Combining landscape and genetic graphs to address key issues in landscape genetics

Savary, Paul; Foltête, Jean‐Christophe; Moal, Hervé; Garnier, Stéphane

doi:10.1007/s10980-022-01489-7

Cited by 7 publications

(4 citation statements)

References 102 publications

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“…In the former case, we set the number of modules in each spatial cost-distance graph to be equal to the optimal number of modules identified in each corresponding genetic graph. Then, we compared these partitions using the Adjusted Rand Index (ARI, Hubert and Arabie (1985)), following the method described by Savary et al (2022). This index takes its maximal value (1) when two nodes from the same module in one graph also belong to the same module in the other graph.…”

Section: Module Partitionsmentioning

confidence: 99%

Gene flow in the city. Unravelling the mechanisms behind the variability in urbanization effects on genetic patterns.

Coulon

2024

PCI Ecology

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Context and objectivesAlthough urbanization is a major driver of biodiversity erosion, it does not affect all species equally. The neutral genetic structure of populations in a given species is affected by both genetic drift and gene flow processes. In cities, the size of animal populations determines drift and can depend on multiple processes, whereas gene flow essentially depends on the ability of species to disperse across urban areas. Considering this, we tested whether variations in dispersal constraints alone could explain the variability of neutral genetic patterns commonly observed in urban areas. Besides, we assessed how the spatial distribution of urban green spaces (UGS) and peri-urban forests acts on these patterns. MethodsWe simulated multi-generational genetic processes in virtual populations of animal species occupying either UGS or forest areas (both considered as a virtual species habitat) within and around 325 European cities. We used three dispersal cost scenarios determining the ability of species to cross the least favorable land cover types, while maintaining population sizes constant among scenarios. We then assessed genetic diversity and genetic differentiation patterns for each city and each habitat types across the three cost scenarios. ResultsOverall, as dispersal across the least favorable land cover types was more constrained, genetic diversity decreased and genetic differentiation increased. Across scenarios, the scale and strength of the relationship between genetic differentiation and dispersal cost-distances varied substantially, alike previously observed empirical genetic patterns. Forest areas contributed more to habitat connectivity than UGS, due to their larger area and mostly peri-urban location. Hence, population-level genetic diversity was higher in forests than in UGS and genetic differentiation was higher between UGS populations than between forest populations. However, interface habitat patches allowing individuals to move between different habitat types seemed to locally buffer these contrasts by promoting gene flow. Discussion and conclusionOur results showed that variations in spatial patterns of dispersal, and thus gene flow, could explain the variability of empirically observed genetic patterns in urban contexts. Besides, the largest habitat areas and biodiversity sources are likely to be found in areas surrounding city centers. This should encourage urban planners to pay attention to the areas promoting dispersal movements between urban habitats (e.g., UGS) and peri-urban habitats (e.g., forests), rather than among urban habitats, when managing urban biodiversity.

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Section: Module Partitionsmentioning

confidence: 99%

Gene flow in the city. Unravelling the mechanisms behind the variability in urbanization effects on genetic patterns.

Coulon

2024

PCI Ecology

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Section: Genetic Data Analysesmentioning

confidence: 99%

How does dispersal shape the genetic patterns of animal populations in European cities? A simulation approach

Savary,

Tannier,

Foltête

et al. 2024

Peer Community Journal

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“…Empirical evidence of functional connectivity among amphibian populations has been compiled either using genetic markers and landscape resistance analyses, or with individual dispersal data (Heintzman & McIntyre, 2021; Lowe & Allendorf, 2010). The combination of both approaches in integrative studies has been rarely conducted (Cayuela, Besnard, et al., 2020; Murphy et al., 2015; Savary, Foltête, Moal, & Garnier, 2022), but has great potential to illuminate the relationships between genetic connectivity and demographic parameters. In this line, capture–mark–recapture (CMR) data allow comprehensive characterization of movement patterns by inferring connectivity graphs built from dispersal kernels based on movement records, demographic and geographical information (Reyes‐Moya et al., 2022); these graphs can, in turn, be contrasted with graphs based on genomic information by comparing inferred clusters, connections and node attributes (Savary, Foltête, Moal, & Garnier, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The combination of both approaches in integrative studies has been rarely conducted (Cayuela, Besnard, et al., 2020; Murphy et al., 2015; Savary, Foltête, Moal, & Garnier, 2022), but has great potential to illuminate the relationships between genetic connectivity and demographic parameters. In this line, capture–mark–recapture (CMR) data allow comprehensive characterization of movement patterns by inferring connectivity graphs built from dispersal kernels based on movement records, demographic and geographical information (Reyes‐Moya et al., 2022); these graphs can, in turn, be contrasted with graphs based on genomic information by comparing inferred clusters, connections and node attributes (Savary, Foltête, Moal, & Garnier, 2022). These two methods provide complementary information: While CMR data provide information on movement patterns, these movements do not necessarily result in successful reproduction.…”

Section: Introductionmentioning

confidence: 99%

Assessing fine‐scale pondscape connectivity with amphibian eyes: An integrative approach using genomic and capture–mark–recapture data

Reyes‐Moya,

Sánchez‐Montes,

Babik

et al. 2023

Molecular Ecology

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In the face of habitat loss, preserving functional connectivity is essential to maintain genetic diversity and the demographic dynamics required for the viability of biotic communities. This requires knowledge of the dispersal behaviour of target species, which can be modelled as kernels, or probability density functions of dispersal distances at increasing geographic distances. We present an integrative approach to investigate the relationships between genetic connectivity and demographic parameters in organisms with low vagility focusing on five syntopic pond‐breeding amphibians. We genotyped 1056 individuals of two anuran and three urodele species (1732–3913 SNPs per species) from populations located in a landscape comprising 64 ponds to characterize fine‐scale genetic structure in a comparative framework, and combined these genetic data with information obtained in a previous 2‐year capture–mark–recapture (CMR) study. Specifically, we contrasted graphs reconstructed from genomic data with connectivity graphs based on dispersal kernels and demographic information obtained from CMR data from previous studies, and assessed the effects of population size, population density, geographical distances, inverse movement probabilities and the presence of habitat patches potentially functioning as stepping stones on genetic differentiation. Our results show a significant effect of local population sizes on patterns of genetic differentiation at small spatial scales. In addition, movement records and cluster‐derived kernels provide robust inferences on most likely dispersal paths that are consistent with genomic inferences on genetic connectivity. The integration of genetic and CMR data holds great potential for understanding genetic connectivity at spatial scales relevant to individual organisms, with applications for the implementation of management actions at the landscape level.

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Combining landscape and genetic graphs to address key issues in landscape genetics

Cited by 7 publications

References 102 publications

Gene flow in the city. Unravelling the mechanisms behind the variability in urbanization effects on genetic patterns.

Gene flow in the city. Unravelling the mechanisms behind the variability in urbanization effects on genetic patterns.

How does dispersal shape the genetic patterns of animal populations in European cities? A simulation approach

Assessing fine‐scale pondscape connectivity with amphibian eyes: An integrative approach using genomic and capture–mark–recapture data

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