Changes In Aboveground Biomass Research Articles

The predictability of near‐term forest biomass change in boreal North America

AbstractClimate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

Mapping aboveground biomass of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress using Sentinel-2 imagery

Moso bamboo (Phyllostachys pubescens) stands as a pivotal economic bamboo species globally, holding substantial potential for carbon sequestration. Accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is crucial due to its close ties with the ecosystem's carbon cycle. Despite the maturation of monitoring techniques for Pantana phyllostachysae Chao, a significant pest of Moso bamboo, its interplay with AGB in these forests remains enigmatic. This study addressed this gap by categorizing P. phyllostachysae's impact on Moso bamboo forests into four levels: healthy, mild damage, moderate damage, and severe damage. By scrutinizing field data, we delved into the shifts in Moso bamboo leaf biomass under P. phyllostachysae stress. Leveraging Sentinel-2A/B imagery, we extracted diverse correlation factors, including original wave bands, vegetation indices, texture attributes, and vegetation's physical and chemical parameters. Subsequently, machine learning algorithms-namely, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were employed to achieve remote sensing inversion of AGB in Moso bamboo forests, accounting for the presence of insect pests. We analyzed the response of Moso bamboo biomass sensitive factors and to further clarify the changes of AGB of Moso bamboo forests under insect pest stress at the remote sensing level ultimately. The results showed that (1) the degree of Moso bamboo leaf biomass damage was positively related to the damage level, which gradually increased from 15.15 % to 59.42 %; (2) the RF algorithm excelled in estimating Moso bamboo forest AGB, particularly in May, and inclusion of insect pest considerations enhanced AGB estimation accuracy; (3) among the four factor types, Band information and vegetation indices emerged as most impactful, and Band5, Band11, Band12, NDVI68a and MSAVI were selected the most often; (4) at the remote sensing level, AGB in Moso bamboo forests significantly varies under P. phyllostachysae stress. Healthy areas demonstrate an AGB of 66.9037 Mg ha−1, while heavily affected regions drop to 52.6591 Mg ha−1. It can be seen that combining pest factors for Moso bamboo biomass estimation solves the problem of rough biomass estimation, and this study provides a more promising method for forest growth monitoring.

Analysis of rotational grazing management for sheep in mixed grassland.

Sown mixed grassland is rarely used for livestock raising and grazing; however, different forages can provide various nutrients for livestock, which may be beneficial to animal health and welfare. We established a sown mixed grassland and adopted a rotational grazing system, monitored the changes in aboveground biomass and sheep weights during the summer grazing period, measured the nutrients of forage by near-infrared spectroscopy, tested the contents of medium- and long-chain fatty acids by gas chromatography, and explored an efficient sheep fattening system that is suitable for agro-pastoral interlacing areas. The results showed that the maximum forage supply in a single grazing paddock was 4.6 kg DM/d, the highest dry matter intake (DMI) was 1.80 kg DM/ewe/d, the average daily weight gain (ADG) was 193.3 g, the DMI and ADG were significantly correlated (P<0.05), and the average feed weight gain ratio (F/G) reached 8.02. The average crude protein and metabolizable energy intake by sheep were 286 g/ewe/d and 18.5 MJ/ewe/d respectively, and the n-6/n-3 ratio of polyunsaturated fatty acids in mutton was 2.84. The results indicated that the sheep fattening system had high feed conversion efficiency, could improve the yield and quality of sheep, and could be promoted in suitable regions.

Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multitemporal Lidar and Landsat

Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multitemporal Lidar and Landsat

Grassland biomass spatiotemporal patterns and response to climate change in eastern Inner Mongolia based on XGBoost model estimates

Aboveground biomass (AGB) is a key index that reflects grassland health and is of great importance for grassland conservation and sustainable utilization. Many previous studies on grassland AGB were based on the analysis of the whole study period, can’t reflect the change of grassland in the short term, which was not conducive to the implementation and revision of policies. And it is still a challenge to accurately describe the response of grassland AGB to climate factors. In this study, we used four machine learning algorithms (kNN, k-nearest neighbor; RF, Random Forest; SVR, Support Vactor Regression; and XGBoost, eXtreme Gradient Boosting) to construct the estimation model of grassland AGB in eastern Inner Mongolia based on grassland AGB measurement data, MODIS(MOD13Q1) data and environmental data (climate and topography). The average distribution of grassland AGB from 2003 to 2021 was calculated based on the optimal grassland AGB estimation model. The changes of grassland AGB from 2003 to 2021 and in every five-year period during the study series were comprehensively analyzed by the Theil–Sen Median trend analysis and the Mann–Kendall test. The response of grassland AGB to climatic factors was investigated using sensitivity analysis. The results showed that (1) the XGBoost model constructed by NDVI, longitude, latitude, temperature, precipitation, elevation, slope, and aspect (NXYTPESA) is the optimal grassland AGB model (R2 = 0.87, RMSE = 14.27 g/m2). (2) The 20-year average grassland AGB in Eastern Inner Mongolia showed heterogeneous, with higher values in the northeast and lower values in the southwest. (3) The inter-annual variation fluctuated substantially, and the overall trend was increasing. Compared with other time periods, most grassland AGBs were in an increasing trend during 2008–2012 (79 %) and 2018–2021 (84 %). (4) The grassland AGB in the study area was regulated by temperature and precipitation, and the influence of precipitation was greater than that of temperature. This study provides a new method for estimating grassland AGB and its changes and a scientific basis for the sustainable development of grassland ecosystems in eastern Inner Mongolia.

Inter- and intra-year forest change detection and monitoring of aboveground biomass dynamics using Sentinel-2 and Landsat

National aboveground forest biomass products enable monitoring of biomass dynamics in a consistent and repeatable manner to inform carbon accounting and sustainable forest management activities. The availability of images of the Earth's surface from a combination of Landsat and Sentinel-2A and -2B provides an opportunity and capability to track intra-year biomass dynamics availing upon a twice weekly acquisition opportunity. We developed an algorithm that leverages these spatially and spectrally compatible data sources, called Tracking Intra- and Inter-year Change (TIIC), to monitor forest change across Canada's forested ecozones (>650Mha) in near-real time. Combining the stand-replacing change information from TIIC with spatially explicit maps of aboveground biomass (AGB), we demonstrate how intra-annual and inter-annual AGB dynamics, including losses due to stand-replacing disturbances and gains from vegetation growth, can be quantified temporally and spatially. Using independent validation data, our results for the focus year 2019 indicate that TIIC, by analysing images from May 30 to September 1, can accurately detect stand-replacing disturbances across Canada's forested ecozones with an overall accuracy of 99% and correctly attribute the type of change (i.e., wildfire or mechanical removal, the latter of which includes timber harvesting) with an overall accuracy of 99%. From an initial producer's accuracy of 23% for the changed class, accuracy increases incrementally as additional images are added throughout the growing season. attaining a producer's accuracy of 98% by the end of the analysis period. Intra-year change information complements information on long-term trends derived from annual time series monitoring over several decades. Our analysis of AGB dynamics indicated that in every ecozone, widespread small positive AGB increments yielded overall AGB gains that were greater than the sum of the large, punctual AGB losses resulting from stand-replacing disturbances. Overall in 2019, forest AGB increased across Canada's forested ecozones by 2.54%. Temporally, 80% of AGB losses stemming from mechanical removal occurred over the winter and are as such concentrated at the beginning of the growing season. In contrast, AGB losses linked to fires happen more stochastically throughout the growing season and occupy a greater area. By way of disturbance type, 36% of 2019 AGB loss was attributed to mechanical removal, and 64% was attributed to wildfire. Following the approach demonstrated herein, AGB changes can be tracked at a temporal frequency informative of forest management practices and ecological processes on the landscape, thereby refining our understanding of AGB dynamics.

Estimation of Aboveground Forest Biomass and Carbon Storage of Bangladesh

Forest biomass helps mitigate climate change impacts through sequestration of atmospheric carbon dioxide and potentially storing it for long periods of time. Deforestation and timber harvesting cause the reduction of forest biomass resulting in the reduced carbon sequestration capacity and alterednatural balance of forest ecosystems. We used remote sensing and GIS tools in the four important forest cover zones within five districts of Bangladesh to compare the aboveground forest biomass (AGB) changes between 2014 and 2020. We found an increased AGB in Sundarban mangrove forest from 89.73 Mg.h-1 in 2014 to 90.76 Mg.h-1 in 2020. Similarly, the AGB was found to be increased for Ukhiya hill forest from 7.89 Mg.h-1 in 2014 to 8.89 Mg.h-1 in 2020. Contrary, the average AGB content in Nijhum Dwip mangrove forest decreased from 44.36 Mg.h-1 in 2014 to 37.46 Mg.h-1 in 2020. The average AGB of Modhupur decidious forest also found to be decreased from 110.01 Mg.h-1 in 2014 to 107.22 Mg.h-1 in 2020. The decreased biomass contents could be attributed to anthropgenic factors as indicated by the presence of human activities and this informatin will be helpful for forest restoration and management in Bangladesh.

Recovery of aboveground biomass, soil carbon stocks and species diversity in tropical montane secondary forests of East Africa

Tropical montane forests are fragile ecosystems that provide a wide range of ecosystem services including hydrological services, biodiversity protection and storing carbon in the above and belowground and soils contributing to climate change mitigation. The world’s tropical montane forests are increasingly exposed to degradation and their recovery after disturbance has not been adequately quantified. Here, using information from 47 plots in three blocks of the Mau Forest Complex of Kenya, we assessed the changes in aboveground biomass (AGB), tree species diversity, soil carbon and nitrogen stocks following forest clearance. AGB recovered at an annual rate of 6.42 Mg ha−1 yr−1 in the first 20 years, the rate then slowed down to 4.46–4.67 Mg ha−1 yr−1 at around 25–30 years. Around 25 years after disturbance, AGB in recovering forests was 70 % (198.32 ± 78.11 Mg ha−1) of the AGB in the old secondary forest (OSF) (282.86 ± 71.64 Mg ha−1) and was statistically indistinguishable. Stem density, species diversity and richness indices did not show significant differences across recovery classes. There was no significant difference in soil carbon and nitrogen stocks across classes with the soil carbon (184.1 ± 41 Mg ha−1) of the young secondary forest (<10 years) being 84.5 % of that of the OSF (217.9 ± 51.8 Mg ha−1). This study reports a rapid rate of AGB and carbon accumulation within 20 years of disturbance, and high levels of species richness in these previously disturbed fragments of tropical montane forests of East Africa.

Destructive selective logging in tropical forests causes soil carbon loss through forest degradation and soil redox change

Soil organic C (SOC) stock in tropical forests can be reduced significantly after logging, not only due to lowered above ground biomass (AGB) and consequentially reduced organic matter (OM) input, but also consumption on sequestrated soil C protected by OM-mineral association. In this study, we studied the loss of mineral-associated OM after logging in the main rooting zone (e.g., 0–30 cm), and examined a hypothetical twofold underlying mechanism of SOC reduction: first, decrease in AGB limiting OM input; second, decrease in soil redox potential (Eh) leading to the reductive dissolution of short-range-ordered iron (SRO Fe) minerals that destabilize some mineral-associated OM. Thirty-five plots at different degradation levels were investigated for both above- and belowground properties. AGB and tree community composition were employed as indicators of forest degradation gradient; multiple physicochemical properties in top-30 cm soils were measured every 10 cm depth. We analysed the relationship between AGB and soil C with the mediator SRO Fe, and investigated the role of soil Eh in linking the changes in AGB and SRO Fe. Our results revealed simultaneous decreases in AGB, SRO Fe and soil C in top-30 cm soils, and SRO Fe mediated 43 % of the hypothetical causal effect of AGB on soil C in deeper (20–30 cm) soil. Soil Eh decreased with decreasing AGB in a logistic pattern, dropping to a reductive regime (Eh7 ≤ 0.05 V) when more than half of the AGB was removed from the pristine level. Mean SRO Fe stock in the reductive soils was significantly lower than that in the oxic soils (Eh7 > 0.05 V), and the loss of SRO Fe correspond to the decomposition of OM in 10–30 cm soils. We suggest that the reductive shift in soil is caused by the weakened evapotranspiration as destructive selective logging inflicts canopy openness and succession failure. This study presents the possibility that forest degradation may reduce the size of protected soil C pool resulting from the loss of soil SRO Fe minerals.

Effect of grazing management strategies on the vegetation parameters and soil nutrients in alpine Kobresia pygmaea meadow on the northeastern Qinghai–Tibetan Plateau

Understanding the impact of grazing management strategies on alpine meadow is of fundamental importance for grassland conservation. However, the vegetation and soil properties in response to the different grazing management strategies implemented on Kobresia pygmaea meadow remains elusive. In this study, a medium-term field experiment with three grazing management strategies (free grazing, FG; no grazing, NG; and reduced grazing, RG) was conducted. The results revealed that the total aboveground biomass (AGB), vegetation cover and litter biomass increased significantly under NG treatment, whereas the total belowground biomass (BGB) decreased significantly in NG plot. Furthermore, the responses of plant functional groups to different grazing management strategies were varied. The AGB of grass increased remarkably under NG treatment compared to FG plot, whereas there were no obvious change in AGB of the sedge, legume, and forb groups. The concentrations of the soil available phosphorus (AP) and ammonia nitrogen (NH4+-N) of RG and FG were considerably different at the 0–5 cm soil layers. In contrast, the soil C:N ratios in all soil layers were similar among the three grazing management strategies. It is worth noting that the soil AP concentration was positively correlated with the total AGB. More importantly, the results supported the functional equilibrium hypothesis for AGB versus BGB, which is based on the unique biological characteristics of K. pygmaea meadow. Our study provides valuable insights into the response of alpine meadow to grazing management strategies and can thus provide guidance for designing management strategies for degraded alpine meadow.

Disease decreases variation in host community structure in an old-field grassland.

Disease may drive variation in host community structure by modifying the interplay of deterministic and stochastic processes that shape communities. For instance, deterministic processes like ecological selection can benefit species less impacted by disease. When communities have higher levels of disease and disease consistently selects for certain host species, this can reduce variation in host community composition. On the other hand, when host communities are less impacted by disease and selection is weaker, stochastic processes (e.g., drift, dispersal) may play a bigger role in host community structure, which can increase variation among communities. While effects of disease on host community structure have been quantified in field experiments, few have addressed the role of disease in modulating variation in structure among host communities. To address this, we conducted a field experiment spanning three years, using a tractable system: foliar fungal pathogens in an old-field grassland community dominated by the grass Lolium arundinaceum, tall fescue. We reduced foliar fungal disease burden in replicate host communities (experimental plots in intact vegetation) in three fungicide regimens that varied in the seasonal duration of fungicide treatment and included a fungicide-free control. We measured host diversity, biomass, and variation in community structure among replicate communities. Disease reduction generally decreased plant richness and increased aboveground biomass relative to communities experiencing ambient levels of disease. These changes in richness and aboveground biomass were consistent across years despite changes in structure of the plant communities over the experiment's three years. Importantly, disease reduction amplified host community variation, suggesting that disease diminished the degree to which host communities were structured by stochastic processes. These results of experimental disease reduction both highlight the potential importance of stochastic processes in plant communities and reveal the potential for disease to regulate variation in host community structure.

Monitoring Spatiotemporal Variation of Individual Tree Biomass Using Multitemporal LiDAR Data

Assessing the spatiotemporal changes in forest aboveground biomass (AGB) provides crucial insights for effective forest carbon stock management, an accurate estimation of forest carbon uptake and release balance, and a deeper understanding of forest dynamics and climate responses. However, existing research in this field often lacks a comprehensive methodology for capturing tree-level AGB dynamics using multitemporal remote sensing techniques. In this study, we quantitatively characterized spatiotemporal variations of tree-level AGB in boreal natural secondary forests in the Greater Khingan Mountains region using multitemporal light detection and ranging (LiDAR) data acquired in 2012, 2016, and 2022. Our methodology emphasized improving the accuracy of individual tree segmentation algorithms by taking advantage of canopy structure heterogeneity. We introduced a novel three-dimensional metric, similar to crown width, integrated with tree height to calculate tree-level AGB. Moreover, we address the challenge of underestimating tree-level metrics resulting from low pulse density, ensuring accurate monitoring of AGB changes for every two acquisitions. The results showed that the LiDAR-based ΔAGB explained 62% to 70% of the variance of field-measured ΔAGB at the tree level. Furthermore, when aggregating the tree-level AGB estimates to the plot level, the results also exhibited robust and reasonable accuracy. We identified the average annual change in tree-level AGB and tree height across the study region, quantifying them at 2.23 kg and 0.25 m, respectively. Furthermore, we highlighted the importance of the Gini coefficient, which represents canopy structure heterogeneity, as a key environmental factor that explains relative AGB change rates at the plot level. Our contribution lies in proposing a comprehensive framework for analyzing tree-level AGB dynamics using multitemporal LiDAR data, paving the way for a nuanced understanding of fine-scale forest dynamics. We argue that LiDAR technology is becoming increasingly valuable in monitoring tree dynamics, enabling the application of high-resolution ecosystem dynamics products to elucidate ecological issues and address environmental challenges.

Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements

Rapid and non-destructive potato above ground biomass (AGB) monitoring is a crucial step in the development of smart agriculture because AGB is closely related to crop growth, yield, and quality. Compared to time-consuming and laborious field surveys, unmanned aerial vehicle (UAV) remote sensing provides a new direction for large-scale AGB monitoring. However, estimating AGB using an optical remote sensing technique usually does not work well because of spectral saturation, but multi-source remote sensing feature fusion (e.g., fusing spectral and structural features) can mitigate that problem. Due to potato crop canopy structure and AGB change greatly during growth, the potential of fusing optical, textural (TFs), and structural features (SFs) for calculating potato AGB at multiple growth stages was unknown. In addition, the ability of optical features, TFs, and SFs and their combinations to estimate potato AGB had not been examined. Vegetation indices (RGB-VIs), TFs, and SFs were extracted from ultra-high spatial resolution RGB images and compared their performances for estimating potato AGB with those of hyperspectral vegetation indices (H-VIs) obtained from UAV hyperspectral images. The results revealed that each type of feature had its own advantages and limitations for potato AGB estimation. Except for canopy volume (CV) in SFs, the best H-VI, RGB-VI, and TF for estimating AGB in both single growth stages and the entire growth period were inconsistent. When estimating AGB with only a single type of feature, the model accuracy in descending order was SFs, TFs, H-VIs, and RGB-VIs. The fusion of any two types of remote sensing features improved AGB estimation model accuracy. Among them, TFs combined with SFs provided the best estimation performance. The fusion of RGB-VIs, TFs, and SFs produced the best AGB estimates precision (R2 = 0.81, RMSE = 207 kg/hm2, NRMSE = 17.40%). Since AGB was effectively estimated under different treatments in the field, the model applicability was confirmed. Using different types of remote sensing features, the Gaussian process regression method produced better estimation results than the partial least squares regression method did. This study provides an economic and effective method for monitoring the potato growth in the field, and thus helps improve farmland production and guide fertilization management.

Grazing-induced changes in soil microclimate and aboveground biomass modulate freeze–thaw processes in a Tibetan alpine meadow

Grazing is the most important disturbance to grasslands, especially on the Tibetan Plateau, which sustains the livelihoods of tens of thousands of herders. However, the effects of grazing intensity and associated changes in vegetation on freeze–thaw processes (FTPs) in seasonal permafrost regions remain unclear. Here, two years of continuous in situ observations from a controlled grazing intensity experiment were used to examine FTPs and influencing factors in an alpine meadow on the Eastern Tibetan Plateau. Our results showed that a higher grazing intensity led to a shorter frozen period, extended freezing and thawing periods, and earlier initiation of freezing and thawing periods. Furthermore, grazing increased soil temperature and active (> 0 °C) and effective (> 10 °C) accumulated temperatures. The soil diurnal temperature range increased linearly with grazing intensity, whereas soil moisture did not vary with soil temperature. Our results demonstrated that the freezing and frozen periods were mainly influenced by soil temperature and the diurnal temperature range, whereas soil moisture played a critical role in the variation in the thawing period. The reduction in aboveground biomass elicited changes in FTPs, including shortening of the frozen period and lengthening of the thawing and freezing periods. Collectively, our results highlight that the combination of grazing-induced changes in vegetation and changes in subsurface temperature and moisture leads to changes in freezing and thawing processes.

Archetypes of remnant West African forest patches, their main characteristics and geographical distribution

Remnant West African forest patches provide crucial ecosystem functions and services while contributing to sustaining the livelihoods of vast numbers of people. The vast majority lie outside of protected areas, although relatively few are managed as sacred forests, which limits their access and use. This lack of protection, together with a growing demand for arable land and forest resources, have accentuated their fragmentation, degradation, and deforestation. There is therefore an urgent need to generate knowledge on their social-ecological characteristics and change pressures to support their conservation. This study investigates what are i) the main biophysical and social-ecological characteristics of remnant forest patches, and ii) the potential change pressures and drivers. Within this scope, we apply archetype analysis to discern processes affecting remnant forest patches. Biophysical and socio-ecological indicators were selected from a published dataset via expert consultation, and nine archetypes were developed by applying a cluster analysis. Evaluating the results in relation to ecoregions and landscape features using high resolution imagery, we identify common underlying social-ecological change pressures and characteristics. The most common archetype (2) is characterized by being close to protected areas and having a low average annual precipitation and cluster along the northern fringe of the study area. The second most common archetype (5) is characterized by lying in highly disturbed landscapes, having undergone biomass losses, and widely distributed throughout central and western Nigeria. Patches of archetype 8 found predominantly in mangrove and swamp forests, exhibit positive above-ground biomass changes and greening trends; we propose that these vegetation changes could benefit conservation measures and carbon sequestration programs. In contrast, archetype 10 patches show both forest and biomass losses and gains and are often encompass fragmented forests in urban/arable landscapes. Identifying such common patterns of anthropogenic and ecological change provides a means of prioritizing regionalized strategies for their conservation and sustainable use.

Drought diminishes aboveground biomass accumulation rate during secondary succession in a tropical forest on Hainan Island, China

With rising temperatures and altered annual precipitation patterns, climatic factors are likely to play an increasingly important role in controlling the biomass accumulation rate of tropical secondary forests. However, the relative importance of biotic and abiotic factors and demographic progresses (growth, mortality, and recruitment) driving biomass dynamics in these ecosystems remain largely unclear, especially in tropical Asia. We explored aboveground biomass (AGB) dynamics in old-growth and secondary tropical forests on Hainan Island, China, across two consecutive five-year intervals (2010–2020). Drought frequency was higher in the second vegetation census interval. We studied the relationships of AGB and its three demographic components with drought frequency, tree diversity, soil nutrient and microorganismal abundance, and successional stage. We found that AGB accumulation rate decreased as AGB increased gradually during secondary succession. The different drought frequency between the two intervals was the dominant factor predicting the net change of AGB. With high-frequency droughts, the loss of AGB intensified and the increment rate of AGB decreased, especially in old-growth forests during second interval (2015–2020). Considering the impact of the three demographic progresses on net change in AGB, the relative influence of mortality was the largest (61.1%), followed by growth (38.3%) and recruitment (0.6%). Successional stage was another major factor affecting AGB and its dynamics, showing a positive relationship with AGB stock and a negative relationship with net AGB change. Species richness and fungal richness had a significant positive association with AGB, while soil available nutrient had a significant negative relationship. Overall, our results show that drought frequency had a strong impact on AGB dynamics, and that the resilience of AGB accumulation to drought decreased in later successional stages. Thus, with the intensification of global climate change, the effects of high-frequency drought events should be taken into account when studying the factors affecting AGB recovery in tropical secondary forests.

Divergent responses of grassland productivity and plant diversity to intra‐annual precipitation variability across climate regions: A global synthesis

Abstract Global warming intensifies the hydrological cycle and may result in changes in the frequency and intensity of precipitation events. However, a knowledge gap still exists about the general influences of intra‐annual precipitation variability on grassland plant diversity and ecosystem function at the global scale. Here, we synthesized field manipulative experiments from 66 publications to quantify the effects of intra‐annual precipitation variability increases (IPVI) on community biomass and plant diversity in grasslands worldwide. At the global scale, we found that IPVI generally increased grassland community above‐ground biomass (AGB) by 6.90%, and decreased grass biomass and soil ammonium content by 10.38% and 25.35% respectively. IPVI increased and decreased total AGB in arid and humid regions, respectively, and IPVI enhanced grassland below‐ground biomass and plant species richness in arid regions, but showed no effect in humid regions. Furthermore, moderate IPVI stimulated plant species richness and community biomass in arid grasslands, while extreme IPVI impaired them in mesic grasslands. Changes in total AGB under IPVI were related to changes in the biomass of plant functional groups, species richness and soil moisture. Structural equation modelling demonstrated that climate conditions (mean annual temperature and mean annual precipitation) and background soil properties (soil sand content and soil organic carbon content) jointly regulated grassland total AGB responses to IPVI across climate regions. Synthesis. Overall, our study shows divergent responses of grassland productivity and diversity to IPVI in arid and humid regions. Accounting for climate, edaphic properties and precipitation variability can help improve our ability to predict the responses of grassland ecosystems to future changes in precipitation at global scales.

Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m2, exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources.

Precipitation and local adaptation drive spatiotemporal variations of aboveground biomass and species richness in Tibetan alpine grasslands.

The Tibetan Plateau contains the highest and largest alpine pasture in the world, which is adapted to the cold and arid climate. It is challenging to understand how the vast alpine grasslands respond to climate change. We aim to test the hypothesis that there is local adaptation in elevational populations of major plant species in Tibetan alpine grasslands, and that the spatiotemporal variations of aboveground biomass (AGB) and species richness (S) can be mainly explained by climate change only when the effect of local adaptation is removed. A 7-year reciprocal transplant experiment was conducted among the distribution center (4950m), upper (5200m) and lower (4650m) limits of alpine Kobresia meadow in central Tibetan Plateau. We observed interannual variations in S and AGB of 5 functional groups and 4 major species, and meteorological factors in each of the three elevations during 2012-2018. Relationships between interannual changes of AGB and climatic factors varied greatly with elevational populations within a species. Elevation of population origin generally had a greater or an equal contribution to interannual variation in AGB of the 4 major species, compared to temperature and precipitation effects. While the effect of local adaptation was removed by calculating differences in AGB and S between elevations of migration and origin, relative changes in AGB and S were mainly explained by precipitation change rather than by temperature change. Our data support the hypothesis, and further provide evidence that the monsoon-adapted alpine grasslands are more sensitive to precipitation change than to warming.

Grazing-induced cattle behaviour modulates the secondary production in a Eurasian steppe ecosystem

Livestock-grassland interactions are among the most important relationships in grazed grassland ecosystems, where herbivores play a crucial role in plant community and ecosystem functions. However, previous studies primarily have focused on the responses of grasslands to grazing, with few focussing on the effects of livestock behaviour that in turn would influence livestock intake and primary and secondary productivity. Through a 2-year grazing intensity experiment with cattle in Eurasian steppe ecosystem, global positioning system (GPS) collars were used to monitor animal movements, where animal locations were recorded at 10-min intervals during the growing season. We used a random forest model and the K-means method to classify animal behaviour and quantified the spatiotemporal movements of the animals. Grazing intensity appeared to be the predominant driver for cattle behaviour. Foraging time, distance travelled, and utilization area ratio (UAR) all increased with grazing intensity. The distance travelled was positively correlated with foraging time, yielding a decreased daily liveweight gain (LWG) except at light grazing. Cattle UAR showed a seasonal pattern and reached the maximum value in August. In addition, the canopy height, above-ground biomass, carbon content, crude protein, and energy content of plants all affected cattle behaviour. Grazing intensity and the resulting change in above-ground biomass and forage quality jointly determined the spatiotemporal characteristics of livestock behaviour. Increased grazing intensity limited forage resources and promoted intraspecific competition of livestock, which induced longer travelling distance and foraging time, and more even spatial distribution when seeking habitat, which ultimately led to a reduction in LWG. In contrast, under light grazing where there were sufficient forage resources, livestock exhibited higher LWG with less foraging time, shorter travelling distance, and more specialized habitat occupation. These findings support the Optimal Foraging Theory and the Ideal Free Distribution model, which may have important implications for grassland ecosystem management and sustainability.