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Global change has altered the nature of disturbance regimes, and megafire events are increasingly common. Megafires result in immediate changes to habitat available to terrestrial wildlife over broad landscapes, yet we know surprisingly little about how such changes shape space use of sensitive species in habitat that remains. Functional responses provide a framework for understanding and predicting changes in space use following habitat alteration, but no previous studies have assessed functional responses as a consequence of megafire. We studied space use and tested for functional responses in habitat use by breeding greater sage‐grouse (Centrocercus urophasianus) before and after landscape‐level changes induced by a >40,000 ha, high‐intensity megafire that burned sagebrush steppe in eastern Idaho, USA. We also incorporated functional responses into predictive resource selection functions (RSFs) to map breeding habitat before and after the fire. Megafire had strong effects on the distribution of available resources and resulted in context‐dependent habitat use that was heterogeneous across different components of habitat. We observed functional responses in the use and selection of a variety of resources (shrubs and herbaceous vegetation) for both nesting and brood rearing. Functional responses in the use of nesting habitat were influenced by the overarching effect of megafire on vegetation, whereas responses during brood rearing appeared to be driven by individual variation in available resources that were conditional on nest locations. Importantly, RSFs built using data collected prior to the burn also had poor transferability for predicting space use in a post‐megafire landscape. These results have strong implications for understanding and predicting how animals respond to a rapidly changing environment, given that increased severity, frequency, and extent of wildfire are consequences of global change with the capacity to reshape ecosystems. We therefore demonstrate a conceptual framework to better understand space use and aid habitat conservation for wildlife in a rapidly changing world. [ABSTRACT FROM AUTHOR]

Documenting the spatial distribution of high-quality habitat patches, the distributions of threats and protected areas, and the vulnerability of habitat patches to changes in environmental conditions is vital for conservation of rare species. Range-wide species distribution models were developed for Black Rails (Laterallus jamaicensis) to predict the distribution of high-quality habitat patches for breeding Eastern Black Rails (L. j. jamaicensis). Overlay analyses were conducted to quantify the distribution of habitat relative to human development and existing protected areas, as well as the vulnerability of the best habitat to future sea level rise. The amount of high-quality habitat varied among states (0.4-7.6% of area) and was relatively rare throughout the subspecies' range (3.3% of area). Human development was common but the amount varied spatially among states (2.2-15.3% of area). Higher-quality breeding habitat was more common on federal lands (9.4% of area) and protected areas (6.4% of area), yet 33-42% of the highest-quality habitat patches were vulnerable to sea level rise of 0.61-1.83 m. Our results imply that even though many of the highest-quality habitat patches may be less likely sites for development they are often vulnerable to rising seas, and thus maintenance of existing high-quality habitat patches may be difficult without management that takes into account the likelihood of future inundation. [ABSTRACT FROM AUTHOR]

European black poplar ( Populus nigra L.) has become threatened by the widespread disappearance of riparian forests in Europe. Gene introgression through hybridization with related, non-indigenous poplar taxa may additionally reduce the population size and genetic diversity of native black poplar. We assessed the exposure of native black poplar in Slovenia to genetic introgression using a combination of habitat and gene flow models. The habitat model was built with data mining on forest inventory data employing classification decision trees. Dispersion distances for pollen, seeds and vegetative parts at 0.9% and 0.001% of adventitious presence threshold were assessed using modified gene flow models. To protect native black poplar, protection zones around native black poplar stands were proposed. Since the population of native black poplar may be exposed to various introgression pressures (e.g., the vicinity of exotic, genetically compatible poplar plantations, or feral populations potentially initiated along transport corridors or rivers), we assessed the intensity of introgression pressure by intersecting the occupied and potential habitat with the introgression pressure areas. In 2010, native black poplar occurred on 43,756 ha (2.2% of the total Slovenian territory), while the suitable habitat extended up to 108,724 ha (5.4%). The protection zones were 0.5–4.6 times larger than the occupied and potential habitat depending on the selected threshold and the type of dispersion vector. In the worst case scenario, 45.1% of protection zones would be exposed to high hybridization pressure. We recommend extending the proposed methodological approach to other regions with endangered black poplar. [ABSTRACT FROM AUTHOR]

The article discusses a study on the distribution of three mountain ungulates species. Topics covered include identifying the habitat suitability models for the species which is a fundamental part of conservation planning, the approach used to produce a habitat suitability map for each of the three species, and the variation in the availability of high-quality habitats for these species.

Abstract: Loss and deterioration of habitats are major threats for Tetrao urogallus in central Europe, where forests are highly fragmented and forest practices have distinctly changed during the last decades. Habitat models are important tools for conservation planning, often relying on presence–absence data. We mapped indirect signs of Tetrao urogallus presence as well as habitat variables over a series of seven study areas in the Austrian Alps, situated on limestone and on silicate rock. We modelled habitat use of Tetrao urogallus with one parametric approach (binary logistic regression) and two machine learning classification algorithms (classification trees and random forests) for both geological substrata separately. All three modelling approaches performed equally well in terms of accuracy or predictive power, but differed in model calibration. Three variables significantly contributed to all three habitat models on limestone and on silicate substrate, respectively, i.e. the cover of field-layer, the cover of dwarf shrubs and the proportion of deciduous trees in forest stands on limestone and the cover of field-layer, the canopy cover and the occurrence of Abies alba and/or Pinus sylvestris in forest stands on silicate rock. Some variables like the cover of Rubus sp. appeared in several models, which are not frequently mentioned in other studies. There have been some explanatory variables, which would have been missed, when applying just one single modelling approach, for example the occurrence of forest edges, the availability of canopy gaps and the supply of ant hills. Our results suggest differing habitat management strategies on limestone and on silicate rock. Considering the large spatial requirements of Tetrao urogallus the necessity of active habitat management for Tetrao urogallus becomes obvious. [Copyright &y& Elsevier]

Abstract: An isolated population of the fisher (Martes pennanti) in the southern Sierra Nevada, California, is threatened by small size and habitat alteration from wildfires, fuels management, and other factors. We assessed the population’s status and conservation options for its habitat using a spatially explicit population model coupled with a fisher probability of occurrence model. The fisher occurrence model was selected from a family of generalized additive models (GAM) generated using numerous environmental variables and fisher detection–nondetection data collected at 228 survey arrays sampled repeatedly during 2002–2006. The selected GAM accounted for 69% of the Akaike weight using total above-ground biomass of trees, latitude-adjusted elevation, and annual precipitation averaged over a 5km2 moving window. We estimated equilibrium population sizes (or carrying capacities) within currently occupied areas, and identified likely population source, sink, and expansion areas, by simulating population processes for 20years using different demographic rates, dispersal distances, and territory sizes. The population model assumed that demographic parameters of fishers scale in proportion to habitat quality as indexed by the calculated probability of fisher occurrence. Based on the most defensible range of parameter values, we estimate fisher carrying capacity at ∼125–250 adults in currently occupied areas. Population expansion into potential habitat in and north of Yosemite National Park has potential to increase population size, but this potential for expansion is predicted to be highly sensitive to mortality rates, which may be elevated in the northern portion of the occupied range by human influences, including roadkill and diseases carried by domestic cats and dogs. [Copyright &y& Elsevier]

Summary 1. Classifications that group rivers and streams with similar ecological characteristics are used increasingly to underpin conservation and resource management planning. Uses include identifying systems that may respond similarly to human activities or management actions, setting guidelines and standards to manage human impacts, interpreting data from inventory (survey) and monitoring, and identifying priority sites for conservation management. 2. Traditional approaches to river classification have been based mostly on delineating landscape units (ecoregions), often by grouping adjacent catchments having similar ecological character. However, use of this approach can be complicated by marked local heterogeneity of river systems. Instead, classifications may be more ecologically relevant if individual river or stream segments having similar environmental conditions are grouped together, independent of their geographic locations. The latter approach also allows the use of more rigorous classification procedures, including newly emerging techniques that optimise the ability of a classification to discriminate patterns in parallel sets of biological data. 3. Here, we explore the use of one of these newer techniques, generalised dissimilarity modelling (GDM), an extension of generalised regression techniques, that defines an optimal set of transformations of candidate environmental predictors to maximise explanation of species turnover in site-based biological data. 4. Using two biological data sets describing the distributions of freshwater fish and macroinvertebrates and a candidate set of functionally relevant environmental variables, we used GDM to identify the variables, weightings and transformations that best explain biological dissimilarities across sites. We then used these as input to a multivariate classification of 567 000 river and stream segments throughout New Zealand. Weightings and transformations of these variables were also specified from the GDM analysis. The matrix of transformed environmental predictors was classified in a two-stage process, using non-hierarchical mediod clustering to define an initial set of 400 groups, with relationships between these groups then defined using hierarchical clustering. 5. The resulting classification better discriminates sites with similar biological character than previous classifications, particularly at higher levels of classification detail. Key factors contributing to this success include the use of detailed, segment-specific environmental variables, coherently accounting for the longitudinal connectivity inherent in rivers including its implications for the construction of biologically relevant predictors, and the use of a modelling technique (GDM) designed to specifically analyse biological turnover and its relationships with environment. [ABSTRACT FROM AUTHOR]

The Mahalanobis distance statistic (D2) has emerged as an effective tool to identify suitable habitat from presence data alone, but there has been no mechanism to select among potential habitat covariates. We propose that the best combination of explanatory variables for a D2 model can be identified by ranking potential models based on the proportion of the entire study area that is classified as potentially suitable habitat given that a predetermined proportion of occupied locations are correctly classified. In effect, our approach seeks to minimize errors of commission, or maximize specificity, while holding the omission error rate constant. We used this approach to identify potentially suitable habitat for the Olympic marmot (Marmota olympus), a declining species endemic to Olympic National Park, Washington, USA. We compared models built with all combinations of 11 habitat variables. A 7-variable model identified 21,143 ha within the park as potentially suitable for marmots, correctly classifying 80% of occupied locations. Additional refinements to the 7-variable model (e.g., eliminating small patches) further reduced the predicted area to 18,579 ha with little reduction in predictive power. Although we sought a model that would allow field workers to find 80% of Olympic marmot locations, in fact, <3% of 376 occupied locations and <9% of abandoned locations were >100 m from habitat predicted by the final model, suggesting that >90% of occupied marmot habitat could be found by observant workers surveying predicted habitat. The model comparison procedure allowed us to identify the suite of covariates that maximized specificity of our model and, thus, limited the amount of less favorable habitat included in the final prediction area. We expect that by maximizing specificity of models built from presence-only data, our model comparison procedure will be useful to conservation practitioners planning reintroductions, searching for rare species, or identifying habitat for protection. [ABSTRACT FROM AUTHOR]

Long-term monitoring programs must use informative yet cost-effective methods. Occupancy estimates that incorporate detection probabilities are used with increasing frequency to describe species status and make management recommendations. Estimating changes in the occupancy of points over time in response to management actions or environmental changes may be especially useful for management of the Palm Springs round-tailed ground squirrel (Spermophilus tereticaudus chlorus), a subspecies covered under the Coachella Valley Multiple Species Habitat Conservation Plan and Natural Community Conservation Plan. In 2002 and 2003, we estimated occupancy and detection probability of ground squirrels across lands modeled as ground squirrel habitat by the Scientific Advisory Committee for the Habitat Conservation Plan and tested a priori hypotheses about how occupancy varied among vegetation and substrate types. In the 2003 study, we asked whether these associations were affected by winter rains after the 2002 drought year. Occupancy in 2003 was estimated at 0.99 (S^E = 0.01) in Western honey mesquite (Prosopis glandulosa) on dunes and hummocks, and occupancy of the remaining modeled habitat was best described by distance to mesquite, with the occupancy probability decreasing with increasing distance from mesquite on dunes or hummocks. The best-supported model in 2002 described the distribution of ground squirrels as a function of only vegetation and substrate type. However, the best-supported models in 2003 suggested that distance to mesquite was a component of the occupancy of non-mesquite vegetation. Mesquite seems to provide high-quality habitat that can support ground squirrels at high occupancy probabilities that may breed successfully every year. In contrast, other vegetation types provide low-quality habitat that can only support ground squirrels at low occupancy probabilities that may only breed occasionally. Mesquite could be an essential refugium during drought years, and the 4 best-supported models in 2003 suggest that restoration of mesquite beginning near currently occupied mesquite patches could be critical for maintaining ground squirrel populations on the preserves. [ABSTRACT FROM AUTHOR]

Because habitat loss and fragmentation threaten giant pandas (Ailuropoda melanoleuca), habitat protection and restoration are important conservation measures for this endangered species. However, distribution and value of potential habitat to giant pandas on a regional scale are not fully known. Therefore, we identified and ranked giant panda habitat in Foping Nature Reserve, Guanyinshan Nature Reserve, and adjacent areas in the Qinling Mountains of China. We used Mahalanobis distance and 11 digital habitat layers to develop a multivariate habitat signature associated with 247 surveyed giant panda locations, which we then applied to the study region. We identified approximately 128 km² of giant panda habitat in Foping Nature Reserve (43.6% of the reserve) and 49 km² in Guanyinshan Nature Reserve (33.6% of the reserve). We defined core habitat areas by incorporating a minimum patch-size criterion (5.5 km²) based on home-range size. Percentage of core habitat area was higher in Foping Nature Reserve (41.8% of the reserve) than Guanyinshan Nature Reserve (26.3% of the reserve). Within the larger analysis region, Foping Nature Reserve contained 32.7% of all core habitat areas we identified, indicating regional importance of the reserve. We observed a negative relationship between distribution of core areas and presence of roads and small villages. Protection of giant panda habitat at lower elevations and improvement of habitat linkages among core habitat areas are important in a regional approach to giant panda conservation. [ABSTRACT FROM AUTHOR]

• Hydrodynamic and feeding environment are the key factors affecting the abundance of Yangtze finless porpoise. • A habitat model for Yangtze finless porpoise is established based on hydrodynamic model. • Water level, surface area and food availability are deteriorating, and the number of Yangtze finless porpoise population in Poyang Lake is declining significantly. • The proposed model can provide guidance for habitat restoration and protection. Poyang Lake, located in the middle and lower reaches of the Yangtze River, is the largest freshwater lake in China and the most important habitat for the Yangtze finless porpoise. With the operation of the Three Gorges Dam, the water ecosystem functions of Poyang Lake are deteriorating, which has negatively influenced the habitat of Yangtze finless porpoise. In this research, quantitative functions of the three environmental factors, flow velocity, water depth and food availability, which have the greatest impact on the abundance of Yangtze finless porpoise were constructed, and then a habitat model of the Yangtze finless porpoise was established based on hydrodynamic model. The model results are in good agreement with the observed data, and the simulation well reproduces the spatial position distribution and abundance of the Yangtze finless porpoise in Poyang Lake. Then, the proposed model is applied to compare three scenarios before and after the operation of Three Gorges Dam, and simulate the evolution of hydrodynamics, feeding environment and Yangtze finless porpoise habitat in Poyang Lake. Two typical parts of river-lake connection channel and the main lake area were analyzed separately. Results show that the water depth, surface area and food availability in Poyang Lake are gradually decreasing, which lead to the weakening and plaque-like fragmentation of the Yangtze finless porpoise habitat. The findings in this study could help to understand how changes in aquatic conditions affect the ecological habitat, and provide scientific guidance for the future protection of Yangtze finless porpoise. Image, graphical abstract [ABSTRACT FROM AUTHOR]