Reseeding native species indirectly enhances degraded desert steppe stability by increasing functional diversity in arid areas.

Understanding the dynamics and persistence of biodiversity patterns over short (contemporary) and long (thousands of years) time scales is crucial for predicting ecosystem changes under global climate and land-use changes. A key challenge is integrating currently scattered ecological data to assess complex vegetation dynamics over time. Here, we present VegVault, an interdisciplinary SQLite database that uniquely integrates paleo- and neo-ecological plot-based vegetation data on a global and millennial scale, directly linking them with functional traits, soil, and climate information. VegVault currently comprises data from BIEN, sPlotOpen, TRY, Neotoma, CHELSA, and WoSIS, providing a comprehensive and ready-to-use resource for researchers across various fields to address questions about past and contemporary biodiversity patterns and their abiotic drivers. To further support the usability of the data, VegVault is complemented by the {vaultkeepr} R package, enabling streamlined data access, extraction, and manipulation. This study introduces the structure, content, and diverse applications of VegVault, emphasizing its potential role in advancing ecological research to improve predictions of biodiversity responses to global climate change.

A thorough understanding of vegetation resilience to climate variability is critical for sustaining ecosystem functions and terrestrial carbon sinks. Although the El Niño-Southern Oscillation (ENSO) is a key driver of global extreme weather events and vegetation dynamics, its impacts on vegetation resilience remain unclear. Here we estimate global present-day (1981-2018) and future (2015-2100) vegetation resilience using a lag-1 autocorrelation analysis of global leaf area index (LAI) time series and investigate its teleconnection to ENSO. Our findings reveal that ENSO significantly affects vegetation resilience across 53% of the global vegetated area. Within these regions, 15% are linked primarily to large-scale atmospheric synchrony with ENSO, 51% are mainly shaped by ENSO-driven local climate anomalies, and the remaining 34% are influenced by both processes. Future projections suggest that the area impacted via ENSO-driven climate anomalies may expand by 7-10%, with Eastern Siberia and northern North America newly affected. Our study provides a coherent global assessment of vegetation resilience sensitivity to ENSO, identifies teleconnected hotspots and potential influential pathways, and informs targeted restoration and climate-adaptive ecosystem governance under climate change.

Abstract. Animal herbivory can have large and diverse impacts on vegetation and hence on the state and function of ecosystems. Despite this, quantitative understanding of vegetation responses to consumption of green leaf tissue by herbivores is currently lacking. The large-scale impacts of changes in herbivore abundance on ecosystem function have yet to be investigated. Process-based modelling can help to quantify how animals affect important processes, such as ecosystem carbon cycling. To do so, we linked the dynamic global vegetation model LPJ-GUESS with Madingley, a model of multi-trophic functional diversity. This implementation allows us to simulate feedbacks between the availability of green vegetation biomass, herbivory and the whole trophic chain in response to monthly consumption of leaf biomass. In the coupled model system, we see an overall reduction in ecosystem productivity (NPP −5.2 %), leaf area index (−9.0 %) and carbon mass (−9.7 %), compared to the stand-alone version of LPJ-GUESS, with the highest impact on carbon mass in the boreal ecosystems (−42 %). We observe ecosystem composition to shift from boreal coniferous forests (without animals) to boreal mixed forests (with animals), as well as a general increase in herbaceous vegetation. Indirect effects like an increased light transfer facilitating growth of lower canopy layers are also captured by the model system. Overall, the results of this study underpin the important role of animals in ecosystem functioning and highlight the important contribution of process-based modelling towards a better understanding of complex food web interconnections.

ABSTRACT Dams have a considerable impact on river systems and lead to changes in riparian vegetation. The Normalized Difference Vegetation Index (NDVI) is widely used for researching ground surface processes and analyzing and monitoring vegetation dynamics. To date, few studies have comprehensively quantified the impact of cascade dams on riparian vegetation NDVI in dam‐reservoir‐river systems. We analyzed the spatial distribution of the mean annual maximum NDVI value (NDVImax) of the riparian areas of the middle‐lower reaches of Hanjiang River (MLHan) from 1986 to 2018 using 30‐m resolution Landsat series data. We explored the spatio‐temporal trends of NDVImax using the Mann‐Kendall test and Sen's slope estimator. Additionally, we quantified the individual and combined influences of natural factors and human activities on NDVImax changes using Geodetector. The main findings and conclusions are as follows: (1) the NDVImax in the riparian zone of the MLHan showed an increasing trend, with a rate of 0.005–0.01 per year from 1986 to 2018; (2) water occurrence was the main driving factor of NDVI change in the study area, contributing over 60% to NDVImax variability, followed by land use/land cover with a 49% contribution. The interaction between land use/land cover and water occurrence accounted for an 80% contribution rate; (3) the section from Cihe Town to the Cuijiaying Water Conservancy Project and the Yuekou‐Wuhan Reach showed a decreasing trend in NDVImax from 1986 to 2018 that likely resulted from the expansion of construction land. Water occurrence had the greatest influence on riparian NDVI change in the study area. This study provides a practical reference for investigating the effects of water occurrence on riparian vegetation.

Numerous studies indicate that the Tibet Autonomous Region’s grasslands have experienced widespread greening since remote sensing data became available. While climate warming and moistening can drive this trend, there is growing interest in quantifying the effect of non-climatic factors, including human activities. A widely used method estimates these effects by comparing potential and actual vegetation productivity. This study focuses on Ngari, a region constrained by both temperature and moisture. We constructed a multiple regression model using climate variables to predict NDVI and to achieve a good fit for as many pixels as possible. Residual trends, analyzed via the Kendall Tau method, reflect vegetation dynamics after removing climatic effects—a form of statistical control. Results show that grassland NDVI in Ngari increased overall (2000–2024), with 73% of pixels showing a positive Kendall Tau (among them 34% were significant at p < 0.05). The best-performing model used July–August SPEI, April–July precipitation, and mean temperature. After removing climate effects, pixels with a positive Kendall Tau rose to 74.1% (among them 21% were significant at p < 0.05), indicating that non-climatic factors exerted a net positive influence on Ngari’s grassland trends from 2000 to 2024.

Spatiotemporal dynamics and heterogeneous driving mechanisms of soil wind erosion in the forest-grassland ecotone: Responses to climate change and vegetation dynamics across aridity types.

How effective are green roofs as building carbon sinks? Empirical evidence linking substrate, depth, and vegetation dynamics in the U.S. Great Plains.

Impacts of anthropogenic disturbance gradients on vegetation dynamics in the hyper-arid desert environment

Nonlinear responses of dryland vegetation GPP to climate and human drivers along aridity gradients in China and Mongolia.

The role of climate in shaping vegetation dynamics and carbon dioxide fluxes in global protected forest landscapes.

Carbon sequestration patterns in the Yellow River Basin of China are governed by the vegetation structural dynamics.

Vegetation and climate dynamics in the south-western mediterranean during MIS 37–31 (∼1.25 - ∼1. 06 Ma): Insights from the marine core ODP site 976

Accurate wheat yield estimation at the farm scale is crucial for food security, market strategies, trade planning, and storage decisions. However, predicting crop production using remote sensing at farm scale presents significant challenges. This research aimed to develop a field-scale wheat yield prediction model using multi-temporal vegetation indices derived from Sentinel-2 MSI imagery for the rabi seasons of 2018–19 and 2019–20 from Badsu village in Alwar district, Rajasthan. Vegetation indices derived from cloud-free Sentinel-2 images spanning the crop growth cycle were processed to generate multiple vegetation indices, grouped into greenness, chlorophyll content, and dryness indicators. Spearman’s rank correlation (ρ) assessed relationships between indices and wheat yield across various phenological stages and their combinations. Linear and multiple linear regression (MLR) models were developed using the most significant indices. Findings indicate that Wide Dynamic Range Vegetation Index (WDRVI), Normalized Green-Red Difference Index (NGRDI), and Normalized Difference Water Index-2 (NDWI2), representing greenness, chlorophyll, and water stress, respectively, exhibited strong correlations with yield, except during harvesting and crown root initiation. The best-performing model achieved an RMSE of 0.47 tons/ha and an R² of 0.74, demonstrating the effectiveness of remote sensing indices for precise wheat yield estimation at the field level in diverse agricultural Conditions.

Spatiotemporal complexity of vegetation dynamics in view of optimal control

Spatiotemporal patterns of secondary vegetation dynamics and potential regional drivers in Pará state, Brazilian Amazon

Dramatic biome changes in China through the Cenozoic Era: Modeling the combined effects of climate, CO2 concentration, and topography on long-term vegetation dynamics

Latin American cities face significant challenges arising from high social and environmental inequity, the impacts of climate change, biodiversity loss, and elevated pollution levels. In response to these challenges, strategies have emerged emphasizing the conservation, restoration, and integration of nature in urban and peri-urban areas to fulfil multiple functions that enhance long-term resilience. Recent research highlights diverse trends of urban vegetation in Latin America, showcasing gains, losses, and persisting inequalities. Despite recognizing the importance of planning and public policies, a notable gap exists in understanding how they in fluence urban vegetation distribution and changes. This research examines the most relevant public policies, programmes, and plans related to urban nature in Santiago (Chile), Bogotá (Colombia), and Lima (Peru), and explores how public policies influence urban vegetation dynamics. We identified a positive impact of urban greening instruments in Latin America, although there is still room for improvement. Public policies should outline concrete implementation actions, detailing budget allocation, required personnel, and robust evaluation mechanisms while reinforcing a strong commitment to equity. This ensures that greening efforts are not only tailored to local socio-ecological conditions but also remain sustainable in the long term. The insights gained from this research offer valuable lessons for urban planners and policymakers to develop more effective strategies promoting equitable and sustainable distribution of urban vegetation, ultimately enhancing urban resilience and quality of life globally.

Monitoring vegetation recovery in abandoned mining areas using Sentinel-2A and UAV data: Evidence from the Hengshanli mine.

ABSTRACT The alteration of the hydrological regime caused by damming remains a critical challenge for river systems. Rio Chama, a highly regulated river, has undergone severe alterations in its hydrological regime due to a series of dams that impact sediment transport mechanisms and riparian vegetation dynamics. This study employs remote sensing to assess reach‐scale changes in riparian vegetation and geomorphology and field‐informed 2D hydrodynamic modelling to determine sediment transport processes. We used a random forest classifier within Google Earth engine and high‐resolution National Agriculture Imagery Program imagery (2011–2022) to assess changes in riparian vegetation and channel planform. We evaluated the impact of different flows on sediment transport mechanisms, along with field‐data–informed sediment distribution at the sub‐reach scale. Following a 2009 high‐flow release, channel width remained relatively stable, although planform changes were observed, including shifts in the channel centre line and localised bank erosion, especially in the sinuous sections. Vegetation expanded over time and encroached along bars, linked to reduced overbank flooding and sustained base flow year‐round. Furthermore, results indicated that even smaller flows can lead to fine sediment displacement, while higher flows are necessary to mobilise coarse sediments. This study offers valuable insights for ecological flow recommendations, particularly for Rio Chama, and supports improved restoration strategies and long‐term river management in other dam‐regulated systems across arid and semi‐arid regions.