Identifying future research needs in landscape genetics: where to from here?

Balkenhol, Niko; Gugerli, Félix; Cushman, Sam; Waits, Lisette P.; Coulon, Aurélie; Arntzen, Jan W.; Holderegger, Rolf; Wagner, Helene H.

doi:10.1007/s10980-009-9334-z

Cited by 173 publications

(169 citation statements)

References 49 publications

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“…Fortunately, patch-scale habitat data are increasingly available at landscape scales, such as mapping vegetation-cover down to single trees using high-resolution satellites (Levin et al 2009). The size and quality of a patch can affect population sizes and migration rates, whereas the intervening matrix may affect success of migrants (Balkenhol et al 2009a(Balkenhol et al , 2009b. For example, White-browed Treecreepers (Climacteris affinis) were absent from patches >3 km from other patches in agricultural land, in contrast to >8 km in native vegetation.…”

Section: Avian Declines and The Need For Understanding Ecological Promentioning

confidence: 99%

“…This information can be applied, for example, to measuring and monitoring populations in disturbed and natural landscapes by enumerating migrants, whether they breed, and, with substantial sampling, may produce matrices of dispersal distances and directions. Direct genetic approaches are profoundly suited to agent-based, spatially explicit modelling for biodiversity management, for which there is an urgent need (Balkenhol et al 2009a).…”

Section: The Fundamental Sources Of Information In Molecular Populatimentioning

confidence: 99%

“…Within the last decade, these approaches have developed very rapidly and been dubbed landscape genetics (Manel et al 2003;Storfer et al 2007;Holderegger and Wagner 2008;Balkenhol et al 2009aBalkenhol et al , 2009b.…”

Section: Landscape Genetics: Relating Spatial Genetic Patterns To Lanmentioning

confidence: 99%

“…The extensive contributions of molecular population biology to ecological management have been reviewed elsewhere (Pearse and Crandall 2004;DeYoung and Honeycutt 2005;Scribner et al 2005;Excoffier and Heckel 2006;Rollins et al 2006;Selkoe and Toonen 2006;Sunnucks and Taylor 2008;Balkenhol et al 2009a;Simmons et al 2010). The main focus here will be on factors associated with understanding population persistence.…”

Section: Introduction To Molecular Population Biologymentioning

confidence: 99%

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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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Section: Avian Declines and The Need For Understanding Ecological Promentioning

confidence: 99%

Section: The Fundamental Sources Of Information In Molecular Populatimentioning

confidence: 99%

Section: Landscape Genetics: Relating Spatial Genetic Patterns To Lanmentioning

confidence: 99%

Section: Introduction To Molecular Population Biologymentioning

confidence: 99%

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Towards modelling persistence of woodland birds: the role of genetics

Sunnucks

2011

Emu - Austral Ornithology

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“…The field of landscape genetics can advance beyond characterizing static landscape pattern–genetic process relationships (Balkenhol et al., 2009). Incorporating landscape change and quantifying the effects of change on SGS over time is vital to improve understanding of anthropogenic impacts on functional connectivity (Bolliger et al., 2010; Segelbacher et al., 2010; Storfer et al., 2010).…”

Section: Discussionmentioning

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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