Landscape Connectivity Measures Research Articles

Spatiotemporal connectivity dynamics in spatially structured populations.

Connectivity is a fundamental concept linking dispersal to the emergent dynamics and persistence of spatially structured populations. Functional measures of connectivity typically seek to integrate aspects of landscape structure and animal movement to describe ecologically meaningful connectedness at the landscape and population scale. Despite this focus on function, traditional measures of landscape connectivity assume it is a static property of the landscape, hence abstracting out the underlying spatiotemporal population dynamics. Connectivity is, arguably, a dynamic property of landscapes, and is inherently related to the spatial distribution of individuals and populations across the landscape. Static representations of connectivity potentially overlook this variation and therefore adopting a dynamic approach should offer improved insights about connectivity and associated ecological processes. Using a large-scale, long-term time series of occupancy data from a metapopulation of water voles Arvicola amphibius, we tested competing hypotheses about how considering the dynamic nature of connectivity improves the ability of spatially explicit occupancy models to recover population dynamics. Iteratively relaxing standing assumptions of connectivity metrics, these models ranged from spatially and temporally fixed connectivity metrics that are widely applied, to the more flexible, but lesser used model that allowed temporally varying connectivity measures that incorporate spatiotemporally dynamic patch occupancy states. Our results provide empirical evidence that demographic weighting using patch occupancy dynamics and temporal variability in connectivity measures are important for describing metapopulation dynamics. We highlight the implications of commonly held assumption in connectivity modelling and demonstrate how they result in different and highly variable predictions of metapopulation capacity. Thus, we argue that the concept of connectivity and its potential applications would benefit from recognizing inherent spatiotemporal variation in connectivity that is explicitly linked to underlying ecological state variables.

An equitable method for evaluating habitat amount and potential occupancy

Landscape connectivity measures based on metapopulation theory were developed over 20 years ago. Initially, they applied classic metapopulation models to simple patch-based representations of landscapes using vector spatial data structures. Realism was improved by developing dynamic estimates of occupancy and metapopulation capacity, the latter providing a measure of the integrated habitat amount. Such measures are used to estimate the ability of habitat networks to support metapopulation persistence. The original methods for occupancy mapping and metapopulation capacity were adapted to work with fine-grained, continuous-value raster data. That step shifted the method outside of the classic metapopulation model which left some methodological issues unresolved; in particular, what has been termed the deceptive paradox of patch-based connectivity whereby perverse and what we describe as inequitable results are obtained through arbitrary circumscription of the analysis grid and through the trading of habitat between habitat quality, extent and connectivity. We provide a solution to this issue and apply it within the frame of Drielsma and Ferrier's (2009) raster-based Rapid Evaluation of Metapopulation Persistence (REMP).We demonstrate our solution using simple hypothetical examples; and in order to demonstrate the practicality of our approach to real-world settings, we apply the approach to habitat suitability mapping of the White-browed Treecreeper (Climacteris affinis) in south eastern New South Wales, Australia.

Fine-scale variation within urban landscapes affects marking patterns and gastrointestinal parasite diversity in red foxes.

Urban areas are often considered to be a hostile environment for wildlife as they are highly fragmented and frequently disturbed. However, these same habitats can contain abundant resources, while lacking many common competitors and predators. The urban environment can have a direct impact on the species living there but can also have indirect effects on their parasites and pathogens. To date, relatively few studies have measured how fine‐scale spatial heterogeneity within urban landscapes can affect parasite transmission and persistence.Here, we surveyed 237 greenspaces across the urban environment of Edinburgh (UK) to investigate how fine‐scale variation in socio‐economic and ecological variables can affect red fox (Vulpes vulpes) marking behavior, gastrointestinal (GI) parasite prevalence, and parasite community diversity.We found that the presence and abundance of red fox fecal markings were nonuniformly distributed across greenspaces and instead were dependent on the ecological characteristics of a site. Specifically, common foraging areas were left largely unmarked, which indicates that suitable resting and denning sites may be limiting factor in urban environments. In addition, the amount of greenspace around each site was positively correlated with overall GI parasite prevalence, species richness, and diversity, highlighting the importance of greenspace (a commonly used measure of landscape connectivity) in determining the composition of the parasite community in urban areas.Our results suggest that fine‐scale variation within urban environments can be important for understanding the ecology of infectious diseases in urban wildlife and could have wider implication for the management of urban carnivores.

Samc: an R package for connectivity modeling with spatial absorbing Markov chains

Quantifying landscape connectivity is fundamental to better understand and predict how populations respond to environmental change. Currently, popular methods to quantify landscape connectivity emphasize how landscape features provide resistance to movement. While many tools are available to quantify landscape resistance, these do not discern between two fundamentally different sources of resistance: movement behavior and mortality. To address this issue, we developed the samc R package that quantifies landscape connectivity using absorbing Markov chain theory. Within this mathematical framework, movements are represented as transient states in the Markov chain, while mortality is represented by transitions to absorbing states. Not only does this framework explicitly account for these different issues, it provides a probabilistic approach that can incorporate both short‐term and long‐term dynamics, as well as species distribution and abundance. The package includes functions to quantify life expectancy, long‐term visitation rates, and various spatially and temporally explicit measures of mortality and movement at the local and landscape scales. These functions in samc have been optimized to find computationally practical solutions in landscapes comprised of > 2 × 106cells. Here, we illustrate the workflow of the samc package with publicly available movement and mortality data on the endangered Florida pantherPuma concolor coryi. This analysis showed that movement and mortality are generally correlated except for locations near roads (areas of high mortality risk) that are within the dispersal range of source locations. This pattern would have been undetectable with current methods that quantify movement resistance. Overall, the samc package provides a means for implementing spatial absorbing Markov chains that can distinguish between movement behavior and mortality resulting in more reliable landscape connectivity measures.

From structure to function: Understanding shrub encroachment in drylands using hydrological and sediment connectivity

Hydrological and sediment connectivity can help us to understand better how physical and process-based linkages govern ecogeomorphic feedbacks and identify locations where degradation is likely to be pronounced. In this study we investigate how hydrological and sediment connectivity affect ecogeomorphic feedbacks in transitional landscapes. We propose a novel approach, the Relative Connectivity Index (RCI), to quantify landscape connectivity which explicitly integrates structural measures of landscape connectivity (SC) with functional measures of hydrological and sediment connectivity (FC) that are derived from runoff and sediment-transport modelling. We use the RCI calculated for runoff (RCIH) and sediment (RCIS) to identify locations and times when functional connectivity exceeds structural connectivity thresholds – where land degradation is likely to be pronounced – and explore how these thresholds are affected by rainfall-event size and antecedent soil-moisture content. We find that there are non-linear increases in RCIH values with an increase in shrub cover, which suggest that ecogeomorphic feedbacks become more important in modifying system structure and function during late stages of shrub encroachment. Thresholds of sediment connectivity appear to be directly related to thresholds of hydrological connectivity, although rainsplash appears to be an important mechanism in creating connected sediment transport where there is no connected runoff. High RCIH values are most widely distributed for the largest (45 mm) rainfall event, whilst high RCIS values are observed to some extent across all stages of the grass to shrub transition for rainfall events as small as 10 mm. Whilst particularly large events have a low return period, they appear to be particularly instrumental in shaping ecogeomorphic feedbacks that are likely to drive catastrophic shifts in ecosystem state. The strength of the indicator approach used here is that it enables identification of regions with pronounced ecogeomorphic feedbacks, which act as potential trigger points for catastrophic shifts in ecosystem state, and thus, we demonstrate how the static limitations of existing approaches to developing connectivity indices may be overcome. The dynamic RCI allows the evaluation of the vulnerability or resilience of a particular system to variable driving mechanisms. The RCI can therefore be used to guide management interventions aimed at reducing or mitigating undesirable ecosystem state change, by focussing on specific locations/regions with high RCI values, to prevent further chances in system structure and function and to maximise the provision of ecosystem services.

Influence of local and landscape factors on distributional dynamics: a species-centred, fitness-based approach.

In spatially structured populations, distributional dynamics are driven by the quantity, connectivity and quality of habitat. Because these drivers are rarely measured directly and simultaneously at relevant scales, information on their relative importance remains unclear. I assessed the influence of both direct and indirect measures of local habitat quality, and of landscape habitat amount and connectivity on long-term territory occupancy dynamics of non-migratory pygmy owls. Direct measures of local habitat quality based on territory-specific reproductive output had greater effects on distribution than landscape factors, but only when spatio-temporal fluxes in performance linked to environmental stochasticity and intraspecific competition were considered. When habitat quality was measured indirectly based on habitat structure, however, landscape factors had greater effects. Although all landscape factors were important, measures of landscape connectivity that were uncorrelated with habitat amount and based on attributes of matrix structure and habitat configuration that influence dispersal movements had greater effects than habitat effective area (amount weighted by quality). Moreover, the influence of connectivity (but not habitat effective area) depended on local habitat quality. Such results suggest the relative importance of local habitat quality in driving distribution has been underestimated and that conservation strategies should vary spatially depending on both local and landscape contexts.

Synthetic Aperture Radar (SAR) images improve habitat suitability models

Context The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors. Objectives The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs). Methods We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model. Results We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species. Conclusions Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.

Model‐based estimators of density and connectivity to inform conservation of spatially structured populations

AbstractConservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model‐based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density‐weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Effects of uncertain cost-surface specification on landscape connectivity measures

Estimates of landscape connectivity are routinely used to inform decision-making by conservation biologists. Most estimates of connectivity rely on cost-surfaces: raster representations of landscapes in which cost values represent the difficulty involved with traversing an area. However, there is considerable uncertainty in the generation of cost-surfaces that have not been widely explored. We investigated the effects of four potential sources of uncertainty in the creation of cost-surfaces: 1) number of landscape classes represented; 2) spatial resolution (grain size); 3) misclassification of edges between landscape classes; and 4) cost values selected for each landscape class. Following a factorial design we simulated multiple cost-surface pairs, each comprising one true surface with no errors and one surface with uncertainty comprised of some combination of the four error sources. We evaluated the relative importance of each source of uncertainty in determining the difference between the least-cost paths (LCPs) costs and resistance distances generated for the true and erroneous cost-surfaces, using four model evaluation metrics. Errors in the underlying geospatial layers produced larger inaccuracies in connectivity estimates than those produced by cost-value errors. Incorrect grain size had the largest overall effect on the accuracy of connectivity estimates. Though the removal of an element class was found to have a large effect on the configuration of connectivity estimates, and the addition of an element class had a large effect on estimates configuration. Our results highlight the importance of minimising and quantifying the uncertainty inherent in the geospatial data used to develop cost-surfaces.

Within-catchment variability in landscape connectivity measures in the Garang catchment, upper Yellow River

The degree of landscape connection has wide-ranging implications for sediment availability, frequency of transport, and the nature of sediment storage within a basin. Two methodological approaches for assessing landscape connectivity are applied within the Garang subcatchment of the upper Yellow River. A GIS-based geomorphometric index was calculated for the catchment and compared to a methodology linking interpretation of satellite imagery and field mapping of sediment storage to slope threshold analysis. Landscapes of the Garang catchment are highly contrasting and can be separated into three zones: i) a highly disconnected upper catchment of low-relief with large inactive sediment stores; ii) a transitional zone where present landscape dynamics are controlled in large part by past incisional processes in the form of large alluvial fan/terrace deposits; and iii) a highly connected and highly dissected landscape within the lower catchment that has little accommodation space for sediment storage. The geomorphometric index shows general agreement with the patterns in landscape connectivity observed through imagery and field investigations. However, it estimates similar levels of connectivity in landscapes with observed (dis)connectivity, reflecting limitations of the calculation. While rapidly calculated geomorphometric indices of landscape connectivity provide useful appraisals of relative within-catchment variability in systems such as the Garang catchment, caution is urged in conducting broader-scale analyses using the same tools. Findings from this study highlight the importance of field-informed appraisals of landscape dynamics, site-specific characteristics and the imprint of historical (evolutionary) context in making such comparative assessments.

Modelling non‐Euclidean movement and landscape connectivity in highly structured ecological networks

Summary Movement is influenced by landscape structure, configuration and geometry, but measuring distance as perceived by animals poses technical and logistical challenges. Instead, movement is typically measured using Euclidean distance, irrespective of location or landscape structure, or is based on arbitrary cost surfaces. A recently proposed extension of spatial capture‐recapture (SCR) models resolves this issue using spatial encounter histories of individuals to calculate least‐cost paths (ecological distance: Ecology, 94, 2013, 287) thereby relaxing the Euclidean assumption. We evaluate the consequences of not accounting for movement heterogeneity when estimating abundance in highly structured landscapes, and demonstrate the value of this approach for estimating biologically realistic space‐use patterns and landscape connectivity. We simulated SCR data in a riparian habitat network, using the ecological distance model under a range of scenarios where space‐use in and around the landscape was increasingly associated with water (i.e. increasingly less Euclidean). To assess the influence of miscalculating distance on estimates of population size, we compared the results from the ecological and Euclidean distance based models. We then demonstrate that the ecological distance model can be used to estimate home range geometry when space use is not symmetrical. Finally, we provide a method for calculating landscape connectivity based on modelled species‐landscape interactions generated from capture‐recapture data. Using ecological distance always produced unbiased estimates of abundance. Explicitly modelling the strength of the species‐landscape interaction provided a direct measure of landscape connectivity and better characterised true home range geometry. Abundance under the Euclidean distance model was increasingly (negatively) biased as space use was more strongly associated with water and, because home ranges are assumed to be symmetrical, produced poor characterisations of home range geometry and no information about landscape connectivity. The ecological distance SCR model uses spatially indexed capture‐recapture data to estimate how activity patterns are influenced by landscape structure. As well as reducing bias in estimates of abundance, this approach provides biologically realistic representations of home range geometry, and direct information about species‐landscape interactions. The incorporation of both structural (landscape) and functional (movement) components of connectivity provides a direct measure of species‐specific landscape connectivity.

Can epidemic control be achieved by altering landscape connectivity in agricultural systems?

Few investigations on host diversification at the landscape scale to control plant disease in agricultural systems are available in the literature. At this scale, landscape connectivity measures how the landscape structure facilitates or impedes the disease spread among host patches. We developed a simulation model, giving a particular attention to the representation of the landscape structures, and we characterized the landscape connectivity by the proportion and the aggregation level of the host varieties, the ability for the pathogen to develop on each host and its ability to disperse. The pathogen dynamics was represented by a matrix population model designed for a generic air-borne foliar disease. This framework was used to establish a detailed assessment of the influence of each landscape connectivity variable on the pathogen population dynamics. When deploying a host with complete resistance to the pathogen along with a susceptible host, mixed landscapes were always found to be more efficient to hamper the disease spread. However, when using a quantitatively resistant host, aggregating the hosts in different regions could result in a better control of the pathogen spread, depending on the proportion and level of resistance of the resistant host and according to a source–sink dynamics between the two hosts. The ability of the pathogen to disperse did not change the results from a qualitative point of view. By accounting explicitly for the landscape features, our approach can be used to guide further data analysis or to evaluate the effectiveness of control strategies designed at the landscape scale.

Habitat Edge Contrast as an Indicator to Prioritize Sites for Ecological Restoration at the Landscape Scale

Forest restoration at the landscape level has required the development of criteria and methods to prioritize sites of restoration. In this study, we analyzed the spatial patterns of habitat contrast edge to prioritize sites for restoration in a protected area in southern Chile. The edge contrast index (ECON) was estimated and mapped for each pairwise combination of habitat type, based on community composition and structure ecological variables. Correlation length, as a measure of landscape connectivity, was calculated to analyze the effect of restoring high-contrast edge zones on focal habitat continuity. Approximately, 20% of the landscape area was composed of patches of high-contrast habitat edge (ECON ≥50%). Out of the total edge length, 91% corresponded to high-contrast edges between the focal habitat and forest plantations of Eucalyptus globulus and Pinus radiata and stands invaded by Acacia spp. Only 9% corresponded to low-contrast edges (< 0.5) with natural forests. The highest ECON corresponded to patches both invaded by alien species (80-90%) and planted with exotic tree species (60-70%) that were adjacent to the focal habitat patches. Zones of high contrast that were dissecting the focal habitat patches were prioritized for restoration. The correlation length increased from 1,822 to 2,211 m for the current and the restoration scenario, respectively. Landscape planning should strive to reduce edges between suitable and unsuitable habitats, taking into account edge sensitivity of forest-dependent species and the spread of invasive species.

Connectivity of agroecosystems: dispersal costs can vary among crops

Knowledge of how habitat heterogeneity affects dispersal is critical for conserving connectivity in current and changing landscapes. However, we generally lack an understanding of how dispersal costs and animal movements vary among crops characteristic of agroecosystems. We hypothesized that a physiological constraint, desiccation risk, influences movement behavior among crops and other matrix habitats (corn, soybean, forest, prairie) in Ambystoma tigrinum (tiger salamander) in Illinois, USA. In a desiccation experiment, salamanders were added to enclosures in four replicate plots of each matrix habitat, and water loss was measured every 12 h for 48 h. Changes in water loss were examined using a linear mixed model. Water loss varied among treatments, over time, and there was a significant treatment-time interaction. Water loss was greater in corn and prairie than in forest and soybean. To assess whether salamanders move through matrix habitats that minimize desiccation, we tracked movements of individuals released on edges between habitats for two treatment combinations: soybean–corn, and soybean–prairie. As predicted based on our desiccation experiment, movements were oriented towards soybean in both cases. Thus, variation in desiccation risk among matrix habitats likely influenced movement decisions by salamanders, although other factors such as predation risk could have contributed to habitat choice. We argue that conceptualizing dispersal cost as uniformly high in all crop types is too simplistic. Estimating crop-specific dispersal costs and movement patterns may be necessary for constructing effective measures of landscape connectivity in agroecosystems.

What do we still need to know about the ecohydrology of riparian zones?

AbstractRiparian zones have become increasingly important elements of river basin ecohydrology given their relevance of nutrient dynamics to water quality management. Most attention has been given to nitrogen cycling, denitrification in particular, but new processes have emerged as potentially important and need to be evaluated. Riparian zones are sensitive locations, so it is important to understand the impact of changes that can fundamentally alter their ecosystem processes and responses. Better understanding of how riparian buffering varies in time and space provides the basis for a catchment‐scale approach, but it seems likely that low‐order basins will remain the focus for research and management, given that low‐order basins constitute the majority of basin area. Finally, it is argued that new ecohydrological indicators are needed that combine innovative biogeochemical parameters and landscape connectivity measures. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Life history and matrix heterogeneity interact to shape metapopulation connectivity in spatially structured environments

Metapopulation models have historically treated a landscape as a collection of habitat patches separated by a matrix of uniformly unsuitable habitat. This perspective is still apparent in many studies of marine metapopulations, in which recruitment variation is generally assumed to be primarily the result of variability in ocean currents and interactions with disperser behavior, with little consideration of spatial structure that can affect disperser viability. We use a simple model of dispersal of marine larvae to demonstrate how heterogeneity in dispersal habitat (i.e., the matrix) can generate substantial spatial variation in recruitment. Furthermore, we show how this heterogeneity can interact with larval life-history variation to create alternative patterns of source-sink dynamics. Finally, we place our results in the context of spatially structured matrix population models, and we propose the damping ratio of the connectivity matrix as a general and novel measure of landscape connectivity that may provide conceptual unification to the fields of metapopulation biology and landscape ecology.

Testing the importance of spatial configuration of winter habitat for woodland caribou: An application of graph theory

Conservation of remaining woodland caribou ( Rangifer tarandus caribou) populations requires land management strategies that not only maintain caribou habitat, but also favour habitat connectivity. This study presents and field tests graph theory-based measures of landscape connectivity, and demonstrate an association between the distribution of woodland caribou and well-connected winter habitat. Cost values for the intervening land cover types were determined based on the probability of selection relative to high-quality winter habitat. Habitat connectivity was then represented by linking high-quality habitat patches along least-cost paths through this parameterized cost surface. A randomization procedure was used to assess the animal’s association with habitat connected at increasing distance thresholds to identify appropriate scales of response. A strong relationship was obtained between large clusters of high-quality winter habitat patches and winter GPS telemetry location points (November 1–March 15) from two woodland caribou herds in Manitoba, Canada. This relationship was stronger when only late winter location points (January 1–March 15) were used. Our results highlight the importance of accounting for the spatial configuration of habitat on the landscape and the intervening land cover types when assessing range quality for woodland caribou. They also provide support for the use of graph theory to assist in identifying core activity areas for woodland caribou and key linkages between these areas and other parts of the landscape.

How does landscape structure influence landscape connectivity?

We investigated the impact of landscape structure on landscape connectivity using a combination of simulation and empirical experiments. In a previous study we documented the movement behaviour of a specialized goldenrod beetle (Trirhabda borealis Blake) in three kinds of patches: habitat (goldenrod) patches and two types of matrix patch (cut vegetation and cut vegetation containing camouflage netting as an impediment to movement). In the current study, we used this information to construct simulation and experimental landscapes consisting of mosaics of these three patch types, to study the effect of landscape structure on landscape connectivity, using the T. borealis beetle as a model system. In the simulation studies, landscape connectivity was based on movements of individual beetles, and was measured in six different ways. The simulations revealed that the six measures of landscape connectivity were influenced by different aspects of landscape structure, suggesting that: (1) landscape connectivity is a poorly defined concept, and (2) the same landscape may have different landscape connectivity values when different measures of landscape connectivity are used. There were two general predictions that held over all measures of landscape connectivity: (1) increasing interpatch distance significantly decreased landscape connectivity and (2) the influence of matrix elements on landscape connectivity was small in comparison to the influence of habitat elements. Empirical mark‐release‐resight experiments using Trirhabda beetles in experimental landscapes supported the simulation results.

Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach

Connectivity determines a large number of ecological functions of the landscape, including seed and animal dispersal, gene flow and disturbance propagation, and is therefore a key to understanding fluxes of matter and energy within land mosaics. Several approaches to quantifying landscape connectivity are possible. Among these, graph theory may be used to represent a landscape as a series of interconnected patches, where flows occur as a result of structural and/or functional patch connectivity. Within this context, we propose the use of a graph-theoretic index (i.e., the Harary index) as a measure of landscape connectivity. Results derived from the analysis of the vegetation map of Palmarola (central Italy) show that, from a statistical and ecological viewpoint, the Harary index may be a better measure of landscape connectivity than more traditional indices derived from transportation geography.

How should we measure landscape connectivity

The methods for measuring landscape connectivity have never been compared or tested for their responses to habitat fragmentation. We simulated movement, mortality and boundary reactions across a wide range of landscape structures to analyze the response of landscape connectivity measures to habitat fragmentation. Landscape connectivity was measured as either dispersal success or search time, based on immigration into all habitat patches in the landscape. Both measures indicated higher connectivity in more fragmented landscapes, a potential for problematic conclusions for conservation plans. We introduce cell immigration as a new measure for landscape connectivity. Cell immigration is the rate of immigration into equal-sized habitat cells in the landscape. It includes both within- and between-patch movement, and shows a negative response to habitat fragmentation. This complies with intuition and existing theoretical work. This method for measuring connectivity is highly robust to reductions in sample size (i.e., number of habitat cells included in the estimate), and we hypothesize that it therefore should be amenable to use in empirical studies. The connectivity measures were weakly correlated to each other and are therefore generally not comparable. We also tested immigration into a single patch as an index of connectivity by comparing it to cell immigration over the landscape. This is essentially a comparison between patch-scale and landscape-scale measurement, and revealed some potential for patch immigration to predict connectivity at the landscape scale. However, this relationship depends on the size of the single patch, the dispersal characteristics of the species, and the amount of habitat in the landscape. We conclude that the response of connectivity measures to habitat fragmentation should be understood before deriving conclusions for conservation management.