references.bib

@misc{olson_terrestrial_2017,
	title = {Terrestrial {Ecoregions} of the {World}: {A} {New} {Map} of {Life} on {Earth}},
	abstract = {This map depicts the 825 terrestrial ecoregions of the globe. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. This comprehensive, global map provides a useful framework for conducting biogeographical or macroecological research, for identifying areas of outstanding biodiversity and conservation priority, for assessing the representation and gaps in conservation efforts worldwide, and for communicating the global distribution of natural communities on earth. We have based ecoregion delineations on hundreds of previous biogeographical studies, and refined and synthesized existing information in regional workshops over 10 years to assemble the global dataset. Ecoregions are nested within two higher-order classifications: biomes (14) and biogeographic realms (8). Together, these nested classification levels provide a framework for comparison among units and the identification of representative habitats and species assemblages. Ecoregions have increasingly been adopted by research scientists, conservation organizations, and donors as a framework for analyzing biodiversity patterns, assessing conservation priorities, and directing effort and support (Ricketts et al. 1999a; Wikramanayake et al. 2001; Ricketts et al. 1999b; Olson \& Dinerstein 1998; Groves et al. 2000; Rosenzweig et al. 2003; and Luck et al. 2003). More on the approach to ecoregion mapping, the logic and design of the framework, and previous and potential uses are discusses in Olson et al. (2001) and Ricketts et al. (1999a).},
	publisher = {BioScience},
	author = {Olson, D.M. and Dinerstein, E.D. and Wikramanayake, N.D and {Burgess, G.V.N.} and {Powell, E.C.} and {Underwood, J.A.} and {D'Amico, I.} and {Itoua, H.E.}},
	year = {2017},
}

@article{yamazaki_high-accuracy_2017,
	title = {A high-accuracy map of global terrain elevations: {Accurate} {Global} {Terrain} {Elevation} map},
	volume = {44},
	issn = {00948276},
	shorttitle = {A high-accuracy map of global terrain elevations},
	url = {http://doi.wiley.com/10.1002/2017GL072874},
	doi = {10.1002/2017GL072874},
	language = {en},
	number = {11},
	urldate = {2023-06-06},
	journal = {Geophysical Research Letters},
	author = {Yamazaki, Dai and Ikeshima, Daiki and Tawatari, Ryunosuke and Yamaguchi, Tomohiro and O'Loughlin, Fiachra and Neal, Jeffery C. and Sampson, Christopher C. and Kanae, Shinjiro and Bates, Paul D.},
	year = {2017},
	pages = {5844--5853},
}

@misc{mapbiomas_project_collection_2020,
	title = {Collection 7 of the {Annual} {Series} of {Land} {Use} and {Land} {Cover} {Maps} of {Brazil}},
	url = {projects/mapbiomas-workspace/public/collection7/mapbiomas_collection70_integration_v2},
	author = {MapBiomas Project},
	year = {2020},
}

@article{anderson_resilient_2016,
	title = {Resilient {Sites} for {Terrestrial} {Conservation} in {Eastern} {North} {America}},
	volume = {28},
	issn = {0888-8892, 1523-1739},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/cobi.12272},
	doi = {10.1111/cobi.12272},
	language = {en},
	number = {4},
	urldate = {2023-02-08},
	journal = {Conservation Biology},
	author = {Anderson, Mark G. and Clark, Melissa and Sheldon, Arlene Olivero},
	year = {2016},
	pages = {959--970},
}

@article{yamazaki_merit_2019,
	title = {{MERIT} {Hydro}: {A} {High}‐{Resolution} {Global} {Hydrography} {Map} {Based} on {Latest} {Topography} {Dataset}},
	volume = {55},
	issn = {0043-1397, 1944-7973},
	shorttitle = {{MERIT} {Hydro}},
	url = {https://onlinelibrary.wiley.com/doi/10.1029/2019WR024873},
	doi = {10.1029/2019WR024873},
	language = {en},
	number = {6},
	urldate = {2023-06-06},
	journal = {Water Resources Research},
	author = {Yamazaki, Dai and Ikeshima, Daiki and Sosa, Jeison and Bates, Paul D. and Allen, George H. and Pavelsky, Tamlin M.},
	month = jun,
	year = {2019},
	pages = {5053--5073},
}

@misc{gumbricht_tropical_2017,
	title = {Tropical and subtropical wetlands distribution version 2},
	url = {https://data.cifor.org/dataset.xhtml?persistentId=doi:10.17528/CIFOR/DATA.00058},
	doi = {10.17528/cifor/data.00058},
	abstract = {Wetlands are important providers of ecosystem services and key regulators of climate change. They positively contribute to global warming through their greenhouse gas emissions, and negatively through the accumulation of organic material in histosols, particularly in peatlands. Our understanding of wetlands’ services is currently constrained by limited knowledge on their distribution, extent, volume, inter-annual flood variability, and disturbance levels. We present an expert system approach to estimate wetland and peatland areas, depths and volumes, which relies on three biophysical indices related to wetland and peat formation: 1. Long-term water supply exceeding atmospheric water demand; 2. Annually or seasonally water-logged soils; 3. A geomorphological position where water is supplied and retained.
  
  The dataset is version 2 with significant improvements compare to previous version. It shows distribution of wetland, peatland and peat depth that covers the tropics and sub tropics (40° N to 60° S; 180° E to -180° W), excluding small islands. It was mapped in 231 meters spatial resolution. The dataset can be viewed in this interactive map: http://www.cifor.org/global-wetlands/.},
	language = {en},
	urldate = {2023-05-26},
	author = {Gumbricht, T. and Román-Cuesta, R. M. and Verchot, L. V. and Herold, M. and Wittmann, F. and Householder, E. and Herold, N. and Murdiyarso, D.},
	year = {2017},
	doi = {10.17528/cifor/data.00058},
}

@article{anderson_resilient_2016-1,
	title = {Resilient and {Connected} {Landscapes} for {Terrestrial} {Conservation}},
	language = {en},
	author = {Anderson, Mark and Barnett, Analie and Clark, Melissa and Prince, Jeremy and Olivero, Sheldon A and Vickery, B},
	year = {2016},
}

@article{anderson_estimating_2014,
	title = {Estimating {Climate} {Resilience} for {Conservation} across {Geophysical} {Settings}},
	volume = {28},
	issn = {0888-8892, 1523-1739},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/cobi.12272},
	doi = {10.1111/cobi.12272},
	abstract = {Conservationists need methods to conserve biological diversity while allowing species and communities to rearrange in response to a changing climate. We developed and tested such a method for northeastern North America that we based on physical features associated with ecological diversity and site resilience to climate change. We comprehensively mapped 30 distinct geophysical settings based on geology and elevation. Within each geophysical setting, we identified sites that were both connected by natural cover and that had relatively more microclimates indicated by diverse topography and elevation gradients. We did this by scoring every 405 ha hexagon in the region for these two characteristics and selecting those that scored {\textgreater}SD 0.5 above the mean combined score for each setting. We hypothesized that these high-scoring sites had the greatest resilience to climate change, and we compared them with sites selected by The Nature Conservancy for their high-quality rare species populations and natural community occurrences. High-scoring sites captured significantly more of the biodiversity sites than expected by chance (p {\textless} 0.0001): 75\% of the 414 target species, 49\% of the 4592 target species locations, and 53\% of the 2170 target community locations. Calcareous bedrock, coarse sand, and fine silt settings scored markedly lower for estimated resilience and had low levels of permanent land protection (average 7\%). Because our method identifies—for every geophysical setting—sites that are the most likely to retain species and functions longer under a changing climate, it reveals natural strongholds for future conservation that would also capture substantial existing biodiversity and correct the bias in current secured lands.},
	language = {en},
	number = {4},
	urldate = {2022-12-07},
	journal = {Conservation Biology},
	author = {Anderson, Mark G. and Clark, Melissa and Sheldon, Arlene Olivero},
	month = aug,
	year = {2014},
	note = {tex.ids= anderson\_estimating\_2014-1
\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/cobi.12272},
	keywords = {Norteamérica, North America, biodiversidad, biodiversity, cambio climático, climate change, conectividad, connectivity, conservation planning, fragmentación, fragmentation, geology, geología, planeación de la conservación, protected areas, Áreas protegidas},
	pages = {959--970},
}

@article{jones_incorporating_2016,
	title = {Incorporating climate change into spatial conservation prioritisation: {A} review},
	volume = {194},
	issn = {0006-3207},
	shorttitle = {Incorporating climate change into spatial conservation prioritisation},
	url = {https://www.sciencedirect.com/science/article/pii/S0006320715301877},
	doi = {10.1016/j.biocon.2015.12.008},
	abstract = {To ensure the long-term persistence of biodiversity, conservation strategies must account for the entire range of climate change impacts. A variety of spatial prioritisation techniques have been developed to incorporate climate change. Here, we provide the first standardised review of these approaches. Using a systematic search, we analysed peer-reviewed spatial prioritisation publications (n=46) and found that the most common approaches (n=41, 89\%) utilised forecasts of species distributions and aimed to either protect future species habitats (n=24, 52\%) or identify climate refugia to shelter species from climate change (n=17, 37\%). Other approaches (n=17, 37\%) used well-established conservation planning principles to combat climate change, aimed at broadly increasing either connectivity (n=11, 24\%) or the degree of heterogeneity of abiotic factors captured in the planning process (n=8, 17\%), with some approaches combining multiple goals. We also find a strong terrestrial focus (n=35, 76\%), and heavy geographical bias towards North America (n=8, 17\%) and Australia (n=11, 24\%). While there is an increasing trend of incorporating climate change into spatial prioritisation, we found that serious gaps in current methodologies still exist. Future research must focus on developing methodologies that allow planners to incorporate human responses to climate change and recognise that discrete climate impacts (e.g. extreme events), which are increasing in frequency and severity, must be addressed within the spatial prioritisation framework. By identifying obvious gaps and highlighting future research needs this review will help practitioners better plan for conservation action in the face of multiple threats including climate change.},
	language = {en},
	urldate = {2023-05-22},
	journal = {Biological Conservation},
	author = {Jones, Kendall R. and Watson, James E. M. and Possingham, Hugh P. and Klein, Carissa J.},
	month = feb,
	year = {2016},
	keywords = {Biodiversity conservation, Climate change, Conservation planning, Direct effects, Extreme events, Human response, Indirect effects, Spatial prioritisation},
	pages = {121--130},
}

@article{carrasco_global_2021,
	title = {Global progress in incorporating climate adaptation into land protection for biodiversity since {Aichi} targets},
	volume = {27},
	issn = {1365-2486},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15511},
	doi = {10.1111/gcb.15511},
	abstract = {Climate adaptation strategies are being developed and implemented to protect biodiversity from the impacts of climate change. A well-established strategy involves the identification and addition of new areas for conservation, and most countries agreed in 2010 to expand the global protected area (PA) network to 17\% by 2020 (Aichi Biodiversity Target 11). Although great efforts to expand the global PA network have been made, the potential of newly established PAs to conserve biodiversity under future climate change remains unclear at the global scale. Here, we conducted the first global-extent, country-level assessment of the contribution of PA network expansion toward three key land prioritization approaches for biodiversity persistence under climate change: protecting climate refugia, protecting abiotic diversity, and increasing connectivity. These approaches avoid uncertainties of biodiversity predictions under climate change as well as the issue of undescribed species. We found that 51\% of the countries created new PAs in locations with lower mean climate velocity (representing better climate refugia) and 58\% added PAs in areas with higher mean abiotic diversity compared to the available, non-human-dominated lands not chosen for protection. However, connectivity among PAs declined in 53\% of the countries, indicating that many new PAs were located far from existing PAs. Lastly, we identified potential improvements for climate adaptation, showing that 94\% of the countries have the opportunity to improve in executing one or more approaches to conserve biodiversity. Most countries (60\%) were associated with multiple opportunities, highlighting the need for integrative strategies that target multiple land protection approaches. Our results demonstrate that a global improvement in the protection of climate refugia, abiotic diversity, and connectivity of reserves is needed to complement land protection informed by existing and projected species distributions. Our study also provides a framework for countries to prioritize land protection for climate adaptation using publicly available data.},
	language = {en},
	number = {9},
	urldate = {2023-05-22},
	journal = {Global Change Biology},
	author = {Carrasco, Luis and Papeş, Monica and Sheldon, Kimberly S. and Giam, Xingli},
	year = {2021},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15511},
	keywords = {abiotic diversity, climate change, climate refugia, climate velocity, connectivity, protected areas},
	pages = {1788--1801},
}

@article{anderson_conserving_2010,
	title = {Conserving the {Stage}: {Climate} {Change} and the {Geophysical} {Underpinnings} of {Species} {Diversity}},
	volume = {5},
	issn = {1932-6203},
	shorttitle = {Conserving the {Stage}},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0011554},
	doi = {10.1371/journal.pone.0011554},
	abstract = {Conservationists have proposed methods for adapting to climate change that assume species distributions are primarily explained by climate variables. The key idea is to use the understanding of species-climate relationships to map corridors and to identify regions of faunal stability or high species turnover. An alternative approach is to adopt an evolutionary timescale and ask ultimately what factors control total diversity, so that over the long run the major drivers of total species richness can be protected. Within a single climatic region, the temperate area encompassing all of the Northeastern U.S. and Maritime Canada, we hypothesized that geologic factors may take precedence over climate in explaining diversity patterns. If geophysical diversity does drive regional diversity, then conserving geophysical settings may offer an approach to conservation that protects diversity under both current and future climates. Here we tested how well geology predicts the species diversity of 14 US states and three Canadian provinces, using a comprehensive new spatial dataset. Results of linear regressions of species diversity on all possible combinations of 23 geophysical and climatic variables indicated that four geophysical factors; the number of geological classes, latitude, elevation range and the amount of calcareous bedrock, predicted species diversity with certainty (adj. R2 = 0.94). To confirm the species-geology relationships we ran an independent test using 18,700 location points for 885 rare species and found that 40\% of the species were restricted to a single geology. Moreover, each geology class supported 5–95 endemic species and chi-square tests confirmed that calcareous bedrock and extreme elevations had significantly more rare species than expected by chance (P{\textless}0.0001), strongly corroborating the regression model. Our results suggest that protecting geophysical settings will conserve the stage for current and future biodiversity and may be a robust alternative to species-level predictions.},
	language = {en},
	number = {7},
	urldate = {2023-05-22},
	journal = {PLOS ONE},
	author = {Anderson, Mark G. and Ferree, Charles E.},
	month = jul,
	year = {2010},
	note = {Publisher: Public Library of Science},
	keywords = {Climate change, Conservation science, Geology, Geophysics, Latitude, Limestone, Sedimentary geology, Species diversity},
	pages = {e11554},
}

@article{theobald_ecologically-relevant_2015,
	title = {Ecologically-{Relevant} {Maps} of {Landforms} and {Physiographic} {Diversity} for {Climate} {Adaptation} {Planning}},
	volume = {10},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619},
	doi = {10.1371/journal.pone.0143619},
	abstract = {Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings.},
	language = {en},
	number = {12},
	urldate = {2022-12-13},
	journal = {PLOS ONE},
	author = {Theobald, David M. and Harrison-Atlas, Dylan and Monahan, William B. and Albano, Christine M.},
	year = {2015},
	note = {Publisher: Public Library of Science},
	keywords = {Biodiversity, Cliffs, Climate change, Conservation science, Landforms, Physical geography, Species diversity, Valleys},
	pages = {e0143619},
}

@article{landau_omniscapejl_2021,
	title = {Omniscape.jl: {Software} to compute omnidirectional landscape connectivity},
	volume = {6},
	issn = {2475-9066},
	shorttitle = {Omniscape.jl},
	url = {https://joss.theoj.org/papers/10.21105/joss.02829},
	doi = {10.21105/joss.02829},
	abstract = {Omniscape.jl is a software package that implements the Omniscape algorithm (McRae et al., 2016) to compute landscape connectivity. It is written in the Julia programming language (Bezanson et al., 2017) to be fast, scalable, and easy-to-use. Circuitscape.jl (Anantharaman et al., 2020), the package on which Omniscape.jl builds and expands, abstracts landscapes as two-dimensional electrical networks and solves for current flow. The current flow that results represents landscape connectivity. Omniscape.jl is novel in that it produces maps of “omni-directional” connectivity, which provide a spatial representation of connectivity between every possible pair of start and endpoints in the landscape. These maps can be used by researchers and landscape managers to understand and predict how ecological processes (e.g., animal movement, disease transmission, gene flow, and fire behavior) are likely to manifest in geographic space. Omniscape.jl makes use of Julia’s native multi-threading, making it readily scalable and deployable to high performance compute nodes. More information on the broader Circuitscape project, which is home to Circuitscape.jl and Omniscape.jl, can be found at circuitscape.org.},
	language = {en},
	number = {57},
	urldate = {2023-03-08},
	journal = {Journal of Open Source Software},
	author = {Landau, Vincent and Shah, Viral and Anantharaman, Ranjan and Hall, Kimberly},
	month = jan,
	year = {2021},
	pages = {2829},
}

@article{hall_circuitscape_2021,
	title = {Circuitscape in {Julia}: {Empowering} {Dynamic} {Approaches} to {Connectivity} {Assessment}},
	volume = {10},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2073-445X},
	shorttitle = {Circuitscape in {Julia}},
	url = {https://www.mdpi.com/2073-445X/10/3/301},
	doi = {10.3390/land10030301},
	abstract = {The conservation field is experiencing a rapid increase in the amount, variety, and quality of spatial data that can help us understand species movement and landscape connectivity patterns. As interest grows in more dynamic representations of movement potential, modelers are often limited by the capacity of their analytic tools to handle these datasets. Technology developments in software and high-performance computing are rapidly emerging in many fields, but uptake within conservation may lag, as our tools or our choice of computing language can constrain our ability to keep pace. We recently updated Circuitscape, a widely used connectivity analysis tool developed by Brad McRae and Viral Shah, by implementing it in Julia, a high-performance computing language. In this initial re-code (Circuitscape 5.0) and later updates, we improved computational efficiency and parallelism, achieving major speed improvements, and enabling assessments across larger extents or with higher resolution data. Here, we reflect on the benefits to conservation of strengthening collaborations with computer scientists, and extract examples from a collection of 572 Circuitscape applications to illustrate how through a decade of repeated investment in the software, applications have been many, varied, and increasingly dynamic. Beyond empowering continued innovations in dynamic connectivity, we expect that faster run times will play an important role in facilitating co-production of connectivity assessments with stakeholders, increasing the likelihood that connectivity science will be incorporated in land use decisions.},
	language = {en},
	number = {3},
	urldate = {2023-03-08},
	journal = {Land},
	author = {Hall, Kimberly R. and Anantharaman, Ranjan and Landau, Vincent A. and Clark, Melissa and Dickson, Brett G. and Jones, Aaron and Platt, Jim and Edelman, Alan and Shah, Viral B.},
	month = mar,
	year = {2021},
	note = {Number: 3
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {Circuitscape, Earth observations, Julia programming language, Omniscape, computational ecology, conservation planning, dynamic connectivity, landscape connectivity},
	pages = {301},
}

@article{gorelick_google_2017,
	title = {Google {Earth} {Engine}: {Planetary}-scale geospatial analysis for everyone},
	url = {https://doi.org/10.1016/j.rse.2017.06.031},
	doi = {10.1016/j.rse.2017.06.031},
	journal = {Remote Sensing of Environment},
	author = {Gorelick, Noel and Hancher, Matt and Dixon, Mike and Ilyushchenko, Simon and Thau, David and Moore, Rebecca},
	year = {2017},
	note = {Publisher: Elsevier},
}

@article{anderson_resilient_2023,
	title = {A resilient and connected network of sites to sustain biodiversity under a changing climate},
	volume = {120},
	url = {https://www.pnas.org/doi/10.1073/pnas.2204434119},
	doi = {10.1073/pnas.2204434119},
	abstract = {Motivated by declines in biodiversity exacerbated by climate change, we identified a network of conservation sites designed to provide resilient habitat for species, while supporting dynamic shifts in ranges and changes in ecosystem composition. Our 12-y study involved 289 scientists in 14 study regions across the conterminous United States (CONUS), and our intent was to support local-, regional-, and national-scale conservation decisions. To ensure that the network represented all species and ecosystems, we stratified CONUS into 68 ecoregions, and, within each, we comprehensively mapped the geophysical settings associated with current ecosystem and species distributions. To identify sites most resilient to climate change, we identified the portion of each geophysical setting with the most topoclimate variability (high landscape diversity) likely to be accessible to dispersers (high local connectedness). These “resilient sites” were overlaid with conservation priority maps from 104 independent assessments to indicate current value in supporting recognized biodiversity. To identify key connectivity areas for sustaining species movement in response to climate change, we codeveloped a fine-scale representation of human modification and ran a circuit-theory-based analysis that emphasized movement potential along geographic climate gradients. Integrating areas with high values for two or more factors, we identified a representative, resilient, and connected network of biodiverse lands covering 35\% of CONUS. Because the network connects climatic gradients across 250,000 biodiversity elements and multiple resilient examples of all geophysical settings in every ecoregion, it could form the spatial foundation for targeted land protection and other conservation strategies to sustain a diverse, dynamic, and adaptive world.},
	number = {7},
	urldate = {2023-03-01},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Anderson, Mark G. and Clark, Melissa and Olivero, Arlene P. and Barnett, Analie R. and Hall, Kimberly R. and Cornett, Meredith W. and Ahlering, Marissa and Schindel, Michael and Unnasch, Bob and Schloss, Carrie and Cameron, D. Richard},
	month = feb,
	year = {2023},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	keywords = {ya},
	pages = {e2204434119},
}

@article{mcrae_isolation_2006,
	title = {Isolation by resistance},
	volume = {60},
	abstract = {Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species’ range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.},
	language = {en},
	number = {8},
	journal = {Evolution},
	author = {McRae, Brad H},
	year = {2006},
	keywords = {ya},
	pages = {1551--1561},
}