Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, no quantitative, spatially-explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here, we use 6,579 georeferenced samples to generate a mechanistic understanding of the patterns of global soil nematode abundance and functional group composition. The resulting maps show that 4.4 ± 0.64  10 20 nematodes (total biomass ~0.3 Gt) inhabit surface soils across the world, with higher abundances in sub-arctic regions (38% of total), than in temperate (24%), or tropical regions (21%). Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes into global biogeochemical models, to predict elemental cycling under current and future climate scenarios.

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis-based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.

In the recent years, many protocols aimed at reproducibly sequencing reduced--genome subsets in non--model organisms have been published. Among them, RAD--sequencing is one of the most widely used. It relies on digesting DNA with specific restriction enzymes and performing size selection on the resulting fragments. Despite its utility, this method is of a limited use with degraded DNA samples, such as those isolated from museum specimens, as these are either less likely to harbor fragments long enough to comprise two restriction sites making possible ligation of the technical sequences required or performing size selection of the resulting fragments. In addition, RAD--sequencing also reveals a suboptimal technique when applied to an evolutionary scale larger than the intra--specific level, as polymophisms in the restriction sites cause loci dropout. Here, we address both of these limitations by a novel method called hybridization RAD (hyRAD). In this method, biotinylated RAD fragments, covering a random fraction of the genome, are used as baits for capturing homologous fragments from samples processed through a classical genomic shotgun sequencing protocol. This simple and cost-effective approach allows sequencing orthologous sequences even from highly degraded DNA samples, opening new avenues of research in the field of museum genomics. Not relying on the restriction site presence, it improves among--sample loci coverage, and can be applied to broader phylogenetic scales. In a trial study, hyRAD allowed us to obtain a large set of orthologous loci from fresh and museum samples from a non--model butterfly species, with over 10.000 single nucleotide polymorphisms present in all eight analyzed specimens, including 58 years old museum samples.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries