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African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAI W ) and herbaceous LAI (LAI H ), respectively, derived from aggregate leaf area index (LAI A ), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAI A ); and (2) partitioned biomass model (LAI P ) comprising LAI H and LAI W . By integrating forage estimates with ancillary environmental variables, we found that LAI P , with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAI P yielded the lowest RMSE (5.9 TLUs) and higher R 2 (0.83), surpassing NDVI and LAI A models RMSE (9.3 TLUs) and R 2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAI P has the potential to reduce basis risk by enhancing IBLI index accuracy.

Surface-water availability, defined as precipitation minus evapotranspiration, can be affected by changes in vegetation. These impacts can be local, due to the modification of evapotranspiration and precipitation, or non-local, due to changes in atmospheric moisture transport. However, the teleconnections of vegetation changes on water availability in downwind regions remain poorly constrained by observations. By linking measurements of local precipitation to a new hydrologically weighted leaf area index that accounts for both local and upwind vegetation contributions, we demonstrate that vegetation changes have increased global water availability at a rate of 0.26 mm yr −2 for the 2001–2018 period. Critically, this increase has attenuated about 15% of the recently observed decline in global water availability. The water availability increase is due to a greater rise in precipitation relative to evapotranspiration for over 53% of the global land surface. We also quantify the potential hydrological impacts of regional vegetation increases at any given location across global land areas. We find that enhanced vegetation is beneficial to both local and downwind water availability for ~45% of the land surface, whereas it is adverse elsewhere, primarily in water-limited or high-elevation regions. Our results highlight the potential strong effects of deliberate vegetation changes, such as afforestation programmes, on water resources beyond local and regional scales. Vegetation change over the past two decades has limited the decline in global water availability by enhancing rainfall over evapotranspiration, according to analysis of observation-based atmospheric moisture transport data.

Global vegetation and associated ecosystem services critically depend on soil moisture availability which has decreased in many regions during the last three decades. While spatial patterns of vegetation sensitivity to global soil water have been recently investigated, long-term changes in vegetation sensitivity to soil water availability are still unclear. Here we assess global vegetation sensitivity to soil moisture during 1982-2017 by applying explainable machine learning with observation-based leaf area index (LAI) and hydro-climate anomaly data. We show that LAI sensitivity to soil moisture significantly increases in many semi-arid and arid regions. LAI sensitivity trends are associated with multiple hydro-climate and ecological variables, and strongest increasing trends occur in the most water-sensitive regions which additionally experience declining precipitation. State-of-the-art land surface models do not reproduce this increasing sensitivity as they misrepresent water-sensitive regions and sensitivity strength. Our sensitivity results imply an increasing ecosystem vulnerability to water availability which can lead to exacerbated reductions in vegetation carbon uptake under future intensified drought, consequently amplifying climate change. Water availability is a major control of vegetation dynamics and terrestrial carbon cycling. Here, the authors show that vegetation sensitivity to soil moisture has been increasing in the last 36 years, especially in (semi)arid areas, and that state-of-the-art land surface models fail to capture this trend.

Satellite observations show that leaf area index (LAI) has increased globally since 1981, but the impact of this vegetation structural change on the global terrestrial carbon cycle has not been systematically evaluated. Through process-based diagnostic ecosystem modeling, we find that the increase in LAI alone was responsible for 12.4% of the accumulated terrestrial carbon sink (95 ± 5 Pg C) from 1981 to 2016, whereas other drivers of CO 2 fertilization, nitrogen deposition, and climate change (temperature, radiation, and precipitation) contributed to 47.0%, 1.1%, and −28.6% of the sink, respectively. The legacy effects of past changes in these drivers prior to 1981 are responsible for the remaining 65.5% of the accumulated sink from 1981 to 2016. These results refine the attribution of the land sink to the various drivers and would help constrain prognostic models that often have large uncertainties in simulating changes in vegetation and their impacts on the global carbon cycle. There lacks systematic analysis on the importance of vegetation structural change in the global terrestrial carbon cycle. Here the authors conducted a multi-model comparison analysis and find that the increase in leaf area index has been responsible for 12.4% of the accumulated terrestrial carbon sink from 1981 to 2016.

Background It is well established that the functioning of terrestrial ecosystems depends on biophysical and biogeochemical feedbacks occurring at the soil-plant-atmosphere (SPA) interface. However, dynamic biophysical and biogeochemical processes that operate at local scales are seldom studied in conjunction with structural ecosystem properties that arise from broad environmental constraints. As a result, the effect of SPA interactions on how ecosystems respond to, and exert influence on, the global environment remains difficult to predict. Scope We review recent findings that link structural and functional SPA interactions and evaluate their potential for predicting ecosystem responses to chronic environmental pressures. Specifically, we propose a quantitative framework for the integrated analysis of three major plant functional groups (evergreen conifers, broadleaf deciduous, and understory shrubs) and their distinct mycorrhizal symbionts under rising levels of carbon dioxide, changing climate, and disturbance regime. First, we explain how symbiotic and competitive strategies involving plants and soil microorganisms influence scale-free patterns of carbon, nutrient, and water use from individual organisms to landscapes. We then focus on the relationship between those patterns and structural traits such as specific leaf area, leaf area index, and soil physical and chemical properties that constrain root connectivity and canopy gas exchange. Finally, we use those relationships to predict how changes in ecosystem structure may affect processes that are important for climate stability. Conclusions On the basis of emerging ecological theory and empirical biophysical and biogeochemical knowledge, we propose ten interpretive hypotheses that serve as a primary set of hierarchical relationships (or scaling rules), by which local SPA interactions can be spatially and temporally aggregated to inform broad climate change mitigation efforts. To this end, we provide a series of numerical formulations that simplify the net outcome of complex SPA interactions as a first step towards anticipating shifts in terrestrial carbon, water, and nutrient cycles.

Plant trait variation drives plant function, community composition, and ecosystem processes. However, our current understanding of trait variation disproportionately relies on aboveground observations. Here we integrate root traits into the global framework of plant form and function. We developed and tested an overarching conceptual framework that integrates two recently identified root trait gradients with a well-established aboveground plant trait framework. We confronted our novel framework with published relationships between above- and belowground trait analogues and with multivariate analyses of aboveground and belowground traits of 2510 species. Our traits represent the leaf- and root conservation gradients (specific leaf area, leaf and root nitrogen concentration and root tissue density), the root collaboration gradient (root diameter and specific root length), and the plant size gradient (plant height and rooting depth). We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth. This perspective on the multi-dimensional nature of plant trait variation better encompasses plant function and influence on the surrounding environment.

BackgroundFuel monitoring data are essential to evaluate wildfire risk, plan management activities and evaluate fuel treatment effects. Terrestrial light detection and ranging (lidar) is a field-based 3D scanning technology with great potential to reduce labor-intensive field measurements and provide new depths of vegetation structure data.AimsTo facilitate the integration of terrestrial lidar into fuel monitoring programs, we developed a model, training process, and Python program that produces canopy fuel, surface fuel and terrain metrics commonly used in fire behavior and fire risk modeling.MethodsWe estimated canopy and surface fuel metrics from terrestrial lidar using a semi-empirical model incorporating physically based modeling of leaf area density and occlusion and a non-destructive model calibration process leveraging Bayesian regression. We compared lidar-derived fuel estimates with conventional fuel estimates across diverse conditions in semi-arid shrubland, woodland and forest in Arizona. We also compared estimates using single- and multiple-scan modes.Key resultsIn single-scan mode, our lidar-derived fuel estimates were significantly related to conventional estimates of total canopy fuel load, maximum canopy bulk density, downed surface fuel load and standing surface fuel load.ImplicationsOur methods provide opportunities to increase the scalability of fuel monitoring to better understand wildfire risk and treatment effectiveness.

Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

Adaptation to drought involves complex interactions of traits that vary within and among species. To date, few data are available to quantify within-species variation in functional traits and they are rarely integrated into mechanistic models to improve predictions of species response to climate change.We quantified intraspecific variation in functional traits of two Hakea species growing along an aridity gradient in southeastern Australia. Measured traits were later used to parameterise the model SurEau to simulate a transplantation experiment to identify the limits of drought tolerance.Embolism resistance varied between species but not across populations. Instead, populations adjusted to drier conditions via contrasting sets of trait trade-offs that facilitated homeostasis of plant water status. The species from relatively mesic climate, Hakea dactyloides, relied on tight stomatal control whereas the species from xeric climate, Hakea leucoptera dramatically increased Huber value and leaf mass per area, while leaf area index (LAI) and epidermal conductance (g(min)) decreased. With trait variability, SurEau predicts the plasticity of LAI and g(min) buffers the impact of increasing aridity on population persistence.Knowledge of within-species variability in multiple drought tolerance traits will be crucial to accurately predict species distributional limits.

Waterlogging, an abiotic stress, severely restricts crop yield in various parts of the world. Thus, we conducted a meta-analysis of 2,419 comparisons from 115 studies to comprehensively evaluate the overall change in crop yield induced by waterlogging in the global region. The results suggested that waterlogging obviously decreased crop yield by 32.9% on average, compared with no waterlogging, which was a result of a reduced 1,000-grain weight (13.67%), biomass (28.89%), plant height (10.68%), net photosynthetic rate ( P n , 39.04%), and leaf area index (LAI, 22.89%). The overall effect of a waterlogging regime on crop yield is related to the crop type; the crop yield reduction varied between wheat (25.53%) and cotton (59.95%), with an overall average value of 36.81% under field conditions. In addition, we also found that compared with no waterlogging, waterlogging in the reproductive growth stage (41.90%) caused a greater yield reduction than in the vegetative growth stage (34.75%). Furthermore, decreases in crop yield were observed with an extension in the waterlogging duration; the greatest decreases in crop yield occurred at 15 < D ≤ 28 (53.19 and 55.96%) under field and potted conditions, respectively. Overall, the results of this meta-analysis showed that waterlogging can decrease crop yield and was mainly affected by crop type, growth stage, and experimental duration.