Genetic diversity in a long‐lived mammal is explained by the past’s demographic shadow and current connectivity

Lehnen, Lisa; Jan, Pierre‐Loup; Besnard, Anne‐Laure; Fourcy, Damien; Kerth, Gerald; Biedermann, Martin; Nyssen, Pierrette; Schorcht, Wigbert; Petit, Éric; Puechmaille, Sébastien J.

doi:10.1111/mec.16123

Cited by 8 publications

(5 citation statements)

References 81 publications

(148 reference statements)

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“…It also increases its genetic differentiation from other populations. On the other hand, if there are other habitat patches within dispersal distance, gene flow events due to dispersal from neighbouring populations can counterbalance this loss of local genetic diversity while limiting genetic differentiation between populations (Frankham 2015;Lehnen et al 2021;Ingvarsson 2001). Understanding precisely how these two components of the genetic structure are influenced by the habitat spatial pattern is crucial in an era when habitat destruction is globally threatening all biodiversity levels (Díaz et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs

et al. 2021

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Genetic structure, i.e. intra-population genetic diversity and inter-population genetic differentiation, is influenced by the amount and spatial configuration of habitat. Measuring the amount of reachable habitat (ARH) makes it possible to describe habitat patterns by considering intra-patch and inter-patch connectivity, dispersal capacities and matrix resistance. Complementary ARH metrics computed under various resistance scenarios are expected to reflect both drift and gene flow influence on genetic structure. Using an empirical genetic dataset concerning the large marsh grasshopper (Stethophyma grossum), we tested whether ARH metrics are good predictors of genetic structure. We further investigated (i) how the components of the ARH influence genetic structure and (ii) which resistance scenario best explains these relationships. We computed local genetic diversity and genetic differentiation indices in genetic graphs, and ARH metrics in the unified and flexible framework offered by landscape graphs, and we tested the relationships between these variables. ARH metrics were relevant predictors of the two components of genetic structure, providing an advantage over commonly used habitat metrics. Although allelic richness was significantly explained by three complementary ARH metrics in the best PLS regression model, private allelic richness and MIW indices were essentially related with the ARH measured outside the focal patch. Considering several matrix resistance scenarios was also key for explaining the different genetic responses. We thus call for further use of ARH metrics in landscape genetics to explain the influence of habitat patterns on the different components of genetic structure.

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Section: Introductionmentioning

confidence: 99%

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs

et al. 2021

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“…Sequential colonization events (Clegg et al, 2002 ; Pruett & Winker, 2005 ), population isolation (Kardos et al, 2021 ; Lehnen et al, 2021 ), and recent anthropogenic bottlenecks (Dussex et al, 2018 ; Spielman et al, 2004 ; von Seth et al, 2021 ) can erode genetic diversity within animal populations. In our focal seals, such effects have led to a gradient of genetic variation from the highly diverse Arctic ringed seal to the genetically very uniform Saimaa ringed seal (Löytynoja et al, 2023 ; Nyman et al, 2014 ; Peart et al, 2020 ; Stoffel et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Loss of species and genetic diversity during colonization: Insights from acanthocephalan parasites in northern European seals

Sromek,

Ylinen,

Kunnasranta

et al. 2023

Ecology and Evolution

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Studies on host–parasite systems that have experienced distributional shifts, range fragmentation, and population declines in the past can provide information regarding how parasite community richness and genetic diversity will change as a result of anthropogenic environmental changes in the future. Here, we studied how sequential postglacial colonization, shifts in habitat, and reduced host population sizes have influenced species richness and genetic diversity of Corynosoma (Acanthocephala: Polymorphidae) parasites in northern European marine, brackish, and freshwater seal populations. We collected Corynosoma population samples from Arctic, Baltic, Ladoga, and Saimaa ringed seal subspecies and Baltic gray seals, and then applied COI barcoding and triple‐enzyme restriction‐site associated DNA (3RAD) sequencing to delimit species, clarify their distributions and community structures, and elucidate patterns of intraspecific gene flow and genetic diversity. Our results showed that Corynosoma species diversity reflected host colonization histories and population sizes, with four species being present in the Arctic, three in the Baltic Sea, two in Lake Ladoga, and only one in Lake Saimaa. We found statistically significant population‐genetic differentiation within all three Corynosoma species that occur in more than one seal (sub)species. Genetic diversity tended to be high in Corynosoma populations originating from Arctic ringed seals and low in the landlocked populations. Our results indicate that acanthocephalan communities in landlocked seal populations are impoverished with respect to both species and intraspecific genetic diversity. Interestingly, the loss of genetic diversity within Corynosoma species seems to have been less drastic than in their seal hosts, possibly due to their large local effective population sizes resulting from high infection intensities and effective intra‐host population mixing. Our study highlights the utility of genomic methods in investigations of community composition and genetic diversity of understudied parasites.

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“…We collected bat droppings at 10 lesser horseshoe bat ( Rhinolophus hipposideros ) maternity roosts in Thuringia (Germany) between 2015 and 2019 (Jan et al, 2019; Lehnen et al, 2021). We sampled each roost twice a year: once in June and once in August, that is, before and after offspring were born.…”

Section: Methodsmentioning

confidence: 99%

How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design

et al. 2023

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Extra-organismal DNA (eoDNA) from material left behind by organisms (noninvasive DNA, e.g., feces, hair) or from environmental samples (eDNA, e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost-and time-intensive replications of genotyping/sequencing procedures.Therefore, system-and site-specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using 10 environmentally diverse bat roosts as a case study, we developed a robust modeling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling-site-specific ideal exposure duration. Maximum humidity was the strongest eoDNA-degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later.The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site-specific simulations revealed that reducing exposure duration to 2-8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA-based species detection, and increasing the accuracy of abundance estimates.

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Genetic diversity in a long‐lived mammal is explained by the past’s demographic shadow and current connectivity

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 8 publications

References 81 publications

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs

Loss of species and genetic diversity during colonization: Insights from acanthocephalan parasites in northern European seals

How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design

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