reduced in a trajectory comparable to a process of stabilizing selection towards a mean below the threshold at which migratory offspring are no longer produced (Fig. 2). Once the populational mean has shifted towards this point or beyond, variation in liability does not respond anymore to selection. Genetic variation may, however, be lost by genetic drift. Further changes in migration propensity of non-migratory populations could be achieved by an increase of genes that increase environmental sensitivity of liability, which would facilitate phenotypic expression by environmental induction. The environmental threshold model of avian migration The threshold model of avian migration was derived from the expression of migratory activity (⫽ zugunruhe) under controlled indoor conditions, and it has hitherto only been tested in the lab. Here, I want to discuss whether the threshold model is applicable to migratory movements in natural populations in the wild and propose an extension of the model, which accounts for environmental variation. This environmental threshold model may help to reconcile opposing views on whether a bird’s decision to migrate or to stay on the breeding ground is determined mainly by its genes or environment (Berthold 1984, Lundberg 1988, Adriaensen et al. 1990). There is strong evidence that migratory restlessness measured under laboratory conditions is correlated with migratory behaviour in the wild. It was shown that both within and among-population differences in migratory behaviour are paralleled by differences in ‘zugunruhe’ (reviewed by Berthold 1996), but there is no one-to-one correspondence, at least not in all species and not in all phases of migration (Gwinner and Czeschlik 1978, Helm 2006). To understand the relationship between migratory activity, as expressed under artificial laboratory conditions, and migratory behaviour in the wild, we need to understand how environmental conditions trigger and modify the expression of migratory behaviour. It is well established that environmental factors such us food availability, density, competition and adverse weather events may trigger migratory movements in the wild, particularly in facultative migratory populations (Farner 1950, Baker 1978, Gauthreaux 1982, Schwabl and Silverin 1990, Berthold 1996, Newton 2008, Boyle 2011). Yet although migratory activity in captivity (⫽ migratory restlessness) had been considered largely insensitive to all environmental conditions, except for the photoperiod (Berthold 1996), under laboratory conditions, the same exogenous factors having an effect on migratory behaviour in the wild may modify the inclination to migrate and migration intensity, at least in some species (Table 1). The sensitivity may vary between individuals from different populations (Chan 1994, 2005), or within populations among individuals differing in age (Ketterson and Nolan 1983) or sex (Coppack and Pulido 2009), or between different phases of the migratory season (Terrill 1987a). Apparently, birds are particularly capable of modifying their migration program if they are short-distance migrants and have a more facultative type of movement, or if they are long-distance migrants close to the termination of the migratory programme (Terrill 1987a). As a consequence, it was suggested that migratory restlessness accurately reflects migratory activity in the wild only in long-distance migrants (Gwinner 1977). In short distance migrants, as well as in apparently resident birds that display migratory activity (Helm 2006, Helm and Gwinner 2006) migratory restlessness, as measured under ‘artificial’ laboratory conditions may reflect ‘the bird’s readiness to respond to external or internal cues, releasing and/or inhibiting actual migration’ (Gwinner and Czeschlik 1978) rather than migratory behaviour. Moreover, Helm (2006) rightly stated that it is the ‘...interactions between genetic disposition and social and ecological environment...’ that will determine whether a bird actually migrates or not. So, how can we account for the discrepancy between migratory restlessness and migratory behaviour in the wild? Clearly, we need a genetic model that takes into account phenotypic response to environmental variation, i.e. phenotypic plasticity. A first attempt to account for the effect of environmental variation on the propensity of migration in birds was made by Chan (2005), in his ‘genetic social model’ (Fig. 4 in Chan 2005). Based on his results on silevereyes Zosterops lateralis he suggested that ‘social influence’ and genetic thresholds for migration may explain differences in migration tendency under different environmental conditions. Here, I propose a more general model, the environmental threshold model (Roff 1994b), which accounts for environmental variation. This model builds on the model developed by Hazel et al. (1998, 2004) to study the evolution of conditional strategies. It assumes that there is genetic variation for both the liability and the threshold (Pulido et al. 1996, Pulido 2007, see Fairbairn and Yadlowski 1997 for a demonstration of this assumption in cricket wing dimorphism), and that both traits may be modified by individual or environmental condition. While there is strong circumstantial evidence supporting the assumption that there is plasticity in the amount of migratory restlessness in response to environmental variation (Table 1) it is not clear whether there is also plasticity in the position of the threshold. Hitherto no experiment has been conducted to separate environmental effects on the incidence and amount of migratory activity. However, rapid changes in migratory status from year to year in individuals and populations suggest that environmental effects on the threshold are important. For simplicity, and because effects of a shift in liability or of the threshold are equivalent Table 1. Studies experimentally demonstrating effects of the social and physical environment on incidence and amount of migratory restlessness in birds Environmental variable Physical variables Temperature Latitudinal information Ecological variables Food availability Social variables Migratory activity of conspecifics Social dominance Reference Palmgren 1937, Putzig 1938, Schildmacher 1937, Siivonen and Palmgren 1937, Merkel 1938 Wagner and Schildmacher 1937, Ketterson and Nolan 1987 Wagner 1937, Gwinner et al. 1985, 1988, Biebach 1985, Terrill 1987b, Fusani et al. 2011 (but see Ramenofsky et al. 2008) Chan 1994 Terrill 1987b 1779 (Roff 1997), only environmental effects on the threshold were considered in the graphical model of Fig. 1. Similar genetic models for dichotomous traits considering environmental variation but assuming fixed liability and variable thresholds have been proposed for the study of conditional strategies (Hazel et al. 2004, Tomkins and Hazel 2007). The environmental threshold model of migration predicts that adaptive changes in migration will depend not only on changes in the threshold but also on its response to environmental variables (e.g. social dominance, food availability). The model further makes clear predictions about environmental sensitivity. Individuals with liability values at the extremes of the distribution, i.e. long-distance migrants and long-established sedentary populations, will be resilient to the effects of environmental changes on their migratory status. Individuals with liability values close to the threshold, however, are expected to readily change migration status, as environmental effects shift them across the threshold (Fig. 1). Environmental variables modifying migratory behaviour may be ecological (e.g. food availability, competition, density, temperature), social (e.g. dominance rank) or intrinsic (e.g. physical condition, Table 1). Sensitivity to these factors as well as the distance of an individual’s liability to the threshold will determine whether an environmental change will shift an individual across the migration threshold, and change its migration status. We generally consider migratory restlessness as a good proxy of the propensity for migration and an indicator of the probability of an individual to change migration in response to environmental changes in the wild (Berthold 1996, 2001). However, if we consider phenotypic response of migratory activity to environmental variation we realize that we cannot readily use results obtained in a laboratory environment to predict migratory behaviour in the wild. The laboratory environment is one particular environment and because of plasticity results are strictly only valid for this very environment. Whether the measured response in the laboratory environment is correlated with migratory activity in the wild will depend on the specific environmental conditions and the reaction norms (Caro et al. 2007 discussed this question in a study of reproductive biology in blue tits). To be able to assess the validity of results obtained in the lab, we therefore need to measure reaction norms for migratory traits, or measure migratory activity, right away, in more natural environments and under more realistic conditions. Partial migration and evolutionary change by genetic accommodation Recently, the change in plasticity in combination with changes in the thresholds that determine developmental pathways has been identified as an important mechanism for adaptation to novel environments (West-Eberhard 2003, 2005, Schwander and Leimar 2011). The term ‘genetic accommodation’ (West-Eberhard 2003) was proposed to describe the shift in the population reaction norm, which could imply changes in the intercept, shape or slope, after exposure to a novel environmental stimulus (Crispo 2007). This change usually involves changes in the degree of canalization, i.e. the reduction of phenotypic variation. It may reduce phe1780 notypic variation if canalization increases or release hidden genetic variation if canalization decreases (Flatt 2005, Crispo 2007). In the evolution from residency to migration, and from migration to residency, genetic accommodation may be of major importance. The environmental threshold model predicts that the transition from facultative migration to obligate partial migration, to complete residency and migratoriness (particularly in long-distance migrants) is a gradual process characterized by increasing canalization, decreasing environmental sensitivity and decreasing phenotypic plasticity (Fig. 1). If individuals from a population with mean liability close to the threshold are exposed to environmental changes they will readily adjust their migratory behaviour by phenotypic plasticity. They will change migratory status from migratory to nonmigratory or vice versa, in direct response to the new environmental conditions. If these conditions persist, the population will respond by evolutionary change. Evolutionary changes in migration status would thus be accompanied by changes in phenotypic control. When selecting for increased migratory activity, we would expect genetic changes to follow phenotypic changes by phenotypic plasticity (⫽ ‘genes as followers’, Schwander and Leimar 2011). In the evolutionary transition from a long-distance migrant to a facultative partial migrant genetic change will come first and then, as migration distance decreases and mean population liability shifts close to the threshold, the environmental control will become more important. These shifts in phenotypic control have important consequences for the potential for and the dynamics of phenotypic adjustment of populations to changing environmental conditions (Schwander and Leimar 2011). For understanding them we need to study genetic and physiological mechanism underlying variation in migration status and of its phenotypic expression. Conclusions Today there is ample evidence supporting the view that the threshold model of migration is valid for describing the genetics of partial migration. Understanding the implications of this model is fundamental for our understanding of evolutionary changes in migration. The preservation of high levels of partly cryptic genetic variation, even under strong selection, may account for repeated and rapid evolutionary changes in migration within and among species. Moreover, the combination of ‘hidden’ genetic variation with a threshold that is phenotypically plastic may explain the apparent ‘de novo’ evolution of migratory behaviour in previously nonmigratory populations, for which there are a number of examples (Newton 2008), suggesting that this is a powerful mechanism to adapt to novel environments. This has been previously proposed as a mechanism of adaptation (West-Eberhard 2003, Schlichting 2008, McGuigan and Sgró 2009, Schwander and Leimar 2011), but it has not yet been considered in migration models. The proposed extension of the standard threshold model that considers phenotypic plasticity in the migration threshold (or the liability) will make this model more useful to study real migration movements in natural populations. It is clear that today an evolutionary model not accounting for the effects of the interaction between genotype and environment is insufficient (Schlichting and Pigliucci 1998). The environmental threshold model offers an alternative to the ‘genetic hypothesis’ and the ‘behavioural – constitutional hypothesis’ for the control of partial migration (Berthold 1984). Clearly, migration has a genetic basis but whether this genetic variation is expressed or not, and, if so, how strongly is also determined by the environment. The environmental threshold model predicts that facultative and obligate partial migration, as well as residency and complete migration, are controlled by the same mechanisms, for which there is some empirical findings in birds (Newton 2008). The environmental threshold model describes the complex relationships, between the propensity to migrate, the amount of migratory activity expressed and the sensitivity of the expression to environmental variation, i.e. canalization and de-canalization of the phenotype. Modelling these relationships for predicting the dynamics and trajectories of evolutionary changes in migration under different environmental conditions is clearly the next step to take. Existing models for the genetics of multifactorial diseases (Stewart 2002) and for conditional strategies (Tomkins and Hazel 2007) could be a starting point for developing more realistic migration models. For testing the predictions of the models we need to integrate the factor ‘environment’ into our experimental studies. This approach has recently been followed for studying the control of migration in fish and amphibians, showing that the development of migratory state is significantly determined by environmental conditions (Olsson et al. 2006, Brodersen et al. 2008, Grayson and Wilbur 2009, Wysujack et al. 2009, Skov et al. 2010). In birds, similar studies are still lacking. The experimental study of migratory behaviour in birds kept under semi-natural conditions will help us to manipulate single environmental variables, thereby quantifying their effect on migratory activity. Moreover, the use of modern tracking techniques, which will make it possible to study individual migratory behaviour in the wild, will help us to verify results obtained in the lab. Both approaches will be necessary to test predictions obtained from genetic migration models. Acknowledgements – I would like to thank Ben Chapman and the CAnMove members for their invitation to contribute to this volume on partial migration. Thanks also to Ben Chapman, Ian Newton and Derek Roff for thoughtful comments on an earlier version of the manuscript. Francisco Pulido is financially supported by grants (RYC-2007-01861 and CGL2009-12397) from the Spanish Ministry of Science and Innovation (MICINN). References Able, K. and Belthoff, J. R. 1998. 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