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Keywords:

  • habitat saturation;
  • heterozygosity-fitness correlation;
  • natal dispersal;
  • philopatry

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

1. Dispersal can be condition- and phenotype-dependent and related to individual genetic differences. Few studies have addressed the relative importance of these factors on dispersal. We studied the factors behind philopatry and dispersal in juvenile Siberian flying squirrels, Pteromys volans L.

2. The dispersal distance and the distances explored before abandoning the natal nest were not related to any of the condition-dependent factors studied such as the area of high-quality habitat or the number of conspecifics near the natal area. In addition, the body mass (a phenotypic trait) of individuals was not related to philopatry and dispersal in flying squirrels.

3. Genetic variability, measured by microsatellite heterozygosity, was positively correlated with dispersal. The correlation was mainly driven by one locus related to the distances explored before abandoning the natal nest.

4. We conclude that condition- and phenotype-dependent factors did not have detectable effects on philopatry and dispersal, but individual heterozygosity was related to dispersal in flying squirrels. Our results suggest that genetic variability is important behind the dispersal of the species.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Individual dispersal, that is, the movement between natal area and first breeding area or between two successive breeding areas, can be condition-, phenotype- and genetic-dependent (Clobert et al. 2009). Condition dependence means that individuals rely on a set of external cues to adjust their dispersal tactics such as local population density and habitat change (Verhulst, Perrins & Riddington 1997; Ims & Hjermann 2001; Matthysen 2005; Wauters et al. 2010). Phenotype dependence means than dispersal propensity correlates with a suite of phenotypic traits, like body size or exploration activity (O’Riain, Jarvis & Faulkes 1996; Dingemanse et al. 2003; Clobert et al. 2009).

Condition-dependent explanations of dispersal can be viewed through the costs of dispersal and the benefits of philopatry, that is, remaining at the natal nest or territory (Anderson 1989; Boutin, Larsen & Berteaux 2000; Solomon 2003; Bowler & Benton 2005). Based on these factors, different hypotheses, which are not mutually exclusive, are proposed to explain the decision to remain philopatric or disperse. For example, the benefits of philopatry hypothesis emphasize the fitness benefits that arise from remaining at the natal nest as opposed to the costs of dispersal (Stacey & Ligon 1991; Solomon 2003). The ecological constraints hypothesis stresses that if there are constraints on natal dispersal, such as the absence of vacant breeding territories due to habitat saturation, the direct fitness cost of remaining philopatric may be small compared to the costs associated with dispersal, such as increased mortality (Emlen 1982; Ims & Hjermann 2001). Condition-dependent factors may or may not be linked to phenotypic differences between dispersers and philopatric individuals.

Phenotypic characteristics specific to dispersers are likely to exist if they somehow benefit the dispersers, by mitigating the costs of dispersal and/or facilitating settlement to a new site (O’Riain et al. 1996; Zera & Denno 1997). These include traits that facilitate movement like the wing muscles and appropriate body shape of some dispersing insects (Zera & Denno 1997). For instance, in vertebrates, body size differences have been reported between dispersers and philopatric individuals (Hanski, Peltonen & Kaski 1991; O’Riain et al. 1996), and dispersers of some species are more active in exploring novel environments than philopatric individuals (Ebenhard 1987; Dingemanse et al. 2003). In addition, physiological, such as hormonal, differences may exist between dispersers and philopatric individuals (Holekamp & Sisk 2003; Vercken 2007). Such phenotypic characteristics of dispersers may be linked to individual genetic differences (Clobert et al. 2009).

That dispersal is related to genetic differences is supported by observations of heritability for this trait (Trefilov et al. 2000; Roff & Fairbairn 2001; Hansson, Bensch & Hasselquist 2003; Doligez, Gustafsson & Part 2009). For example, dispersal has been recently linked to a single gene polymorphism in the melitaean butterfly (Haag et al. 2005; Niitepõld et al. 2009). One genetic factor that is observed to correlate positively with different fitness related traits is individual heterozygosity measured with neutral markers such as microsatellites (heterozygosity-fitness correlation, HFC; Coltman & Slate 2003; Pemberton 2004). Traits observed to be linked to heterozygosity include juvenile survival (Coulson et al. 1998), disease susceptibility (Acevedo-Whitehouse et al. 2003), parasite burden (Coltman et al. 1999), social dominance (Tiira et al. 2006) and reproductive success (Slate et al. 2000). The two main hypotheses behind HFCs are the global effects hypothesis, that is, wide-scale genomic heterozygosity which may be linked to inbreeding, and the local effects hypothesis. According to the local effects hypothesis, the markers studied are associated to fitness loci in the local chromosomal vicinity. Theoretical work (Balloux, Amos & Coulson 2004; Slate et al. 2004) and recent empirical studies (Da Silva et al. 2008; Acevedo-Whitehouse et al. 2009; Amos & Acevedo-Whitehouse 2009) seem to support the local effects hypothesis (but see Szulkin, Bierne & David 2010). It has also been shown that linkage disequilibrium (non-random association of alleles at two or more loci), which may help explain the link between neutral markers and fitness related genes, can extend over many hundreds of kilobases in the genome (see refs. in Chapman et al. 2009). In any case, a conclusive explanation for the genetic process behind the observed HFCs remains unclear. However, HFCs seem to be a general phenomenon in many wild vertebrate populations (Chapman et al. 2009). Thus, we can expect that this phenomenon may also be related to dispersal behaviour, because leaving the natal area is one of the biggest challenges in the life of many species. Indeed, it is observed in two previous vertebrate studies that inbreeding may shorten dispersal distances (Schiegg et al. 2006; Szulkin & Sheldon 2008).

Here, we study whether condition dependence, phenotype dependence or individual heterozygosity are related to philopatry and dispersal in juvenile Siberian flying squirrels, Pteromys volans L. We predict that: (a) if dispersal is related to genetic variation, individual levels of microsatellite heterozygosity will correlate with dispersal; (b) body mass differences between dispersers and philopatric individuals would indicate a phenotype dependence of dispersal; (c) if dispersal is condition dependent and is driven by habitat saturation or the benefits of philopatry, then the likelihood of dispersal should be lower when (i) the area of habitat near the natal nest and the number of nest sites within the natal area are large; (ii) the number of same-sex conspecifics is low and/or the number of opposite-sex conspecifics is high close to the natal nest; and (iii) the body mass and heterozygosity of juveniles (these likely being linked to dominance status) is high.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Study species

Siberian flying squirrels are nocturnal, arboreal rodents, which nest or roost in tree cavities, nest boxes and dreys in spruce-dominated boreal forests. Natal dispersal is the main form of population redistribution in the flying squirrel (Hanski & Selonen 2009). The onset of dispersal takes place during late summer of the birth year (Selonen & Hanski 2004; Hanski & Selonen 2009). Dispersal distances are long: the mean is around 2 km and straight-line distances of up to 9 km have been measured between the natal and settlement nests (Hanski & Selonen 2009). Dispersal distances are not related to the structural connectivity of the landscape (Selonen & Hanski 2004). Juveniles are divided into philopatric individuals and dispersers based on moved distances (Selonen & Hanski 2004, 2006). Philopatric individuals moved <400 m from the natal nest indicating that they may be in proximity of their mother’s home range. This distance of 400 m is two times the radius of a circle enclosing the average female home range (8 ha), thus ensuring that individuals dispersing more than this should not be in contact with their mother. Using this criterion a fraction of males (38%) remains philopatric, but almost all females leave the natal area (Hanski & Selonen 2009). Before the decision to disperse, individuals move around the natal site and perform exploratory forays outside the natal area (Selonen & Hanski 2006). Both dispersing and philopatric juveniles perform exploration (Selonen & Hanski 2006). Thus, both dispersers and philopatric individuals have some knowledge of the landscape around the natal site. There may be competitive exclusion, especially between females which seem to be territorial, but males live in overlapping home ranges with several males and females. Both sexes may mate with multiple partners, but females seem to be the dominant sex because adult females are slightly heavier, and we have observed females evicting males from the nest cavity. A female flying squirrel can have one or two litters in one summer. First litters are born in late April and second litters in June (Hanski & Selonen 2009).

Study areas and data collection

We conducted our study in three study areas in southern Finland (Iitti, Anjalankoski and Nuuksio) during 1997–2004 (plus a few additional individuals in 2008). On average, 24% of these areas consisted of spruce-dominated forest, that is, potential flying squirrel habitat (Selonen, Hanski & Stevens 2001). The landscape matrix of each area was dominated by managed forest areas, pine forests and fields (for more information on the study areas and landscape structure, see Selonen et al. 2001; Selonen & Hanski 2004). The landscape of the study areas was functionally continuous for dispersers, which were able to move between forest patches and visited several patches (Selonen & Hanski 2004; Selonen, Hanski & Desrochers 2007).

We used data from 53 male and 39 female juvenile flying squirrels (the same data as in Hanski & Selonen 2009; with six additional individuals). Of these, 20 males and 1 female were classified as philopatric. Six males and 11 females were from a second litter the rest being from a first litter. Juveniles were captured in their nest cavity (natural or nest box) between June and July and radio-collared. The batteries in the radio-collars usually lasted until January–February. During the dispersal period (July–September), flying squirrels were located approximately five times a week (day and night, one fix per individual per night and day), but less frequently before and after the dispersal period. Nest sites were located during the daytime (for more information, see Selonen & Hanski 2006, 2010a).

The data included juveniles from 41 families (full and half siblings from ≥1 year). Siblings move in separate areas before dispersal, disperse in different directions, settle far away from each other and are independent from their parents (Selonen & Hanski 2010b). However, family effects on dispersal distance are expected as the landscape data around the natal site was similar for individuals within a family, and siblings were genetically similar. Thus, we analysed the data using the family as a repeated-measures subject (see below). All the families originated from separate areas.

Condition- and phenotype-dependent variables explaining dispersal

A flying squirrel’s dispersal distance (continuous variable) and decision to remain philopatric (binomial variable) were used as dependent variables in the analyses. The latter analysis was restricted to males, because female flying squirrels rarely remain philopatric (Hanski & Selonen 2009). In addition, for both sexes, we used movement distances before abandoning the natal site, that is, during the exploration phase of dispersal (from now on ‘exploration distance’, see Selonen & Hanski 2010a), as a dependent variable in the analyses. Maximum exploration distances are on average around 500 m (straight-line distances between natal nest and nightly location fixes) and some of the dispersers eventually settle to sites located during this phase of dispersal (Selonen & Hanski 2006, 2010a). Maximum exploration distances strongly correlate with mean exploration distances which are around 200 m [35 males and 19 females with 13 ± 4 (mean ± 1 SD) fixes per individual; data published in Selonen & Hanski 2006]. We used mean exploration distances in the analyses. Exploration distances do not correlate with dispersal distance in flying squirrels (Selonen & Hanski 2006). Both of these are a function of movement activity, but the dispersal distance is also related, for example, to the location of vacant settlement sites in the landscape. Juveniles were weighed when trapped, and based on successive weighings we calculated a daily body mass increase (unfortunately, we lacked other body measures than body mass; for more information see Hanski & Selonen 2009). The body mass of individuals was standardized based on daily growth to correspond the body mass on 1st July.

The predictor variables were: (i) the standardized body mass (a phenotypic variable), and the variables describing the environment within and around the natal site and the litter attributes for juveniles. (ii) The area of high-quality habitat around the natal nest. This was calculated within a buffer area with a radius of 500 m for males and 1000 m for females. The reason for the larger buffer area for females was that few of the females settle <500 m from the natal nest (also the 500 m buffer area was tested for females, but this did not affect the results). A high-quality habitat included large aspens and other deciduous trees (mean ± SD percentage tree cover by all deciduous trees 24 ± 18%) that provided food and cavity resources for flying squirrels. The large aspen is the main source of cavities and an important food source for flying squirrels. The flying squirrel is closely associated with the aspen, which seems to be reflected by its fur colour. All the dispersers in our study areas also seem to target selecting habitat with large aspens (Selonen et al. 2007). (iii) The number of cavities within a mother’s home range. In half of the cases, the female home range was known (published in Selonen et al. 2001; Rantala 2003). For the rest of the juveniles, we used the average of 8 ha for a female’s home range (100% Minimum convex polygon). (iv) The number of same-sex siblings in the litter (litter size in our data varies between 1 and 4 young, with a mean of 2·8). This variable was included to analyse its effect in different model combinations, although the effects of the number of same- and opposite-sex siblings on dispersal have been analysed in more detail and no effect of dispersal has been found (Selonen & Hanski 2010b). (v) The number of same- and opposite-sex conspecifics around the natal nest (500 m for males and 1000 m for females; 44 juveniles in Anjalankoski). The latter analysis was restricted to the Anjalankoski study area where we captured all the resident individuals in the area (based on mark–recapture analysis with program MARK 6·0; Selonen, Hanski & Painter 2010). The effects of different two-way interactions of predictor variables were analysed. Recently, the effects of parents on juvenile dispersal have been analysed and no effect on dispersal has been found (Selonen & Hanski 2010b).

Microsatellite heterozygosity

We measured standardized heterozygosity using seven microsatellite markers. For more information on the DNA extraction and polymerase chain reaction (PCR) primers and protocols, see Painter, Selonen & Hanski (2004). In short, hair samples were taken from juveniles captured from nests when they were small. All seven loci were amplified via the PCR, and the products run on an ABI Prism 377 automated sequencer (Applied Biosystems, California, USA) to separate the alleles. All seven microsatellite loci were polymorphic with between 6 and 14 alleles. The mean HO and HE varied between 0·56–0·77 and 0·53–0·75, respectively. Deviations from the Hardy–Weinberg equilibrium were not found for loci/site and site/loci tests. We have not found any indication of null alleles or allelic dropout (Selonen, Painter & Hanski 2005).

Several multilocus measures have recently been employed to infer genetic variability. However, to avoid pseudoreplication researchers are encouraged to report only one of these genetic metrics (Chapman et al. 2009). We used standardized heterozygosity as it is found to be an effective and widely used measure of genetic diversity (Chapman et al. 2009). Standardization of multilocus heterozygosity seeks to ensure that the level of heterozygosity of all individuals is measured comparably even in those cases where some individuals have been typed for less loci. It is equivalent to heterozygosity, but is computed as the proportion of heterozygous typed loci divided by the mean heterozygosity of typed loci in the population. We did not use data for individuals with less than five typed loci. Microsatellite data were missing for four juveniles. To analyse whether some of the seven loci (Pvol10, Pvol41, Pvol74, PvolE1, PvolE5, PvolE6 and PvolE10; Painter et al. 2004) had more influence than others on exploration and dispersal distances we analysed each locus separately and estimated whether heterozygosity correlated between the seven loci (see below). The tendency of heterozygosity to be correlated among loci may indicate inbreeding related to global effects behind heterozygosity (Balloux et al. 2004). In our data, the body mass of an individual and standardized heterozygosity were independent variables (n = 79, r = −0·04, P = 0·72).

Statistical analyses

For statistical analysis, we used general linear models (GLM) with the family (siblings from 41 families, see above) as a repeated-measures subject. When dispersal distance (continuous variable) was the dependent variable, we used the MIXED procedure in SAS 9·2 with Kenward-Roger adjusted error degrees of freedom. When the decision to remain philopatric (binomial variable) was a dependent variable, we used the GENMOD procedure with generalized estimation equations (SAS 9·2). Study area, sex and litter (the first or second litter of the summer) were always included as class variables in the models. The effects of these variables on dispersal distance have already been analysed (Hanski & Selonen 2009), so, here we only control the effect of these variables and do not report the results for these variables. Other predictor variables (condition- and phenotype-dependent variables and heterozygosity, see above) were analysed in separate models, because sample sizes differed among the tests. Interactions were omitted from the final models, unless significant, using stepwise backward selection and eliminating non-significant parameters. All continuous variables (except standardized heterozygosity) were log-transformed to achieve homogeneity of variances and normality of errors.

For single-locus tests, we replaced standardized heterozygosity in the above described models with a class variable homozygous vs. heterozygous for each locus. To test whether heterozygosity was correlated among the loci we used statistical computing software R 2·9·2 (http://www.r-project.org) extension Rhh (Alho, Välimäki & Merilä 2010). The Rhh computes heterozygosity–heterozygosity correlation among the loci, that is, a simulated mean correlation (r) and 95% confidence intervals between multilocus heterozygosity estimates calculated from random samples of loci. This correlation should be positive if multilocus heterozygosity carries a signature of global genomic heterozygosity (Balloux et al. 2004).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The decision to remain philopatric as opposed to the decision to disperse by male flying squirrels (n = 53) was not explained by any of the studied variables (Table 1). Similarly, the dispersal distance for both sexes (n = 92) was not explained by any of the condition-dependent variables: the area of high-quality habitat around the natal nest (GLM: F1,41·7 = 0·42, P = 0·52), the number of cavities within a mother’s home range (F1,63 = 0·56, P = 0·46), the number of same-sex siblings (F1,84·9 = 0·41, P = 0·53) or the number of same- or opposite-sex adults around the natal nest (n = 44 in Anjalankoski where we had density information; same-sex: F1,34·5 = 0·06, P = 0·81; opposite-sex: F1,34·5 = 0·12, P = 0·73). In addition, dispersal distance was not explained by the body mass of individuals (standardized on 1st July; n = 92, F1,85 = 2·7, P = 0·11). None of the two-way interactions of the above mentioned variables explained the dispersal distance (all inline image).

Table 1.   Predictors for the decision to remain philopatric or to disperse in male flying squirrels (binomial model, with generalized estimation equations in proc GENMOD, SAS)
 Philopatric males n = 18Dispersing males n = 35CoefficientSEChange in deviance (χ2)d.f.P
  1. aFirst litter only, body mass standardized on the first of July.

  2. bHigh-quality habitat within a 500 m buffer area from the natal nest.

  3. cCavities within mothers’ home range.

  4. dWithin a 500 m buffer area from the natal nest, in study area A, where we had density information n = 7 and n = 15 for philopatric and dispersing males, respectively.

Body mass (g)a63·5 ± 8·366·4 ± 9·7−4·93·91·310·3
Standardized heterozygosity0·93 ± 0·221·06 ± 0·28−1·62·71·110·3
Area of habitat (ha)b17·8 ± 10·517·7 ± 9·00·030·031·210·3
Number of same-sex siblings0·6 ± 0·50·7 ± 0·6−0·30·70·110·7
Number of natal cavitiesc7·2 ± 3·66·4 ± 3·90·070·070·910·3
Number of same-sex adultsd2·7 ± 1·82·7 ± 1·2−0·180·320·210·7
Number of opposite-sex adultsd2·0 ± 0·82·0 ± 1·4−0·020·080·110·8

Standardized heterozygosity was positively correlated with dispersal distance (n = 88, GLM: F1,72 = 3·98, P = 0·04; Fig. 1a) and more clearly with exploration distances, that is, distances moved before abandoning the natal nest (n = 51, F1,44·7 = 13·4, P = 0·0007; Fig. 1b). None of the two-way interactions between genetic diversity and condition-dependent variables (or body mass) explained the exploration or dispersal distances (all inline image).

image

Figure 1.  Heterozygosity-dispersal correlation in juvenile flying squirrels. (a) Standardized heterozygosity vs. dispersal distance (log transformed; linear regression: r2 = 0.03), (b) standardized heterozygosity vs. exploration distance (log transformed; r2 = 0.25), i.e. distances moved before abandoning the natal site.

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The test for heterozygosity–heterozygosity correlation indicated that there was no correlation among the loci (r = 0·01, 95% confidence interval: −0·09 to 0·13), suggesting that one or more loci might be contributing disproportionally to the heterozygosity estimate. Indeed, when analysing correlations between single-locus heterozygosity and dispersal/exploration distance, we found that the correlation was significant only for locus PvolE6 vs. exploration distance (F1,45 = 10·7, P = 0·002; all the other single-locus heterozygosities explaining exploration or dispersal distances: inline image, i.e. correlation between PvolE6 and dispersal distance also was insignificant). This locus indicated an average increase in the explored distance from 130 to 225 m and from 180 to 270 m for females and males, respectively. However, the trend was similar for the other loci (Pvol10, Pvol41, Pvol74, PvolE1, PvolE5 and PvolE10), that is, exploration distances were larger for heterozygous than for homozygous individuals (on average 52 ± 30 m; male and female comparisons for six loci). Dispersal distance was larger for heterozygous than homozygous loci in 10 out of 14 comparisons (7 loci for both males and females).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The dispersal of flying squirrels was not related to any of the studied condition-dependent factors such as the area of high-quality habitat or the number of conspecifics near the natal nest. Neither was the measured phenotypic trait, body mass of individuals, related to philopatry and dispersal in flying squirrels. Instead, microsatellite heterozygosity correlated positively with dispersal.

Heterozygosity was not correlated among the studied loci and the observed correlation between exploration distance and heterozygosity seemed to be driven by a single locus (PvolE6). These results support the local effects hypothesis for HFC (Da Silva et al. 2008; Acevedo-Whitehouse et al. 2009), but the possible link between PvolE6 and dispersal related genes remains unproven. Although statistically insignificant, heterozygosity at each of the remaining loci also suggested an association with increased exploration distances. Consequently, we cannot rule out global genomic heterozygosity also having some role behind the observed correlation between heterozygosity and dispersal in flying squirrels. Indeed, separating local effects from inbreeding related global effects may be difficult in natural populations (Szulkin et al. 2010).

In general, observed HFCs are relatively weak (effect sizes r being clearly <0·1, Chapman et al. 2009), but we observed a strong correlation between heterozygosity and exploration distances. This strong correlation may have been the result of mere chance. Alternatively, it could be expected that heterozygosity correlations might be stronger for dispersal related traits than for some other traits. The reason for this is that dispersers are bound to environmental variability compared to resident individuals. Thus, theoretically, it can be expected that dispersers may be linked to increased heterozygosity due to the necessary diversity of alleles required to cope with varying environments (episodic heterozygote advantage, Samollow & Soulé 1983). For example, in the Glanville fritillary butterfly, Melitaea cinxia, individuals heterozygous for the PGI allozyme genotype had increased movement activity (Haag et al. 2005; Niitepõld et al. 2009).

For flying squirrels, the observed correlation with heterozygosity was less clear for the dispersal distance than for the exploration distance. Exploration distances are part of the dispersal process in flying squirrels as many short-distance dispersers settle in habitats located during this phase of dispersal (Selonen & Hanski 2006). However, the behaviour of short- and long-distance dispersers differs (Selonen & Hanski 2010a; V. Selonen & I.K. Hanski, unpublished data) and consequently exploration distances do not explain the dispersal distances of the latter. In any case, for short-distance dispersers exploration activity is likely linked to dispersal distance. One possibility is that heterozygosity was primarily related to exploration distance, the observed weak correlation between dispersal distance and heterozygosity being the outcome of the previous relationship. It is also possible that the exploration distance was not directly linked to heterozygosity either, but through some covariate trait. For example, for some non-mammalian species, it has been observed that personality differences with regard to boldness in exploring novel habitats may affect dispersal (Dingemanse et al. 2003), and boldness can be related to genetic diversity (Vilhunen et al. 2008). Risk-taking related behaviour is also linked to dispersal in American red squirrels, Tamiasciurus hudsonicus (Boon, Réale & Boutin 2008). In flying squirrels, boldness might explain the exploration distances of short-distance dispersers, that is, how far juveniles were willing to move while exploring the surroundings of the natal site. In other words, boldness, exploration distances and heterozygosity could be linked to each other. However, this possibility remains unproven for flying squirrels.

We found that flying squirrels that dispersed farther were significantly more heterozygous than less dispersing individuals. One general observation of genetic diversity studies is that more heterozygous individuals seem to be more ‘fit’ (Chapman et al. 2009), having, for example, less parasites (Coltman et al. 1999) and surviving better (Coulson et al. 1998). Thus, it seems logical to assume that in the flying squirrel the more dispersing individuals are not the ones in poor condition that are forced to leave the natal site. This conclusion is supported by the observation that long-distance dispersers leave earlier than short-distance dispersers (Hanski & Selonen 2009). In addition, although we could not find significant body mass differences between juveniles, if anything long-distance dispersers were not the lightest individuals. Larger body mass could be expected if dispersers need to take into account expenses related to dispersal, for example, by putting on weight for hard times to come, as observed in some other vertebrate species (e.g. O’Riain et al. 1996; Barbraud, Johnson & Bertault 2003). The alternative hypothesis, that dispersers are in poor condition, has been observed when dispersal is related to the avoidance of parasites (Suhonen, Honkavaara & Rantala 2010) or when dispersal is due to habitat saturation (Matthysen 2005) as is the case for many cooperatively breeding species (Pruett-Jones & Lewis 1990; Walters, Copeyon & Carter 1992; Lucia et al. 2008).

Despite the correlation between dispersal and heterozygosity, there were no significant heterozygosity (or body mass) differences between philopatric and dispersing males (note: changing our cutline of philopatry from 100 m up to 1000 m did not change our results, analyses not shown). We also observed earlier that exploration distances are similar between philopatric and dispersing males (Selonen & Hanski 2006). Thus, philopatric males seem to be quite similar to short-distance dispersers in flying squirrels. One interpretation of this result could be that philopatric individuals are simply a subset of short-distance dispersers, the difference being that the former end up settling at or near the natal site and the latter further away. In another forest squirrel, the American red squirrel, philopatry is much more common (Haughland & Larsen 2004) than in flying squirrels. One reason for this is that red squirrel mothers sometimes give up part or all of their food cache sites within their territory to their offspring (Boutin et al. 2000). In flying squirrels, we only observed one case where the mother may have left the home range for her daughter, and in general this kind of behaviour is rare in flying squirrels (Hanski & Selonen 2009; Selonen & Hanski 2010b). Instead, philopatry may pose future problems for both daughters and sons in flying squirrels due to the presence of the mother in the natal site (Hanski & Selonen 2009; Selonen & Hanski 2010b). In terms of habitat use, the red squirrel is also more of a habitat generalist than the flying squirrel, resulting in a more restricted habitat selection for flying squirrel dispersers (Haughland & Larsen 2004; Selonen et al. 2007).

Surprisingly, we did not find any evidence that any of the condition-dependent factors studied influenced dispersal. Despite these results we expected that the amount of conspecifics near the natal site would have influenced dispersal decisions. However, we found no such correlation. Partly, this may be due to limitations in our data, but it may also be associated with the complexity of the dispersal process in the flying squirrel (Selonen & Hanski 2006, 2010a; Selonen et al. 2007), which may complicate the detection of possible weak correlations. Thus, although our results support the hypothesis that dispersal is related to individual genetic differences, we are not arguing that these are the only factor behind dispersal in the flying squirrel. Indeed, many other studies have found condition-dependent effects on dispersal in mammals and other taxa (Wauters & Dhondt 1993; Solomon 2003; Clobert et al. 2009; Wong 2010). However, many of these studies are from species with group-living or cooperative breeding systems or with high population densities. Condition-dependent factors could have a more important role in determining the dispersal decisions of these species (see for example, Ekman, Eggers & Griesser 2002; Pasinelli & Walters 2002; Solomon 2003) compared to more solitary species, like flying squirrels. No cooperation between relatives occurs in the flying squirrel (Selonen & Hanski 2010b). On the contrary, there seems to be competition between females for home ranges and nest and food resources therein (Hanski & Selonen 2009).

Recent studies have shown that significant adaptations that enhance dispersal can evolve quickly in natural populations (Simmons & Thomas 2004; Phillips et al. 2006). However, few have studied the relative importance of condition-, phenotype-, and genetic-dependent factors on dispersal determinism (Ronce et al. 2001; Hansson et al. 2003; Clobert et al. 2009). For the Siberian flying squirrel, we would expect that dispersal is determined by multiple factors that are related to each other as observed in other species (Solomon 2003; Clobert et al. 2009). However, here we could not find evidence that natal dispersal in flying squirrels was due to condition- or phenotype-dependent factors, genetic heterozygosity being the only factor with detectable effects on dispersal.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We thank Mikko Hannonen, Petri Ihalempiä, Maarit Jokinen, Eva Kallio, Henna Piha, Heikki Savolainen and Paul Stevens for their help in the field. Jodie Painter and Salla Rantala assisted with microsatellite analysis. André Desrochers and Tero Klemola gave valuable comments on an earlier draft of the manuscript. The study was financially supported by the Finnish Ministries of the Environment and Education, the Emil Aaltonen foundation, the Ella and Georg Ehrnrooth Foundation, the Kone Foundation and the Maj and Tor Nessling Foundation.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References