Consequences of population topology for studying gene flow using link‐based landscape genetic methods

Strien, Maarten J. van

doi:10.1002/ece3.3075

Cited by 12 publications

(14 citation statements)

References 85 publications

(144 reference statements)

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“…We suggest including such population pairs in link-based inferences because their genetic divergence should reflect landscape influence on gene flow. Our view contrasts with the exclusive use of population pairs that are within migration range of each other recommended by others when assessing the effect of landscape on gene flow (Keller et al, 2013;Van Strien et al, 2015;Van Strien, 2017). Therefore, a reliable pruning method to estimate landscape resistance to gene flow should identify population pairs whose genetic differentiation reflects the long term gene flow between them.…”

Section: How To Prune a Genetic Graph To Infer Landscape Resistance Tmentioning

confidence: 81%

Analysing landscape effects on dispersal networks and gene flow with genetic graphs

Savary

Foltête

Moal³

et al. 2021

Molecular Ecology Resources

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Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used i) to identify direct dispersal paths followed by propagules or ii) to quantify landscape effects on multigenerational gene flow. However, the influence of their construction parameters remains to be explored. Using a simulation approach, we constructed genetic graphs using several pruning methods (geographical distance thresholds, topological constraints, statistical inference) and genetic distances to weight graph links (F ST , D PS , Euclidean genetic distances). We then compared the capacity of these different graphs to i) identify the precise topology of the dispersal network and ii) to infer landscape resistance to gene flow from the relationship between cost-distances and genetic distances. Although not always clear-cut, our results showed that methods based on geographical distance thresholds seem to better identify dispersal networks in most cases. More interestingly, our study demonstrates that a subselection of pairwise distances through graph pruning (thereby reducing the number of data points) can counter-intuitively lead to improved inferences of landscape effects on dispersal. Finally, we showed that genetic distances such as the D PS or Euclidean genetic distances should be preferred over the F ST for landscape effect inference as they respond faster to landscape changes.

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Section: How To Prune a Genetic Graph To Infer Landscape Resistance Tmentioning

confidence: 81%

Analysing landscape effects on dispersal networks and gene flow with genetic graphs

Savary

Foltête

Moal³

et al. 2021

Molecular Ecology Resources

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“…Since sensitive methods are needed to detect an effect of landscape on potentially complex disease patterns, choosing an appropriate sampling design is critical. Ideally, all populations should be sampled (van Strien, 2017). However, if not all populations are sampled, tests should be performed to assess inference sensitivity to missing populations (van Strien, 2017).…”

Section: Spatial Sampling Regimementioning

confidence: 99%

“…Ideally, all populations should be sampled (van Strien, 2017). However, if not all populations are sampled, tests should be performed to assess inference sensitivity to missing populations (van Strien, 2017). Other possible sampling designs include: random, linear, systematic, and cluster.…”

Section: Spatial Sampling Regimementioning

confidence: 99%

Landscape Genetics: A Toolbox for Studying Vector-Borne Diseases

Hemming-Schroeder

Salazar

et al. 2018

Front. Ecol. Evol.

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Landscape genetics aims to quantify the effect of landscape on gene flow. Broadly, the approach involves measuring genetic variation, quantifying landscape heterogeneity, and statistically testing the link between both genetic variation and landscape heterogeneity. This approach has been widely used by conservation biologists, for example to identify barriers restricting movement in threatened populations. More recently, landscape genetics has been used to study the epidemiology of infectious diseases, such as chronic wasting disease, raccoon rabies, and malaria. This method can be useful in identifying potential hotspot areas of disease movement for targeted public health interventions and containment of disease and drug resistance. However, vector-borne disease epidemiology is particularly complex, as it is affected by the movement of both the vector and human or vertebrate host. This feature could potentially inhibit the ability to detect the effect of landscape on gene flow, since the ecology of vectors and hosts are likely different and potentially conflicting. Here, we provide a summary of the latest innovations in the field of landscape genetics with a focus on those that could help increase the power to detect landscape effects in vector-borne human disease studies. We also provide a recommended framework for studying vector-borne diseases using a landscape genetics approach. Landscape genetics has the potential to be a powerful tool for the field of vector-borne disease epidemiology but has so far been underutilized. The provided synthesis of tools and considerations for conducting a landscape genetics study of a vector-borne disease aim to bridge the gap between the two disciplines.

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“…When building a genetic graph, the construction method should always be guided by the specific research question (Miele et al, 2019). For example, an important step in this process is graph pruning, which consists in removing some links and should be performed differently if the aim of the analysis is (i) to identify single generation (direct) dispersal paths (Boulanger et al, 2020;Dyer, 2015) or (ii) to infer landscape effects on dispersal from the genetic differentiation measurements between populations connected on the graph (Savary, P. et al, in correction;Van Strien (2017)). Indeed, in the first case, paths that are not within reach of individuals given their dispersal capacities should be removed in order to represent the dispersal network topology.…”

Section: Accepted Article 1 Introductionmentioning

confidence: 99%

“…In the second case, links corresponding to multi‐generational indirect dispersal can be conserved given that they reflect the genetic connectivity emerging over generations due to stepping‐stone dispersal (Boulanger et al., 2020; Saura et al., 2014). In both cases, several graph pruning methods can be used and must be chosen accordingly (Greenbaum & Fefferman, 2017; Van Strien, 2017). Apart from these link‐level analyses, once genetic graphs have been constructed, they can be analysed at the node‐ and boundary‐levels (Wagner & Fortin, 2013), according to the research question.…”

Section: Introductionmentioning

confidence: 99%

graph4lg: A package for constructing and analysing graphs for landscape genetics in R

Savary

Foltête

Moal³

et al. 2020

Methods Ecol Evol

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Consequences of population topology for studying gene flow using link‐based landscape genetic methods

Cited by 12 publications

References 85 publications

Analysing landscape effects on dispersal networks and gene flow with genetic graphs

Analysing landscape effects on dispersal networks and gene flow with genetic graphs

Landscape Genetics: A Toolbox for Studying Vector-Borne Diseases

graph4lg: A package for constructing and analysing graphs for landscape genetics in R

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