Search Results (325)

Forest ecosystems provide essential ecological functions in the context of accelerating climate change. However, evaluating their conservation values and conditions remains challenging due to conceptual and methodological ambiguities. In particular, ecosystem integrity and ecosystem authenticity are often conflated in vegetation-based assessments, despite representing distinct dimensions of ecosystem condition. This study advances vegetation-based assessments by explicitly decoupling ecosystem integrity from ecosystem authenticity, while integrating spatial completeness, vegetation patterns and quality, and successional–disturbance attributes into a unified operational framework for reserve-level diagnosis and comparison. The resulting indices enable managers to distinguish boundary-driven limitations of landscape integrity from internal vegetation conditions that persist in near-natural states, thus enhancing interpretability for conservation planning in the context of climate change. Using standardized forest resource survey data and spatial analysis, we constructed two composite indices: Forest Ecosystem Integrity (FEI) and Forest Ecosystem Authenticity (FEA). These indices were applied to two adjacent cold-temperate forest nature reserves, Hanma and Huzhong, in the Greater Khingan Mountains of northeastern China, as well as to a merged spatial scenario. The results demonstrate consistently high ecosystem authenticity (>90%) across all study areas, indicating strong naturalness and successional maturity. In contrast, ecosystem integrity remains moderate (63–69%), primarily constrained by the low spatial completeness of conservation units. The spatial integration of the two reserves significantly improved ecosystem integrity without compromising authenticity, highlighting the role of boundary configuration in conservation effectiveness. By operationalizing integrity and authenticity as complementary yet distinct dimensions, this study provides a reproducible framework for evaluating forest ecosystem conditions and offers practical insights for the design of protected area networks and adaptive management in cold-temperate forest regions. Full article

Climate change is rapidly altering the distribution and abundance of species, with significant impacts on regional ecosystems, including reduced ecosystem services and the loss of biodiversity. Accurately predicting changes in the distribution and abundance of taxa under future climate scenarios is, therefore, crucial. In South Korea, beetle data collected via pitfall traps from approximately 300 forest sites between 2007 and 2009 (30 families, 4 genera, and 150 species) were used to forecast changes in their abundance and distribution under climate change scenarios RCP 4.5 and 8.5. This study evaluated the accuracy of those predictions using data from a subsequent survey conducted between 2017 and 2019. We compared species richness, abundance, changes in abundance (i.e., number of individuals), and occurrence (i.e., number of occupied sites) using data from 273 sites that were surveyed in both the initial (2007–2009) and follow-up (2017–2019) periods. All four parameters were found to be significantly influenced by the identifiers. This identifier bias was attributed to the omission of morphologically similar species in the initial survey or the loss of individuals during the preparation process of dry specimens. As a result, increases in abundance and distribution appear to have been affected by identification errors, whereas decreases more closely reflect actual ecological changes. When the comparison between predicted and observed results was restricted to taxa with reduced abundance and distribution, the number of taxa that matched the predictions was significantly higher than that of those that did not. Based on ease of identification, abundance, and sensitivity to climate change, we selected a set of indicator taxa (four families, two genera, and seven species) for climate change monitoring. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

Itaúba (Mezilaurus itauba (Meisn.) Taub. ex Mez) is an Amazonian forest tree whose high-quality timber has driven sustained commercial exploitation, leading to its classification as threatened with extinction. This systematic scoping review synthesizes the current scientific knowledge on M. itauba. A systematic search of the Web of Science, Scopus, and SciELO databases retrieved studies published in English, Portuguese, and Spanish. Sixty-eight articles were analyzed using quantitative and qualitative approaches. Publications were concentrated between 2012 and 2025, largely derived from research conducted in Brazil and disseminated mainly through national journals. Overall, the literature is dominated by studies on wood technological properties, whereas research on the ecology and silviculture of M. itauba remains limited and often methodologically insufficient to support effective conservation actions. Based on the synthesis of identified knowledge gaps, we highlight as research priorities (i) the generation of empirical data on field performance across developmental stages, from nursery based seedling production to establishment and growth under open field and managed forest conditions; (ii) advancement of knowledge on genetic attributes, including structure and adaptive potential, to support conservation strategies and the selection of planting material; and (iii) integration of ecological interactions, ecophysiological responses, and regeneration processes into applied management frameworks capable of informing evidence based public policies. Addressing these priorities is essential to support conservation planning and the sustainable management of M. itauba. Full article

Tropical Andean forests are biodiversity hotspots that have been transformed by anthropogenic activities, making ecosystem regeneration and restoration essential for their recovery. This study evaluated floristic composition, forest structure, and diversity in three land cover types within tropical Andean ecosystems: riparian forest (RF), natural regeneration (NR), and ecological restoration areas (RE). Vegetation was inventoried using standardized plots, recording species composition, diameter, and height. Basal area, size class distribution, and vertical structure were estimated. The Shannon Wiener and Simpson indices were evaluated. RF showed the highest structural complexity and basal area among the evaluated cover types, followed by ER, whereas NR showed the lowest values. NR showed the highest diversity values and a predominance of individuals in lower diameter and height classes, reflecting active recruitment and intermediate successional stages. Segment ER exhibited lower diversity and intermediate structural development, consistent with shorter recovery periods and limitations in restoration design. Overall, the integration of floristic, structural, and diversity attributes indicates distinct successional trajectories, conditioned by land-use history, disturbance intensity, and environmental heterogeneity. These findings highlight the great potential for natural regeneration under reduced anthropogenic pressure and emphasize the need to integrate passive and active restoration strategies to enhance biodiversity and resilience in Andean tropical forests. Full article

Accurately quantifying the sensitivity of alpine vegetation to climate change is a key prerequisite for formulating regional climate change adaptation policies. The sensitivity of the fragile alpine grasslands on the Tibetan Plateau to climate change has received widespread attention. However, the spatiotemporal dynamics and driving mechanisms of this sensitivity are still unclear under continuous warming and wetting. This study, based on MODIS_NDVI and meteorological data from 2000 to 2023, constructed a dynamic Vegetation Sensitivity Index (VSI) framework and integrated Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with Shapley Additive exPlanations (SHAP) attribution analysis to reveal the spatiotemporal evolution characteristics and driving mechanisms of vegetation sensitivity on the Tibetan Plateau. The results show that (1) the VSI of alpine grasslands exhibited a spatial pattern of higher values in the southwest and lower values in the northeast, with an overall upward trend. Specifically, 56.31% of the region showed an increase in the VSI, with the upward trend being more pronounced in the northern plateau. (2) The dominant role of different climate factors varied regionally; vegetation sensitivity to precipitation increased in the northern plateau, and temperature sensitivity decreased in the central plateau, while sensitivity to solar radiation significantly increased in the central plateau. (3) SHAP attribution analysis indicated that elevation was the core factor driving VSI differentiation, showing a higher sensitivity at higher elevations, with lower growth rates. These findings reveal the dynamic evolution of vegetation sensitivity under the warming and wetting climate trend and its elevation-regulated mechanism, providing important scientific insights for regional ecological adaptation management. Full article

Habitat utilization is a critical determinant of animal survival and reproductive success. Clarifying species-specific habitat preferences provides essential insights into ecological requirements and forms the basis for sound conservation planning. The Temminck’s Tragopan (Tragopan temminckii), a medium-sized, sexually dimorphic pheasant endemic to montane forests of central and southern China, is classified as a nationally protected Class II species. Nevertheless, its fine-scale habitat selection during the breeding season remains inadequately documented. In 2024, we conducted a field investigation in the Laojunshan National Nature Reserve, Sichuan Province, to examine microhabitat use during this critical period. Our analysis revealed a significant preference for sites characterized by greater tree and bamboo height, higher canopy and bamboo cover, increased litter coverage, and taller shrub layers. In contrast, the species consistently avoided locations dominated by dense, tall herbaceous vegetation. Principal Component Analysis identified six principal components, collectively explaining 71.78% of the total environmental variance. The first component was primarily associated with bamboo structural attributes, the second with tree-layer structure, and the third with proximity to forest edges and streams. These findings indicate that effective conservation of this pheasant requires targeted forest management practices that preserve this specific suite of habitat characteristics, which are essential for ensuring reproductive success and long-term population viability. Full article

Wildfire regimes are undergoing rapid transformation under anthropogenic climate change, with major implications for biodiversity, carbon cycling, and ecosystem resilience. This systematic review synthesizes findings from 42 studies across global, continental, and regional scales to assess emerging patterns in fire frequency, intensity, and seasonality, and to identify climatic, ecological, and anthropogenic drivers shaping these changes. Across biomes, evidence shows increasingly fire-conducive conditions driven by rising temperatures, vapor-pressure deficit, and intensifying drought, with climate model projections indicating amplification of extreme fire weather this century. Boreal ecosystems show heightened fire danger and carbon-cycle vulnerability; Mediterranean and Iberian regions face extended fire seasons and faster spread rates; tropical forests, particularly the Amazon, are shifting toward more flammable states due to drought–fragmentation interactions; and savannas display divergent moisture- and fuel-limited dynamics influenced by climate and land use. These results highlight the emergence of biome-specific fire–climate–fuel feedback that may push certain ecosystems toward alternative stable states. The review underscores the need for improved attribution frameworks, integration of fire–vegetation–carbon feedback into Earth system models, and development of adaptive, regionally tailored fire-management strategies. Full article

The Three-North Shelterbelt Forest Program (TNSFP) region in northern China, a critical ecological zone, has experienced significant changes in vegetation coverage and water availability under climate change. However, a comprehensive understanding of how vegetation growth responds to both water deficit and surplus remains limited. This study systematically assessed the spatiotemporal dynamics of vegetation responses to atmospheric water constraints (represented by the Standardized Precipitation Evapotranspiration Index (SPEI)) and soil moisture constraints (represented by the Standardized Soil Moisture Index (SSMI)) across the TNSFP region from 2001 to 2022. Our results revealed a compound water constraint pattern: soil moisture deficit dominated vegetation limitation across 46.41–67.88% of the region, particularly in the middle (28–100 cm) and deep (100–289 cm) layers, while atmospheric water surplus also substantially affected 37.35% of the area. From 2001 to 2022, vegetation has shown weakening correlations with atmospheric and shallow-soil moisture, but strengthening coupling with middle- and deep-soil moisture, indicating a growing dependence on deep water resources. Furthermore, the response times of vegetation to water deficit and water surplus have been reduced, indicating that vegetation growth was increasingly restricted by water deficit while being less constrained by water surplus during the period. Attribution analysis identified that air temperature exerted a stronger influence than precipitation on vegetation–water relationships over the study period. This study improved the understanding of vegetation–water interactions under combined climate and land use change, providing critical scientific support for land use-targeted adaptive management in arid and semi-arid regions. Full article

Terrestrial ecosystems in Hunan Province have undergone marked yet spatially heterogeneous vegetation changes under concurrent climate change and intensifying human activities. The aim of this study is to resolve how vegetation responses vary among land-use types by quantifying kernel Normalized Difference Vegetation Index (kNDVI) dynamics during 2000–2023 using precipitation, temperature, and solar radiation, coupled with trend analysis and a partial-derivative-based attribution. Mean kNDVI increased overall at 0.0016 yr⁻¹; vegetation improved over 76.30% of the area, whereas 5.72% of the area experienced degradation. Built-up land exhibited the largest degraded fraction (35.04%). Human activities and temperature emerged as the dominant drivers of kNDVI change, contributing 62.25% and 27.92%, respectively, while precipitation (3.08%) and solar radiation (6.77%) played comparatively minor roles. Spatially, human activities primarily controlled vegetation dynamics in plains and urban clusters (~78% of the area), whereas temperature constrained vegetation in high-elevation mountain ranges. Analysis along the human footprint (HFP) gradient reveals that driver composition remains steady in resilient ecosystems (farmland and forest), despite increasing anthropogenic pressure, whereas fragile ecosystems (grassland and bareland) exhibited pronounced volatility and heightened sensitivity to environmental constraints. These findings provide a quantitative basis for developing sustainable ecological security strategies, incorporating region-specific measures such as adaptive afforestation, sustainable agricultural management, and strict ecological protection, to enhance ecosystem resilience by prioritizing the climate resilience of mountain forests and the stability of fragile grassland systems. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Plant functional traits are key to understanding species performance, community assembly and ecosystem processes. Fruit and seed traits play an important role in early life-cycle processes by influencing seed dispersal, germination, and establishment, ultimately shaping plant regeneration and ecosystem dynamics. While global initiatives such as TRY and Seed Information Database (SID) have assembled extensive trait data, coverage of reproductive traits remains limited, and high-quality images of diaspores are particularly scarce, particularly in subtropical Asia. To address this need, we created an open-source, comprehensive database of fruit and seed traits, accompanied by diaspore images against a high-contrast background. This dataset documents 684 species in 128 families recorded in Hong Kong and provides standardised measurements of morphological attributes (e.g., length, mass, number of seeds per fruit) and dispersal characteristics (e.g., presence of appendages). Our measurements were validated against previously published records of common species in Hong Kong, showing strong consistency with R² = 0.80 (p < 0.001) for fruit dry mass and R² = 0.91 (p < 0.001) for seed dry mass, respectively. This database provides a valuable resource for trait-based ecology, forest dynamics and conservation biology. Additionally, it supports applications in ecological restoration, habitat management, and predicting plant responses to environmental change. This initiative enhances our understanding of trait-based ecology by complementing global initiatives such as TRY and SID and improving the representation of reproductive traits from subtropical Asia, a region that is underrepresented in existing global databases. Full article

The mangrove ecosystems in Muara Angke, Jakarta, serve as a national benchmark for sustainable mangrove management in Indonesia, yet face significant urban pressures threatening their long-term viability. This study evaluates the ecological integrity and governance effectiveness of this critical ecosystem, covering Wildlife Reserve, Nature Park, Protected Forest, and Production Forest areas totaling 327.7 hectares. An exploratory mixed-methods approach was employed over four months (June–September 2025), integrating vegetation diversity assessments through plot sampling, avifauna surveys via point count methods, herpetofauna identification using Visual Encounter Surveys, water quality assessments through systematic literature review, geospatial analysis of mangrove dynamics using Sentinel-2A imagery (2015–2025), and social-governance evaluation using close-ended questionnaires and One Score One Criteria Scoring System. Results revealed moderate to severe water pollution with phosphate and nitrate exceeding standards, moderate vegetation diversity (13 species; Shannon-Wiener H′ = 1.466–1.728), high avifaunal diversity (55 species; H′ = 3.54) confirming significance along the East Asian-Australasian Flyway, and significant sediment accretion (32 hectares) attributed to coastal reclamation. Management evaluation identified critical conservation compliance deficiencies (score 1.43/7). The findings indicate urgent need for integrated interventions including pollution control, ecosystem-based restoration, enhanced monitoring, and cross-sector policy integration to prevent rapid mangrove degradation and ensure sustainability of this ecologically significant urban mangrove ecosystem. Full article

The world faces increasing food, environmental, and human security issues, primarily attributed to an overburdened agricultural sector struggling to keep pace with rising population and demand for food, energy, and fiber. Advances in food production and agriculture, especially with monoculture farming, have continued to meet these demands but at a high price regarding resource depletion and environmental devastation. This is especially severe in developing world areas with rural populations with thin resource margins. Regenerative agriculture has emerged as a solution to provide shielding for food production, ensure environmental protection, and promote social equity while addressing many of these issues. Regenerative agriculture food production aims to restore soils, forests, waterways, and the atmosphere and operate with lower offsite negative environmental and social impacts. This review discusses the fundamental principles and practices of sustainable plant protection for regenerative farming. It focuses on the role of biological and ecological processes, reduces non-renewable inputs, and aims to incorporate traditional ecological knowledge into pest control practices. It offers essential transition strategies, including critical changes from conventional integrated pest management (IPM) to agro-ecological crop protection, focusing on systemic approaches to design agroecosystems. It also reaffirms the importance of a vast diversity of pest control methods that are culturally, mechanistically, physically, and biologically appropriate for regenerative farming practices. Ultimately, the aim is to encourage ecological, economic, and social sustainability for the future of more resilient and controlled agricultural practices. Full article

Coconut palms (Cocos nucifera L.) are a critical economic and ecological resource in Wenchang City, Hainan. Accurate mapping of their spatial distribution is essential for precision agricultural planning and effective pest and disease management. However, in tropical monsoon regions, persistent cloud cover, spectral similarity with other evergreen species, and redundancy among high-dimensional features hinder the performance of optical classification. To address these challenges, we developed a scalable multi-source remote sensing framework on the Google Earth Engine (GEE) with an emphasis on species-oriented feature design rather than generic feature stacking. The framework integrates Sentinel-1 SAR, Sentinel-2 MSI, and SRTM topographic data to construct a 42-dimensional feature set encompassing spectral, polarimetric, textural, and topographic attributes. Using Random Forest (RF) importance ranking and out-of-bag (OOB) error analysis, an optimal 15-feature subset was identified. Four feature combination schemes were designed to assess the contribution of each data source. The fused dataset achieved an overall accuracy (OA) of 92.51% (Kappa = 0.8928), while the RF-OOB optimized subset maintained a comparable OA of 92.83% (Kappa = 0.8975) with a 64% reduction in dimensionality. Canopy Water Index (CWI), Green Chlorophyll Index (GCI), and VV-polarized backscattering coefficient (${\mathsf{\sigma }}_{\mathrm{V}\mathrm{V}}$) were identified as the most discriminative features. Independent UAV validation (0.07 m resolution) in a 50 km² area of Chongxing Town confirmed the model’s robustness (OA = 90.17%, Kappa = 0.8617). This study provides an efficient and robust framework for large-scale monitoring of tropical economic forests such as coconut palms. Full article