Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

14 p.Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Indicating soil quality in cacao-based agroforestry systems and old-growth forests: The potential of soil macrofauna assemblage

International audienceSoil quality or health is a fuzzy concept that has been vigorously criticized due to the extreme variability of soil and the difficulty of linking soil indicators to soil functions and sustainability. In most soil quality studies some obvious factors or typologies are used as a basis to select the “best indicators” of soil quality, i.e. those that best explain the differences among the plots under study. This is not the case for a variety of natural or agroecosystems including the Talamanca cacaobased agroforestry systems (AFS), which present neither a preestablished typology nor a clear framework to evaluate their soil quality. This situation required a selection of indicators based on the literature that was oriented by the nonequilibrium thermodynamic theory. A framework was elaborated through full and minimum indicator sets of baseline soil physical and chemical indicators, along with macrofauna groups. A minimum set of four wellaccepted abiotic soil quality indicators (bulk density, sum of bases, pH and carbon) was able to separate cacao AFS plots and forests into five distinct clusters along a lowtohigh “soil quality” gradient. The AFS rated as “good” soil quality did not differ from the forest. Abundances of selected macrofauna groups were well correlated with these indicators and helped elucidate the soil quality clusters identified. In particular, high predator abundance indicated proper energy flow and confirmed the high abiotic soil quality, thus confirming the potential of macrofauna groups as apt soil quality indicators. However, these indicators need to be tailored to local conditions. Consequentially, cacaobased AFS in Talamanca are able to conserve soil and provide a high level of soilrelated ecological services. Considering the soil an open system where the nonequilibrium thermodynamic theory applies successfully guided indicator selection and could help to reformulate the soil quality definition

Shade tree identity rather than diversity influences soil macrofauna in cacao-based agroforestry systems

Humanity is facing a rapid decline in global biodiversity, caused mainly by tropical deforestation through industrial and smallholder agriculture. However, smallholder agriculture landscapes host areas containing home gardens and other agroforestry systems (AFS) proved highly relevant for soil and biodiversity conservation. The positive interactions between aboveground and belowground biodiversity probably constitute a key element to promote the efficiency of these agro-ecosystems. To determine whether a relationship exists between tree and soil macrofauna diversity and composition, we compared cacao AFS with contrasted tree diversity along a topography and forest cover gradient in Talamanca, Costa Rica. To determine which components of the shade tree species, community structure (density, richness, Shannon, Pielou), and agroforest floor best explain the macrofauna community composition and structure, we constructed the “best models” based on the tree composition, tree structure, and agroforest floor as explanatory matrices in redundancy analyses. Macrofauna composition was best explained by a mix of tree species and litter attributes (R2=26.5 %), whereas macrofauna and vegetation co-vary significantly with topography (R2=12 %). Macrofauna structure is best explained by a selection of seven tree species (R2=41.2 %). The co-variation with topography remained low (R2=10.9 %). Tree evenness (Pielou index) explained only 7 % of macrofauna community structure while other diversity indices were not correlated with macrofauna composition or structure. The soil macrofauna was therefore more influenced by tree identity and litter composition than by the overall diversity of the tree community. This information is important for designing the optimal combinations of species for the intensification of production and provision of ecosystem services in cacao-based AFS