Genetic structure of fragmented southern populations of African Cape buffalo (Syncerus caffer caffer)

peer reviewedBackground African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases. In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the “genetic health” of free ranging southern African Cape buffalo populations (S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites. Results The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity (mean allelic richness (Ar) of 7.217, average genetic diversity over loci of 0.594, mean private alleles (Pa) of 11), low differentiation, and an absence of an inbreeding depression signal (mean FIS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift (FIS = 0.062, mean Ar = 6.160, Pa = 2). This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century. Conclusions We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes. The splitting time of N and C clusters suggests that the current pattern results from human-induced factors and/or from the aridification process that occurred during the Holocene period. The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases). Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study

How and how much does RAD-seq bias genetic diversity estimates?

Abstract Background RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Results Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to â idealâ empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. Conclusion The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where polymorphism does not exceed 2 %, the bias is of minor importance in the face of other sources of uncertainty, such as heterogeneous bases composition or technical artefacts. The neutral panmictic model provides a practical mean to correct the bias through ABC, albeit with some imprecisions. More elaborate ABC methods might integrate additional parameters, such as population structure and selection, but their opposite effects could hinder accurate corrections.Abstract Background RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Results Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to â idealâ empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. Conclusion The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where polymorphism does not exceed 2 %, the bias is of minor importance in the face of other sources of uncertainty, such as heterogeneous bases composition or technical artefacts. The neutral panmictic model provides a practical mean to correct the bias through ABC, albeit with some imprecisions. More elaborate ABC methods might integrate additional parameters, such as population structure and selection, but their opposite effects could hinder accurate corrections

Additional file 1: Table S1. of How and how much does RAD-seq bias genetic diversity estimates?

Genomic sequences used for the in silico RAD-seq experiments. 2. Polymorphism heterogeneity along the genome of Schizophyllum commune. Figure S1. Theoretical and observed distributions of genetic distances (number of SNPs between RAD tags) between two American S. commune individuals (A10 and A13, on the left) and between 2 Russian individuals (K1 and K3, on the right). Blue: observed distribution of genetic distances; red: Poisson distribution, expected under a model of homogeneous polymorphism along the genome. Kolmogorov-Smirnov test, D = 0.2404, p-value

How and how much does RAD-seq bias genetic diversity estimates?

Abstract Background RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Results Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to â idealâ empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. Conclusion The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where polymorphism does not exceed 2Â %, the bias is of minor importance in the face of other sources of uncertainty, such as heterogeneous bases composition or technical artefacts. The neutral panmictic model provides a practical mean to correct the bias through ABC, albeit with some imprecisions. More elaborate ABC methods might integrate additional parameters, such as population structure and selection, but their opposite effects could hinder accurate corrections

Data from: Different response-effect trait relationships underlie contrasting responses to two chemical stressors

1. Trait-based approaches predict ecosystem functioning under environmental change by relating traits predicting changes in species densities (response traits) to traits driving ecosystem functioning (effect traits). Stressors can however affect ecosystem functioning not only by altering species densities, but also by directly changing species effect traits. 2. We first identified the response traits predicting the cell density of 18 marine benthic diatom strains along gradients of two chemical stressors (a pesticide and a metal, atrazine and copper). We then tested if response traits could predict stressor-induced changes in ecosystem functioning, i.e. changes in the effect traits driving the diatoms’ potential contribution to primary production, sediment stabilization and energy content in intertidal systems. Finally, we examined if changes in density and changes in ecosystem functioning were correlated, to assess whether species capable of growing under stressful conditions could maintain their contribution to ecosystem functioning. 3. The relationship between response traits and stressor-induced changes in density and ecosystem functioning was different depending on stressor type: a set of intercorrelated morphological traits predicted changes in both density and ecosystem functioning under metal stress, with large cells being more stress-resistant. Changes in density and changes in ecosystem functioning were positively related: diatoms whose density was least affected by the metal were also able to sustain functioning under metal exposure. 4. In contrast, the capacity for mixotrophic growth predicted changes in density, but not changes in ecosystem functioning under pesticide stress. Pesticide effects on density and on ecosystem functioning were negatively related for energy content and sediment stabilization, indicating a limited capacity of pesticide-tolerant diatoms to maintain their contribution to ecosystem functioning. Synthesis. Ecosystem functioning under stress can depend on whether response traits driving changes in density also predict direct stress effects on the species’ contribution to ecosystem functioning. Based on our results, we expect a disproportionate loss of functioning when traits driving species densities do not allow to maintain ecosystem functioning under stress.1. Trait-based approaches predict ecosystem functioning under environmental change by relating traits predicting changes in species densities (response traits) to traits driving ecosystem functioning (effect traits). Stressors can however affect ecosystem functioning not only by altering species densities, but also by directly changing species effect traits. 2. We first identified the response traits predicting the cell density of 18 marine benthic diatom strains along gradients of two chemical stressors (a pesticide and a metal, atrazine and copper). We then tested if response traits could predict stressor-induced changes in ecosystem functioning, i.e. changes in the effect traits driving the diatoms’ potential contribution to primary production, sediment stabilization and energy content in intertidal systems. Finally, we examined if changes in density and changes in ecosystem functioning were correlated, to assess whether species capable of growing under stressful conditions could maintain their contribution to ecosystem functioning. 3. The relationship between response traits and stressor-induced changes in density and ecosystem functioning was different depending on stressor type: a set of intercorrelated morphological traits predicted changes in both density and ecosystem functioning under metal stress, with large cells being more stress-resistant. Changes in density and changes in ecosystem functioning were positively related: diatoms whose density was least affected by the metal were also able to sustain functioning under metal exposure. 4. In contrast, the capacity for mixotrophic growth predicted changes in density, but not changes in ecosystem functioning under pesticide stress. Pesticide effects on density and on ecosystem functioning were negatively related for energy content and sediment stabilization, indicating a limited capacity of pesticide-tolerant diatoms to maintain their contribution to ecosystem functioning. Synthesis. Ecosystem functioning under stress can depend on whether response traits driving changes in density also predict direct stress effects on the species’ contribution to ecosystem functioning. Based on our results, we expect a disproportionate loss of functioning when traits driving species densities do not allow to maintain ecosystem functioning under stress

Data from: Dispersal dynamics in food webs

Studies of food webs suggest that limited nonrandom dispersal can play an important role in structuring food webs. It is not clear, however, whether density-dependent dispersal fits empirical patterns of food webs better than density-independent dispersal. Here, we study a spatially distributed food web, using a series of population-dispersal models that contrast density-independent and density-dependent dispersal in landscapes where sampled sites are either homogeneously or heterogeneously distributed. These models are fitted to empirical data, allowing us to infer mechanisms that are consistent with the data. Our results show that models with density-dependent dispersal fit the α, β, and γ tritrophic richness observed in empirical data best. Our results also show that density-dependent dispersal leads to a critical distance threshold beyond which site similarity (i.e., β tritrophic richness) starts to decrease much faster. Such a threshold can also be detected in the empirical data. In contrast, models with density-independent dispersal do not predict such a threshold. Moreover, preferential dispersal from more centrally located sites to peripheral sites does not provide a better fit to empirical data when compared with symmetric dispersal between sites. Our results suggest that nonrandom dispersal in heterogeneous landscapes is an important driver that shapes local and regional richness (i.e., α and γ tritrophic richness, respectively) as well as the distance-decay relationship (i.e., β tritrophic richness) in food webs

Comparing 16S rDNA amplicon sequencing and hybridization capture for pea aphid microbiota diversity analysis

Abstract Objective Targeted sequencing of 16S rDNA amplicons is routinely used for microbial community profiling but this method suffers several limitations such as bias affinity of universal primers and short read size. Gene capture by hybridization represents a promising alternative. Here we used a metagenomic extract from the pea aphid Acyrthosiphon pisum to compare the performances of two widely used PCR primer pairs with DNA capture, based on solution hybrid selection. Results All methods produced an exhaustive description of the 8 bacterial taxa known to be present in this sample. In addition, the methods yielded similar quantitative results, with the number of reads strongly correlating with quantitative PCR controls. Both methods can thus be considered as qualitatively and quantitatively robust on such a sample with low microbial complexity

MOESM1 of Comparing 16S rDNA amplicon sequencing and hybridization capture for pea aphid microbiota diversity analysis

Additional file 1: Table S1. Primers used for quantitative PCR

MOESM3 of Comparing 16S rDNA amplicon sequencing and hybridization capture for pea aphid microbiota diversity analysis

Additional file 3: Table S2. Normalized and relative abundances of the different OTUs

MOESM2 of Comparing 16S rDNA amplicon sequencing and hybridization capture for pea aphid microbiota diversity analysis

Additional file 2. Details on sequence data processing