Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.

Modelling ecological niches across vast distribution ranges in remote, high mountain regions like the Himalayas faces several data limitations, in particular nonavailability of species occurrence data and fine-scale environmental information of sufficiently high quality. Remotely sensed data provide key advantages such as frequent, complete, and long-term observations of land surface parameters with full spatial coverage. The objective of this study is to evaluate modelled climate data as well as remotely sensed data for modelling the ecological niche of Betula utilis in the subalpine and alpine belts of the Himalayan region covering the entire Himalayan arc. Using generalized linear models (GLM), we aim at testing factors controlling the species distribution under current climate conditions. We evaluate the additional predictive capacity of remotely sensed variables, namely remotely sensed topography and vegetation phenology data (phenological traits), as well as the capability to substitute bioclimatic variables from downscaled numerical models by remotely sensed annual land surface temperature parameters. The best performing model utilized bioclimatic variables, topography, and phenological traits, and explained over 69% of variance, while models exclusively based on remotely sensed data reached 65% of explained variance. In summary, models based on bioclimatic variables and topography combined with phenological traits led to a refined prediction of the current niche of B. utilis, whereas models using solely climate data consistently resulted in overpredictions. Our results suggest that remotely sensed phenological traits can be applied beneficially as supplements to improve model accuracy and to refine the prediction of the species niche. We conclude that the combination of remotely sensed land surface temperature parameters is promising, in particular in regions where sufficient fine-scale climate data are not available.

Habitat use by cervids can have large effects on vegetation. Therefore, factors influencing habitat use are pertinent to ecology, nature conservation as well as management of forests and hunting. The aim of this study was to identify factors influencing habitat use by red and roe deer, Cervus elaphus and Capreolus capreolus , in the Lüneburger Heide, Germany. We used faecal pellet counts as an indicator of habitat use and applied generalized linear models to conclude that habitat use by both cervids was largely explained by the availability of food plants. In particular, the occurrence of bilberry Vaccinium myrtillus and deciduous trees such as beeches Fagus sylvatica were of major importance. Nonetheless, red and roe deer showed preferences for different plant species, but for both deer, the presence of large trees and conifers, with exception of young Norway spruce Picea abies providing shelter for roe deer, was less attractive. Furthermore, the proximity of mineral licks was important for both cervids. Relative to roe deer, red deer showed a stronger avoidance of well‐used paths. Surprisingly, landscape features such as distance to settlements and elevated hunting standshad no significant effect. Our study demonstrates similarities and differences in habitat use of two important herbivores of the Northern temperate forests. We propose that managers establish ‘decoy’ sites for deer: areas with a high proportion of ground vegetation and young trees of lower forestry value. This will serve to divert deer from valuable trees and thereby enhance biodiversity. These open stands could be interspersed with dense islands of young coniferous trees to offer shelter and should be distant to frequently used pathways. Mineral licks may help enhance use of these sites. Providing an attractive resource for both species of cervids is the best way to manage large herbivores in economically valuable forests.

The sensitivity and response of climatic treelines in the Himalayas to climate change is still being debated. Regeneration of tree species in the treeline ecotone is considered a sensitivity indicator and thus of great scientific interest. The aim of this study is to detect predictor variables for regeneration densities of the major tree species in central Himalayan treeline ecotones (Abies spectabilis, Betula utilis, Rhododendron campanulatum), analysing five development stages from seedling to mature tree. We applied negative binomial generalized linear models with predictors selected from a wide range of soil, topography, climate and stand characteristic variables. We found considerably varying predictors across the tree species and their stages of development. Soil conditions, topography and climate, as well as competing and facilitating tree species, had high predictive power for population densities. These predictors were clearly species- and development stage-specific. Predictors’ spatial- and development-specific heterogeneity induce a high degree of complexity and diversify any potentially linear response of tree population densities and treeline position to changing climatic conditions.

Precisely detecting and attributing changes in vegetation greenness is crucial for sustainable ecosystem management. The normalized difference vegetation index (NDVI) is highly responsive to changes in vegetation cover and is essential for assessing vegetation dynamics. This study integrates a digital elevation model (DEM) with climate data (temperature, precipitation, evapotranspiration, and solar radiation) and MODIS-NDVI imagery (2000–2019) to investigate NDVI fluctuations and their correlation with climate change in the central Himalaya. Trend analysis of NDVI time-series data examined vegetation response influenced by elevation, aspect, and slope. The results revealed significant spatial and temporal NDVI variations, with an overall increase of 0.01 per decade (p < 0.05), indicating gradual vegetation improvement, though 26.3% of the area (107,138 km²) showed a decreasing trend. NDVI trends increased with elevation, peaking at 2000–2500 m, and then declined up to 4000 m, where they stabilized. While trends varied across slopes, they were independent of the aspect. Spearman correlation analysis revealed elevation-dependent vegetation responses to climate. Below 1000 masl, the NDVI was negatively correlated with temperature and evapotranspiration and positively with precipitation. At higher elevations (>4000 masl), temperature and evapotranspiration positively correlated with the NDVI, suggesting warming supports growth. These findings highlight complex interactions between vegetation, climate, and topography that are crucial for targeted ecosystem management.