• Energy Research

Regional carbon emissions related to forest cover change (FCC) and wood harvest (WH) are critical for the accurate estimates of global carbon balance over an extended time period. Using remote sensing and inventory data, this study provides a comprehensive record of FCC, WH, and their integrated carbon emissions between 1908 and 2015 in the dry temperate regions of Pakistan. Results demonstrate a significant decline in forest area (21,034 ha) at an annual rate of 0.56% from 1973 to 2015. The total WH was 24.84 million m3 (0.23 million m3 yr−1) between 1908 and 2015. Deforestation was responsible for a net loss of 1.39 million Mg C (0.018 million Mg C yr−1), while WH-related carbon emissions accounted for 11.29 million Mg C (0.52 million Mg C yr−1). The present results indicate that under the existing FCC and WH harvest scenario, the forests are acting as a net source of 0.29 million Mg C yr−1. Agriculture expansion and the heavy dependency of local communities on the forest’s resources, exclusion of conservation and local communities from forest management, insufficient monitoring, and weak law-enforcement were the striking drivers of FCC, WH, and their related emissions. These findings suggest that to maintain forest carbon and meet the communities’ requirements, counter approaches such as agriculture incentives, agroforestry, trophy hunting, alternative energy sources, and inclusion of conservation and secure community-based management are needed.

Regional carbon emissions related to forest cover change (FCC) and wood harvest (WH) are critical for the accurate estimates of global carbon balance over an extended time period. Using remote sensing and inventory data, this study provides a comprehensive record of FCC, WH, and their integrated carbon emissions between 1908 and 2015 in the dry temperate regions of Pakistan. Results demonstrate a significant decline in forest area (21,034 ha) at an annual rate of 0.56% from 1973 to 2015. The total WH was 24.84 million m3 (0.23 million m3 yr−1) between 1908 and 2015. Deforestation was responsible for a net loss of 1.39 million Mg C (0.018 million Mg C yr−1), while WH-related carbon emissions accounted for 11.29 million Mg C (0.52 million Mg C yr−1). The present results indicate that under the existing FCC and WH harvest scenario, the forests are acting as a net source of 0.29 million Mg C yr−1. Agriculture expansion and the heavy dependency of local communities on the forest’s resources, exclusion of conservation and local communities from forest management, insufficient monitoring, and weak law-enforcement were the striking drivers of FCC, WH, and their related emissions. These findings suggest that to maintain forest carbon and meet the communities’ requirements, counter approaches such as agriculture incentives, agroforestry, trophy hunting, alternative energy sources, and inclusion of conservation and secure community-based management are needed.

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.

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.

Accurate evaluation of fish stock biomass is essential for effective conservation management and targeted species enhancement efforts. However, this remains challenging owing to limited data availability. Therefore, we present an integrated modeling framework combining catch per unit effort with ensemble species distribution modeling called CPUESDM, which explicitly assesses and validates the spatial distribution of stock biomass for freshwater fish species with limited data, applied to Herzensteinia microcephalus. The core algorithm incorporates the Leslie regression model, ensemble species distribution modeling, and exploratory spatial interpolation techniques. We found that H. microcephalus biomass in the Yangtze River source area yielded an initial estimate of 113.52 tons. Our validation results demonstrate high accuracy with a Cohen's kappa coefficient of 0.78 and root mean square error of 0.05. Furthermore, our spatially-explicit, global, absolute biomass density map effectively identified areas with high and low concentrations of biomass distribution centers. Additionally, this study offers access to the source code, example raw data, and a step-by-step instruction manual for other researchers using field data to explore the application of this model. Our findings can help inform for future conservation efforts around fish stock biomass estimation, especially for endangered species.

Accurate evaluation of fish stock biomass is essential for effective conservation management and targeted species enhancement efforts. However, this remains challenging owing to limited data availability. Therefore, we present an integrated modeling framework combining catch per unit effort with ensemble species distribution modeling called CPUESDM, which explicitly assesses and validates the spatial distribution of stock biomass for freshwater fish species with limited data, applied to Herzensteinia microcephalus. The core algorithm incorporates the Leslie regression model, ensemble species distribution modeling, and exploratory spatial interpolation techniques. We found that H. microcephalus biomass in the Yangtze River source area yielded an initial estimate of 113.52 tons. Our validation results demonstrate high accuracy with a Cohen's kappa coefficient of 0.78 and root mean square error of 0.05. Furthermore, our spatially-explicit, global, absolute biomass density map effectively identified areas with high and low concentrations of biomass distribution centers. Additionally, this study offers access to the source code, example raw data, and a step-by-step instruction manual for other researchers using field data to explore the application of this model. Our findings can help inform for future conservation efforts around fish stock biomass estimation, especially for endangered species.

Habitat degradation and species range contraction due to land use/land cover changes (LULCC) is a major threat to global biodiversity. The ever-growing human population has trespassed deep into the natural habitat of many species via the expansion of agricultural lands and infrastructural development. Carnivore species are particularly at risk, as they demand conserved and well-connected habitat with minimum to no anthropogenic disturbance. In Pakistan, the snow leopard (Panthera uncia) is found in three mountain ranges—the Himalayas, Hindukush, and Karakoram. Despite this being one of the harshest environments on the planet, a large population of humans reside here and exploit surrounding natural resources to meet their needs. Keeping in view this exponentially growing population and its potential impacts on at-risk species like the snow leopard, we used geographic information systems (GIS) and remote sensing with the aim of identifying and quantifying LULCC across snow leopard range in Pakistan for the years 2000, 2010, and 2020. A massive expansion of 1804.13 km2 (163%) was observed in the built-up area during the study period. Similarly, an increase of 3177.74 km2 (153%) was observed in agricultural land. Barren mountain land increased by 12,368.39 km2 (28%) while forest land decreased by 2478.43 km2 (28%) and area with snow cover decreased by 14,799.83 km2 (52%). Drivers of these large-scale changes are likely the expanding human population and climate change. The overall quality and quantity of snow leopard habitat in Pakistan has drastically changed in the last 20 years and could be compromised. Swift and direct conservation actions to monitor LULCC are recommended to reduce any associated negative impacts on species preservation efforts. In the future, a series of extensive field surveys and studies should be carried out to monitor key drivers of LULCC across the observed area.

Habitat degradation and species range contraction due to land use/land cover changes (LULCC) is a major threat to global biodiversity. The ever-growing human population has trespassed deep into the natural habitat of many species via the expansion of agricultural lands and infrastructural development. Carnivore species are particularly at risk, as they demand conserved and well-connected habitat with minimum to no anthropogenic disturbance. In Pakistan, the snow leopard (Panthera uncia) is found in three mountain ranges—the Himalayas, Hindukush, and Karakoram. Despite this being one of the harshest environments on the planet, a large population of humans reside here and exploit surrounding natural resources to meet their needs. Keeping in view this exponentially growing population and its potential impacts on at-risk species like the snow leopard, we used geographic information systems (GIS) and remote sensing with the aim of identifying and quantifying LULCC across snow leopard range in Pakistan for the years 2000, 2010, and 2020. A massive expansion of 1804.13 km2 (163%) was observed in the built-up area during the study period. Similarly, an increase of 3177.74 km2 (153%) was observed in agricultural land. Barren mountain land increased by 12,368.39 km2 (28%) while forest land decreased by 2478.43 km2 (28%) and area with snow cover decreased by 14,799.83 km2 (52%). Drivers of these large-scale changes are likely the expanding human population and climate change. The overall quality and quantity of snow leopard habitat in Pakistan has drastically changed in the last 20 years and could be compromised. Swift and direct conservation actions to monitor LULCC are recommended to reduce any associated negative impacts on species preservation efforts. In the future, a series of extensive field surveys and studies should be carried out to monitor key drivers of LULCC across the observed area.