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Background: Grasslands provide a wide range of ecosystem services (ESs). However, there is currently no method for easily diagnosing the level of ESs produced. Our aim was to develop ES indicators based on botanical surveys, which are readily available data and integrative of grassland spatiotemporal variability. Methods: Based on academic knowledge and expertise, we identified several simple vegetation criteria that we aggregated using a multicriteria analysis tool to construct indicators of the level of ESs provided by grasslands. In this study, the indicators were calculated from over 2000 botanical surveys spread over a wide biogeographical gradient. Results: Analyses of correlation between the various indicators show that "forage supply" and "diversity conservation" were not correlated. "Forage availability" and "nitrogen availability for the vegetation" were positively linked together and negatively linked to the robustness of the plant community to extreme events. A temporal approach highlights that the "biodiversity conservation" score decreased from 1970 to 2010 and that "nitrogen availability for the vegetation" was lower in 1970 and 1980 than in 2000 and 2010. Conclusions: These results show that our aggregation method based on a large data set of botanical surveys could be appropriate for studying temporal dynamics of ESs. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely used to assess the vegetation of an ecosystem. This report presents three data sets from UAVs used on a savannah‐type ecosystem in Senegal. Two data sets contain an orthomosaic and a surface and terrain digital model (UAV outputs), along with associated field measurements on the tree and herbaceous layers, while the third data set contains an orthomosaic and digital surface model, along with field measurements on the herbaceous layers. One data set was compiled over the entire rainy season on the same plot (plot dataset). One was compiled across a landscape at the 7000 ha Dahra Djoloff research center (CRZ‐dahra Centre de recherche zootechnique de Dahra). The third data set was compiled across Senegal from the North to the Southeast of the country with wide variability in climate and soil conditions. All the data sets are in Open Access in the Zenodo repository. These data sets could be used in many different studies: calibration between field measurements and UAV outputs on a large scale, or used as an intermediate step between field and satellite images. [ABSTRACT FROM AUTHOR]

Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost‐efficient way. Structure from Motion (SfM) is an image analysis process that can create a variety of 3D spatial models as well as 2D orthomosaics from a set of images. Computed from Unmanned Aerial Vehicle (UAV) and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape, and national (across Senegal). All images were processed using PIX4D software (photogrammetry software) and were used to extract vegetation indices and heights. A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g m−² for fresh mass (20% relative error) and 60 g m−² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystems. [ABSTRACT FROM AUTHOR]

Understanding how drivers of change affect ecosystem services (ES) is of great importance. Indicators of ES can be developed based on biophysical measures and be used to investigate the service flow from ecosystems to socio-ecological systems. However, the ES concept is multivariate and the use of normalized composite indicators reduces complexity and facilitates communication between science and policy. The aim of this study is to analyze how land use change affects ES and species richness and how the effects are modified by environmental factors by using composite indicators based on biophysical indicators. Using multivariate and regression analyses, we analyze the effect of grazing management abandonment in semi-natural grasslands in Norway on six ES: nutrient cycling, pollination, forage quality, aesthetics and global and regional climate regulation in addition to species richness along soil and climate gradients. Nutrient cycling, forage quality, regional climate regulation, aesthetics and species richness are larger in managed compared to abandoned grasslands. There are trade-offs among ES as different management strategies provide various ES and these trade-offs vary along environmental gradients. Management policies that aim to conserve ES need to have conservation goals that are context dependent, should recognize ES trade-offs and be adapted to local conditions. [ABSTRACT FROM AUTHOR]

Questions: What are the effects of abandonment on plant diversity in semi-natural grasslands? Do the effects of abandonment on taxonomic and functional diversity vary along environmental gradients of climate and soil? Location: West and mid-Norway. Methods: Plant composition was surveyed in 110 subplots of 4 m2 in 14 sites across grazed and abandoned semi-natural grasslands. Climate data were extracted and soil composition analysed. To reduce the number of explanatory variables and deal with collinearity, we performed PCA. Data on the plant species vegetative height (H), leaf dry matter content (LDMC), specific leaf area (SLA), seed mass (SM) and number of seeds per plant (SNP) for 175 species were extracted from the LEDA database. Measures of plant diversity (species richness, CWM of functional traits and functional diversity (evenness and range)) were calculated for each subplot. To estimate the effects of abandonment on plant diversity and examine how these effects are moderated by gradients in soil and climate, we fitted mixed models to the data including site as a random effect. Results: Species richness in the subplots was lower in abandoned semi-natural grasslands, especially on more calcareous soils. CWM H, LDMC and SM were higher in abandoned semi-natural grasslands. CWM LDMC was only higher in the driest subplots. The ranges in H, SLA and SM, as well as evenness in LDMC were also higher in abandoned semi-natural grasslands,but the range in LDMC was lower. Conclusions: It is important to assess both taxonomic and functional diversity to understand ecosystem processes. The species pool in ecosystems influenced by a long history of intermediate grazing includes a high proportion of low stature, grazing-tolerant plant species. Abandonment of extensive land-use practices will cause a decline in taxonomic diversity (plant species richness) in such systems due to increased abundance of plants with high stature that outcompete the lower, grazing-tolerant plants. This process is predominant especially if moisture, soil fertility and pH are at intermediate levels. Changes in species communities due to abandonment will also influence ecosystem functioning, such as nutrient turnover and fodder production resilience. [ABSTRACT FROM AUTHOR]

Highlights: [•] LAI was split by layer in coffee agroforestry system and upscaled using remote sensing. [•] Ten years dynamics of LAI were monitored and related to Ecosystem Services and Management. [•] Yield was best predicted with N-fertilizer, LAI and percentage of pruning. [•] LAI could significantly affect the partitioning between blue and green water. [•] LAI is a promising scalable indicator of Ecosystem Services. [Copyright &y& Elsevier]

Functional trait databases are powerful tools in ecology, though most of them contain large amounts of missing values. The goal of this study was to test the effect of imputation methods on the evaluation of trait values at species level and on the subsequent calculation of functional diversity indices at community level using functional trait databases. Two simple imputation methods (average and median), two methods based on ecological hypotheses, and one multiple imputation method were tested using a large plant trait database, together with the influence of the percentage of missing data and differences between functional traits. At community level, the complete-case approach and three functional diversity indices calculated from grassland plant communities were included. At the species level, one of the methods based on ecological hypothesis was for all traits more accurate than imputation with average or median values, but the multiple imputation method was superior for most of the traits. The method based on functional proximity between species was the best method for traits with an unbalanced distribution, while the method based on the existence of relationships between traits was the best for traits with a balanced distribution. The ranking of the grassland communities for their functional diversity indices was not robust with the complete-case approach, even for low percentages of missing data. With the imputation methods based on ecological hypotheses, functional diversity indices could be computed with a maximum of 30% of missing data, without affecting the ranking between grassland communities. The multiple imputation method performed well, but not better than single imputation based on ecological hypothesis and adapted to the distribution of the trait values for the functional identity and range of the communities. Ecological studies using functional trait databases have to deal with missing data using imputation methods corresponding to their specific needs and making the most out of the information available in the databases. Within this framework, this study indicates the possibilities and limits of single imputation methods based on ecological hypothesis and concludes that they could be useful when studying the ranking of communities for their functional diversity indices. [ABSTRACT FROM AUTHOR]

Monitoring available feed for livestock is a key factor for developing pastoralism in the Sahel, and satellite images has proven useful in monitoring dry mass on large spatial scales. This approach requires field measurements of dry mass (herbaceous and woody plants) to calibrate such models based on Earth observation data. However, the need for representative field measurements can be a challenge when considering the low spatial resolution of available satellite data. Unmanned Aerial Vehicles (UAV) can produce very high-resolution images, so we tested UAVs as an intermediate step between field measurements and satellite images, to bridge the difference in spatial scale. We used 43 orthomosaics from a red-green-blue (RGB) UAV sensor in combination with field measurements of herbaceous and woody dry biomass at sites located primarily in the northern/central and southernmost parts of Senegal. We developed a dry mass model trained with filed observed measurements to be applied on the UAV orthomosaics. The dry mass information obtained from these UAV maps was subsequently related to vegetation indices derived from Sentinel-2 data to produce a national-scale 10 m spatial resolution baseline map of herbaceous and woody dry mass for Senegal in 2020. We obtained a high correlation between dry mass derived from UAV and Sentinel-2 indices (R² = 0.91), suggesting a robust basis for national-scale mapping. Lastly, our map was compared with a state-of-the-art annual reference map based on satellite remote sensing. This comparison showed a difference of 21 million tons of dry mass at national level. We concluded that bridging the spatial gap between field and satellite observations using spatially representative UAV data collection is a cost-effective approach for accurate mapping of dry mass at national level using freely available Sentinel-2 satellite data. [ABSTRACT FROM AUTHOR]

Several components of predator functional diversity have been hypothesized to influence prey suppression through either niche complementarity or mass ratio effects. Nevertheless, most studies have used a functional group approach when assessing the role of these predators in ecosystem functioning. By adopting a trait‐based approach, we evaluated the relative contributions of carabid diversity components in predicting prey suppression. Our results highlight the importance of both taxonomic and functional diversity components of carabids as key drivers of prey suppression. Prey suppression was best predicted by carabid densities, with the dominance of Poecilus cupreus potentially driving the positive effect of community total abundance through the mass ratio effect. Prey suppression increased with increasing the density of large carabids. In addition, carabid eye diameter and antennal length were key functional traits for predicting prey suppression. Furthermore, prey suppression increased with increasing carabid functional richness following the niche complementarity effect. In contrast to functional richness, functional evenness and functional divergence of carabid communities were weakly correlated with prey suppression. By identifying which diversity components of carabid communities contribute the most to increase prey suppression, our results can guide efforts aiming to predict the relationship between diversity of these predators and ecosystem functioning. [ABSTRACT FROM AUTHOR]

In drylands, water availability is the main limiting factor for plant growth. Topographic depressions in the Sahel that reveal a more favorable water balance display greater tree diversity compared to their surroundings. However, the environmental factors that drive differences in botanical composition between depressions remain unknown. Here, we investigated the depression features, and the landscape and connectivity metrics responsible for woody vegetation composition in a savanna of Senegal. We mapped depressions in a 700 km2 area and inventoried woody vegetation in 61 depressions varying in area. Simple linear models were applied between (i) indicators of richness and botanical composition and (ii) depression attributes, landscape and connectivity metrics. We found that the area of a depression increased species richness. The number of vegetation strata and the presence of a temporary pond also positively influenced species richness. Yet, while large depressions always had high richness, some small depressions did too. We were not able to identify additional factors to explain this diversity between small depressions. In the context of decreasing richness and composition shift observed in the Sahel over recent decades, depressions, as biodiversity hotspots, are key elements in ecosystem functioning. Further studies are needed to understand other potential drivers of species diversity, such as soil water availability. • Species richness increases with depression area. • Some small depressions have a similar species richness than the largest. • Landscape and connectivity metrics weakly influence species diversity. • Rare and depression-specific species present higher water requirements. • Difference in water availability among depressions should be further investigated. [ABSTRACT FROM AUTHOR]

Assessing the spatial variability of soil organic carbon (SOC) is crucial for SOC monitoring and comparing management options. Topsoil (0–5 cm) SOC concentrations were surveyed in a coffee agroforestry watershed (0.9 km 2 ) on Andisols in Costa Rica with uniform farm management. We encountered high values and large spatial variations of SOC, from 48.1 to 172 g kg − 1 in the dry combustion set (SOC ref ; n = 72) used for calibrating the visible-near-infrared reflectance spectroscopy (VNIRS) samples (SOC VNIRS ; 350–2500 nm; n = 520). VNIRS using partial least squares regression was effective in predicting SOC ( R 2 = 0.85; a root mean square error (RMSE) = 12.3 g kg − 1 ) and proved an effective proxy measurement. We assessed several topographic, vegetation and andic soil property variables, of which only the latter (metal–humus complexes and allophanes) displayed strong correlations with SOC ref concentrations. We compared Random Forest and three geostatistical approaches for the interpolation of SOC in unsampled locations. Ordinary kriging with SOC ref yielded an RMSE of 28.0 g kg − 1 . Random Forest was successful in incorporating many weakly and non-linearly correlated covariates with SOC (RMSE = 14.7 g kg − 1 ), provided Al p (the sodium pyrophosphate extractable aluminum), the best predictor of SOC ( r = 0.85) but also the most costly variable to acquire. Co-kriging with Al p also showed high reduction in RMSE (16.0 g kg − 1 ). Co-kriging with SOC VNIRS only showed marginal reduction in RMSE to 24.2 g kg − 1 due to the presence of a high nugget effect. Local variability of SOC in this volcanic agroforestry watershed was dominated by andic properties whereas topographic or vegetation variables had very little impact. Estimation of SOC variability is recommended using inexpensive proxy measurements like VNIRS (RMSE = 12.3 g kg − 1 ) rather than spatial interpolation techniques. [ABSTRACT FROM AUTHOR]