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Context: Preserving functional connectivity is a conservation priority to secure the long-term viability of geographically dispersed subpopulations, such as the jaguar (Panthera onca) populations in Central America. However, managing connectivity in this region is limited due to the scarcity of local assessments of the connectivity between existing populations, some of which exhibit low levels of gene flow and genetic admixture. Objectives: We selected the jaguar as a model species to understand how gene flow of large carnivores is shaped in a heavily human-impacted landscape in the Neotropics. We assessed the impact of landscape features and spatial scale on jaguar gene flow across northern Central America, predicted synoptic, landscape-wide functional connectivity across the region; and compared connectivity predictions based on models of gene flow, habitat suitability, and expert knowledge. Methods: We employed genetic data based on 335 faecal samples collected from 72 individual jaguars. We parameterized gene flow resistance surfaces using linear mixed effects models and the maximum likelihood population-effects method. We evaluated nine landscape variables at six spatial scales, selecting the optimal scale and transformation for each variable according to univariate models and AIC. To predict jaguar gene flow, we developed multivariate models and implemented resistant kernels to forecast functional connectivity between jaguar populations across the study region under three dispersal distance scenarios. Furthermore, we compared the connectivity estimates based on gene flow against those based on habitat suitability and the corridors delineated by expert knowledge in the region. Results: Low resistance to jaguar gene flow was associated with greater tree cover and vegetation, lower areas of built-up, and intermediate distances from water bodies. Notably, tree cover affected jaguar gene flow on a smaller scale compared to the rest of variables. Higher connectivity, indicated by lower resistance, was found within and around the largest jaguar conservation units (JCUs) such as the Reserva-de-Biosfera-Transfronteriza, Selva-Maya and Maya-Mountains. This contrasted with the smallest JCUs, like Sierra-Santa-Cruz and Sierra-de-las-Minas in eastern Guatemala, and Cordillera-Nombre-de-Dios in Honduras. Across the region, lower connectivity was observed in the Caribbean connection between eastern Guatemala and midwestern Honduras, as well as in the Honduran and Nicaraguan Miskito area. Models based on gene flow and habitat suitability were similar in their predictions of areas of high connectivity; however, the habitat suitability models predicted larger areas of low connectivity than did the gene flow models. Moreover, the expert knowledge corridors were consistent with areas of high and medium connectivity as predicted by the gene flow model. Conclusions: Gene flow of jaguars is positively driven by the presence of forest and water sources, while human impact (built-up and non-vegetation areas) has a negative effect. Areas of lowest resistance largely correspond to the location of JCUs, which serve as crucial reservoirs of high-quality jaguar habitat within the region. While the largest JCU in the region displayed high connectivity, the low connectivity between the smallest JCUs (i.e. Sierra-Santa-Cruz, Sierra-de-las-Minas and Cordillera-Nombre-de-Dios) underscores the need for conservation attention in these areas. Conservation and management actions such as habitat loss-prevention/restoration and anthropogenic impact mitigation should be prioritized in the binational region of Guatemala-Honduras, a key connectivity bottleneck between the species’ northern and southern ranges. Similarly, attention is warranted in the Honduran-Nicaraguan Miskito area. [ABSTRACT FROM AUTHOR]

• Given the wide application of connectivity modelling tools in applied conservation planning, it is crucial to broadly evaluate how these models perform across resistance, movement, and population structure conditions in predicting genetic diversity patterns. • Our goal was to investigate how the predictions of three connectivity models were related to spatial patterns of genetic diversity complex landscapes, considering factors such as population structure, resistance, genetic drift, genetic disequilibrium, and organism movement abilities. • We used CDPOP, an individual-based, spatially explicit population and genetic simulation model, to simulate genetic diversity across these resistance surfaces. We then simulated landscape connectivity with several popular connectivity methods, including resistant kernels, Circuitscape, and Pathwalker, and evaluated how well they were able to predict spatial patterns of genetic diversity. • Resistant kernel outperformed other connectivity methods in predicting landscape patterns of genetic diversity. The strongest relationships occurred when the population process has created spatial structure but has not yet led to significant genetic diversity loss due to drift. • Our results confirm the strong relationship between genetic diversity and population connectivity, and suggest that computationally efficient incidence function algorithms, such as resistant kernel methods, are well suited to predicting functional connectivity. There have been few evaluations of how well different connectivity modelling methods are able to predict the spatial genetic structure and genetic diversity of populations residing in complex landscapes. Given the wide application of connectivity modelling tools in applied conservation planning, it is crucial to broadly evaluate how these models perform across resistance, movement, and population structure conditions in predicting genetic diversity patterns. Such evaluations are critical to provide rigorous, biologically based guidance for conservation and management applications. Our goal was to investigate how the predictions of three connectivity models were related to spatial patterns of genetic diversity complex landscapes, considering factors such as population structure, resistance, genetic drift, genetic disequilibrium, and organism movement abilities. We evaluated the performance of several connectivity methods across seven a priori landscape resistance surfaces to provide a broad assessment of their performance. We used CDPOP, an individual-based, spatially explicit population and genetic simulation model, to simulate genetic diversity across these resistance surfaces. This provided a pool of genetic diversity patterns that were the response factor in our simulation experiment. We then simulated landscape connectivity with several popular connectivity methods, including resistant kernels, Circuitscape, and Pathwalker, and evaluated how well they were able to predict spatial patterns of genetic diversity. Resistant kernel outperformed other connectivity methods in predicting landscape patterns of genetic diversity. The strongest relationships occurred when the population process has created spatial structure but has not yet led to significant genetic diversity loss due to drift. The time lag disequilibrium was relatively short. Long simulation times resulted in severe reduction in prediction ability due to drift. Resistant kernel predictions were much more strongly related to spatial patterns of genetic diversity than were predictions produced by Circuitscape and Pathwalker, across a large combination of population structures. Strong relationships exist between functional connectivity and genetic diversity, with clearer and stronger associations seen in spatial patterns of allelic richness compared to heterozygosity or spatial effective population size. Our results confirm the strong relationship between genetic diversity and population connectivity, and suggest that computationally efficient incidence function algorithms, such as resistant kernel methods, are well suited to predicting functional connectivity. [Display omitted] [ABSTRACT FROM AUTHOR]

Context: Wildlife distributions are often subdivided into discrete conservation units to aid in implementing management and conservation objectives. Habitat suitability models, resistance surfaces, and resistant kernels provide tools for delineating spatially explicit conservation units but guidelines for parameterizing resistant kernels are generally lacking. Objectives: We used the federally threatened eastern indigo snake (Drymarchon couperi) as a case study for calibrating resistant kernels using observed movement data and resistance surfaces to help delineate habitat-based conservation units. Methods: We simulated eastern indigo snake movements under different resistance surface and resistant kernel parameterizations and selected the scenario that produced simulated movement distances that best approximated the maximum observed annual movement distance. We used our calibrated resistant kernel to model range-wide connectivity and compared delineated conservation units to Euclidean distance-based population units from the recent eastern indigo snake species status assessment (SSA). Results: We identified a total of 255 eastern indigo snake conservation units, with numerous large (2500–5000 ha of suitable habitat) conservation units across the eastern indigo snake distribution. There was substantial variation in the degree of overlap with the SSA population units likely reflecting the spatial heterogeneity in habitat suitability and landscape resistance. Conclusion: Our calibration approach is widely applicable to other systems for parameterizing biologically meaningful resistant kernels. Our conservation units can be used to prioritize future eastern indigo snake conservation efforts, identify areas where more survey work is needed, or identify small, isolated populations with high extinction risks. [ABSTRACT FROM AUTHOR]

Habitat loss and fragmentation are widely acknowledged as the main driver of the decline of giant panda populations. The Chinese government has made great efforts to protect this charming species and has made remarkable achievements, such as population growth and habitat expansion. However, habitat fragmentation has not been reversed. Protecting giant pandas in a large spatial extent needs to identify core habitat patches and corridors connecting them. This study used an equal‐sampling multiscale random forest habitat model to predict a habitat suitability map for the giant panda. Then, we applied the resistant kernel method and factorial least‐cost path analysis to identify core habitats connected by panda dispersal and corridors among panda occurrences, respectively. Finally, we evaluated the effectiveness of current protected areas in representing core habitats and corridors. Our results showed high scale dependence of giant panda habitat selection. Giant pandas strongly respond to bamboo percentage and elevation at a relatively fine scale (1 km), whereas they respond to anthropogenic factors at a coarse scale (≥2 km). Dispersal ability has significant effects on core habitats extent and population fragmentation evaluation. Under medium and high dispersal ability scenarios (12,000 and 20,000 cost units), most giant panda habitats in the Qionglai mountain are predicted to be well connected by dispersal. The proportion of core habitats covered by protected areas varied between 38% and 43% under different dispersal ability scenarios, highlighting significant gaps in the protected area network. Similarly, only 43% of corridors that connect giant panda occurrences were protected. Our results can provide crucial information for conservation managers to develop wise strategies to safeguard the long‐term viability of the giant panda population. [ABSTRACT FROM AUTHOR]

Context: Habitat loss and fragmentation are main drivers of biodiversity loss and are occurring at an accelerating rate globally. Carnivores are often subject to persecution and conflict with humans, typically reside in low densities and require large areas for core habitat and dispersal, which makes them especially vulnerable to habitat loss and fragmentation. Objectives: This paper identifies, maps and analyses habitat core areas and connectivity linkages for three mountain-residing carnivore species of high conservation importance in Iran: Persian leopard (Panthera pardus saxicolor), Eurasian lynx (Lynx lynx) and Pallas's cat (Otocolobus manul). Methods: We used ensemble habitat suitability modeling, and compared the accuracy of ensemble models with seven separate models based on AUC and TSS. We applied resistant kernel and factorial least-cost path analyses to identify population core areas and corridors across the full distributions of the three species in Iran. Results: Mean annual temperature, vegetation greenness (NDVI), and slope were among the most important predictor variables for all three species. We found ensemble modeling outcompeted all single-method models in terms of AUC. We found low overlap between predicted corridor locations of our modeled species with Protected Areas. Conclusions: Given the fragmented populations of our studied species in Iran, conserving them will require integrated landscape-level management to protect corridors and enhance connectivity, especially outside of Protected Areas. Optimized landscape management to conserve these species will likely promote conservation of montane landscapes and their inhabitants in Iran. [ABSTRACT FROM AUTHOR]

Abstract Habitat loss and fragmentation are widely acknowledged as the main driver of the decline of giant panda populations. The Chinese government has made great efforts to protect this charming species and has made remarkable achievements, such as population growth and habitat expansion. However, habitat fragmentation has not been reversed. Protecting giant pandas in a large spatial extent needs to identify core habitat patches and corridors connecting them. This study used an equal‐sampling multiscale random forest habitat model to predict a habitat suitability map for the giant panda. Then, we applied the resistant kernel method and factorial least‐cost path analysis to identify core habitats connected by panda dispersal and corridors among panda occurrences, respectively. Finally, we evaluated the effectiveness of current protected areas in representing core habitats and corridors. Our results showed high scale dependence of giant panda habitat selection. Giant pandas strongly respond to bamboo percentage and elevation at a relatively fine scale (1 km), whereas they respond to anthropogenic factors at a coarse scale (≥2 km). Dispersal ability has significant effects on core habitats extent and population fragmentation evaluation. Under medium and high dispersal ability scenarios (12,000 and 20,000 cost units), most giant panda habitats in the Qionglai mountain are predicted to be well connected by dispersal. The proportion of core habitats covered by protected areas varied between 38% and 43% under different dispersal ability scenarios, highlighting significant gaps in the protected area network. Similarly, only 43% of corridors that connect giant panda occurrences were protected. Our results can provide crucial information for conservation managers to develop wise strategies to safeguard the long‐term viability of the giant panda population.

Abstract The Amur tiger (Panthera tigris altaica) is critically endangered and also the subspecies of the tiger with the most restoration potential in China. It is challenging to protect large‐ranging carnivores like tigers under increasing pressure of human development. To provide a more technically robust foundation for tiger habitat conservation prioritization, we conducted a comprehensively empirical analysis based on a broadly collected occurrence dataset of tigers and their prey. We modeled tiger distribution by running an ensemble model integrating nine different algorithms. We found that the ensemble model performed well and outperformed any individual model regarding the discrimination ability. We used cumulative resistant kernel analysis to identify core habitats as with high predicted movement density and used factorial least‐cost paths to model corridors among tiger occurrence locations. We found that core habitats for Amur tigers are distributed in three mountain areas, namely, eastern Wanda Mountain, southern Zhangguangcailing, and Laoyeling‐Dalongling. We found significant protection gaps as existing protected areas only cover less than one‐fourth of predicted core habitats, but this proportion will rise significantly with the establishment of the Northeast China Tiger and Leopard National Park. Furthermore, we ranked spatial priorities for the expansion of the protected area network, simultaneously considering biological and socioeconomic dimensions under the Zonation framework. Our study presented the most up‐to‐date and detailed maps of the predicted potential distribution and area of the most important habitats for the Amur tigers in China, which can provide quantitative guidance in the effort to maximize the efficiency of conservation initiatives at a regional scale.

The Amur tiger (Panthera tigris altaica) is critically endangered and also the subspecies of the tiger with the most restoration potential in China. It is challenging to protect large‐ranging carnivores like tigers under increasing pressure of human development. To provide a more technically robust foundation for tiger habitat conservation prioritization, we conducted a comprehensively empirical analysis based on a broadly collected occurrence dataset of tigers and their prey. We modeled tiger distribution by running an ensemble model integrating nine different algorithms. We found that the ensemble model performed well and outperformed any individual model regarding the discrimination ability. We used cumulative resistant kernel analysis to identify core habitats as with high predicted movement density and used factorial least‐cost paths to model corridors among tiger occurrence locations. We found that core habitats for Amur tigers are distributed in three mountain areas, namely, eastern Wanda Mountain, southern Zhangguangcailing, and Laoyeling‐Dalongling. We found significant protection gaps as existing protected areas only cover less than one‐fourth of predicted core habitats, but this proportion will rise significantly with the establishment of the Northeast China Tiger and Leopard National Park. Furthermore, we ranked spatial priorities for the expansion of the protected area network, simultaneously considering biological and socioeconomic dimensions under the Zonation framework. Our study presented the most up‐to‐date and detailed maps of the predicted potential distribution and area of the most important habitats for the Amur tigers in China, which can provide quantitative guidance in the effort to maximize the efficiency of conservation initiatives at a regional scale. [ABSTRACT FROM AUTHOR]

Abstract In this study, we assess potential habitats and connectivity for the common leopard (Panthera pardus) in the Indian part of the Kailash Sacred Landscape to predict suitable areas for future dispersal within the landscape. We used a modeling-based approach, which incorporates sixteen landscape variables to identify priority areas for leopard conservation in the Kailash Sacred Landscape. We opportunistically collected 205 presence locations across the 7120 km2 landscape during 2009–2015. The importance of each variable was evaluated using univariate regression analysis across five different spatial scales. The best spatial scale of each response variable was selected based on lowest Akaike's Information Criterion (AIC). Due to multicollinearity in the landscape variables we computed Principal Components and used eight of the sixteen components as predictors in a multivariate generalized linear model of habitat suitability. A resistance surface model was developed to compute the leopard movement or connectivity across the landscape. We identified corridors between the largest forest patches in the landscape with UNICOR. Here we predict that a large area of the landscape comprising suitable leopard habitat is mostly found at the middle elevational range. This covers about 19.13% of the study area, of which 0.06% area was found to be highly suitable, 4.19% suitable and 14.87% moderately suitable. Further, we have also identified four priority connective corridors. We propose that the conservation of the leopard in this region requires immediate attention by applying integrated landscape-level management to arrest their recent high levels of mortality risk caused due to Human–Leopard conflict. [ABSTRACT FROM AUTHOR]

African buffalo the primary source of foot and mouth disease (FMD) infection for livestock in South Africa. Predicting the spatial drivers and patterns of buffalo–cattle contact risk is crucial for developing effective FMD mitigation strategies. Therefore, the goal of this study was to predict fine‐scale, seasonal contact risk between cattle and buffaloes straying into communal lands adjacent to Kruger National Park. This study provides the first application of the cumulative resistant kernel method to calculate contact risk between two species. We built resistance surfaces from resource utilization models of buffalo and cattle and calculated the intersection of resistant kernels of the two species. This revealed that the contact risk is influenced by seasonality, water sources and fence strength, and the magnitude of contact risk is largely driven by buffalo and cattle dispersal abilities. The probability of contact was higher in the dry season, with hotspots along a main river and the weakest parts of the perimeter fence. In the wet season, contact risk was more diffuse and less concentrated along the main river and near settlements. The new approach of intersecting cumulative resistant kernels of two species can produce quantitative predictive maps of animals’ contact risk and help identify potential hotspots of disease transmission. [ABSTRACT FROM AUTHOR]

The forests of Borneo support some of the highest biodiversity in the world, yet have experienced among the world's highest rates of deforestation. Such rapid forest loss and associated fragmentation reduces the availability of suitable habitat for wildlife and creates dispersion barriers. Understanding the prevalence and impacts of this anthropogenic disturbance, and developing ways in which to mitigate such changes, is thus critical to the conservation of Borneo's wildlife. Here, we applied a path selection function with conditional logistic regression and used it to develop a resistance surface for a population of Sunda clouded leopards ( Neofelis diardi ) residing within a fragmented and human dominated landscape in Malaysian Borneo. We used cumulative resistant kernel and factorial least-cost path analysis to predict how connectivity may change in response to four future scenarios involving conversion of remaining unproductive forest to palm oil plantations, conversion of unproductive palm oil back to forest, and restoration of a riparian buffer zone along the river, and combination of the two forest restoration scenarios. We showed that Sunda clouded leopard movement is facilitated by forest canopy cover and resisted by non-forest vegetation, particularly recently cleared/planted and underproductive (flooded) plantation areas with low canopy closure. By combining resistant kernel and factorial least-cost path modelling we mapped core areas and the main linkages among them, and identified several key pinch points that may limit regional connectivity of the population. We predict that Sunda clouded leopard connectivity in the region can be greatly enhanced through the protection of privately owned forest patches and the reforestation of underproductive oil palm plantation areas, and creation of a forested buffer zone along the river. Conversely, we show that if the region's unprotected forests were to be converted to plantations then connectivity across the Kinabatangan floodplain would be significantly reduced. [ABSTRACT FROM AUTHOR]

Aim Central Iran is a priority area for biodiversity conservation, which is threatened by encroachment on core habitats and fragmentation by roads. The goal of this study was to identify core areas and connectivity corridors for a set of desert carnivores by predicting habitat suitability and calculating resistant kernel, factorial least-cost path modelling and graph network indices. Location Iran. Methods We used an ensemble model ( EM) of habitat suitability methods to predict the potential habitats of leopard, cheetah, caracal, wild cat, sand cat and grey wolf and used resistant kernel and factorial least-cost path modelling to identify important core habitats and corridors between patches. We also used a graph network analysis to quantify the importance of each core patch to landscape connectivity. Results Potential habitats of the studied carnivores appeared to be strongly influenced by prey density, annual precipitation, topographical roughness, shrubland density and anthropogenic factors. Most of the core patches were covered by protected areas and no-hunting areas. This may be attributed to the relatively high resistance outside protected areas leading to isolated occupied patches. Patch importance to connectivity was significantly correlated with patch extent, density of dispersing individuals and probability of occurrence in the core patch. Main conclusions Our findings revealed that prey abundance in core habitat is critically important, and has higher influence than habitat area per se. In addition, our analysis provided the first map of landscape connectivity for multiple species in Iran and revealed that conserving these species requires integrated landscape-level management to reduce mortality risk and protect core areas and linkages among them. These results will assist the development of multispecies conservation strategies to protect core areas for carnivores. [ABSTRACT FROM AUTHOR]

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway crossings by American black bears in the northern Rocky Mountains, USA. We found that a resistance surface derived directly from movement data greatly outperformed a resistance surface produced from analysis of genetic differentiation, despite their heuristic similarities. Our analysis also suggested differences in the performance of different connectivity modeling approaches. Factorial least cost paths appeared to slightly outperform other methods on the movement-derived resistance surface, but had very poor performance on the resistance surface obtained from multi-model landscape genetic analysis. Cumulative resistant kernels appeared to offer the best combination of high predictive performance and sensitivity to differences in resistance surface parameterization. Our analysis highlights that even when two resistance surfaces include the same variables and have a high spatial correlation of resistance values, they may perform very differently in predicting animal movement and population connectivity.

Context: Connectivity models for animal movement frequently use resistance surfaces, but rarely incorporate actual movement data and multiple scale drivers of landscape resistance. Objectives: Using GPS data, we developed a multi-scale model of landscape resistance for tiger ( Panthera tigris) dispersal in central India and evaluated the performance, interpretation and predictions against single scale models. Methods: Six dispersing tiger paths were subjected to a path level analysis with conditional logistic regression to parameterize a resistance surface. We evaluated for 21 scales of available habitat and selected the best scale for each variable. We derived a scale-optimized multivariate path selection function and predicted landscape resistance across the landscape. Results: The tigers preferred to move along areas with forest cover at relatively high elevations along the ridges with rugged topography at broad scale, while avoiding areas with agriculture-village matrix at fine scale. We found that the scale that was most supported by Akaike's information criterion was not always the scale that maximized the magnitude (effect size) of the relationship. Further, the multi-scale optimized model differed substantially from the single scale models in terms of variable importance, magnitude of coefficients and predictions of connectivity. Conclusions: Our results demonstrate that the variables in landscape resistance models produce markedly different predictions of population connectivity depending on the scales of analyses and interpretation. Thus, scale optimization in parameterization is critical for appropriate inferences and sound management strategies. [ABSTRACT FROM AUTHOR]

Context: Habitat loss and fragmentation are among the major drivers of population declines and extinction, particularly in large carnivores. Connectivity models provide practical tools for assessing fragmentation effects and developing mitigation or conservation responses. To be useful to conservation practitioners, connectivity models need to incorporate multiple scales and include realistic scenarios based on potential changes to habitat and anthropogenic pressures. This will help to prioritize conservation efforts in a changing landscape. Objectives: The goal of our paper was to evaluate differences in population connectivity for lions ( Panthera leo) across the Kavango-Zambezi Trans-frontier Conservation Area (KAZA) under different landscape change scenarios and a range of dispersal distances. Methods: We used an empirically optimized resistance surface, based on analysis of movement pathways of dispersing lions in southern Africa to calculate resistant kernel connectivity. We assessed changes in connectivity across nine landscape change scenarios, under each of which we explored the behavior of lions with eight different dispersal abilities. Results: Our results demonstrate that reductions in the extent of the protected area network and/or fencing protected areas will result in large declines in the extent of population connectivity, across all modeled dispersal abilities. Creation of corridors or erection of fences strategically placed to funnel dispersers between protected areas increased overall connectivity of the population. Conclusions: Our results strongly suggest that the most effective means of maintaining long-term population connectivity of lions in the KAZA region involves retaining the current protected area network, augmented with protected corridors or strategic fencing to direct dispersing individuals towards suitable habitat and away from potential conflict areas. [ABSTRACT FROM AUTHOR]

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway crossings by American black bears in the northern Rocky Mountains, USA. We found that a resistance surface derived directly from movement data greatly outperformed a resistance surface produced from analysis of genetic differentiation, despite their heuristic similarities. Our analysis also suggested differences in the performance of different connectivity modeling approaches. Factorial least cost paths appeared to slightly outperform other methods on the movement-derived resistance surface, but had very poor performance on the resistance surface obtained from multi-model landscape genetic analysis. Cumulative resistant kernels appeared to offer the best combination of high predictive performance and sensitivity to differences in resistance surface parameterization.Our analysis highlights that even when two resistance surfaces include the same variables and have a high spatial correlation of resistance values, they may perform very differently in predicting animal movement and population connectivity. [ABSTRACT FROM AUTHOR]

Habitat loss and fragmentation are widely recognized as among the most important threats to global biodiversity. New analytical approaches are providing an improved ability to predict the effects of landscape change on population connectivity at vast spatial extents. This paper presents an analysis of population connectivity for three species of conservation concern [swift fox ( Vulpes velox); lesser prairie-chicken ( Tympanuchus pallidicinctus); massasuaga ( Sistrurus catenatus)] across the American Great Plains region. We used factorial least-cost path and resistant kernel analyses to predict effects of landscape conditions on corridor network connectivity. Our predictions of population connectivity provide testable hypotheses about the location of core habitats, corridors, and barriers to movement. The results indicate that connectivity is more sensitive to a species' dispersal ability than variation in landscape resistance to movement. Thus, it may prove difficult to optimize conservation strategies to maintain population connectivity for multiple species with disparate dispersal abilities and independent distributions. [ABSTRACT FROM AUTHOR]

When ecological models are used to guide conservation decisions, these models should be based upon substantial data and should be applied at appropriate spatial scales. Yet, ecologists are usually faced with scarce data and must often make subjective choices about scale. To handle limited data, the use of expert panels to parameterize models has become common. However, few studies evaluate the success of expert panels in improving models. In this study, I examine a recent resistant kernel model designed to prioritize amphibian breeding habitat for conservation. I compare the predictive ability of the model as originally parameterized by an expert panel to the predictive ability of simpler models. I optimize parameter values for spatial scale and landscape resistance using 896 ponds from 5 studies of spotted salamanders (Ambystoma maculatum) and wood frogs (Lithobates sylvaticus) in Massachusetts and Rhode Island. In predicting amphibian distributions, models examined in this study that relied upon expert-derived resistance values performed worse than null models with uninformative resistance values. The failure of the resistant kernel model offers support for the use of simple models in the face of complex ecological problems. The best scale for measuring upland habitat in these models was in the range of 1000–3000m, an order of magnitude larger than the salamander migration scale previously proposed for wetland buffer zones. [Copyright &y& Elsevier]

Resistance surfaces are often used to fill gaps in our knowledge surrounding animal movement and are frequently the basis for modeling connectivity associated with conservation initiatives. However, the methods for quantifying resistance surfaces are varied and there is no general consensus on the appropriate choice of environmental data or analytical approaches. We provide a comprehensive review of the literature on this topic to highlight methods used and identify knowledge gaps. Our review includes 96 papers that parameterized resistance surfaces (sometimes using multiple approaches) for a variety of taxa. Data types used included expert opinion ( n = 76), detection ( n = 23), relocation ( n = 8), pathway ( n = 2), and genetic ( n = 28). We organized the papers into three main analytical approaches; one-stage expert opinion, one-stage empirical, and two-stage empirical, each of which was represented by 43, 22, and 36 papers, respectively. We further organized the empirical approaches into five main resource selection functions; point ( n = 16), matrix ( n = 38), home range ( n = 3), step ( n = 1), and pathway ( n = 1). We found a general lack of justification for choice of environmental variables and their thematic and spatial representation, a heavy reliance on expert opinion and detection data, and a tendency to confound movement behavior and resource use. Future research needs include comparative analyses on the choice of environmental variables and their spatial and thematic scales, and on the various biological data types used to estimate resistance. Comparative analyses amongst analytical processes is also needed, as well as transparency in reporting on uncertainty in parameter estimates and sensitivity of final resistance surfaces, especially if the resistance surfaces are to be used for conservation and planning purposes. [ABSTRACT FROM AUTHOR]

Effective broad-spectrum biodiversity conservation requires that conservation strategies simultaneously meet the needs of multiple species. However, little is known about how maintaining habitat connectivity for one species or species group may also act as an umbrella for other species. We evaluated the degree to which predicted connected habitat for each of 144 different hypothetical organisms expressing range of dispersal abilities and ecological responses to elevation, roads and land cover function as an indicators of connected habitat for the others in the U.S. Northern Rocky Mountains. We used resistant kernel modeling to map the extent of the study area predicted to be connected by dispersal for each species. At relatively large dispersal abilities there was extensive overlap between connected habitat for most organisms and much of the study area is predicted to provide connected habitat for all hypothetical organisms simultaneously. In contrast, at low to medium dispersal abilities there was much less intersection of habitat connected by dispersal. We found that habitat specialists with limited dispersal ability are weak indicators of others, and likewise are weakly indicated by others. We evaluated the effectiveness of three carnivores as connectivity umbrellas for many species. All three carnivore species performed significantly worse as connectivity umbrellas than the average across the simulated species. These species are associated with high elevation forested habitats. It is the low elevation and non-forest habitats that are most at risk of habitat loss and fragmentation in the study area, suggesting that a carnivore umbrella may miss many species most at risk. [Copyright &y& Elsevier]