Optimizing dispersal and corridor models using landscape genetics

Epps, Clinton W.; Wehausen, John D.; Bleich, Vernon C.; Torres, Steven G.; Brashares, Justin S.

doi:10.1111/j.1365-2664.2007.01325.x

Cited by 286 publications

(259 citation statements)

References 35 publications

Supporting

Mentioning

253

Contrasting

Unclassified

Order By: Relevance

“…Landscape genetic analyses using LCP models assume that genetic distances between population pairs increase with cost-weighted distances measured along the optimal (leastcost) routes connecting them (9,(13)(14)(15)(16). We calculated LCP distances (reflecting the shortest within-habitat routes connecting sample pairs; Figs.…”

Section: Ibd Predictionsmentioning

confidence: 99%

“…This hinders hypothesis testing and leaves conservation planners without validated metrics of how landscape change will affect genetic connectivity. Although a growing number of studies are incorporating landscape data into genetic predictions and conservation plans using least-cost path (LCP) modeling (9,(13)(14)(15)(16), gene flow among real populations is not restricted to single, optimal pathways, as LCP models assume; rather, it occurs over multiple pathways and often involves indirect allele movements spanning many generations (17). Thus, to support conservation planning and to enable hypothesis testing in landscape genetics, theoretically justified models of how landscape features facilitate or impede gene flow over multiple, direct and indirect pathways are needed.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Circuit theory predicts gene flow in plant and animal populations

McRae

Beier²

2007

Proc. Natl. Acad. Sci. U.S.A.

814

700

View full text Add to dashboard Cite

Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.Gulo gulo ͉ isolation by resistance ͉ landscape connectivity ͉ Swietenia macrophylla ͉ landscape genetics P reserving and restoring connectivity for broad-scale ecological processes, such as dispersal and gene flow, has become a major conservation priority (1, 2). Conservation organizations are investing considerable resources-and asking governments to do the same-to set aside land to promote connectivity (3). A major impediment to this goal is the difficulty in predicting how different land use, climate change, or reserve design scenarios will affect connectivity, and conservation planning decisions are often made without quantifying benefits for the ecological processes they are meant to conserve. If scarce conservation dollars are to be spent effectively, conservation biologists need clear, efficient, and reliable tools relating landscape composition and pattern to important ecological processes (4).Gene flow is a critical ecological process with conservation benefits ranging from promoting the persistence of small populations to spreading adaptive traits in changing environments (5-8). Because of these important ecological and evolutionary roles, a new and rapidly growing field-landscape genetics-is primarily dedicated to understanding and predicting how landscape characteristics affect gene flow (5). By combining genetic, computational, and spatial analytic tools unavailable a decade ago, the interdisciplinary field has yielded insights relevant not only to conservation (e.g., refs. 8 and 9), but to fields such as evolution (10), infectious disease ecology (11), and population ecology (12).Yet progress in all of these fields has been hampered by a lack of models capable of predicting gene flow from landscape structure. As a result, typical landscape genetic analyses simply detect genetic discontinuities and propose ad hoc explanations based on coincident landscape features, rather than testing a priori predictions of how such features are expected to influence genetic structure. This hinders hypothesis testing and leaves conserva...

show abstract

Section: Ibd Predictionsmentioning

confidence: 99%

mentioning

confidence: 99%

Circuit theory predicts gene flow in plant and animal populations

McRae

Beier²

2007

Proc. Natl. Acad. Sci. U.S.A.

814

700

View full text Add to dashboard Cite

show abstract

“…The approach has been widely applied in conservation biology and landscape ecology as a means to identify paths that support gene flow between populations of organisms that require high-quality habitat linkages (Macdonald & Johnson 2001). Indeed, a common outcome of these studies is that distances defined by functional environmental models better explain observed dispersal, population dynamics or genetic differentiation than Euclidean distance, reinforcing the need for improved geographical measures of ecological connectivity to predict rates and likelihoods of spread (Michels et al 2001;Adriaensen et al 2003;Stevens et al 2006;Epps et al 2007).…”

Section: Functional Environmental Modelsmentioning

confidence: 99%

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

Remais

Akullian

Ding

et al. 2010

J. R. Soc. Interface.

View full text Add to dashboard Cite

The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of internode EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.

show abstract

“…Opportunities for dispersal may exist in southern Africa because protected areas are within close proximity of each other and are embedded in a matrix of mostly untransformed habitats with few people (Cushman et al 2010;Mittermeier et al 2003). Furthermore, elephants are ideal candidates for designing corridors because they are an umbrella species, often co-occurring with other species of conservation concern (Epps et al 2007). In this paper, we will assess the potential for connectivity between existing elephant populations in southern Africa using circuit theory and resource selection function (RSF) models.…”

Section: Introductionmentioning

confidence: 99%

Functional connectivity within conservation networks: Delineating corridors for African elephants

Roever

Aarde

Leggett

2013

Biological Conservation

105

100

View full text Add to dashboard Cite

Managing multiple parks, reserves, and conservation areas collectively as conservation networks is a recent, yet growing trend. But in order for these networks to be ecologically viable, the functional connectivity of the landscape must be ensured. We assessed the connectivity between six African savanna elephant populations in southern Africa to test whether existing conservation networks were functioning and to identify other areas that could benefit from being managed as conservation networks. We used resource selection function models to create an index of habitat selection by males and female elephants. We employed this habitat use index as a resistance surface, and applied circuit theory to assess connectivity between adjacent elephant populations within six clusters of protected areas across southern Africa. Circuit theory current flow maps predicted a high likelihood of connectivity in the central portion of our study area (i.e. between the Chobe, Kafue, Luangwa, and Zambezi cluster). Main factors limiting connectivity across the study area were high human density in the east and a lack of surface water in the west. These factors effectively isolate elephants in the Etosha cluster in Namibia and Niassa clusters in Mozambique from the central region. Our models further identified two clusters where elephants might benefit from being managed as part of a conservation network, 1) northern Zambia and Malawi and 2) northern Mozambique.We conclude that using habitat selection and circuit theory models to identify conservation networks is a data-based method that can be applied to other focal species to identify and conserve functional connectivity.

show abstract

Optimizing dispersal and corridor models using landscape genetics

Cited by 286 publications

References 35 publications

Circuit theory predicts gene flow in plant and animal populations

Circuit theory predicts gene flow in plant and animal populations

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

Functional connectivity within conservation networks: Delineating corridors for African elephants

Contact Info

Product

Resources

About