Anthropogenic climate change is a major driver of biodiversity loss, causing range shifts, population decline, and extinction. While ecological niche modeling (ENM) has provided insights into climate change impacts, it often assumes uniform climate responses across populations. Ecologic genomics, investigating genomic variation along environmental gradients, reveals intraspecific variation in climate adaptation, suggesting differential responses to climate change. However, this intraspecific variation has been rarely incorporated into ENMs. Mountainous areas, harboring high biodiversity and endemism, are particularly vulnerable due to complex topography and ecological stratification. Species in these areas are often locally adapted to spatially heterogeneous environments. This study integrates ecological genomics and ENM to investigate population-specific responses to future climate change in two bird species in the Sino-Himalayan Mountains: the Green-backed tit (*Parus monticolus*) and Elliot's laughingthrush (*Trochalopteron elliotii*). These species, distributed across different elevations, offer an excellent system to examine intraspecific variation in genotype-climate associations and its influence on climate change vulnerability. The study aims to combine ecological genomics and niche modeling to evaluate population-specific responses to future climate change, considering genomic offset (mismatch in genotype-climate association between current and future climates) and niche suitability change, to identify potential donor populations for evolutionary rescue, and to assess potential rescue routes using a landscape genetic approach.

Previous studies have highlighted the widespread impact of climate change on biodiversity, leading to local extinctions and range shifts. ENMs, predicting species distributions under different climate scenarios, have become valuable tools, but they primarily focus on abiotic and biotic factors, often neglecting intraspecific variation in climate adaptation. Recent ecological genomic studies have shown that different populations within a species can exhibit distinct genotype-climate associations, emphasizing the need for incorporating this variation into vulnerability assessments. However, most ENMs still assume uniform climate responses, neglecting the potential for local adaptation and differential responses to climate change. Mountainous regions, characterized by high biodiversity and complex environmental gradients, present a unique challenge for understanding climate change impacts, as species inhabiting these regions often exhibit high levels of local adaptation and genetic diversity. Previous studies have started to address this gap by integrating ecological genomics into ENMs, showing the potential for improved predictions of climate change vulnerability. This review underscores the importance of considering intraspecific variation in climate change impact assessment and the need for incorporating ecological genomics into ENMs, particularly for mountainous species.

This study combined ecological genomics and niche modeling to evaluate the population-specific responses of two mountainous bird species, *Parus monticolus* and *Trochalopteron elliotii*, to future climate change. A de novo genome assembly was generated for *T. elliotii*, facilitating the analysis of genome-wide resequencing data from multiple individuals across the species' distribution ranges. **1. De novo Genome Assembly and Annotation:** A high-quality reference genome for *T. elliotii* was assembled using 10X Genomics linked-read data. The completeness of the assembly was assessed using BUSCO, and protein-coding genes were annotated using a homologue-based approach. **2. Intraspecific Variation in Genotype-Climate Association:** Genome-wide resequencing data from multiple individuals of both species were generated. GradientForest, a machine-learning approach, was used to identify climatic variables associated with genetic variation. Three complementary approaches (LFMM, RDA, dbRDA) were then used to identify SNPs significantly associated with the top climatic variables. **3. Genomic Offset to Future Climate Change:** Genomic offset, measuring the mismatch in genotype-climate association between current and future climates, was calculated using gradientForest under various climate change scenarios (four climate models, two emission scenarios (RCP4.5 and RCP8.5), two time horizons (2050 and 2070)). A parallel analysis using Generalized Dissimilarity Modelling (GDM) was conducted to validate the results. **4. Ecological Niche Modeling:** Ecological niche modeling (ENM) was performed using an ensemble approach, combining outputs from multiple modeling algorithms (Maxent, Generalized Boosted Model, Generalized Additive Model, and Multivariate Adaptive Regression Splines). Niche suitability change (NSC) was calculated for different climate-tolerant groups under future climate conditions. **5. Combining Genomic Offset and NSC:** A genome-niche index was developed to integrate genomic offset and NSC, providing a comprehensive measure of climate change vulnerability. The Artificial Bee Colony (ABC) algorithm was used to determine the optimal weighting of genomic offset and NSC in the index. **6. Landscape Genetic Analysis:** Landscape genetic analysis was performed to assess landscape connectivity, using Circuitscape to model resistance surfaces based on elevation, slope, habitat suitability, and land cover. Linear mixed-effects models were employed to estimate the influence of these landscape features on gene flow. **7. Demographic Model Tests:** Fastsimcoal was used to test for migration between identified climate-tolerant groups, comparing different demographic models (no gene flow, secondary gene flow, continuous gene flow) using AIC for model selection.

The study revealed significant intraspecific variation in genotype-climate associations and responses to future climate change. Cold-tolerant populations exhibited greater genomic offset but experienced less niche suitability decline compared to warm-tolerant populations. The genome-niche index identified populations in central areas of the Sino-Himalayan Mountains as potential donors for evolutionary rescue due to their minor genome-niche interruption. Landscape genetic analysis suggested potential rescue routes via dispersal corridors, primarily westward toward areas predicted to have expanding suitable niches. Functional enrichment analysis of climate-associated genes revealed enrichment in catalytic and metabolic processes, and some genes previously identified in cold-tolerant vertebrates. Notably, the *CRB1* gene showed a strong association with temperature adaptation across both species. Specifically: * **Genomic Offset:** Populations in western regions (*T. elliotii* in the southern Tibetan zone and *P. monticolus* in the eastern Himalayan zone) showed greater genomic offset than those in eastern and southern regions. * **Niche Suitability Change:** Warm-tolerant populations faced a much greater decrease in niche suitability under future climate scenarios compared to cold-tolerant populations. * **Genome-Niche Index:** Populations in central areas of the Southwest Mountains had the least genome-niche interruption, making them potential donors for evolutionary rescue. * **Landscape Connectivity:** Landscape genetic analysis showed potential for dispersal and evolutionary rescue via westward migration towards expanding suitable niches in the southern Tibetan zone (*T. elliotii*) and the eastern Himalayan zone (*P. monticolus*).

The findings highlight the importance of integrating genomic offset and niche suitability for comprehensive climate change vulnerability assessments. The study demonstrates that cold-tolerant populations, while showing higher genomic offset, are less vulnerable due to less significant niche suitability decline and potential for upslope migration in the heterogeneous mountain environments. Warm-tolerant populations, even with relatively lower genomic offset, face substantial risks due to predicted niche suitability decline and lack of suitable uphill migration options. The identification of potential donor populations and their connectivity offers valuable guidance for targeted conservation efforts. The integration of ecological genomics and ENM improves predictions of species vulnerability to climate change. Future research should investigate the physiological tolerance and functional tests of climate-sensitive genes to enhance understanding of species' adaptive potential. Moreover, exploring interactions between local adaptation, phenotypic plasticity, and interspecific interactions will lead to a more refined understanding of climate change-driven species vulnerability.

This study underscores the critical need to consider intraspecific variation in climate change vulnerability assessments. The integration of genomic offset, niche suitability, and landscape connectivity provides a powerful framework for predicting species responses and guiding effective conservation strategies. The identification of potential donor populations and their connectivity offers crucial information for prioritizing conservation actions. Future research should focus on expanding these analyses to other species and regions, and incorporating further factors influencing evolutionary responses to climate change to refine vulnerability predictions.

The study's scope was limited to two bird species in the Sino-Himalayan Mountains. The sample sizes for some localities were relatively small, although a deep-sequencing strategy was employed to mitigate this issue. While the study considered multiple climate models and emission scenarios, uncertainty remains regarding future climate projections. Landscape connectivity modeling relied on specific assumptions about resistance surfaces, which could impact the accuracy of dispersal predictions. The functional role of identified genes needs further experimental validation.