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Globally, animals that are range-restricted are frequently becoming species of conservation concern, in part due to competitive exclusion by phylogenetically and ecologically similar species that are more tolerant of human disturbance. However, climate and land use changes to natural landscapes can create pockets of refugia for range-restricted species. Western gray squirrels (Sciurus griseus) are native to the west coast of North America, principally California and western Oregon. Over the past several decades, Western Gray Squirrel populations have declined in human-dominated areas, with increased competition from introduced congeneric species native to eastern North America cited as a primary driver. Despite declines in their established range west of the Pacific Crest in western North America, western gray squirrels are extending their range into the Great Basin, where they were not historically found. Using a network of remote camera traps deployed across the Sierra Nevada–Great Basin ecotone in northwestern Nevada, we detected western gray squirrels across 16 of 100 camera-trapping sites. The majority of detections were located in piñon–juniper woodland, a land cover type not previously occupied by this species. Occupancy modeling revealed that western gray squirrels were equally likely to occur in piñon–juniper woodland compared to mature pine forest that they occupy elsewhere in their range. A species distribution model parameterized with historical gray squirrel observations (pre-1950), indicated increased climatic suitability for the species on the eastern side of the Sierra Nevada in recent decades, which may have facilitated this range expansion. Our findings reveal the potential for species declining in their historical range to colonize novel habitats that become increasingly suitable as a result of human-driven changes to ecosystems. [ABSTRACT FROM AUTHOR]

Monitoring the dynamics of land use and land cover (LULC) is imperative in the changing climate and evolving urbanization patterns worldwide. The shifts in land use have a significant impact on the hydrological response of watersheds across the globe. Several studies have applied machine learning (ML) algorithms using historical LULC maps along with elevation data and slope for predicting future LULC projections. However, the influence of other driving factors such as socio-economic and climatological factors has not been thoroughly explored. In the present study, a sensitivity analysis approach was adopted to understand the effect of both physical (elevation, slope, aspect, etc.) and socio-economic factors such as population density, distance to built-up, and distance to road and rail, as well as climatic factors (mean precipitation) on the accuracy of LULC prediction in the Brahmani and Baitarni (BB) basin of Eastern India. Additionally, in the absence of the recent LULC maps of the basin, three ML algorithms, i.e., random forest (RF), classified and regression trees (CART), and support vector machine (SVM) were utilized for LULC classification for the years 2007, 2014, and 2021 on Google earth engine (GEE) cloud computing platform. Among the three algorithms, RF performed best for classifying built-up areas along with all the other classes as compared to CART and SVM. The prediction results revealed that the proximity to built-up and population growth dominates in modeling LULC over physical factors such as elevation and slope. The analysis of historical data revealed an increase of 351% in built-up areas over the past years (2007–2021), with a corresponding decline in forest and water areas by 12% and 36% respectively. While the future predictions highlighted an increase in built-up class ranging from 11 to 38% during the years 2028–2070, the forested areas are anticipated to decline by 4 to 16%. The overall findings of the present study suggested that the BB basin, despite being primarily agricultural with a significant forest cover, is undergoing rapid expansion of built-up areas through the encroachment of agricultural and forested lands, which could have far-reaching implications for the region's ecosystem services and sustainability. [ABSTRACT FROM AUTHOR]

Human activities have drastically reshaped global forest ecosystems in recent decades. However, identifying the origins, magnitude, and primary effects of these actions, especially in East Asia (EA), with a long history of human activity, remains challenging. Crucially, unraveling the early anthropogenic impacts on forest ecosystems has substantial implications for characterizing the Anthropocene. In this study, we collated and synthesized Holocene arboreal pollen (AP) sequences from 61 EA lake basins, which demonstrated an overall expansion of forest vegetation during the early to middle Holocene, followed by a retreat from 5 cal. ka BP. The cooling and drying climate during the mid- and late Holocene proved inimical to forest proliferation across EA. Survival imperatives are likely to drive humans to adopt grazing and farming, causing a transformation from forest land to agro-pastoral land. This change further accelerated forest ecosystem degradation. By dividing EA into five subregions, we observed asynchronous declines in forest vegetation from 8 cal. ka BP to 3 cal. ka BP. This trend is primarily attributed to human impacts, given the pronounced coincidence between forest reduction and emergence and prosperity of Neolithic agriculture in each region. Significantly, the positive feedback loop between population growth and agricultural expansion can amplify human impacts. Coupled with climatic effects, these factors resulted in EA's forest vegetation plummeting to its lowest level since 10 cal. ka BP at 2.1 cal. ka BP. Furthermore, the anthropogenic influences on EA forest vegetation mirror land use, soil erosion, and vegetation variability observed globally in terms of timeline. These alterations are driven by human activities, almost coinciding with shifts in atmospheric greenhouse gas levels away from their natural trajectory. We, therefore, argue that the Anthropocene definition should consider the aggregate environmental impact of human activities over millennia, rather than restricting to the most recent centuries. [ABSTRACT FROM AUTHOR]