Vegetation Response Research Articles

Detecting Vegetation Stress in Mixed Forest Ecosystems Through the Joint Use of Tree‐Water Monitoring and Land Surface Modeling

AbstractRecent European heatwaves have significantly impacted forest ecosystems, leading to increased plant water stress. Advances in land surface models aim to improve the representation of vegetation drought responses by incorporating plant hydraulics into the plant functional type (PFT) classification system. However, reliance on PFTs may inadequately capture the diverse plant hydraulic traits (PHTs), potentially biasing transpiration and vegetation water stress representations. The detection of vegetation drought stress is further complicated by the mixing of different tree species and forest patches. This study uses the Community Land Model version 5.0 to simulate an experimental mixed‐forest catchment with configurations representing standalone, patched mixed, and fully‐mixed forests. Biome‐generic, PFT‐specific, or species‐specific PHTs are employed. Results emphasize the crucial role of accurately representing mixed forests in reproducing observed vegetation water stress and transpiration fluxes for both broadleaf and needleleaf tree species. The dominant vegetation fraction is a key determinant, influencing aggregated vegetation response patterns. Segregation level in PHT parameterizations shapes differences between observed and simulated transpiration fluxes. Simulated root water potential emerges as a potential metric for detecting vegetation stress periods. However, the model's plant hydraulic system has limitations in reproducing the long‐term effects of extreme weather events on needleleaf tree species. These findings highlight the complexity of modeling mixed forests and underscore the need for improved representation of plant diversity in land surface models to enhance the understanding of vegetation water stress under changing climate conditions.

Development and application of a simplified biophysical model to study deltaic and coastal ecosystems

Many coastal regions around the globe experience land loss due to high relative sea level rise rates, declining sediment supply, and a host of other anthropogenic factors. To evaluate possible restoration strategies, we developed a computationally efficient Simplified Biophysical Model (SBM). The SBM includes hydrodynamic, morphodynamic, and marsh inundation components. The hydrodynamic and mineral sediment processes of the SBM are based on open source Delft3D models. The marsh inundation MATLAB module is a simplified vegetation response to salinity and inundation used to estimate annual organic accretion rates. Organic accretion is added annually to the morphodynamic calculations of mineral sediment. The typical run time for the SBM for an area of 4500 km2 is ∼0.8, 2.5, and 4.7 days real time for one, three, and five decade simulations, respectively which is considered a computationally efficient for modeling decadal landscape evolution. The utility of the SBM was demonstrated through an application to assess the performance of a sediment diversion in the Barataria Basin in Louisiana, USA. The model results demonstrate the importance of incorporating the impact of salinity and inundation effects on the resilience of marshes subjected to high rates of relative sea level rise. Further, the sediment reduction analysis confirms the critical impact of the Mississippi River sediment decline on potential land building from sediment diversions where 1.5%, and 3% annual declining rates show a reduction in net land gain due to Mid-Barataria Sediment Diversion operation of 30% and 50 %, respectively. This highlights the importance of considering the Mississippi River sediment supply decline in restoration projects’ planning and specifically, sediment diversions.

Temperature Is Likely an Important Omission in Interpreting Vegetation Optical Depth

AbstractVegetation optical depth (VOD) satellite microwave retrievals provide significant insights into vegetation water content and responses to hydroclimatic changes. While VOD variations are commonly linked to dry biomass and live fuel moisture content (LFMC), the impact of canopy temperature (Tc) remains overlooked in large‐scale studies. Here, we investigated the impact of Tc on L‐band (1.4 GHz) and X‐band (10.7 GHz) VOD at diurnal and seasonal timescales. Synthetic benchmark VOD was created using realistic fields of Tc, LFMC, and biomass in an electromagnetic model. Perturbation experiments revealed that Tc strongly affects diurnal VOD variations at both L‐band and X‐band. Seasonally, while biomass emerges as the largest contributor to VOD variations in 70% (at X‐band) and 90% (at L‐band) of our study region, Tc and LFMC still play substantial roles. The findings stress the importance of refining retrieval algorithms to distinguish Tc, LFMC, and biomass effects for future VOD applications in ecohydrology.

Increased stress from compound drought and heat events on vegetation

Compound drought and heat events (CDHEs), which are frequently occurring compound extreme climate events, have garnered considerable attention because of their detrimental effects on ecosystems. However, the intricacies of the spatial and temporal distributions of different durations of compound events, along with the variability in vegetation responses remain unclear. Here, we delineated the CDHEs based on meteorological observation data and investigated the spatial and temporal characteristics of CDHEs from 1993 to 2020 using the Theil-Sen trend test and Mann–Kendall nonparametric test. Furthermore, we utilized sliding correlation analysis to evaluate the impacts of CDHEs on vegetation among different climatic regions and ecosystems. Our findings indicate significant increasing trends in both the frequency and persistence of CDHEs from 1993 to 2020. The average trend of CDHEs frequency across different duration periods amounted to 13.80 %/decade. The fractional contribution of CDHEs lasting more than three days exhibited a significant increase, with an average trend of 2.00 %/decade. We also observed that vegetation is most significantly affected by compound events lasting 5–9 days. During the study period, the geographical extent of vegetation significantly impacted by CDHEs expanded by 0.89 %, correlation strength increased by 0.02, and lag time decreased by 0.25 months. These insights highlight the growing impact of CDHEs on vegetation under climate change, improving our understanding of vegetation responses to these compound events.

Investigating the temporal lag and accumulation effect of climatic factors on vegetation photosynthetic activity over subtropical China

Monitoring vegetation photosynthesis in China’s subtropical regions using remote sensing is challenging because of the complex ecosystems and climate variability. Previous studies often pay less attention on the influence of multiple climatic factors on the temporal effects (lag and accumulation) of vegetation photosynthesis, thereby underestimating their impact. This study utilizes a dataset comprising Solar-induced chlorophyll fluorescence (SIF) data (GOSIF product), MODIS Land Cover product (MCD12C1), and various climatic variables. Analytical methods including Theil-Sen Median trend analysis, the Mann-Kendall test, partial correlation analysis, and the optimal parameter-based geographical detector (OPGD) model were employed to explore the temporal dynamics of subtropical vegetation SIF responses to climatic factors and to identify their climate drivers in subtropical China. The study findings indicate that (1) vegetation photosynthesis, as indicated by SIF, exhibited an increasing trend in the majority of Chinese subtropical regions, which constitute over 80 % of the study area, with particularly pronounced enhancements in the southern and central western parts of the Chinese subtropics. (2) Soil moisture primarily exhibits lag effects on SIF, particularly in evergreen needleleaf forests, deciduous broadleaf forests, and mixed forests, whereas temperature does not exhibit significant temporal effects. Solar radiation and vapor pressure deficits impact SIF through both lag and accumulation effects. Under the lag and accumulation effects, the proportion of significant correlations between climatic factors and vegetation SIF increases by 36.71 % ∼ 43.8 %, excluding temperature. (3) Temperature is the dominant factor affecting vegetation SIF, particularly in the evergreen needleleaf forest. Interactions between climatic factors have a significantly stronger influence on SIF than individual factors. Notably, the explanatory power of the vapor pressure deficit increases substantially when it interacts with other factors. Studying the lag and accumulation effects of climatic factors on photosynthesis aids in accurately predicting vegetation responses to climate change, thereby improving the accuracy of global carbon cycle models and guiding the development of carbon sequestration management strategies.

Impacts of multiple temporal and spatial scale drought on grassland vegetation dynamics in the Tibetan Plateau region of China, 1982–2015

The Tibetan Plateau is a sensitive area to global climate change, and the special natural environmental conditions have nurtured extremely fragile vegetation and its ecosystems, making it one of the ideal regions to study the response of vegetation to climate change. In this study, we used the drought index (SPEI) and the vegetation index (NDVI) as drought and green indicators, respectively. The spatial and temporal variation patterns of vegetation greenness on the Tibetan Plateau from 1982 to 2015 were analysed, and the response of vegetation greenness to dry and wet changes was investigated. The results showed that the grassland vegetation improved overall (slope = 0.00015, P < 0.05) and degraded locally during 34 years. The NDVI of grassland vegetation showed an increasing trend from 1982 to 2008 (slope = 0.00005, P = 0.86), and a significant decreasing trend from 2009 to 2015 (slope = −0.002, P < 0.05). The degraded areas of grassland NDVI were mainly distributed in wet areas. Meanwhile, the Tibetan Plateau as a whole showed a non-significant trend of increasing drought, with a mitigating trend of drought in dry areas and an increasing trend of drought in wet areas. The probability of vegetation growth loss was higher with increasing drought. The vulnerability of grassland vegetation to drought events was significantly higher in the humid region than in the arid region. Under the current drought intensification, the risk of loss of grassland vegetation to drought in the humid zone will increase. This suggests that we need to pay more attention to the stability of vegetation in the humid region and drought trends.

Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods

Ecological water diversion projects (EWDP) are an effective management tool for restoring oasis ecosystems in arid regions. Given the potential for drier climatic conditions in arid regions in the future, it is essential to develop water diversion strategies that can adapt to climate change and reduce the risk of oasis ecosystem degradation. Here, this study used a Bayesian optimization-based long- and short-term memory (BO-LSTM) model to determine the optimal amount of water diversion needed to maintain healthy growth of oasis vegetation under future climate change scenarios in the Qingtu Oasis, which is a typical downstream oasis of inland rivers restored by EWDP in China. The results showed that the BO-LSTM model effectively captured the response of oasis vegetation to changes in water inundation areas and drought stress with low computational cost and high accuracy. The study revealed that regional vegetation became more vulnerable than previously thought when extreme drought and a drying trend were taken into account. It was found that if the amount of water entering the oasis ranges from 10 to 15 million m3, there will be a decline in the growth of oasis vegetation as indicated by the normalized difference vegetation index (NDVI). Even if current levels of water diversion (20 million m3) are maintained, oasis vegetation may still face growth decline due to meteorological drought. The optimal amount of water diversion was determined to be 25 million m3, resulting in a 21.7% increase in NDVI regardless of drought events. This study represents an innovative approach as it couples EWDP, climate change, and oasis vegetation dynamics based on deep learning models, which unveils divergent responses of oasis vegetation to climate change and EWDP and verifies a non-linear relationship between water diversion amounts and ecological benefits generated.

Elevated CO2 concentrations contribute to a closer relationship between vegetation growth and water availability in the Northern Hemisphere mid-latitudes

The Northern Hemisphere mid-latitudes, with large human populations and terrestrial carbon sinks, have a high demand for and dependence on water resources. Despite the growing interest in vegetation responses to drought under climate change in this region, our understanding of changes in the relationship between vegetation growth and water availability (referred to as Rvw) remains limited. Here, we aim to explore the Rvw and its drivers in the Northern Hemisphere mid-latitudes between 1982 and 2015. We used the satellite-derived normalized difference vegetation index (NDVI) and the fine-resolution Palmer drought severity index (PDSI) as proxies for vegetation growth and water availability, respectively. The trend analysis results showed that changes in NDVI and PDSI were asynchronous over the past three decades. Moreover, we analyzed the spatiotemporal patterns of the correlation coefficient between NDVI and PDSI. The results indicated that the Rvw was getting closer in more areas over the period, but there were differences across ecosystems. Specifically, most croplands and grasslands were primarily constrained by water deficit, which was getting stronger; however, most forests were primarily constrained by water surplus, which was getting weaker. Furthermore, our random forest regression models indicated that the dominant driver of changes in the NDVI-PDSI correlation was atmospheric carbon dioxide (CO2) in more than 45% of grid cells. In addition, the partial correlation analysis results demonstrated that elevated CO2 concentrations not only boosted vegetation growth through the fertilizer effect but also indirectly enhanced water availability by improving water use efficiency. Overall, this study highlights the important role of atmospheric CO2 in mediating the Rvw under climate change, implying a potential link between vegetation greening and drought risk.

Temperature-induced germination pulses above the alpine tree line

ABSTRACT Subject to a long research tradition, the tree line is considered an important biogeographic indicator of climate changes and associated range shifts. Realized tree line positions and the potential tree line isotherm are, however, rarely in equilibrium because trees are unable to track rapid temperature variations. Often ignored in tree line research, this dilemma constrains the suitability of tree line trees for understanding alpine vegetation responses to anthropogenic warming. Here, we present combined dendrochronological and wood anatomical assessments of 1,351 seedlings and saplings from three subalpine forest species—larch (Larix decidua Mill.), pine (Pinus cembra L.), and spruce (Picea abies)—collected between ~2,200 and 2,600 m.a.s.l. in the Swiss Alps. We found evidence for temperature-induced, pulse-like seedling germination, rather than a continuous, long-term upward movement. Though the species spread across overlapping elevational ranges, larch was found at the highest elevations, followed by spruce and pine. Surprisingly, we found a varying age structure, with no sign of decreasing age toward higher elevations. Spring and summer temperatures promoted germination pulses, but postgermination survival was likely facilitated by species-specific plant traits. Our study demonstrates the importance of seedling and sapling data from above the tree line to understand prevailing vegetation dynamics at cold temperature extremes and also suggests future tree line advancement in the Swiss Alps.

Surface soil phytolith assemblages across an altitudinal transect in the Qilian Mountains of Northwestern China, and their implications for palaeoecologic analysis in arid alpine regions

Reconstructing changes in altitudinal vegetation belts is crucial for comprehending vegetation response to past environmental changes in arid alpine regions, however, proxy data are limited. In this study, we report 51 surface soil phytolith samples from various vegetation types across an altitudinal transect in the arid alpine region of the Qilian Mountains, to elucidate the relationships between vegetation types and phytolith assemblages. Our results show that phytolith morphotypes in surface soil samples could be categorized into 16 classes, with Gobbet, Crenate, Rondel, Elongate entire, and Acutebulbosus being the predominant classes. The characteristics of surface soil phytolith assemblages vary across an altitudinal transect, reflecting differences in local vegetation. Discriminant analysis identifies Cubic, Elongate entire, Crenate, Gobbet, and Rondel as discriminant variables that correlated with five vegetation types (desert vegetation, mountain steppe vegetation, mountain coniferous forest vegetation, subalpine shrub meadow vegetation, and alpine meadow vegetation). We establish five discriminant functions based on the relationships between vegetation types and phytolith assemblages, demonstrating a high accuracy of our method for distinguishing between different vegetation types. These discriminant functions are applicable for distinguishing between diverse vegetation types in the mountains of arid inland regions of Asia. They are also effective in identifying coniferous forest vegetation in mid-latitude mountains. Therefore, these discriminant functions hold significant potential for reconstructing changes in altitudinal vegetation belts in mountainous regions and determining the vegetative types recorded by terminal lakes in arid regions. Our study introduces a new perspective on palaeoecologic analysis in arid alpine regions, advancing our understanding of ecosystem dynamics in response to environmental fluctuations.

Water quality and wetland vegetation responses to water level variations in a university stormwater reuse reservoir: Nature-based approaches to campus water sustainability

In response to climate-driven water shortages, Duke University in 2014 constructed a water reuse reservoir and wetland complex (Pond) to capture urban stormwater and recycle water to provide campus cooling and reduce downstream loading of nutrients and sediment into Jordan Lake, a regional water supply. We postulated that even with significant water level changes due to withdrawals, the Pond would function to reduce downstream nutrients and sediment once wetland plants became established in the littoral zone. Throughout the project (2015–2021), baseflow nutrient concentrations downstream decreased, with Unfiltered Total Nitrogen (UTN) falling by 44 % and Unfiltered Total Phosphorus (UTP) by 50 %. Storm mean concentrations decreased by 31 % for UTN, 54 % for UTP, and 72 % for Total Suspended Solids (TSS). The annual reductions in mass fluxes (UTN, UTP, and TSS) were between 58 and 85 % across a range of storm intensities. Regardless of water level, temperature, pH, and oxygen concentrations downstream were not significantly changed. Between 2015 and 2020, a littoral survey of planted and naturally introduced species showed that wetter years resulted in a greater number of species across a gradient of three inundation zones (i.e., moist, wet, and aquatic). Conversely, dryer years resulted in fewer species across overlapping zones. The dominant plants that successfully colonized the Pond are all obligate wetland species despite the Pond's highly variable water depths and periods of inundation. The final plant populations were dominated by invasive native species supporting the self-design theory of plant succession as nearly half of the original planted species died. The reuse Pond design (pond-wetland complex) showed the capability of using stormwater runoff for campus cooling while improving water quality services and providing habitat for wetland plants. Thus, campuses with watershed runoff capture capability should consider a nature-based recycling approach as part of their water sustainability program.

Novel Use of Image Time-Series to Distinguish Dryland Vegetation Responses to Wet and Dry Years

Novel Use of Image Time-Series to Distinguish Dryland Vegetation Responses to Wet and Dry Years

Vegetation growth responses to climate change: A cross-scale analysis of biological memory and time lags using tree ring and satellite data.

Vegetation growth is affected by past growth rates and climate variability. However, the impacts of vegetation growth carryover (VGC; biotic) and lagged climatic effects (LCE; abiotic) on tree stem radial growth may be decoupled from photosynthetic capacity, as higher photosynthesis does not always translate into greater growth. To assess the interaction of tree-species level VGC and LCE with ecosystem-scale photosynthetic processes, we utilized tree-ring width (TRW) data for three tree species: Castanopsis eyrei (CE), Castanea henryi (CH, Chinese chinquapin), and Liquidambar formosana (LF, Chinese sweet gum), along with satellite-based data on canopy greenness (EVI, enhanced vegetation index), leaf area index (LAI), and gross primary productivity (GPP). We used vector autoregressive models, impulse response functions, and forecast error variance decomposition to analyze the duration, intensity, and drivers of VGC and of LCE response to precipitation, temperature, and sunshine duration. The results showed that at the tree-species level, VGC in TRW was strongest in the first year, with an average 77% reduction in response intensity by the fourth year. VGC and LCE exhibited species-specific patterns; compared to CE and CH (diffuse-porous species), LF (ring-porous species) exhibited stronger VGC but weaker LCE. For photosynthetic capacity at the ecosystem scale (EVI, LAI, and GPP), VGC and LCE occurred within 96 days. Our study demonstrates that VGC effects play a dominant role in vegetation function and productivity, and that vegetation responses to previous growth states are decoupled from climatic variability. Additionally, we discovered the possibility for tree-ring growth to be decoupled from canopy condition. Investigating VGC and LCE of multiple indicators of vegetation growth at multiple scales has the potential to improve the accuracy of terrestrial global change models.

Application of a Standardized Impact Monitoring Protocol (SIMP) to assess biological weed control projects in the State of Idaho, USA

In the USA, the lack of systematic post-release assessments of weed biological control projects demonstrating quantitative effects of biological control agents on target weed densities, population biology, and/or vegetation responses remain a concern. While there are numerous quantitative assessments, overall conclusions are limited in scope by the spatial scale covered, duration of study, potentially confounding factors, or weed parameters measured. The United States Department of Agriculture’s Animal Plant Health Inspection Service is the regulatory agency responsible for biological control agent releases. USDA APHIS requires post-release impact monitoring of target weed populations as part of release permits issued for new biological control agents. However, large scale, long-term impact assessments are rarely conducted for a variety of reasons. To address this problem, biocontrol practitioners developed a standardized impact monitoring protocol (SIMP) for the State of Idaho in 2007. The intent was to collect ecological data which are sufficiently robust for quantitative analysis, while minimizing time spent in the field to make data collection appealing to citizen scientists and land managers. SIMP has since been implemented for ten weed biocontrol systems, with more than fifteen years of monitoring data for some systems. SIMP data are collected along permanent transects and include weed density and size parameters, categorical vegetation community and biocontrol agent abundance data. Data are repeated measures, which are well suited for dynamic population modeling and can be coupled with environmental factors (e.g., climate or soil data) to analyze how processes like climate (and climate change) can drive variation in weed biocontrol agent interactions. SIMP data can be combined with detailed weed, nontarget plant and/or biocontrol agent surveys to add analytical data. Finally, SIMP has also been implemented for two weeds for which biological control agents have not yet been introduced to provide weed and community vegetation data prior to releases.

Quantifying nonlinear responses of vegetation to hydro-climatic changes in mountainous Southwest China

Quantifying nonlinear responses of vegetation to hydro-climatic changes in mountainous Southwest China

Enhancing precision in coastal dunes vegetation mapping: ultra-high resolution hierarchical classification at the individual plant level

ABSTRACT The classification of ultra-high-resolution (UHR) imagery, characterized by spatial resolutions exceeding 10 cm, presents opportunities and challenges distinct from lower-resolution counterparts. Particularly, challenges are pronounced in some scenarios, such as mapping plant species in coastal environments, where similar vegetation responses and small plant sizes pose additional difficulties. The present work addressed such issues by developing a UHR vegetation cover classification model at the single plant level using data from uncrewed aerial systems (UASs) equipped with a multispectral optical sensor. The model was tested across the San Rossore Regional Park (Italy), where three pilot areas were defined as training-test-validation sites. The proposed solution consists of a hierarchical two-level-of-detail machine learning model based on object-based image analysis (OBIA) and random forest. This model considers spectral features and indices, elevation, and texture and can classify twelve plant species and two service classes (debris and sand) within the study areas. Train and test were carried out utilizing UAS flight data collected during two specific phenological periods and precise field data derived from in-situ vegetation surveys, which provided 937 herbaceous and shrub samples. The model performance was evaluated based on the error matrix and 50-fold stratified cross-validation method, obtaining an overall accuracy (OA) of 0.76 and a standard deviation of 0.08. Such assessment underscored the crucial role of texture information, in addition to radiometric and elevation. Finally, the model was tested against an unseen dataset, proving its transferability (OA equal to 0.62). Although the discussion highlights some aspects to be further improved and claims for future research, the first version of this hierarchical classification model demonstrated its potential for mapping and monitoring coastal sand dune ecosystems, providing data for understanding and, eventually, modeling ecological and biogeomorphological dynamics.

Vegetation response to large-scale mountain excavation and city construction projects on the Loess Plateau of China

Since 2012, the “Mountain Excavation and City Construction” (MECC) project has been implemented extensively on the Loess Plateau of China, transforming gullies into flat land for urban sprawl by leveling loess hilltops to fill in valleys. However, this unprecedented human activity has caused widespread controversy over its unknown potential ecological impacts. Quantitative assessment of the impacts of the MECC project on the vegetation is key to ecological management and restoration. Taking the largest MECC project area on the Loess Plateau, Yan'an New District (YND), as the study area, this study investigated the spatiotemporal pattern of vegetation dynamics before and after the implementation of the MECC project using a multitemporal normalized difference vegetation index (NDVI) time series from 2009 to 2023 and explored the response of vegetation dynamics to the large-scale MECC project. The results showed that the vegetation dynamics in the YND exhibited significant spatial and temporal heterogeneity due to the MECC project, with the vegetation in the project-affected areas showing rapid damage followed by slow recovery. Vegetation damage occurred only in the project-affected area, and 84 % of these areas began recovery within 10 years, indicating the limited impact of the large-scale MECC project on the regional vegetation. The strong correlation between vegetation dynamics and the MECC project suggested that the destruction and recovery of vegetation in the project-affected areas was mainly under anthropogenic control, which highlights the importance of targeted ecological policies. Specifically, the MECC project induced local anthropogenic damage to the plant population structure during the land creation period, but regeneration and rational allocation of the vegetation were achieved through urbanization, gradually forming a new balanced ecological environment. These findings will contribute to a full understanding of the response of vegetation to such large-scale engineering activities and help local governments adopt projects or policies that facilitate vegetation recovery.

Spatio-temporal dynamics of fairy circles in Namibia are driven by rainfall and soil infiltrability

ContextNamibia’s fairy circles (FCs) form an extraordinary vegetation pattern along the Namib Desert. Recent evidence from multiple fieldwork activities is increasingly supporting the view that FCs result from biomass-water feedbacks and plant self-organization.ObjectivesTo shed light on these biomass-water feedbacks, we focused here on a temporal analysis of the spatial FC patterns with regard to vegetation response after rainfall events.MethodsWe analyzed the distribution of FCs in 10 drone-mapped study plots of the Namib and related their spatial patterns to the soil infiltrability. Additionally, we mapped three plots repeatedly during the rainfall seasons 2020–2023 to assess how the emerging grasses within FCs changed the FC patterns after rainfall.ResultsWe found that the most regular, spatially periodic, FCs occurred in areas with deep aeolian sands where rain water infiltrates very quickly and homogenously across the study plot, which enables the most symmetric competitive interactions between the grasses. After ample rainfall following a drought period, between 58 and 34% of all mature FCs revegetated. These 1092 closing FCs were 73 times more than the 15 new FCs that formed during the same time. The closing FCs occurred in areas where there was locally a higher density of FCs, which act as underground water sources for the surrounding grasses.ConclusionsOur study shows that the dynamic vegetation response to rainfall and soil water is the key driver of the FC patterns. Overall, the research underlines that Namibia’s fairy circles are a self-organized emergent vegetation pattern that is driven by biomass-water feedbacks and the competition of grasses for limiting water resources.

Indirect non-linear effects of landscape patterns on vegetation growth in Kunming City

Urban greening is becoming an important strategy in improving urban ecosystem services and sustainability. Identifying the response of vegetation to urbanization and urban landscape patterns can help in planning for urban greening. Urbanization may lead to both direct and indirect effects on vegetation, and the indirect effects of urbanization on vegetation growth (UIE-VG) have been paid much attention recently in large scale. In this study, we investigated the spatiotemporal evolution of UIE-VG and the effects of landscape patterns on UIE-VG using the boosted regression tree model and remotely sensed data. An increase in average UIE-VG from 4 to 56% was found during urbanization of Kunming, the case study area in southwest China. However, UIE-VG exhibited high variations due to landscape pattern changes at the local scale. Overall, area-related and aggregation-related landscape metrics had greater effects on UIE-VG than the other metrics. The increase and aggregation of built-up land enhanced UIE-VG by 3.1–81.3% while the increase and aggregation of unused land and waterbodies reduced UIE-VG by 0.7–20.6%. Moreover, we found that the large and aggregated vegetation areas may mitigate the negative UIE-VG in low urbanization areas. Our findings have important implications for integrating urban landscape planning into sustainable urban greening strategies.

Understanding woody plant encroachment: A plant functional trait approach

AbstractThe increasing density of woody plants threatens the integrity of grassy ecosystems. It remains unclear if such encroachment can be explained mostly by direct effects of resources on woody plant growth or by indirect effects of disturbances imposing tree recruitment limitation. Here, we investigate whether woody plant functional traits provide a mechanistic understanding of the complex relationships between these resource and disturbance effects. We first assess the role of rainfall, soil fertility, texture, and geomorphology to explain variation in woody plant encroachment (WPE) following livestock grazing and consequent fire suppression across the Serengeti ecosystem. Second, we explore trait‐environment relationships and how these mediate vegetation response to fire suppression. We find that WPE is strongest in areas with high soil fertility, high rainfall, and intermediate catena positions. These conditions also promote woody plant communities characterized by small stature and seed sizes smaller relative to a comparative baseline within the Serengeti ecosystem, alongside high recruit densities (linked to a recruitment‐stature trade‐off). The positioning of species along this “recruitment‐stature axis” predicted woody stem density increase in livestock sites. Structural equation modeling suggested a causal pathway where environmental factors shape the community trait composition, subsequently influencing woody recruit numbers. These numbers, in turn, predicted an area's vulnerability to WPE. Our study underscores the importance of trait‐environment relationships in predicting the impact of human alterations on local vegetation change. Understanding how environmental factors directly (resources) and indirectly (legacy effects and plant traits) determine WPE supports the development of process‐based ecosystem structure and function models.