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.     

The impact of climate change and potential distribution of the endangered white winged wood duck (Asarcornis scutulata, 1882) in Indian eastern Himalaya

The White-Winged Wood duck (Asarcornis scutulata) is an endangered forest wetland bird currently on the verge of extinction due to an array of anthropogenic pressures. It has been reported that global climate change could affect the distribution of many bird species globally. Therefore, an understanding the potential distribution of the White-Winged Wood duck in future climate scenarios could facilitate the creation of immediate conservation plans and the mitigation of subsequent threats. This is the first ever study on the distribution of White-Winged Wood Duck (WWWD) where Representative Concentration Pathway (RCP) 8.5 scenario was used to forecast the distribution of the WWWD in the Indian Eastern Himalayan region in the 2050s and 2070s. The study revealed that 1.87 % of the total area of IEH has the high potential distribution of WWWD. The state of Assam alone includes 1.68 % of the highly potential habitat in the region. It was predicted that 436.61 km² of highly potential habitat would be lost by 2070. Changes in the annual temperature range, precipitation in the wettest months (June to September), and precipitation decrease in the warmest quarter (October to December) would result in the loss of highly potential habitats. Under the influence of climate change, the habitat of WWWD in the eastern part of the region is likely to shift towards the western part. It was found that there will be a decline in potential habitat in the Indian states of Arunachal Pradesh, Assam, Nagaland, and Tripura located in the IEH under future climate scenarios. The potential of areas located at the Bhutan and Assam border would increase for supporting WWWD as this species' requires the average annual precipitation about 1000–1200 mm. However, the simultaneous anthropogenic activity would further destroy potential habitats in the future. The current study has provided baseline data on the potential distribution of WWWD in the IEH region for immediate conservation management plans.     

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.     

Identifying climate refugia and its potential impact on Tibetan brown bear (Ursus arctos pruinosus) in Sanjiangyuan National Park,China

Climate change has direct impacts on wildlife and future biodiversity protection efforts. Vulnerability assessment and habitat connectivity analyses are necessary for drafting effective conservation strategies for threatened species such as the Tibetan brown bear (Ursus arctos pruinosus). We used the maximum entropy (MaxEnt) model to assess the current (1950–2000) and future (2041–2060) habitat suitability by combining bioclimatic and environmental variables, and identified potential climate refugia for Tibetan brown bears in Sanjiangyuan National Park, China. Next, we selected Circuit model to simulate potential migration paths based on current and future climatically suitable habitat. Results indicate a total area of potential suitable habitat under the current climate scenario of approximately 31,649.46 km², of which 28,778.29 km² would be unsuitable by the 2050s. Potentially suitable habitat under the future climate scenario was projected to cover an area of 23,738.6 km². Climate refugia occupied 2,871.17 km², primarily in the midwestern and northeastern regions of Yangtze River Zone, as well as the northern region of Yellow River Zone. The altitude of climate refugia ranged from 4,307 to 5,524 m, with 52.93% lying at altitudes between 4,300 and 4,600 m. Refugia were mainly distributed on bare rock, alpine steppe, and alpine meadow. Corridors linking areas of potentially suitable brown bear habitat and a substantial portion of paths with low‐resistance value were distributed in climate refugia. We recommend various actions to ameliorate the impact of climate change on brown bears, such as protecting climatically suitable habitat, establishing habitat corridors, restructuring conservation areas, and strengthening monitoring efforts.     

Potential distribution of Notopterygium incisum Ting ex H. T. Chang and its predicted responses to climate change based on a comprehensive habitat suitability model

Notopterygium incisum Ting ex H. T. Chang is a rare and endangered traditional Chinese medicinal plant. In this research, we built a comprehensive habitat suitability (CHS) model to analyze the potential suitable habitat distribution of this species in the present and future in China. First, using nine different algorithms, we built an ensemble model to explore the possible impacts of climate change on the habitat distribution of this species. Then, based on this model, we built a CHS model to further identify the distribution characteristics of N. incisum‐suitable habitats in three time periods (current, 2050s, and 2070s) while considering the effects of soil and vegetation conditions. The results indicated that the current suitable habitat for N. incisum covers approximately 83.76 × 10³ km², and these locations were concentrated in the Tibet Autonomous Region, Gansu Province, Qinghai Province, and Sichuan Province. In the future, the areas of suitable habitat for N. incisum would significantly decrease and would be 69.53 × 10³ km² and 60.21 × 10³ km² in the 2050s and 2070s, respectively. However, the area of marginally suitable habitat would remain relatively stable. This study provides a more reliable and comprehensive method for modelling the current and future distributions of N. incisum, and it provides valuable insights for highlighting priority areas for medicinal plant conservation and resource utilization.     

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

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

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

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

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.     

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy‐deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate‐only model shows that only 11.64% (17,190 km²) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km² (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate‐only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.     

Adult activity and temperature preference drives region-wide damselfly (Zygoptera) distributions under a warming climate

We analysed a recently completed statewide odonate Atlas using multivariate linear models. Within a phylogenetically explicit framework, we developed a suite of data-derived traits to assess the mechanistic distributional drivers of 59 species of damselflies in New York State (NYS). We found that length of the flight season (adult breeding activity period) mediated by thermal preference drives regional distributions at broad (10⁵ km²) scales. Species that had longer adult flight periods, in conjunction with longer growing seasons, had significantly wider distributions. These intrinsic traits shape species'' responses to changing climates and the mechanisms behind such range shifts are fitness-based metapopulation processes that adjust phenology to the prevailing habitat and climate regime through a photoperiod filter.     

Predicting current and future distributions of Mentha pulegium L. in Tunisia under climate change conditions,using the MaxEnt model

Climate change and human activities have caused the degeneration of the natural habitats of medicinal plants. Mentha pulegium L. is one of the most common medicinal plants in Tunisia that features high economic and ecological values. Predicting species' suitable habitats, through modeling, has evolved as a useful tool for the assessment of resource conservation to protect medicinal plants. Herein, we used MaxEnt model to predict current and future distributions of M. pulegium under two representative concentration pathways (RCP2.6 and RCP8.5) for the years 2050 and 2070. MaxEnt modeling was in the “Excellent” category since all the AUCs were above 0.9. Results showed that high and moderate suitable habitats for the current distribution of M. pulegium encompassed ca. 9929 km² and 16,423 km², respectively. These areas are mainly located in North Tunisia. Precipitation of the coldest quarter (Bio19) was identified as the most critical factor shaping M. pulegium distribution. Compared to the current distribution, the highly and moderately suitable areas for M. pulegium under the two RCPs (RCP2.6 and RCP8.5) would decrease in the 2050s and 2070s. The model projected a shift of the suitable area from Northeastward to Center-eastward. These results may provide a useful tool for developing adaptive management strategies to enhance M. pulegium protection and sustainable utilization in the context of global climate change.     

Predicting potential distribution and identifying priority areas for conservation of the lowland tapir (Tapirus terrestris) in Peruvian Amazon

A key conservation biology tool is the information on the geographic distribution of species as well as the variables driving those patterns. Here, we used maximum entropy modeling, MaxEnt, to model the total potential distribution of Tapirus terrestris, classified as “Vulnerable” on the IUCN Red List of Threatened Species. In this study, we recorded 117 occurrence records and considered 18 environmental variables. The total potential distribution area covers 96,055.6 km², meaning 12.3 % of the territory of the Peruvian Amazon, with “high potential” habitat covering 3,891.36 km², “moderate potential” habitat covering 22,849.5 km², and “low potential” habitat covering 69,314.7 km². Natural Protected Areas (NPAs) shelter 32.2 % (30,966.2 km²) of the total potential distribution area of the species, being the Bahuaja Sonene and Manu National Parks, the NPAs with the largest total potential distribution, 8,220.2 km² and 7,619.7 km² respectively. Eventually, 67.8 % (65,089.4 km²) of the total potential distribution were identified without any type of protection category by SINANPE and its complementary categories; therefore, we consider this area as a priority for the conservation of T. terrestris in Peru.     

Climate and habitat availability determine 20th century changes in a butterfly's range margin

Evidence of anthropogenic global climate change is accumulating, but its potential consequences for insect distributions have received little attention. We use a ''climate response surface'' model to investigate distribution changes at the northern margin of the speckled wood butterfly, Pararge aegeria. We relate its current European distribution to a combination of three bioclimatic variables. We document that P. aegeria has expanded its northern margin substantially since 1940, that changes in this species'' distribution over the past 100 years are likely to have been due to climate change, and that P. aegeria will have the potential to shift its range margin substantially northwards under predicted future climate change. At current rates of expansion, this species could potentially colonize all newly available climatically suitable habitat in the UK over the next 50 years or more. However, fragmentation of habitats can affect colonization, and we show that availability of habitat may be constraining range expansion of this species at its northern margin in the UK. These lag effects may be even more pronounced in less-mobile species inhabiting more fragmented landscapes, and highlight how habitat distribution will be crucial in predicting species'' responses to future climate change.     

Differential effects of climate and species interactions on range limits at a hybrid zone: potential direct and indirect impacts of climate change

The relative contributions of climate versus interspecific interactions in shaping species distributions have important implications for closely related species at contact zones. When hybridization occurs within a contact zone, these factors regulate hybrid zone location and movement. While a hybrid zone''s position may depend on both climate and interactions between the hybridizing species, little is known about how these factors interact to affect hybrid zone dynamics. Here, we utilize SDM (species distribution modeling) both to characterize the factors affecting the current location of a moving North American avian hybrid zone and to predict potential direct and indirect effects of climate change on future distributions. We focus on two passerine species that hybridize where their ranges meet, the Black‐capped (Poecile atricapillus) and Carolina (P. carolinensis) chickadee. Our contemporary climate models predict the occurrence of climatically suitable habitat extending beyond the hybrid zone for P. atricapillus only, suggesting that interspecific interactions primarily regulate this range boundary in P. atricapillus, while climatic factors regulate P. carolinensis. Year 2050 climate models predict a drastic northward shift in suitable habitat for P. carolinensis. Because of the greater importance of interspecific interactions for regulating the southern range limit of P. atricapillus, these climate‐mediated shifts in the distribution of P. carolinensis may indirectly lead to a range retraction in P. atricapillus. Together, our results highlight the ways climate change can both directly and indirectly affect species distributions and hybrid zone location. In addition, our study lends support to the longstanding hypothesis that abiotic factors regulate species'' poleward range limits, while biotic factors shape equatorial range limits.     

Habitat use of the endangered golden‐rumped sengi Rhynchocyon chrysopygus

The endangered golden‐rumped sengi are found only in Arabuko‐Sokoke Forest with 395.4 km² of forest habitat, and perhaps in a few isolated forest and thicket fragments of total area less than 30 km² all within central coastal Kenya. Understanding its habitat use is an important requirement to develop better conservation measures for the species and its remaining forest habitat. A more reliable method for monitoring its status is also needed. We used the Bayesian occupancy modelling with camera trap data and habitat mapping to characterise the species habitat use in the Arabuko‐Sokoke Forest. The species uses 328 km² (95% CI: 289–364 km²) of Arabuko‐Sokoke Forest habitat, and its site use increases with distance from forest edge, with the highest site use in the Cynometra thicket (0.93; 95% CI: 0.82–1). Its use of the mixed forest habitat has been significantly reduced following years of logging of Afzelia quanzensis. We recommend the use of modelled occupancy, interpreted as the proportion of area used by the species, to monitor the species status. Occupancy models account for detection probability, and heterogeneity in site use and detection can be incorporated. Estimated territory sizes can be combined to obtain abundance estimates.     

Projected impacts of climate change on snow leopard habitat in Qinghai Province,China

Assessing species’ vulnerability to climate change is a prerequisite for developing effective strategies to reduce emerging climate‐related threats. We used the maximum entropy algorithm (MaxEnt model) to assess potential changes in suitable snow leopard (Panthera uncia) habitat in Qinghai Province, China, under a mild climate change scenario. Our results showed that the area of suitable snow leopard habitat in Qinghai Province was 302,821 km² under current conditions and 228,997 km² under the 2050s climatic scenario, with a mean upward shift in elevation of 90 m. At present, nature reserves protect 38.78% of currently suitable habitat and will protect 42.56% of future suitable habitat. Current areas of climate refugia amounted to 212,341 km² and are mainly distributed in the Sanjiangyuan region, Qilian mountains, and surrounding areas. Our results provide valuable information for formulating strategies to meet future conservation challenges brought on by climate stress. We suggest that conservation efforts in Qinghai Province should focus on protecting areas of climate refugia and on maintaining or building corridors when planning for future species management.     

The distribution and decline of a widespread butterfly Lycaena phlaeas in a pastoral landscape

1. Ecological specialists are often regarded as most likely to be threatened by anthropogenic habitat changes but few relevant data are available on changes in the status of widespread species. 2. Grid square distribution maps have been used widely to measure rates of decline and target conservation resources but it is known that coarse grain mapping is not appropriate to identify declines in widespread species that initially contain numerous local populations per grid cell. Changes in the status of widespread species need to be quantified. 3. Present‐day habitat associations, determined from over 2000 transect counts, combined with data on historical and present‐day habitat distributions, reveal that the area of occupancy and population‐level rate of decline of the Small Copper butterfly Lycaena phlaeas is likely to have been of the order of 92 and 89% respectively, in 35 km² of North Wales. Similar data on the species' major host plants Rumex acetosa and R. acetosella indicate possible declines in area occupied of 48 and 91%. If a 1‐km² grid was applied to the landscape, and if L. phlaeas, R. acetosa, and R. acetosella had occupied all 1‐km² cells in the study area in 1901 (non‐limestone cells for R. acetosella only), their declines would only have been recorded as 15, 9, and 35% respectively. 4. Many declining ecological specialists are threatened with extinction because of their initial rarity. At a population level, however, they may or may not be declining faster than less specialised species. The results presented here illustrate that some widespread species may have declined as much as many of Britain's rarities.     

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China

Climate change might pose an additional threat to the already vulnerable giant panda (Ailuropoda melanoleuca). Effective conservation efforts require projections of vulnerability of the giant panda in facing climate change and proactive strategies to reduce emerging climate‐related threats. We used the maximum entropy model to assess the vulnerability of giant panda to climate change in the Qinling Mountains of China. The results of modeling included the following findings: (1) the area of suitable habitat for giant pandas was projected to decrease by 281 km² from climate change by the 2050s; (2) the mean elevation of suitable habitat of giant panda was predicted to shift 30 m higher due to climate change over this period; (3) the network of nature reserves protect 61.73% of current suitable habitat for the species, and 59.23% of future suitable habitat; (4) current suitable habitat mainly located in Chenggu, Taibai, and Yangxian counties (with a total area of 987 km²) was predicted to be vulnerable. Assessing the vulnerability of giant panda provided adaptive strategies for conservation programs and national park construction. We proposed adaptation strategies to ameliorate the predicted impacts of climate change on giant panda, including establishing and adjusting reserves, establishing habitat corridors, improving adaptive capacity to climate change, and strengthening monitoring of giant panda.     

The influence of coarse‐scale environmental features on current and predicted future distributions of narrow‐range endemic crayfish populations

1. A major limitation to effective management of narrow‐range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2. Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate‐change scenarios. 3. The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 65–87% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4. Current models created using two spatial resolutions (1 and 4.5 km²) showed that fine‐resolution data more accurately represented current distributions. For three of the four species, the 1‐km² resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1‐km² resolution models were more accurate than 4.5‐km² resolution models. 5. Future projected (4.5‐km² resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low‐emission scenario, whereas two of four species would be severely restricted in range under moderate–high emissions. Discrepancies in the two emission scenarios probably relate to the exclusion of behavioural adaptations from species‐distribution models. 6. These model predictions illustrate possible impacts of climate change on narrow‐range endemic crayfish populations. The predictions do not account for biotic interactions, migration, local habitat conditions or species adaptation. However, we identified the constraining landscape features acting on these populations that provide a framework for addressing habitat needs at a fine scale and developing targeted and systematic monitoring programmes.     

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site‐specific frequency distributions of occurrence probabilities across a species' range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1–42.5 thousand km; this was predicted to decline to 0.5–7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.