26 research outputs found
Data from: Genetic diversity in a long-lived mammal is explained by the past's demographic shadow and current connectivity
No full textWithin-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
Genetic diversity and associated explanatory variables
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 [email protected]
Funding provided by: RESPONSE exchange grantCrossref Funder Registry ID: http://dx.doi.org/NoneAward Number: (awarded to PLJ)Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: RTG 2010 Research Training Program (German ScienceFunding provided by: PROCOPE/DAADCrossref Funder Registry ID: Award Number: project number 57211773 (awarded to SJP)Funding provided by: PROCOPE/PHCCrossref Funder Registry ID: Award Number: project number 35454SB (awarded to EJP)Funding provided by: Office National des ForetsCrossref Funder Registry ID
Using Approximate Bayesian Computation to infer sex ratios from acoustic data
No full textPopulation sex ratios are of high ecological relevance, but are challenging to determine in species lacking conspicuous external cues indicating their sex. Acoustic sexing is an option if vocalizations differ between sexes, but is precluded by overlapping distributions of the values of male and female vocalizations in many species. A method allowing the inference of sex ratios despite such an overlap will therefore greatly increase the information extractable from acoustic data. To meet this demand, we developed a novel approach using Approximate Bayesian Computation (ABC) to infer the sex ratio of populations from acoustic data. Additionally, parameters characterizing the male and female distribution of acoustic values (mean and standard deviation) are inferred. This information is then used to probabilistically assign a sex to a single acoustic signal. We furthermore develop a simpler means of sex ratio estimation based on the exclusion of calls from the overlap zone. Applying our methods to simulated data demonstrates that sex ratio and acoustic parameter characteristics of males and females are reliably inferred by the ABC approach. Applying both the ABC and the exclusion method to empirical datasets (echolocation calls recorded in colonies of lesser horseshoe bats, Rhinolophus hipposideros) provides similar sex ratios as molecular sexing. Our methods aim to facilitate evidence-based conservation, and to benefit scientists investigating ecological or conservation questions related to sex- or group specific behaviour across a wide range of organisms emitting acoustic signals. The developed methodology is non-invasive, low-cost and time-efficient, thus allowing the study of many sites and individuals. We provide an R-script for the easy application of the method and discuss potential future extensions and fields of applications. The script can be easily adapted to account for numerous biological systems by adjusting the type and number of groups to be distinguished (e.g. age, social rank, cryptic species) and the acoustic parameters investigated.</div
Proportion of males and peak frequencies of the studied colonies.
No full textThe proportion of males was estimated with the genetic (Gen.), acoustic ABC (ABC), and acoustic 99.9% exclusion methods. The 95% highest density interval (HDI) is presented for parameters estimated via the ABC approach. Peak frequencies were estimated with the acoustic ABC approach.</p
Influence of the proportion of males on the global percentage of correct sex assignment of calls when male and female mean peak frequencies differ by 1–5 kHz.
No full textValues for different sample sizes (cf. Fig 7) and sexes are combined.</p
Proportion of males estimated via the ABC method versus the simulated (true) value.
No full textFor 100, 500, 1000, 2500, 5000, and 10000 calls (same datasets as Figs 2 and 3). The dashed line depicts a perfect match between simulated and estimated values.</p
Root mean square error (RMSE) for different sex ratios estimated via the exclusion method using thresholds of 95% (blue), 99% (yellow), and 99.9% (green), and via the ABC approach (black).
No full textBlocks correspond to different sample sizes as indicated. The X-axis represents the simulated proportion of males (from 0 to 1 in steps of 0.05, same datasets as Figs 2–4).</p
Historische Gebäude als biodiverser Lebensraum und Objekt der Denkmalpflege (Abschlussbericht)
No full textMean peak frequency of males (top) and females (middle) and the within-sex (equal for males and females) standard deviation of calls (bottom).
No full textEstimated via ABC for the same datasets as in Fig 2, but plotted separately for the six different sample sizes (represented by blocks).</p
