Disentangling the effects of historic vs. contemporary landscape structure on population genetic divergence

Zellmer, Amanda J.; Knowles, L. Lacey

doi:10.1111/j.1365-294x.2009.04305.x

Cited by 105 publications

(124 citation statements)

References 80 publications

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“…Some organisms showed the same patterns as found in our study (Keyghobadi et al, 2005b;Zellmer and Knowles, 2009), whereas others showed the opposite (Orsini et al, 2008). These results highlight the importance of accounting for historical patterns, such as past landscape structure and historical demography, when analyzing the effect of contemporary landscape structure on genetic diversity and differentiation.…”

Section: Genetic Differentiationsupporting

confidence: 68%

“…Landscape metrics related to forest amount and matrix resistance were Tables S4 and S5), and thus we used the matrix resistance as a proxy to discuss habitat loss and fragmentation. Genetic variability and differentiation may reflect not only contemporary landscape structure but also historical processes such as past gene flow and population fluctuations (Zellmer and Knowles, 2009). Thus, along with contemporary landscape metrics, we also calculated historical effective population size in each site (metric for node-level analyses) and historical genetic connectivity among sites (metric for link-level analyses), because of their potential effect on current genetic diversity and genetic differentiation.…”

Section: Landscape Mapping and Explanatory Covariatesmentioning

confidence: 99%

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Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm

et al. 2015

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Population genetics theory predicts loss in genetic variability because of drift and inbreeding in isolated plant populations; however, it has been argued that long-distance pollination and seed dispersal may be able to maintain gene flow, even in highly fragmented landscapes. We tested how historical effective population size, historical migration and contemporary landscape structure, such as forest cover, patch isolation and matrix resistance, affect genetic variability and differentiation of seedlings in a tropical palm (Euterpe edulis) in a human-modified rainforest. We sampled 16 sites within five landscapes in the Brazilian Atlantic forest and assessed genetic variability and differentiation using eight microsatellite loci. Using a model selection approach, none of the covariates explained the variation observed in inbreeding coefficients among populations. The variation in genetic diversity among sites was best explained by historical effective population size. Allelic richness was best explained by historical effective population size and matrix resistance, whereas genetic differentiation was explained by matrix resistance. Coalescence analysis revealed high historical migration between sites within landscapes and constant historical population sizes, showing that the genetic differentiation is most likely due to recent changes caused by habitat loss and fragmentation. Overall, recent landscape changes have a greater influence on among-population genetic variation than historical gene flow process. As immediate restoration actions in landscapes with low forest amount, the development of more permeable matrices to allow the movement of pollinators and seed dispersers may be an effective strategy to maintain microevolutionary processes.

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Section: Genetic Differentiationsupporting

confidence: 68%

Section: Landscape Mapping and Explanatory Covariatesmentioning

confidence: 99%

Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm

et al. 2015

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“…Because genetic divergences carry the signature of contemporary impacts as well as historical ones, historical demography underpins the ability to distinguish between gene flow among populations and divergence in isolation among recently subdivided populations, critical to understanding current connectivity (Pavlacky et al 2009;Zellmer and Knowles 2009). …”

Section: Evolutionary Relationships Among Population Units -Phylogeogmentioning

confidence: 99%

Towards modelling persistence of woodland birds: the role of genetics

Sunnucks

2011

Emu - Austral Ornithology

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Abstract. Assessing how environmental change affects the probability of persistence of organisms requires an understanding of dispersal through, and occupation of, landscapes, and the associated demographic outcomes. Projections of differences in persistence probability can then be made under different scenarios of land-use and global environmental change. Rates and distances of dispersal, and demographic change and trajectories, are difficult to measure accurately, but genetic approaches can make major contributions. For two decades the field of molecular ecology has been providing useful life-history information relevant to population management, including key ecological attributes such as disease-resistance and thermal biology, mobility, dispersal and gene flow, habitat connectivity, the spatial and temporal scales of population processes, and demography. Genetic estimators of these factors could be employed to a much greater extent than they are currently. To facilitate this increased use, genetic estimates of functional connectivity (mobility and gene flow of organisms) and demography need to be integrated directly into decision-making processes. Population genetics is well suited to Bayesian approaches, with associated benefits including the ability to consider many factors, and estimation of error and parameter sensitivities. Genetic estimators based on the mobility and reproductive success of individual organisms and their key ecological traits can make unique contributions alongside other types of data into agent-based, spatially explicit modelling approaches of real landscape scenarios at the range of scales needed by managers. Virtually all the tools to do this exist. It is imperative that genetic samples be collected for contemporary and future analyses.

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“…There is increased interest in examining how landscape configuration assessed at different times has influenced contemporary SGS (Landguth et al., 2010; Pavlacky, Goldizen, Prentis, Nicholls, & Lowe, 2009; Zellmer & Knowles, 2009). Yet, few if any, landscape genetic studies have applied a time series approach using multiple genetic and landscape data sets collected at the same time points.…”

Section: Introductionmentioning

confidence: 99%

“…Explicitly adding a temporal component in landscape genetics analyses may provide valuable additional resolution on directional trends in gene flow (Martensen et al., 2017; Wagner & Fortin, 2013). By quantifying concurrent changes in SGS and landscape structure over time, we only improve the long‐term predictive power of the effects of ongoing or future landscape change on genetic connectivity (Zellmer & Knowles, 2009). …”

Section: Introductionmentioning

confidence: 99%

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

Draheim

Moore

Fortin

et al. 2018

Evolutionary Applications

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Landscape genetic studies typically focus on the evolutionary processes that give rise to spatial patterns that are quantified at a single point in time. Although landscape change is widely recognized as a strong driver of microevolutionary processes, few landscape genetic studies have directly evaluated the change in spatial genetic structure (SGS) over time with concurrent changes in landscape pattern. We introduce a novel approach to analyze landscape genetic data through time. We demonstrate this approach using genotyped samples (n = 569) from a large black bear (Ursus americanus) population in Michigan (USA) that were harvested during 3 years (2002, 2006, and 2010). We identified areas that were consistently occupied over this 9‐year period and quantified temporal variation in SGS. Then, we evaluated alternative hypotheses about effects of changes in landscape features (e.g., deforestation or crop conversion) on fine‐scale SGS among years using spatial autoregressive modeling and model selection. Relative measures of landscape change such as magnitude of landscape change (i.e., number of patches changing from suitable to unsuitable states or vice versa), and during later periods, measures of fragmentation (i.e., patch aggregation and cohesion) were associated with change in SGS. Our results stress the importance of conducting time series studies for the conservation and management of wildlife inhabiting rapidly changing landscapes.

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Disentangling the effects of historic vs. contemporary landscape structure on population genetic divergence

Cited by 105 publications

References 80 publications

Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm

Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm

Towards modelling persistence of woodland birds: the role of genetics

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

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