Functional trait composition of carabid beetle communities predicts prey suppression through both mass ratio and niche complementarity mechanisms.

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.

More than one quarter of Africa's tree cover is found outside areas previously classified as forest.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Estimating herbaceous aboveground biomass in Sahelian rangelands using Structure from Motion data collected on the ground and by UAV.

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.

FLOTROP, a massive contribution to plant diversity data for open ecosystems in northern tropical Africa.

The FLOTROP dataset contains numerous plant observations (around 340,000 occurrences) in northern tropical Africa (from the 5th to 25th parallel north) in open ecosystems (savannah and steppe). They were collected by multiple collectors between 1920 and 2012 and were managed by Philippe Daget. These observations are probably the most important and unique source of plant distribution over the Sahel area. The data are now available in the Global Biodiversity Information Facility, Tela Botanica website, and as maps in the African Plant Database. For the overall area involved, this dataset has increased by 40% the data available in the GBIF. For some countries between the 15th and 21st parallel north, the FLOTROP dataset has increased available occurrences 10-fold compared to the data existing in the GBIF.

Filling the gap in functional trait databases: use of ecological hypotheses to replace missing data.

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.