A small mammal’s map: identifying and improving the large-scale and cross-border habitat connectivity for the hazel dormouse <Emphasis Type="Italic">Muscardinus avellanarius</Emphasis> in a fragmented agricultural landscape

Fragmentation and habitat loss pose major threats to global biodiversity. Especially forest dwelling species with small ranges and high habitat specialisation are affected by ongoing land use change. Building projects for infrastructural purposes, expanding settlements, and extensive agricultural areas are assumed to have a high impact on these species. The European Habitat’s Directive aims to conserve and restore habitat networks to lower these impacts. We propose that the idea of securing habitat networks for protected species should be incorporated within large scale landscape planning e.g. by modelling and improving corridors for umbrella species. Within a Danish-German project we developed a model demonstrating potential connecting corridors for Muscardinus avellanarius, a specialised forest dwelling rodent species with low dispersal ability. We used presence data and eco-geographical variables to find a data based time-efficient procedure which may be applied also in other species for future landscape planning. The habitat suitability model shows that the hazel dormouse occupies a narrow niche with highly suitable habitats comprising edge habitats. It indicates the preference of forest patches, linear structures and networks of the mentioned habitats. Along with connectivity the diversity of suitable habitats diversity is the major factor predicting hazel dormouse presence. For conservation management, we calculated habitat corridors and highlight sections with missing connections. This allowed us to accentuate regions of high management interest. The results represent the foundation not only for this cross-border conservation project, but also for long-term dormouse conservation on a federal-state level.     

Habitat suitability modelling for species at risk is sensitive to algorithm and scale: A case study of Blanding's turtle,Emydoidea blandingii,in Ontario,Canada

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boosted regression trees; and maximum entropy modelling. The area under the receiver characteristic operating curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912 when using random background and from 0.727 to 0.741 with target-group background. E. blandingii habitat suitability was best predicted by air temperature, wetland area, open water area, road density, and cropland area. Habitat suitability increased with increasing air temperature and wetland area, and decreased with increasing cropland area. Low road density and open water increased habitat suitability, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps for species at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offer insight on the habitat requirements of species at risk and help guide the development of management plans. Our results suggest that E. blandingii management plans should promote the protection of terrestrial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currently occupied habitat, and identify movement corridors for isolated populations.     

Ecological modelling for the conservation of Gluta travancorica Bedd. - An endemic tree species of southern Western Ghats,India

Endemic species are highly adapted to grow exclusively in a specific geographical area. The goal of the current study is to determine the probable habitat distribution range of the narrowly endemic species Gluta travancorica. An ecological niche modelling is carried out, using four different models viz., BioClim, MaxEnt, Random Forest and Deep Neural Networks (DNN). A total of 506 G. travancorica cluster locations were surveyed and used for this study with thirty different ecogeographic, edaphic and bioclimatic environmental parameters. After a preliminary investigation using multi-collinearity analysis, soil parameter variables like pH, cation exchange capacity (CEC), silt and clay content are used for final modelling. Factor analysis of ecological niche revealed the soil parameters like pH, CEC, silt and clay content as the key predictors. The result is validated using true skill statistics, sensitivity, specificity, kappa statistic and AUC-ROC. Results of the present study show that DNN have exceptional prediction performance, demonstrated by its AUC score of 0.959. DNN model projected 32.37% (938.18 km²) of the study region to have a ‘highly suitable habitat’, whereas 67.63% (1960.82 km²) was classified as having ‘low habitat suitability’. Besides, back-to-field assessments have also proven DNN's potential in predicting the habitat suitability of G. travancorica. The study results will facilitate the prioritization of conservation and seedling restoration strategies. The forest range explored in this work is a component of one of the most important global biodiversity hotspots, and it has significant implications for regional biodiversity conservation.     

Scenario-Led Habitat Modelling of Land Use Change Impacts on Key Species

Accurate predictions of the impacts of future land use change on species of conservation concern can help to inform policy-makers and improve conservation measures. If predictions are spatially explicit, predicted consequences of likely land use changes could be accessible to land managers at a scale relevant to their working landscape. We introduce a method, based on open source software, which integrates habitat suitability modelling with scenario-building, and illustrate its use by investigating the effects of alternative land use change scenarios on landscape suitability for black grouse Tetrao tetrix. Expert opinion was used to construct five near-future (twenty years) scenarios for the 800 km² study site in upland Scotland. For each scenario, the cover of different land use types was altered by 5–30% from 20 random starting locations and changes in habitat suitability assessed by projecting a MaxEnt suitability model onto each simulated landscape. A scenario converting grazed land to moorland and open forestry was the most beneficial for black grouse, and ‘increased grazing’ (the opposite conversion) the most detrimental. Positioning of new landscape blocks was shown to be important in some situations. Increasing the area of open-canopy forestry caused a proportional decrease in suitability, but suitability gains for the ‘reduced grazing’ scenario were nonlinear. ‘Scenario-led’ landscape simulation models can be applied in assessments of the impacts of land use change both on individual species and also on diversity and community measures, or ecosystem services. A next step would be to include landscape configuration more explicitly in the simulation models, both to make them more realistic, and to examine the effects of habitat placement more thoroughly. In this example, the recommended policy would be incentives on grazing reduction to benefit black grouse.     

Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m – 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.     

Integrating GIS and expert judgment in a multi-criteria analysis to map and develop a habitat suitability index: A case study of large mammals on the Malayan Peninsula

Many habitat patches in tropical landscapes have become less suitable for wildlife due to an increase in anthropogenic disturbances. An index of habitat suitability based on the ecological factors that collectively determine the suitability of an organism's habitat is important for conservation planning. However, a widely accepted and comprehensive multi-criteria habitat suitability index for umbrella species is still lacking, particularly in areas where information related to the biology and ecology of the species of interest is not available. Therefore we develop preliminary habitat maps and measure the degree of habitat suitability for large mammals, focusing on four umbrella species in the State of Selangor, Peninsular Malaysia: Panthera tigris jacksoni (Malayan tiger), Tapirus indicus (Malayan tapir), Helarctos malayanus malayanus (Malayan sun bear), and Rusa unicolor cambojensis (sambar deer). The former two are endangered and the latter two are vulnerable according to the IUCN Red List. The suitability of habitat patches for each species was measured across the entire study area as well as in nine wildlife protected areas by integrating GIS data and expert opinion. Expert opinions were used as the source of information regarding the stresses faced by the species because there was insufficient information available from ground surveys.We developed an index and maps of habitat suitability for each species, which were then integrated to represent a combined index (ranging from 0 to 27) and spatially explicit maps of the area's habitat suitability for large mammals. The average large mammal habitat suitability index value of the State of Selangor (9) indicates that many habitat patches have become unsuitable for such species. Of the nine wildlife protected areas, Fraser's Hill (22), Sungai Dusun (22), and Bukit Kutu (21) are very suitable; Klang Gate (20) and Templers Park (17) are suitable; and the remaining four are unsuitable for large mammals. We assume that this preliminary habitat suitability index and mapping are useful for conservation planning of wildlife habitats at both landscape and regional scales, as well as providing an initial foundation for revision by future research with significant new information.     

Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW

Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.     

Are Regional Habitat Models Useful at a Local-Scale? A Case Study of Threatened and Common Insectivorous Bats in South-Eastern Australia

Habitat modelling and predictive mapping are important tools for conservation planning, particularly for lesser known species such as many insectivorous bats. However, the scale at which modelling is undertaken can affect the predictive accuracy and restrict the use of the model at different scales. We assessed the validity of existing regional-scale habitat models at a local-scale and contrasted the habitat use of two morphologically similar species with differing conservation status (Mormopterus norfolkensis and Mormopterus species 2). We used negative binomial generalised linear models created from indices of activity and environmental variables collected from systematic acoustic surveys. We found that habitat type (based on vegetation community) best explained activity of both species, which were more active in floodplain areas, with most foraging activity recorded in the freshwater wetland habitat type. The threatened M. norfolkensis avoided urban areas, which contrasts with M. species 2 which occurred frequently in urban bushland. We found that the broad habitat types predicted from local-scale models were generally consistent with those from regional-scale models. However, threshold-dependent accuracy measures indicated a poor fit and we advise caution be applied when using the regional models at a fine scale, particularly when the consequences of false negatives or positives are severe. Additionally, our study illustrates that habitat type classifications can be important predictors and we suggest they are more practical for conservation than complex combinations of raw variables, as they are easily communicated to land managers.     

A Presence-Only Model of Suitable Roosting Habitat for the Endangered Indiana Bat in the Southern Appalachians

We know little about how forest bats, which are cryptic and mobile, use roosts on a landscape scale. For widely distributed species like the endangered Indiana bat Myotis sodalis, identifying landscape-scale roost habitat associations will be important for managing the species in different regions where it occurs. For example, in the southern Appalachian Mountains, USA, M. sodalis roosts are scattered across a heavily forested landscape, which makes protecting individual roosts impractical during large-scale management activities. We created a predictive spatial model of summer roosting habitat to identify important predictors using the presence-only modeling program MaxEnt and an information theoretic approach for model comparison. Two of 26 candidate models together accounted for >0.93 of AICc weights. Elevation and forest type were top predictors of presence; aspect north/south and distance-to-ridge were also important. The final average best model indicated that 5% of the study area was suitable habitat and 0.5% was optimal. This model matched our field observations that, in the southern Appalachian Mountains, optimal roosting habitat for M. sodalis is near the ridge top in south-facing mixed pine-hardwood forests at elevations from 260–575 m. Our findings, coupled with data from other studies, suggest M. sodalis is flexible in roost habitat selection across different ecoregions with varying topography and land use patterns. We caution that, while mature pine-hardwood forests are important now, specific areas of suitable and optimal habitat will change over time. Combining the information theoretic approach with presence-only models makes it possible to develop landscape-scale habitat suitability maps for forest bats.     

Integrating climate and landscape models to prioritize areas and conservation strategies for an endangered arboreal primate

Species distributions are influenced by both climate conditions and landscape structure. Here we propose an integrated analysis of climatic and landscape niche-based models for a forest-dependent primate, the endangered black lion tamarin (Leontopithecus chrysopygus). We applied both climate and landscape variables to predict the distribution of this tamarin and used this information to prioritize strategic areas more accurately. We anticipated that this approach would be beneficial for the selection of pertinent conservation strategies for this flagship species. First, we built climate and landscape niche-based models separately, combining seven algorithms, to infer processes acting on the species distribution at different scales. Subsequently, we combined climate and landscape models using the EcoLand Analysis. Our results suggest that historic and current landscape fragmentation and modification had profoundly adverse effects on the distribution of the black lion tamarins. The models indicated just 2096 km² (out of an original distribution of 92,239 km²) of suitable areas for both climate and landscape. Of this suitable area, the species is currently present in less than 40%, which represents less than 1% of its original distribution. Based on the combined map, we determined the western and southeast regions of the species range to be priority areas for its conservation. We identified areas with high climatic and high landscape suitability, which overlap with the remaining forest fragments in both regions, for habitat conservation and population management. We suggest that areas with high climatic but low landscape suitability should be prioritized for habitat management and restoration. Areas with high landscape suitability and low climatic suitability, such as the Paranapiacaba mountain range should be considered in light of projected climate change scenarios. Our case study illustrates that a combined approach of climatic and landscape niche-based modeling can be useful for establishing focused conservation measures that may increase the likelihood of success.     

Forecasting suitable areas for wind turbine occurrence to proactively improve wildlife conservation

The United States is rapidly expanding production of renewable energy to meet increased energy demands and reduce greenhouse gas emissions. Wind energy is at the forefront of this transition. A central challenge is understanding the nexus between wind energy development and its capacity for negative effects on wildlife causing population declines and habitat loss. Collaboration among conservationists and developers, early in the planning process, is crucial for minimizing wind-wildlife conflicts. Such collaborations require data showing where wind and wildlife impacts occur. To meet this challenge and inform decision-making, we provide natural resource agencies and stakeholders information regarding where future wind turbines may occur, and the potential affects on natural resource management, including the conservation of priority species and their habitats. We developed a machine learning model predicting suitability of wind turbine occurrence (hereafter, wind turbine suitability) across an eight-state region in the United States, representing some of the richest areas of wind potential. Our model incorporates predictor variables related to infrastructure, land ownership, meteorology, and topography. We additionally created a constraint layer indicating areas where wind would likely not be developed because of zoning, protected lands, and restricted federal agency proximity guidelines. We demonstrate how the predictive wind turbine suitability model informs conservation planning by incorporating animal movement models, relative abundance models coupled with spatial conservation planning software, and population density models for three exemplar, high priority species often affected by wind energy: whooping cranes (Grus americana), golden eagles (Aquila chrysaetos), and lesser prairie-chickens (Tympanuchus pallidicinctus). By merging the wind turbine and biological models, we identified conservation priority areas (i.e., places sharing high suitability for wind turbines and species use), and places where wind expansion could minimally affect these species. We use our “species-wind turbine occurrence relationships” to demonstrate applications, illustrating how forecasting areas of wind turbine suitability promotes wildlife conservation. These relationships inform wind energy siting to reduce negative ecological impacts while promoting environmental and economic viability.     

Factors affecting seasonal habitat use,and predicted range of two tropical deer in Indonesian rainforest

There is an urgent recognized need for conservation of tropical forest deer. In order to identify some environmental factors affecting conservation, we analyzed the seasonal habitat use of two Indonesian deer species, Axis kuhlii in Bawean Island and Muntiacus muntjak in south-western Java Island, in response to several physical, climatic, biological, and anthropogenic variables. Camera trapping was performed in different habitat types during both wet and dry season to record these elusive species. The highest number of photographs was recorded in secondary forest and during the dry season for both Bawean deer and red muntjac. In models, anthropogenic and climatic variables were the main predictors of habitat use. Distances to cultivated area and to settlement were the most important for A. kuhlii in the dry season. Distances to cultivated area and annual rainfall were significant for M. muntjak in both seasons. Then we modelled their predictive range using Maximum entropy modelling (Maxent). We concluded that forest landscape is the fundamental scale for deer management, and that secondary forests are potentially important landscape elements for deer conservation. Important areas for conservation were identified accounting of habitat transformation in both study areas.     

Using Landscape and Bioclimatic Features to Predict the Distribution of Lions,Leopards and Spotted Hyaenas in Tanzania's Ruaha Landscape

Tanzania''s Ruaha landscape is an international priority area for large carnivores, supporting over 10% of the world''s lions and important populations of leopards and spotted hyaenas. However, lack of ecological data on large carnivore distribution and habitat use hinders the development of effective carnivore conservation strategies in this critical landscape. Therefore, the study aimed to (i) identify the most significant ecogeographical variables influencing the potential distribution of lions, leopards and spotted hyaenas across the Ruaha landscape; (ii) identify zones with highest suitability for harbouring those species; and (iii) use species distribution modelling algorithms (SDMs) to define important areas for conservation of large carnivores. Habitat suitability was calculated based on environmental features from georeferenced presence-only carnivore location data. Potential distribution of large carnivores appeared to be strongly influenced by water availability; highly suitable areas were situated close to rivers and experienced above average annual precipitation. Net primary productivity and tree cover also exerted some influence on habitat suitability. All three species showed relatively narrow niche breadth and low tolerance to changes in habitat characteristics. From 21,050 km² assessed, 8.1% (1,702 km²) emerged as highly suitable for all three large carnivores collectively. Of that area, 95.4% (1,624 km²) was located within 30 km of the Park-village border, raising concerns about human-carnivore conflict. This was of particular concern for spotted hyaenas, as they were located significantly closer to the Park boundary than lions and leopards. This study provides the first map of potential carnivore distribution across the globally important Ruaha landscape, and demonstrates that SDMs can be effective for understanding large carnivore habitat requirements in poorly sampled areas. This approach could have relevance for many other important wildlife areas that only have limited, haphazard presence-only data, but which urgently require strategic conservation planning.     

Identifying potential conservation areas in the Cuitzeo Lake basin,Mexico by multitemporal analysis of landscape connectivity

Lake Cuitzeo basin is an important ecological area subjected to strong human pressure on forest covers that are key elements for the long-term support of biodiversity. We studied landscape connectivity changes for the years 1975, 1996, 2000, 2003 and 2008 to identify potential conservation areas in the basin. We modeled potential distributions of the Mexican bobcat (Lynx rufus escuinapae) and the ringtail (Bassariscus astutus) – two terrestrial mammal focal species with contrasting dispersal capacities – and we determined their habitat availability and suitability in the basin. We then identified their optimal habitat patches and produced landscape cumulative resistance maps, estimated least-cost paths (graph theory approach), and elaborated current flow maps (circuit theory approach). For evaluation of landscape connectivity, we applied an integral index of connectivity (IIC) to each study period, and determined individual habitat patch contribution to the overall landscape connectivity. The IIC index had very low values associated with reduced availability of focal species habitat. However, our study showed the conservation importance of the surface of optimal habitat patch areas. The combined application of a graph-based approach and current flow mapping were useful, and complementary both in terms of estimating potential dispersal corridors and identifying high probability dispersal areas. This indicated that landscape connectivity analysis is a useful tool for identification of potential conservation areas and for local landscape planning.     

Potential biodiversity map of bird species (Passeriformes): Analyses of ecological niche,environmental characterization and identification of priority conservation areas in southern Patagonia

Different methodologies try to identify priority conservation areas (PCA) to improve habitat conservation and decrease human pressures over bird species at coarse-scale. Map of potential biodiversity (PB) can identify PCA (high PB values) at different scale levels by considering ecological requirements and distributions through potential habitat suitability (PHS) models. The aim was to elaborate a map of PB of bird species based on PHS models to spatially identify PCA in Santa Cruz, Argentina. Moreover, we want to analysis species’ ecology requirements, and evaluate PB values and spatially identify PCA through two scale levels. We computed 47 models using Environmental Niche Factor Analysis (ENFA) on Biomapper software. Each model was visualized and combined to get a unique map of PB. We analyzed ecological requirements by specialization and marginality and PHS maps. Moreover, considering natural environments (regional level) and forest types’ cover (forest landscape level), we evaluated PB values using ANOVAs and identified PCA under different human pressures, using human footprint (HPF) map. Bird species related to Nothofagus forests were most specialist and exhibited a narrower potential distribution than grassland species. At regional level, Magellanic grass steppes displayed the highest PB values, where most of the PCA had high HPF values. At forest landscape level, ecotone N. antarctica forests had the highest PB values, where PCA with low HFP values were outside current protected networking. We conclude that combining PHS models and the map of PB allowed us to improve bird distribution studies and to assist biodiversity conservation strategies under human pressures.     

Microhabitat relationships among five lizard species associated with granite outcrops in fragmented agricultural landscapes of south‐eastern Australia

A fundamental part of developing effective biodiversity conservation is to understand what factors affect the distribution and abundance of particular species. However, there is a paucity of data on ecological requirements and habitat relationships for many species, especially for groups such as reptiles. Furthermore, it is not clear whether habitat relationships for particular species in a given environment are transferable to other environments within their geographical range. This has implications for the type of ‘landscape model’ used to guide management decisions in different environments worldwide. To test the hypothesis that species‐specific habitat relationships are transferable to other environments, we present microhabitat models for five common lizard species from a poorly studied habitat – insular granite outcrops, and then compared these relationships with studies from other environments in south‐eastern Australia. We recorded twelve species from five families, representing 699 individuals, from 44 outcrops in the south‐west slopes of New South Wales. Five lizard species were abundant and accounted for 95% of all observations: Egernia striolata, Ctenotus robustus, Cryptoblepharus carnabyi, Morethia boulengeri and Carlia tetradactyla (Scincidae). Linear regression modelling revealed suites of different variables related to the abundance patterns of individual species, some of which were broadly congruent with those measured for each species in other environments. However, additional variables, particular to rocky environments, were found to relate to reptile abundance in this environment. This finding means that species' habitat relationships in one habitat may not be readily transferable to other environments, even those relatively close by. Based on these data, management decisions targeting reptile conservation in agricultural landscapes, which contain rocky outcrops, will be best guided by landscape models that not only recognize gradients in habitat suitability, but are also flexible enough to incorporate intraspecies habitat variability.     

Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species

Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.     

Novel insights into habitat suitability for Amazonian freshwater mussels linked with hydraulic and landscape drivers

Novel insights into habitat suitability for two Unionida freshwater mussels, Castalia ambigua Lamarck, 1819 (Hyriidae) and Anodontites elongatus (Swainson, 1823) (Mycetopodidae), are presented on the basis of hydraulic variables linked with the riverbed in six 500‐m reaches in an eastern Amazonian river basin. Within the reaches, there was strong habitat heterogeneity in hydrodynamics and substrate composition. In addition, we investigated stressors based on landscape modification that are associated with declines in mussel density. We measured hydraulic variables for each 500‐m reach, and landscape stressors at two spatial scales (subcatchment and riparian buffer forest). We used the Random Forest algorithm, a tree‐based model, to predict the hydraulic variables linked with habitat suitability for mussels, and to predict which landscape stressors were most associated with mussel density declines. Both mussel species were linked with low substrate heterogeneity and greater riverbed stability (low Froude and Reynolds numbers), especially at high flow (low stream power). Different sediment grain size preferences were observed between mussel species: Castalia ambigua was associated with medium sand and Anodontites elongatus with medium and fine sand. Declines in mussel density were associated with modifications linked to urbanization at small scales (riparian buffer forest), especially with percent of and distance from rural settlements, distance to the nearest street, and road density. In summary, the high variance explained in both hydraulic and landscape models indicated high predictive power, suggesting that our findings may be extrapolated and used as a baseline to test hypotheses of habitat suitability in other Amazonian rivers for Castalia ambigua and Anodontites elongatus and also for other freshwater mussel species. Our results highlight the urgent need for aquatic habitat conservation to maintain sheltered habitats during high flow as well as mitigate the effects of landscape modifications at the riparian buffer scale, both of which are important for maintaining dense mussel populations and habitat quality.     

Implications of Climate Change for Bird Conservation in the Southwestern U.S. under Three Alternative Futures

Future expected changes in climate and human activity threaten many riparian habitats, particularly in the southwestern U.S. Using Maximum Entropy (MaxEnt3.3.3) modeling, we characterized habitat relationships and generated spatial predictions of habitat suitability for the Lucy’s warbler (Oreothlypis luciae), the Southwestern willow flycatcher (Empidonax traillii extimus) and the Western yellow-billed cuckoo (Coccyzus americanus). Our goal was to provide site- and species-specific information that can be used by managers to identify areas for habitat conservation and/or restoration along the Rio Grande in New Mexico. We created models of suitable habitat for each species based on collection and survey samples and climate, biophysical, and vegetation data. We projected habitat suitability under future climates by applying these models to conditions generated from three climate models for 2030, 2060 and 2090. By comparing current and future distributions, we identified how habitats are likely to change as a result of changing climate and the consequences of those changes for these bird species. We also examined whether land ownership of high value sites shifts under changing climate conditions. Habitat suitability models performed well. Biophysical characteristics were more important that climate conditions for predicting habitat suitability with distance to water being the single most important predictor. Climate, though less important, was still influential and led to declines of suitable habitat of more than 60% by 2090. For all species, suitable habitat tended to shrink over time within the study area leaving a few core areas of high importance. Overall, climate changes will increase habitat fragmentation and reduce breeding habitat patch size. The best strategy for conserving bird species within the Rio Grande will include measures to maintain and restore critical habitat refugia. This study provides an example of a presence-only habitat model that can be used to inform the management of species at intermediate scales.     

Assessing environmental requirements effects on forest fragmentation sensitivity in two arboreal rodents

The study of the effect of habitat fragmentation on species that inhabit residual patches requires the investigation of the relationship existing between species distribution and landscape components. To understand which components of landscape mosaics are more influential for species’ persistence, we compared the distribution of two arboreal rodents proved to be sensitive to habitat fragmentation, the hazel dormouse Muscardinus avellanarius and the red squirrel Sciurus vulgaris. Their occurrence in residual oak woods in central Italy was studied with nest-boxes and hair-tubes, developing a new method for hair analysis. Their distributions were analysed considering patch, matrix composition and configuration, and landscape vegetation variables. The two species showed a different degree of plasticity, with the squirrel being significantly more specialised at the landscape scale. The comparison of the two distribution patterns highlighted the influence of different ecological constraints and the existence of different strategies to cope with fragmentation. Patch size and patch attributes were generally weaker determinants of occurrence, compared to landscape metrics. The squirrel presence was significantly influenced by the presence of shared perimeter between hedgerows and woods and by the lack of isolation of the residual patches, suggesting the use of several fragments to compensate the low habitat quality. Conversely the hazel dormouse seemed to be more affected by the internal management of the woods, and in particular by the mean DBH. Our results highlight how the recognition of the extrinsic constraints and the influence of multi-scale habitat selection may help guiding land use management, to ensure species conservation in profoundly exploited landscapes.