Soil organisms provide crucial ecosystem services that support human life. However, little is known about their diversity, distribution, and the threats affecting them. Here, we compiled a global dataset of 60 sampled earthworm communities from over 7000 sites in 56 countries to predict patterns in earthworm diversity, abundance, and biomass. We identify the environmental drivers shaping these patterns. Local species richness and abundance typically peaked at higher latitudes, while biomass peaked in the tropics, patterns opposite to those observed in aboveground organisms. Similar to many aboveground taxa, climate variables were more important in shaping earthworm communities than soil properties or habitat 65 cover. These findings highlight that, while the environmental drivers are similar, conservation strategies to conserve aboveground biodiversity might not be appropriate for earthworm diversity, especially in a changing climate.

The physiochemical properties of soils of two different types of forests (pure Shorea robusta and mixed Shorea robusta) were analyzed. Soil samples were collected from both types of forest and analyzed for texture, pH, organic matter, humus content, water holding capacity, nitrogen, phosphorous and potassium. In both the pure and mixed forest, soil was sandy loam (60.12% and 50.58% sand, 28.59% and 35.24% silt and 11.12 and 22.41% clay, respectively). The pH value was lower in pure forest (4.33) than in the mixed forest (5.26), and so were phosphorus and water holding capacity. The higher values of humus, organic matter, nitrogen and potassium (7.34%, 2.42%, 0.117%, 267.73 kg/ha, respectively) were found in pure forest. The higher levels of soil nutrients in the pure forest were due partly to reduction in the loss of top soil and partly to the increased supply of nutrients in the form of leaf litter and biomass from the larger number of sal trees and their saplings.

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Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson’s correlation coefficient (r) and Jaccard’s index (J)–two of the most common metrics for correlation analysis of presence-absence data–can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson’s correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard’s index of similarity (J) can yield improvements over Pearson’s correlation coefficient. However, the standard null model for Jaccard’s index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard’s index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

The Great Plains of North America encompass approximately 1,30 0,0 0 0 km 2 of land from Texas to Saskatchewan. The integrity of these lands is under continual assault by long-established and newlyarrived invasive plant species, which can threaten native species and diminish land values and ecological goods and services by degrading desired grassland resources. The Great Plains are a mixture of privately and publicly owned lands, which leads to a patchwork of varying management goals and strategies for controlling invasive plants. Continually updated knowledge is required for efficient and effective management of threats posed by changing environments and invasive plants. Here we discuss current challenges, contemporary management strategies, and management tools and their integration, in hopes of presenting a knowledge resource for new and experienced land managers and others involved in making decisions regarding invasive plant management in the Great Plains.

A mounting body of research suggests that invasive nonnative earthworms substantially alter microbial communities, including arbuscular mycorrhizal fungi (AMF). These changes to AMF can cascade to affect plant communities and vertebrate populations. Despite these research advances, relatively little is known about (1) the mechanisms behind earthworms' effects on AMF and (2) the factors that determine the outcomes of earthworm-AMF interactions (i.e., whether AMF abundance is increased or decreased and subsequent effects on plants). We predict that AMF-mediated effects of nonnative earthworms on ecosystems are nearly universal because (1) AMF are important components of most terrestrial ecosystems, (2) nonnative earthworms have become established in nearly every type of terrestrial ecosystem, and (3) nonnative earthworms, due to their burrowing and feeding behavior, greatly affect AMF with potentially profound concomitant effects on plant communities. We highlight the multiple direct and indirect effects of nonnative earthworms on plants and review what is currently known about the interaction between earthworms and AMF. We also illustrate how the effects of nonnative earthworms on plant-AMF mutualisms can alter the structure and stability of aboveground plant communities, as well as the vertebrate communities relying on these habitats. Integrative studies that assess the interactive effects of earthworms and AMF can provide new insights into the role that belowground ecosystem engineers play in altering aboveground ecological processes. Understanding these processes may improve our ability to predict the structure of plant and animal communities in earthworm-invaded regions and to develop management strategies that limit the numerous undesired impacts of earthworms.

Abstract. Understanding the relative importance of environmental and anthropogenic factors in driving plant community structure, including relative dominance of native and non-native species, helps predict community responses to biological invasions. To assess factors influencing plant communities on San Clemente Island, USA, we conducted an islandwide vegetation survey in which we measured plant species richness and percent cover of native and non-native plants, as well as physical environmental variables, soil chemical properties, abundance of soil microbial functional groups (e.g., arbuscular mycorrhizal fungi [AMF]), and a human disturbance variable (distance to road). We found that total plant species richness decreased with increasing non-native plant cover, soil pH, and AMF abundance. Native plant cover increased with increasing distance to a major paved road and decreased with increasing soil moisture and pH. Non-native plant cover decreased with increasing distance to a major paved road and increased with increasing soil moisture, AMF abundance, and from southwest to northeast, a geographic/climatic gradient that represents increasing moisture. Nonmetric multidimensional scaling ordination further illustrated that trends in plant community composition were correlated with elevation, distance to a major paved road, and soil moisture, organic matter, and ammonium. These results suggest complex effects of physical environmental, soil chemical, and human-related factors on plant community structure on an oceanic island, and moreover, that different factors affect cover of native and non-native plants. Notably, our observation of apparent moisture limitation of non-native plants suggests that, in some contexts, drought conditions can limit plant invasions and may even represent an opportunity for efficient control or eradication of invasive plants. The apparent negative effect of non-native plants on native plant cover and overall plant species richness represents a conservation concern for native biodiversity on oceanic islands and suggests the potential for community reassembly as invasive species increasingly dominate due to anthropogenic disturbances.

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