Habitat quality of source patches and connectivity in fragmented landscapes

Because spatial connectivity is critical to dispersal success and persistence of species in highly fragmented landscapes, the way that we envision and measure connectivity is consequential for biodiversity conservation. Connectivity metrics used for predictive modeling of spatial turnover and patch occupancy for metapopulations, such as with Incidence Function Models (IFM), incorporate distances to and sizes of possible source populations. Here, our focus is on whether habitat quality of source patches also is considered in these connectivity metrics. We propose that effective areas (weighted by habitat quality) of source patches should be better surrogates for population size and dispersal potential compared to unadjusted patch areas. Our review of a representative sample of the literature revealed that only 12.5% of studies incorporated habitat quality of source patches into IFM-type connectivity metrics. Quality of source patches generally was not taken into account in studies even if habitat quality of focal patches was included in analyses. We provide an empirical example for a metapopulation of a rare wetland species, the round-tailed muskrat (Neofiber alleni), demonstrating that a connectivity metric based on effective areas of source patches better predicts patch colonization and occupancy than a metric that used simple patch areas. The ongoing integration of landscape ecology and metapopulation dynamics could be hastened by incorporating habitat quality of source patches into spatial connectivity metrics applied to species conservation in fragmented landscapes.     

Characterizing the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscapes

Aim To examine the effects of forest fragmentation on the distribution of the entire wild giant panda (Ailuropoda melanoleuca) population, and to propose a modelling approach for monitoring the spatial distribution and habitat of pandas at the landscape scale using Moderate Resolution Imaging Spectro‐radiometer (MODIS) enhanced vegetation index (EVI) time‐series data. Location Five mountain ranges in south‐western China (Qinling, Minshan, Qionglai, Xiangling and Liangshan). Methods Giant panda pseudo‐absence data were generated from data on panda occurrences obtained from the third national giant panda survey. To quantify the fragmentation of forests, 26 fragmentation metrics were derived from 16‐day composite MODIS 250‐m EVI multi‐temporal data and eight of these metrics were selected following factor analysis. The differences between panda presence and panda absence were examined by applying significance testing. A forward stepwise logistic regression was then applied to explore the relationship between panda distribution and forest fragmentation. Results Forest patch size, edge density and patch aggregation were found to have significant roles in determining the distribution of pandas. Patches of dense forest occupied by giant pandas were significantly larger, closer together and more contiguous than patches where giant pandas were not recorded. Forest fragmentation is least in the Qinling Mountains, while the Xiangling and Liangshan regions have most fragmentation. Using the selected landscape metrics, the logistic regression model predicted the distribution of giant pandas with an overall accuracy of 72.5% (κ = 0.45). However, when a knowledge‐based control for elevation and slope was applied to the regression, the overall accuracy of the model improved to 77.6% (κ = 0.55). Main conclusions Giant pandas appear sensitive to patch size and isolation effects associated with fragmentation of dense forest, implying that the design of effective conservation areas for wild giant pandas must include large and dense forest patches that are adjacent to other similar patches. The approach developed here is applicable for analysing the spatial distribution of the giant panda from multi‐temporal MODIS 250‐m EVI data and landscape metrics at the landscape scale.     

Habitat‐mediated timing of migration in polar bears: an individual perspective

Migration phenology is largely determined by how animals respond to seasonal changes in environmental conditions. Our perception of the relationship between migratory behavior and environmental cues can vary depending on the spatial scale at which these interactions are measured. Understanding the behavioral mechanisms behind population‐scale movements requires knowledge of how individuals respond to local cues. We show how time‐to‐event models can be used to predict what factors are associated with the timing of an individual's migratory behavior using data from GPS collared polar bears (Ursus maritimus) that move seasonally between sea ice and terrestrial habitats. We found the concentration of sea ice that bears experience at a local level, along with the duration of exposure to these conditions, was most associated with individual migration timing. Our results corroborate studies that assume thresholds of >50% sea ice concentration are necessary for suitable polar bear habitat; however, continued periods (e.g., days to weeks) of exposure to suboptimal ice concentrations during seasonal melting were required before the proportion of bears migrating to land increased substantially. Time‐to‐event models are advantageous for examining individual movement patterns because they account for the idea that animals make decisions based on an accumulation of knowledge from the landscapes they move through and not simply the environment they are exposed to at the time of a decision. Understanding the migration behavior of polar bears moving between terrestrial and marine habitat, at multiple spatiotemporal scales, will be a major aspect of quantifying observed and potential demographic responses to climate‐induced environmental changes.     

Using connectivity metrics in conservation planning – when does habitat quality matter?

Aim The objective of conservation planning is often to prioritize patches based on their estimated contribution to metapopulation or metacommunity viability. The contribution that an individual patch makes will depend on its intrinsic characteristics, such as habitat quality, as well as its location relative to other patches, its connectivity. Here we systematically evaluate five patch value metrics to determine the importance of including an estimate of habitat quality into the metrics. Location We tested the metrics in landscapes designed to represent different degrees of variability in patch quality and different levels of patch aggregation. Methods In each landscape, we simulated population dynamics using a spatially explicit, continuous time metapopulation model linked to within patch logistic growth models. We tested five metrics that are used to estimate the contribution that a patch makes to metapopulation viability: two versions of the probability of connectivity index, two versions of patch centrality (a graph theory metric) and the metapopulation capacity metric. Results All metrics performed best in environments where patch quality was very variable and high quality patches were aggregated. Metrics that incorporated some measure of patch quality did better in all environments, but did particularly well in environments with high variance of patch quality and spatial aggregation of good quality patches. Main conclusions Including an estimate of patch quality significantly increased the ability of a given connectivity metric to rank correctly habitat patches according to their contribution to metapopulation viability. Incorporating patch quality is particularly important in landscapes where habitat quality is highly variable and good quality patches are spatially aggregated. However, caution should be used when applying patch metrics to homogeneous landscapes, even if good estimates of patch quality are available. Our results demonstrate that landscape structure and the degree of variability in patch quality need to be assessed prior to selecting a suitable method for estimating patch value.     

Indirect effects of sea ice loss on summer‐fall habitat and behaviour for sympatric populations of an Arctic marine predator

Aim

Climate change is fundamentally altering habitats, with complex consequences for species across the globe. The Arctic has warmed 2–3 times faster than the global average, and unprecedented sea ice loss can have multiple outcomes for ice‐associated marine predators. Our goal was to assess impacts of sea ice loss on population‐specific habitat and behaviour of a migratory Arctic cetacean.

Location

Arctic Ocean.

Methods

Using satellite telemetry data collected during summer‐fall from sympatric beluga whale (Delphinapterus leucas) populations (“Chukchi” and “Beaufort” belugas), we applied generalized estimating equations to evaluate shifts in sea ice habitat associations and diving behaviour during two periods: 1993–2002 (“early”) and 2004–2012 (“late”). We used resource selection functions to assess changes in sea ice selection as well as predict trends in habitat selection and “optimal” habitat, based on satellite‐derived sea ice data from 1990 to 2014.

Results

Sea ice cover declined substantially between periods, and Chukchi belugas specifically used significantly lower sea ice concentrations during the late than early period. Use of bathymetric features did not change between periods for either population. Population‐specific sea ice selection, predicted habitat and the amount of optimal habitat also generally did not change during 1990–2014. Chukchi belugas tracked during 2007–2012 made significantly more long‐duration and deeper dives than those tracked during 1998–2002.

Main conclusions

Taken together, our results suggest bathymetric parameters are consistent predictors of summer‐fall beluga habitat rather than selection for specific sea ice conditions during recent sea ice loss. Beluga whales were able to mediate habitat change despite their sea ice associations. However, trends towards prolonged and deeper diving possibly indicate shifting foraging opportunities associated with ecological changes that occur in concert with sea ice loss. Our results highlight that responses by some Arctic marine wildlife can be indirect and variable among populations, which could be included in predictions for the future.

     

Important marine habitat off east Antarctica revealed by two decades of multi‐species predator tracking

Satellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide‐ranging meso‐ and top‐predator species: Adélie Pygoscelis adeliae and emperor Aptenodytes forsteri penguins, light‐mantled albatross Phoebetria palpebrata, Antarctic fur seals Arctocephalus gazella, southern elephant seals Mirounga leonina, and Weddell seals Leptonychotes weddellii. The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea‐ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. The results emphasize the importance of on‐shore and sea‐ice processes to Antarctic marine ecosystems. Our study provides ocean‐basin‐scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts.     

Sea ice resource selection models for polar bears in the Barents Sea subpopulation

The extent, thickness and age of Arctic sea ice has dramatically declined since the late 1990s, and these trends are predicted to continue. Exploring the habitat use of sea‐ice‐dependent species can help us understand which resources they use and how their distribution responds to a changing environment. The goal of this study was to develop predictive models of the habitat use of an Arctic apex predator. Polar bear Ursus maritimus habitat use in the Barents Sea subpopulation was modelled with seasonal resource selection functions (RSFs) using satellite‐linked telemetry data from 294 collars deployed on female polar bears between 1991 and 2015. Polar bears selected habitat in the Marginal Ice Zone, with a preference for intermediate sea ice concentrations (40–80%). They spent most time in areas with relatively short travel distances to 15 or 75% ice concentration, and during spring and autumn they exhibited a preference for sea ice areas over the continental shelf or over shallower bathymetry). Predictions of the distribution of polar bears in the Barents Sea area can be made for specific sea ice scenarios using these models. Two such predictive distribution maps based on the autumn seasonal model were made and validated against two independent polar bear survey datasets collected in August 2004 and August 2015. The distribution of optimal polar bear habitat has shifted strongly northwards in all seasons of the year during the 25 yr study period.     

SPATIALLY CORRELATED EXTINCTIONS SELECT FOR LESS EMIGRATION BUT LARGER DISPERSAL DISTANCES IN THE SPIDER MITE TETRANYCHUS URTICAE

Dispersal is a central process to almost all species on earth, as it connects spatially structured populations and thereby increases population persistence. Dispersal is subject to (rapid) evolution and local patch extinctions are an important selective force in this context. In contrast to the randomly distributed local extinctions considered in most theoretical studies, habitat fragmentation or other anthropogenic interventions will lead to spatially correlated extinction patterns. Under such conditions natural selection is thought to lead to more long‐distance dispersal, but this theoretical prediction has not yet been verified empirically. We test this prediction in experimental spatially structured populations of the spider mite Tetranychus urticae and supplement these empirical results with insights from an individual‐based evolutionary model. We demonstrate that the spatial correlation of local extinctions changes the entire distribution of dispersal distances (dispersal kernel) and selects for overall less emigration but more long‐distance dispersal.     

Food‐web structure varies along environmental gradients in a high‐latitude marine ecosystem

Large‐scale patterns in species diversity and community composition are associated with environmental gradients, but the implications of these patterns for food‐web structure are still unclear. Here, we investigated how spatial patterns in food‐web structure are associated with environmental gradients in the Barents Sea, a highly productive shelf sea of the Arctic Ocean. We compared food webs from 25 subregions in the Barents Sea and examined spatial correlations among food‐web metrics, and between metrics and spatial variability in seawater temperature, bottom depth and number of days with ice cover. Several food‐web metrics were positively associated with seawater temperature: connectance, level of omnivory, clustering, cannibalism, and high variability in generalism, while other food‐web metrics such as modularity and vulnerability were positively associated with sea ice and negatively with temperature. Food‐web metrics positively associated with habitat heterogeneity were: number of species, link density, omnivory, path length, and trophic level. This finding suggests that habitat heterogeneity promotes food‐web complexity in terms of number of species and link density. Our analyses reveal that spatial variation in food‐web structure along the environmental gradients is partly related to species turnover. However, the higher interaction turnover compared to species turnover along these gradients indicates a consistent modification of food‐web structure, implying that interacting species may co‐vary in space. In conclusion, our study shows how environmental heterogeneity, via environmental filtering, influences not only turnover in species composition, but also the structure of food webs over large spatial scales.     

Spatial segregation of sympatric marten and fishers: the influence of landscapes and species‐scapes

Co‐occurring species are rarely considered as a factor influencing habitat selection. However, niche theory predicts that sharing resources, predators, and other interspecific interactions can limit the environmental conditions under which a species may exist. How does the spatial distribution of one species affect that of another within shared landscapes? We tested whether sympatric marten Martes americana and fishers M. pennanti in a mountain landscape in Alberta, Canada exhibit local‐scale spatial segregation, beyond differential habitat selection. We modelled marten and fisher distribution in relation to remotely‐sensed habitat data and species co‐occurrence, using generalized linear models and information‐theoretic model selection. Marten and fishers selected different habitat types and showed different responses to habitat fragmentation. Even after accounting for these differences, the absence of one species significantly explained the occurrence of the other. We conclude that the spatial distribution of marten and fishers influences habitat selection by each other at landscape scales, and hypothesize that this pattern may result from competition in a spatially heterogeneous environment. Species‐habitat models that consider only resources may fail to capture key predictors of species’ occurrence. Reliable prediction and inference requires that ecologists expand from landscapes to also include species‐scapes: a spatial plane of species interactions that combines with resources to drive species’ distributions.     

Ecological metrics predict connectivity better than geographic distance

We use microsatellite loci to examine genetic structure of the Florida scrub lizard (Sceloporus woodi) and test for the effects of landscape variables at the scale of neighboring patches. We evaluate ecological metrics of connectivity with genetics data, which to our knowledge is the first application of these particular metrics to landscape-level genetics studies in Florida scrub. Florida scrub is a highly threatened ecosystem in which habitat patches are remnants of a previously widespread xeric landscape. Analysis of mitochondrial DNA (mtDNA) has shown that landscape structure influenced the evolutionary history of the Florida scrub lizard (S. woodi) across its range. Our results concur with these mtDNA studies in documenting divergence between xeric ridge systems and also demonstrate divergence at very local scales. Both least-cost distance and pairwise isolation (a metric used in ecological studies that includes patch size, quality and a modified isolation index) were better predictors of genetic distance than Euclidean distance, indicating that mesic and hydric habitat influence spatial patterns in genetic variation. Our results support the need for focusing on spatial distribution of scrub habitat at the scale of neighboring patches, as well as regionally, in conservation management and restoration. Also, our study points to the value of integrating landscape ecology metrics into landscape genetics.     

The response of shrubland patterns' properties to rainfall changes and to the catastrophic removal of plants in semi-arid regions predicted by Reaction–Diffusion simulations

Extended areas of the Mediterranean and semi-arid ecosystems are predicted to face decreased water availability, alongside increased human disturbances owing to an increase in population during this century. The use of geosimulations is instrumental for studying the expected ecosystem's response to predicted changes in habitat conditions due to the lack of field data at appropriate spatial and temporal resolutions over wide regional extents throughout sufficient time spans. Computational simulations, based on reaction‐diffusion equations (RDE), were performed in order to quantitatively assess the form of shrubland pattern changes in response to decreasing and increasing rainfall regimes and during recovery following catastrophic removal of plants, which would result from fires or droughts. Patch pattern properties were analyzed using the Shannon–Wiener fragmentation (SW) metric (= Ʃ Si Ln Si, where Si is the area fraction of patch i of n patches) and the edge ratio (ER) metric (= sum of edge area/sum of patches' area). The SW fragmentation change during pattern formation is characterized by 3 phases, where in the first phase there is decreased fragmentation, and the third phase represents the evolution of equilibrium. The second phase is the most interesting one, where we have observed pattern regularization obtained by rearranging the shrubs' patches while increasing the fragmentation of the shrub patches. Such regularization phases seem to be a primary characteristic of self-organized behavior in these ecosystems. The general form of pattern properties change with decreasing or increasing rainfall according to SW fragmentation levels reached at equilibrium, which revealed a non-linear configuration with three divergence points. At these divergence points, the pattern evolution trajectories diverge according to the rainfall change rates. The most important divergence point occurs when rainfall drops below the critical desertification level. Whereas a slow reduction in rainfall would allow the shrub patches to be maintained below this critical rainfall level, rapid changes would cause immediate desertification. Edge ratios are closely linked to rainfall levels, and thus, they may provide early warnings and allow changes in the habitat conditions to be monitored due to climate changes.     

Predicting immigration of two species in contrasting landscapes: effects of scale, patch size and isolation

Migration is a key process for spatially structured populations. We examined how a variety of patch based metrics commonly used to predict the number of immigrants to a habitat patch performed based on data from three different years, in two distinct insect systems. The first system was an herbivorous beetle inhabiting patches of its host plant within a 'typical' patch network. In this system there were numerous patches located relatively close to one another, given the beetle's dispersal ability. The second system consisted of a butterfly inhabiting a series of 17 subalpine meadows. Here, the patches were arranged in a linear fashion and were more distant from each other. Overall, we found that the best models incorporating aspects of patch size and/or isolation explained a large (30–40%) amount of deviance in immigration, but there were considerable differences between the systems. For the first system, we found that metrics including the size of the target patch explained the highest proportion of deviance in immigrant numbers, while metrics based only on interpatch distances explained very little deviance. The situation was reversed for the second system. Metrics including the size of the target patch explained little deviance, while metrics based on the distance between patches explained the bulk of deviance in the number of immigrants. The results of our study show that the effects of patch size and isolation on the number of immigrants are highly important, but dependent on spatial scale, the organism studied, and how it responds to the spatial arrangement of patches. Correspondingly, there will be no single generalized metric to predict immigration for all cases. Given the dependency of the results on the system studied, we recommend that future studies provide explicit data on habitat areas and dispersal distance relative to interpatch distance to allow for meaningful comparison among organisms and systems.     

Alpine glacial relict species losing out to climate change: The case of the fragmented mountain hare population (Lepus timidus) in the Alps

Alpine and Arctic species are considered to be particularly vulnerable to climate change, which is expected to cause habitat loss, fragmentation and—ultimately—extinction of cold‐adapted species. However, the impact of climate change on glacial relict populations is not well understood, and specific recommendations for adaptive conservation management are lacking. We focused on the mountain hare (Lepus timidus) as a model species and modelled species distribution in combination with patch and landscape‐based connectivity metrics. They were derived from graph‐theory models to quantify changes in species distribution and to estimate the current and future importance of habitat patches for overall population connectivity. Models were calibrated based on 1,046 locations of species presence distributed across three biogeographic regions in the Swiss Alps and extrapolated according to two IPCC scenarios of climate change (RCP 4.5 & 8.5), each represented by three downscaled global climate models. The models predicted an average habitat loss of 35% (22%–55%) by 2100, mainly due to an increase in temperature during the reproductive season. An increase in habitat fragmentation was reflected in a 43% decrease in patch size, a 17% increase in the number of habitat patches and a 34% increase in inter‐patch distance. However, the predicted changes in habitat availability and connectivity varied considerably between biogeographic regions: Whereas the greatest habitat losses with an increase in inter‐patch distance were predicted at the southern and northern edges of the species’ Alpine distribution, the greatest increase in patch number and decrease in patch size is expected in the central Swiss Alps. Finally, both the number of isolated habitat patches and the number of patches crucial for maintaining the habitat network increased under the different variants of climate change. Focusing conservation action on the central Swiss Alps may help mitigate the predicted effects of climate change on population connectivity.     

Population substructure and space use of Foxe Basin polar bears

Climate change has been identified as a major driver of habitat change, particularly for sea ice-dependent species such as the polar bear (Ursus maritimus). Population structure and space use of polar bears have been challenging to quantify because of their circumpolar distribution and tendency to range over large areas. Knowledge of movement patterns, home range, and habitat is needed for conservation and management. This is the first study to examine the spatial ecology of polar bears in the Foxe Basin management unit of Nunavut, Canada. Foxe Basin is in the mid-Arctic, part of the seasonal sea ice ecoregion and it is being negatively affected by climate change. Our objectives were to examine intrapopulation spatial structure, to determine movement patterns, and to consider how polar bear movements may respond to changing sea ice habitat conditions. Hierarchical and fuzzy cluster analyses were used to assess intrapopulation spatial structure of geographic position system satellite-collared female polar bears. Seasonal and annual movement metrics (home range, movement rates, time on ice) and home-range fidelity (static and dynamic overlap) were compared to examine the influence of regional sea ice on movements. The polar bears were distributed in three spatial clusters, and there were differences in the movement metrics between clusters that may reflect sea ice habitat conditions. Within the clusters, bears moved independently of each other. Annual and seasonal home-range fidelity was observed, and the bears used two movement patterns: on-ice range residency and annual migration. We predict that home-range fidelity may decline as the spatial and temporal predictability of sea ice changes. These new findings also provide baseline information for managing and monitoring this polar bear population.     

Characterizing habitat suitability for a central‐place forager in a dynamic marine environment

Characterizing habitat suitability for a marine predator requires an understanding of the environmental heterogeneity and variability over the range in which a population moves during a particular life cycle. Female California sea lions (Zalophus californianus) are central‐place foragers and are particularly constrained while provisioning their young. During this time, habitat selection is a function of prey availability and proximity to the rookery, which has important implications for reproductive and population success. We explore how lactating females may select habitat and respond to environmental variability over broad spatial and temporal scales within the California Current System. We combine near‐real‐time remotely sensed satellite oceanography, animal tracking data (n = 72) from November to February over multiple years (2003–2009) and Generalized Additive Mixed Models (GAMMs) to determine the probability of sea lion occurrence based on environmental covariates. Results indicate that sea lion presence is associated with cool ( <14°C ), productive waters, shallow depths, increased eddy activity, and positive sea‐level anomalies. Predictive habitat maps generated from these biophysical associations suggest winter foraging areas are spatially consistent in the nearshore and offshore environments, except during the 2004–2005 winter, which coincided with an El Niño event. Here, we show how a species distribution model can provide broadscale information on the distribution of female California sea lions during an important life history stage and its implications for population dynamics and spatial management.     

Landscape size affects the relative importance of habitat amount,habitat fragmentation,and matrix quality on forest birds

It is important to understand the relative effects of landscape habitat loss, habitat fragmentation, and matrix quality on biodiversity, so that potential management options can be appropriately ranked. However, their effects and relative importance may change with the size of the landscape considered because the multiple (and potentially conflicting) ecological processes that are influenced by landscape structure occur at different spatial scales (e.g. dispersal, predation, foraging). We estimated the relative effects of habitat loss, habitat fragmentation, and matrix quality (measured as the amount of forest, the proportion of forest area contained in large core forests, and the density of roads respectively) on fragmentation‐sensitive forest birds in southern Ontario, Canada using a range of landscape sizes (0.8–310 km²). We used three complementary statistical approaches to estimate relative effects of these correlated landscape factors – 1) multiple regression, 2) information theoretic (AIC) estimates of the most parsimonious model, and 3) multi‐model inference to average effects across all supported models. We controlled for spatial autocorrelation, local habitat, roadside sampling bias, time of day, season, habitat heterogeneity, and the interaction between the effects of habitat amount and fragmentation. We found that relative effects of habitat amount and fragmentation were scale dependent; habitat amount had a consistently positive effect that was consistent over more than two orders of magnitude in landscape area (~1–300 km²). In contrast, the effects of habitat fragmentation depended on the size of the landscape considered. Indeed, for veery Catharus fuscescens, habitat fragmentation had positive effects at one scale and negative effects at another. The effects of matrix quality were generally weak and changed little with scale. For the number of fragmentation sensitive species and the presence of veery, habitat amount was most important in large landscapes and habitat fragmentation in small landscapes but for the presence of ovenbird Seiurus aurocapilla, habitat amount was most important at all scales.     

landscapemetrics: an open‐source R tool to calculate landscape metrics

Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data.     

Coherence,conservation and patch‐occupancy analysis

Spatial coherence (synchrony) among subpopulations poses a danger to the metacommunity, as it increases the risk of regional extinction. When this effect is significant, the use of inference techniques based on the stochastic patch occupancy model (SPOM) may be inadequate, since SPOMs assume that each habitat patch is either occupied or empty, thereby neglecting the intra‐patch dynamics. Here we suggest a general classification of the dynamics that allows the identification, in a model‐independent manner, of the regimes where coherence effects are strong. We also present a new technique, based on patch occupancy (presence/absence) data, for identifying the role of spatial coherence in the stabilization of a metapopulation. If the chance of a local extinction grows with the connectivity, this implies that spatial synchronization is too strong and that regional‐scale extinction becomes possible. When this scenario occurs, a decrease in the movement of individuals (habitat fragmentation, reduced dispersal rates) has a positive effect on the sustainability of the spatially distributed population. The results of individual based simulations of a spatially structured population are analyzed with SPOM and the regime where the two‐state approximation fails is identified.     

Jaguar Panthera onca Habitat Modeling in Landscapes Facing High Land‐use Transformation Pressure—Findings from Mato Grosso,Brazil

The modeling of top predators' habitats and the understanding of their environmental requirements in landscapes facing high land‐use transformation pressure have long‐standing importance for the development of conservation strategies. Multi‐distance spatial cluster analysis and logistic regression with environmental weighting for pseudo‐absence designation were applied to understand spatial patterns of jaguar occurrence in Mato Grosso state (Central Western Brazil). This location has been under intense deforestation pressure since the 1970s and is historically one of the most important jaguar habitats in the world. By using a model of five independent variables, we were able to achieve a 73.2 percent success rate of case/non‐case classification and indicate not only a general loss of habitat suitability, but also an increasing interruption of potential migration corridors in the state. Our analysis on a regional scale demonstrates the importance of forest and savannah woodland for jaguar habitat maintenance in the Mato Grosso state. The jaguar species demonstrates a sensitivity to landscape fragmentation, which can be parameterized for improved model building by metrics such as edge density and patch size. Comparisons with previous studies in South America show that parameter selection for jaguar habitat modeling is highly scale‐dependent and that habitat suitability in partially transformed landscapes could be maintained if fragmentation is minimized. Recent land‐use transformation, however, has significantly weakened the conservation status of the Pantanal‐Amazon corridor.