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We combined observations of bobcats ( Lynx rufus) from bowhunters with remotely-sensed data to build models that describe habitat and relative abundance of this species in the agricultural landscape of Iowa, USA. We calculated landscape composition and configuration from publicly available land cover, census, road, hydrologic, and elevation data. We used multiple regression models to examine county-level associations between several explanatory variables and relative abundance of bobcats reported by surveyed bowhunters in each county. The most influential explanatory variables in the models were metrics associated with the presence of grassland, including Conservation Reserve, along with configuration of this perennial habitat with forests, although human population density and abundance of eastern cottontails ( Sylvilagus floridanus) also correlated with abundance of bobcats. Validation of predictions against 3 years of independent data provided confidence in the models, with 66% of predictions within 1 bobcat/1,000 hunter-hours and 95% within 5 bobcats/1,000 hunter-hours of observed values. Once we accounted for landscape differences, no residual spatial trend was evident, despite relatively recent bobcat recolonization of Iowa. Models suggested that future range expansion of the bobcat population may be possible in some northern Iowa counties where habitat composition is similar to counties in southern Iowa where bobcats are abundant. Results from the county-level model have been useful to the Iowa Department of Natural Resources in evaluating the expansion of this once rare species and for delineating harvest opportunities. © 2011 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Identification of critical habitat in estuarine nursery areas is an important conservation and management objective. Habitat can be viewed as a mosaic of both temporally variable environmental features and spatially variable structural features that combine to define optimal habitat. Effective models of juvenile distributions should account for individual movement, as well as the full suite of habitat variability including both spatial and temporal components. We have extended a terrestrial model of small-scale movement patterns to describe habitat choices of an index juvenile fish in an estuarine nursery system. Movement of small juvenile fishes was found to be influenced by both spatial and temporal patterns in habitat quality, and it was a balanced mix of both that resulted in an optimal distribution. Fishes that perceive habitat on a scale much smaller than the scale of spatial heterogeneity may respond to temporal change as a movement cue allowing for more deterministic outcomes at larger scales despite perceptual limitations. These model outcomes suggest a hierarchical approach is best for describing habitat choice in juvenile fishes and this approach will be used in the future to explore individual and population responses to predictable habitat change. [Copyright &y& Elsevier]

The neotropical frog Eleutherodactylus johnstonei (Anura: Eleutherodactylidae) has been referred to as a highly invasive species on grounds of its wide distribution and is expected to extend its range significantly based on recent climate model assumptions. The frog was introduced to a number of South American mainland localities, including French Guiana. Other species of the genus have become invasive on oceanic islands such as Hawaii where they pose serious biological and economical problems. No data are available on the invasion status of E. johnstonei populations from coastal South America. Here we present the first assessment of the status of a mainland population from French Guiana 10 years after the introduction of the species. We found that the species has not spread significantly within the last ten years and so far does not exhibit a high invasion potential. Frogs were restricted to urban garden habitats showing significantly higher abundance in gardens containing ornamental potted plants. Distribution patterns were best explained by two dispersal-related factors operating at different scales: (1) distance of occupied sites to nearest population indicating short-distance active dispersal using stepping stone habitats, and (2) distance of occupied sites to shore with a distinct bimodal pattern indicating long-distance passive or jump-dispersal, most likely due to the exchange or transfer of ornamental plants. Even though past and current invasion potential can be considered low, we advocate the implementation of a regional monitoring scheme and detailed studies on biotic interactions with native fauna. [ABSTRACT FROM AUTHOR]

The Northern Spotted Owl (Strix occidentalis caurina) population is declining throughout its range in the United States and adjacent Canada and is facing increasing pressure from the invading Barred Owl (Strix varia). In this study, we characterize Spotted Owl habitat associations and develop 2 new habitat selection models for the eastern Washington Cascade Range. Topographic and habitat data were compiled at 2 scales (0.25 and 1.0 mi) around 224 Spotted Owl activity centers, or sites, and at 160 random locations in the same geographic region, and used to develop models for predicting owl distributions. Univariate analysis found that owl sites occurred below 5000-ft elevation and were more likely to occur as area in the >71% crown-cover class increased. Owl sites were found to be more likely to occur closer to streams and to be rare in the Subalpine Fir (Abies lasiocarpa) vegetation type. The 9-25" tree size-class was a significant predictor of the distribution of owl sites. Habitat models were constructed that were moderately successful at predicting owl-site distribution. Models from the largest scale tested (1.0-mi radius) were the most predictive, at 80% accuracy. Top-ranked models included overstory canopy cover, tree size, elevation, precipitation, distance to stream, and tree species as predictors. The resulting models can be used to help identify likely sites for surveys and to inform conservation and landscape management activities associated with forest-health restoration. [ABSTRACT FROM AUTHOR]

The article presents a study on the use of incorporating time-lagged oceanographic data into the predictive habitat models of dolphin acoustic occurrence. The researchers built habitat models within a generalized additive model (GAM) framework, after which autonomously recorded echolocation click occurrence was incorporated with solar and lunar temporal indices, upwelling indices and remotely sensed oceanographic variables. They also investigated the relationships between dolphin acoustic activity and environmental variables within a GAM framework. They found that the temporal predictive models of cetacean habitat offer researchers the capacity to assess how dynamic ocean processes impact the occurrence of cetaceans in a particular area of interest.

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]

An improvement of a metapopulation model is developed and used to analyze the behaviors of abundant and extinct species in the system analytically. By approximating the direct effect rate of habitat destructions on individual species using differentiable functions, the model enables us to discuss the equilibrium and stability of the system. Special cases of the habitat destruction functions, constant, piecewise linear, exponential and periodic functions, are discussed analytically and numerically. The conditions for system stability are also given. [Copyright &y& Elsevier]

Abstract: Habitat models for animal species are important tools in conservation planning. We assessed the need to consider several scales in a case study for three amphibian and two grasshopper species in the post-mining landscapes near Leipzig (Germany). The two species groups were selected because habitat analyses for grasshoppers are usually conducted on one scale only whereas amphibians are thought to depend on more than one spatial scale. First, we analysed how the preference to single habitat variables changed across nested scales. Most environmental variables were only significant for a habitat model on one or two scales, with the smallest scale being particularly important. On larger scales, other variables became significant, which cannot be recognized on lower scales. Similar preferences across scales occurred in only 13 out of 79 cases and in 3 out of 79 cases the preference and avoidance for the same variable were even reversed among scales. Second, we developed habitat models by using a logistic regression on every scale and for all combinations of scales and analysed how the quality of habitat models changed with the scales considered. To achieve a sufficient accuracy of the habitat models with a minimum number of variables, at least two scales were required for all species except for Bufo viridis, for which a single scale, the microscale, was sufficient. Only for the European tree frog (Hyla arborea), at least three scales were required. The results indicate that the quality of habitat models increases with the number of surveyed variables and with the number of scales, but costs increase too. Searching for simplifications in multi-scaled habitat models, we suggest that 2 or 3 scales should be a suitable trade-off, when attempting to define a suitable microscale. [ABSTRACT FROM AUTHOR]

Both observational and modelling studies of the natural environment are characterised by their ‘grain’ and ‘extent’, the smallest and largest scales represented in time and space. These are imposed scales that should be chosen to ensure that the natural scales of the system are captured in the study. A simple cellular automata model of habitat represents only the presence or absence of vegetation, with global and local interactions described by four empirical parameters. Such a model can be formulated as a nonlinear Markov equation for the habitat probability. The equation produces inherent space and time scales that may be considered as transition scales or the scales for recovery from disturbance. However, if the resolution of the model is changed, the empirical parameters must be changed to preserve the properties of the system. Further, changes in the spatial resolution lead to different interpretations of the spatial structure. In particular, as the resolution is reduced, the apparent dominance of one habitat type over the other increases. The model provides an ability to compare both field and model investigations conducted at different resolutions in time and space. [Copyright &y& Elsevier]

The goal of the Endangered Species Act is to improve the chances of listed species’ survival by increasing population levels (US Fish and Wildlife Service in American burying beetle ( Nicrophorus americanus) recovery plan. Newton Corner, MA, p 80, ). If successful, a species will be delisted, but in order to achieve the goal of species recovery the demography, habitat preferences, reproductive biology, and cause of the species decline must be understood. Like many rare invertebrates, information about the endangered American burying beetle ( Nicrophorus americanus) prior to listing consisted of the taxonomic description and morphological characterization. Surveys for N. americanus provide data that can be integrated into spatial models to help predict suitable habitat. Our objective was to model the potential distribution of N. americanus and to evaluate these models ability to generate maps of potential habitat, thus focusing recovering efforts. We chose six modelling algorithms that utilized both presence and absence data from beetle surveys conducted throughout eastern Oklahoma. Using area under the curve (AUC) as our evaluation statistic, we found that ten of the twelve models performed within the AUC index category of “potentially useful” (AUC 0.7–0.9). Models utilizing presence only data performed well compared to models built with presence/absence data. This may indicate the weakness of using absence data to indicate unsuitable habitat. Lack of integration into the model of biotic interactions may also be affecting model performance. To improve model performance, the causes of N. americanus’s endangered status and its population shrinkage should be considered. Although the best models were not highly accurate, the map of suitable habitat can help to inform conservation biologists of areas with a likelihood of N. americanus presence. Overgenerous models can mislead conservation planners in thinking that more areas are highly suitable. If resources are limited for planning preserves and areas of reintroduction, it may be better to be conservative and to limit consideration to the most suitable habitat. [ABSTRACT FROM AUTHOR]

Geographic information systems (GIS) allow researchers to make cost-effective, spatially explicit predictions of species’ distributions across broad geographic areas. However, there has been little research on whether using fine-scale habitat data collected in the field could produce more robust models of species’ distributions. Here we used radio-telemetry data collected on a declining species, the North American wood turtle ( Glyptemys insculpta), to test whether fine-scale habitat variables were better predictors of occurrence than land-cover and topography variables measured in a GIS. Patterns of male and female occurrence were similar in the spring; however, females used a much wider array of land-cover types and topographic positions in the summer and early fall, making it difficult for GIS-based models to accurately predict female occurrence at this time of year. Males on the other hand consistently selected flat, low-elevation, riparian areas throughout the year, and this consistency in turn led to the development of a strong GIS-based model. These results demonstrate the importance of taking a more sex-specific and temporally dynamic view of the environmental niche. [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]

Reduction of bycatch through spatial management is one possible approach in fisheries management. One requirement is description of the habitat of the species in question, and this is now possible for a range of species using data collected from electronic tags. Here we report on a spatial management approach used to limit unwanted capture of southern bluefin tuna (SBT, Thunnus maccoyii) in the eastern Australia longline fishery. The approach combines a habitat model conditioned with temperature preference data from 56 pop-up satellite archival tags deployed on SBT, and an ocean model to produce near real-time habitat predictions utilized by fishery managers during the fishing season. Three habitat types based on probability of SBT occurrence are identified in the fishing region, and spatial zoning by managers is used to regulate fisher access to these zones. This management approach has been used since 2003, and despite dynamic spatial management often being resisted by fishers or managers because of perceived implementation complexity, there has been an increase in complexity of the management actions over the 6-yr period. Managers now request more habitat predictions each season, update the spatial zoning more frequently, and apply more complicated management lines dividing the habitat types. This has resulted in an improved fit to the modeled habitat predictions, and as catch per unit effort (CPUE) of SBT is related to the habitat type, has contributed to the overall success of the management approach. This implemented management approach illustrates that managers and fishers can successfully implement complex dynamic spatial zoning. [ABSTRACT FROM AUTHOR]

Habitat loss and habitat isolation have contributed to declines in the green and golden bell frog ( Litoria aurea) over much of its former range in south-eastern Australia. Understanding the parameters critical for the persistence of extant populations is required to halt further declines. We assessed waterbody occupancy in a local network of potential L. aurea breeding habitats on Kooragang Island, Australia, by conducting surveys over two years (2000 and 2001) at 30 waterbodies, and using site-occupancy models. The probability that a waterbody was used by L. aurea in 2000 increased with increasing waterbody area, and decreasing distance to the nearest waterbody where L. aurea was detected and where reproductive recruitment was observed. Large waterbodies where high numbers of L. aurea were detected in 2000 were more likely to be used in 2001. The probability that a waterbody used in 2000 was not used in 2001 (i.e., turnover) decreased with increasing waterbody area and increasing numbers of L. aurea detected at the site in 2000. Our results demonstrate the potential for frog abundance, immigration, and recruitment to reduce local turnover. We recommend that conservation strategies for extant populations of L. aurea protect mosaics of wetland habitat and maintain connectivity among waterbodies. [ABSTRACT FROM AUTHOR]

Abstract: To incorporate ruffed grouse (Bonasa umbellus) habitat planning in forest management, it is necessary for managers to understand factors contributing to grouse habitat use. Previous studies examining ruffed grouse drumming habitat documented relationships between drumming grouse and broad vegetation categories (e.g., northern hardwoods, young aspen [Populus spp.], oak [Quercus spp.]), but few studies have documented how drumming grouse respond to ecological variations in site conditions of aspen or other vegetation types that might be used. Our objectives were to determine the utility of habitat type classifications in predicting the occurrence of ruffed grouse drumming habitat in the western Upper Peninsula of Michigan, and demonstrate how classifications may be used to understand how forest management may affect ruffed grouse habitat. We used survey routes on state land and conducted drumming surveys during mid-April and early May at 78 points in 2005 and 2006. We recorded the number of drumming males heard at each point, the azimuth to where the grouse was heard, and a qualitative measure of distance to determine in which forest stands grouse were drumming. Using GIS, we determined the specific vegetation type, age class, and habitat type, evaluated habitat suitability, and determined a suitability score for areas in which grouse were drumming. We constructed a logistic regression model that calculated the probability of grouse use of areas for drumming based on vegetation characteristics at used and random locations. Our results indicated that the probability of grouse use of an area for drumming is based on inherent site characteristics (i.e., habitat type) and habitat suitability. The model is useful for planning forest management activities and understanding how grouse may respond to spatial or temporal changes in vegetation through succession or manipulation. [Copyright &y& Elsevier]

Aim To determine whether different abundances of introduced species of Cinchona (Rubiaceae) affect species composition and facilitate species richness in managed tropical forests, to test whether any facilitative effects on understorey species depend on forest type, and to investigate whether facilitative effects can be attributed to the ‘substitutive facilitation model’. Location Makawao Forest Reserve on Maui, Hawai’i, USA. Methods Cinchona species ( Cinchona pubescens and Cinchona calisaya) were mapped within various forest types. In three forest types (ageing Eucalyptus and Pinus plantations, and near-natural Acacia koa forests), we analysed environmental parameters (e.g. canopy cover, litter cover, pH value and soil depth) and the species composition of Cinchona-invaded and non-invaded plots; data were compared based on Cinchona cover and forest types. Habitat modelling for several endemic species and tree ferns was carried out to test whether Cinchona cover is an important variable for the probability of occurrence of these endemics. Results Cinchona species have naturalized mainly in Eucalyptus and Pinus plantations and Acacia koa forests and here add an additional shrub layer. In contrast to other studies, we revealed facilitative effects of Cinchona on native species within all forest types. Species richness is about 20% higher in invaded plots than in non-invaded plots, and these show a nearly 50% higher proportion of endemic species, including tree ferns. The proportion of endemics even increases with increasing Cinchona cover. For several endemics, Cinchona is found to be an important variable for the probability of occurrence, and the removal of Cinchona cover as an explanatory variable lowers the model fit. In addition to Cinchona, variables delineating vegetation structure and light availability have a strong effect on the model fit. Main conclusions In the structurally simplified Hawaiian forests studied, Cinchona facilitated endemic species in accordance with the ‘substitutive facilitation model’. This contrasts with the results of an earlier study in the naturally treeless Galápagos highlands, which revealed a sharp decrease in the abundance of endemics under Cinchona canopy. These results illustrate that, through the same structural change (addition of a vegetation layer), an invasive species may exert divergent effects across different ecosystem types. The facilitation of endemic understorey species by invasive tree species in managed forests leads to a dilemma in conservation but also to new perspectives for ecosystem restoration. [ABSTRACT FROM AUTHOR]

The article presents a study on the efficient use of light detection and ranging (LIDAR)-derived measurements to measure the vegetation structure for passerine birds in riparian habitat at the Central Valley in California. The use of LIDAR imagery is capable of generating estimates of canopy height, stem diameter and biomass. Moreover, the study intends to support the predictive performance of vegetation measurements of LIDAR imagery at one hectare scale.

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]

We developed predictive habitat models for a bighorn sheep (Ovis canadensis) population in the Peninsular Ranges of southern California, USA, using 2 Geographic Information System modeling techniques, Ecological Niche Factor Analysis (ENFA) and Genetic Algorithm for Rule-set Production (GARP). We used .16,000 Global Positioning System locations from 34 animals in 5 subpopulations to develop and test ENFA and GARP models, and we then compared these models to each other and to the expert-based model presented in the United States Fish and Wildlife Service's Recovery Plan for this population. Based on a suite of evaluation methods, we found both ENFA and GARP to provide useful predictions of habitat; however, models developed with GARP appeared to have higher predictive power. Habitat delineations resulting from GARP models were similar to the expert-based model, affirming that the expert-based model provided a useful delineation of bighorn sheep habitat in the Peninsular Ranges. In addition, all 3 models identified continuous bighorn sheep habitat from the northern to southern extent of our study area, indicating that the Recovery Plan's recommendation of maintaining habitat connectivity throughout the range is an appropriate goal. [ABSTRACT FROM AUTHOR]

The importance of the spatial as well as the temporal structure of habitat patches for urban biodiversity has been recognised, but rarely quantified. In dynamic environments the rate of habitat destruction and recreation (i.e. the landscape turnover rate), the minimum amount of potential habitat, its spatial configuration as well as the environmental conditions determining habitat quality are crucial factors for species occurrence. We analysed species responses to environmental parameters and to the spatio-temporal configuration of urban brownfield habitats in a multi-species approach (37 plant and 43 insect species). Species presence/absence data and soil parameters, site age, vegetation structure and landscape context were recorded by random stratified sampling at 133 study plots in industrial areas in the city of Bremen (Germany). Based on the field data, we predicted species occurrences by species distribution models using a multi-model inference approach. Predicted species communities were driven by successional age both at the scale of a single building lot and at the landscape scale. Minimum average succession time of brownfield habitats required to support all and especially regionally rare species depended on the proportion of available open space; the larger the potential habitat area the faster the acceptable turnover. Most plant, grasshopper, and leafhopper species modelled could be maintained at an intermediate turnover rate (mean age of 10-15 years) and a proportion of open sites of at least 40%. Our modelling approach provides the opportunity of inferring optimal spatio-temporal landscape configurations for urban conservation management from patch scale species environment relationships. The results indicate that urban planning should incorporate land use dynamics into the management of urban biodiversity. [ABSTRACT FROM AUTHOR]

Murase, H., Nagashima, H., Yonezaki, S., Matsukura, R., and Kitakado, T. 2009. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. – ICES Journal of Marine Science, 66: 1417–1424.A generalized additive model (GAM) was applied to fishery-survey data to reveal the influences of environmental factors on the distribution patterns of Japanese anchovy (Engraulis japonicus), sand lance (Ammodytes personatus), and krill (Euphausia pacifica). Echosounder and physical-oceanographic data were collected in Sendai Bay, Japan, in spring 2005. A hierarchical model was used with two spatial strata: (i) presence and absence of each species; and (ii) biomass density of each species, given its presence; and six environmental covariates (surface water temperature, salinity, and chlorophyll, and near-seabed water temperature, salinity, and depth). The results indicate non-linear responses of the two indices to the environmental covariates. In addition, the biomasses estimated by the GAMs were comparable with estimates based on conventional, stratified-random sampling for each species. GAMs are very useful for (i) investigating the effects of environmental factors on the distributions of biological organisms, and (ii) predicting the distributions of animal densities in unsurveyed areas. [ABSTRACT FROM PUBLISHER]

Abstract: Effective management of natural populations depends heavily on the capacity for understanding and predicting their habitat requirements. For endemic and threatened species like the mara (Dolichotis patagonum), this information is extremely necessary for populations survival. I investigated and constructed a mathematical habitat model for the mara, with the capacity to predict the probability of finding animals in a particular environment. In the Monte Desert, maras prefer habitats with low shrub density and high percentage of bare soil. The area used by each couple of maras was 7.73 ha and shows an aggregation pattern. Contrary to expectation, about 94% of these areas were in habitats created by human activities. Grazed, burned and crop areas seem to be prefered by maras when natural open habitat disappear. This information and the results of habitat requirements can be used to monitor the status of mara populations and predict potential population extinctions according to habitat variables. [Copyright &y& Elsevier]

With the implementation of large-scale water diversion projects, the reduction of river flow in water diversion areas may impact the ecological environment of the river. In this study, we used integrated hydrodynamic, habitat, and risk modeling methods to produce an ecological risk time series based on the consequences of departures from the optimum environmental flow range of a river, quantifying the extent to which flow regulation affects fish habitats. Taking the water diversion project in central Yunnan as an example, we used this method to produce a risk time series of fish habitats. This time series shows that the ecological risk increases when the river flow deviates from the optimal range of environmental flow. The degree of risk is different in different periods: in wet years, the risk is negligible; in normal years, the risk is between acceptable and critical, and in dry years, the risk is above critical. The research results can provide a basis for the optimal allocation of water resources to meet environmental flow requirements. • Large drops in river flow in diversion areas may impact the ecological environment. • Our model captured risk dynamics of a river system following water diversion. • Our model generated a time series of risks rather than a single point risk. • Eco-environmental risk increases with river flow deviating from the optimal range. • This study offers a basis to allocate water to meet eco-environmental requirements. [ABSTRACT FROM AUTHOR]

Habitat modeling is one of the most common practices in ecology today, aimed at understanding complex associations between species and an array of environmental, bioclimatic, and anthropogenic factors. This review of studies of seven species of terrestrial bears (Ursidae) occupying four continents examines how habitat models have been employed, and the functionality of their predictions for management and conservation. Bear occurrence data have been obtained at the population level, as presence points (e.g., sign surveys or camera trapping), or as locations of individual radio-collared animals. Radio-collars provide greater insights into how bears interact with their environment and variability within populations; they are more commonly used in North America and Europe than in South America and Asia. Salient problematic issues apparent from this review included: biases in presence data; predictor variables being poor surrogates of actual behavioral drivers; predictor variables applied at a biologically inappropriate scale; and over-use of data repositories that tend to detach investigators from the species. In several cases, multiple models in the same area yielded different predictions; new presence data occurred outside the range of predicted suitable habitat; and future range projections, based on where bears presently exist, underestimated their adaptability. Findings here are likely relevant to other taxa. [ABSTRACT FROM AUTHOR]

Abstract: The Middle Spotted Woodpecker (Dendrocopos medius) is the bird species which Germany has the greatest global responsibility to protect. It is an umbrella species for the entire assemblage of animals associated with mature broadleaved trees, especially oak. Even though well studied in small to medium scale stands, the validity of habitat suitability analysis for this species in larger forests has not previously been proved. Aim of this study was to test suitability of permanent forest inventory plots for modelling its distribution in a 17,000ha forest landscape and to derive habitat threshold values as a basis for formulating management guidelines. Based on 150 randomly selected 12.5ha plots we identified mean age and basal area of oaks as the most important habitat factors using a backward selection logistic model. Internal validation showed an AUC of 0.89 and a of 0.58. Determination of thresholds using maximally selected rank statistics found higher probability of occurrence in stands with a mean age >95 years. Above that age the probability increased again in stands with more than 6.4m2 basal area oak/ha. Our results show that widely available forest inventory data can serve as a valuable basis for monitoring the Middle Spotted Woodpecker, either within the framework of the Natura 2000 Network, or more generally in integrated forest management with the aim of providing suitable habitats for the entire assemblage of species on old deciduous trees, especially oak. [Copyright &y& Elsevier]

Fuel reduction in bushland adjacent to urban development is an important component of bushfire management to protect lives and properties. In many urban areas, the objective of property protection by fuel reduction conflicts with biodiversity management objectives. Conserving threatened species in such situations will require information on spatial distributions of these species in the landscape. We used GIS modelling to predict the likely impacts of strategic fire advantage zones (SFAZs) on two threatened species in the Shoalhaven region of NSW: the eastern bristlebird and the glossy black cockatoo. We used current knowledge of the association between these animals and vegetation to predict habitat suitability, overlaid residential areas on this habitat-suitability map, and then applied buffers around the residential areas to represent minimum (250 m) and maximum (450 m) SFAZs scenarios. For eastern bristlebirds, 4,000 ha of suitable habitat occurred in the study area, and nearly 5% of this would become unsuitable with 450 m SFAZs. For the cockatoos, approximately 9% of 30,000 ha of suitable habitat would be altered with 450 m SFAZs. The GIS models provide the information needed for more creative bushfire mitigation activities that could deliver both the conservation of endangered species and protection of human assets. [ABSTRACT FROM AUTHOR]

Abstract: The aim of the study was the development of habitat models for Nephtys species (Polychaeta: Nephtyidae). The investigation area was the German Bight, the southeastern part of the North Sea. Models were developed based on field data collected between 2000 and 2006. In addition, data on environmental variables were retrieved from long-term monitoring data sets and from the sediment map by Figge [Figge, K., 1981. Nordsee. Sedimentverteilung in der Deutschen Bucht. Map No. 2900. Publisher: Deutsches Hydrographisches Institut, Hamburg]. The statistical modelling technique used was multivariate adaptive regression splines (MARS). Models were fitted individually for each species. Evaluation of predictive discrimination and predictive accuracy of the developed models was by calculation of the area under the receiver operating curve (AUC) or sensitivity and specificity, respectively. Habitat models with best predictive fit were selected for the presentation of habitat suitability maps. Six Nepthys species were found: Nephtys assimilis, N. caeca, N. cirrosa, N. hombergii, N. incisa and N. longosetosa. N. hombergii was most common whereas N. incisa and N. longosetosa were rare. Habitat preferences varied considerably among the species. For all investigated Nephtys species except N. longosetosa a habitat model could be developed based on four predictor variables. The habitat models with best predictive fit were those for N. cirrosa and N. hombergii. The N. caeca habitat model was of limited predictive accuracy and only accept predictive discrimination. The number of predictors as well as the relative importance of the respective predictors in the model varied among the different species. Direct comparison of most suitable habitats for the different species based on modelling revealed that in the mostly sandy regions parallel to the German coast in water depths up to 20 m an overlap between N. caeca, N. hombergii and N. cirrosa exists. In the deeper central German Bight with mostly fine sands with increased mud contents N. hombergii, N. assimilis and, at least partially and rare in numbers, N. incisa co-occur. It can be concluded that important sediment characteristics like grain size median and mud content as well as water depth and mean salinity are useful parameters to describe the habitat requirements of most Nepthys species in the German Bight. However, additional variables need to be incorporated into such analyses. [Copyright &y& Elsevier]

Questions: 1. How many traits associated with persistence and regeneration are necessary to predict the response of plants to soil fertility and disturbance? 2. Are correlated changes in trait expressions linked to the response of functional groups to fertility and disturbance? Locution: Lower Frankonia, Germany. Methods: On 120 plots located in managed and abandoned grasslands, fields, and vineyards, we recorded species composition, disturbance intensity, soil water and nutrients, and ten candidate traits for 75 species. We used a novel method which is based on three steps: (1) logistic regression to separate responsive from non-responsive species; (2) iterative clustering of all possible combinations of the candidate traits including regression of each cluster in response to the environmental variables; (3) selection of the trait combination that performed best with respect to goodness of fit of all clusters from this combination. Bivariate trait relationships across functional groups were analysed with reduced major axis regression (RMA). Results: The parsimonious trait combination consisted of life span, specific leaf area (SLA), canopy height, and seed number. The 'acquisitive' functional groups in terms of SLA and height were linked to higher fertility and earlier disturbance, while the 'retentive' groups related to lower fertility and later disturbance. Investment in reproduction, however, displayed a reverse relation. SLA and canopy height showed correlated shifts in two pairs of co-occursing functional groups. Conclusions: A small number of traits is sufficient to predict the response of species. Plants need a higher increment in SLA to reach the same height, if start of disturbance is earlier. Linkages between traits shift from generative to vegetative with increasing fertility and earlier start of disturbance. Functional groups enable shifts in scaling relationships between traits to be analysed, in contrast to the analysis of single traits. [ABSTRACT FROM AUTHOR]

The Pacific sand lance ( Ammodytes hexapterus) is an ecologically important prey species for many vertebrate marine predators in the Pacific Northwest. In this study, we examined the use of intertidal habitat by young of the year (YOY) sand lance in Barkley Sound, British Columbia. We selected 14 of the original 29 independent environmental variables for modelling based on univariate analysis for variable selection. These were then used to model sand lance presence-absence using a classification tree approach. Based on our models we found that sand lance avoided mud and intertidal eelgrass. For sites that had very little mud and no intertidal eelgrass, sand lance preferred sediment size mean ≥1,918 μm or sites with sediment size mean ≤1,918 μm but with relatively well sorted sediment (sorting values ≤2.56 SDs, used as a heterogeneity index of the substrate grain size). Adjacent subtidal characteristics were not found to be important at this scale of study. This suggests intertidal substrate characteristics and presence-absence of intertidal eelgrass are the main influences on occurrence of YOY in the intertidal. Our results support the hypothesis that sand lance are associated with particular sediment types, however intertidal sediment types used by sand lance in our study differ from known preferences of sand lance for subtidal sediment types. This difference may be due to unique habitat constraints for intertidal versus subtidal regions. Although it is unknown if the results of this study are widely applicable, the results begin to identify intertidal habitat features that are important for Pacific sand lance. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the incidence of the A. chilensis disease syndrome in the “16 de Octubre” Valley (Chubut, Argentinean Patagonia) was related to landscape climatic, topographic and edaphic attributes, using remote sensing, geographic information systems and statistical methods. A strong relationship between the occurrence and incidence of the A. chilensis disease syndrome and site variables related to poor soil drainage was found. Non-allophanized soils with fine textures on flat and wavy soil phases, geomorphologies associated to alluvial processes, and low elevations and gentle slopes were positively related to the incidence of the disease. These relationships at a landscape scale agree with previous studies carried out at the stand level. A logistic predictive model of diseased occurrence was developed for the study area considering aspect, elevation, slope, mean annual precipitation and soil phase (classified according to predominant slopes). [Copyright &y& Elsevier]

Abstract: When plant cover is measured by the pin-point method the results are traditionally reported by the observed mean and the variance of the number of hits. However, in order to model plant cover data it would be advantageous to have a model that describes the stochastic ecological processes. Here a hierarchical stochastic model of zero-inflated generalised binomial distributed data is introduced. The model specifically represents two important characteristics of the distribution of plant species: plant species do not occur everywhere possible within their realised niche and the data will consequently be zero-inflated, and even if a plant species do occur in the area the number of hits typically will be overdispersed relative to the binomial distribution due to a non-random distribution of individual plants. The stochastic model is applied using different regression models with a varying degree of complexity on hierarchical pin-point cover data of 155 plant species at 10 different habitat types regressed on the measured carbon/nitrogen ratio. The plant cover data displayed a considerable amount of intra-class positive correlation with an average intra-class correlation of 0.31. The degree of intra-class positive correlation differed significantly among habitat types, and generally, it may be concluded that it is critical to take intra-class correlation into account when modelling pin-point cover data. [Copyright &y& Elsevier]

Abstract: Northern Ohio supports a small population of greater sandhill cranes (Grus canadensis tabida) that is currently listed as state-endangered. Population restoration efforts are currently under consideration, although it is not known if habitats in the state can support additional nesting pairs. Accurate estimates of breeding pair carrying capacity are necessary before conservation efforts can be effectively developed and implemented. We estimated carrying capacity for nesting sandhill cranes using habitat suitability and spatial optimization models. We first developed a spatially explicit habitat suitability index (HSI) model to identify suitable nesting sites at five locations in northern Ohio. We then used the HSI output to estimate the carrying capacity at each location. We modeled carrying capacity as an anti-covering location problem, a spatial optimization model that determines the maximum number of breeding pairs an area can support, given that nests must be spaced 3000m apart. Our results indicate that habitats in Ohio where cranes currently breed are near carrying capacity, while unoccupied suitable habitats are available in other portions of the state. This analysis enables wildlife managers to identify priority locations for crane conservation in Ohio and to determine which restoration efforts (e.g. habitat restoration or population augmentation) are most likely to succeed at each location. Our methodology provides an important and innovative conservation tool that can be applied to other species with strong attachment to sites (e.g. nest or den) that are optimally spaced at some minimum distance from conspecifics, competitors, predators, or sources of disturbance. [Copyright &y& Elsevier]

Populations of the threatened Growling Grass Frog Litoric raniformis in metropolitan Melbourne currently occur in human-made habitats and there is little information on their current status. We conducted systematic field surveys at 28 waterbodies within a population distributed over agricultural land in the Pakenham area, on the south-eastern urban-fringe of Melbourne,Victoria, during the 2005/06 breeding season. Our aim was to identify key areas for the conservation of L raniformis.We recorded L raniformis at 14 waterbodies where we marked 31 males and 16 females. Occupied waterbodies had a greater proportion of submerged vegetation, a higher pH, and were situated close to other occupied waterbodies, generally < 200 m.The adult male and female population size was estimated at 45 ± 10 (± S.E.) and 23 ± 8 individuals, respectively. There was high site fidelity at occupied waterbodies (> 80% recapture rates). Recruitment was recorded in only two waterbodies. Two males and four females were recorded moving 15 - 20 m between adjacent waterbodies, whereas one additional male moved 130 m. Dispersal and movement corridors important for the persistence of L raniformis in the study area include agricultural drains and Gum Scrub Creek. High priority conservation areas are waterbodies situated in close proximity to the creek and Melbourne - Bairnsdale railway reserve, and clusters of occupied waterbodies to the south. [ABSTRACT FROM AUTHOR]

Habitat size, habitat isolation and habitat quality are regarded as the main determinants of butterfly occurrence in fragmented landscapes. To analyze the relationship between the occurrence of the butterfly Cupido minimus and these factors, patch occupancy of the immature stages in patches of its host plant Anthyllis vulneraria was studied in the nature reserve Hohe Wann in Bavaria (Germany). In 2001 and 2002, 82 A. vulneraria patches were surveyed for the presence of C. minimus larvae. The occurrence was largely affected by the size of the food plant patches. In a habitat model that uses multiple logistic regression, the type of management and habitat connectivity are further determinants of species distribution. Internal and temporal validation demonstrate the stability and robustness of the developed habitat models. Additionally, it was proved that the colonization rate of C. minimus was significantly influenced by the distance to the next occupied Anthyllis patch. Concerning long-term survival of (meta-) populations in fragmented landscapes, the results show that lower habitat quality may be compensated by higher connectivity between host plant patches. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Declining population numbers coupled with the growing evidence of global change have focussed attention on the critically endangered riverine rabbit (Bunolagus monticularis) endemic to South Africa. The aim of this study is to develop a habitat model to aid in the identification of isolated populations, offer opportunities for re-introduction or introduction, and guide future conservation efforts by assessing the possible impacts of global change. We attempt a novel approach where plant species which afford the riverine rabbit cover from predation and its primary food sources are modelled utilising the same technique and are included as a predictor variable in the habitat model for both current and future projections of potential habitat. Inclusion of this proximal variable as well as riparian areas yields a more parsimonious habitat model than using climatic variables alone. Results suggest that unsurveyed suitable habitat east of Victoria West might harbour previously overlooked isolated populations or offer new opportunities for re-introductions. Future climatic conditions under the most severe general circulation model for the region (HADCM3) suggest that, on average, in excess of 96% of the current habitat could become unsuitable, mitigated only slightly by a possible 7% increase in range in adjacent upper catchment areas. Consideration of existing land transformation increases this range reduction by a further 1%. Given that ex situ captive breeding programmes have met with no success and that the bulk of future potential range lies well outside of the currently known and surveyed areas the current adaptation options of conservancy establishment and captive breeding need to be re-evaluated. Without positive human intervention the future of the critically endangered riverine rabbit under conditions of global change seems certain. [Copyright &y& Elsevier]

After nearly a century of decline and range contraction in the northeastern United States, moose (Alces alces) have re-colonized Adirondack Park, New York due to improved habitat and adjacent source populations. In this paper 1 present the results of 2 Habitat Suitability Index (HSI) models used to examine the pattern of moose recovery in Adirondack Park. Sighting data collected in 1980-1999 by the New York State Department of Environmental Conservation were used to compare moose locations with 3 suitability levels of moose habitat predicted by the HSI models. The 2 models indicated that most of Adirondack Park was a combination of suitable (49-73%) and most suitable habitat (10-35%) for moose; the majority (53-77%) of sightings occurred in suitable habitat. However, the distribution of moose locations derived from sighting data might have been influenced by where human recreational activity occurred because sighting locations were not well correlated with the most suitable habitat. The combined analysis of the sighting locations and the HSI models provided valuable insight into the current and potential occupation and distribution of moose in Adirondack Park. [ABSTRACT FROM AUTHOR]

The article analyzes recent survey data and maps environmental variables integrated over a home-range-scale habitat models for fisher (Martes Pennanti) in California. It aims to determine habitat factors associated with the current distribution of fishers, and test whether those factors differ for widely disjunct northern and southern populations. The authors note that the analyses were designed to probe whether poor habitat quality can explain the current absence of fishers in the historically occupied central and northern Sierra Nevada region. Results and implications of the study are discussed.

Controversy over bobcat (Lynx rufus) management in the northern Lower Peninsula of Michigan (NLP), USA, stimulated a need for information on the distribution of Michigan bobcats. From March 2003 to October 2004, we conducted a radiotelemetry and scentstation survey study of bobcats in the NLP. We developed a spatial model to predict bobcat distribution throughout the NLP based on bobcat area requirements, habitat and landscape variables derived from remotely sensed land-cover data, and a multivariate distance statistic. Bobcat 50% minimum convex polygon core areas were comprised of more lowland forest (51%), nonforested wetlands (9%), and streams (3%) than the surrounding NLP. The NLP was comprised primarily of upland forest (44%) and field (32%). Habitat in the northeast and central regions of the NLP was most similar to the habitat composition of bobcat core areas. This model will be useful in aiding Michigan wildlife management agencies with assessing the status and distribution of the NLP bobcat population by identifying areas important to bobcats and supporting the development of regional strategies for carnivore conservation. [ABSTRACT FROM AUTHOR]

Anthropogenic management of dynamic ecosystems has led to decline of species dependent on processes that maintain suitable habitat, particularly on barrier islands. By evaluating environmental variables over large geographic areas, remote-sensing data and geographic information systems (GIS) hold increasing promise for management of these unique habitats and their species associates. We used light detection and ranging (LIDAR) data to extract habitat variables for Amaranthus pumilus, a federally threatened flowering annual of the Atlantic barrier islands. We asked: (1) can habitat variables for A. pumilus be extracted from remote-sensing data, and (2) can these variables be used to model suitable habitat? We extracted topographic habitat variables for naturally occurring plants and evaluated habitat using multiple statistical techniques and other published model performance measures. We found that elevation was the most limiting topographic variable controlling the occurrence of Amaranthus pumilus. The most occurrences fell with in a 1.23 m range relative to local mean high water. Additionally, we used digital imagery collected concurrently with the LIDAR data to assess the role of vegetation cover in A. pumilus distribution. The occurrence of seabeach amaranth in previous years also was factored into the models. The models performed well, predicting 46%-100% of the plant occurrences using as little as 2% of the habitat. Amaranthus pumilus can potentially serve as a conservation "umbrella" for coastal biodiversity. The methods presented here for identification of A. pumilus habitat, using GIS and model construction of potential habitat, can be applied to other species of concern, including nesting shorebirds and sea turtles. [ABSTRACT FROM AUTHOR]

1. When invasive species are first detected in a new environment there is often a demand for information about the potential for the organism to spread and create impacts. Uraba lugens (Lepidoptera: Nolidae) is an Australian native moth that has invaded New Zealand in what are presumed to be two separate episodes. After U. lugens was found in Auckland in 2001 aclimex™ model was prepared to gauge the potential for the moth to spread and inflict damage in New Zealand. Inconsistencies in fitting model parameters to the then known native distribution, indicated that the known native distribution was probably incomplete, and that the unknown part of its range was critical for defining its likely range limits in New Zealand. 2. A synthetic sex pheromone trapping survey was used to ascertain the altitudinal and upper rainfall range limits of U. lugens in part of its native range in Australia. The survey extended the known range of U. lugens into higher-altitude and higher-rainfall zones of Tasmania. We used the expanded distribution information to refine aclimex model, and to project the climate suitability for U. lugens with particular emphasis on New Zealand. 3. The projected climatic suitability of New Zealand indicates that the area likely to be at risk of invasion by U. lugens is considerably more extensive than was indicated from its historical distribution records in Australia. It now includes all the eucalypt forestry areas. Similarly, the global potential distribution covers the major eucalypt forestry regions of the world. 4. The minimum heat sum necessary to complete a generation for U. lugens that was associated with its range limits was considerably lower than that predicted by average development rates gained from constant temperature and daylength studies. Possible explanations for this anomaly are discussed. 5. Synthesis and applications. The lack of suitable data on the distribution of organisms is perhaps the single most common challenge for ecological climate modellers. Trapping along climatic gradients with a synthetic pheromone lure offers a cheap, rapid means of ascertaining the climatic distribution of suitable high profile insects. [ABSTRACT FROM AUTHOR]

Summary: The integration of spatial information concerning animal species into static, rule-based spatially explicit non-probabilistic models for decision-making regarding the planning of landscapes and regions provides generalised habitat-described landscape-structural parameters. As a basis for an individually developed model, a discussion is first of all presented which involves general data and parametric requirements, necessary for the development of a species-referenced, spatially explicit model for analysis and evaluation. The parameters necessary for an assessment of habitat characteristics of birds in Central Europe will be discussed on the basis of landscape and structural information, using the Corn Bunting (Emberiza calandra) as an example. A spatial analysis and assessment procedure supported by geographical information system (GIS) for this species has been developed for the definition of regulations and assessment categories and subsequently applied to the example of an open agricultural landscape in Saxony-Anhalt. Within the area of examination of approximately 42.4km2, 56 songbird perches were located (density of 1.34territories/km2). A comparison with the 45 mapped territories from the year 2004 indicated a good correlation with the model assumptions. Indeed, 16 of these 45 territories were only briefly occupied and the establishment of breeding pairs was ascertained in only 17 territories. The analysis and assessment model as presented yielded reality-based results after the utilisation of relatively little landscape-structural entry data, and is well suited for support of the decision-making process for spatial planning. The model framework presented in this paper can be modified and transferred to other species. [Copyright &y& Elsevier]

The eastern diamondback rattlesnake (Crotalus adamanteus) and timber rattlesnake (Crotalus horridus) are sympatric throughout most of southern Georgia, USA. We used rattlesnake sightings to quantify and compare habitat use by these 2 species in the Gulf Coastal Plain. At the largest scale examined, univariate statistics and logistic regression models indicated that eastern diamondback rattlesnakes were associated with roads but not with any of the specific habitat types we examined. In contrast, timber rattlesnakes were closely associated with hardwood habitat and riverine systems but not with roads and edges. To effectively conserve and manage both species in the Southeast, a habitat matrix of large intact patches of both hardwood and pine (Pinus spp.) forest may be necessary. [ABSTRACT FROM AUTHOR]

Prior to making general inferences or predictions from habitat models, their generalizability requires thorough assessment. However, systematic testing of model generality is often claimed, but rarely done. We used existing models for phytophagous insects (grasshoppers and leafhoppers) from a study on urban brownfields. Data for model building had been collected in two major cities of Northern Germany, Berlin and Bremen. We transferred these models to test data from another year (Bremen, 30 model transfers), and to test data from different geographic regions (transfer from Berlin to Bremen and vice versa, 30 model transfers). We evaluated discriminatory ability as well as model calibration for the test data. Most transfers (28 in time, 27 in space) were successful, i.e. occupied sites within the test data were assigned higher occurrence probabilities than unoccupied sites, the threshold independent c-index for the test data exceeded chance. Our results indicated that models built on the larger dataset (147 plots, Bremen) were more general than the ones basing on the smaller dataset (89 plots, Berlin). The overall good transferability had three important drawbacks: (1) models were mostly not well calibrated to the test data, thus predicted occurrence probabilities may not be used as absolute values, but as ordinal ranks. (2) Model fit to the test data often decreased considerably compared to the training data. (3) Dichotomising occurrence probabilities to presence/absence predictions required prior information about species prevalence. Assigning presences to the sites with the highest predicted occurrence probabilities, with the number of presences corresponding to the prevalence, proofed to be a comparatively simple and reliable way of dichotomising predictions. Still, it only allowed predictions exceeding chance for 19 model transfers in time and 23 transfers in space, and required information about species’ prevalences. We qualitatively compared pairs of models for 10 species, with one model basing on the Bremen data, one on the Berlin data. Both models had been built with the same modeling technique. Vegetation structure variables were largely comparable between models. It seemed that they were more directly related to species’ occurrences and thus more general than landscape context variables and soil parameters. [Copyright &y& Elsevier]

Capercaillie (Tetrao urogallus) is a large, endangered forest grouse species with narrow habitat preferences and large spatial requirements that make it susceptible to habitat changes at different spatial scales. Our aim was to evaluate the relative power of variables relating to forest versus landscape structure in predicting capercaillie occurrence at different spatial scales. We investigated capercaillie-habitat relationships at the scales of forest stand and forest-stand mosaic in 2 Swiss regions. We assessed forest structure from aerial photographs in 52 study plots each 5 km². We classified plots into one of 3 categories denoting the observed local population trend (stable, declining, extinct), and we compared forest structure between categories. At the stand scale, we used presence-absence data for grid cells within the plots to build predictive habitat models based on logistic regression. At this scale, habitat models that included only variables relating to forest structure explained the occurrence of capercaillie only in part, whereas variables selected by the models differed between regions. Including variables relating to landscape features improved the models significantly. At the scale of stand mosaic, variables describing forest structure (e.g., mean canopy cover, proportion of open forest, and proportion of multistoried forest) differed between plot categories. We conclude that small-scale forest structure has limited power to predict capercaillie occurrence at the stand scale, but that it explains well at the scale of the stand mosaic. Including variables for landscape structure improves predictions at the forest-stand scale. Habitat models built with data from one region cannot be expected to predict the species occurrence in other regions well. Thus, multiscale approaches are necessary to better understand species-habitat relationships. Our results can help regional authorities and forest-management planners to identify areas where suitable habitat for capercaillie is not available in the required proportion and, thus, where management actions are needed to improve habitat suitability. [ABSTRACT FROM AUTHOR]

Current circumstances — that the majority of species distribution records exist as presence-only data (e.g. from museums and herbaria), and that there is an established need for predictions of species distributions — mean that scientists and conservation managers seek to develop robust methods for using these data. Such methods must, in particular, accommodate the difficulties caused by lack of reliable information about sites where species are absent. Here we test two approaches for overcoming these difficulties, analysing a range of data sets using the technique of multivariate adaptive regression splines (MARS). MARS is closely related to regression techniques such as generalized additive models (GAMs) that are commonly and successfully used in modelling species distributions, but has particular advantages in its analytical speed and the ease of transfer of analysis results to other computational environments such as a Geographic Information System. MARS also has the advantage that it can model multiple responses, meaning that it can combine information from a set of species to determine the dominant environmental drivers of variation in species composition. We use data from 226 species from six regions of the world, and demonstrate the use of MARS for distribution modelling using presence-only data. We test whether (1) the type of data used to represent absence or background and (2) the signal from multiple species affect predictive performance, by evaluating predictions at completely independent sites where genuine presence–absence data were recorded. Models developed with absences inferred from the total set of presence-only sites for a biological group, and using simultaneous analysis of multiple species to inform the choice of predictor variables, performed better than models in which species were analysed singly, or in which pseudo-absences were drawn randomly from the study area. The methods are fast, relatively simple to understand, and useful for situations where data are limited. A tutorial is included. [ABSTRACT FROM AUTHOR]

Aggregation of reserve networks is generally considered desirable for biological and economic reasons: aggregation reduces negative edge effects and facilitates metapopulation dynamics, which plausibly leads to improved persistence of species. Economically, aggregated networks are less expensive to manage than fragmented ones. Therefore, many reserve-design methods use qualitative heuristics, such as distance-based criteria or boundary-length penalties to induce reserve aggregation. We devised a quantitative method that introduces aggregation into reserve networks. We call the method the boundary-quality penalty (BQP) because the biological value of a land unit (grid cell) is penalized when the unit occurs close enough to the edge of a reserve such that a fragmentation or edge effect would reduce population densities in the reserved cell. The BQP can be estimated for any habitat model that includes neighborhood (connectivity) effects, and it can be introduced into reserve selection software in a standardized manner. We used the BQP in a reserve-design case study of the Hunter Valley of southeastern Australia. The BQP resulted in a more highly aggregated reserve network structure. The degree of aggregation required was specified by observed (albeit modeled) biological responses to fragmentation. Estimating the effects of fragmentation on individual species and incorporating estimated effects in the objective function of reserve-selection algorithms is a coherent and defensible way to select aggregated reserves. We implemented the BQP in the context of the Zonation method, but it could as well be implemented into any other spatially explicit reserve-planning framework. [ABSTRACT FROM AUTHOR]

Questions: Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative kernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods? Methods: Non-parametric multiplicative regression (NPMR) was developed from the premises that: the response variable has a minimum of zero and a physiologically-determined maximum, species respond simultaneously to multiple ecological actors, he response to any one factor is conditioned by the values of other factors, and that if any of the factors is intolerable then the response is zero. Key features of NPMR are interactive effects of predictors, no need to specify an overall model form in advance, and built-in controls on overfitting. The effectiveness of the method is demonstrated with simulated and real data sets. Results: Empirical and theoretical relationships of species response to multiple interacting predictors can be represented effectively by multiplicative kernel smoothers. NPMR allows us to abandon simplistic assumptions about overall model form, while embracing the ecological truism that habitat factors interact. [ABSTRACT FROM AUTHOR]