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Abstract
Dispersal is a fundamental process in ecology influencing the genetic structure and the viability of populations. Understanding how variable factors influence the dispersal of the population is becoming an important question in animal ecology. To date, geographic distance and geographic barriers are often considered as main factors impacting dispersal, but their effects are variable depending on different conditions. In general, geographic barriers affect more significantly than geographic distance on dispersal. In rapidly expanding populations, however, geographic barriers have less effect on dispersal than geographic distance. The effects of both geographic distance and geographic barriers in low-density populations with patchy distributions are poorly understood. By using a panel of 10 microsatellite loci we investigated the genetic structure of three patchy-distributed populations of the Greater long-tailed hamster (Tscherskia triton) from Raoyang, Guan and Shunyi counties of the North China Plain. The results showed that (i) high genetic diversity and differentiation exist in three geographic populations with patchy distributions; (ii) gene flow occurs among these three populations with physical barriers of Beijing city and Hutuo River, which potentially restricted the dispersal of the animal; (iii) the gene flow is negatively correlated with the geographic distance, while the genetic distance shows the positive correlation. Our results suggest that the effect of the physical barriers is conditional-dependent, including barrier capacity or individual potentially dispersal ability. Geographic distance also acts as an important factor affecting dispersal for the patchy distributed geographic populations. So, gene flow is effective, even at relatively long distances, in balancing the effect of geographic barrier in this study.
Citation: Xue H, Zhong M, Xu J, Xu L (2014) Geographic Distance Affects Dispersal of the Patchy Distributed Greater Long-Tailed Hamster (Tscherskia triton). PLoS ONE 9(6): e99540. https://doi.org/10.1371/journal.pone.0099540
Editor: Baohong Zhang, East Carolina University, United States of America
Received: March 22, 2014; Accepted: May 16, 2014; Published: June 9, 2014
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction.
Funding: This study was supported by National Natural Science Foundation of China (31270417 and 31300304) and the Shandong Provincial Natural Science Foundation (ZR2010CL013). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Population persistence strongly depends on its own evolutionary capacity which in turn relies on the genetic variability. The capacity of genetic variability within and among populations results from many processes involving mutation, dispersal, genetic drift and selection. Dispersal refers to the movement of an organism from one place to another, which plays a fundamental role in population biology and conservation because it influences the genetic structure as well as the persistence of populations [1]. Dispersal could restrict the genetic differentiation in yellow warblers [2], and it tends to be considered as one of the main causes in maintaining the high genetic diversity of rodent populations [3].
It is well known that dispersal is conditional-dependent. There are a number of potential driving forces identified for dispersal including kin competition, inbreeding, resource competition and environmental stochasticity [1], [4]. How these factors work for dispersal varies among species according to their life histories and how they interact with the environment. Dispersal needs costs, which are important for the success of dispersal. The costs on dispersal are paid during dispersal movements [5] or prior invested for increasing the dispersal capacity [6]. For most animals, the cost and benefit of dispersal vary in space and time as well as among different species. The profitability of dispersal as a life history strategy varies, and a plastic dispersal strategy is expected to accommodate to this variation [7], [8].
Some previous studies have focused on the effects of several main factors on dispersal, such as population density [9]–[13] and sex [14], [15]. Changes in population densities lead to the changes in the social and competitive environment over time and will eventually cause dispersal. This is called density-dependent dispersal. The dispersal rates change as the density of the population change. Specifically, empirical and demographical data provided the evidence that negative density-dependent dispersal is prevalent in voles [9]–[11], while positive density-dependent dispersal is proposed in rodents[12], [13]. In particular groups of animals, the propensity to disperse has sex bias with different dispersal rates between males and females. For examples, most mammals show male-biased dispersal pattern meaning that males disperse more frequently and farther than females [14], whereas the female-biased dispersal mainly occurs in birds [15].
Geographic distance is also an important factor affecting dispersal. As the costs of movement increase with distance, a successful dispersal is often considered to occur when the distance between patches decreases [16]. The isolated distance of a patch, which is apart from other patches, will strongly impact the cost of dispersal as costs of movement increase with travel distance. Whether dispersal propensity is actually sensitive to the isolated degree of a patch depends on the ability to estimate the isolated degree. Isolation could potentially be assessed by several different methods [17]. Exploratory movement is one of prevalent method to assess the location of suitable habitat, depending on the inter-patch distance and the movement capacity of the animal. The perception of suitable habitat is important to estimate the distance to other patch without actually travelling the full distance [17], which was supported by data collected from field studies [18], [19]. The butterfly Maniola jurtina uses a non-random, systematic dispersal strategy and can detect and orient towards habitat from distances of 100–150 m [17]. The genetic and geographic distance between populations are generally positively correlated [20]–[23], suggesting an isolation-by-distance effect. However, several studies have suggested no correlation existing between geographic and genetic distances [24]–[28]. In our study, we expected to investigate the relationship between the geographic and genetic distance for the patchy-distributed hamster populations to better understand how geographic distance affect the population dispersal.
In addition to distance, geographic barriers are also thought to be the important factors affecting dispersal [12]. Because of the dramatically increased use of land, habitat fragmentation is more and more obvious. Habitat fragmentation can increase the probability of local extinctions by destroying effective metapopulation structures. Rivers [29], roads [30]–[32] and valleys [33] all act as geographic barriers for the dispersal of some animal populations, but not for others. For example, rivers may act as physical barriers limiting the dispersal from one edge to the other for northern cavefish [34] and white-tailed deer [29], while they don't work for Chimpanzee [35] and Euglossini [36]. This divergence may be determined by the differences of dispersal abilities of particular animals or populations [36]. In this study, we chose three patchy-distributed Greater long-tailed hamster populations which were significantly isolated by Hutuo river and Beijing city respectively (Fig.1). Hutuo River is the main water source of Shijiazhuang County, Hebei Province, and it is 513.3 km in length and 46,000 square kilometers in watershed area. The river stores water annually and could be a potential physical barrier for dispersal. Beijing is the capital of the People's Republic of China and the center of politics, culture, education and international exchange. Beijing bears a great amount of people with lots of constructions and transportations which could restrict the dispersal of the Greater long-tailed hamster.
The Greater long-tailed hamster (Tscherskia triton) is widely distributed in croplands of North China [37], and it is one of dominant rodent species in the North China Plains [13]. Population abundance of the Greater long-tailed hamster varies greatly in space [13]. Here, we assessed the genetic differentiation and the gene flow among the three patchy-distributed Greater long-tailed hamster populations from Raoyang, Guan and Shunyi Counties of the North China Plain by using 10 polymorphic microsatellite loci, and to evaluate the role of geographic barriers and geographic distances for the dispersal to better understand the dispersal mechanism of animal populations. Rodents as an important functional group in ecosystem play an important role in the balance of ecosystem. Understanding the dispersal mechanism of the rodent population is very important for making reasonable and effective control methods.
Materials and Methods
Ethics statement
The Greater long-tailed hamsters in this study were captured in Raoyang, Guan and Shunyi Counties of the North China Plain, which were permitted by Wugong Station for Pest Monitoring and Forecasting, Hebei Province, China. All measures for the hamsters in this study were inspected and approved by Institutional Animal Care and Use Committee of the Institute of Zoology, Chinese Academy of Sciences (Permit Number: IOZ11012). All researchers had received appropriate training and affirmed before conducting animal studies.
Sampling collection
The Greater long-tailed hamsters were captured using wooden-iron traps in Raoyang, Guan and Shunyi Counties of the North China Plain in autumn, 2000. The wooden-iron traps are box-shaped, with a wooden-side at the bottom and iron sheets with holes at other sides. One small side of the traps could be open or closed, and the other sides are fixed. The wooden-iron trap works by the leverage principle. The trap is 20 cm in length, 12 cm in width and 14 cm in height. The wooden side as bottom is baited with peanuts and equipped with spring device. When the Greater long-tailed hamsters step on the wood plate to eat and the spring set up release, the hamsters will be closed in the trap and then euthanized by CO2 asphyxiation immediately. These operations were approved by the Institute of Zoology, Chinese Academy of Sciences. Captured hamsters were numbered, sexed, weighed, dissected, and measured to provide estimates of body size, age, and reproductive condition [38]. Sampling sites Guan and Shunyi are isolated by Beijing city which is often considered as a physical barrier since it bears a great amount of people with lots of constructions and transportations. Guan and Raoyang sites are isolated by Hutuo River, which stores water annually and were potential barriers to dispersal for the Greater long-tailed hamsters (Fig. 1). More than 25 permanent plots were chosen in each sampling site, covering most crop types. Each plot contained two trapping lines and interval distance of 25–30 m. Twenty-five traps were placed along each line with an interval of 5 m. Trappings were conducted for 3 consecutive days two weeks followed by two weeks of non-trapping each month, to minimize the effects of removing too many animals. Based on the suggestion from a previous study that the samples should contain at least 20 individuals to obtain accurate estimates of genetic distance [39], we used ninety-three individuals in this study, including 30 from Raoyang, 31 from Guan and 32 from Shunyi. So, the sample size of each population in our study meets the requirements. To reduce the sampling errors, the distance between the sampling sites in the same population was more than 25 m.
DNA extraction
Tissues were fixed in 90% ethanol, preserved in formalin solution and kept in the animal ecological Laboratory, Institute of Zoology, Chinese Academy of Sciences for more than 10 yrs. The genomic DNA was extracted from liver tissues using an improved phenol-chloroform extraction method [40]. DNA content is comparatively higher in liver tissue than others, and no fat matrix exist in liver tissue, therefore liver tissue is an ideal material for high DNA extraction efficiency. In order to prevent contamination in the process of DNA extraction, benches and plastic ware was cleaned with 10% bleach and sterile water and then exposed to ultraviolet (UV) light for 30 min. We used 10 extraction controls, none of which produced positive amplification during subsequent polymerase chain reaction (PCR).
Genetic analyses
Ten microsatellite loci of the Greater long-tailed hamster (Table S1) were used on the basis of its high amplification efficiency and rich polymorphism [41]. The variation of each locus was examined by PCR containing 50 mM KCl, 10 mM Tris-HCl, 2.5 mM MgCl2, 0.2 mM each dNTP, 1 U of Taq DNA polymerase (Promega), 10 pM forward and reverse primers, and approximately 2 ng of template DNA in 25 µL. Amplifications began with a 5-min denaturing step at 94°C, followed by 30–35 cycles of the following thermal reaction: denaturing at 94°C for 45 s, annealing at 47∼55°C for 45 s, and extending at 72°C for 1 min, with a final extension for 5 min at 72°C. All products were analyzed in an ABI 377 instrument (Perkin-Elmer Applied Biosystems, Foster City, California) and the gel analysis was performed by using GENESCAN3.1 (Perkin-Elmer Applied Biosystem).
Measures of Genetic Variation
Genetic variation within populations were assessed by using the measures including allelic richness (AR, number of alleles independent of sample size), Shannon's Information index (I), observed (HO) and expected (HE) heterozygosity. FSTAT version 2.9.2 [42] was used to calculate the measures for each locus. Hardy–Weinberg equilibrium tests were carried out by the Markov chain method [43], [44] in GENEPOP version 3.3[45]. Differentiation between various populations was estimated by the FST value [46].
An analysis of molecular variance (AMOVA) with Arlequin version 2.000 [47] was used to detect how much the genetic variance occupies the covariance components at various hierarchical levels. The three hierarchical levels were as follows: 1) within individuals, 2) among individuals within populations, and 3) among populations. The fixation indices FIS, FIT, and FST were calculated and the significant levels were tested respectively. Genetic distance [48] and gene flow [49] were calculated using the POPGENE version 1.31.
Geographic distances between sampling sites were calculated based on the approximate center of the sampling areas. Correlations between geographic distances and Nei's standard genetic distance [50] were calculated with the R-PACKAGE-module Mantel [51]. The statistical significance of the relationships was determined with 10 000 randomizations. The multiple regression analysis in the Mantel [51] were carried out to exclude the effects of the geographic barrier and the geographic distance on genetic differentiation for the three Greater long-tailed hamsters populations of Raoyang, Guan and Shunyi Counties.
Results
Genetic diversity
Among 10 microsatellite loci, there are 3 to 11 alleles with a mean of 6.1 alleles per locus (Table S1). Within populations, the mean number of alleles per locus ranged from 2.8 to 3.5 and allelic richness from 3.31 to 3.57 (Table 1). Observed heterozygosity was 0.557, 0.629 and 0.684 and expected heterozygosity was 0.601, 0.615 and 0.647 for Raoyang, Guan and Shunyi populations, respectively. No locus was found to deviate significantly from the Hardy-Weinberg equilibrium within each of three populations.
Some alleles were more restricted, while others showed wide range of geographic distribution as shown in Table S2. Twenty-seven of 61 alleles were found only in one population, and not in the other two populations. For example, the alleles of 436, 438, 440, 444 and 450 at GYA66 locus were only detected in Raoyang population. The numbers of alleles observed in different geographic populations were 35, 28, 33 in Raoyang, Guan and Shunyi geographic population, respectively. There are 11, 4, 6, 5, 6, 6, 6, 9, 5 and 3 alleles at locus GYA66, GYA136, GYA183, GYA189, GYB13, GYB47, GYA185, GY103, GYB28 and GYA181 respectively for all the Greater long-tailed hamsters tested in this study.
Estimates of allelic richness (AR), Shannon's Information index (I), observed (HO) and expected (HE) heterozygosity for the microsatellites in each population are shown in Table S3. The mean values of the parameters AR, I, Ho, and He were 3.57, 1.93, 0.56 and 0.60 for Raoyang population, 3.31, 1.86, 0.63 and 0.62 for Guan population and 3.47, 1.97, 0.68 and 0.65 for Shunyi population, respectively. The parameters of AR, I, Ho, and He show that genetic diversity exists in Raoyang, Guan and Shunyi populations. No significant difference on the genetic diversity level was detected from the used parameters among the three populations.
Genetic differentiation
Three pairwise estimates of FST were significant. The highest FST value (FST = 0.0531, P = 0.0038) was observed between the populations of Raoyang and Shunyi. The lowest FST value (FST = 0.0327, P = 0.0041) was observed between the populations of Guan and Shunyi. The FST value (FST = 0.0359, P = 0.0027) between the populations of Raoyang and Guan was moderate. The FST values of populations separated by the Hutuo River were higher than the ones separated by Beijing city,which indicates that the Hutuo River has higher barrier capacity than Beijing city for the Greater long-tailed hamsters. This leads to the higher genetic difference between the two sides populations of the Hutuo River than those of Beijing city.
The AMOVA showed that 71.6% of the variance is explained by within-individual variation, 8.9% by variation between individuals within population, and 19.5% by variation between populations. Although the total genetic variation is mainly from the variation of within-individuals, there was more than twice as much variation between populations as the one between individuals within populations. The overall F-statistics revealed the significance for FIS = 0.0281 (P = 0.0032), FIT = 0.0437 (P = 0.0053) and FST = 0.0382 (P = 0.0039). Wright (1978) identified the problem of interpreting FST values as an absolute value based on highly polymorphic loci and proposed that a FST<0.05 could indicate a considerable population differentiation [52]. Our significant FST value suggests that the genetic differentiation exists among three tested Greater long-tailed hamster populations.
Correlations between geographic and genetic distance
Genetic and geographic distances among the three examined patchy distributed geographic populations were summarized in Table 2. The genetic distance between the Raoyang and Shunyi populations (1.84) was found to be larger than the Guan and Shunyi (0.25) populations and the genetic distance between the Raoyang and Guan populations was moderate (1.19), suggesting a more distant genetic relationship between the Raoyang and Shunyi populations than the latter. This shows a direct proportional relationship between the genetic distance and the geographic distance (r = 0.98, P = 0.008), as shown in Fig.2.
Gene flows between the different examined geographic populations were also summarized in Table 2. Higher level of gene flow exists between the Raoyang and Guan geographic populations than the Raoyang and Shunyi populations. The gene flow was inversely proportional to the geographic distance (r = −0.99, P = 0.017) as shown in Fig.2.
Discussion
Generally, the populations that are isolated from one another by geographic barrier can evolve variable characters in order to adapt to local environments. Genetic variation of a population may lead to the character differentiation resulting from natural selection. To some extent analyzing the genetic structure of populations is very important to understand the population dynamics [53], [54]. The divergence of populations is usually investigated by using experimental genomic data [55], [56], as well as theoretical and empirical data [57], [58]. As a consequence, genetic variation was suggested to be the basis for populations to adapt to the environmental changes. Therefore, the importance of genetic diversity for the viability of populations is generally recognized [59]–[62].
From our study, we examined the genetic structure of three populations of Greater long-tailed hamster and found the significant genetic differentiation exist among these three patchy-distributed populations, suggesting the geographic distance plays an critical role in the genetic differentiation of these three patchy populations despite geographic barriers act on them to a certain degree. We have identified the correlation among genetic distance, gene flow and geographic distance and found that geographic distance is positively correlated with the genetic distance and is negatively correlated with the gene flow.
Genetic diversity within populations
By using 10 microsatellites, we were able to detect comparable values of expected heterozygosity and allelic richness in three patchy distributed geographic populations. Mean allelic richnesses (AR) are 3.566, 3.314, and 3.474, and expected heterozygosities (He) are 0.601, 0.615, and 0.647, respectively for Raoyang, Guan and Shunyi populations. The values of the expected heterozygosity (He) for individual geese ranged from 0.38 to 0.51[63] and for noble scallop ranged from 0.119 to 0.459 [64]. The expected heterozygosities (He) detected in three populations are higher than those of the geese [63] and the noble scallop [64]. Therefore, we believe there is high genetic diversity existing within three patchy-distributed populations, which indicates high viability leading to fluctuation of hamster populations. At this point, our results are consistent with many previous studies [13], [14], [38], [65]. Gene flow between different populations is usually considered as an important factor leading to high genetic diversity within populations. The effective gene flow works for large populations with relative long geographic distances by balancing the effect of fragmentation [66]. In this study, our results also support the previous hypotheses that gene flow between different geographic populations could be critical resulting in high genetic diversity within populations, which is in accordance with our former study [13].
Genetic differentiation and geographic distance effect
Natural selection, genetic drift and gene flow are main causes leading to genetic differentiation in populations. When populations are sufficiently isolated from one another selection and drift would enhance genetic differences, while oppositely, gene flow precludes their differentiation [67]. In large geographic areas, a variety of continuous differentiated populations would bring about from the interaction process of selection, genetic drift and gene flow, ranging from shared population to high degree differentiated populations [67]. Genetic differentiation may be a sign of the differences in ecological traits within a species' range, potentially leading to distinct ecotype-specific responses to different climates [68]–[70].
In this study, significant genetic differences were found among three geographic populations. High values of differentiation (FST = 0.0531, 0.0327, and 0.0359) exist among the three geographic populations. Population differentiation can result from two genetic processes. 1) Populations may be isolated from one another by geographic barriers and therefore gene flow will be reduced among local populations. 2) Population differentiation level may increase with the increased geographic distance because the magnitude of gene flow declines with extended length of the geographic distance.
Geographic distance [16], [23] and geographic barriers [23], [71] are main factors affecting dispersal, which is an important cause affecting genetic variability [71], [72]. It is well known that geographic distance is negatively correlated with the dispersal in continuous distributed populations [20]–[23], but plays variable roles in patchy-distributed populations [24]–[28]. It is apparent that geographic barriers prevent dispersal. Studies have found that habitat discontinuities increase genetic differentiation in marine environments [73], [74]. Barriers, such as oceans, mountains, huge city communities, rivers etc., have more important effects on dispersal than geographic distance [71]. For example, rivers were identified as a major gene flow barrier for the army ant Eciton burchellii [75]. Sea lochs were the most important red deer gene flow barriers, followed by mountain slopes, roads and forests [71]. Former studies have shown that geographic distance and geographic barrier have prevented the dispersal at various levels among different species and barrier types. In this study, however, gene flow is effective, even at relatively long distances, in balancing the effect of geographic barrier under the interaction of geographic distance and geographic barriers.
Our study examined three geographic populations which are isolated by Beijing city or Hutuo river respectively as physical barriers, with far geographic distance among them. The correlation analyses among genetic distance, geographic distance and gene flow (Fig 2) showed significant isolation by distance despite the presence of gene flow. Thus, our results strongly agree with studies previously demonstrated [20], [21], [23] indicating the importance of analyzing the effects of distance on population differentiation. In this study, geographic distance explained 68.27% of the genetic differentiation, and only 31.73% of the genetic variance was explained by the geographic barrier, which suggests geographic distance has a significant effect on the dispersal of the patchy-distributed hamster populations, which is in accordance with former studies [20], [21], [23]. Therefore, geographic distance may partly account for the genetic differentiation among Raoyang, Guan and Shunyi populations, which was in accordance with the results obtained through former researches [76], [77].
However, several studies have suggested that no correlation exists between geographic and genetic distances among populations [24]–[28]. In this case, the geographic barrier has high capacity, even as absolute barriers and no gene flow exists between different geographic populations. While in our study, the barriers of Beijing city and Hutuo river are less restricted than what were expected.
Populations separated by the Hutuo river showed high genetic differentiation than populations on opposite sides of the Beijing city, suggesting that Hutuo river has higher barrier capacity than Beijing city for the Greater long-tailed hamster populations. In research of small mammal species, such as voles and ground beetles, barrier effect of roads to gene flow has clearly been shown [78], [79]. Human-made barriers, including highways and developed areas, act as absolute barriers for the populations of desert bighorn sheep (Ovis canadensis nelsoni) indicating no gene flow exists between the populations from two sides of the barriers [80]. Interestingly, gene flow of Alaskan brown bear (Ursus arctos) was found to be reduced or sometimes absent between four insular populations when separated by stretches of sea water of a few kilometres, but continuous gene flow between populations was detected when the stretches of sea water were much narrower at approximately 600 m [81], indicating that the span of sea water significantly affects the barrier capacity and the Alaskan brown bear can conquest the sea water barrier with width less than 600 m. Therefore, the restricted capacity of one physical barrier in various states could be different, even for the same species. Beijing is the metropolis of China possessing lots of roads which was considered to have high barrier capacity theoretically. Nevertheless, from our data, the less barrier capacity for Beijing city to hamster populations was indicated than that was expected. We speculate that some Greater long-tailed hamster may disperse by the underground path, so ground environment, including transportation and construction, has less barrier capacity, and the specific reasons need to be further studied.
Conclusion
Genetic diversity and genetic differentiation exist in the three examined hamster populations. The barriers in this study acting on populations are less restricted than expected. Furthermore, the genetic differentiation is positively correlated with the geographic distance, while the gene flow shows a negative correlation. Geographic distance may act as one of main causes for the genetic differentiation of the patchy distributed hamster populations.
Supporting Information
Table S1.
Characterization of the microsatellite loci in Greater long-tailed hamster (Tscherskia triton).
https://doi.org/10.1371/journal.pone.0099540.s001
(DOC)
Table S2.
Alleles and their frequencies for the ten microsatellite markers in the three Tscherskia triton populations.
https://doi.org/10.1371/journal.pone.0099540.s002
(DOC)
Table S3.
Genetic diversity indices within three Tscherskia triton populations at ten microsatellite markers.
https://doi.org/10.1371/journal.pone.0099540.s003
(DOC)
Table S4.
Genotypes for the Greater long-tailed hamsters examined in this study at 10 microsatellite loci.
https://doi.org/10.1371/journal.pone.0099540.s004
(DOC)
Acknowledgments
The authors are deeply grateful to the project of the National Science Foundation of China (31270417 and 31300304), and the Shandong Provincial Natural Science Foundation (ZR2010CL013) for their financial support, and to Zhibin Zhang and Tongqin Xu for providing specimens.
Author Contributions
Conceived and designed the experiments: LX MZ. Performed the experiments: HX JX. Analyzed the data: HX JX. Contributed reagents/materials/analysis tools: LX JX HX MZ. Contributed to the writing of the manuscript: HX MZ.
References
- 1.
Clobert J, Danchin E, Dhondt AA, Nichols JD (2001) Dispersal. Oxford, UK: Oxford University Press.
- 2. Chaves JA, Parker PG, Smith TB (2012) Origin and population history of a recent colonizer, the yellow warbler in Galapagos and Cocos Islands. Journal of Evolutionary Biology 25: 509–521.
- 3. Berthier K, Charbonnel N, Galan M, Chaval Y, Cosson JF (2006) Migration and recovery of the genetic diversity during the increasing density phase in cyclic vole populations. Molecular Ecology 15: 2665–2676.
- 4. Johnson ML, Gaines MS (1990) Evolution of dispersal: theoretical models and empirical tests using birds and mammals. Annual Review of Ecology and Systematics 21: 449–480.
- 5. Waser PM, Creel SR, Lucas JR (1994) Death and Disappearance - Estimating Mortality Risks Associated with Philopatry and Dispersal. Behavioral Ecology 5: 135–141.
- 6. Denno RF, Olmstead KL, McCloud ES (1989) Reproductive cost of flight capability: a comparison of life history traits in wing dimorphic planthoppers. Ecological Entomology 14: 31–44.
- 7.
Ronce O, Olivieri I, Clobert J, Danchin E (2001) Perspectives on the study of dispersal evolution. In Dispersal (eds. Clobert J, Danchin E, Dhondt AA, Nichols JD), pp. 341–357. Oxford University Press, Oxford.
- 8. Massot M, Clobert J, Lorenzon P, Rossi JM (2002) Condition-dependent dispersal and ontogeny of the dispersal behaviour: an experimental approach. Journal of Animal Ecology 71: 253–261.
- 9. Andreassen HP, Ims RA (2001) Dispersal in patchy vole populations: role of patch configuration, density-dependence and demography. Ecology 82: 2911–2926.
- 10. Lin YK, Batzli GO (2001) The influence of habitat quality on dispersal, demography, and population dynamics of voles. Ecological Monographs 71: 245–275.
- 11. Ims RA, Andreassen HP (2005) Density dependent dispersal and spatial population dynamics. Proceedings of the Royal Society B 272: 913–918.
- 12. Gaines MS, McClenaghan LR (1980) Dispersal in small mammals. Annual Review of Ecology and Systematics 11: 163–196.
- 13. Xu LX, Xue HL, Song MJ, Zhao QH, Dong JP, et al. (2013) Variation of Genetic Diversity in a Rapidly Expanding Population of the Greater Long-Tailed Hamster (Tscherskia triton) as Revealed by Microsatellites. PLoS ONE 8(1): e54171
- 14. Song MJ, Zhang ZB, Neumann K, Gattermann R (2005) Sex-biased dispersal of Greater long-tailed hamster (Tscherskia triton) revealed by microsatellites. Canadian Journal of Zoology 83: 773–779.
- 15. Goudet J, Perrin N, Waser P (2002) Tests for sex-biased dispersal using biparentally inherited genetic markers. Molecular Ecology 11: 1103–1114.
- 16. Bowler DE, Benton TG (2009) Variation in dispersal mortality and dispersal propensity among individuals: the effects of age, sex and resource availability. Journal of Animal Ecology 78: 1234–1241.
- 17. Conradt L, Roper TJ, Thomas CD (2001) Dispersal behaviour of individuals in metapopulations of two British butterflies. Oikos 95: 416–424.
- 18. Kuussaari Mikko, Nieminen Marko, Hanski Llkka (1996) An experimental study of migration in the Glanville fritillary butterfly Melitaea Cinxia. Journal of Animal Ecology 65: 791–801.
- 19. Serrano D, Tella JL (2003) Dispersal within a spatially structured population of lesser kestrels: The role of spatial isolation and conspecific attraction. Journal of Animal Ecology 72: 400–410.
- 20. Jensen H, Moe R, Hagen IJ, Holand AM, Kekkonen J, et al. (2013) Genetic variation and structure of house sparrow populations: is there an island effect? Molecular Ecology 22(7): 1792–805.
- 21. Küpper C, Edwards SV, Kosztolányi A, Alrashidi M, Burke T, et al. (2012) High gene flow on a continental scale in the polyandrous Kentish plover Charadrius alexandrinus. Molecular Ecology 21(23): 5864–79.
- 22. Alda F, García J, García JT, Suárez-Seoane S (2013) Local genetic structure on breeding grounds of a long-distance migrant passerine: the bluethroat (Luscinia svecica) in Spain. Journal of Heredity 104(1): 36–46.
- 23. Igawa T, Oumi S, Katsuren S, Sumida M (2013) Population structure and landscape genetics of two endangered frog species of genus Odorrana: different scenarios on two islands. Heredity 110(1): 46–56.
- 24. Ryman N (1983) Patterns of distribution of biochemical genetic variation in salmonids: differences between species. Aquaculture 33: 1–21.
- 25. Nielsen O, Nielsen K, Stewart REA (1996) Serologic evidence of Brucella spp. Exposure in Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida) of Arctic Canada. Arctic 49: 383–386.
- 26. Li J, Zhang Y, Wang ZY, He KL, Wang Q (2010) Genetic differentiation and gene flow among different geographical populations of the Asian corn borer, Ostrinia furnacalis (Lepidoptera: Crambidae) in China estimated by mitochondrial CO gene sequences. Acta Entomologica Sinica 53(10): 1135–1143.
- 27. Wei SJ, Shi BC, Gong YJ, Jin GH, Chen XX, et al. (2013) Genetic Structure and Demographic History Reveal Migration of the Diamondback Moth Plutella xylostella (Lepidoptera: Plutellidae) from the Southern to Northern Regions of China. PLoS One 8(4): e59654
- 28. An HS, Lee JW, Park JY, Jung HT (2013) Genetic structure of the Korean black scraper Thamnaconus modestus inferred from microsatellite marker analysis. Molecular Biology Report 40(5): 3445–3456.
- 29. Robinson SJ, Samuel MD, Lopez DL, Shelton P (2012) The walk is never random: subtle landscape effects shape gene flow in a continuous white-tailed deer population in the Midwestern United States. Molecular Ecology 21(17): 4190–205.
- 30. Irene K, Carlo RL (2003) Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proceedings of the Royal Soceity B 270: 417–423.
- 31. Mader HJ (1984) Animal habitat isolation by roads and agricultural fields. Biology Conservation 29: 81–96.
- 32. Magnus W, Arnd S (2001) The impact of habitat fragmentation and social structure on the population genetics of roe deer (Capreolus capreolus L.) in Central Europe. Heredity 86: 703–715.
- 33. Hedin M, Starrett J, Hayashi C (2013) Crossing the uncrossable: novel trans-valley biogeographic patterns revealed in the genetic history of low-dispersal mygalomorph spiders (Antrodiaetidae, Antrodiaetus) from California. Molecular Ecology 22(2): 508–526.
- 34. Niemiller ML, McCandless JR, Reynolds RG, Caddle J, Near TJ, et al. (2013) Effects of climatic and geological processes during the pleistocene on the evolutionary history of the northern cavefish, Amblyopsis spelaea (teleostei: amblyopsidae). Evolution 67(4): 1011–1025.
- 35. Piel AK, Stewart FA, Pintea L, Li Y, Ramirez MA, et al. (2013) The Malagarasi River Does Not Form an Absolute Barrier to Chimpanzee Movement in Western Tanzania. PLoS One 8(3): e58965
- 36. da Rocha Filho LC, de Campos Muradas Cerântola N, Garófalo CA, Imperatriz-Fonseca VL, Del Lama MA (2013) Genetic differentiation of the Euglossini (Hymenoptera, Apidae) populations on a mainland coastal plain and an island in southeastern Brazil. Genetica 141(1-3): 65–74.
- 37.
Luo ZX, Chen W, Gao W (2000) Fauna Sinica Mammalia in China, pp. 63–75.Science Press, Beijing (in Chinese).
- 38. Dong JP, Li CH, Zhang ZB (2010) Density-dependent genetic variation in dynamic populations of the Greater long-tailed hamster (Tscherskia triton). Journal of Mammalogy 91: 200–207.
- 39. Kalinowski ST (2005) Do polymorphic loci require large sample sizes to estimate genetic distances? Heredity 94: 33–36.
- 40. Xu LX, Zhang ZB, Song MJ, Cao XP, Wang FS, et al. (2002) A method of extracting genomic DNA from animal specimen preserved in formalin. Acta Zoologica Sinica 48: 264–269.
- 41. Xu LX, Song MJ, Guo Y, Kong FH, Zhang ZB (2007) The highly polymorphic microsatellite markers for the greater long-tailed hamster (Tscherskia triton). Molecular Ecology Notes 7: 617–619.
- 42.
Goudet J (2001) FSTAT, a program to estimate and test gene diversities and fixation indices. Version 2.9.3. Available from http://www.unil.ch/izea/softwares/fstat.html. Updated from Goudet(1995)
- 43. Guo SW, Thompson EA (1992) Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48: 361–372.
- 44. Raymond M, Rousset F (1995a) An exact test for population differentiation. Evolution 49: 1280–1283.
- 45. Raymond M, Rousset F (1995b) Genepop (version-1.2) —population-genetics software for exact tests and ecumenicism. Journal of Heredity 86: 248–249.
- 46. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population-structure. Evolution 38: 1358–1370.
- 47.
Schneider S, Roessli D, Excoffier L (2000) Arlequin: a software for population genetic data. Geneva (Switzerland): Genetics and Biometry Laboratory, University of Geneva.
- 48. Nei M (1972) Genetic distance between populations. American Naturalist 106: 283–292.
- 49.
Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New York, NY.
- 50. Nei M (1978) Estimation of average heterozygosity of individuals.Genetics. 89: 538–590.
- 51.
Casgrain P (2001) Development release of the R-package 4.0. Montreal (Canada): University of Montreal.
- 52.
Wright S (1978) Evolution and the genetics of populations: Vol. 4. Variability within and among natural populations. Chicago (IL): University of Chicago Press.
- 53. Boonstra R, Krebs CJ, Stenseth N C (1998) Population cycles in small mammals: the problem of explaining the low phase. Ecology 79: 1479–1488.
- 54. Krebs CJ (1996) Population cycles revisited. Journal of Mammalogy 77: 8–24.
- 55. Ayres MP, Scriber JM (1994) Local adaptation to regional climates in Papilio canadensis (Lepidoptera: Papilionidae). Ecological Monographs 64: 465–482.
- 56. Hoffmann AA, Hallas R, Sinclair C, Mitrovski PP (2001) Levels of variation in stress resistance in Drosophila among strains, local populations, and geographic regions: patterns for desiccation, starvation, cold resistance, and associated traits. Evolution 55: 1621–1630.
- 57. Gunter LE, Tuskan GA, Gunderson CA, Norby RJ (2000) Genetic variation and spatial structure in sugar maple (Acer saccharum Marsh.) and implications for predicted global-scale environmental change. Global Change Biology 6: 335–344.
- 58. Hellmann JJ, Pineda-Krch M (2007) Constraints and reinforcement on adaptation under climate change: selection of genetically correlated traits. Biological Conservation 137: 599–609.
- 59.
Frankham R, Ballou JD, Briscoe DA (2002) Introduction to Conservation Genetics. Cambridge University Press, Cambridge, UK.
- 60. Hansson B, Westerberg L (2002) On the correlation between heterozygosity and fitness in natural populations. Molecular Ecology 11: 2467–2474.
- 61. Reed D, Frankham R (2003) Correlation between fitness and genetic diversity. Conservation Biology 17: 230–237.
- 62. Schmitt T, Hewitt GM (2004) The genetic pattern of population threat and loss: a case study of butterflies. Molecular Ecology 13: 21–31.
- 63. Parada Rafa, Ksiazkiewicz Juliusz, Kawka Magdalena, Jaszczak Kazimierz (2012) Studies on resources of genetic diversity in conservative flocks of geese using microsatellite DNA polymorphic markers. Molecular Bioogyl Reports 39: 5291–5297.
- 64. Wang Y, Fu D, Xia J (2013) The genetic diversity of the noble scallop (Chlamys nobilis, Reeve 1852) in China assessed using five microsatellite markers. Marine Genomics 9: 63–67.
- 65. Xie J, Zhang ZB (2006) Genetic diversity decreases as population density declines: Implications of temporal variation in mitochondrial haplotype frequencies in a natural population of Tscherskia triton. Integrative Zoology 1: 188–193.
- 66. Leonardi S, Piovani P, Scalfi M, Piotti A, Giannini R, et al. (2012) Effect of habitat fragmentation on the genetic diversity and structure of peripheral populations of beech in Central Italy. Journal of Heredity 103(3): 408–417.
- 67. Zakharov EV, Hellmann JJ (2008) Genetic differentiation across a latitudinal gradient in two co-occurring butterfly species: revealing population differences in a context of climate change. Molecular Ecology 17(1): 189–208.
- 68. Davis AJ, Jenkinson LS, Lawton JH, Shorrocks B, Wood S (1998) Making mistakes when predicting shifts in species range in response to global warming. Nature 391: 783–786.
- 69. Michaux JR, Libois R, Filippucci MG (2005) So close and so different: comparative phylogeography of two small mammal species, the Yellow-necked field mouse (Apodemus flavicollis) and the Wood-mouse (Apodemus sylvaticus) in the Western Palearctic region. Heredity 94: 52–63.
- 70. Hewitt GM (2001) Speciation, hybrid zones and phylogeography - or seeing genes in space and time. Molecular Ecology 10: 537–549.
- 71. Perez-espona S, Perez-barberia FJ, Mcleod JE, Jiggins CD, Gordon IJ, et al. (2008) Landscape features affect gene flow of Scottish Highland red deer (Cervus elaphus). Molecular Ecology 17: 981–996.
- 72. Dingemanse NJ, Both C, van Noordwijk AJ, Rutten AL, Drent PJ (2003) Natal dispersal and personalities in great tits (Parus major). Proceedings of the Royal Soceity Lond B 270: 741–747.
- 73. Riginos C, Nachman MW (2001) Population subdivision in marine environments: the contributions of isolation by distance, discontinuous habitat, and biogeography to genetic differentiation in a blennioid fish, Axoclinus nigricaudus. Molecular Ecology 10: 1439–1453.
- 74. Billot C, Engel CR, Rousvoal S, Kloareg B, Valero M (2003) Current patterns, habitat discontinuities and population genetic structure: the case of the kelp Laminaria digitata in the English Channel. Marine Ecology Progress Series 253: 111–121.
- 75. Perez-espona S, Mcleod JE, Franks NR (2012) Landscape genetics of a top neotropical predator. Molecular Ecology 21: 5969–5985.
- 76. Nangong ZY, Gao BJ, Liu JX, Yang J (2008) Genetic diversity of geographical populations of four Dendrolimus species (Lepidoptera: Lasiocampidae) in China based on allozyme analysis. Acta Entomologica Sinica 51: 417–423.
- 77. Zhao HT, Gao PF, Jiang YS, Yang GY, Chen TZ (2008) RAPD Analysis Among Different Populations of Apis Cerana in China. Acta Zootaxonomica Sinica 33: 89–96.
- 78. Gerlach G, Musolf K (2000) Fragmentation of landscape as a cause for genetic subdivision in bank voles. Conservation Biology 14: 1066–1074.
- 79. Keller I, Nentwig W, Largiader CR (2004) Recent habitat fragmentation due to roads can lead to significant genetic differentiation in an abundant flight-less ground beetle. Molecular Ecology 13: 2983–2994.
- 80. Epps CW, Palsboll PJ, Wehausen JD, Roderick GK, Ramey RB, et al. (2005) Highways block gene flow and cause a rapid decline in genetic diversity of desert bighorn sheep. Ecology Letters 8: 1029–1038.
- 81. Paetkau D, Shields GF, Strobeck C (1998) Gene flow between insular, coastal and interior populations of brown bears in Alaska. Molecular Ecology 7: 1283–1292.