Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

Grassland cover dynamics and their relationship with climatic factors in China from 1982 to 2021

China possesses abundant grassland resources, making it imperative to comprehend the influence of climate change on Chinese grassland ecosystems. Nonetheless, the impact pathways and lag effects of climate factors on various grassland types in this region at multiple temporal scales are still to be investigated in long-term sequences. This study investigated the dynamics of grassland FVC (fractional vegetation cover), temperature, precipitation, and drought from 1982 to 2021 using trend analysis, multiple linear regression, path analysis, and correlation analysis and explored the dominant, direct, indirect, and time-lag effects of climate factors on different grassland types at multiple time scales. Precipitation-grassland correlation pathways dominated the annual-scale grassland FVC. The correlation path of temperature to grassland FVC and the direct path of temperature dominated spring grassland FVC. The correlation path of drought to grassland FVC and the direct path of drought dominated summer grassland FVC. The correlation path of temperature to grassland FVC and the direct path of temperature dominated autumn and winter grassland FVC. The effects of temperature and precipitation on alpine and subalpine meadows, desert grasslands, and alpine and subalpine plains grasslands had a 1-month lag. The response to drought exhibited a 1-month lag in desert grasslands, a 2-month lag in alpine and subalpine meadows, plains grasslands, meadows, and alpine and subalpine plains grasslands, and a 3-month lag in sloped grasslands. This study seeks to provide a scientific reference to reveal the impact of climate change on grasslands and to protect grassland ecosystems.

Effect of Soil Environment on Species Diversity of Desert Plant Communities

Desert ecosystems possess an astonishing biodiversity and are rich in endangered species. This study investigated characteristics of species diversity and soil environmental factors in three major deserts of China's Alxa Plateau. The Alxa Desert included 183 plant species belonging to 109 genera and 35 families. The highest numbers of plant species belonged to the Compositae, Gramineae, and Chenopodiaceae families. The research area belongs to the semi-shrub and small semi-shrub deserts in temperate deserts. Species diversity was low, with the Shannon-Wiener index (H') of shrub-herb = shrub > herb > tree. The Pielou evenness index (E) of shrub herb vegetation was the lowest, indicating more enriched species and fewer sparse species in the community, and that these types of vegetation had the characteristics of rich and obviously dominant species. Redundancy analysis (RDA) and correlations between the comprehensive plant community biodiversity index and soil factors indicated that soil-available phosphorus (NP), organic matter (SOM), and electrical conductivity (EC) had significant impacts on community species diversity. The herbaceous shrub community exhibited the highest H', Simpson index (D), species richness index (S), soil moisture (SW), and soil nutrients. Planting Calligonum mongolicum, Ephedra membranacea, Artemisia annua, and Phragmites australis to form a typical desert shrub community for community diversity protection is recommended to effectively protect and restore desert ecosystems.

Features of Natural Succession of Ex-Arable Forest Steppe Grassland (from Western Romania) under the Influence of Climate

Important land surfaces from hill and mountain areas from the northern hemisphere formerly used for cropping were abandoned. Often, the abandoned land evolved by natural succession to grassland, shrubland or even to forest. The main goal of this paper is to bring new datasets necessary for the understanding of the evolution of ex-arable grassland vegetation from the forest steppe area into relationship with climate. The researches were performed in the locality of Grădinari (Caraş-Severin County, Western Romania) on an ex-arable plot abandoned since 1995. The vegetation data were collected for 19 years (time interval 2003-2021). The analyzed vegetation features were floristic composition, biodiversity and pastoral value. The climate data considered were air temperature and rainfall amount. The vegetation and climate data were correlated statistically, with a view to highlighting the potential impact of the temperature and rainfalls during the evolution of succession process on the grassland's floristic composition, biodiversity and pastoral value. The pressure of the increased temperatures on the natural restoration process of the biodiversity and pastoral value of ex-arable forest steppe grassland could, at least partially, be mitigated by random grazing and mulching works.

Research Progress of Grassland Ecosystem Structure and Stability and Inspiration for Improving Its Service Capacity in the Karst Desertification Control

The structure and stability of grassland ecosystems have a significant impact on biodiversity, material cycling and productivity for ecosystem services. However, the issue of the structure and stability of grassland ecosystems has not been systematically reviewed. Based on the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, we used the systematic-review method and screened 133 papers to describe and analyze the frontiers of research into the structure and stability of grassland ecosystems. The research results showed that: (1) The number of articles about the structure and stability of grassland ecosystems is gradually increasing, and the research themes are becoming increasingly diverse. (2) There is a high degree of consistency between the study area and the spatial distribution of grassland. (3) Based on the changes in ecosystem patterns and their interrelationships with ecosystem processes, we reviewed the research progress and landmark results on the structure, stability, structure-stability relationship and their influencing factors of grassland ecosystems; among them, the study of structure is the main research focus (51.12%), followed by the study of the influencing factors of structure and stability (37.57%). (4) Key scientific questions on structural optimization, stability enhancement and harmonizing the relationship between structure and stability are explored. (5) Based on the background of karst desertification control (KDC) and its geographical characteristics, three insights are proposed to optimize the spatial allocation, enhance the stability of grassland for rocky desertification control and coordinate the regulation mechanism of grassland structure and stability. This study provided some references for grassland managers and relevant policy makers to optimize the structure and enhance the stability of grassland ecosystems. It also provided important insights to enhance the service capacity of grassland ecosystems in KDC.

Analysis of Land Use Change Drivers and Simulation of Different Future Scenarios: Taking Shanxi Province of China as an Example

This study analyzed change and spatial patterns of land use in Shanxi from 2000 to 2020. The drivers of land use and cover change (LUCC) in cultivated lands, forest lands, grasslands, and rural construction areas were explored from four dimensions, including population, natural environment, location traffic, and economic development. The CA-Markov model was used to simulate the scenarios of natural growth (NG), ecological protection (EP), economic development (ED), food security (FS), ecological protection-economic development (EP-ED), and ecological protection-food security (EP-FS) in 2030. The results indicated that: (1) The conversion to built-up areas primarily dominated the LUCC processes, and their expansion was mainly to the detriment of the cultivated lands and grasslands during 2000-2020. (2) From 2000 to 2020, population, economy, and land productivity were the main factors of LUCC; the interaction of drivers for the increase of cultivated lands, forest lands, grasslands, and rural construction areas showed enhancement. (3) Under the NG, ED, and EP-ED scenarios, the rural construction areas would have increased significantly, while under the FS and EP-FS scenarios, the cultivated lands would only just have increased. These future land use scenarios can inform decision-makers to make sound decisions that balance socio-economic, ecological, and food security benefits.

Evaluation of fatty acid and antioxidant variation in a complex pasture system as compared to standard cattle feed in the Great Lakes region

As the demand for grass-fed ruminant products keeps increasing, more data are needed to assess the nutritional value of feedstuffs, especially pastures. In addition, global climate change adds another challenge to the management of grasslands with projections of changing temperature and precipitation patterns. Consequently, the variations in bioactive compounds such as fatty acids and antioxidants in feeds will be harder to predict. Therefore, it is critical to report region and time-specific results of the nutritional value of feeds intended for ruminant nutrition. The objectives of this study were to compare the antioxidant and fatty acid content of commonly used feedstuffs including a complex pasture mixture from the Great Lakes Region and a traditional grain-based diet, and to assess the variations of these bioactive compounds in the pasture over the course of two grazing seasons. Weather parameters including temperature and rainfall were recorded for the length of the study. Feed samples were collected between June and September 2019 and 2020 and analyzed for nutrient composition, chlorophyll A and B, carotenoids, and total phenols. Fatty acids were analyzed by GC-MS. Correlations were reported to analyze the relationship between individual plant species, antioxidants, and fatty acids. We observed higher antioxidant parameters in the pasture compared to the grain diet. Total polyunsaturated fatty acids were higher in the pasture including α-linolenic acid while the grain diet was higher in n-6 polyunsaturated fatty acids including linoleic acid. The n-6:n-3 ratio was more beneficial in the pasture and was 50–90 times higher in the grain diet. Variations in the fatty acid profile of the pasture were observed and varied between 2019 and 2020. Plant growth cycles, climatic conditions, and grazing methods were hypothesized to cause these changes. Altogether, this study increased our knowledge about the nutritional value of feedstuffs and will help ranchers and researchers to better understand the variations of bioactive content based on region, season, and climatic conditions.

Quantifying the Aboveground Biomass (AGB) of Gobi Desert Shrub Communities in Northwestern China Based on Unmanned Aerial Vehicle (UAV) RGB Images

Shrubs are an important part of the Gobi Desert ecosystem, and their aboveground biomass (AGB) is an important manifestation of the productivity of the Gobi Desert ecosystem. Characterizing the biophysical properties of low-stature vegetation such as shrubs in the Gobi Desert via conventional field surveys and satellite remote sensing images is challenging. The AGB of shrubs had been estimated from spectral variables taken from high-resolution images obtained by unmanned aerial vehicle (UAV) in the Gobi Desert, Xinjiang, China, using vegetation feature metrics. The main results were as follows: (1) Based on the UAV images, several RGB vegetation indices (RGB VIs) were selected to extract the vegetation coverage, and it was found that the excess green index (EXG) had the highest accuracy and the overall extraction accuracy of vegetation coverage reached 97.00%. (2) According to field sample plot surveys, the AGB and shrub crown area of single shrubs in the Gobi Desert were in line with a power model. From the bottom of the alluvial fan to the top of the alluvial fan, as the altitude increased, the AGB of the vegetation communities showed an increasing trend: the AGB of the vegetation communities at the bottom of the alluvial fan was 2–90 g/m2, while that at the top of the alluvial fan was 60–201 g/m2. (3) Vegetation coverage (based on the UAV image EXG index) and AGB showed a good correlation. The two conform to the relationship model (R2 = 0.897) and the expression is Y = 1167.341 x0.946, where Y is the AGB of the sample plots in units g/m2 and x is the vegetation coverage extracted by the VI. (4) The predicted AGB values of Gobi Desert shrubs using UAV RGB images based on a power model were closer to the actual observed AGB values. The study findings provide a more efficient, accurate, and low-cost method for estimating vegetation coverage and AGB of Gobi Desert shrubs.

Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe

Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and other background factors in natural grassland. Unmanned aerial vehicle (UAV)-based imaging spectroscopy provides the possibility of soil information removal by virtue of its high spatial and spectral resolution. In this study, UAV-imaging spectroscopy data with a spatial resolution of 0.2 m obtained in two sites of typical alpine steppe within the Sanjiangyuan National Nature Reserve were used to analyze the relationships between four spectral diversity metrics (coefficient of variation based on NDVI (CVNDVI), coefficient of variation based on multiple bands (CVMulti), minimum convex hull volume (CHV) and minimum convex hull area (CHA)) and two species diversity indices (species richness and the Shannon–Wiener index). Meanwhile, two soil removal methods (based on NDVI threshold and the linear spectral unmixing model) were used to investigate the impact of soil on species diversity estimation. The results showed that the Shannon–Wiener index had a better response to spectral diversity than species richness, and CVMulti showed the best correlation with the Shannon–Wiener index between the four spectral diversity metrics after removing soil information using the linear spectral unmixing model. It indicated that the estimation ability of spectral diversity to species diversity was significantly improved after removing the soil information. Our findings demonstrated the applicability of the spectral variation hypothesis in natural grassland, and illustrated the impact of soil on species diversity estimation.

Plant Diversity, Functional Group Composition and Legumes Effects versus Fertilisation on the Yield and Forage Quality

Elevating plant diversity and functional group composition amount in the swards may contribute to lower N fertiliser use. The excessive use of fertilisers in agriculture is one of the causes of environmental pollution issues. We investigated the effects of plant diversity, functional community composition, and fertilisation on the dry matter yield and its quality at the Lithuanian Research Centre for Agriculture and Forestry, Central Lithuania. The study aimed to determine the productivity potential of single-species and multi-species swards with three, four, six, and eight plant species in the mixtures including four grasses and four legumes. Two experimental backgrounds were used with N0 and N150 kg ha−1 yr−1 for all treatments. In the two-year experiment manipulating species richness and functional group diversity had a positive effect on the dry matter yield and produced better quality of the forage when compared with single-species swards. Crude protein in the forage of grass–legume mixtures was significantly greater than for grass monocultures. Investigating fertilisation background was a concern; it had a positive effect on the single-species sward yield but decreased the yield of multi-species swards.

Arbuscular Mycorrhizal Fungi and Nutrition Determine the Outcome of Competition Between Lolium multiflorum and Trifolium subterraneum

Arbuscular mycorrhizal fungi (AMF) may affect competitive plant interactions, which are considered a prevalent force in shaping plant communities. Aiming at understanding the role of AMF in the competition between two pasture species and its dependence on soil nutritional status, a pot experiment with mycorrhizal and non-mycorrhizal Lolium multiflorum and Trifolium subterraneum was conducted, with manipulation of species composition (five levels), and nitrogen (N)- and phosphorus (P)- fertilization (three levels). In the non-mycorrhizal state, interspecific competition did not play a major role. However, in the presence of AMF, Lolium was the strongest competitor, with this species being facilitated by Trifolium. While N-fertilization did not change the competitive balance, P-fertilization gave Lolium, a competitive advantage over Trifolium. The effect of AMF on the competitive outcome may be driven by differential C-P trade benefits, with Lolium modulating carbon investment in the mycorrhizal network and the arbuscule/vesicle ratio at the cost of Trifolium.

Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects

The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.

Confronting an individual-based simulation model with empirical community patterns of grasslands

Grasslands contribute to global biogeochemical cycles and can host a high number of plant species. Both-species dynamics and biogeochemical fluxes-are influenced by abiotic and biotic environmental factors, management and natural disturbances. In order to understand and project grassland dynamics under global change, vegetation models which explicitly capture all relevant processes and drivers are required. However, the parameterization of such models is often challenging. Here, we report on testing an individual- and process-based model for simulating the dynamics and structure of a grassland experiment in temperate Europe. We parameterized the model for three species and confront simulated grassland dynamics with empirical observations of their monocultures and one two-species mixture. The model reproduces general trends of vegetation patterns (vegetation cover and height, aboveground biomass and leaf area index) for the monocultures and two-species community. For example, the model simulates well an average annual grassland cover of 70% in the species mixture (observed cover of 77%), but also shows mismatches with specific observation values (e.g. for aboveground biomass). By a sensitivity analysis of the applied inverse model parameterization method, we demonstrate that multiple vegetation attributes are important for a successful parameterization while leaf area index revealed to be of highest relevance. Results of our study pinpoint to the need of improved grassland measurements (esp. of temporally higher resolution) in close combination with advanced modelling approaches.

Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the Betula and Salix genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R2 = 0.888; validation R2 = 0.774) nearly as well as the top multivariate linear regression models (calibration R2 = 0.896; validation R2 > 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.

A Metaecoepidemic Model of Grassland Ecosystem with Only Consumers' Migration

Metaecoepidemic models generalize metapopulation systems, combining local population dynamics with inter-patch migration coupled with an epidemic proliferation. A resource-consumer model is introduced with an ecosystem composed by two patches, in which consumers can freely move. A disease affects resources of the second patch. This situation corresponds to a grassland-herbivore environment, where one patch, managed in an extensive way, has a wider plant diversity, while the other one is highly fertilized leading to an important forage production. The latter is also subject to a fungal disease. Herbivores both feed on healthy or infected crop and can freely migrate between the two patches. A preliminary investigation focuses on behaviors emerging from some parts of the model, respectively, formed by uncoupled patches and by the purely demographic coupled model. Equilibria of the whole system are assessed and characterized. Results are then compared with the purely demographic model to highlight the role of the disease in this dynamics. A thorough numerical investigation of the model completes this analysis to assess the system behavior near each equilibrium. System bifurcations have also been explored as well as the response of the system equilibria to parameter perturbations. The disease eradication is possible under suitable circumstances. Coexistence of the five populations through persistent oscillations is also possible, but it is not at a stable level.

Forecasting semi-arid biome shifts in the Anthropocene

Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

Relevance of Intermittent Rivers and Streams in Agricultural Landscape and Their Impact on Provided Ecosystem Services-A Mediterranean Case Study

Ecosystem services (ES), as an interconnection of the landscape mosaic pieces, along with temporal rivers (IRES) are an object of research for environmental planners and ecological economists, among other specialists. This study presents (i) a review on the importance of IRES and the services they can provide to agricultural landscapes; (ii) a classification tool to assess the impact of IRES to provide ES by agricultural landscapes; (iii) the application of the proposed classification to the Caia River in order to identify the importance of this intermittent river for its surrounding agricultural landscape. The classification of the ES follows the Common International Classification of Ecosystem (CICES) classification that was adapted for the purposes of this study. Firstly, the list of ES provided by agricultural landscape was elaborated. In the next step, we assessed the potential of IRES to provide ES. Next, IRES impacts to ES within the agricultural landscape were evaluated according to observations from the conducted field monitoring in the study area. This study focuses on the relevance of the intermittent Caia River-a transboundary river in Spain and Portugal-and its ephemeral tributaries in the agricultural landscape. Our study estimates that each hydrological phase of IRES increases the ES provided by the agricultural landscape. However, the dry phase can potentially have negative impacts on several services. The intensification of the agricultural sector is the main provision of the water resource within the Caia River basin, but we were able to identify several other ES that were positively impacted. The present study is in line with the conclusions of other authors who state that IRES constitute a valuable resource which should not be underestimated by society.

Characterizing Livestock Production in Portuguese Sown Rainfed Grasslands: Applying the Inverse Approach to a Process-Based Model

Grasslands are a crucial resource that supports animal grazing and provides other ecosystem services. We estimated the main properties of Portuguese sown biodiverse permanent pastures rich in legumes (SBP) starting from measured data for soil organic carbon (SOC) and using the Rothamsted Carbon Model. Starting from a dataset of SOC, aboveground production (AGP) and stocking rates (SR) in SBP, we used an inverse approach to estimate root to shoot (RS) ratios, livestock dung (LD), livestock intake (LI) and the ratio between easily decomposable and resistant plant material. Results for the best fit show that AGP and belowground productivity is approximately the same (RS is equal to 0.96). Animals consume 61% of the AGP, which is within the acceptable range of protein and energy intake. Carbon inputs from dung are also within the range found in the literature (1.53 t C/livestock unit). Inputs from litter are equally distributed between decomposable and resistant material. We applied these parameters in RothC for a dataset from different sites that only comprises SOC to calculate AGP and SR. AGP and SR were consistently lower in this case, because these pastures did not receive adequate technical support. These results highlight the mechanisms for carbon sequestration in SBP.