Within-species genetic diversity is crucial for the persistence and integrity of populations and ecosystems. Conservation actions require an understanding of factors influencing genetic diversity, especially in the context of global change. Both population size and connectivity are factors greatly influencing genetic diversity; the relative importance of these factors can however change through time. Hence, quantifying the degree to which population size or genetic connectivity are shaping genetic diversity, and at which ecological time scale (past or present), is challenging, yet essential for the development of efficient conservation strategies. In this study, we estimated the genetic diversity of 42 colonies of Rhinolophus hipposideros, a long-lived mammal vulnerable to global change, sampling locations spanning its continental northern range. We present an integrative approach that disentangles and quantifies the contribution of different connectivity measures in addition to contemporary colony size and historic bottlenecks in shaping genetic diversity. In our study, the best model explained 64% of the variation in genetic diversity. It included historic bottlenecks, contemporary colony sizes, connectivity and a negative interaction between the latter two. Contemporary connectivity explained most genetic diversity when considering a 65 km radius around the focal colonies, emphasizing the large geographic scale at which the positive impact of connectivity on genetic diversity is most profound and hence the minimum scale at which conservation should be planned. Our results highlight that the relative importance of the two main factors shaping genetic diversity varies through time, emphasizing the relevance of disentangling them to ensure appropriate conservation strategies.
Microsatellite genotypes
Csv file containing distinct (consensus) genotypes used in the analysis. ID: Population names as in Table 1 and Fig. S7. Columns two to nine: names of microsatellite loci. Each allele is coded by three digits, resulting in a six digit code per locus per sample.
Genotypes.csv
The file "Data_Table_Genetic_diversity_in_a_long-lived_mammal.csv" is a data table with headers that contains genetic and connectivity data on R. hipposideros maternity colonies. Each record (=line) corresponds to a different R. hipposideros maternity colony. The variables on each line include the following:
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Variable Column(s) Type
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PopID 1 Character
Hs 2 Numeric
AllelicRichness 3 Numeric
BayesFactorMSVAR 4 Numeric
BottleneckStatus 5 Logical
NearestNeighbour 6 Numeric
NbIndXXXXkm2 7-68 Integer
NbColXXXXkm2 69-130 Integer
WeightedNumberofIndividual 131 Numeric
ForestAreaXXXXkm2 132-193 Numeric
ForestPerimeterXXXXkm2 194-255 Numeric
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Each XXXX in the variable name corresponds to an integer between 100 and 6000 (see below). These variables have the following definitions:
PopID is the bat colony identification code
Hs is the expected heterozygosity of the bat colony
AllelicRichness is the allelic richness of the colony corrected for sample size
BayesFactorMSVAR is the Bayes Factor obtained through MSVAR computation
BottleneckStatus is the result of MSVAR computation as a logical (TRUE : Bottlenecked population, FALSE : Non-bottlenecked)
NearestNeighbour is the distance to the nearest bat colony, in meters.
NbIndXXXXkm2 is the number of R. hipposideros from other maternity colonies contained in a buffer centered on the colony within an area of XXXX km2.
NbColXXXXkm2 is the number of others R. hipposideros maternity colonies contained in a buffer centered on the colony within an area of XXXX km2.
WeightedNumberofIndividual is the number of R. hipposideros from other maternity colonies weighted by the negative exponential dispersal kernel
ForestAreaXXXXkm2 is the area (m²) of mixed and deciduous forest contained in a buffer centered on the colony within an area of XXXX km2.
ForestPerimeterXXXXkm2 is the perimeter (m) of mixed and deciduous forest patches contained in a buffer centered on the colony within an area of XXXX km2.
Further details and explanations about the origin and computation of these variables are provided in the Materials & Methods section of the paper "Genetic diversity in a long-lived mammal is explained by the past’s demographic shadow and current connectivity" by L. Lehnen, P.-L. Jan, A.-L. Besnard, D. Fourcy, G. Kerth, M. Biedermann, P. Nyssen, W. Schorcht, E. J. Petit, S.J. Puechmaille.
For additional information, please send an e-mail to pierreloup.jan@gmail.com
