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Scots pine (Pinus sylvestris L.) is an essential species for biodiversity and ecosystem services in Türkiye, yet it is becoming increasingly vulnerable to climate change, especially in climatically marginal areas such as Central Anatolia. This study used MaxEnt modeling along with CHELSA V2.1 climate projections to evaluate the current and future distribution of Scots pine under three Shared Socioeconomic Pathways (SSP1 2.6, SSP3 7.0, SSP5 8.5) projected for the year 2100. The key climatic factors influencing habitat suitability include precipitation seasonality (Bio15) and temperature seasonality (Bio7). The results show that while 34% of Central Anatolia is currently suitable for Scots pine, habitat suitability could decline by 91% under SSP5 8.5, leaving only 4% of the region viable for the species by 2100. This significant reduction highlights the uncertain future of Scots pine populations in the area. Unlike previous research, this study provides a high-resolution analysis that incorporates fine-scale environmental and topographical variables, emphasizing the importance of mid-altitude refugia as potential climate shelters. Aligning with Sustainable Development Goal 15 (SDG15), this study underscores the need to incorporate climate projections into forest management practices. The findings contribute to a broader understanding of climate-induced range shifts and inform adaptive conservation strategies for other vulnerable tree species in semiarid regions.

Aim To determine the global position of tree line isotherms, compare it with observed local tree limits on islands and mainlands, and disentangle the potential drivers of a difference between tree line and local tree limit. Location Global. Time period 1979–2013. Major taxa studied Trees. Methods We modelled the potential climatic tree line based on monthly temperatures and precipitation for the period 1979–2013. We then compared the potential tree line based on climate to observed tree limits at 26 oceanic islands, 55 continental islands and 382 mainland locations. The differences between potential tree line and observed tree limits was then analysed by regression with the islands’ maximum elevation, age, isolation, and area. Additionally, we estimated growing season temperature niches for 16,041 species known to occur in the vicinity of the studied tree lines, and compared them across mainlands, and islands of continental and oceanic origin. Results Observed local tree limits differ up to 2,066 m from the potential tree line at the mainland on oceanic islands. Climatic effects are responsible for a difference of up to 1,296 m between tree lines of mainland regions and oceanic islands (but only for 756 m for continental islands). On oceanic islands, a remaining difference of up to 829 m correlates with the isolation and the maximum elevation of an island. Floras of oceanic islands are however depauperate with respect to potential tree line species and species show an affinity to higher growing season temperatures. Main conclusions Climate can explain about half of the differences between observed local tree limits and potential tree lines between the mainland and continental and oceanic islands. The remaining difference can be attributed to the higher isolation of oceanic islands, especially in the tropics, and as a consequence, a more depauperate flora and a lack of tree species that are able to grow at the tree line.

Global climate change is a process with dramatic consequences for ecosystems, and changes that may occur in the potential distribution of plant communities especially draw attention. This study aimed to reveal the potential distribution modeling and mapping of the Oriental spruce (Picea orientalis L.), distributed in a limited area, using current and future (year 2100) climate scenarios in Turkey. The maximum entropy method for potential distribution and Chelsa V2.1 technical specification IPSL-CM6A-LR scenarios (SSP126-SSP370-SSP585) were preferred to reveal the effect of climate change. Results for the current were in the “excellent” category with training and test data AUC 0.981 and 0.977, respectively. The variables contributing to the model were the precipitation amount of the driest month, mean diurnal air temperature range, annual precipitation amount, and mean annual air temperature. Variables contributing to the current model were analysed using the SSP126, SSP370, and SSP585 scenarios of the year 2100. It was assessed that the potential distribution for 2100 decreases according to SSP126, was fragmented according to SSP370, and decreased according to the SSP585 scenario. As a result, the authors determined that the high potential distribution is reduced 61% when the current mapping of Oriental spruce is compared with the SSP585 mapping.

The significance of land surface temperature (LST) and near-surface air temperature (T AIR ) extends to various applications, including the exploration of urban heat islands. Understanding urban heat islands is crucial for comprehending the intricate interactions among urbanization, climate dynamics, and human well-being. However, many aspects of these topics remain understudied. In this study, we conducted a comprehensive analysis of LST and T AIR , covering day and night and spanning all four seasons of a full year. We used global datasets and applied non-spatial and spatial analysis techniques in the Amman-Zarqa urban region, a typical arid to semiarid environment. The study had three primary objectives: (1) Assess how different human settlement types influence the variations in LST and T AIR across space and time. (2) Examine the spatial and temporal attributes of the relationships between T AIR and LST. (3) Synthesize insights regarding the spatial and temporal characteristics of urban heat islands in arid to semiarid environments. The findings unveiled that urban centers consistently exhibit the lowest daytime LST and maximum and minimum T AIR , across all seasons when compared to other human settlement types. Nighttime LST displayed more variable patterns. Urban centers act as surface urban cool islands during the day and canopy layer urban cool islands both day and night throughout the seasons. The presence of surface urban heat or cool islands at night is barely noticeable. Daytime and nighttime LST play a significant role in explaining the variability in maximum and minimum T AIR across all seasons, with the relationships exhibiting variations ranging from positive to non-significant to negative, influenced by location and seasonal changes. During the daytime, LST consistently exceeds T AIR across all seasons, whereas this relationship displays greater variability at night. The findings of this study hold significant implications for sustainable urban planning and efforts to combat the effects of urban heat islands.

Invasion risks may be influenced either negatively or positively by climate change, depending on the species. These can be predicted with species distribution models, but projections can be strongly affected by the source of the environmental data (climate data source, Global Circulation Models GCM and Shared Socio-economic Pathways SSP). We modelled the distribution of Phelsuma grandis and P. laticauda, two Malagasy reptiles that are spreading globally. We accounted for drivers of spread and establishment using socio-economic factors (e.g., distance from ports) and two climate data sources, i.e., Climatologies at High Resolution for the Earth’s and Land Surface Areas (CHELSA) and Worldclim. We further quantified the degree of agreement in invasion risk models that utilised CHELSA and Worldclim data for current and future conditions. Most areas identified as highly exposed to invasion risks were consistently identified (e.g. in Caribbean and Pacific Islands). However, projected risks differed locally. We also found notable differences in quantitative invasion risk (3% difference in suitability scores for P. laticauda and up to 14% for P. grandis) under current conditions. Despite both species native distributions overlapping substantially, climate change will drive opposite responses on invasion risks by 2070 (decrease for P. grandis, increase for P. laticauda). Overall, projections of future invasion risks were the most affected by climate data source, followed by SSP. Our results highlight that assessments of current and future invasion risks are sensitive to the climate data source, especially in islands. We stress the need to account for multiple climatologies when assessing invasion risks.

Climate change is reshaping forest ecosystems, presenting urgent and complex challenges that demand attention. In this context, research that quantifies interactions between climate and forests is substantial. However, modelling at a spatial resolution relevant for ecological processes presents a significant challenge, especially given the diverse geographical contexts in which it is applied. In our study, we aimed to assess the effects of applying CHELSA v.2.1 and WorldClim v2.1 data on bioclimatic analysis within the Río Puelo catchment area in northern Patagonia. To achieve this, we inter-compared and evaluated present and future bioclimates, drawing on data from both climate datasets. Our findings underscore substantial consistency between both datasets for temperature variables, confirming the reliability of both for temperature analysis. However, a strong contrast emerges in precipitation predictions, with significant discrepancies highlighted by minimal overlap in bioclimatic classes, particularly in steep and elevated terrains. Thus, while CHELSA and WorldClim provide valuable temperature data for northern Patagonia, their use for precipitation analysis requires careful consideration of their limitations and potential inaccuracies. Nevertheless, our bioclimatic analyses of both datasets under different scenarios reveal a uniform decline in mountain climates currently occupied by N. pumilio, with projections suggesting a sharp decrease in their coverage under future climate scenarios.

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.

This study employed ensemble species distribution models (SDMs) using the “biomod2” package and different General Circulation Models (GCMs) to assess the impacts of climate change on the potential distribution of Pinus cembroides in Mexico. Using presence and pseudo-absence data, along with bioclimatic variables from CHELSA v2.1, future habitat suitability was projected for the near future (2041-2060) and far future (2061-2080) under two CMIP6 scenarios (SSP245 and SSP585). Our results predict that under future climate conditions, P. cembroides will likely undergo substantial range contractions, with losses of approximately 65%-85% of the current suitable habitat and no colonization of novel areas. Temperature-related predictors, particularly Bio8 (mean temperature of the wettest quarter) and Bio9 (mean temperature of the driest quarter) were identified as the primary drivers of the species’ distribution. These results suggest that under warming scenarios, P. cembroides will be confined to high elevation refugia, thereby increasing fragmentation and reducing its adaptive capacity. Overall, our findings provide a critical baseline for adaptive forest management strategies, such as assisted migration and the conservation of high elevation refugia, to mitigate the impacts of climate change on P. cembroides.

Bioclimatic classifications provide critical insights into the relationships between climatic variables and the geographic distribution of organisms. Advances in high-resolution climate data, geobotanical integration, and spatial analysis techniques have improved the delineation of bioclimatic units, enabling more precise characterization of terrestrial ecosystems. This study characterizes the bioclimatic conditions of Jalisco, Mexico, through the identification of bioclimatic units and variants using bioclimatic indices and parameters. High-resolution climate data (1980–2018) from the CHELSA database and GIS-based spatial analysis were employed to delineate bioclimatic patterns and their correlation with climatophyllous potential vegetation. The results identified one macrobioclimate and two bioclimates—Tropical pluviseasonal (56.62%) and Tropical xeric (43.38%)—as well as two bioclimatic variants, six thermotypes, and seven ombrotypes. Notably, 49.84% of the territory exhibits bioclimatic variants, and a total of 42 isobioclimates were associated with 14 types of climatophyllous potential vegetation. These findings provide a foundation for understanding vegetation dynamics and support territorial planning and land management. The integration of remote sensing and bioclimatic analysis enhances the identification of spatial heterogeneity in climate–vegetation relationships, facilitating applications in ecological modeling, drought assessment, and conservation planning. This study contributes to ongoing research on terrestrial ecosystem functioning, aligning with current advancements in remote sensing-based environmental analysis.

Timely and accurate information on forest composition is crucial for ecosystem conservation and management tasks. Information regarding the distribution and extent of forested areas can be derived through the classification of satellite imagery. However, optical data alone are often insufficient to achieve the required accuracy due to the similarity in spectral characteristics among tree species, particularly in mountainous regions. One approach to improving the accuracy of forest classification is the integration of auxiliary environmental data. This paper presents the results of research conducted in the Slyudyanskoye Forestry area in the Irkutsk Region. A dataset comprising 101 variables was collected, including Sentinel-2 bands, vegetation indices, and climatic, soil, and topographic data, as well as forest canopy height. The classification was performed using the Random Forest machine learning method. The results demonstrated that auxiliary environmental data significantly improved the performance of the tree species classification model, with the overall accuracy increasing from 49.59% (using only Sentinel-2 bands) to 80.69% (combining spectral data with auxiliary variables). The most significant improvement in accuracy was achieved through the incorporation of climatic and soil features. The most important variables were the shortwave infrared band B11, forest canopy height, the length of the growing season, and the number of days with snow cover.