Entropy-based early detection of critical transitions in spatial vegetation fields.

In semiarid regions, vegetated ecosystems can display abrupt and unexpected changes, i.e., transitions to different states, due to drifting or time-varying parameters, with severe consequences for the ecosystem and the communities depending on it. Despite intensive research, the early identification of an approaching critical point from observations is still an open challenge. Many data analysis techniques have been proposed, but their performance depends on the system and on the characteristics of the observed data (the resolution, the level of noise, the existence of unobserved variables, etc.). Here, we propose an entropy-based approach to identify an upcoming transition in spatiotemporal data. We apply this approach to observational vegetation data and simulations from two models of vegetation dynamics to infer the arrival of an abrupt shift to an arid state. We show that the permutation entropy (PE) computed from the probabilities of two-dimensional ordinal patterns may provide an early warning indicator of an approaching tipping point, as it may display a maximum (or minimum) before decreasing (or increasing) as the transition approaches. Like other spatial early warning indicators, the spatial permutation entropy does not need a time series of the system dynamics, and it is suited for spatially extended systems evolving on long time scales, like vegetation plots. We quantify its performance and show that, depending on the system and data, the performance can be better, similar or worse than the spatial correlation. Hence, we propose the spatial PE as an additional indicator to try to anticipate regime shifts in vegetated ecosystems.

Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India.

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

1.36 million years of Mediterranean forest refugium dynamics in response to glacial-interglacial cycle strength.

The sediment record from Lake Ohrid (Southwestern Balkans) represents the longest continuous lake archive in Europe, extending back to 1.36 Ma. We reconstruct the vegetation history based on pollen analysis of the DEEP core to reveal changes in vegetation cover and forest diversity during glacial-interglacial (G-IG) cycles and early basin development. The earliest lake phase saw a significantly different composition rich in relict tree taxa and few herbs. Subsequent establishment of a permanent steppic herb association around 1.2 Ma implies a threshold response to changes in moisture availability and temperature and gradual adjustment of the basin morphology. A change in the character of G-IG cycles during the Early-Middle Pleistocene Transition is reflected in the record by reorganization of the vegetation from obliquity- to eccentricity-paced cycles. Based on a quantitative analysis of tree taxa richness, the first large-scale decline in tree diversity occurred around 0.94 Ma. Subsequent variations in tree richness were largely driven by the amplitude and duration of G-IG cycles. Significant tree richness declines occurred in periods with abundant dry herb associations, pointing to aridity affecting tree population survival. Assessment of long-term legacy effects between global climate and regional vegetation change reveals a significant influence of cool interglacial conditions on subsequent glacial vegetation composition and diversity. This effect is contrary to observations at high latitudes, where glacial intensity is known to control subsequent interglacial vegetation, and the evidence demonstrates that the Lake Ohrid catchment functioned as a refugium for both thermophilous and temperate tree species.

Investigating the long-term response of plateau vegetation productivity to extreme climate: insights from a case study in Qinghai Province, China.

Over the past three decades, there has been a significant global climate change characterized by an increase in the intensity and frequency of extreme climate events. The vegetation status in Qinghai Province has undergone substantial changes, which are more pronounced than other regions in the Qinghai-Tibet Plateau. However, a clear understanding of the response characteristics of plateau vegetation to extreme climate events is currently lacking. In this study, we investigated the response of net primary productivity (NPP) to different forms of extreme climate events across regions characterized by varying levels of aridity and elevation gradients. Specifically, we observed a significant increase in NPP in relatively arid regions. Our findings indicate that, in relatively arid regions, single episodes of high-intensity precipitation have a pronounced positive effect (higher correlation) on NPP. Furthermore, in high-elevation regions (4000-6000 m), both the intensity and frequency of precipitation events are crucial factors for the increase in regional NPP. However, continuous precipitation can have significant negative impacts on certain areas within relatively wet regions. Regarding temperature, a reduction in the number of frost days within a year has been shown to lead to a significant increase in NPP in arid regions. This reduction allows vegetation growth rate to increase in regions where it was limited by low temperatures. Vegetation conditions in drought-poor regions are expected to continue to improve as extreme precipitation intensifies and extreme low-temperature events decrease.

Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin.

Solar-induced chlorophyll fluorescence (SIF) has been used since its discovery to characterize vegetation photosynthesis and is an effective tool for monitoring vegetation dynamics. Its response to meteorological drought enhances our comprehension of the ecological consequences and adaptive mechanisms of plants facing water scarcity, informing more efficient resource management and efforts in mitigating climate change. This study investigates the spatial and temporal patterns of SIF and examines how vegetation SIF in the Yellow River Basin (YRB) responds to meteorological drought. The findings reveal a gradual southeast-to-northwest decline in SIF across the Yellow River Basin, with an overall increase-from 0.1083 W m-2µm-1sr-1 in 2001 to 0.1468 W m-2µm-1sr-1 in 2019. Approximately 96% of the YRB manifests an upward SIF trend, with 75% of these areas reaching statistical significance. The Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 4 months (The SPEI-4), based on the Liang-Kleeman information flow method, is identified as the most suitable drought index, adeptly characterizing the causal relationship influencing SIF variations. As drought intensified, the SPEI-4 index markedly deviated from the baseline, resulting in a decrease in SIF values to their lowest value; subsequently, as drought lessened, it gravitated towards the baseline, and SIF values began to gradually increase, eventually recovering to near their annual maximum. The key finding is that the variability of SIF with SPEI is relatively pronounced in the early growing season, with forests demonstrating superior resilience compared to grasslands and croplands. The responsiveness of vegetation SIF to SPEI can facilitate the establishment of effective drought early warning systems and promote the rational planning of water resources, thereby mitigating the impacts of climate change.

Plant fire-adaptive traits mediate long-term fire recurrence impact on the potential supply capacity of ecosystem services and their resilience.

Fire-induced changes in vegetation composition due to fire-regime intensification are leading to alterations in ecosystem services that might threaten their future sustainability. Fire recurrence, in particular, could be a key driver shaping ecosystem service resilience in fire-prone ecosystems. This study evaluates the impact of fire recurrence, over twenty-four years, on the potential supply capacity of ten regulating, provisioning, and cultural services selected as critical services by stakeholders and experts. We assessed fire effects in four fire-prone landscapes dominated by species with different functional-traits response to fire (i.e., obligate seeder vs resprouter species). Trends in the potential supply capacity linked to fire recurrence were estimated by applying a supervised classification of Land Use and Land Cover (LULC) classes performed using Landsat imagery, associated to an ecosystem service capacity matrix adapted to the local socio-ecological context. In landscapes dominated by seeders, fire recurrence broke off the potential supply capacity of services traditionally associated to mature forest cover (i.e., the predicted probability of a decrease in the potential supply capacity of climate regulation, timber, wood fuel, mushroom production, tourism, landscape aesthetic, and cultural heritage occurred with high fire recurrence). In landscapes dominated by resprouter species, the effect of fire recurrence was partially buffered in the short-term after fire and no substantial differences in trends of change were found (i.e., equal predicted probability in the potential supply capacity of ecosystem services regardless of fire recurrence). We detected two new opportunities for ecosystems service supply associated to fire recurrence: livestock and honey production, especially in sites dominated by seeders. These findings provide valuable information aiming at recovering post-fire ecosystem service potential supply to partially counterbalance the loss in the socio-ecological system. When the main post-fire restoration goal is preserving ecosystem service resilience in fire-prone ecosystems, establishing management strategies focused on promoting resprouter species could aid mitigating the fire-driven loss of their supply capacity.

Quantifying climate variability and regional anthropogenic influence on vegetation dynamics in northwest India.

To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (-0.07 °C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 °C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R2 = 0.92), although a negative relationship is apparent between LST and vegetation (R2 = -0.83). Additionally, increased LST in the low-elevation parts of the study area impacted PET (R2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.

Pattern Formation in Mesic Savannas.

We analyze a spatially extended version of a well-known model of forest-savanna dynamics, which presents as a system of nonlinear partial integro-differential equations, and study necessary conditions for pattern-forming bifurcations. Homogeneous solutions dominate the dynamics of the standard forest-savanna model, regardless of the length scales of the various spatial processes considered. However, several different pattern-forming scenarios are possible upon including spatial resource limitation, such as competition for water, soil nutrients, or herbivory effects. Using numerical simulations and continuation, we study the nature of the resulting patterns as a function of system parameters and length scales, uncovering subcritical pattern-forming bifurcations and observing significant regions of multistability for realistic parameter regimes. Finally, we discuss our results in the context of extant savanna-forest modeling efforts and highlight ongoing challenges in building a unifying mathematical model for savannas across different rainfall levels.

Should time-lag and time-accumulation effects of climate be considered in attribution of vegetation dynamics? Case study of China's temperate grassland region.

Although the time-lag and time-accumulation effects (TLTAEs) of climatic factors on vegetation growth have been investigated extensively, the uncertainties caused by disregarding TLTAEs in the attribution analysis of long-term changes in vegetation remain unclear. This hinders our understanding of the associated changes in ecosystems and the effects of climate change. In this study, using multiple methods, we evaluate the biases of attribution analyses of vegetation dynamics caused by the non-consideration of TLTAEs in the temperate grassland region (TGR) of China from 2000 to 2019. Based on the datasets of the normalized difference vegetation index (NDVI), temperature (TMP), precipitation (PRE), and solar radiation (SR), the temporal reaction patterns of vegetation are analyzed, and the relationships among these variables under two scenarios (considering and disregarding TLTAEs) are compared. The results indicate that most areas of the TGR show a greening trend. A time-lag or time-accumulation effect of the three climatic variables is observed in most areas with significant spatial differences. The lagged times of the vegetation response to PRE are particularly prominent, with an average of 2.12 months in the TGR. When the TLTAE is considered, the areas where changes in the NDVI are affected by climatic factors expanded significantly, whereas the explanatory power of climate change on NDVI change increased by an average of 9.3% in the TGR; these improvements are more prominent in relatively arid areas. This study highlights the importance of including TLTAEs in the attribution of vegetation dynamics and the assessment of climatic effects on ecosystems.

Effects of drought and climate factors on vegetation dynamics in Central Asia from 1982 to 2020.

Ecological security and ecosystem stability in Central Asia depend heavily on the local vegetation. Vegetation dynamics and the response and hysteresis relationships to climate factors and drought on multiple scales over long time series in the region still need to be further explored. Using the net primary productivity (NPP) values as the vegetation change index of interest, in this study, we analyzed vegetation dynamics in Central Asia from 1982 to 2020 and assessed the responses and time lags of vegetation to climate factors and drought. The results showed that NPP gradually decreased from north to south and from east to west. Vegetation was distributed along both sides of the mountains. The temperatures rose from northeast to southwest, while precipitation gradually increased from southwest to northeast. The proportion of dry and wet years was as follows: normal (56.41%) > slightly dry (28.2%) > slightly humid (15.39%). Precipitation and drought conditions were positively correlated with NPP during the growing season, while temperature was negatively correlated with NPP. Increased spring temperature, precipitation, and drought conditions positively affected vegetation, while sustained summer temperature resulted in suppressed vegetation growth. Autumn vegetation was positively affected by temperature and drought, and precipitation was negatively correlated with autumn vegetation. Increasing winter temperatures promoted vegetation growth. The time lag between NPP and temperature gradually increased from northeast to southwest, and the time lag between NPP and precipitation gradually increased from south to north. Spring temperatures had the greatest beneficial impact on forestlands; summer climatic factors and drought had little effect on shrublands; the autumn climate exhibited small differences in its influence of each plant type; and winter temperatures had the greatest positive effect on grasslands. No time lag effect was found between any of the four vegetation types and precipitation. A one-month lag was found between cultivated lands and temperature; a two-month lag was found between forestlands and temperature; and a one-month lag was found between forestlands and drought and between shrublands and drought. The results can provide a scientific foundation for the sustainable development and management of ecosystems.

Decoupling the response of vegetation dynamics to asymmetric warming over the Qinghai-Tibet plateau from 2001 to 2020.

Global land surface air temperature data show that in the past 50 years, the rate of nighttime warming has been much faster than that of daytime, with the minimum daily temperature (Tmin) increasing about 40% faster than the maximum daily temperature (Tmax), resulting in a decreased diurnal temperature difference. The Qinghai-Tibet Plateau (QTP) is known as the "roof of the world", where temperatures have risen twice as fast as the global average warming rate in the last few decades. The factors affecting vegetation growth on the QTP are complex and still not fully understood to some extent. Previous studies paid less attention to the explanations of the complicated interactions and pathways between elements that influence vegetation growth, such as climate (especially asymmetric warming) and topography. In this study, we characterized the spatial and temporal trends of vegetation coverage and investigated the response of vegetation dynamics to asymmetric warming and topography in the QTP during 2001-2020 using trend analysis, partial correlation analysis, and partial least squares structural equation model (PLS-SEM) analysis. We found that from 2001 to 2020, the entire QTP demonstrated a greening trend in the growing season (April to October) at a rate of 0.0006/a (p < 0.05). The spatial distribution pattern of partial correlation between NDVI and Tmax differed from that of NDVI and Tmin. PLS-SEM results indicated that asymmetric warming (both Tmax and Tmin) had a consistent effect on vegetation development by directly promoting greening in the QTP, with NDVI values being more sensitive to Tmin, while topographic factors, especially elevation, mainly played an indirect role in influencing vegetation growth by affecting climate change. This study offers new insights into how vegetation responds to asymmetric warming and references for local ecological preservation.

Seasonal shifts from plant diversity to consumer control of grassland productivity.

Plant biodiversity and consumers are important mediators of energy and carbon fluxes in grasslands, but their effects on within-season variation of plant biomass production are poorly understood. Here we measure variation in control of plant biomass by consumers and plant diversity throughout the growing season and their impact on plant biomass phenology. To do this, we analysed 5 years of biweekly biomass measures (NDVI) in an experiment manipulating plant species richness and three consumer groups (foliar fungi, soil fungi and arthropods). Positive plant diversity effects on biomass were greatest early in the growing season, whereas the foliar fungicide and insecticide treatments increased biomass most late in the season. Additionally, diverse plots and plots containing foliar fungi reached maximum biomass almost a month earlier than monocultures and plots treated with foliar fungicide, demonstrating the dynamic and interactive roles that biodiversity and consumers play in regulating biomass production through the growing season.

Simulating environmentally-sensitive tree recruitment in vegetation demographic models.

Vegetation demographic models (VDMs) endeavor to predict how global forests will respond to climate change. This requires simulating which trees, if any, are able to recruit under changing environmental conditions. We present a new recruitment scheme for VDMs in which functional-type-specific recruitment rates are sensitive to light, soil moisture and the productivity of reproductive trees. We evaluate the scheme by predicting tree recruitment for four tropical tree functional types under varying meteorology and canopy structure at Barro Colorado Island, Panama. We compare predictions to those of a current VDM, quantitative observations and ecological expectations. We find that the scheme improves the magnitude and rank order of recruitment rates among functional types and captures recruitment limitations in response to variable understory light, soil moisture and precipitation regimes. Our results indicate that adopting this framework will improve VDM capacity to predict functional-type-specific tree recruitment in response to climate change, thereby improving predictions of future forest distribution, composition and function.

Forest regeneration within Earth system models: current process representations and ways forward.

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications.

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Spatiotemporal variations in evapotranspiration and its influencing factors in the semiarid Hailar river basin, Northern China.

Evapotranspiration (ET) is a critical variable in the world's water cycle, and plays a significant role in estimating the impact of environmental change on the regional hydrothermal cycle. Moreover, as an essential of eco-hydrological processes, changes in ET may exceptionally impact the local climate and provide indicative information on the eco-system's functioning. The Hailar River Basin (HRB), located in northern China, is one of the most sensitive areas to climate warming. Under the influence of climate change in recent years, the vegetation dynamics of the basin have been significant and have had profound effects on the regional water cycle conditions and hydrological processes. The HRB is located in a semiarid region and ET is the main mode of water consumption. The ET response to climate change and vegetation dynamics is the focus of research on ecohydrological processes in this basin. In this study, a distributed hydrological model, the BTOPMC model, is used to evaluate the actual ET in the HRB from 1981 to 2020, based on in situ meteorological data as well as LAI data obtained by satellite remote sensing. The seasonal, interannual and spatial dynamics of ET were characterized. The contribution of meteorological factors to ET was calculated by sensitivity analysis and multiple linear regression analysis, and the predominant elements influencing the difference in ET in the HRB were also discussed. The results show that: (1) estimated ET values can clarify over 85% of the seasonal variation in the observed values (R2= 0.79, P < 0.001; R2= 0.84, P < 0.001), which demonstrates that the model has a high precision. (2) Over the past 40 years, the annual ET has shown a clear increasing trend and a large spatial heterogeneity in its spatial distribution, which is consistent with the trend of vegetation. It mainly shows that the eastern forest area is larger than the central forest-grass transition area and the western meadow steppe area. (3) Sensitivity and influential factor contribution analyses show that the main factor driving interannual variability in ET is climate warming, followed by precipitation. At the same time, vegetation dynamics also play a crucial role in ET, especially in areas with different vegetation types and high coverage, while climatic factors also have a strong influence on ET indirectly through vegetation. Due to its special geographic location, the HRB is more sensitive to global climate change and is a typical ecologically fragile area. Therefore, this study has important scientific value and social significance for maintaining ecological security and the sustainable use of water resources.

Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis.

The increase in drought frequency in recent years is considered as an important factor affecting vegetation diversity. Understanding the responses of vegetation dynamics to drought is helpful to reveal the behavioral mechanisms of terrestrial ecosystems and propose effective drought control measures. In this study, long time series of Normalized Difference Vegetation Index (NDVI) and Solar-induced chlorophyll fluorescence (SIF) were used to analyze the vegetation dynamics in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought was evaluated, and the corresponding differences among different vegetation types were revealed. Based on an improved partial wavelet coherence (PWC) analysis, the influences of teleconnection factors (i.e., large-scale climate patterns and solar activity) on the response relationship between meteorological drought and vegetation were quantitatively analyzed to determine the roles of factors. The results indicate that (a) vegetation in the PRB showed an increasing trend from 2001 to 2019, and the SIF increased more than that of NDVI; (b) the vegetation response time (VRT) based on NDVI (VRTN) was typically 4-6 months, while the VRT based on SIF (VRTS) was typically 2-4 months. The VRT was shortest in the woody savannas and longest in the evergreen broadleaf forests. (c) The relationship between the SIF and meteorological drought was more significant than that between the NDVI and meteorological drought. (d) There was a significant positive correlation between meteorological drought and vegetation in the period of 8-20 years. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and sunspots were important driving factors affecting the response relationship between drought and vegetation. Specifically, the PDO had the greatest impacts among these factors.

Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020.

Quantification of the spatio-temporal trends in vegetation dynamics and its drivers is crucial to ensure sustainable management of ecosystems. The north-eastern state of Meghalaya possessing an idiosyncratic climatic regime has been undergoing tremendous pressure in the past decades considering the recent climate change scenario. A robust trend analysis has been performed using the MODIS NDVI (MOD13Q1) data (2001-2020) along with multi-source gridded climate data (precipitation and temperature) to detect changes in the vegetation dynamics and corresponding climatic variables by employing the Theil-Sen Median trend test and Mann-Kendall test (τ). The spatial variability of trends was gauged with respect to 7 major forest types, administrative boundaries and different elevational gradients found in the area. Results revealed a large positive inter-annual trend (85.48%) with a minimal negative trend (14.52%) in the annual mean NDVI. Mean Annual Precipitation presents a negative trend in 66.97% of the area mainly concentrated in the eastern portion of the state while the western portion displays a positive trend in about 33.03% of the area. Temperature exhibits a 98% positive trend in Meghalaya. Pettitt Change Point Detection revealed three major breakpoints viz., 2010, 2012 and 2014 in the NDVI values from 2001 to 2020 over the forested region of Meghalaya. A consistent future vegetation trend (87.78%) in Meghalaya was identified through Hurst Exponent. A positive correlation between vegetation and temperature was observed in about 82.81% of the area. The western portion of the state was seen to reflect a clear correlation between NDVI and rainfall as compared to the eastern portion where NDVI is correlated more with temperature than rainfall. A gradual deviation of rainfall towards the west was identified which might be feedback of the increasing significant greening observed in the state in the recent decades. This study, therefore, serves as a decadal archive of forest dynamics and also provides an insight into the long-term impact of climate change on vegetation which would further help in investigating and projecting the future ecosystem dynamics in Meghalaya.

Root vascular traits differ systematically between African savanna tree and grass species, with implications for water use.

PREMISE: Belowground functional traits play a significant role in determining plant water-use strategies and plant performance, but we lack data on root traits across communities, particularly in the tropical savanna biome, where vegetation dynamics are hypothesized to be strongly driven by tree-grass functional differences in water use. METHODS: We grew seedlings of 21 tree and 18 grass species (N = 5 individuals per species) from the southern African savanna biome under greenhouse conditions and collected fine-root segments from plants for histological analysis. We identified and measured xylem vessels in 539 individual root cross sections. We then quantified six root vascular anatomy traits and tested them for phylogenetic signals and tree-grass differences in trait values associated with vessel size, number, and hydraulic conductivity. RESULTS: Grass roots had larger root xylem vessels than trees, a higher proportion of their root cross-sectional area comprised vessels, and they had higher estimated axial conductivities than trees, while trees had a higher number of vessels per root cross-sectional area than grasses did. We found evidence of associations between trait values and phylogenetic relatedness in most of these traits across tree species, but not grasses. CONCLUSIONS: Our findings support the hypothesis that grass roots have higher water transport capacity than tree roots in terms of maximum axial conductivity, consistent with the observation that grasses are more "aggressive" water users than trees under conditions of high soil moisture availability. Our study identifies root functional traits that may drive differential responses of trees and grasses to soil moisture availability.

Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai-Tibetan Plateau.

Alpine grasslands on the Qinghai-Tibetan Plateau are sensitive and vulnerable to climate change and human activities. Climate warming and overgrazing have already caused degradation in a large fraction of alpine grasslands on this plateau. However, it remains unclear how human activities (mainly livestock grazing) regulates vegetation dynamics under climate change. Here, alpine grassland productivity (substituted with the normalized difference vegetation index, NDVI) is hypothesized to vary in a nonlinear trajectory to follow climate fluctuations and human disturbances. With generalized additive mixed modelling (GAMM) and residual-trend (RESTREND) analysis together, both magnitude and direction of climatic (in terms of temperature, precipitation, and radiation) and anthropogenic impacts on NDVI variation were examined across alpine meadows, steppes, and desert-steppes on the Qinghai-Tibetan Plateau. The results revealed that accelerating warming and greening, respectively, took place in 76.2% and 78.8% of alpine grasslands on the Qinghai-Tibetan Plateau. The relative importance of temperature, precipitation, and radiation impacts was comparable, between 20.4% and 24.8%, and combined to explain 66.2% of NDVI variance at the pixel scale. The human influence was strengthening and weakening, respectively, in 15.5% and 14.3% of grassland pixels, being slightly larger than any sole climatic variable across the entire plateau. Anthropogenic and climatic factors can be in opposite ways to affect alpine grasslands, even within the same grassland type, likely regulated by plant community assembly and species functional traits. Therefore, the underlying mechanisms of how plant functional diversity regulates nonlinear ecosystem response to climatic and anthropogenic stresses should be carefully explored in the future.