Large-scale afforestation on the Loess Plateau, costing hundreds of billions of Chinese yuan, has increased vegetation cover but also depleted soil water, raising concerns about the long-term ecosystem sustainability. While debates continue over suitable afforestation areas and precipitation thresholds, the potential role of alternative stable states, a captivating nonlinear dynamical phenomenon, in afforestation success has been largely overlooked. Here, we combined a systematic field survey (4,875 sites, survey mileage of 80,000 km) with a minimal model to explore potential alternative vegetation states, using tree cover as a state variable along a mean annual precipitation gradient. The results showed a clear signature of alternative states of tree cover across the Loess Plateau: Within the 350 to 500 mm mean annual precipitation range, three vegetation states coexist, which are identified as treeless (cover < 5%), open woodland (5 to 50%), and forest (cover > 50%). For areas with mean annual precipitation between 500 and 700 mm, the ecosystem displays bistability consisting of an open woodland and a forest state. Our minimal model revealed that vegetation-precipitation positive feedbacks expanded the range over which alternative vegetation states are permitted and shifted the associated thresholds. Regime shifts between the alternative vegetation states have a strong impact on carbon storage potential, suggesting that afforestation strategies should prioritize bistable and tristable zones where restoration is feasible. These findings provide a framework for afforestation planning and advance the theory of alternative stable states in dryland forests.

The Colombian Amazon faces persistent tensions between biodiversity conservation and rural livelihoods, while territorially grounded productive alternatives remain limited. This study assesses the feasibility of a cacao-based agroforestry system as a sustainable value-chain strategy in Puerto Nariño (Amazonas) and Puerto Caicedo (Putumayo), Colombia. Using participatory action research and mixed methods (100 semi-structured interviews, participatory mapping, techno-economic scenario modeling, and MICMAC structural analysis), the study integrates local knowledge, productivity projections, and territorial governance assessment. The analysis indicates that cacao can be integrated into Amazonian chagra systems without introducing external species, preserving sociocultural compatibility and ecological continuity. Under empirically calibrated productivity assumptions and nine cost–price scenarios, projected annual revenues range from USD 1200 to 2550 per hectare, with an average net present value of USD 3596 over 30 years. MICMAC results identify community governance and institutional articulation as key enabling conditions shaping value-chain feasibility in both territories. Rather than proposing a universal model, the findings suggest that cacao-based agroforestry may strengthen food security and income diversification when embedded in locally legitimate institutions. These results are prospective and should be further assessed through pilot implementations and participatory monitoring.

UPLC-Q-Exactive MS-based metabolomics integrated with species distribution modeling and AI-driven target prediction: Identifying geographical markers and functional mechanisms of Chaenomeles speciosa fruits.

Understanding how medicinal plant distributions shift in response to climate change is essential for developing forward-looking conservation strategies. Cibotium barometz (L.) J. Sm., a tree fern from the family Dicksoniaceae, is not only ecologically significant but also holds considerable medicinal value. Despite its importance, wild populations of this species have been steadily declining due to ongoing habitat loss and unsustainable harvesting. To address this concern, we constructed a multi-model ensemble framework that integrated nine different algorithms, including Generalized Linear Models, various machine learning approaches, and a MaxEnt model optimized through ENMeval using a regularization multiplier of 2 and a feature class of LQH. Using this modeling framework, we simulated the habitat suitability dynamics of C. barometz under current climate conditions (1970-2000) and two future periods (2050s and 2090s) across four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Our analysis identified water availability and low temperature stress as the primary factors limiting the species' distribution. The suitable range for precipitation during the driest quarter extends from 3.25 to 640.20 mm, with optimal conditions occurring when precipitation reaches at least 96.84 mm. Annual precipitation suitable for the species lies between 74.58 and 4209.60 mm, and the most favorable range falls between 3834.10 and 4209.60 mm. While the minimum temperature of the coldest month can vary from -35.41 to 22.35 °C, optimal survival requires temperatures of 8.79 °C or higher. In addition, the species grows best within an annual temperature range of 16.25 to 27.92 °C, with an optimum around 20.47 °C. Projections based on the multi model ensemble suggest that future climate warming may lead to a southwestward shift in the centroid of suitable habitat for this species. By the 2090s, under the SSP245, SSP370, and SSP585 scenarios, the centroid shifts southwestward by 331.3 km, 335.1 km, and 180.2 km, respectively. Meanwhile, areas with high habitat suitability are expected to retreat toward mid-to-high elevation zones, especially in southeastern Yunnan, southern Guizhou, and western Guangxi. The effects of different emission pathways vary considerably; under the high-emission SSP585 scenario, the reduction in total suitable area is projected to be more severe and habitat fragmentation more extensive compared to the low-emission SSP126 pathway. In contrast, implementing ambitious emissions reduction measures could play a key role in supporting the long-term stability of C. barometz populations. This study clarifies how this species responds to climate change and the spatial strategies it may adopt, providing a scientific basis and spatial references for conserving its germplasm resources, restoring its habitats, and advancing its sustainable use.

Phortica okadai and Phortica variegata are the primary vectors of the zoonotic eyeworm Thelazia callipaeda, which infects humans and various mammals. Climate change and intensified human activities have altered the potential suitable habitats of these vectors, posing a risk of expanded T. callipaeda transmission. This study aims to predict the current potential suitable habitats and future distribution patterns of the two species, providing a scientific basis for vector-borne disease prevention and control. Species occurrence records were compiled from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/) and systematic literature reviews. The MaxEnt model was utilized to identify key environmental determinants influencing vector distribution. Climate data from WorldClim, future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), elevation data, and Human Footprint Index (HFP) were integrated to predict potential suitable habitats and future distributions (2041-2060) across China and Europe. The key environmental drivers for P. okadai are warmest quarter precipitation, HFP, and temperature seasonality, and for P. variegata they are HFP, coldest quarter precipitation, and temperature annual range. Currently, the suitable habitats of P. okadai are concentrated in central, eastern, and northeastern coastal China, with only sporadic low-suitability patches recorded in Europe. P. variegata exhibits a wide distribution across the UK, France, Belgium, and Italy, with nearly the entire Mediterranean coastal belt and its associated offshore islands falling within its suitable range. Under future climate scenarios, the suitable area of P. okadai is projected to expand significantly in Central/Western Europe (Italy, Austria, Switzerland, and western Russia). In contrast, the suitable habitats of P. variegata will shift significantly: The central-southern-eastern European transitional belt will lose almost all suitable habitat across scenarios, while the Mediterranean littoral and its offshore islands remain climatically suitable. The suitable area for P. okadai is projected to increase significantly, whereas that for P. variegata is expected to decline. Temperature and precipitation emerge as primary drivers of these contrasting distribution shifts. These findings underscore the need for enhanced vector surveillance and control strategies for T. callipaeda, particularly regarding the expanding P. okadai populations in Europe.

Climate change plays a critical role in shaping the geographical distribution and ecological dynamics of plant species. Amomum Roxb., a genus with important medicinal and culinary uses, is particularly sensitive to changes in temperature and precipitation. This study compiled occurrence records for 24 Amomum Roxb. species in China to analyze their overall distribution pattern. From these, we selected several key species for detailed ecological niche modeling using DIVA-GIS and MaxEnt to project their suitable habitat shifts under future climate scenarios. A. villosum, A. tsaoko, and A. longiligulare had the widest geographic distributions, primarily in southern China. Modeling results indicate a moderate expansion of suitable habitats for A. longiligulare (+ 12.83%, 17.8 km²) and A. tsaoko (+ 5.43%, 10.1 km²) by the end of the century. In contrast, sharp reductions are projected for A. gagnepainii (− 40.7%, 48.6 km²) and A. kravanh (− 22.27%, 16.7 km²). The most influential bioclimatic factors were the minimum temperature of the coldest month (BIO6) and annual temperature range (BIO7), highlighting the vulnerability of these thermophilic plants to extreme temperature fluctuations. These findings provide critical insights for biodiversity conservation and suggest targeted management strategies for climate-resilient cultivation of Amomum Roxb. resources.

ABSTRACT This study presents the first continent‐wide assessment of long‐term (1974–2023) summer heat stress in Africa using the Universal Thermal Climate Index (UTCI) derived from ERA5‐HEAT reanalysis datasets. Employing a grid‐specific definition of summer, we analysed UTCI trends across spatial (continental to city scale) and temporal (decadal to hourly) dimensions. While annual UTCI anomalies range 0.2°C–1.6°C, substantial intensification emerges at finer scales. The frequency, duration and spatial extent of ‘very strong’ (38°C–46°C) and ‘extreme’ (≥ 46°C) heat stress have increased markedly, particularly, in the Sahel. Heat‐stressed hours (UTCI &gt; 32°C) rose by 2%–25% across most countries, with extreme events doubling in 3–5 and 6–8 days episodes. City‐level analyses reveal escalating risks in large urban centres like Cairo, Lagos and Kano. As climate projections indicate further intensification, the findings underscore the urgent need for targeted heat‐health adaptation and early warning systems to protect vulnerable populations across the continent.

The short-lived progeny of radon (222Rn) and thoron (22⁰Rn) are significant global sources of ionizing radiation exposure and require continuous environmental monitoring due to health risks associated with inhalation. This study examines the spatial distribution of radon and thoron in soil, their mass and surface exhalation rates, and the mineralogical factors that influence beach sands along the coastline of the Chennai megacity in southeast India. A total of twenty-four intertidal soil samples were analyzed using a RAD7 radon-thoron monitor (Durridge Co., USA) and an electrostatic solid-state alpha detector, optimized specifically for measuring thoron. The radon mass exhalation rates ranged from 2 to 12mBqkg-1h-1 (mean: 2.42mBqkg-1h-1), whereas thoron surface exhalation rates varied widely from 162 to 31,623Bqm-2h-1 (mean: 3,688.08Bqm-2h-1). The highest soil-gas radon concentrations, ranging from 4.5 to 12Bq/m3, were recorded in Kokkilamedu, an area rich in riverine placer deposits. The exhalation rates of radon and thoron are inversely proportional to grain size, with finer sediments exhibiting higher release rates than coarser sediments. However, higher heavy mineral content, increased bulk density, and finer grain fractions can also restrict exhalation, indicating a strong mineralogical and textural influence on radionuclide mobility. The sediments contain up to 12.5% heavy minerals, including ilmenite, zircon, and monazite, contributing to gamma radiation levels of up to 7.5 µR/h. Annual effective dose rates range from 0.12 to 0.45mSv/y, remaining within global safety limits. Elevated thoron exhalation in monazite-rich areas highlights the need for regular radiological monitoring, particularly for workers in placer deposits. Overall, these findings provide baseline data on the radiological characteristics and sediment provenance of the Chennai coastal zone, supporting environmental risk management and public health planning.

A 14 500-year multi-proxy reconstruction of climate and environmental change in Eastern Baltics

Understanding how species-area relationships (SARs) vary with elevation as well as how elevational richness patterns vary across spatial scales is critical for biodiversity conservation in montane systems. On Mt Wutai of China, we sampled sets of nested plots and collected moss species along the elevational gradient. We examined how the c-value (richness per unit area) and z-value (rate of richness increase with area) of SARs vary with elevation and how elevational richness patterns vary under different scales. We analyzed the driving factors behind the variation of c- and z-values along the elevational gradient as well as the variation of elevational richness patterns across spatial scales through a machine learning method. Last, we explored how the driving factors of elevational richness patterns vary with spatial scale. We found a positively skewed hump-shaped pattern in c-values along the elevational gradient and a monotonic increasing trend in z-values with rising elevation. NPP and precipitation of the driest month (Bio14) were the most influential predictors for the variation of c- and z-values, respectively. A positively skewed hump-shaped pattern in species richness along the elevational gradient was found at small spatial scales, whereas a decelerating increasing trend with a less distinct mid-elevation peak was found at larger spatial scales. A stronger relationship between elevational richness pattern and environmental variables was detected as sampling scale increased. With increasing spatial scale, the relative importance of the mid-domain effect and Bio14 declined, whereas that of NDVI, NPP, and annual temperature range rose significantly when explaining variations in species richness. The scale-dependent elevational richness patterns of mosses, marked by a fine-scale mid-elevation peak and shifting predictor importance, are driven by their sensitivity to microhabitat, climate, energy, and scale-dependent ecological processes. Given scale-dependent elevational richness patterns, we emphasize the need to establish an integrated "large-scale guiding, small-scale refining" conservation framework.

The clearness index (CI) is a key indicator of atmospheric transparency and solar energy availability. This study examines the temporal and spatial variability of the CI in seven representative cities of Iraq (Basra, Nakheb, Kut, Baghdad, Rutba, Kirkuk, and Shakhan) using POWER/NASA reanalysis monthly data from 2005 to 2024. The CI values were analyzed to examine seasonal and annual behavior, long-term trends, the relationship with elevation, and the spatial analysis. Polynomial regression model and ordinary Kriging technique were applied to better fit temporal annual variations and to make the spatial distribution of CI, respectively. The results revealed a pronounced seasonal cycle, with the highest CIs in June and the lowest in December, and an annual range of ~0.65-0.73 across the study area. Elevation exerts a positive but limited influence: highland and desert plateau regions generally exhibit clearer atmospheric conditions than low-lying urban areas. Long-term analysis indicates that the nationwide annual CI follows a cubic polynomial regression with an overall negative trend across all studied cities. There is evident regional variability: desert cities such as Rutba and Nakheb remain relatively stable, while more urbanized or humid areas, including Baghdad, Basra, and Kirkuk, exhibit stronger declines. Reduced CI lowers available solar irradiance, so policymakers should focus on air quality, dust storm control, and adaptive solar energy strategies to ensure long-term sustainability.

This paper shows the projected climatic changes in the Romanian Carpathians under the SSP370 climate scenario for nine biometeorological indices, at fine spatial resolution, over two horizons: 2041–2070 and 2071–2100. The projections are based on the GFDL-ESM4 model developped by the National Oceanic and Atmospheric Administration, USA. The following indices (computed from daily data) were extracted from the CHELSA database: mean annual air temperature, mean diurnal air temperature range, isothermality, mean daily minimum air temperature of the coldest month, annual range of air temperature, annual precipitation amount, growing season length, accumulated precipitation amount within the growing season, mean temperature of the growing season. The results show tiny increases in mean annual air temperature (compared to the reference period 1981–2010) for both near and distant future. The isothermality (defined as the ratio between the average daily and annual air temperature variation) is also very stable. The annual amount of precipitation shows very small deviations, with negative values in the southwest and positive in the northeast. The growing season length is projected to increase in most areas until the end of the century.

Cryphalus dilutus is an emerging invasive pest of tropical and subtropical regions, with Mangifera indica and Ficus carica being its primary host plants. Larval damage caused by this insect can lead to severe tree wilting, posing a direct threat to agricultural production and ecological security. Native to South Asia, C. dilutus has established introduced populations in the Near East, Mexico, and other areas. In recent years, it has invaded multiple regions, including southern China and southern Italy. Given the widespread global distribution of host plants and the intensification of climate change, their distribution ranges are expected to expand. However, research assessing the potential global geographical distribution of this pest under climate change is lacking. In this study, we used the Random Forest model to predict the potential distribution range of C. dilutus. Under historical climatic conditions between 1970 and 2000, suitable climatic regions for C. dilutus were primarily distributed across southern China, southeastern Brazil, southeastern Mexico, the Congo Basin periphery, and the Iberian Peninsula, with a total area of 12,192.42 × 104 km2. The Temperature Annual Range and Precipitation of Warmest Quarter were identified as key environmental determinants that shaped its distribution. Under the future RCP4.5 climate scenario projected for the 2050s, the total suitable area for C. dilutus is projected to contract. Specifically, high-, medium-, and low-suitability areas are projected to decline by 52.77%, 62.39%, and 24.02%, respectively. While the total area of the very low zones is expected to increase, the total area of the suitable region has been reduced to 11,891.17 ×104 km2. Future climate change is expected to drive the distribution northward to high-altitude areas and inland areas. Model projections indicate a poleward expansion of the fundamental climatic niche, with climatic suitability increasing in high-latitude and high-altitude regions, such as Northern Europe and western North America. Conversely, current core tropical habitats in the Indian subcontinent and the Amazon Basin are projected to face significant habitat degradation due to thermal stress. Agricultural regions previously considered relatively safe due to climatic constraints, such as northern China, the midwestern United States, and Eastern Europe, may face new challenges from pest infestation. These findings underscore the importance of proactive monitoring and implementation of preventive measures. This provides crucial decision support for countries and regions to formulate precise pest control strategies and offers a theoretical basis for early monitoring and prevention of cross-border invasions on a global scale.

Abstract Primula palinuri Petagna is a narrow endemic species confined to Tyrrhenian coastal cliff habitats in southern Italy and threatened by climate change and anthropogenic pressures. We assessed the current, past and future habitat suitability of P. palinuri in Italy using a macroecological framework based on ecological niche modelling and GIS analyses. Furthermore, focusing on the present distribution, we conducted a conservation gap analysis to quantify the proportion of suitable habitat included within protected areas. Our models identified precipitation of the driest and wettest months, annual temperature range and distance from limestone substrates and grazing areas as the main contributors to model performance. The highest suitability values were concentrated around Palinuro (currently the area with the greatest population density) and along the southern Cilento region. Additionally, projections highlighted two geographically isolated areas outside the calibration range—Liguria and Tuscany—as potentially suitable for the occurrence of P. palinuri . Conservation gap analysis revealed that 41% of the predicted suitable habitat lies outside protected areas. Paleoclimatic projetcions indicated a significant expansion of climatically suitable areas during the Last Glacial Maximum, while showed a marked range contraction throughout the Last Interglacial and Mid-Holocene. Future climate projections (2040–2100) consistently predicted a marked reduction in suitable habitat, irrespective of the emission scenario considered. Overall, our results underscore the high vulnerability of P. palinuri to ongoing climatic and land-use changes and emphasise the urgent need for integrated conservation strategies to ensure its long-term persistence.

• Trematode genetic diversity patterns as a tool for biodiversity conservation. • Hotspot of trematode genetic diversity in Mexico and its Protected Natural Areas. • The genetic diversity of parasites as an indicator of biodiversity in Mexico. • The environmental variables can influence the genetic diversity of trematodes. Genetic diversity (GD) is a fundamental component of biodiversity that remains largely overlooked in conservation planning, especially for parasitic taxa. Trematodes are among the most diverse and ecologically important parasitic groups, although their GD across regions remains poorly characterized. Here we analyze the nucleotide diversity (π) and haplotype diversity (Hd) of mitochondrial (COI) and nuclear (28S) genes using sequences available in public datasets to: (i) represent the spatial patterns genetic diversity at the family level of trematodes across Mexican biogeographic provinces and Protected Natural Areas (PNAs); (ii) identify regions with the highest GD (hotspots); and (iii) to explore how environmental factors influence genetic diversity patterns. We identified some GD patterns, as well as GD hotspots in center and southeastern Mexico, particularly in the states of Michoacán, Estado de México, Veracruz, Tabasco, Chiapas, and Oaxaca. Correlation and model selection analysis revealed multiples environmental variables that can influence the GD of trematodes, as temperature seasonality (BIO4), max temperature of warmest month (BIO5), annual temperature range (BIO7), precipitation of the wettest quarter (BIO16), precipitation of warmest quarter (BIO18) and vegetation type. Furthermore, we found that 37 of 67 PNAs in the southeast overlapped with cells mapped with high-GD, suggesting that existing PNAs may preserve GD. However, public databases are still limited, highlight the need to promote more targeted studies that include parasitic taxa in conservation initiatives. This work contributes to the integration of genetic indicators into biodiversity monitoring, in line with the objectives of the Kunming-Montreal Global Biodiversity Framework.

ABSTRACT Groundwater pumping‐induced reductions in streamflow (known as ‘streamflow depletion’) have been documented worldwide, but potential impacts of streamflow depletion on stream temperature are not well understood. Here, we use two types of models to identify potential impacts of pumping on stream temperature across the conterminous United States (CONUS) to determine which aspects of a stream's annual thermograph (thermal signatures) can be used to monitor and manage streamflow depletion impacts on stream temperature. We used long‐term streamflow and stream temperature data from 30 streamgages across CONUS and surrogate models of streamflow depletion to analyse potential stream temperature impacts at each site. We compared two different stream temperature modelling approaches: (i) a process‐based energy balance model and (ii) statistical regression models based on air temperature and stream discharge. We calculated a suite of thermal signatures under depleted and non‐depleted conditions for each stream and found that maximum annual 7‐day temperature and annual temperature range are potentially the most sensitive to streamflow depletion, with potential changes of at least 2°C at &gt; 70% of the sites when using the process‐based model. We also found that the regression‐based models predicted much less sensitivity of stream temperature to streamflow depletion than the process‐based model. This work provides an initial evaluation and sensitivity analysis of the potential impacts of streamflow depletion on stream temperature. We demonstrate that stream temperature may be most sensitive to pumping in streams with a high proportion of flow sourced from relatively cold groundwater inputs, and that regression‐based stream temperature models may underpredict stream temperature changes caused by streamflow depletion.

Understanding how geographic and climatic gradients shape genetic architecture is a central goal of evolutionary ecology. In Taiwan, mammals show varied divergence: low-mobility species such as mole-shrews and Formosan wood mice exhibit strong north-south splits, and surprisingly, similar patterns occur in mobile taxa like Formosan serow and sambar deer. In contrast, other mobile species, including flying squirrels and Reeves's muntjac, show weak or no population structure in prior studies. This recurring north-south divergence across ecologically diverse taxa suggests that shared environmental gradients, beyond historical isolation, drive parallel population structures. If so, species occupying similar habitats may exhibit comparable genetic breaks regardless of life-history traits. Prior mitochondrial studies likely missed fine-scale structure in muntjac; high-resolution SNP data now offer improved resolution. Here, we analyzed genome-wide SNPs from 71 Taiwanese Reeves's muntjac and comparative Chinese samples. We detected deep divergence from Chinese muntjac (~0.24 MYA), and further north-south subdivision within Taiwan (~0.06 MYA). Demographic modeling revealed a complex history involving glacial isolation and asymmetric gene flow, mainly from north to south. Within Taiwan, genetic differentiation was shaped by both geography and climate, especially temperature annual range (Bio7), with niche models showing environmental separation. Selection scans identified PLA2-associated genes, potentially linked to thermal adaptation. This is the first study to demonstrate that both geographic and environmental heterogeneity jointly contribute to mammalian divergence in Taiwan. The repeated north-south split across ecologically diverse species highlights shared climatic and topographic factors driving parallel population structure in Taiwan's montane ecosystems.

Anurans are profoundly affected by the ongoing biodiversity crisis. Understanding the drivers of their population decline is key to guiding management strategies and prioritize conservation efforts. Population trends have recently become a popular indicator of extinction risk, yet comprehensive global-scale assessments are still scarce, particularly those that account for phylogenetic nonindependence. In this study, we assess the ecological and environmental factors associated with population decline in the world's anurans. We conducted a phylogenetic generalized least squares analysis using large-scale datasets of population trend (as indicated by their IUCN status), morphology, geographical distribution, and climate variables across 5246 globally distributed species. Specifically, we tested whether body size (BS), range size, annual mean temperature (AMT), temperature annual range (TAR), climate moisture index (CMI), latitude, and environmental prevalence (i.e., relative availability of climate conditions in the geographical space) affect population trends. A large majority of evaluated species were in decline. Range size and TAR were negatively correlated with decline, whereas latitude was positively correlated. Climatic prevalence was not correlated with decline, although declining species often showed lower prevalence values. The findings underscore the critical state of anuran populations, which may worsen in the future due to synergistic effects with climate change. Therefore, we recommend initiatives, such as the establishment of protected areas with multiple narrowly-distributed species, and the increase of the population trend assessment coverage.

ABSTRACT Among different forms of biodiversity, endemic species exceptionally experience a higher risk of extinction and therefore merit urgent research attention and conservation priority. In India—one of the world's megabiodiverse countries—the availability of biodiversity data is largely insufficient, thereby hampering the national and global conservation efforts. To bridge these knowledge gaps, here we present a novel biodiversity dataset on endemic trees of India. Leveraging this dataset, we ask: what is the extent of diversity in the endemic tree flora of India, how is it distributed, and what are its key climatic and environmental drivers? The dataset documents 737 endemic tree taxa, including 693 species, 9 subspecies and 35 varieties in India, which represent ~20% of the country's total tree flora. We found that the distribution of the endemic tree flora varied significantly across different regions of this continental‐scale country, with southern and eastern states harboring the highest diversity (64%). Similarly, the regions falling under the wet tropical biome harbor the highest proportion of endemic tree species (~65%). Out of the 13 selected drivers, temperature seasonality showed maximum contribution (~33%) in explaining the variation of endemic tree species distribution across the country, followed by temperature annual range, mean diurnal range, forest cover and elevation width. To date, only 222 species (~30%) have been evaluated for threat status, while the majority (70%) still remain unevaluated. Looking ahead, we highlight the scope of our findings in advancing biodiversity synthesis research in this world's most populous country and in guiding national conservation and restoration efforts with wide implications.

Climate change in Iran is significant, as reduced rainfall adversely affects both biological and social systems. This study aims to long-term predict rainfall changes based on social and economic scenarios from the sixth climate change report (Hist_SSP126_SSP245_SSP585) in the Kermanshah synoptic station. Different machine learning models, have been employed to analyze data from three CMIP6 public circulation models. These models are well-established for classification and prediction tasks. The ML-based downscaling models will estimate monthly rainfall for three time periods: 2026–2050, 2051–2075, and 2076–2100. These predictions will be made under three different scenarios: SSP1, SSP2, and SSP5. Historical monthly rainfall data from a Kermanshah station (1990–2014) have been divided for model training and testing. The models were checked and adjusted using MAE, MSE, RMSE, R², and NSE to see how well they performed. Results show no significant changes in the prediction results for SVR and RF models, with the best climate models varying by region. In all scenarios, the CANESM5 model closely matches the Random Forest predictions. Projected declines in annual rainfall range from 31% to 33% across scenarios and periods, with a multi-scenario average of 32% by 2100.