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The tourism industry is a significant contribution to the economy of many countries, including Pakistan. However, its activities often have a negative impact on the environment, particularly related to climate change. Notably, Pakistan ranks fifth among countries most affected by climate change, which requires a targeted analysis of the tourism sector to determine its potential impacts. Despite the critical nature of this issue, there is currently a lack of research that examines how climate change specifically impacts Pakistan's tourism industry. This study aims to address this gap by using the Tourism Climate Index (TCI) to assess the impact of climate change on the suitability of different tourism locations across the country. Our results show that popular tourist destinations such as Gilgit-Baltistan experience their peak season in the warmer third quarter (summer), which corresponds to the highest observed TCI values. This suggests that warmer temperatures could boost tourism activity and spending in these regions. Furthermore, our analysis shows that air temperature plays a crucial role in determining tourist comfort levels and significantly influences tourists' TCI values and sensory experiences. The results of this study show that the TCI methodology can be an effective tool for further research into the geographical impacts of climate change on tourism. By identifying necessary changes due to climatic changes, future studies could provide valuable insights into how the tourism industry can adapt to and reduce its environmental footprint.

Population abundance and density estimates play important roles in biodiversity conservation assessment and can lead to prioritization of conservation efforts, strategies, and management. The Calabar angwantibo (Arctocebus calabarensis) is a poorly studied, Near-Threatened nocturnal, arboreal mammal species occurring only in the lowland moist tropical rainforest blocks of southeastern Nigeria and southwestern Cameroon. Like other arboreal nocturnal mammals, there are gaps in knowledge of the distribution and abundance of this species, which may be facing population declines due to habitat loss and hunting. In this study, we investigated the abundance and density of A. calabarensis in the Oban Hills Region of Nigeria. We conducted systematic distance sampling survey along 32 transects in different habitats in a 1227 km of survey efforts. A total of 41 sightings of A. calabarensis were detected, resulting in an abundance of 7345 individuals (95% CI = 1.51–4.37) with an estimated density of 2.57 animals/km2. The estimated population abundance is 6515 individuals in closed-canopy forests and 830 individuals in secondary forests, with encounter rates of 0.52 individual/km2 and 0.60 individuals/km2 in the closed canopy and secondary forest habitats. The global estimates encounter rate of A. calabarensis across the habitat types is 0.33 individuals/km2 and population abundance of 4456 individuals. Our findings indicate that the A. calabarensis populations can adapt to low changes in forest habitat modifications resulting from increasing and widespread forest disturbance by human-dominated activity, which is giving way to forest clearance for agriculture cultivation and infrastructural development. Our findings help to fill a knowledge gap regarding this species and may help establish a baseline for future management, population monitoring, and conservation of the cryptic population of A. calabarensis in Cross-Sanaga Forests.

The inevitable impacts of climate change have reverberated across ecosystems and caused substantial global biodiversity loss. Climate-induced habitat loss has contributed to range shifts at both species and community levels. Given the importance of identifying suitable habitats for at-risk species, it is imperative to assess potential current and future distributions, and to understand influential environmental factors. Like many species, the Demoiselle crane is not immune to climatic pressures. Khyber Pakhtunkhwa and Balochistan provinces in Pakistan are known wintering grounds for this species. Given that Pakistan is among the top five countries facing devastating effects of climate change, this study sought to conduct species distribution modeling under climate change using data collected during 4 years of field surveys. We developed a Maximum Entropy distribution model to predict the current and projected future distribution of the species across the study area. Future habitat projections for 2050 and 2070 were carried out using two representative concentration pathways (RCP 4.5 and RCP 8.5) under three global circulation models, including HADGEM2-AO, BCC-CSM1-1, and CCSM4. The most influential factors shaping Demoiselle Crane habitat suitability included the temperature seasonality, annual mean temperature, terrain ruggedness index, and human population density, all of which contributed significantly to the suitability (81.3%). The model identified 35% of the study area as moderately suitable (134,068 km2) and highly suitable (27,911 km2) habitat for the species under current climatic conditions. Under changing climate scenarios, our model predicted a major loss of the species’ current suitable habitat, with shrinkage and shift towards western–central areas along the Pakistan–Afghanistan boarder. The RCP 8.5, which is the extreme climate change scenario, portrays particularly severe consequences, with habitat losses reaching 65% in 2050 and 85% in 2070. This comprehensive study provides useful insights into the Demoiselle Crane habitat’s current and future dynamics in Pakistan.

Insecticide application is still a standard method in pest management. However, the current regions for insecticide application usually focus on the host of the pest but ignore the migration pathways, continually resulting in pests’ periodic outbreaks. This research provides a valuable modeling framework to identify optimal regions for insecticide application to solve this problem. This modeling framework optimizes insecticide areas based on habitat suitability and landscape connectivity, combining the MaxEnt and circuit theory. We conducted a case study in Beijing, China, to elucidate the application of this modeling framework. The input data included 517 occurrence points of Semiothisa cinerearia collected in the fieldwork and the environmental variables related to the eco-physiological characteristics of this species. The results indicated that the Chinese scholar tree (Styphnolobium japonicum) was the most vulnerable species. Increasing the pesticide region by 11.9% at some crucial corridors of the target species, the pest management effectiveness increased by 27.7%, compared with the pesticide applied only to the Chinese scholar trees. In summary, this study proposed a convenient and efficient modeling methodology for planning optimal regions for insecticide application.

Species distribution models have been widely used to assess changes in species distribution under climate change. However, these models often neglect anthropogenic drivers of range shifts or contractions, and general circulation models may introduce uncertainties. Therefore, quantifying the impacts of both climate change and anthropogenic disturbance on species distribution remains a challenge. Long-term historical data may overcome the uncertainties introduced by variability in global climate models and thus provide ecologists with comprehensive information on the long-term effects of anthropogenic disturbance. The black-billed capercaillie (Tetrao urogalloides) has undergone a dramatic population decline since the late 20th century and is endangered in China. We used ensemble forecasting to assess the impact of climate change and anthropogenic disturbance on the distribution of this species in northeast China from the 1970s to the 2000s. Our results suggested a decline in the predicted distribution area from 339,638 km2 in the 1970s to 219,083 km2 in the 2000s, representing a total reduction of 35.50%. We estimated that the range contraction caused by climate change equated to 22,038 km2, while that due to anthropogenic disturbance to 98,517 km2 over this time period. Therefore, the observed range contraction for this species is primarily attributable to anthropogenic disturbance.

In an increasingly urbanized world, habitat fragmentation and lack of corridors among the fragmented habitats in urbanized ecosystems have compromised the survival of many species both at regional and global scale. Green corridors that connect isolated remnant habitat patches (e.g. parks) can increase connectivity and provide ecosystem services in cities. In the current study, we introduce an open-source ArcGIS toolset, the Gravity model toolbox, a programmed ArcGIS tool to map and prioritize the potential green corridors. The new toolset is based on the least-cost modelling and the improved gravity model, comprising of two script tools, the gravity model tool, and the network analysis tool. The gravity model tool can produce a spatially-explicit map of potential corridors for urban green space, as well as prioritize these corridors considering the opportunity cost. The network analysis tool is aimed at offering multiple scenarios of corridor networks based on the demands of users. To elucidate how the toolbox could be used by the urban planners and ecology researchers, a case study of the toolbox was conducted in the Tongzhou District, Beijing, China. This study presents a novel approach to assess urban connectivity and a systematic methodology for planning greening networks that connects landscape ecology with practical planning and designing considerations to maximize social and ecological functions of urban green space.

Northern regions of Pakistan support a relatively large population of wild ungulates, the preferred prey of sympatric carnivores. The Asiatic ibex (Capra Ibex Sibirica) is one such an ungulate species which also serves as an important trophy animal. The maintenance of trophy hunting programs rely on estimates of harvestable population sizes derived from rigorous methods. The present study successfully used the double observer-based Capture-Mark-Recapture (CMR) method to produce a reliable and accurate estimate of the Asiatic ibex population in the Community Control Hunting Areas (CCHAs) of Socterabad, Gojal watershed and Khunjerab National Park (KNP). Surveys were conducted from February to March 2018 and from March to April 2019. The total ibex population was calculated to be 1075 individuals (95%CI ± 670) with a density of 1.43 ibex/km2 in Gojal watershed, followed by Socterabad with 856 individuals (95%CI ± 680) and a density of 6.24ibex/km2, and lastly KNP with 463 individuals (95%CI ± 93.5) and a density of 0.14ibex/km2. A total of 52 herds were sighted in Gojal watershed with mean size of 19 ibex/herd (SE ± 3.2). In Socterabad, 28 herds were sighted with mean size of 16.07 ibex/herd (SE ± 2.4) and in KNP 28 herds were sighted with average recorded size of 16.5 ibex/herd (SE ± 3.4). In KNP Sex ratios of female to young, female to yearling and female to male were 1:0.7, 1:0.4, and 1:0.5 respectively. The detection probability of observer two was less than observer one. Ibex biomass recorded is insufficient for current recorded snow leopard (Panthera uncia) and wolf (Canis lupus) population in the area. Our study validates the use of Capture Mark Recapture as a viable tool in discerning ungulate populations, and shows that the population of the Asiatic ibex is viable in the study area, making it suitable for trophy hunting programs but need to modify the hunting law.

Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990–2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990–2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r = − 0.155 (p > 0.005), −0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (−12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ∼10.74 °C and ∼12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

Species conservation actions are guided by available information on the biogeography of the protected species. In this study, we integrated the occurrence data of Siberian musk deer (Moschus moschiferus L.) collected from 2019 to 2021 with species distribution models to estimate the species’ potential distribution in Northeast China. We then identified conservation priority areas using a core-area zonation algorithm. In addition, we analyzed core patch fragmentation using FRAGSTATS. Lastly, we identified potential connectivity corridors and constructed a potential protection network based on the least-cost path and the circuit theory. The results showed concentrations of M. moschiferus in the northern Greater Khingan Mountains, the southeastern Lesser Khingan Mountains, and the eastern Changbai Mountains, with a potential distribution area of 127,442.14 km2. Conservation priority areas included 41 core patches with an area of 106,306.43 km2. Patch fragmentation mainly occurred in the Changbai Mountains and the Lesser Khingan Mountains. We constructed an ecological network composed of 41 core patches and 69 linkages for M. moschiferus in Northeast China. The results suggest that the Greater Khingan Mountains represent the most suitable area to maintain the stability of M. moschiferus populations in Northeast China. Considering the high habitat quality requirements of M. moschiferus and its endangered status, we propose that the Chinese government accelerates the construction of the Greater Khingan Mountains National Park and the Lesser Khingan Mountains National Park and enlarges the Northeast China Tiger and Leopard National Park to address the fragmentation of protected areas and the habitat of M. moschiferus.

Global warming has negative impacts on the distribution of large ungulates, particularly for species occupying narrow distributional ranges. Knowledge of how climate change will affect future distributions is imperative for designing effective conservation action plans for at risk species such as the Himalayan gray goral (HGG), a cliff-dwelling mountainous goat. We sought to evaluate the potential distribution of Himalayan gray goral (HGG) under future climate change scenarios using ensemble modeling approaches. HGG data were obtained from previous published surveys, publications, and occurrence records ranging from 1985 to 2018. we also conducted survey in 2017–2018 using double observer method based on capture mark recapture. (Suryawanshi et al., 2012; Tumursukh et al., 2015). Later on we double check the record and remove double observation. After quality control screening, 139 records remained for analysis. Resulting species distribution models (SDMs) results showed sufficient internal evaluation metrics, with all TSS values being > 0.7. The random forest (RF) modelling technique had on average the lowest true skill statistics (TSS) value, However the multivariate adaptive regression splines (MARS) modelling technique had the highest. The ensemble modelling internal evaluation metrics indicated adequate results with values ranging from 0.827 to 0.843. Annual mean temperature (Bio1) and annual precipitation (Bio12) were found to be the most important climatic variables impacting the potential distribution of HGG. HGG habitat determined to be suitable in both current and future climate scenarios decreased in all Representative Concentration Pathways (RCPs) scenarios with the exception of RCPs 2.6. Suitable habitat in both current and future climate scenarios remained consistent in the time periods of 2050 and 2070 under RCP4.5 while fluctuating in 2030, 2050 and 2070 under RCP 2.6. However, the suitable habitat under current and future scenarios declined in 2030 under RCPs 4.5 and in 2030, 2050, and 2070 in scenarios RCPs 8.5. Currently suitable HGG habitat was located in an area where the species is known to be locally extinct. Further work is necessary to determine the key drivers of local extinction events in an effort to mitigate population crashes. Our work will assist in formulating conservation actions for the HGG in the context of climate change, and provide a platform for continued monitoring efforts of the species. Keywords: Niche change, Climate change, Habitat change, Habitat prediction, Ungulates, Capture-mark recapture

Located at the foothills of the Himalayan Mountains, subtropical and moist temperate forests of Pakistan are very rich in flora and fauna. However, due to increased illegal and uncontrolled harvesting of wood, agricultural activities, and urbanization, these forests are fast disappearing. The recent expansion of human activities resulting illegal and uncontrolled harvesting, agricultural activities, and urbanization is a cause for concern. Using Landsat imagery, Markov Chain and Cellular Automata, this study focused on the quantitative assessment of spatiotemporal land use and land cover changes during 1998, 2008, 2018 and a simulation of 2028. In addition, a forest inventory survey of biomass and carbon sink were respectively calculated for these subtropical broad-leaved evergreen, subtropical chirpine and moist temperate forests. Results showed biomass was 560.56 ± 104.33 Mg ha−1, 350.95 ± 104.33 Mg ha−1 and 153.63 ± 104.33 Mg ha−1 in moist temperate, subtropical chirpine and subtropical broad-leaved forests respectively. Meanwhile, carbon was 313.94 ± 44.78 Mg C ha−1, 221.34 ± 44.78 Mg C ha−1 and 131.77 ± 44.78 Mg C ha−1 in moist temperate, subtropical chirpine and subtropical broad-leaved forests respectively. During the study period, land-use and land cover changes showed forest land changed from 40936.77 ha to 36709.23 ha, agricultural land from 4220.46 to 10374.64 ha, and built-up area from 1497.60 to 5395.12 ha. The average annual biomass and carbon loss were respectively 50.34 Gg ha−1yr−1 and 31.33 Gg C ha−1 yr−1. The information derived from this study could assist in the development of appropriate sustainable forest management policies in Pakistan. Keywords: Land use change, Biomass, Carbon, Himalayan mountains, Markov chain, Urbanization

Pastoralist−wolf conflict over livestock depredation is the main factor affecting conservation of grey wolf worldwide. Very limited research has been carried out to evaluate the pattern and nature of livestock depredation by wolf. This study aims to determine the status and nature of human−wolf conflict across different villages in the Hind Kush region of Pakistan during the period January 2016−December 2016. For this purpose, a total of 110 local male respondents from all walks of life were interviewed using a semi-structured questionnaire. The grey wolf was declared as a common species in the area by 51.3% of the locals with an annual sighting rate of 0.46 each. During the year (2016), a total of 358 livestock were lost to grey wolf predation and disease. Of the total livestock loss, grey wolf was held responsible for a total 101 livestock losses. Goat and sheep were the most vulnerable prey species as they accounted for 80 (79.2%) of the total reported depredations. Out of the total economic loss (USD 46,736, USD 424.87/household), grey wolf was accountable for USD 11,910 (USD 108.27 per household), while disease contributed 34,826 (USD 316.6 per household). High depredation was observed during the summer season 58.42% (n = 59) followed by spring and autumn. Unattended livestock were more prone to grey wolf attack during free grazing in forests. Most of the respondents (75.45%) showed aggressive and negative attitudes towards grey wolf. The herders shared more negative attitude (z = −3.21, p = 0.001) than businessman towards the species. Herders having larger herd size displayed more deleterious behavior towards wolves than those having smaller herd size. Active herding techniques, vaccinating livestock, educating locals about wildlife importance, and initiating compensating schemes for affected families could be helpful to decrease negative perceptions.

Accurate estimation of tree position, diameter at breast height (DBH), and tree height measurements is an important task in forest inventory. Mobile Laser Scanning (MLS) is an important solution. However, the poor global navigation satellite system (GNSS) coverage under the canopy makes the MLS system unable to provide globally-consistent point cloud data, and thus, it cannot accurately estimate the forest attributes. SLAM could be an alternative for solutions dependent on GNSS. In this paper, a mobile phone with RGB-D SLAM was used to estimate tree position, DBH, and tree height in real-time. The main aims of this paper include (1) designing an algorithm to estimate the DBH and position of the tree using the point cloud from the time-of-flight (TOF) camera and camera pose; (2) designing an algorithm to measure tree height using the perspective projection principle of a camera and the camera pose; and (3) showing the measurement results to the observer using augmented reality (AR) technology to allow the observer to intuitively judge the accuracy of the measurement results and re-estimate the measurement results if needed. The device was tested in nine square plots with 12 m sides. The tree position estimations were unbiased and had a root mean square error (RMSE) of 0.12 m in both the x-axis and y-axis directions; the DBH estimations had a 0.33 cm (1.78%) BIAS and a 1.26 cm (6.39%) root mean square error (RMSE); the tree height estimations had a 0.15 m (1.08%) BIAS and a 1.11 m (7.43%) RMSE. The results showed that the mobile phone with RGB-D SLAM is a potential tool for obtaining accurate measurements of tree position, DBH, and tree height.

Enriching forest resource inventory is important to ensure the sustainable management of forest ecosystems. Obtaining forest inventory data from the field has always been difficult, laborious, time consuming, and expensive. Advances in integrating photogrammetry and computer vision have helped researchers develop some numeric algorithms and methods that can turn 2D (images) into 3D (point clouds) and are highly applicable to forestry. This paper aimed to develop a new, highly accurate methodology that extracts sample plot parameters based on continuous terrestrial photogrammetry. For this purpose, we designed and implemented a terrestrial observation instrument combining real-time kinematic (RTK) and charge-coupled device (CCD) continuous photography. Then, according to the set observation plan, three independent experimental plots were continuously photographed and the 3D point cloud of the plot was generated. From this 3D point cloud, the tree position coordinates, tree DBHs, tree heights, and other plot characteristics of the forest were extracted. The plot characteristics obtained from the 3D point cloud were compared with the measurement data obtained from the field to check the accuracy of our methodology. We obtained the position coordinates of the trees with the positioning accuracy (RMSE) of 0.162 m to 0.201 m. The relative root mean square error (rRMSE) of the trunk diameter measurements was 3.07% to 4.51%, which met the accuracy requirements of traditional forestry surveys. The hypsometrical measurements were due to the occlusion of the forest canopy and the estimated rRMSE was 11.26% to 11.91%, which is still good reference data. Furthermore, these image-based point cloud data also have portable observation instruments, low data collection costs, high field measurement efficiency, automatic data processing, and they can directly extract tree geographic location information, which may be interesting and important for certain applications such as the protection of registered famous trees. For forest inventory, continuous terrestrial photogrammetry with its unique advantages is a solution that deserves future attention in the field of tree detection and ecological construction.