Coupling inter-patch movement models and landscape graph to assess functional connectivity

Landscape connectivity is a key process for the functioning and persistence of spatially-structured populations in fragmented landscapes. Butterflies are particularly sensitive to landscape change and are excellent model organisms to study landscape connectivity. Here, we infer functional connectivity from the assessment of the selection of different landscape elements in a highly fragmented landscape in the Île-de-France region (France). Firstly we measured the butterfly preferences of the Large White butterfly (Pieris brassicae) in different landscape elements using individual release experiments. Secondly, we used an inter-patch movement model based on butterfly choices to build the selection map of the landscape elements to moving butterflies. From this map, functional connectivity network of P. brassicae was modelled using landscape graph-based approach. In our study area, we identified nine components/groups of connected habitat patches, eight of them located in urbanized areas, whereas the last one covered the more rural areas. Eventually, we provided elements to validate the predictions of our model with independent experiments of mass release-recapture of butterflies. Our study shows (1) the efficiency of our inter-patch movement model based on species preferences in predicting complex ecological processes such as dispersal and (2) how inter-patch movement model results coupled to landscape graph can assess landscape functional connectivity at large spatial scales.     

Mapping landscape corridors

Corridors are important geographic features for biological conservation and biodiversity assessment. The identification and mapping of corridors is usually based on visual interpretations of movement patterns (functional corridors) or habitat maps (structural corridors). We present a method for automated corridor mapping with morphological image processing, and demonstrate the approach with a forest map derived from satellite imagery of northern Slovakia. We show how the approach can be used to differentiate between relatively narrow (‘line’) and wide (‘strip’) structural corridors by mapping corridors at multiple scales of observation, and indicate how to map functional corridors with maps of observed or simulated organism movement. An application to environmental reporting is demonstrated by assessing structural forest corridors in relation to forest types in northern Slovakia.     

A Flexible Approach for Assessing Functional Landscape Connectivity,with Application to Greater Sage-Grouse (Centrocercus urophasianus)

Connectivity of animal populations is an increasingly prominent concern in fragmented landscapes, yet existing methodological and conceptual approaches implicitly assume the presence of, or need for, discrete corridors. We tested this assumption by developing a flexible conceptual approach that does not assume, but allows for, the presence of discrete movement corridors. We quantified functional connectivity habitat for greater sage-grouse (Centrocercus urophasianus) across a large landscape in central western North America. We assigned sample locations to a movement state (encamped, traveling and relocating), and used Global Positioning System (GPS) location data and conditional logistic regression to estimate state-specific resource selection functions. Patterns of resource selection during different movement states reflected selection for sagebrush and general avoidance of rough topography and anthropogenic features. Distinct connectivity corridors were not common in the 5,625 km² study area. Rather, broad areas functioned as generally high or low quality connectivity habitat. A comprehensive map predicting the quality of connectivity habitat across the study area validated well based on a set of GPS locations from independent greater sage-grouse. The functional relationship between greater sage-grouse and the landscape did not always conform to the idea of a discrete corridor. A more flexible consideration of landscape connectivity may improve the efficacy of management actions by aligning those actions with the spatial patterns by which animals interact with the landscape.     

A graph-based approach to investigating the influence of the landscape on population spread processes

Modelling of landscape connectivity is a key point in the study of the movement of populations within a given landscape. For studies focused on the preservation of biodiversity, graph-based methods provide an interesting framework to investigate the landscape influence on population spread processes. Such an approach is described here, based on the mapping of landscape categories in habitat patches, including a diachronic data set describing the population spread within the habitat patches. A minimum planar graph was built by computing spatial distances between all pairs of neighbouring patches. From this structure, two types of analysis are proposed: one focused on the links of the graph and consists in correlating spatial distances and gap indicators computed from the diachronic data. The other was based on the correlations between population data and connectivity metrics at the patch level. As an example, this approach was applied to the spread of the fossorial water vole on the Jura plateau (France), with annual population data covering eleven years from 1989 to 2000. Link analysis allowed to find an optimal set of resistance values used in the least-cost distances computations, and thus to build a relevant graph. From this graph, patch analysis displayed a cyclic correlation between a metric based on potential dispersal flux and the population density, outlining the strong role of landscape connectivity in the population spread. The present study clearly shows that landscape modelling and graph-based approach can produce parameters which are consistent with field observations and thus pave the way to simulating the effect of landscape modification on population dynamics.     

Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits

Landscape connectivity is crucial for many ecological processes, including dispersal, gene flow, demographic rescue, and movement in response to climate change. As a result, governmental and non-governmental organizations are focusing efforts to map and conserve areas that facilitate movement to maintain population connectivity and promote climate adaptation. In contrast, little focus has been placed on identifying barriers—landscape features which impede movement between ecologically important areas—where restoration could most improve connectivity. Yet knowing where barriers most strongly reduce connectivity can complement traditional analyses aimed at mapping best movement routes. We introduce a novel method to detect important barriers and provide example applications. Our method uses GIS neighborhood analyses in conjunction with effective distance analyses to detect barriers that, if removed, would significantly improve connectivity. Applicable in least-cost, circuit-theoretic, and simulation modeling frameworks, the method detects both complete (impermeable) barriers and those that impede but do not completely block movement. Barrier mapping complements corridor mapping by broadening the range of connectivity conservation alternatives available to practitioners. The method can help practitioners move beyond maintaining currently important areas to restoring and enhancing connectivity through active barrier removal. It can inform decisions on trade-offs between restoration and protection; for example, purchasing an intact corridor may be substantially more costly than restoring a barrier that blocks an alternative corridor. And it extends the concept of centrality to barriers, highlighting areas that most diminish connectivity across broad networks. Identifying which modeled barriers have the greatest impact can also help prioritize error checking of land cover data and collection of field data to improve connectivity maps. Barrier detection provides a different way to view the landscape, broadening thinking about connectivity and fragmentation while increasing conservation options.     

基于鸟类边缘种行为的景观连接度研究——空间句法的反规划应用

在城市森林及其他生态景观的研究与规划中,结合特定物种(使用者)行为的景观连接度已得到业界越来越多的重视。为了给基于物种行为的景观连接度提供新的建模和分析理念,拟将建筑及城市规划学科中一种可靠的人工智能语言通过反规划思想引入景观生态学领域,通过模拟演绎展示空间句法在研究中的优越性。鉴于鸟类边缘种的栖息地景观构型特点,将空间句法轴线图借用于基于鸟类边缘种行为的景观连接度研究。首先介绍了传统景观连接度的量化方法及物种迁移模型,通过文献综述归纳出此类研究方法的局限性;接着,由反规划启蒙提出将空间句法模型引入"边缘种-栖息地"互动研究的设想;最后,以贺州城市森林规划为例,利用空间句法轴线图探究基于鸟类边缘种行为的景观连接度。模拟实验旨在展示轴线图模型能反映物种对不同景观元素非等权重的选择行为,并可根据物种邻域感知信息预测其复合尺度的行为。     

基于电路理论的南京城市绿色基础设施格局优化

城市绿色基础设施的连通性与格局优化能显著提升城市的生物多样性和可持续发展能力,对维持城市生态系统的健康与稳定具有重要意义。基于电路理论构建了南京市主城区绿色基础设施景观格局,根据电流密度分析斑块、廊道重要性,并借助移动窗口搜索法识别障碍点,提出南京市景观格局优化策略。研究结果表明:(1)南京主城区景观破碎化程度较高,40%的生境斑块(约为28.18 km2)对连通性的贡献较低,南部重要廊道的数量最多,局部簇团成网,网络结构较为复杂,其次为中部,且河流廊道(秦淮河)是其主要廊道类型,北部廊道数量最少,斑块多呈孤岛分布;(2)研究区共有155处障碍点,其中84.5%面积小于5hm2,可见主城区景观连通性仍有较大的提升空间。研究丰富了城市绿色基础设施景观格局的构建方法,对南京主城区绿色基础设施的连通性与格局优化具有一定的实践指导意义与参考价值。     

Movement Patterns of Frugivorous Birds Promote Functional Connectivity among Chaco Serrano Woodland Fragments in Argentina

Movement patterns of frugivorous birds may be altered in anthropogenically fragmented landscapes, with possible consequences for seed dispersal and plant recruitment. We studied the movement patterns and functional connectivity of six frugivorous bird species (Colaptes melanochloros, Thraupis bonariensis, Pitangus sulphuratus, Saltator aurantiirostris, Turdus amaurochalinus, and Elaenia spp.) in a fragmented Chaco‐woodland landscape in Argentina. We recorded the directions of bird movements (arrivals and departures) and whether their destination was oriented toward a specific neighboring fragment. We evaluated the movement rates, distance of interpatch movement, and functional connectivity within the landscape for the six bird species. We applied a novel approach, graph theory, to represent bird movement patterns in the landscape and the functional connections among fragments for each bird species. Bird movements were recorded at point‐count stations established along the edges of each fragment. The directions of arrival and departure movements from and to neighboring fragments revealed complex movement patterns. However, the destination of bird movements after leaving the focal fragments was usually concentrated on only a few neighboring fragments of different sizes. Pitangus sulphuratus and T. bonariensis showed larger movement rates and higher functional connectivity (number of graphs and functional area) than the other frugivorous species. The functional connectivity mediated by movement of frugivorous birds may promote seed dispersal of many bird‐dispersed plant species. As forest loss and fragmentation of Chaco subtropical forests increase, understanding the pivotal role of mobile links exerted by avian seed dispersers is vital to maintaining and conserving this unique ecosystem.     

Spatial distribution, connectivity, and the influence of scale: habitat availability for the endangered Mona Island rock iguana

The Caribbean region is one of the five leading biodiversity hotspots in the world. Analysis of the spatial structure of critical habitats and how it affects endemic species in this region is essential baseline information for biodiversity monitoring and management. We quantified and evaluated the spatial structure and connectivity of depression forests on Mona Island and their potential impact on Mona Island rock iguana habitat, as a framework to assess spatial distribution, connectivity, and the issue of scale in small and widely dispersed habitats. Using IKONOS imagery, we mapped and delineated depression forests at four different scales (minimum mapping units: <100, 100, 500, and 1,000 m), and calculated landscape metrics describing their spatial structure, and connectivity, for each map resolution. Our approach resulted in a more detailed map than previously described maps, providing better information on habitat connectivity for iguanas. The comparison of the island landscape mapped at different scales provided evidence on how changing scales affect the output of spatial metrics and may have a significant impact when planning decisions and assigning conservation priorities. It also highlighted the importance of adequate ecological scales when addressing landscape management and conservation priorities. The analysis of landscapes at multiple scales provided a mechanism to evaluate the role of patch detection and its effect on the interpretation of connectivity and spatial structure of suitable areas for species with small and widely dispersed habitats. These methodologies can be applied other species, in different environments, with similar limitations related to connectivity and habitat availability.     

Modelling the influence of landscape connectivity on animal distribution: a functional grain approach

Landscape change may reduce the connectivity of landscapes and impact the movement of animals. If movement processes have been influenced by landscape connectivity, we hypothesize that animals may distribute themselves in larger connected regions of the landscape in order to minimize the movement costs associated with obtaining required resources and avoiding predators. We adopt the term functional grain to describe a set of functionally connected regions. In this spatial pattern, each region describes a contiguous area of the landscape within which an animal may move freely below a threshold amount of movement cost. We used telemetry data from woodland caribou Rangifer tarandus caribou to test hypothetical functional grains where connectivity was determined by the spatial configuration of resource patches (patch only), by the resistance to movement presented by landscape features (resistance only), and by a combination of the two (patch + resistance). To identify these functional grains, we used a grains of connectivity approach, and introduced a novel lattice‐based variant of this method to build the resistance only model. We developed a measure of fit that describes caribou distribution with respect to larger functionally connected regions in the grain, and used this to ask: 1) are seasonal caribou locations consistent with a random functional grain, implying that landscape connectivity has not shaped their distribution? 2) Given a functional grain model, are seasonal caribou locations distributed in larger functionally connected regions than random points, implying a response to the shape, size, and location of the connected regions. We found support for landscape connectivity influencing animal distribution using grains based on a landscape resistance model, and that support varied between behaviourally defined seasons. We also discuss how our novel lattice approach may be valuable for highly mobile mammals and other species where the identification of resource patches is a limitation.     

Habitat size and number in multi-habitat landscapes: a model approach based on species-area curves

This paper discusses species diversity in simple multi-habitat environments. Its main purpose is to present simple mathematical and graphical models on how landscape patterns affect species numbers. The idea is to build models of species diversity in multi-habitat landscapes by combining species-area curves for different habitats. Predictions are made about how variables such as species richness and species overlap between habitats influence the proportion of the total landscape each habitat should constitute, and how many habitats it should be divided into in order to be able to sustain the maximal number of species. Habitat size and numbers are the only factors discussed here, not habitat spatial patterns. Among the predictions are: 1) where there are differences in species diversity between habitats, optimal landscape patterns contain larger proportions of species rich habitats. 2) Species overlap between habitats shifts the optimum further towards larger proportions of species rich habitat types. 3) Species overlap also shifts the optimum towards fewer habitat types. 4) Species diversity in landscapes with large species overlap is more resistant to changes in landscape (or reserve) size. This type of model approach can produce theories useful to nature and landscape management in general, and the design of nature reserves and national parks in particular.     

Species–area models to assess biodiversity change in multi-habitat landscapes: The importance of species habitat affinity

Species–area relationships (SARs) are a common tool to assess the impacts of habitat loss on species diversity. Species–area models that include habitat effects may better describe biodiversity patterns; also the shape of the SAR may be best described by other models than the classical power model. We compared the fit of 24 SAR models, i.e. eight basic models using three approaches: (i) single-habitat models, (ii) multi-habitat models which account for the effect of the habitat composition on total species diversity (= choros models) and (iii) multi-habitat models which also account for the differential use of habitats by different species groups (= countryside models). We use plant diversity data from a multi-habitat landscape in NW Portugal. Countryside models had the best fit both when predicting species–area patterns of species groups and of total species richness. Overall, choros models had a better fit than single-habitat models. We also tested the application of multi-habitat models to land-use change scenarios. Estimates of species richness using the choros model only depended on the number of habitats in the landscape. In contrast, for the countryside model, estimates of species richness varied continuously with the relative proportion of the different habitat types in the landscape, and projections suggest that land-use change impacts may be moderated by a species’ ability to use multiple habitats in the landscape. We argue that the countryside SAR is a better model to assess the impacts of land-use changes than the single-habitat SAR or the choros model, as species often face habitat change instead of real habitat loss, and species response to change is contingent on their differential use of habitats in the landscape.     

Breaking functional connectivity into components: a novel approach using an individual-based model, and first outcomes

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting 'functional connectivity', namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict.     

Gene flow in complex landscapes: testing multiple hypotheses with causal modeling

Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000-km(2) study area in northern Idaho, with 110 landscape-resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected heterozygosity=0.79). Landscape-resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.     

Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices

The assessment of landscape spatial patterns is a key issue in landscape management. Landscape pattern indices (LPIs) are tools appropriate for analyzing landscape spatial patterns. LPIs are often derived from raster land cover maps that are extracted from remotely sensed data through hard classification. However, pixel-based hard classification methods suffer from the mixed pixel problem (in which pixels contain more than one land cover class), making for inaccurate classification maps and LPIs. In addition, LPIs generated by hard classification methods are characterized by grain sizes (the sampling unit sizes) that limit the derived landscape pattern to a certain scale. Sub-pixel mapping (SPM) models can enable fine-scale estimation of the spatial patterns of land cover classes without requiring additional data; hence, this is an appropriate downscaling method for land cover mapping. The fraction images generated by soft classification estimate the area proportion of each land cover class within each pixel, and using these images as input enables SPM models to alleviate the mixed pixel problem. At the same time, by transforming fraction images into a finer-scaled hard classification map, SPM models can minimize the influence of grain size on LPIs calculation. In this research, simulated landscape thematic patterns that can provide different landscape spatial patterns, eight commonly used LPIs and a SPM model that maximizes the spatial dependence between neighbouring sub-pixels were applied to assess the efficiency of deriving LPIs from sub-pixel model maps. Results showed that the SPM model can more precisely characterize landscape patterns than hard classification methods can. Landscape fragmentation, class abundance, the uncertainty in SPM, and the spatial resolution of the remotely sensed data influenced LPIs derived from sub-pixel maps. The largest patch index, landscape division, and patch cohesion derived from remotely sensed data with different spatial resolutions through the SPM model were suitable for inter-comparison, whereas the patch density, mean patch area, edge density, landscape shape index, and area-weighted mean shape index derived from the sub-pixel maps were sensitive to the spatial resolution of the remotely sensed data.     

Beyond species distribution modeling: A landscape genetics approach to investigating range shifts under future climate change

Understanding how biodiversity will respond to future climate change is a major conservation and societal challenge. Climate change is predicted to force many species to shift their ranges in pursuit of suitable conditions. This study aims to use landscape genetics, the study of the effects of environmental heterogeneity on the spatial distribution of genetic variation, as a predictive tool to assess how species will shift their ranges to track climatic changes and inform conservation measures that will facilitate movement. The approach is based on three steps: 1) using species distribution models (SDMs) to predict suitable ranges under future climate change, 2) using the landscape genetics framework to identify landscape variables that impede or facilitate movement, and 3) extrapolating the effect of landscape connectivity on range shifts in response to future climate change. I show how this approach can be implemented using the publicly available genetic dataset of the grey long-eared bat, Plecotus austriacus, in the Iberian Peninsula. Forest cover gradient was the main landscape variable affecting genetic connectivity between colonies. Forest availability is likely to limit future range shifts in response to climate change, primarily over the central plateau, but important range shift pathways have been identified along the eastern and western coasts. I provide outputs that can be directly used by conservation managers and review the viability of the approach. Using landscape genetics as a predictive tool in combination with SDMs enables the identification of potential pathways, whose loss can affect the ability of species to shift their range into future climatically suitable areas, and the appropriate conservation management measures to increase landscape connectivity and facilitate movement.     

Divergent landscape effects on population connectivity in two co-occurring amphibian species

The physical and environmental attributes of landscapes often shape patterns of population connectivity by influencing dispersal and gene flow. Landscape effects on movement are typically evaluated for single species. However, inferences from multiple species are required for multi‐species management strategies increasingly being applied in conservation. In this study, I compared the spatial genetic patterns of two amphibian species across the northeastern United States and estimated the influence of specific landscape features on the observed genetic structure. The spotted salamander (Ambystoma maculatum) and wood frog (Rana sylvatica) share many ecological attributes related to habitat use, phenology and site fidelity. However, I hypothesized that important differences in their movement patterns and life history would create distinct genetic patterns for each species. Using 14 microsatellite loci, I tested for differences in the level of genetic differentiation between the two species across 22 breeding ponds. The effects of eight landscape features were also estimated by evaluating 32 landscape resistance models. Spotted salamanders exhibited significantly higher genetic differentiation than wood frogs. Different landscape features were also identified as potential drivers of the genetic patterns in each species, with little overlap in model support between species. Collectively, these results provide strong evidence that these two amphibian species interact with the landscape in measurably different ways. The distinct genetic patterns observed are consistent with key differences in movement ability and life history between A. maculatum and R. sylvatica. These results highlight the importance of considering more than one species when assessing the impacts of the landscape matrix on population connectivity, even for ecologically similar species within the same habitats.     

Identifying potential conservation areas in the Cuitzeo Lake basin,Mexico by multitemporal analysis of landscape connectivity

Lake Cuitzeo basin is an important ecological area subjected to strong human pressure on forest covers that are key elements for the long-term support of biodiversity. We studied landscape connectivity changes for the years 1975, 1996, 2000, 2003 and 2008 to identify potential conservation areas in the basin. We modeled potential distributions of the Mexican bobcat (Lynx rufus escuinapae) and the ringtail (Bassariscus astutus) – two terrestrial mammal focal species with contrasting dispersal capacities – and we determined their habitat availability and suitability in the basin. We then identified their optimal habitat patches and produced landscape cumulative resistance maps, estimated least-cost paths (graph theory approach), and elaborated current flow maps (circuit theory approach). For evaluation of landscape connectivity, we applied an integral index of connectivity (IIC) to each study period, and determined individual habitat patch contribution to the overall landscape connectivity. The IIC index had very low values associated with reduced availability of focal species habitat. However, our study showed the conservation importance of the surface of optimal habitat patch areas. The combined application of a graph-based approach and current flow mapping were useful, and complementary both in terms of estimating potential dispersal corridors and identifying high probability dispersal areas. This indicated that landscape connectivity analysis is a useful tool for identification of potential conservation areas and for local landscape planning.     

景观生态学中生态连接度研究进展

生态连接度对生物迁移扩散、基因流动、干扰扩散等生态过程具有重要作用,是目前景观生态学研究的热点内容.生态连接度是测度景观对于资源斑块间运动的促进或者阻碍作用程度的指标.它主要基于渗透和图论两大理论,通过实验、模型模拟以及指数等量化方法描述区域景观结构和功能的变化,广泛应用在自然景观和城市景观格局优化中,对生物多样性保护以及城市开放空间规划具有重要作用.介绍生态连接度的理论基础、评价方法,应用以及主要结论,并对景观生态学中生态连接度的未来研究方向进行展望,以促进生态连接度研究的进一步发展.     

Genetic differentiation and gene flow among populations of the alpine butterfly, Parnassius smintheus, vary with landscape connectivity

Levels of gene flow among populations vary both inter- and intraspecifically, and understanding the ecological bases of variation in levels of gene flow represents an important link between the ecological and evolutionary dynamics of populations. The effects of habitat spatial structure on gene flow have received considerable attention; however, most studies have been conducted at a single spatial scale and without background data on how individual movement is affected by landscape features. We examined the influence of habitat connectivity on inferred levels of gene flow in a high-altitude, meadow-dwelling butterfly, Parnassius smintheus. For this species, we had background data on the effects of landscape structure on both individual movement and on small-scale population genetic differentiation. We compared genetic differentiation and patterns of isolation by distance, based on variation at seven microsatellite loci, among three regions representing two levels of connectivity of high-altitude, nonforested habitats. We found that reduced connectivity of habitats, resulting from more forest cover at high altitudes, was associated with greater genetic differentiation among populations (higher estimated FST), a breakdown of isolation by distance, and overall lower levels of inferred gene flow. These observed differences were consistent with expectations based on our knowledge of the movement behaviour of this species and on previous population genetic analyses conducted at the smaller spatial scale. Our results indicate that the role of gene flow may vary among groups of populations depending on the interplay between individual movement and the structure of the surrounding landscape.