Download Evolutionary genetics of partial migration the threshold model of

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
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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).
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