Willow Warbler Phylloscopus trochilus habitat in woods with different structure and management in southern England

Capsule Use of Light Detection and Ranging (LiDAR) data identified suitable Willow Warbler habitat based on mean vegetation height. This habitat model provided maps of distribution and occupation of suitable habitat.

Aims To identify habitat associations in woods with different vegetation structure and management systems during a period of low Willow Warbler populations.

Methods Locations of all Willow Warblers were mapped during the breeding season in three woods of contrasting management; recent low intervention, actively coppiced woodland and high forest with clear‐fells. Height profile models of each wood were derived from airborne LiDAR. The mean vegetation height at locations with Willow Warblers and a sample from the rest of the wood were used to produce models of optimum habitat and breadth of habitat occupied in each wood. The habitat model was then used to produce maps of suitable habitat.

Results The habitat models did not differ between woods, with highest probability of Willow Warbler occurrence in mean vegetation heights of 3.7–5.3 m. Habitat of heights 6–11 m appeared less suitable, being only partly occupied. Habitat maps showed that habitat of suitable height was only occupied when it occurred as large patches; smaller patches (mostly <0.5 ha) and edges along rides and fields were not used.

Conclusion The use of LiDAR derived measures of vegetation height identified areas of suitable habitat for Willow Warblers. Willow Warblers occupied areas of low mean vegetation height either as early successional or open canopy woodland in all woods. Height‐based habitat maps can identify areas of suitable habitat within larger expanses of heterogeneous woodland and are a potentially useful tool in assessing changes in extent of what are often temporary patches of habitat.     

High resolution habitat suitability modelling for an endemic restricted-range Hawaiian insect (<Emphasis Type="Italic">Nysius wekiuicola</Emphasis>, Hemiptera: Lygaeidae)

Determining potentially suitable habitat is critical for effective species conservation and management, but can be challenging in remote or sensitive areas. An approach that combines non-intrusive spatial data collection techniques and supporting field data can lead to a better understanding of landscape-scale species distributions. Here we present two habitat suitability models, at 1 and 10 m resolutions, for the endemic wēkiu bug Nysius wekiuicola, a poorly-understood resident scavenging arthropod species present on the summit of Maunakea in Hawai‘i. Our models reveal that the wēkiu bug, restricted almost entirely to portions of cinder cones above 3500 m elevation, has a high degree of habitat specificity and represents a classically rare species. Across the 55 km² study area, 850 ha of potentially suitable habitat were identified at the 10 percentile training threshold, with the core area located at the true summit. Our results show that elevation and surficial mineralogy were the strongest predictors of suitable habitat, with lesser contributions from aspect and slope. Climatic variables also likely influence wēkiu bug distribution patterns, but were not included in our models due to the coarseness of available climate data and high correlation between variables. Relatively minor differences between the two models, in terms of identifying the locations and amount of suitable wēkiu bug habitat, and a higher measure of performance for the 10 m resolution model, suggest that coarser resolution input variables may characterize suitable habitat more efficiently than very fine 1 m resolution data. The suitability models generated as a result of this study will be directly incorporated into conservation management and restoration goals, and can easily be adapted for other arthropod species, leading to a more holistic understanding of metacommunity dynamics at the Maunakea summit.     

Occupancy Patterns in a Reintroduced Fisher Population during Reestablishment

Monitoring population performance in the years following species reintroductions is key to assessing population restoration success and evaluating assumptions made in planning species restoration programs. From 2008–2010 we translocated 90 fishers (Pekania pennanti) from British Columbia, Canada, to Washington's Olympic Peninsula, USA, providing the opportunity to evaluate modeling assumptions used to identify the most suitable reintroduction areas in Washington and enhance understanding of fisher habitat associations in the late-successional forest ecosystems in the coastal Pacific Northwest. From 2013–2016, we deployed 788 motion-sensing cameras and hair (DNA)-snaring devices distributed among 263 24-km² primary sampling units across the Olympic Peninsula. Our objectives were to determine whether occupancy patterns of the reestablishing population supported assumptions of the initial habitat assessment models, whether the population had expanded or shifted in distribution since the initial reintroductions, compare physical habitat attributes among land-management designations, and determine whether the founding fishers had successfully reproduced. We predicted that site occupancy by fishers would be associated with landscapes characterized by high proportional coverage of dense forest canopies and medium-sized and large trees, a diversity of stand structural classes, and area near the administrative boundary separating wilderness from more intensively managed forest lands. We detected fishers across designated wilderness, federal lands outside of wilderness, and other land designations in proportion to land availability on the Peninsula. We found negligible support for predictions that occupancy by fishers was associated with percent forest cover, tree-size class, or structural class diversity. Rather, occupancy was strongly associated with lands near the wilderness boundary on both sides. We speculate that the boundary between wilderness and more intensively managed forest lands provided fishers with the most suitable prey in proximity to contiguous expanses of low- to mid-elevation late-successional forests that provided optimal resting, denning, and security values. Occupancy patterns shifted toward the west and south along a precipitation gradient during the study, indicating that population distribution had not yet stabilized 5–8 years following translocation. Genetic results indicated that ≥2 generations of fishers have been produced on the Peninsula. Annual occupancy rates across the Peninsula (0.08–0.24) were lower than in other previously studied and established fisher populations, indicating that not all habitat was fully occupied or that initial estimates of the extent of habitat was overestimated. The strong selection fishers exhibited for wilderness edge and weak selection against extensive forested wilderness areas suggested that habitat managers should strive for maintaining a suitable interspersion of required forest structures and biotic habitat components, such as prey resource availability. © 2019 The Wildlife Society.     

Identifying habitat linkages to maintain connectivity for corridor dwellers in a fragmented landscape

Anthropogenic habitat fragmentation typically precedes conservation planning; maintaining remaining linkages among core habitat areas can thus become a key conservation objective. Identifying linkages for dispersal and ensuring those linkages have long-term protection and management are challenging tasks for wildlife managers. These tasks can be especially daunting for smaller species with low mobility, termed corridor dwellers, which must maintain sustainable populations within corridors. Between May 2007 and June 2009, we collected occurrence locations for a corridor dweller, the Palm Springs pocket mouse (Perognathus longimembris bangsii), from museums, previous research, and our own field sampling. We used those data to model their suitable niche space and then identify suitable linkages between proposed conservation areas. We used a partitioned Mahalanobis D² statistic to create a spatially explicit niche model describing the distribution of a suitable niche space, and we validated the model statistically, with live trapping and with burrowing owl (Athene cunnicularia) diets. Our model identified soil characteristics, topographic ruggedness, and vegetation as variables delimiting Palm Springs pocket mouse habitat; sand content of the soils was an especially important characteristic. Our historic distribution model identified 120,000–90,000 ha as historically potential Palm Springs pocket mouse habitat; roughly 39% of that has been lost to more recent development. Most of the remaining suitable habitat occurred in the northwestern portion of the valley. We modeled habitat within core reserves as well as within proposed linkages between those reserves as having high similarity to known occupied habitats. Live trapping in areas with high (≥0.95) Habitat Suitability Index (HSI) values resulted in captures at 66% of those locations and, along with burrowing owl diets, refined a qualitative model as to what constituted a suitable Palm Springs pocket mouse corridor. While most corridor analyses have focused on mobile species which may traverse corridors in hours, days, or weeks, linkages for corridor dwellers must include habitat for sustaining multi-generational populations. This requires evaluating whether continuous suitable habitat exists within proposed corridors. Our research demonstrates how niche modeling can provide a landscape-scale view of the distribution of suitable habitat to evaluate conservation objectives for connectivity. © 2011 The Wildlife Society.     

Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.     

Modeling the Potential Distribution of Bacillus anthracis under Multiple Climate Change Scenarios for Kazakhstan

Anthrax, caused by the bacterium Bacillus anthracis, is a zoonotic disease that persists throughout much of the world in livestock, wildlife, and secondarily infects humans. This is true across much of Central Asia, and particularly the Steppe region, including Kazakhstan. This study employed the Genetic Algorithm for Rule-set Prediction (GARP) to model the current and future geographic distribution of Bacillus anthracis in Kazakhstan based on the A2 and B2 IPCC SRES climate change scenarios using a 5-variable data set at 55 km² and 8 km² and a 6-variable BioClim data set at 8 km². Future models suggest large areas predicted under current conditions may be reduced by 2050 with the A2 model predicting ∼14–16% loss across the three spatial resolutions. There was greater variability in the B2 models across scenarios predicting ∼15% loss at 55 km², ∼34% loss at 8 km², and ∼30% loss with the BioClim variables. Only very small areas of habitat expansion into new areas were predicted by either A2 or B2 in any models. Greater areas of habitat loss are predicted in the southern regions of Kazakhstan by A2 and B2 models, while moderate habitat loss is also predicted in the northern regions by either B2 model at 8 km². Anthrax disease control relies mainly on livestock vaccination and proper carcass disposal, both of which require adequate surveillance. In many situations, including that of Kazakhstan, vaccine resources are limited, and understanding the geographic distribution of the organism, in tandem with current data on livestock population dynamics, can aid in properly allocating doses. While speculative, contemplating future changes in livestock distributions and B. anthracis spore promoting environments can be useful for establishing future surveillance priorities. This study may also have broader applications to global public health surveillance relating to other diseases in addition to B. anthracis.     

Predictive Model of Habitat Suitability for the Marbled Murrelet in Western Washington

Abstract: The marbled murrelet (Brachyramphus marmoratus) is a small Pacific seabird with a breeding range that extends from the Aleutian Islands to central California. Throughout most of its breeding range, it uses mature and old-growth coniferous forests as nesting habitat. Although most murrelets seem to nest within 60 km of the coast, occupied nesting habitat has been identified as far as 84 km from the ocean in Washington state. Due to the extensive inland distances within which birds are known to breed, the area requiring surveys to identify breeding sites can be enormous. Therefore, the standard 2-year survey protocol can be expensive and time-consuming for forest management agencies and companies to administer. We developed a logistic regression model to determine whether a suite of forest structural characteristics could be used to reliably predict occupancy of a forest patch by marbled murrelets. We tested the performance of the final model using cross-validation procedures and a sample of independent sites. We used 50 sites surveyed for marbled murrelets to estimate the model, and 48 independent sites were available to test model performance. All 50 sites were on private forestland owned by Rayonier located in the western lowlands of Olympic Peninsula within the Sitka spruce and western hemlock transition zones. We sampled forest habitat at each site, and we collected information on 15 explanatory variables. The best-fitting logistic regression model contained variables that measured number of canopy layers (P<0.001, approx. F test) and mistletoe (Arceuthobium sp.) abundance (P = 0.031, approx. F test). The model misclassified 2 of 33 (94% correct) unoccupied sites as occupied using a classification cut-off (c) of c=0.25. In the other direction, under cross-validation the final model misclassified 2 of 17 (88% correct) occupied sites as unoccupied. On a test of the model against an independent sample, using a classification cut-off value of c = 0.25, the final model correctly classified 36 of 48 sites (75% correct). The final model misclassified 3 of 31 occupied sites as unoccupied (90% correct). Use of predictive models could greatly reduce the amount of forest that requires surveys by screening out those sites with little probability of use and by focusing remaining effort on higher probability sites, resulting in a higher likelihood of identifying occupied sites and thereby more efficiently conserving marbled murrelet nesting habitat.     

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Identifying the factors predicting the high‐elevation suitable habitats of Central Asian argali wild sheep and how these suitable habitats are affected by the changing climate regimes could help address conservation and management efforts and identify future critical habitat for the species in eastern Tajikistan. This study used environmental niche models (ENMs) to map and compare potential present and future distributions of suitable environmental conditions for Marco Polo argali. Argali occurrence points were collected during field surveys conducted from 2009 to 2016. Our models showed that terrain ruggedness and annual mean temperature had strong correlations on argali distribution. We then used two greenhouse gas concentration trajectories (RCP 4.5 and RCP 8.5) for two future time periods (2050 and 2070) to model the impacts of climate change on Marco Polo argali habitat. Results indicated a decline of suitable habitat with majority of losses observed at lower elevations (3,300–4,300 m). Models that considered all variables (climatic and nonclimatic) predicted losses of present suitable areas of 60.6% (6,928 km²) and 63.2% (7,219 km²) by 2050 and 2070, respectively. Results also showed averaged habitat gains of 46.2% (6,106 km²) at much higher elevations (4,500–6,900 m) and that elevational shifts of habitat use could occur in the future. Our results could provide information for conservation planning for this near threatened species in the region.     

A novel method for targeting survey effort to identify new bat roosts using habitat suitability modelling

In the UK, four out of 18 bat species are listed on the EU Habitats Directive, including the lesser horseshoe bat (Rhinolophus hipposideros), and their population status is closely monitored by visiting known roosts. R. hipposideros predominantly form maternity roosts in buildings, but roosts are impermanent features in the landscape and their distribution changes as bats form new roosts and abandon others. Locating new roosts requires intensive surveys which are challenging and inefficient. In this study, we provide a novel model-based strategy to identify potential R. hipposideros maternity roost sites that can be used to monitor bat populations. First, we model potential maternity roost habitat using record centre data on roost locations across Wales, Great Britain. We then constrain the area identified from modelling using record centre data on locations of bats in areas with no known roosts. We used two variable selection methods and three pseudo-absence data sets (random background points, random points in buildings and target group selection of mammal records) to produce six habitat suitability models. The three pseudo-absence data sets produced different habitat suitability maps, demonstrating the influence of pseudo-absence selection on species distribution models. The six models were combined using weighted mean average to produce an ensemble model that performed better than individual models and that indicated high levels of congruence in areas predicted to have high habitat suitability for maternity roosts. Our model revealed an extensive area (6523 km²; 31% of the area of Wales) containing 18,051 buildings in suitable habitat. Using record centre data on bat activity outside commuting range from known roosts reduced the potential survey area to 133 km² (0.6% of the area of Wales) and 207 buildings. Our modelling outputs can be used to direct volunteers and bat surveyors in more targeted and efficient searches.     

Assessing habitat suitability for tiger in the fragmented Terai Arc Landscape of India and Nepal

Tiger Panthera tigris populations have declined dramatically in the Terai Arc Landscape (TAL; India and Nepal), and remaining populations are highly fragmented and endangered. As part of a research program to aid tiger management by identifying critical areas for conservation, we aimed to 1) identify the factors which affect the distribution of tigers in the TAL; 2) explore the role of spatial scale in habitat selection; 3) map potentially suitable habitats; and 4) assess the quality of potential corridors linking suitable habitats. We used an approach based on presence and pseudo‐absence data, combining ecological niche factor analysis and generalized linear models. We used an information‐theoretic approach to compare our data on tiger presence with different hypotheses on tiger habitat selection (i.e. protective habitat, prey species, human disturbance), and spatial scales. All hypotheses yielded models with high prediction accuracy (>79%). The most parsimonious model included variables characterizing habitat suitability of the 2 main prey species. More detailed assessment of potentially suitable areas using an extended source‐sink approach suggested that most of the habitats outside the protected areas were attractive sink‐like habitats (i.e. they suffered high levels of human disturbance in otherwise good habitats). Overall, 24% (ca 18 500 km²) of the study area was predicted as suitable (probability cut‐off p>0.5), approximately 7% of which is under protection. Our models showed that protecting the remaining concentrations of tigers requires focusing management efforts on specific areas outside the currently protected areas. These are characterized by good natural suitability; however, they suffer from a high level of human disturbance. Our models underscore the importance of minimizing human disturbances in these areas to avoid that they act as attractive sinks but act as corridors between existing subpopulations.     

Anticipated climate and land‐cover changes reveal refuge areas for Borneo's orang‐utans

Habitat loss and climate change pose a double jeopardy for many threatened taxa, making the identification of optimal habitat for the future a conservation priority. Using a case study of the endangered Bornean orang‐utan, we identify environmental refuges by integrating bioclimatic models with projected deforestation and oil‐palm agriculture suitability from the 1950s to 2080s. We coupled a maximum entropy algorithm with information on habitat needs to predict suitable habitat for the present day and 1950s. We then projected to the 2020s, 2050s and 2080s in models incorporating only land‐cover change, climate change or both processes combined. For future climate, we incorporated projections from four model and emission scenario combinations. For future land cover, we developed spatial deforestation predictions from 10 years of satellite data. Refuges were delineated as suitable forested habitats identified by all models that were also unsuitable for oil palm – a major threat to tropical biodiversity. Our analyses indicate that in 2010 up to 260 000 km² of Borneo was suitable habitat within the core orang‐utan range; an 18–24% reduction since the 1950s. Land‐cover models predicted further decline of 15–30% by the 2080s. Although habitat extent under future climate conditions varied among projections, there was majority consensus, particularly in north‐eastern and western regions. Across projections habitat loss due to climate change alone averaged 63% by 2080, but 74% when also considering land‐cover change. Refuge areas amounted to 2000–42 000 km² depending on thresholds used, with 900–17 000 km² outside the current species range. We demonstrate that efforts to halt deforestation could mediate some orang‐utan habitat loss, but further decline of the most suitable areas is to be expected given projected changes to climate. Protected refuge areas could therefore become increasingly important for ongoing translocation efforts. We present an approach to help identify such areas for highly threatened species given environmental changes expected this century.     

Habitat fragmentation and extinction thresholds on fractal landscapes

Habitat fragmentation is a potentially critical factor in determining population persistence. In this paper, we explore the effect of fragmentation when the fragmentation follows a fractal pattern. The habitat is divided into patches, each of which is suitable or unsuitable. Suitable patches are either occupied or unoccupied, and change state depending on rates of colonization and local extinction. We compare the behaviour of two models: a spatially implicit patch-occupancy (PO) model and a spatially explicit cellular automaton (CA) model. The PO model has two fixed points: extinction, and a stable equilibrium with a fixed proportion of occupied patches. Global extinction results when habitat destruction reduces the proportion of suitable patches below a critical threshold. The PO model successfully recreates the extinction patterns found in other models. We translated the PO model into a stochastic cellular automaton. Fractal arrangements of suitable and unsuitable patches were used to simulate habitat fragmentation. We found that: (i) a population on a fractal landscape can tolerate more habitat destruction than predicted by the patch-occupancy model, and (ii) the extinction threshold decreases as the fractal dimension of the landscape decreases. These effects cannot be seen in spatially implicit models. Landscape struc-ture plays a vital role in mediating the effects of habitat fragmentation on persistence.     

Identifying Habitat Linkages for American Black Bears in North Carolina,USA

ABSTRACT Understanding landscape structure and the role of habitat linkages is important to managing wildlife populations in fragmented landscapes. We present a data-based method for identifying local- and regional-scale habitat linkages for American black bears (Ursus americanus) on the Albemarle-Pamlico Peninsula of North Carolina, USA. We used weights-of-evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (n = 35 bears). The model included 3 variables measured at a 0.2-km² scale: forest cohesion, forest diversity, and forest-agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10-fold model validation. Forest cohesion and forest-agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4-lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.     

Canada lynx occurrence and forest management in the Acadian Forest

We evaluated patterns of occurrence and non-occurrence for Canada lynx (Lynx canadensis) across a 16,530-km² study area in Maine to provide a better understanding of lynx habitat selection and habitat ecology on commercially managed forestlands in the Acadian Forest. Because of the influence of forest structure on lynx habitat selection and abundance of their primary prey, the snowshoe hare (Lepus americanus), and to improve our ability to build robust models, we used habitat information derived from a time series of Landsat satellite imagery spanning the period 1973–2004. We defined and mapped 10 forest types based on forest harvest history, time since harvest, and current forest condition. We compared a suite of models to evaluate relative influences of forest composition, habitat patch configuration, and hare density on habitat selection by lynx at the landscape scale. Occupied areas had greater average hare densities and percentage of mature conifer. Average hare density in occupied areas (0.74 hares/ha) was greater than in unoccupied areas (0.62 hares/ha), but was less than previous research has suggested may be necessary to support lynx populations in the southern portion of the species' range. No occupied areas occurred where average hare density was <0.5 hares/ha. Average hare density at the landscape-scale was strongly influenced by amount of high-quality hare habitat (i.e., conifer or mixedwood regenerating forest, 15–35 yr post-harvest). Edge density between mature conifer and high-quality hare habitat was substantially greater in occupied areas compared to unoccupied areas. Juxtaposition of those 2 forest types may provide edge habitat where lynx experience easier travel and improved access to prey in landscapes with extensive areas of high-quality hare habitat where travel and access may be somewhat limited by high understory stem density. Probability of occurrence declined nonlinearly with changes in hare density and percent mature conifer forest in the landscape; thus, suitability of currently occupied landscapes could change markedly with future changes in landscape-level hare densities and changing habitat associated with forest management. Where lynx conservation is a priority, we recommend that managers focus on creating and maintaining a minimum of 27% high-quality hare habitat within 100-km² areas to promote landscape-scale hare densities >0.5 hares/ha. © The Wildlife Society, 2013     

Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

Greater Sage-Grouse Nesting Habitat: The Importance of Managing at Multiple Scales

Abstract: Considering habitat selection at multiple scales is essential to fully understand habitat requirements and management needs for wildlife species of concern. We used a hierarchical information-theoretic approach and variance decomposition techniques to analyze habitat selection using local-scale habitat variables measured in the field and landscape-scale variables derived with a Geographic Information System (GIS) for nesting greater sage-grouse (Centrocercus urophasianus) in the Powder River Basin (PRB), Montana and Wyoming, USA, 2003–2007. We investigated relationships between habitat features that can and cannot be mapped in a GIS to provide insights into interpretation of landscape-scale—only GIS models. We produced models of habitat selection at both local and landscape scales and across scales, yet multiscale models had overwhelming statistical and biological support. Variance decomposition showed that local-scale measures explained the most pure variation (50%) in sage-grouse nesting-habitat selection. Landscape-scale features explained 20% of pure variation and shared 30% with local-scale features. Both local- and landscape-scale habitat features are important in sage-grouse nesting-habitat selection because each scale explained both pure and shared variation. Our landscape-scale model was accurate in predicting priority landscapes where sage-grouse nests would occur and is, therefore, useful in providing landscape context for management decisions. It accurately predicted locations of independent sage-grouse nests (validation R² = 0.99) and showed good discriminatory ability with >90% of nests located within only 40% of the study area. Our landscape-scale model also accurately predicted independent lek locations. We estimated twice the amount of predicted nesting habitat within 3 km of leks compared to random locations in the PRB. Likewise we estimated 1.8 times more predicted nesting habitat within 10 km of leks compared to random locations. These results support predictions of the hotspot theory of lek placement. Local-scale habitat variables that cannot currently be mapped in a GIS strongly influence sage-grouse nest-site selection, but only within priority nesting habitats defined at the landscape scale. Our results indicate that habitat treatments for nesting sage-grouse applied in areas with an unsuitable landscape context are unlikely to achieve desired conservation results.     

Ensemble modeling to predict habitat suitability for a large‐scale disturbance specialist

To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black‐backed Woodpeckers (Picoides arcticus; a burned‐forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D² models) and various combinations of four environmental variables describing burn severity, the north–south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled “ensemble predictions,” consisting of the number of models (0–8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., “no‐analogue”) environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black‐backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned‐forest landscapes where black‐backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large‐scale disturbance specialists.     

How Many Wolves (Canis lupus) Fit into Germany? The Role of Assumptions in Predictive Rule-Based Habitat Models for Habitat Generalists

Due to legislative protection, many species, including large carnivores, are currently recolonizing Europe. To address the impending human-wildlife conflicts in advance, predictive habitat models can be used to determine potentially suitable habitat and areas likely to be recolonized. As field data are often limited, quantitative rule based models or the extrapolation of results from other studies are often the techniques of choice. Using the wolf (Canis lupus) in Germany as a model for habitat generalists, we developed a habitat model based on the location and extent of twelve existing wolf home ranges in Eastern Germany, current knowledge on wolf biology, different habitat modeling techniques and various input data to analyze ten different input parameter sets and address the following questions: (1) How do a priori assumptions and different input data or habitat modeling techniques affect the abundance and distribution of potentially suitable wolf habitat and the number of wolf packs in Germany? (2) In a synthesis across input parameter sets, what areas are predicted to be most suitable? (3) Are existing wolf pack home ranges in Eastern Germany consistent with current knowledge on wolf biology and habitat relationships? Our results indicate that depending on which assumptions on habitat relationships are applied in the model and which modeling techniques are chosen, the amount of potentially suitable habitat estimated varies greatly. Depending on a priori assumptions, Germany could accommodate between 154 and 1769 wolf packs. The locations of the existing wolf pack home ranges in Eastern Germany indicate that wolves are able to adapt to areas densely populated by humans, but are limited to areas with low road densities. Our analysis suggests that predictive habitat maps in general, should be interpreted with caution and illustrates the risk for habitat modelers to concentrate on only one selection of habitat factors or modeling technique.     

Estimating Carrying Capacity for Sea Otters in British Columbia

ABSTRACT We estimated carrying capacity for sea otters (Enhydra lutris) in the coastal waters of British Columbia, Canada, by characterizing habitat according to the complexity of nearshore intertidal and sub-tidal contours. We modeled the total area of complex habitat on the west coast of Vancouver Island by first calculating the complexity of the Checleset Bay-Kyuquot Sound (CB-KS) region, where sea otters have been at equilibrium since the mid-1990s. We then identified similarly complex areas on the west coast of Vancouver Island (WCVI model), and adapted the model to identify areas of similar complexity along the entire British Columbia coast (BC model). Using survey data from the CB-KS region, we calculated otter densities for the habitat predicted by the 2 models. The density estimates for CB-KS were 3.93 otters/km² and 2.53 otters/km² for the WCVI and BC models, respectively, and the resulting 2 estimates of west coast of Vancouver Island complex habitat carrying capacity were not significantly different (WCVI model: 5,123, 95% CI = 3,337–7,104; BC model: 4,883, 95% CI = 3,223–6,832). The BC model identified the region presently occupied by otters on the central British Columbia coast, but the amount of coast-wide habitat it predicted (5,862 km²) was relatively small, and the associated carrying capacity estimate (14,831, 95% CI = 9,790–20,751) was low compared to historical accounts. We suggest that our model captured a type of high-quality or optimum habitat prevalent on the west coast of Vancouver Island, typified by the CB-KS region, and that suitable sea otter habitat elsewhere on the coast must include other habitat characteristics. We therefore calculated a linear, coast-wide carrying capacity of 52,459 sea otters (95% CI = 34,264–73,489)—a more realistic upper limit to sea otters in British Columbia. Our carrying capacity estimates are helping set population recovery targets for sea otters in Canada, and our habitat predictions represent a first step in Critical Habitat identification. This habitat-based approach to estimating carrying capacity is likely suitable for other nonmigratory, density-dependent species.     

Quantifying components of risk for European woody species under climate change

Estimates of species extinction risk under climate change are generally based on differences in present and future climatically suitable areas. However, the locations of potentially suitable future environments (affecting establishment success), and the degree of climatic suitability in already occupied and new locations (affecting population viability) may be equally important determinants of risk. A species considered to be at low risk because its future distribution is predicted to be large, may actually be at high risk if these areas are out of reach, given the species' dispersal and migration rates or if all future suitable locations are only marginally suitable and the species is unlikely to build viable populations in competition with other species. Using bioclimatic models of 17 representative European woody species, we expand on current ways of risk assessment and suggest additional measures based on (a) the distance between presently occupied areas and areas predicted to be climatically suitable in the future and (b) the degree of change in climatic suitability in presently occupied and unoccupied locations. Species of boreal and temperate deciduous forests are predicted to face higher risk from loss of climatically suitable area than species from warmer and drier parts of Europe by 2095 using both the moderate B1 and the severe A1FI emission scenario. However, the average distance from currently occupied locations to areas predicted suitable in the future is generally shorter for boreal species than for southern species. Areas currently occupied will become more suitable for boreal and temperate species than for Mediterranean species whereas new suitable areas outside a species' current range are expected to show greater increases in suitability for Mediterranean species than for boreal and temperate species. Such additional risk measures can be easily derived and should give a more comprehensive picture of the risk species are likely to face under climate change.