Soil carbon and plant richness relationships differ among grassland types, disturbance history and plant functional groups

Understanding the relationship of soil carbon storage and species diversity in grasslands can provide insights into managing these ecosystems. We studied relationships among soil C and plant species richness within ~ 9700 ha of grasslands in Colorado, US. Using 141 grassland transects, we tested how soil C was related to plant species richness, grassland type, soil texture, and prairie dog presence. Soil C was significantly, positively related to plant species richness, while native perennial graminoid species richness exhibited an even stronger positive relationship. However, the relationship of soil C and plant richness was not found in all three grassland types studied, but instead was unique to the most common grassland type, mixed grass prairie, and absent from both xeric tallgrass and mesic tallgrass prairie. The presence of a single indicator species, Andropogon gerardii, showed a significant, positive relationship with soil carbon. Our best possible model explained 45% of the variance in soil C using species richness, grassland type, and their interaction. Surprisingly, soil C was negatively related to soil clay, suggesting that surface clays amplify evaporation and water runoff rather than protecting soil organic matter from decomposition. Soil C was negatively related to prairie dog presence, suggesting that prairie dogs do not enhance soil carbon sequestration; in fact, prairie dog occupied sites had significantly lower soil C, likely related to loss of topsoil from prairie dog colonies. Our results suggest that management for species richness provides the co-benefit of soil C storage, and high clay and prairie dog disturbance compromises both.

Mapping of Kobresia pygmaea Community Based on Umanned Aerial Vehicle Technology and Gaofen Remote Sensing Data in Alpine Meadow Grassland: A Case Study in Eastern of Qinghai–Tibetan Plateau

The Kobresia pygmaea (KP) community is a key succession stage of alpine meadow degradation on the Qinghai–Tibet Plateau (QTP). However, most of the grassland classification and mapping studies have been performed at the grassland type level. The spatial distribution and impact factors of KP on the QTP are still unclear. In this study, field measurements of the grassland vegetation community in the eastern part of the QTP (Counties of Zeku, Henan and Maqu) from 2015 to 2019 were acquired using unmanned aerial vehicle (UAV) technology. The machine learning algorithms for grassland vegetation community classification were constructed by combining Gaofen satellite images and topographic indices. Then, the spatial distribution of KP community was mapped. The results showed that: (1) For all field observed sites, the alpine meadow vegetation communities demonstrated a considerable spatial heterogeneity. The traditional classification methods can hardly distinguish those communities due to the high similarity of their spectral characteristics. (2) The random forest method based on the combination of satellite vegetation indices, texture feature and topographic indices exhibited the best performance in three counties, with overall accuracy and Kappa coefficient ranged from 74.06% to 83.92% and 0.65 to 0.80, respectively. (3) As a whole, the area of KP community reached 1434.07 km2, and accounted for 7.20% of the study area. We concluded that the combination of satellite remote sensing, UAV surveying and machine learning can be used for KP classification and mapping at community level.

Soil Nitrogen Dynamics in a Managed Temperate Grassland Under Changed Climatic Conditions

Grasslands are one of the most common biomes in the world with a wide range of ecosystem services. Nevertheless, quantitative data on the change in nitrogen dynamics in extensively managed temperate grasslands caused by a shift from energy- to water-limited climatic conditions have not yet been reported. In this study, we experimentally studied this shift by translocating undisturbed soil monoliths from an energy-limited site (Rollesbroich) to a water-limited site (Selhausen). The soil monoliths were contained in weighable lysimeters and monitored for their water and nitrogen balance in the period between 2012 and 2018. At the water-limited site (Selhausen), annual plant nitrogen uptake decreased due to water stress compared to the energy-limited site (Rollesbroich), while nitrogen uptake was higher at the beginning of the growing period. Possibly because of this lower plant uptake, the lysimeters at the water-limited site showed an increased inorganic nitrogen concentration in the soil solution, indicating a higher net mineralization rate. The N2O gas emissions and nitrogen leaching remained low at both sites. Our findings suggest that in the short term, fertilizer should consequently be applied early in the growing period to increase nitrogen uptake and decrease nitrogen losses. Moreover, a shift from energy-limited to water-limited conditions will have a limited effect on gaseous nitrogen emissions and nitrate concentrations in the groundwater in the grassland type of this study because higher nitrogen concentrations are (over-) compensated by lower leaching rates.

Multisensor SAR and optical estimation of grassland above-ground biomass and LAI: a case study for the Mazia valley in South Tyrol

<p>Grasslands are a predominant land cover form, responsible for ecosystem services such as slope stabilization, water and carbon storage or fodder provision for livestock. At the same time, altering climatic effects and human activities have influenced the natural growth pattern and condition of alpine grasslands over the past decades. Mountainous areas are projected to be particularly impacted by climatic changes and management practices. Nowadays, a wide variety and different installations of Earth observation systems are available to monitor and predict grassland growth and status, to evidence ecosystem services such as biodiversity, the fodder availability or to highlight the effectiveness of management practices.</p><p>In this study Support Vector Regression (SVR) and Random Forest (RF) machine learning techniques were used to estimate the aboveground biomass, plant water content and the leaf area index (LAI). As input, we combined hyperspectral imagery from field spectrometers, optical Sentinel-2 data as well as SAR data from Sentinel-1. The models were tested targeting approximately 250 biomass and LAI samples taken from 2017 to 2020 on grasslands in the Mazia/Matsch valley, located in South Tyrol (Italy). The dataset was divided based on grassland type (meadow and pasture) the growth period (up to three growth periods a year for meadows), as well as the year, to analyze the modelled predictions based on the growing stage of the vegetation.</p><p>The results obtained using the integration of the datasets are very promising in the meadow, with R<sup>2</sup> reaching ranging from 0.5 to 0.8 for the biomass and from 0.6 to 0.8 for the LAI retrieval. At the same time, the division in growth phases shows a slightly higher correlation than during the first and second growing periods, indicating that the irregular growth after the last harvest of the year affects the capability of prediction of LAI and above-ground biomass. However, the predictability worsens on high biomass and LAI values before the harvest takes place, thus indicating an impact of the saturation in the optical data and revealing the need for additional data sources or an alternated weighting of the predictors in the models. The results on the pasture show that the prediction of LAI and biomass with optical and SAR data is difficult to achieve (mean R<sup>2</sup> ranging from 0.3 to 0.4) given the natural heterogeneity in growth within the test area. Additional datasets such as cattle movement or the slope information could represent a valuable source of information for further LAI and biomass growth analyses in mountainous areas.</p><p>This research is part of the 2019-2021 project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.</p>

Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China

Soil erosion in the Three-River Headwaters (TRH) region has continued to intensify in recent decades due to human activities and climate change. To reverse this situation, the Chinese central government has launched the Subsidy and Incentive System for Grassland Conservation (SISGC). As a sign of the effectiveness of SISGC implementation, the dynamic changes of soil erosion can provide timely feedback for decision makers and managers. The Revised Universal Soil Loss Equation (RUSLE) model was used to simulate the spatial distribution of soil erosion before and after SISGC implementation, and Mann–Kendall (MK) test to reveal the effect of policy implementation. The results showed that: (1) the soil erosion in the TRH was mainly mild (83.83% of the total eroded area), and the average soil erosion rate and the total erosion were 13.63 t ha−1 y−1 and 323.58 × 106 t y−1 respectively before SISGC implementation; (2) SISGC implementation has curbed soil erosion. After SISGC implementation, the total soil erosion decreased by 3.80%, which showed obvious differences between grassland types; (3) The influences of SISGC were mainly because it has increased vegetation cover, further decreasing soil erosion. However, soil erosion in Alpine grassland has deteriorated, indicating direct targeted policymaking should be on the agenda. Furthermore, SISGC should be continued and grassland-type-oriented to restore the grassland ecosystem.

Simulating the spatiotemporal variations in aboveground biomass in Inner Mongolian grasslands under environmental changes

Grassland aboveground biomass (AGB) is a critical component of the global carbon cycle and reflects ecosystem productivity. Although it is widely acknowledged that dynamics of grassland biomass is significantly regulated by climate change, in situ evidence at meaningfully large spatiotemporal scales is limited. Here, we combine biomass measurements from six long-term (> 30 years) experiments and data in existing literatures to explore the spatiotemporal changes in AGB in Inner Mongolian temperate grasslands. We show that, on average, annual AGB over the past 4 decades is 2561, 1496 and 835 kg ha−1, respectively, in meadow steppe, typical steppe and desert steppe in Inner Mongolia. The spatiotemporal changes of AGB are regulated by interactions of climatic attributes, edaphic properties, grassland type and livestock. Using a machine-learning-based approach, we map annual AGB (from 1981 to 2100) across the Inner Mongolian grasslands at the spatial resolution of 1 km. We find that on the regional scale, meadow steppe has the highest annual AGB, followed by typical and desert steppe. Future climate change characterized mainly by warming could lead to a general decrease in grassland AGB. Under climate change, on average, compared with the historical AGB (i.e. average of 1981–2019), the AGB at the end of this century (i.e. average of 2080–2100) would decrease by 14 % under Representative Concentration Pathway (RCP) 4.5 and 28 % under RCP8.5. If the carbon dioxide (CO2) enrichment effect on AGB is considered, however, the estimated decreases in future AGB can be reversed due to the growing atmospheric CO2 concentrations under both RCP4.5 and RCP8.5. The projected changes in AGB show large spatial and temporal disparities across different grassland types and RCP scenarios. Our study demonstrates the accuracy of predictions in AGB using a modelling approach driven by several readily obtainable environmental variables and provides new data at a large scale and fine resolution extrapolated from field measurements.

Species co-occurrence networks of ground beetles in managed grasslands

Grassland biodiversity, including traditional rural biotopes maintained by traditional agricultural practices, has become threatened worldwide. Road verges have been suggested to be complementary or compensatory habitats for species inhabiting grasslands. Species co-occurrence patterns linked with species traits can be used to separate between the different mechanisms (stochasticity, environmental filtering, biotic interactions) behind community structure. Here, we study species co-occurrence networks and underlying mechanisms of ground beetle species (Carabidae) in three different managed grassland types (meadows, pastures, road verges, n = 12 in each type) in Central Finland. We aimed to find out whether road verges can be considered as compensatory to traditional rural biotopes (meadows and pastures). We found that stochasticity explained over 90% of the pairwise co-occurrences, and the non-random co-occurrences were best explained by environmental filtering, regardless of the grassland type. However, the identities and traits of the species showing non-random co-occurrences differed among the habitat types. Thus, environmental factors behind environmental filtering differ among the habitat types and are related to the site-specific characteristics and variation therein. This poses challenges to habitat management since the species’ response to management action may depend on the site-specific characteristics. Although road verges are not fully compensatory to meadows and pastures, the high similarity of species richness and the high level of shared species suggest that for carabids road verges may be corridors connecting the sparse network of the remaining traditional rural biotopes.