The ecological environment of mountain-hill-plain transition zones is shaped by terrain constraints, ecosystem resilience, and human pressures. Translating multi-source environmental indicators into decision-relevant information for restoration prioritization and zoning management is essential for regional sustainability. This study developed an ecological vulnerability assessment system for the eastern Dabie Mountains, China, using 15 indicators organized around ecological sensitivity, ecosystem resilience, and ecological pressure. Ecological vulnerability from 2000 to 2020 was evaluated using combined AHP-entropy weighting and a unified Jenks natural breaks classification. A transition matrix, hotspot analysis, and Geodetector were then applied to identify class-transition trajectories, spatial clustering patterns, and dominant drivers. Results show an overall decline in ecological vulnerability, although the improvement was not spatially uniform and rebound risks persisted locally. From 2000 to 2020, the proportion of non-vulnerable and slightly vulnerable areas increased from 26.17% to 42.40%, while highly and extremely vulnerable areas decreased from 40.43% to 25.96%. Overall, 47.44% of the study area shifted to lower vulnerability classes, 41.65% remained stable, and 10.91% deteriorated. Annual precipitation, mean annual temperature, ecosystem type, NDVI, and biodiversity abundance were the main explanatory factors of spatial differentiation, whereas construction land proportion and nighttime light became more influential in the later period. These findings indicate ecological improvement in the eastern Dabie Mountains, but agricultural intensification areas, urban expansion margins, and parts of the river-lake plains remain priorities for ecological restoration, dynamic monitoring, and construction control. This study provides a transferable framework for ecological vulnerability diagnosis and sustainability-oriented zoning management in similar transition zones.

Forest ecosystem water conservation is an important ecohydrological regulating service for sustaining mountain watersheds under climate change. This study assessed water-conservation services in the eastern Dabie Mountains of China by integrating the InVEST Water Yield module under the Budyko framework, a Coupling Coordination Degree (CCD) model, and pixel-scale Theil–Sen, Mann–Kendall, and Ordinary Least Squares analyses. The study covered 1985–2024 through ten benchmark years (1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2022, and 2024). Mean water-conservation capacity across the benchmark-year maps was 362.7 mm. The OLS slope was 1.40 mm/a, while the two-sided MK test was non-significant (Z_MK = 0.54, p = 0.592), indicating a weak positive tendency in the benchmark-year sequence. Year-to-year variation was closely associated with hydroclimatic conditions, with canopy interception, litter storage, soil-water storage, and flow attenuation providing process pathways within the model framework. Water-conservation capacity reached 534.4 mm in the wet 2020 benchmark year, declined to 268.2 mm during the 2022 drought, and recovered to 369.6 mm in 2024. Comparison with area-scaled official bulletin surface-water references for seven overlapping years yielded a mean absolute percentage error of 5.21% and absolute relative errors of 0.00%–17.62%. High-value service areas were concentrated in the central forested massif, whereas low-value and coordination-imbalance areas were more common along agricultural and urbanizing margins. The integrated framework links service intensity, coordination status, and historical tendency to support differentiated watershed management and to identify spatial contrasts together with benchmark-year hydroclimatic responses.

Mountain–hill–plain transition zones combine terrain sensitivity, agricultural land use, and construction pressure and therefore require spatially differentiated management. This study develops a pressure-oriented framework that distinguishes composite ecological vulnerability from soil-erosion pressure and translates their multi-period co-occurrence into management demand. Using multi-source raster data for 2000, 2005, 2010, 2015, and 2020 on a common 30 m reference grid, we constructed an ecological vulnerability index (EVI) from 14 ecological, environmental, and human-activity indicators using a sensitivity–resilience–pressure framework, combined analytic hierarchy process (AHP)–entropy weighting, and unified natural breaks. Soil erosion intensity was estimated with the Revised Universal Soil Loss Equation (RUSLE) using digital elevation model (DEM)-derived flow accumulation, a capped effective slope length, and a fractional vegetation cover (FVC)-based cover-management factor. The EVI and RUSLE dimensions were integrated through a two-dimensional pressure matrix, a Coupled Pressure Index (CPI), and an Integrated Management Demand Index (IMDI). High vulnerability was concentrated in northern, urban-fringe, and selected agricultural areas, whereas moderate-or-above erosion was concentrated on slopes, in gullies, and on hilly farmland; areas subject to both pressures were spatially selective. The largest EVI weights were assigned to mean annual precipitation (0.3841), mean annual temperature (0.1727), ecosystem type (0.0820), biological abundance (0.0810), and built-up land ratio (0.0746). Non-vulnerable and slightly vulnerable areas increased from 40.62% to 64.27%, whereas highly and extremely vulnerable areas decreased from 38.98% to 23.29%. Moderate-or-above soil erosion accounted for 13.56% in 2020. The five-zone comprehensive management classification comprised stable conservation (40.19%), soil and water conservation priority (13.73%), ecological vulnerability regulation (27.94%), integrated management priority (1.48%), and transition management (16.66%). By linking ecological diagnosis, erosion-pressure identification, persistence analysis, and county-level zoning, the framework provides an interpretable spatial basis for prioritizing ecological conservation, soil and water conservation, and land-use management in mountain–hill–plain transition regions.