Gene dispersal and outbreeding in a philopatric mammal

Winters, Jonathan B.; Waser, Peter M.

doi:10.1046/j.1365-294x.2003.01896.x

Cited by 68 publications

(69 citation statements)

References 49 publications

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“…The maximum number of burrows per individual reported here is slightly lower than the highest number recorded (up to 8 burrows) for this species (Behrends et al, 1986;Jones, 1989). Other species, such as D. spectabilis (Jones, 1984;Winters and Waser, 2003) and D. ingens (Cooper and Randall, 2007), use 1-3 burrows per individual, similar to our results. Subadult individuals occupied slightly fewer burrows per individual than juveniles and adults (Fig.…”

Section: Patterns Of Burrow Use By the Kangaroo Rat At San Jose Islandsupporting

confidence: 83%

Spatial relationships between burrows of an insular population of Dipodomys merriami

Vázquez¹,

Álvarez-Castañeda²

2011

Mammalian Biology

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a b s t r a c tFor semi-fossorial rodents, descriptions of the distribution and use of burrows can advance in understanding the spatial and social organization of their population. This study describes the use and spatial relationships between burrows of the kangaroo rat (Dipodomys merriami insularis), a subspecies that is possibly on the verge of extinction. Individuals were captured and recaptured in an area of 79.8 hectares located at San José Island, Gulf of California, Mexico, during both the breeding (BRED, from March to May) and the non-breeding (NON-BRED, from October to November) seasons of 2008. Our results show that females and males use a median of 1-2 burrows; factors such as age class, season and frequency of captures account for the number of burrows used by individuals. Few individuals shared their burrows (9.2% during BRED and 1.1% during NON-BRED). The burrows showed a clumped spatial distribution in both sampling periods. The spatial relationship between burrows is characterized by the fact that animals of opposite sex are closer to each other than same-sex individuals, and this spatial relationship between nearest-neighbor burrows is influenced by age class. Our results show that D. m. insularis displays burrow use and spatial organization patterns similar to those observed in other populations of D. merriami.

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Section: Patterns Of Burrow Use By the Kangaroo Rat At San Jose Islandsupporting

confidence: 83%

Spatial relationships between burrows of an insular population of Dipodomys merriami

Vázquez¹,

Álvarez-Castañeda²

2011

Mammalian Biology

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“…For higher dispersal, the variant considered by Vekemans and Hardy (2004) provides more accurate upper confidence bounds for Dσ 2 (Watts et al, 2007). Several comparisons have found agreement within a factor of two with independently derived demographic estimates (Rousset, 2000;Sumner et al, 2001;Winters and Waser, 2003;Fenster et al, 2003;Broquet et al, 2006;Watts et al, 2007). Whether this is considered an important discrepancy or not will depend on the accuracy expected from such analyses, but this is certainly much better than usually reported (see e.g.…”

Section: Isolation By Distancementioning

confidence: 84%

Inferences from Spatial Population Genetics

Rousset

2007

Handbook of Statistical Genetics

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This chapter reviews theoretical models and statistical methods for inference from genetic data in subdivided populations. With few exceptions, these methods are based on neutral models of genetic differentiation and have been mainly concerned with estimation of dispersal rates. However, simulation-based methods allow to draw inferences under models involving additional demographic processes such as changes in dispersal rates over time. The formulation and main results of migration matrix, island, and isolation-by-distance models, are briefly described. The definition and basic properties of F -statistics are reviewed, and moment methods for their estimation are contrasted with likelihood methods. Then, the application of the different methodologies to simple biological scenarios is reviewed. Their practical performance is discussed in light of comparisons with demographic estimates, as well as of their robustness to different assumptions and of concepts of separation of timescale. INTRODUCTIONSince the advent of molecular markers in population genetics, there have been many efforts to define methods of inference from the spatial genetic structure of populations. This chapter can only review a small selection of them including, in particular, some recent developments of simulation-based likelihood methods, and also of less sophisticated methods in so far as they provide analytical insight and proven performance in realistic conditions. With few exceptions, I will focus on allele frequency data; some methods for other types of data are described in Chapter 29.The perspective taken in this review is that studies of spatial population structure are conducted in order to make inferences about parameters considered important for the evolution of natural populations, for example, for the dynamics of adaptation. Thus, 945 Handbook of Statistical Genetics, Third Edition . E dited by D . J. Balding, M . Bishop and C. Cannings. © 2007 John Wiley & Sons, L td. I SBN: 978-0-470-05830-5 946 F. ROUSSETall such analyses should ultimately be based on models of evolution in subdivided populations. This would lead to the identification of important parameters in such processes and to the formulation of appropriate statistical models to estimate them (assuming it is useful to estimate them in order to test the models). In this perspective, the material reviewed below may seem imperfect not only because the statistical models are approximate but also because the important evolutionary parameters are not always clearly identified.In all inferences, we will consider a total sample from a population structured by restricted dispersal in a number of demes (a technical term used in the analysis of the models) or subpopulations (a somewhat looser term). The population concept must be carefully distinguished from another concept of 'population' often considered in statistics, which actually refers to the probability distribution of samples under some model. In general the value of a variable in the biological population is not the expected...

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“…2 We used Cervus (Marshall et al 1998) to infer parentage for all raccoons with at least one candidate parent and accepted a parental assignment when Cervus assignment confidence was >95% and if there was at most one mismatch between the parent and putative offspring (e.g., a mutation, scoring error, or null allele). Analysis for parentage was performed in two stages (see Winters and Waser 2003;Waser et al 2006). First, we conducted parentage analyses (maternity and paternity) assuming neither parent was known.…”

Section: Sex-biased Dispersalmentioning

confidence: 99%

Population genetic structure of raccoons (Procyon lotor) inhabiting a highly fragmented landscape

et al. 2009

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The raccoon (Procyon lotor (L., 1758)), a generalist mesocarnivore, has thrived in anthropogenically fragmented landscapes. In this study we utilized 13 microsatellites to analyze the population genetic structure in raccoons occupying an agriculturally fragmented habitat. Our analyses revealed low, but significant, levels of genetic structure (FST = 0.022, P < 0.001) among 645 raccoons inhabiting 29 spatially distinct habitat patches (neighborhoods). We found no evidence for the presence of either isolation by distance or barriers. However, spatial autocorrelation revealed evidence for social structure at the raccoon-neighborhood scale. About 30% of the variance in levels of social structure among raccoon neighborhoods was explained by effects of habitat-patch size and the percentage of land cover represented by forest around the individual habitat patch. We found strong evidence of sex-biased dispersal using direct (parentage analysis) and indirect (relatedness and assignment indices) genetic estimators. However, our analysis revealed that among dispersing raccoons there was no difference in dispersal distance between sexes and that dispersal events are randomly distributed with respect to distance. Our study provides evidence that demographic and behavioral processes affecting raccoon spatial organization may be most critical at very fine scales of biological organization, such as within individual habitat patches.Résumé : Le raton laveur (Procyon lotor (L., 1758)), un mésocarnivore généraliste, a prospéré dans les paysages fragmentés par l'activité humaine. Nous utilisons, dans notre étude, 13 microsatellites pour déterminer la structure génétique de la population chez des ratons laveurs qui vivent dans un habitat fragmenté par l'agriculture. Nos analyses révèlent des niveaux bas, mais significatifs, de structure génétique (F ST = 0,022, P < 0,001) parmi 645 ratons laveurs vivant dans 29 taches d'habitat distinctes (quartiers). Il n'y a aucune indication d'isolement par la distance, ni de barrières. Cependant, l'autocorrélation spatiale donne des indications d'une structure sociale à l'échelle des quartiers de ratons laveurs. Environ 30 % de la variance des niveaux de structure sociale entre les quartiers de ratons laveurs s'explique par les effets de la taille de la tache d'habitat et le pourcentage de la couverture du sol autour des taches individuelles d'habitat qui est repré-senté par de la forêt. Des estimateurs génétiques directs (analyse de filiation) et indirects (indices de consanguinité et d'attribution) indiquent clairement une dispersion variable en fonction du sexe. Cependant, notre analyse révèle que, parmi les ratons laveurs qui se dispersent, il n'y a pas de différence de distance de dispersion en fonction des sexes et que les événe-ments de dispersion sont répartis au hasard en fonction de la distance. Notre étude fournit des indications que les processus démographiques et comportementaux qui affectent l'organisation spatiale des ratons laveurs peuvent agir de la façon la plus critique aux éch...

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Gene dispersal and outbreeding in a philopatric mammal

Cited by 68 publications

References 49 publications

Spatial relationships between burrows of an insular population of Dipodomys merriami

Spatial relationships between burrows of an insular population of Dipodomys merriami

Inferences from Spatial Population Genetics

Population genetic structure of raccoons (Procyon lotor) inhabiting a highly fragmented landscape

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