How to deal with ground truthing affected by human‐induced habitat change?: Identifying high‐quality habitats for the Critically Endangered Red Siskin

Species distribution models (SDM) can be valuable for identifying key habitats for conservation management of threatened taxa, but anthropogenic habitat change can undermine SDM accuracy. We used data for the Red Siskin (Spinus cucullatus), a critically endangered bird and ground truthing to examine anthropogenic habitat change as a source of SDM inaccuracy. We aimed to estimate: (1) the Red Siskin's historic distribution in Venezuela; (2) the portion of this historic distribution lost to vegetation degradation; and (3) the location of key habitats or areas with both, a high probability of historic occurrence and a low probability of vegetation degradation. We ground‐truthed 191 locations and used expert opinion as well as landscape characteristics to classify species' habitat suitability as excellent, good, acceptable, or poor. We fit a Random Forest model (RF) and Enhanced Vegetation Index (EVI) time series to evaluate the accuracy and precision of the expert categorization of habitat suitability. We estimated the probability of historic occurrence by fitting a MaxLike model using 88 presence records (1960–2013) and data on forest cover and aridity index. Of the entire study area, 23% (20,696 km²) had a historic probability of Red Siskin occurrence over 0.743. Furthermore, 85% of ground‐truthed locations had substantial reductions in mean EVI, resulting in key habitats totaling just 976 km², in small blocks in the western and central regions. Decline in Area of Occupancy over 15 years was between 40% and 95%, corresponding to an extinction risk category between Vulnerable and Critically Endangered. Relating key habitats with other landscape features revealed significant risks and opportunities for proposed conservation interventions, including the fact that ongoing vegetation degradation could limit the establishment of reintroduced populations in eastern areas, while the conservation of remaining key habitats on private lands could be improved with biodiversity‐friendly agri‐ and silviculture programs.     

Expansion risk of the toxic dinoflagellate Gymnodinium catenatum blooms in Chinese waters under climate change

The paralytic shellfish poison toxin (PST)-producing dinoflagellate, Gymnodinium catenatum, frequently blooms in China, posing a threat to food safety and human health. To understand the drivers of G. catenatum blooms and predict potential habitats for G. catenatum under climate change, samples from occurrence localities and environmental datasets from multiple agencies were aggregated and used to model the habitat suitability of G. catenatum in the China Sea using a maximum entropy model (Maxent). The accumulated variable contributions for the Maxent model were defined to measure the importance of key predictors in the model. The most important environmental variables were distance to the coastline, depth of seawater, and long-term average of the minimum annual temperature. This highlights the main reasons why G. catenatum blooms always occur in coastal waters. Occurrence probabilities higher than 0.66 were defined as habitats with high suitability for shellfish management and aquaculture. Projected habitats with high suitability in Haizhou Bay, coastal waters along the western Taiwan Strait, and Bohai Bay remained stable with increasing temperature by 2100, regardless of the IPCC Representative Concentration Pathways (RCPs). However, those in the China Sea would be reduced overall, leading to a northward movement of the center of integrated habitats. Habitats with a spatial area of >6000 km² in the Bohai Sea, Yellow Sea, and South China Sea and >23,000 km² in the East China Sea would be exposed to high risk under low greenhouse gas emission scenarios (RCP2.6).     

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.     

Anticipating Knowledge to Inform Species Management: Predicting Spatially Explicit Habitat Suitability of a Colonial Vulture Spreading Its Range

Background

The knowledge of both potential distribution and habitat suitability is fundamental in spreading species to inform in advance management and conservation planning. After a severe decline in the past decades, the griffon vulture (Gyps fulvus) is now spreading its breeding range towards the northwest in Spain and Europe. Because of its key ecological function, anticipated spatial knowledge is required to inform appropriately both vulture and ecosystem management.

Methodology/Findings

Here we used maximum entropy (Maxent) models to determine the habitat suitability of potential and current breeding distribution of the griffon vulture using presence-only data (N = 124 colonies) in north-western Spain. The most relevant ecological factors shaping this habitat suitability were also identified. The resulting model had a high predictive performance and was able to predict species'' historical distribution. 7.5% (∼1,850 km²) of the study area resulted to be suitable breeding habitat, most of which (∼70%) is already occupied by the species. Cliff availability and livestock density, especially of sheep and goats, around 10 km of the colonies were the fundamental factors determining breeding habitat suitability for this species.

Conclusions/Significance

Griffon vultures could still spread 50–60 km towards the west, increasing their breeding range in 1,782 km². According to our results, 7.22% of the area suitable for griffon vulture will be affected by wind farms, so our results could help to better plan wind farm locations. The approach here developed could be useful to inform management of reintroductions and recovery programmes currently being implemented for both the griffon vulture and other threatened vulture species.     

The ability of climate envelope models to predict the effect of climate change on species distributions

Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO₂ conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.     

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.     

基于MAXENT模型的贺兰山西坡啮齿动物生境适宜性评价

贺兰山因其拥有独特的植物垂直分布带而十分适宜啮齿动物生存,但自保护区生态恢复以来并未见有研究评价啮齿动物在贺兰山的生境适宜性,使得其分布现状未知。使用GIS技术和MAXENT模型对内蒙古贺兰山国家级自然保护区6种主要啮齿动物进行生境适宜性状况评价及预测,探究啮齿动物在贺兰山的分布现状。结果表明：影响6种啮齿动物的主要环境因子为海拔、坡度和距矿区距离,海拔越高、坡度越大及距矿区距离越近均使啮齿动物生存适宜性降低;两两鼠种生境适宜面积叠加发现,大林姬鼠和阿拉善黄鼠适宜生境重叠面积最大(261.37 km~2),短尾仓鼠和子午沙鼠的适宜生境重叠面积最小(19.00 km~2);6种主要鼠种均适宜的生境面积交集仅有17.14 km~2,占贺兰山总面积的0.47%,6种主要鼠种均不适宜的生境面积有2985.23 km~2,占贺兰山总面积的81.21%。研究表明,啮齿动物栖息地距矿区距离仍是影响其适宜生境的重要因素之一,建议相关部门加强对废弃矿区采取措施,改善保护区啮齿动物生境质量。     

A Current Perspective on the Historical Geographic Distribution of the Endangered Muriquis (Brachyteles spp.): Implications for Conservation

The muriqui (Brachyteles spp.), endemic to the Atlantic Forest of Brazil, is the largest primate in South America and is endangered, mainly due to habitat loss. Its distribution limits are still uncertain and need to be resolved in order to determine their true conservation status. Species distribution modeling (SDM) has been used to estimate potential species distributions, even when information is incomplete. Here, we developed an environmental suitability model for the two endangered species of muriqui (Brachyteles hypoxanthus and B. arachnoides) using Maxent software. Due to historical absence of muriquis, areas with predicted high habitat suitability yet historically never occupied, were excluded from the predicted historical distribution. Combining that information with the model, it is evident that rivers are potential dispersal barriers for the muriquis. Moreover, although the two species are environmentally separated in a large part of its distribution, there is a potential contact zone where the species apparently do not overlap. This separation might be due to either a physical (i.e., Serra da Mantiqueira mountains) or a biotic barrier (the species exclude one another). Therefore, in addition to environmental characteristics, physical and biotic barriers potentially shaped the limits of the muriqui historical range. Based on these considerations, we proposed the adjustment of their historical distributional limits. Currently only 7.6% of the predicted historical distribution of B. hypoxanthus and 12.9% of B. arachnoides remains forested and able to sustain viable muriqui populations. In addition to measurement of habitat loss we also identified areas for conservation concern where new muriqui populations might be found.     

Integrating bryophytes into a forest management plan: lessons from grid-mapping in the forest of Soignes (Belgium)

258 bryophyte species were recorded in the 4 380 ha of the forest of Soignes (Belgium). The specific richness is artificially increased by many bryophytes not characteristic of forests such as epilithic species and weeds. In addition, certain species characteristic of ancient woodlands, like Anomodon viticulosus, Brachythecium plumosum, Homalia trichomanoides, Homalothecium sericeum, Rhizomnium punctatum, Taxiphyllum wissgrillii and Thamnobryum alopecurum were found on the concrete of the drainage network. Thus, the richness of the forest of Soignes is partly due to the ability of the bryophytes to colonize habitats of substitution. This ability suggests that the use of bryophytes as indicators of ancient woodlands must be properly tested. Two species, Ephemerum stellatum and Orthotrichum consimile, belong to the European Red List of Bryophytes, while 20 other species are considered as rare or threatened in Belgium. In addition, the forest of Soignes represents a remarkable disjunct area of distribution in Belgium for a number of other species. The distribution of species richness is patchy, ranging from 37 species·km⁻² to 122 species·km^−2, with an average of 70.5 species·km⁻². The effects of current management practices on the bryoflora are documented, and suggestions for integrating bryophyte conservation with logging and recreation are made.     

A species distribution model for pine marten (<Emphasis Type="Italic">Martes martes</Emphasis>) in the least forested region of Europe

Species distribution modelling is a useful technique that provides data on factors that can influence a species range, identify high suitability areas and model future scenarios. The pine marten (Martes martes) has undergone major historical declines in abundance and distribution in Northern Ireland, similar to that which has occurred throughout its range. Currently, the species is in a phase of range expansion in Northern Ireland, in what is the least forested landscape in Europe. To assess the suitability of this environment for pine marten re-establishment, presence only distribution data combined with landcover data at a 10-km scale were used in a species distribution modelling study using Maxent. The results indicated that approximately 32% (4500 km²) of the land area of Northern Ireland had a high probability of pine marten occurrence. Pine marten distribution was positively associated with the extent of conifer forest landcover types, which also had the highest single attribute contribution to the model. Landcover types that were negatively associated with pine marten distribution included the extent of open, dwarf and urban areas.     

Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.     

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.     

Population status and distribution modelling of the critically endangered riverine rabbit (Bunolagus monticularis)

The riverine rabbit (Bunolagus monticularis) is one of the most endangered mammals in the world due to fragmentation of its habitat in the semi‐arid Karoo region of South Africa, to which it is endemic. It is an umbrella conservation species for the riparian shrubland associated with the seasonal drainage system of the Karoo, where its presence is an indicator of ecosystem health. In this study, we analysed historical survey data to derive an improved assessment of the current B. monticularis population status and distribution. Geospatial analysis was conducted using geographical information systems, and distribution modelling was performed using Maxent. Extent of occurrence for the species is 54,227 km², and area of occupancy is 2943 km². Estimates of 157–207 mature individuals confirm an alarmingly small species population size, and it appears that no subpopulation has >50 mature individuals. Our findings thus support the continued classification of this species as ‘critically endangered’ under IUCN Red List criteria. However, with most remaining habitat occurring outside of protected areas, and with habitat loss being exacerbated by climate change, a viable conservation plan remains elusive.     

Unveiling the Conservation Biogeography of a Data-Deficient Endangered Bird Species under Climate Change

It remains a challenge to identify the geographical patterns and underlying environmental associations of species with unique ecological niches and distinct behaviors. This in turn hinders our understanding of the ecology as well as effective conservation management of threatened species. The white-eared night heron (Gorsachius magnificus) is a non-migratory nocturnal bird species that has a patchy distribution in the mountainous forests of East Asia. It is currently categorized as “Endangered” on the IUCN Red List, primarily due to its restricted range and fragmented habitat. To improve our knowledge of the biogeography and conservation of this species, we modeled the geographical pattern of its suitable habitat and evaluated the potential impacts of climate change using ecological niche modeling with a maximum entropy approach implemented in Maxent. Our results indicated that the amount of suitable habitat in all of East Asia was about 130 000 km², which can be spatially subdivided into several mountain ranges in southern and southwestern China and northern Vietnam. The extent of suitable habitat range may shrink by more than 35% under a predicted changing climate when assuming the most pessimistic condition of dispersal, while some more suitable habitat would be available if the heron could disperse unrestrainedly. The significant future changes in habitat suitability suggested for Gorsachius magnificus urge caution in any downgrading of Red List status that may be considered. Our results also discern potentially suitable areas for future survey efforts on new populations. Overall, this study demonstrates that ecological niche modeling offers an important tool for evaluating the habitat suitability and potential impacts of climate change on an enigmatic and endangered species based on limited presence data.     

Species Distribution Modelling predicts habitat suitability and reduction of suitable habitat under future climatic scenario for Sclerophrys perreti: A critically endangered Nigerian endemic toad

Sclerophrys perreti is a critically endangered Nigerian native frog currently imperilled by human activities. A better understanding of its potential distribution and habitat suitability will aid in conservation; however, such knowledge is limited for S. perreti. Herein, we used a species distribution model (SDM) approach with all known occurrence data (n = 22) from our field surveys and primary literature, and environmental variable predictors (19 bioclimatic variables, elevation and land cover) to elucidate habitat suitability and impact of climate change on this species. The SDM showed that temperature and precipitation were the predictors of habitat suitability for S. perreti with precipitation seasonality as the strongest predictor of habitat suitability. The following variable also had a significant effect on habitat suitability: temperature seasonality, temperature annual range, precipitation of driest month, mean temperature of wettest quarter and isothermality. The model predicted current suitable habitat for S. perreti covering an area of 1,115 km². However, this habitat is predicted to experience 60% reduction by 2050 owing to changes in temperature and precipitation. SDM also showed that suitable habitat exists in south-eastern range of the inselberg with predicted low impact of climate change compared to other ranges. Therefore, this study recommends improved conservation measures through collaborations and stakeholder's meeting with local farmers for the management and protection of S. perreti.     

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.     

松嫩平原苍鹭秋季栖息地选择及适宜性分布

苍鹭（Ardea cinerea）是松嫩平原湿地的常见鸟种,松嫩平原也是苍鹭重要的栖息地。为了了解苍鹭潜在栖息地的适宜性分布,利用GPS/GSM卫星跟踪技术,结合遥感影像和地理信息系统,应用Maxent模型对松嫩平原苍鹭秋季潜在的栖息地进行了评价,并对其适宜性分布进行了分析。结果显示：水源距离和绿度指数是影响松嫩平原苍鹭秋季栖息地适宜性的重要环境变量;松嫩平原内苍鹭适宜栖息地面积为2761.06 km²（占研究区域的1.24%）,主要分布在大庆（756.86 km²,占适宜栖息地面积的27.41%）、白城（537.14 km²,占适宜栖息地面积的19.45%）、齐齐哈尔（439.43 km²,占适宜栖息地面积的15.92%）等地市行政区,以大庆市杜尔伯特蒙古族自治县（429.90 km²,占适宜栖息地面积的15.57%）、白城市镇赉县（334.92 km²,占适宜栖息地面积的12.13%）、大庆市肇源县（185.54 km²,占适宜栖息地面积的6.72%）等县级行政区为主;其中,15.79%的适宜栖息地依次受到莫莫格保护区（10.34%）、扎龙保护区（3.47%）、向海保护区（0.67%）、查干湖保护区（0.54%）、大布苏保护区（0.41%）、乌裕尔河保护区（0.36%）等国家级自然保护区的保护。建议对未受到保护的零星小面积栖息地给与更多关注。     

Predictive mapping of two endemic oak tree species under climate change scenarios in a semiarid region: Range overlap and implications for conservation

Quercus infectoria and Quercus libani are two important species distributed across most of the Kurdistan Region of Iraq's mountain ranges (KRI). They have significant ecological, medicinal, and socioeconomic values. Recent studies have documented how plant distributions have been impacted by climate change. This study's goal is to establish the existing distributions of both species, measure the consequences of prospective environmental conditions on their distributions, predict possible habitat distributions, map the overlapped habitat ranges for the species in the KRI, and identify the key factors influencing their distributions. For these aims, distribution data points of the species, different environmental factors, including the existing climate, three emission predictions for the 2050s, 2070s, and 2090s of two general circulation models (GCMs), a machine learning approach, and geospatial techniques were used. Modeling revealed that the total magnitude of the habitat increase for the species would be less than the overall magnitude of the habitat contraction. The yearly mean temperature, yearly precipitation, and minimum temperature during the coldest period mostly alter the target species' geographic dispersion. Across the three emission scenarios of the both models, Q. infectoria habitat would contract by 2760.9–2856.9 km² (5.36–5.55%), 2856.9–3357.2 km² (5.55–6.52%) and 2822.1–3400.2 km² (5.48–6.60%), whereas it would expand by 1153.3–1638.9 km² (2.24–3.18%), 761.0–1556.8 km² (1.48–3.02%), and 721.5–1547.1 km² (1.40–3.00%) for the 2050s, 2070s, and 2090s, respectively. A similar pattern was also noted for Q. libani. The two species' habitat ranges in KRI would be considerably reduced due to climate change. The species' estimated area would extend mostly to the east and southeast of the KRI at high altitudes. The mountain areas, notably those where the species overlap by 1767.2–1807.5 km² (3.43–3.51%) for the two GCMs, must be the primary objective of conservation efforts. This research presents new baseline data for future research on mountain forest ecosystems and the techniques of biodiversity conservation to reduce climate change's effects in Iraq.     

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.     

Modelling potential impacts of climate change on the bioclimatic envelope and conservation of the Maned Wolf (Chrysocyon brachyurus)

Forecasting the influence of climatic changes on the distribution of the Maned Wolf (Chrysocyon brachyurus) is important for the conservation of the species. We explored the environmental characteristics than best explain the current distribution of the species, modelled the past and present distribution, projected the niche model into the future, and identified suitable areas for conservation. Niche modelling was performed using Maxent and 21 environmental variables. For past conditions, we considered the Last Glacial Maximum (LGM) and the mid-Holocene (MH) climates. For future conditions, we used the A2a greenhouse gas emission scenario for 2050. Four General Circulation Models (FGOALS 1.0, HADCM3, IPSL-CM4 and MIROC 3.2) were used. The resulting niche model (AUC = 0.89 ± 0.02) predicts maximum probability of presence at precipitation of 106 mm during the coldest quarter, of 396 mm during the warmest quarter, and in totally flat areas. The suitable area for the Maned Wolf currently covers 4,320,364 km². For the LGM, there were inter-model differences in predicted areas (from 819,324 km² to 6,395,886 km²) and in geographic location. The MH models showed drastic changes with respect to the present and considerable inter-model variation. Predictions for 2050 show significant (at least 33%) reductions in distribution. Only a minor fraction (39%) of the current distribution can be considered stable for the period LGM-2050. The FGOALS model was the best option for projecting species occurrence into the future because it included the three localities known for the Maned Wolf from the late Pleistocene and predicts stable areas that coincide with spatial patterns of genetic diversity. The FGOALS projection for 2050 predicts a 33% reduction in suitable habitats, indicating some stable areas (central South America) that will probably be key sites for the conservation of the species.