Biodiversity, a multidimensional property of natural systems, is difficult to quantify partly because of the multitude of indices proposed for this purpose. Indices aim to describe general properties of communities that allow us to compare different regions, taxa, and trophic levels. Therefore, they are of fundamental importance for environmental monitoring and conservation, although there is no consensus about which indices are more appropriate and informative. We tested several common diversity indices in a range of simple to complex statistical analyses in order to determine whether some were better suited for certain analyses than others. We used data collected around the focal plant Plantago lanceolata on 60 temperate grassland plots embedded in an agricultural landscape to explore relationships between the common diversity indices of species richness (S), Shannon’s diversity (H’), Simpson’s diversity (D1), Simpson’s dominance (D2), Simpson’s evenness (E), and Berger–Parker dominance (BP). We calculated each of these indices for herbaceous plants, arbuscular mycorrhizal fungi, aboveground arthropods, belowground insect larvae, and P. lanceolata molecular and chemical diversity. Including these trait-based measures of diversity allowed us to test whether or not they behaved similarly to the better studied species diversity. We used path analysis to determine whether compound indices detected more relationships between diversities of different organisms and traits than more basic indices. In the path models, more paths were significant when using H’, even though all models except that with E were equally reliable. This demonstrates that while common diversity indices may appear interchangeable in simple analyses, when considering complex interactions, the choice of index can profoundly alter the interpretation of results. Data mining in order to identify the index producing the most significant results should be avoided, but simultaneously considering analyses using multiple indices can provide greater insight into the interactions in a system.
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.
Extreme arid regions in the worlds' major deserts are typified by quartz pavement terrain. Cryptic hypolithic communities colonize the ventral surface of quartz rocks and this habitat is characterized by a relative lack of environmental and trophic complexity. Combined with readily identifiable major environmental stressors this provides a tractable model system for determining the relative role of stochastic and deterministic drivers in community assembly. Through analyzing an original, worldwide data set of 16S rRNA-gene defined bacterial communities from the most extreme deserts on the Earth, we show that functional assemblages within the communities were subject to different assembly influences. Null models applied to the photosynthetic assemblage revealed that stochastic processes exerted most effect on the assemblage, although the level of community dissimilarity varied between continents in a manner not always consistent with neutral models. The heterotrophic assemblages displayed signatures of niche processes across four continents, whereas in other cases they conformed to neutral predictions. Importantly, for continents where neutrality was either rejected or accepted, assembly drivers differed between the two functional groups. This study demonstrates that multi-trophic microbial systems may not be fully described by a single set of niche or neutral assembly rules and that stochasticity is likely a major determinant of such systems, with significant variation in the influence of these determinants on a global scale.
Microbial communities are enigmatically diverse. We propose a novel view of processes likely affecting microbial assemblages, which could be viewed as the Great American Interchange en miniature: the wholesale exchange among microbial communities resulting from moving pieces of the environment containing entire assemblages. Incidental evidence for such 'community coalescence' is accumulating, but such processes are rarely studied, likely because of the absence of suitable terminology or a conceptual framework. We provide the nucleus for such a conceptual foundation for the study of community coalescence, examining factors shaping these events, links to bodies of ecological theory, and we suggest modeling approaches for understanding coalescent communities. We argue for the systematic study of community coalescence because of important functional and applied consequences.
A major challenge for advancing our understanding of the functional role of soil microbial communities is to link changes in their structure and function under climate change. To address this challenge requires new understanding of the mechanisms that underlie the capacity of soil microbial communities to resist and recover from climate extremes. Here, we synthesize emerging understanding of the intrinsic and extrinsic factors that influence the resistance and resilience of soil microbial communities to climate extremes, with a focus on drought, and identify drivers that might trigger abrupt changes to alternative states. We highlight research challenges and propose a path for advancing our understanding of the resistance and resilience of soil microbial communities to climate extremes, and of their vulnerability to transitions to alternative states, including the use of trait-based approaches. We identify a need for new approaches to quantify resistance and resilience of soil microbial communities, and to identify thresholds for transitions to alternative states. We show how high-resolution time series coupled with gradient designs will enable detecting response patterns to interacting drivers. Finally, to account for extrinsic factors, we suggest that future studies should use environmental gradients to track soil microbial community responses to climate extremes in space and time. This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions’.
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