Future Vegetation Research Articles

Effects of Single‐Axis and Fixed‐Tilt Photovoltaic Array Construction on the Soil Seed Bank Characteristics in Semi‐Arid Grasslands

ABSTRACTWith the rapid global development of photovoltaic power generation, research on its impact on land and ecosystems has become increasingly significant. However, its impact on soil seed bank characteristics has yet to be better assessed. In this study, monitoring plots were established in a semi‐arid grassland undergoing solar energy development. This setup allowed us, for the first time, to investigate how soil seed bank characteristics respond to the construction of two typical photovoltaic array systems: single‐axis and fixed‐tilt systems. This study demonstrated that in both single‐axis and fixed‐tilt systems, the establishment of photovoltaic arrays resulted in a significant increase in soil seed density, with seed counts rising by approximately 47.5% compared with control sites without arrays. The aggregation effect of soil seed density under the photovoltaic array primarily occurred in the 0–10 cm soil layer. The soil seed density under the single‐axis arrays was higher than that under the fixed‐tilt arrays. The construction of photovoltaic arrays resets local soil and directly changes the micro‐environment—including reductions in solar radiation, decreases in average temperature by 0.1°C, and wind speed decreases by 1.5 m/s—which negatively affected the richness and diversity of the soil seed bank, resulting in a 21.1% decrease in species richness and a 10.1% reduction in seed diversity. Furthermore, this study highlights that seed germination in semi‐arid grasslands is under pressure due to environmental changes associated with photovoltaic construction areas. Specifically, soil moisture and organic matter were the key factors affecting the vegetation restoration potential of the entire construction area. We recommend selecting the single‐axis system of photovoltaic components. This selection is crucial, which considers both energy production efficiency and supports the facilitation of future vegetation ecosystem succession. Altogether, this study provides information for future land‐use planning in photovoltaic construction areas and sustainable development of photovoltaic power generation.

New insights into a sensitive life stage: hydraulics of tree seedlings in their first growing season.

The first year in a tree's life is characterized by distinct morphological changes, requiring constant adjustments of the hydraulic system. Despite their importance for the natural regeneration of forests and future vegetation composition, little has been known about the hydraulics of tree seedlings. At different times across the first growing season, we analysed xylem area-specific (Kshoot_Axyl) and leaf area-specific (Kshoot_L) shoot hydraulic conductance, as well as embolism resistance of three temperate conifer trees, two angiosperm trees and one angiosperm shrub, and related findings to cell osmotic parameters and xylem anatomical traits. Over the first 10 wk after germination, Kshoot_Axyl and Kshoot_L sharply decreased, then remained stable until the end of the growing season. Embolism resistance was remarkably low in the youngest stages but, coupled with an increase in cell wall reinforcement, significantly increased towards autumn. Contemporaneously, water potential at turgor loss and osmotic potential at saturation decreased. Independent of lineage, species and growth form, the transition from primary to secondary xylem resulted in a less efficient but increasingly more embolism-resistant hydraulic system, enabling stable water supply under increasing risk for low water potentials.

Spatiotemporal Dynamics and Driving Factors of Vegetation Greenness in Typical Tourist Region: A Case Study of Hainan Island, China

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China.

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

AbstractVegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Study on the Constraint Effect of Vegetation on Ecosystem Services in the Yellow River Basin

Ecosystem services (ESs) serve as the foundation for sustaining human life and development, with vegetation status playing a crucial role in influencing the supply of these services. This study focuses on the Yellow River Basin (YRB), where we quantitatively examined the main ESs indicators from 2010 to 2020. We explored the trends in fractional vegetation cover (FVC) and ESs, as well as the constraint relationship between FVC and total ecosystem services (TES). The findings are as follows. (1) From 2010 to 2020, FVC, landscape aesthetics (LA), soil conservation (SC), food production (FP), and TES in the YRB demonstrated an upward trend, whereas water yield (WY) exhibited a downward trend. (2) A constraint relationship exists between FVC and LA, SC, WY, and TES, with the constraint line taking on a hump-like shape. (3) The threshold value of the constraint line between FVC and LA, SC, WY, and TES are approximately 80%. Below this value, FVC does not impose a constraint effect on LA, SC, WY, and TES, but above 80%, a strong constraint effect emerges, leading to a reduction in LA, SC, WY, and TES. These results offer a valuable data reference for guiding future vegetation restoration and ecological engineering efforts in the region.

Plant functional type and peat properties determine elemental transfer in boreal mire vegetation

Uptake of elements into plants is an integral part of many environmental impact assessments. Typically, the plant uptake is determined using an empirical soil-to-plant transfer factor (CR). The elemental concentrations in plants are expected to vary with plant species and plant functional type (PFT), but also according to soil and element properties. Specifically, the uptake of essential elements is regulated, and likely less related to soil concentrations than the uptake of non-essential elements. In this study, the impact of PFT, species and environmental factors on the CR of mire plants was tested. The plants included in the study were four common boreal peatland species (Andromeda polifolia, Vaccinium oxycoccus, Eriophorum vaginatum and Carex rostrata) sampled from 40 minerogenic mires along an age gradient.The results show that while plant species and PFT (heathers and sedges) are the main determinants of the CR value, also environmental factors, such as peat C:N ratio, are important. Further, concentrations of essential elements in plants were only weakly correlated to peat concentrations, whereas the correlation was stronger for non-essential elements and elements utilized at trace amounts.The results of this study verify that CR values may vary substantially between peatland plant species and PFTs. Further, the results suggest that it is relevant to include effects of PFTs on CR and among-species variation in environmental impact assessments. This is because the PFT may have a large impact on the exposure pathways to humans, which could, for example, be berries or animal feed, and also due to the uncertainties of the composition of the future vegetation communities. Since CR varies systematically with several soil properties, there may be potential for adjusting the CR values for expected environmental changes, and thereby reduce the uncertainties of empirical CR values determined from a broad range of environmental conditions.

Characteristics of deep soil layer water deficit under different artificial vegetation types of the Loess Plateau, China

AbstractSoil water is a crucial factor for the growth of vegetation and sustainable development in water‐limited areas. After large‐scale vegetation restoration on the Chinese Loess Plateau, understanding the relationship between vegetation and deep soil moisture has become a crucial focus in current research. In this study, artificial forest (Pinus tabulaeformis [PT], Robinia pseudoacacia [RP] and Platycladus orientalis [PO]), apple orchard (AO), secondary forest (SF) and farmland (FL) were selected as the research objects, and grassland (GL) as the control, using soil‐drilling techniques. We systematically monitored the soil water content of 0–10 m soil layer over two hydrological years, and explored the effects of different vegetation types on soil water deficiency. The results showed that: (1) The deep soil water various significantly among different vegetation types. Compared with GL, the soil water content in all forest land was generally lower, and this difference became more pronounced in deeper soil layer (>7 m), which indicating the depth of the influence of vegetation on soil water has reached 10 m. (2) The mean soil water deficit size (SWDS) values of PT (0.14), RP (0.17), PO (0.07), AO (0.15), SF (0.10) and FL (0.27) in 0–1 m were all positive, indicating that surface soil water had accumulated during more than half of the sampling periods. In the 2–10 m soil layer, mean SWDS was negative in all vegetation types except in FL, leading to soil desiccation. SWDS was found to fluctuate with soil depth. (3) SWDS was affected by a combination of soil properties and vegetation growth. Our results indicate that the current afforestation model could lead to the deficiency of deep soil water. Therefore, it is imperative to make reasonable vegetation structure according to the available local soil and water resources in future vegetation allocation and management.

Incorporating biotic interactions to better model current and future vegetation of the maritime Antarctic

Maritime Antarctica's harsh abiotic conditions forged simple terrestrial ecosystems, mostly constituted of bryophytes, lichens, and vascular plants. Though biotic interactions are, together with abiotic factors, thought to help shape this ecosystem, influencing species' distribution and, indirectly, mediating their response to climate, the importance of these interactions is still fairly unknown. We modeled current and future abundance patterns of bryophytes, lichens, and vascular plants, accounting for biotic interactions and abiotic drivers, along a climatic gradient in maritime Antarctica. The influence of regional climate and other drivers was modeled using structural equation models, with and without biotic interactions. Models with biotic interactions performed better; the one offering higher ecological support was used to estimate current and future spatial distributions of vegetation. Results suggest that plants are confined to lower elevations, negatively impacting bryophytes and lichens, whereas at higher elevations both climate and other drivers influence bryophytes and lichens. Our findings strongly support the use of biotic interactions to predict the spatial distribution of Antarctic vegetation.

Spatiotemporal analysis of different vegetation indices and relation to meteorological parameters over a tropical urban location and its surroundings

The paper investigates the long-term spatiotemporal characteristics of various satellite-derived vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), as well as Gross Primary Productivity (GPP) and Sun-induced Chlorophyll Fluorescence (SIF) over the Kolkata conurbation and its surrounding areas from 2003 to 2016. Additionally, it analyzes the correlation between these vegetation indices and atmospheric parameters like rainfall, soil moisture (SM), evapotranspiration (ET), and land surface temperature (LST). Monthly variations of these parameters are observed, and inter-annual variability is examined using linear regression techniques. The study also observes the time average spatial correlation between vegetation indices and weather parameters. Moreover, it investigates the time-lag effect (0, 1, 2, and 3 months) using Pearson correlation coefficient analysis between VI and other meteorological parameters. NDVI and EVI exhibit maximum correlation with rainfall, SM, ET, and LST within specific lag periods. NDVI and EVI show a slow response rate to rainfall, and their sensitivity depends on SM and ET. A positive correlation is observed between NDVI and ET, indicating that NDVI increases with vaporized water in the atmosphere. A negative correlation is noted between NDVI and LST in the region studied. The study’s insights are valuable for predicting future vegetation index characteristics based on meteorological parameters in tropical urban areas like Kolkata and its surroundings. This predictive capability can aid in mitigating adverse weather effects on vegetation.

Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China

Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.

Inferring on fluvial resilience from multi-temporal high-resolution topography and geomorphic unit diversity

The resilience of a river corridor represents its ability to withstand and recover from disturbances. Quantifying fluvial resilience in the face of various stressors is essential for integrating ecology and geomorphology in a context of river management. Geomorphic unit diversity analysis emerges as a valuable tool for characterizing and quantifying fluvial resilience to disturbances due to its inherent connection with fluvial dynamics. This paper aims to analyse and quantify the fluvial resilience of a wandering gravel-bed river affected by natural (e.g., floods) and human-induced (e.g., instream gravel mining) stressors. To achieve this goal, we use multi-temporal high-resolution topographic surveys of the Upper River Cinca (South-Central Pyrenees) spanning from 2014 to 2020. By employing the Geomorphic Unit Tool (GUT) on these surveys, we can map geomorphic units over time, quantify their diversity, and study geomorphic adjustments through morphodynamic signatures, altogether allowing inferring fluvial resilience.Our findings reveal that topographic changes (i.e., erosion and sedimentation) correlate with the type of stressor: maintenance works and gravel mining lead to degradation, while floods induce aggradation. Geomorphic Unit Diversity decreases following channel disturbances caused by gravel mining but rebounds after periods primarily stressed by floods, returning to pre-impact levels within six years. Geomorphic adjustments, such as channel incision and mid bar development, reflect erosion and sedimentation processes respectively, with channel maintenance works and floods being the main drivers. Despite the recovery of the geomorphic unit diversity within the reach, the deficit resulting from gravel extraction remains unresolved, perpetuating a degrading trend that poses risks in reducing lateral connectivity and could potentially catalyse future vegetation encroachment in bars and floodplains, causing changes on flood conveyance and hydraulics. Monitoring the river's geomorphic diversity provides crucial insights for effective conservation and management decisions regarding land use, development, and conservation along riverbanks, thereby sustaining or enhancing fluvial system resilience.

Forecasting Future Vegetation Dynamics under SSP/RCP Pathways under Spatially Changing Climate and Human Activities Conditions

Vegetation dynamics result from the interaction between human activities and climate change. Numerous studies have investigated the contributions of human activities and climate change to vegetation cover dynamics using statistical methods. However, these studies have not focused much on the spatially non-stationary effects of human activities on vegetation cover changes and future trends. Taking the Three Gorges Reservoir (TGR) area as the case study area, it was divided into 32 combinations by considering the spatially varying effects of five factors related to human activity and climate change, including gross domestic product (GDP), population, land use change, precipitation, and temperature. Regression in terms of pixels was then performed for each combination at the pixel scale. The result showed that from 2001 to 2020, the annual average normalized digital vegetation index (NDVI) in the TGR area exhibited an upward trend (slope = 0.0051, p < 0.01), with the mean NDVI increasing from 0.53 to 0.64. Compared with the regression with climate variables, the proposed model improved the R2 value from 0.2567 to 0.6484, with the p-value in the t-test reduced from 0.2579 to 0.0056. It indicated that changes in vegetation were dominated by human activities and climate change in 48.77% and 3.19% of the TGR area, respectively, and 43.70% of the vegetation coverage was dominated by both human activities and climate change. This study also predicted the future NDVI according to the shared socioeconomic pathways (SSPs) and representative concentration pathway (RCP) scenarios provided by the Intergovernmental Panel on Climate Change. It suggests that, assuming future regional policies are the same as the historical policies in the TGR, the SSP5–8.5 scenario would have the highest and fastest growth in average NDVI, with the average NDVI increasing from 0.68 to 0.89, because of the large increase in the GDP, lower population in this scenario, and adequate hydrothermal conditions.

Night lights observations significantly improve the explainability of intra-annual vegetation growth globally

Understanding the underlying mechanism of vegetation growth is of great significance to improve our knowledge of how vegetation growth responds to its surrounding environment, thereby benefiting the prediction of future vegetation growth and guiding environmental management. However, human impacts on vegetation growth, especially its intra-annual variability, still represent a knowledge gap. Night Lights (NL) have been demonstrated as an effective indicator to characterize human activities, but little is known about the potential improvement of intra-annual vegetation growth using seasonal NL observations. To address this gap, we investigated and quantified the explainability improvement of intra-annual vegetation growth by establishing a multiple linear regression model for vegetation growth (indicated by Normalized Difference Vegetation Index, NDVI) with human factor (indicated by NL observations here) and three climatic factors, i.e., temperature, water availability, and solar radiation using the Principal Components Regression (PCR) method. Results indicate that NL observations significantly improve our understanding of intra-annual vegetation growth globally. Model explainability, i.e., adjusted R2 metric of the PCR model, was comparatively improved by 54 % on average with a median value of 11 % when taking NL observations into consideration. Such improvement occurred in 82 % of the whole investigation pixels. We found that the improvement of model explanatory power was significant in regions where both NL and NDVI trends were large, except for the case where both of their trends were negative. At the country-level, the improvement of model explanatory power increases as GDP decreases, illustrating a greater improvement in a lower middle-income country than that in a high-income country. Our findings emphasize the importance of considering human activities (indicated by NL here) in vegetation growth, offering novel insights into the explanation of intra-annual vegetation growth.

Quantifying the Impacts of Dry–Wet Combination Events on Vegetation Vulnerability in the Loess Plateau under a Changing Environment

Extreme drought and flood events, as well as their combined events, pose significant challenges to global sustainable socio-economic development and ecological health. However, the impact of dry–wet combination events (DWCEs) on vegetation vulnerability remains to be investigated. The Loess Plateau (LP) was selected as the study area to explore the response time of vegetation to precipitation index changes by optimal correlation coefficient; then, the impact of different DWCEs on vegetation vulnerability under moderate and severe scenarios was analyzed; finally, a vegetation loss probability model was constructed based on the copula function and Bayesian framework, to quantify the vegetation loss probability under DWCEs stress. The results indicate that: (1) normalized difference vegetation index (NDVI) shows an upward trend in spring, summer, and autumn, with the proportion of areas are 90.5%, 86.2%, and 95.4%, respectively, and show an insignificant trend in winter; (2) the response time of vegetation to precipitation index changes tends to be one or two seasons; (3) moderate scenarios have more influence than severe scenarios, dry-to-wet events (DWEs), wet-to-dry events (WDE) and continuous dry events (CDE) in spring-summer have a significant impact on summer vegetation of Ningxia and Shanxi, and WDE and CDE have a higher impact on autumn vegetation. (4) in terms of the probability of vegetation loss, DWE, and CDE cause higher losses to summer vegetation, while WDE and CDE cause higher losses to autumn vegetation. This study quantifies the impact of adjacent seasonal DWCE stress on future vegetation vulnerability.

The Time Lag Effects and Interaction among Climate, Soil Moisture, and Vegetation from In Situ Monitoring Measurements across China

The interaction between soil moisture (SM) and vegetation dynamics has been proven in previous studies. In situ measurements have provided reliable data to investigate and validate the time effect in different zones, which is important in the hydrology and agriculture fields. There were 845 SM in situ monitoring measurements utilized with the correlation between SM and vegetation across various soil depths and climate zones in China. The impact of climate and teleconnection factors on SM and the leaf area index (LAI) are also discussed. The results indicate that SM increases from northwest to southeast in China. The time lag responses of SM to temperature, precipitation, relative humidity, and sunshine duration are 0–3 days, 3–7 days, 1–3 days, and 3–15 days, respectively. The LAI is most strongly correlated with the climate of the current month. When the LAI leads SM, a negative correlation is observed, whereas a positive correlation is observed when SM leads the LAI. This proves that vegetation growth restricts the increase in SM, and soil drying further restricts the growth of vegetation. There was a response time of 2–4 months between the LAI and SM. The effect of vegetation and deeper SM was significant in the arid zone, while they were coupled with shallow SM in the humid zone. Additionally, the El Niño–Southern Oscillation (ENSO) showed a significant positive correlation with SM in 2015–2016 with signals of 9–14 months. The results provide support for balancing the contradiction between future vegetation restoration and water resource scarcity.

Analyzing uncertainty and constraining projections for future vegetation in mid-to-high-latitude Asia

Mid-to-high-latitude Asia (MHA) is one of the regions most impacted by global warming and is highly vulnerable to the effects of climate change on its ecosystems. The future vegetation changes in this region are still uncertain, warranting a comprehensive investigation. In this study, the authors conducted a comparative analysis of leaf area index (LAI) projections by models in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) under high emission scenarios (RCP8.5 and SSP5–8.5, respectively). It was found that the CMIP6 models offer greater consistency with historical observations and demonstrate reduced prediction uncertainty compared to the CMIP5 models. There are strong linear relationships between the historical and future vegetation characteristics among the CMIP6 models, which enables a constrained projection of future vegetation based on historical vegetation observations. The model projections suggest a prospective overall increase in the mean, interannual variability, and seasonal amplitude of LAI in the MHA region in the future period (2061–2100) in comparison to the historical period (1985–2014). These results underscore the imperative need to enhance the understanding of ecosystem responses to climate extremes in this region.摘要亚洲中高纬地区是受全球变暖影响最严重的地区之一, 其生态系统高度受到气候变化的影响. 然而, 该地区未来植被变化仍不确定, 需要进行全面调查. 在这项研究中, 作者比较分析了耦合模式比较计划第五和第六阶段 (CMIP5和CMIP6) 中高排放情景 (分别为RCP8.5和SSP5–8.5) 下的叶面积指数 (LAI) 预测. 分析发现, CMIP6模式的LAI结果与历史观测数据更为一致, 并且相比CMIP5模式表现出更小的预测不确定性. CMIP6模式的历史和未来植被特征之间具有强线性关系, 这使得基于历史植被观测进行未来植被预测成为可能. 预测表明, 未来 (2061–2100年) 与历史时期 (1985–2014年) 相比, 亚洲中高纬度地区LAI的平均值, 年际变率和季节振幅将整体增加. 研究结果强调了提高对该地区生态系统应对气候极端事件的理解的重要性.

Updating knowledge of vegetation belts on a complex oceanic island after 20 years under the effect of climate change

Climate change is causing major changes in terrestrial ecosystems and biomes around the world. This is particularly concerning in oceanic islands, considered reservoirs of biodiversity, even more in those with a significant altitudinal gradient and high complexity in the vegetation they potentially harbour. Here, in Tenerife (Canary Islands), we have evaluated the changes in potential vegetation belts during the last 20 years by comparing them with a previous study. Considering the intimate linkage between vegetation and climate, we used a methodology based on phytosociological knowledge, ordination techniques and geostatistics, using multivariate spatial interpolations of bioclimatic data. This has allowed us to spatially detect the variations experienced by eight vegetation units during the last 20 years and incorporating a set of vulnerability metrics. New bioclimatic and vegetation cartography are provided according to the current scenario studied (1990–2019). Our results indicate that summit vegetation, humid laurel forest and thermo-sclerophyllous woodland are the habitats that have experienced a very high area loss and mismatch index, strong changes, if we consider that we are only comparing a period of 20 years. Simultaneously, the more xeric vegetation belts, the dry laurel forest and the pine forest would have benefited from this new warmer and drier climate, by gaining area and experiencing strong upward movements. These changes have not been spatially uniform, indicating that the elevational gradient studied not explain completely our results, showing the influence of the complex island topography. Effective landscape management should consider current remnants, transition capacity and movement limitations to better understand current and future vegetation responses in a global change context.

Revealing the Hidden Consequences of Increased Soil Moisture Storage in Greening Drylands

Vegetation primarily draws water from soil moisture (SM), with restoration in drylands often reducing SM storage (SMS). However, anomalies have been detected in the Beijing–Tianjin Sand Source Region (BTSSR) of China via the Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE). This study quantified the sources of increased SMS in drylands to elucidate the effects of vegetation restoration on SMS. The results indicated the following: (1) In vegetated drylands, 46.2% experienced a significant increase in SMS while 53.8% remained stable; both were positively correlated with the normalised difference vegetation index (NDVI). (2) The increase in SMS was accompanied by a decrease in groundwater storage (GWS), as indicated by the significant correlation coefficients of −0.710 and −0.569 for SMS and GWS, respectively. Furthermore, GWS served as the primary source of water for vegetation. (3) The results of the redundancy analysis (RDA) indicated that the initial vegetation, the driver of the observed trend of increased SMS and decreased GWS, accounted for 50.3% of the variability in water storage. Therefore, to sustain dryland ecosystems, we recommend that future vegetation restoration projects give due consideration to the water balance while concurrently strengthening the dynamic monitoring of SMS and GWS.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Projected distributions of Mongolian rangeland vegetation under future climate conditions

Abstract Mongolian herder households maintain the health and condition of their livestock by adapting to the characteristics of the local vegetation distribution. Thus, predicting future vegetation changes is important for stable livestock grazing and sustainable rangeland use. We predicted the distributional extent of rangeland vegetation, specifically desert steppe, steppe and meadow steppe communities, for the period 2081–2100, based on vegetation data obtained from a previous study. Rangeland vegetation data collected in Mongolia (43–50° N, 87–119° E) between 2012 and 2016 (278 plots) were classified into community types. Species distribution modeling was conducted using a maximum entropy (MaxEnt) model. Distribution data for desert steppe, steppe and meadow steppe communities were used as objective variables, and bioclimatic data obtained from WorldClim were used as explanatory variables. CMIP6-downscaled future climate projections provided by WorldClim were used for future prediction. The area under the curve values for the desert steppe, steppe and meadow steppe models were 0.850, 0.847 and 0.873, respectively. Suitable habitat was projected to shrink under all scenarios and for all communities with climate change. The extent of reduction in potential suitable areas was greatest for meadow steppe communities. Our results indicate that meadow steppe communities will transition to steppe communities with future climate change.