Does the protected area network preserve bird species of conservation concern in a rapidly changing climate?

Species ranges are expected to move polewards following the changing climate, which poses novel challenges to the protected area network, particularly at northern latitudes. Here we study how well protected areas are likely to sustain populations of birds of conservation concern under a changing climate in northern Europe, in Finland. We fitted bioclimatic envelope models generated for 100 bird species to climate scenario data for the years 2051–2080 and three alternative emission scenarios in a 10-km grid system to predict changes in the species probability of occurrence. We related the projected changes in the climatic suitability to the amount of protected preferred habitat for the study species in the 10-km grid cells, and based on the cover of four main CORINE Land Cover classes in each conservation area in Finland. The probability of occurrence of all species (except marshland birds) decreased according to all scenarios, the decline being greatest in southern and smallest in northern boreal zones. This decline was slightly greater in unprotected than in protected areas for species of forests, mires and mountain habitats. The climatically suitable areas for the species were predicted to shift northwards, but the potential gain of southern species of conservation concern appears not to compensate for the loss of northern species. Thus, a representative protected area network is needed in all boreal zones. Overall, our results show that species-specific habitat preferences and habitat availability should be taken into account when assessing the efficiency of a protected area network in a changing climate.     

Rarity,geography, and plant exposure to global change in the California Floristic Province

Aim

Rarity and geographic aspects of species distributions mediate their vulnerability to global change. We explore the relationships between species rarity and geography and their exposure to climate and land use change in a biodiversity hotspot.

Location

California, USA.

Taxa

One hundred and six terrestrial plants.

Methods

We estimated four rarity traits: range size, niche breadth, number of habitat patches, and patch isolation; and three geographic traits: mean elevation, topographic heterogeneity, and distance to coast. We used species distribution models to measure species exposure—predicted change in continuous habitat suitability within currently occupied habitat—under climate and land use change scenarios. Using regression models, decision-tree models and variance partitioning, we assessed the relationships between species rarity, geography, and exposure to climate and land use change.

Results

Rarity, geography and greenhouse gas emissions scenario explained >35% of variance in climate change exposure and >61% for land use change exposure. While rarity traits (range size and number of habitat patches) were most important for explaining species exposure to climate change, geographic traits (elevation and topographic heterogeneity) were more strongly associated with species' exposure to land use change.

Main conclusions

Species with restricted range sizes and low topographic heterogeneity across their distributions were predicted to be the most exposed to climate change, while species at low elevations were the most exposed to habitat loss via land use change. However, even some broadly distributed species were projected to lose >70% of their currently suitable habitat due to climate and land use change if they are in geographically vulnerable areas, emphasizing the need to consider both species rarity traits and geography in vulnerability assessments.     

Habitat-based statistical models for predicting the spatial distribution of butterflies and day-flying moths in a fragmented landscape

1.  Most species' surveys and biodiversity inventories are limited by time and money. Therefore, it would be extremely useful to develop predictive models of animal distributions based on habitat, and to use these models to estimate species' densities and range sizes in poorly sampled regions.
2.  In this study, two sets of data were collected. The first set consisted of over 2000 butterfly transect counts, which were used to determine the relative density of each species in 16 major habitat types in a 35-km² area of fragmented landscape in north-west Wales. For the second set of data, the area was divided into 140 cells using a 500-m grid, and the extent of each habitat and the presence or absence of each butterfly and moth species was determined for each cell.
3.  Logistic regression was used to model the relationship between species' distribution and predicted density, based on habitat extent, in each grid square. The resultant models were used to predict butterfly distributions and occupancy at a range of spatial scales.
4.  Using a jack-knife procedure, our models successfully reclassified the presence or absence of species in a high percentage of grid squares (mean 83% agreement). There were highly significant relationships between the modelled probability of species occurring at regional and local scales and the number of grid squares occupied at those scales.
5.  We conclude that basic habitat data can be used to predict insect distributions and relative densities reasonably well within a fragmented landscape. It remains to be seen how accurate these predictions will be over a wider area.     

How do species respond to climate change along an elevation gradient? A case study of the grey‐headed robin (Heteromyias albispecularis)

Climate is predicted to change rapidly in the current century, which may lead to shifts of species' ranges, reduced populations and extinctions. Predicting the responses of species abundance to climate change can provide valuable information to quantify climate change impacts and inform their management and conservation, but most studies have been limited to changes in habitat area due to a lack of abundance data. Here, we use generalized linear model and Bayesian information criteria to develop a predictive model based on the abundance of the grey‐headed robin (GHR) and the data of climatic environmental variables. The model is validated by leave‐one‐out cross‐validation and equivalence tests. The responses of GHR abundance, population size and habitat area by elevation are predicted under the current climate and 15 climate change scenarios. The model predicts that when temperature increases, abundance of GHR displays a positive response at high elevation, but a negative response at low elevation. High precipitation at the higher elevations is a limiting factor to GHR and any reduction in precipitation at high elevation creates a more suitable environment, leading to an increase in abundance of GHR, whereas changes in precipitation have little impact at low elevation. The loss of habitat is much more than would otherwise be assumed in response to climate change. Temperature increase is the predominant factor leading to habitat loss, whereas changes in precipitation play a secondary role. When climate changes, the species not only loses part of its habitat but also suffers a loss in its population size in the remaining habitat. Population size declines more than the habitat area under all considered climate change scenarios, which implies that the species might become extinct long before the complete loss of its habitat. This study suggests that some species might experience much more severe impacts from climate change than predicted from models of habitat area alone. Management policies based on predictions of habitat area decline using occurrence data need to be re‐evaluated and alternative measures need to be developed to conserve species in the face of rapid climate change.     

Assessing how habitat loss restricts the geographic range of Neotropical anurans

Habitat loss and fragmentation exert unquestionable negative effects in a wide range of taxa on both regional and local scales. However, there is a debate over whether habitat change impacts geographic species distribution. We assess how habitat loss restricts large-scale species distribution on a geographic scale for four South American anurans that are known to occur in well conserved habitats, yet which are absent in others that are close by and more degraded. We used occurrence records of each species in Brazil and performed different modeling algorithms to compare ensemble distribution models generated by two different sets of predictors: a climate-only versus a climate-habitat procedure. We found that the distribution area predicted by the climate-only procedure was larger than that of the climate-habitat procedure for all species. The areas not predicted by the climate-habitat but predicted by the climate-only procedure for all species are commonly located in inland areas in southeastern Brazil, which coincides with areas that have suffered the most from habitat loss in the country. Plotting the predictions against well-surveyed areas where the species have not been recorded, we found evidence that habitat loss may have restricted the current geographic ranges of Hypsiboas faber and Rhinella ornata. Finally, modeling approaches incorporating habitat landscape metrics, particularly for habitat-specialist species, may be a helpful tool for identifying areas that harbored these species before deforestation took place.     

Local ecology and geographic ranges of plants in the Bishop Creek watershed of the eastern Sierra Nevada, California, USA

Aim The physiological requirements and tolerances of a species partially determine both its habitat preferences within a community and its broader geographic range. Therefore, we predicted that local ecology should be correlated with geographic distribution. We tested for a correlation between local ecology and range size, and we attempted to account for this correlation by the climate of the range. Location Bishop Creek Watershed, on the eastern side of the Sierra Nevada, California. Methods We recorded all plant species growing in each of 263 plots in the montane to alpine zones of the watershed. The local habitat preferences of 282 species were described in terms of wetness, elevation, soil, and amount of shade. The size and centre of the geographic range for each species were determined from regional floras. Results Wetness preference within the watershed was significantly correlated with range size. Specifically, plants of wet sites had larger ranges that extend to the north, whereas plants of dry sites tended to have smaller ranges centred to the east. The correlation between local wetness preference and range size was entirely explained by the location of the range centre of the species. Main conclusions A possible reason for the large ranges of mesophilic plants in our study area is that mesic habitats are continuous throughout the western Cordillera, while dry alpine habitats are isolated by valleys to the east. The correspondence between local ecology and geographic distributions implies evolutionary stasis in the niches of these plant species.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

Identifying refugia and corridors under climate change conditions for the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) in Hubei Province,China

Using a case study of an isolated management unit of Sichuan snub‐nosed monkey (Rhinopithecus roxellana), we assess the extent that climate change will impact the species’ habitat distribution in the current period and projected into the 2050s. We identify refugia that could maintain the population under climate change and determine dispersal paths for movement of the population to future suitable habitats. Hubei Province, China. We identified climate refugia and potential movements by integrating bioclimatic models with circuit theory and least‐cost model for the current period (1960–1990) and the 2050s (2041–2060). We coupled a maximum entropy algorithm to predict suitable habitat for the current and projected future periods. Suitable habitat areas that were identified during both time periods and that also satisfied home range and dispersal distance conditions were delineated as refugia. We mapped potential movements measured as current flow and linked current and future habitats using least‐cost corridors. Our results indicate up to 1,119 km² of currently suitable habitat within the study range. Based on our projections, a habitat loss of 67.2% due to climate change may occur by the 2050s, resulting in a reduced suitable habitat area of 406 km² and very little new habitat. The refugia areas amounted to 286 km² and were located in Shennongjia National Park and Badong Natural Reserve. Several connecting corridors between the current and future habitats, which are important for potential movements, were identified. Our assessment of the species predicted a trajectory of habitat loss following anticipated future climate change. We believe conservation efforts should focus on refugia and corridors when planning for future species management. This study will assist conservationists in determining high‐priority regions for effective maintenance of the endangered population under climate change and will encourage increased habitat connectivity.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.     

Species richness, area and climate correlates

Aim Species richness–area theory predicts that more species should be found if one samples a larger area. To avoid biases from comparing species richness in areas of very different sizes, area is often controlled by counting the numbers of co‐occupying species in near‐equal area grid cells. The assumption is that variation in grid cell size accrued from working in a three‐dimensional world is negligible. Here we provide a first test of this idea. We measure the surface area of c. 50 × 50 km and c. 220 × 220 km grid cells across western Europe. We then ask how variation in the area of grid cells affects: (1) the selection of climate variables entering a species richness model; and (2) the accuracy of models in predicting species richness in unsampled grid cells. Location Western Europe. Methods Models are developed for European plant, breeding bird, mammal and herptile species richness using seven climate variables. Generalized additive models are used to relate species richness, climate and area. Results We found that variation in the grid cell area was large (50 × 50 km: 8–3311 km²; 220 × 220: 193–55,100 km²), but this did not affect the selection of variables in the models. Similarly, the predictive accuracy was affected only marginally by exclusion of area within models developed at the c. 50 × 50 km grid cells, although predictive accuracy suffered greater reductions when area was not included as a covariate in models developed for c. 220 × 220 km grid cells. Main conclusions Our results support the assumption that variation in near‐equal area cells may be of second‐order importance for models explaining or predicting species richness in relation to climate, although there is a possibility that drops in accuracy might increase with grid cell size. The results are, however, contingent on this particular data set, grain and extent of the analyses, and more empirical work is required.     

Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.     

气候变化对丹顶鹤秋季迁徙路线潜在生境适宜性的影响

目前全球变暖趋势的加剧对丹顶鹤等大型濒危水禽的栖息地造成了严重的威胁。由于监测方法和技术手段的限制,丹顶鹤在迁徙路线上潜在生境的分布范围尚不清楚,气候变化对丹顶鹤迁徙路线生境适宜性的影响机理有待进一步研究。基于138个丹顶鹤样本分布信息和19种环境变量数据,利用 BIOMOD2 软件包构建了丹顶鹤潜在生境评价的组合模型,对丹顶鹤在亚洲东部秋季迁徙路线上的生境适宜性进行数值模拟,并预测SSP1.2-6气候背景下2021-2040年、2041-2060年、2061-2080年、2081-2100年四个不同阶段的丹顶鹤潜在生境范围的变化趋势。研究结果表明:与单模型的模拟结果相比,集成9种单模型的BIOMOD2组合模型预测精度更高。集成模型的重要性分析表明,气温日较差是丹顶鹤生境适宜性变化的最重要的影响因子。受气候变化的影响2021-2040年、2041-2060年、2061-2080年、2081-2100年丹顶鹤潜在生境的面积将分别减少到2.60×10⁵km²、2.58×10⁵km²、2.75×10⁵km²、2.56×10⁵km²,迁徙路线上胶东半岛和环渤海地区适栖生境面积减少的最为显著。本研究对于迁徙路线上珍稀水禽潜在适宜生境的模拟及全球变化背景下珍稀水禽栖息地的保育和修复具有重要意义。     

Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae

Aim

To assess how habitat loss and climate change interact in affecting the range dynamics of species and to quantify how predicted range dynamics depend on demographic properties of species and the severity of environmental change.

Location

South African Cape Floristic Region.

Methods

We use data‐driven demographic models to assess the impacts of past habitat loss and future climate change on range size, range filing and abundances of eight species of woody plants (Proteaceae). The species‐specific models employ a hybrid approach that simulates population dynamics and long‐distance dispersal on top of expected spatio‐temporal dynamics of suitable habitat.

Results

Climate change was mainly predicted to reduce range size and range filling (because of a combination of strong habitat shifts with low migration ability). In contrast, habitat loss mostly decreased mean local abundance. For most species and response measures, the combination of habitat loss and climate change had the most severe effect. Yet, this combined effect was mostly smaller than expected from adding or multiplying effects of the individual environmental drivers. This seems to be because climate change shifts suitable habitats to regions less affected by habitat loss. Interspecific variation in range size responses depended mostly on the severity of environmental change, whereas responses in range filling and local abundance depended mostly on demographic properties of species. While most surviving populations concentrated in areas that remain climatically suitable, refugia for multiple species were overestimated by simply overlying habitat models and ignoring demography.

Main conclusions

Demographic models of range dynamics can simultaneously predict the response of range size, abundance and range filling to multiple drivers of environmental change. Demographic knowledge is particularly needed to predict abundance responses and to identify areas that can serve as biodiversity refugia under climate change. These findings highlight the need for data‐driven, demographic assessments in conservation biogeography.

     

The Importance of Biologically Relevant Microclimates in Habitat Suitability Assessments

Predicting habitat suitability under climate change is vital to conserving biodiversity. However, current species distribution models rely on coarse scale climate data, whereas fine scale microclimate data may be necessary to assess habitat suitability and generate predictive models. Here, we evaluate disparities between temperature data at the coarse scale from weather stations versus fine-scale data measured in microhabitats required for a climate-sensitive mammal, the American pika (Ochotona princeps). We collected two years of temperature data in occupied talus habitats predicted to be suitable (high elevation) and unsuitable (low elevation) by the bioclimatic envelope approach. At low elevations, talus surface and interstitial microclimates drastically differed from ambient temperatures measured on-site and at a nearby weather station. Interstitial talus temperatures were frequently decoupled from high ambient temperatures, resulting in instantaneous disparities of over 30°C between these two measurements. Microhabitat temperatures were also highly heterogeneous, such that temperature measurements within the same patch of talus were not more correlated than measurements at distant patches. An experimental manipulation revealed that vegetation cover may cool the talus surface by up to 10°C during the summer, which may contribute to this spatial heterogeneity. Finally, low elevation microclimates were milder and less variable than typical alpine habitat, suggesting that, counter to species distribution model predictions, these seemingly unsuitable habitats may actually be better refugia for this species under climate change. These results highlight the importance of fine-scale microhabitat data in habitat assessments and underscore the notion that some critical refugia may be counterintuitive.     

气候变化背景下秦岭地区羚牛生境脆弱性评估及适应性保护对策

脆弱性是指物种受气候变化影响的程度,开展脆弱性评估工作有助于人类认识气候变化对野生动物的影响,为制定野生动物适应气候变化的保护对策提供科学依据。采用最大熵模型评估气候变化背景下秦岭地区羚牛(Budorcas taxicolor bedfordi)生境脆弱性。结果表明：(1)当前秦岭地区羚牛适宜生境总面积为6473 km²,到2050s年,预测秦岭地区羚牛适宜生境总面积为4217 km²,减少34.85%,羚牛适宜生境将向更高海拔地区转移,转移约210 m;(2)已建保护区覆盖49.82%当前羚牛适宜生境,尚有3248 km²的适宜生境处于保护区之外;到2050s年,保护区覆盖了43.87%适宜生境,尚有2367 km²的适宜生境未被保护;(3)到2050s年,当前分布在太白县、佛坪县、洋县和宁陕县等地区的3490 km²羚牛适宜生境将会成为生境脆弱区域,丧失53.92%;(4)分布在秦岭核心区域的2983 km²当前和2050s年保持不变适宜生境,将成为羚...     

气候变化和人为干扰对中国三种金丝猴地理分布的未来影响

气候变化和人为干扰正成为物种多样性丧失的重要驱动力.本文基于MaxEnt模型,探讨气候变化和人为干扰对中国3种金丝猴(川金丝猴Rhinopithiecus roxellana、滇金丝猴R.bieti和黔金丝猴R.brelichi)地理分布变迁的影响:(1)气候变化和人为干扰导致3种金丝猴在2000年和未来(2050年)...     

Predicting the distribution of the invasive alien Heracleum mantegazzianum at two different spatial scales

Modelling the distribution of invasive alien species is widely used for predicting future dispersal, response to climate change, and effects of management, but little information is available on the scale dependence of spatial models. This study is focused on Heracleum mantegazzianum , a problematic invasive plant in central and north-western Europe. The main objective was to model the current distribution of this species at national (43,000 km²) and regional scale (4900 km²) using autologistic regression with a Danish data set. Presence–absence data were used in a grid system with 5 × 5 km² or 2 × 2 km² as basic units. To avoid misleading presence–absence models and unreliable probability values due to unbalanced data, the prevalence was used as cut-off value, and a favourability function was applied to the model predictions. The national model showed a widespread distribution of H. mantegazzianum with highest habitat suitability in the eastern and northern parts of the country where human population density is high, winters more severe and/or loamy soils more common. At a regional scale the distribution of H. mantegazzianum is associated with alluvial sand cover, high human population density, spring precipitation, and presence of the species in neighbour grid units. The observed widespread national distribution is likely the result of anthropogenic spread of this ornamental plant, while the locally clumped distribution suggests that H. mantegazzianum naturally spreads mainly over short distances. The current distribution in Denmark resembles an intermediate invasion stage where long-distance dispersal is less important, while spread from suitable neighbour habitats is significant. The study demonstrates that the favourability function leads to improved mapping standards for invasive species.     

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

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

Does seasonal fine‐tuning of climatic variables improve the performance of bioclimatic envelope models for migratory birds?

We examined the influence of 'seasonal fine-tuning' of climatic variables on the performance of bioclimatic envelope models of migrating birds. Using climate data and national bird atlas data from a 10 × 10 km uniform grid system in Finland, we tested whether the replacement of one 'baseline' set of variables including summer (June–August) temperature and precipitation variables with climate variables tailored ('fine-tuned') for each species individually improved the bird-climate models. The fine-tuning was conducted on the basis of time of arrival and early breeding of the species. Two generalized additive models (GAMs) were constructed for each of the 63 bird species studied, employing (1) the baseline climate variables and (2) the fine-tuned climate variables. Model performance was measured as explanatory power (deviance change) and predictive power (area under the curve; AUC) statistics derived from cross-validation. Fine-tuned climate variables provided, in many cases, statistically significantly improved model performance compared to using the same baseline set of variables for all the species. Model improvements mainly concerned bird species arriving and starting their breeding in May–June. We conclude that the use of the fine-tuned climate variables tailored for each species individually on the basis of their arrival and critical breeding periods can provide important benefits for bioclimatic modelling.     

Modeling of habitat suitability of Asiatic black bear (Ursus thibetanus gedrosianus) in Iran in future

Future changes in climate are imminent and they threat endangered and rare species due to habitat destruction. The Asiatic black bear (Ursus thibetanus gedrosianus) is a rare and vulnerable species whose habitat fragmentation and habitat loss decreased the size of its population significantly. Climate change is another threat to this species that is investigated in this research work. Aiming at this goal, ten species distribution models (SDMs) were applied as helpful tools for evaluating the potential effectiveness of climate change in habitat suitability of Asiatic black bear in Iran. Potential dispersal of Asiatic black bear was modeled as a function of 32 environmental variables for the current time and 2070 for 44 climate change scenarios (CC scenario) of future climate. Our results showed that modeling result depended on type of model. Our results confirmed that one of the greatest threats in the near future for Asiatic black bear was the change of suitable habitat due to climate change. All the CC scenarios showed that migration of this species would be to the north and west areas with higher elevation and that an increase in area would be more than a decrease in area in all scenarios. Recognizing and protecting potential future habitats are of the important activities to conserve this species and identify areas with conservation priority.