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Climate change is the most significant threat to global biodiversity, risking extinction for many species due to their limited adaptability to rapidly changing environmental conditions, such as temperature, precipitation, and other climate variables. Illicium griffithii , an endangered tree with ecological and medicinal value, remains understudied, particularly in Arunachal Pradesh. The aim of the study is to identify key environmental variables influencing the current distribution of I. griffithii and to predict the potential distribution under current and future climatic scenarios (SSP245 and SSP585). We used an ensemble modeling approach that integrates five species distribution models (SDMs). After multicollinearity test, we utilized fifteen environmental variables including bioclimatic variables, soil properties, topographical variables, and evapotranspiration to predict the potential distribution of I. griffithii . The study revealed that the current distribution is predominantly influenced by isothermality, nitrogen content at 0–5 cm depth, clay content at 0–5 cm depth, and seasonality of precipitation, with a total contribution rate of 42.6%. The ensemble model performed robustly and found to be excellent performance based on AUC of 0.94 and TSS of 0.83. The total highly suitable area for I. griffithii spans 722.72 km 2 in the current scenario, primarily located in West Kameng, Tawang, and East Kameng districts. West Kameng stands out as the largest high-suitability area, which covers 592.83 km 2 and contributing a substantial 82.03% of the total suitable area. However, under the SSP585 future climate scenario (2041–2060), projections reveal a concerning decline in highly suitable areas. The area is expected to shrink by over 5.05%, decreasing from 722.72 to 686.25 km 2 . The results have highlighted the vulnerability of I. griffithii under future climatic scenario. Hence, forest managers should prioritize conserving suitable habitats in West Kameng, Tawang, and East Kameng districts of Arunachal Pradesh by implementing habitat restoration, assisted migration and ex situ conservation strategies that can mitigate climate change impacts.

Bedrock landslides are the primary agent of hillslope erosion, and mass wasting, and an essential source of sediment flux to the fluvial network in the mountainous terrain, in particular, in the Himalayan mountain belt. To understand the characteristics of the landscape, we calculate geomorphic matrices including the topographic variables, longitudinal and topographic swath profile, channel steepness index, and stream length gradient index to analyze the spatial distribution of landslide occurrences over landscape evolution. The intensity of rainfall gradient and topographic variables were spatially correlated with the erosion and exhumation rates of the studied catchment. Our analysis suggests that the zones with slope ranges of 24°–28°, relief ranges of 800–1000 m, and elevation ranges of 1500–1700 m, which coincide with the rainfall intensity range of 2500–2700 mm/year in the Teesta river catchment, have the highest probability of frequently occurring landslides. Higher tectonic activity is principally responsible for the landslide over the Higher Himalaya to the north of the Main Central Thrust (MCT)–Main Boundary Thrust (MBT) along the orographic barrier. In contrast, litho-tectonics regulates and mostly triggers landslides adjacent to the MCT–MBT structural affinity dominated by rainfall intensity. Our observation suggests that erosion rates frequently exceed long-term exhumation rates and are spatially more variable. Moreover, they exhibit significantly divergent spatial patterns, which suggests that the processes governing these rates are independent. Exhumation rates have been shown to decrease from south to north over geological periods, rising in the southwest region at ~ 1.2 mm/year and decreasing to ~ 0.5 mm/year in the northernmost region of the Teesta catchment. Long-term exhumation rates are not correlated with geomorphic or climatic variables. The highest apparent erosion rates (5 mm/year) are seen in the catchment that crosses the MCT Zone, however, these rates appear to have been severely impacted by recent landslides. Conversely, changes in rainfall rate do not appear to significantly impact either rate of long-term exhumation or erosion in the Teesta catchment.

Topographic variables such as slope and elevation partially explain spatial variations in aboveground biomass (AGB) within landscapes. Human activities that impact vegetation, such as cattle grazing and shifting cultivation, often follow topographic features and also play a key role in determining AGB patterns, although these effects may be moderated by accessibility. In this study, we evaluated the potential to predict AGB in a rural landscape, using a set of topographical variables in combination with indicators of accessibility. We modeled linear and non‐linear relationships between AGB, topographic variables within the territorial boundaries of six rural communities, and distance to roads. Linear models showed that elevation, slope, topographic wetness index, and tangential curvature could explain up to 21% of AGB. Non‐linear models found threshold values for the relationship between AGB and diffuse insolation, topographic position index at 19 × 19 pixels scale and differentiated between groups of communities, improving AGB predictions to 33%. We also found a continuous and positive effect on AGB with increased distance from roads, but also a piecewise relationship that improves the understanding of intensity of human activities. These findings could enable AGB baselines to be constructed at landscape level using freely available data from topographic maps. Such baselines may be of use in national programs under the international policy Reducing Emissions from Deforestation and Forest Degradation.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non‐model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype–environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole‐genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high‐resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine‐grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Flooding is a recurrent climate-related hazard in southern Thailand that frequently disrupts schooling and undermines educational continuity. Despite the critical importance of school-based disaster preparedness, there is limited empirical evidence explaining the drivers of flood preparedness among primary school teachers in climate-vulnerable regions. This study aimed to identify the cognitive, experiential, and topographic factors correlated with flood knowledge, flood risk perception (FRP), and flood preparedness (FP) among primary school teachers in Nakhon Si Thammarat province. A cross-sectional survey was conducted with 745 teachers using a structured questionnaire that covered sociodemographic characteristics, flood experience, training, information sources, and regional topography (elevation, slope, and distance to river). Spearman’s rank correlation and Generalized Linear Models (GLMs) were applied to examine the relationships and predictive factors. The findings revealed that topographic factors, specifically distance to the nearest river, were significantly associated with teachers’ flood knowledge, while school elevation was significantly related to FRP. Community-based information was a strong predictor of flood knowledge. Furthermore, prior flood experience, first-aid training, access to school-based information networks, and FRP were identified as key drivers of FP. Moreover, the negative relationships were found between flood knowledge and FP suggest that preparedness is influenced by complex cognitive and behavioral mechanisms rather than knowledge alone. These findings highlight the importance of integrating topographic risk information, experiential learning, and community-based information networks into school-based disaster preparedness programs rather than relying solely on knowledge. These findings offer practical implications for designing targeted teacher training and school-based disaster risk reduction (DRR) strategies in climate-vulnerable settings.

The knowledge and prediction of spatial distribution of forest fire is essential for improving fire prevention strategies in forest areas. Forest fire susceptibility maps of the Babolrood Watershed in the Mazandaran Province of Iran were obtained from random forest, artificial neural network and logistic regression models. The important factors identified to affect forest fires include first and secondary topography, climate, vegetation cover and related human activities. Forest fire susceptibility maps were prepared using three models and the accuracy of the results was evaluated using validation datasets, kappa coefficient (K) and area under the receiver operating characteristic curve (AUC). All three methods produced forest fire susceptibility maps of reasonable accuracy; artificial neural network model with K = 0.61 and AUC = 0.88; random forest model with K = 0.64 and AUC = 0.93 and logistic regression model with K = 0.52 and AUC = 0.79. These results showed that the accuracy of forest fire susceptibility map obtained from the random forest method was slightly higher. According to the random forest results, 6.18% and 16.08% of the study area had very high and high potential for fire occurrence respectively. In general, the aforementioned methods can be applied for forest fire susceptibility mapping in forest areas with similar conditions.

Understanding the interaction between life history, demography and population genetics in threatened species is critical for the conservations of viable populations. In the context of habitat loss and fragmentation, identifying the factors that underpin the structuring of genetic variation within populations can allow conservationists to evaluate habitat quality and connectivity and help to design dispersal corridors effectively. In this study, we carried out a detailed, fine‐scale landscape genetic investigation of a giant panda population from the Qinling Mountains for the first time. With a large microsatellite data set and complementary analysis methods, we examined the role of isolation‐by‐barriers (IBB), isolation‐by‐distance (IBD) and isolation‐by‐resistance (IBR) in shaping the pattern of genetic variation in this giant panda population. We found that the Qinling population comprises one continuous genetic cluster, and among the landscape hypotheses tested, gene flow was found to be correlated with resistance gradients for two topographic factors, slope aspect and topographic complexity, rather than geographical distance or barriers. Gene flow was inferred to be facilitated by easterly slope aspect and to be constrained by topographically complex landscapes. These factors are related to benign microclimatic conditions for both the pandas and the food resources they rely on and more accessible topographic conditions for movement, respectively. We identified optimal corridors based on these results, aiming to promote gene flow between human‐induced habitat fragments. These findings provide insight into the permeability and affinities of giant panda habitats and offer important reference for the conservation of the giant panda and its habitat.

Climate change is a key driver of bark beetle outbreaks (BBO), influencing their dynamics through complex interactions between climatic anomalies and topographic features. This study examines these dynamics using a Bayesian Generalized Additive Mixed Spatiotemporal Model implemented in R-INLA. We analyze the effects of Climate Moisture Index (CMI) anomalies, Topographic Wetness Index (TWI), Relative Slope Position (RSP), and Slope Aspect on BBO density (BBO-D) across multiple municipalities in Michoacán and Estado de México, Mexico, from 2009 to 2021. The model accounts for spatiotemporal effects and year-specific variations not explained by the explanatory variables. Our findings reveal that seasonal droughts have a delayed yet significant impact on BBO-D, with topographic features modulating these effects. The interaction between CMI anomalies and topographic variables regulates BBO dynamics, where certain conditions amplify outbreak density while others mitigate it. Notably, RSP and TWI influence the effect of CMI anomalies differently. The greatest increase in BBO-D occurs when the CMI anomaly in the coldest and driest month of the third year prior to the outbreak interacts with RSP. Conversely, the interaction between the CMI anomaly in the driest month of the year before the outbreak and TWI is linked to a decline in BBO-D. This suggests that areas with higher water accumulation or relatively high elevations experience greater outbreak density. Additionally, slope aspects at 180° and 270° further amplify BBO-D. These results emphasize the importance of considering both climatic anomalies and topographic conditions in understanding BBO patterns. Our approach provides a methodological framework for predicting future outbreaks under changing climatic conditions, aiding in proactive forest management strategies.

Understanding topography effects on soil properties is vital to modelling landscape hydrology and establishing sustainable on-field management practices. This research focuses on an arable area (117 km2) in Southwestern Ethiopia where agricultural fields and bush cover are the dominant land uses. We postulate that adapting either of the soil data resources, coarse resolution FAO-UNESCO (Food and Agriculture Organization of the United Nations Educational, Scientific and Cultural Organization) soil data or pedo-transfer functions (PTFs) is not reliable to indicate future watershed management directions. The FAO-UNESCO data does not account for scale issues and assigns the same soil property at different landscape gradients. The PTFs, on the other hand, do not account for environmental effects and fail to provide all the required data. In this regard, mapping soil property spatial dynamics can help understand landscape physicochemical processes and corresponding land use changes. For this purpose, soil samples were collected across the watershed following a gridded sampling scheme. In areas with heterogeneous topography, soil is spatially variable as influenced by land use and slope. To understand the spatial variation, this research develops indicators, such as topographic index, soil topographic wetness index, elevation, aspect, and slope. Pearson correlation (r), among others, was used to investigate terrain effects on selected soil properties: organic matter (OM), available water content (AWC), sand content (%), clay content (%), silt content (%), electrical conductivity (EC), moist bulk density (MBD), and saturated hydraulic conductivity (Ksat). The results show that there were statistically significant correlations between elevation-based variables and soil physical properties. Among the variables considered, the ‘r’ value between topographic index and soil attributes (i.e., OM, EC, AWC, sand, clay, silt, and Ksat) were 0.66, 0.5, 0.7, 0.55, 0.62, 0.4, and 0.66, respectively. In conclusion, while understanding topography effects on soil properties is vital, implementing either FAO-UNESCO or PTFs soil data do not provide appropriate information pertaining to scale issues.

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.