Methods and approaches to advance soil macroecology
Bibliographic record
Abstract
Abstract Motivation and aim Soil biodiversity is central to ecosystem function and services. It represents most of terrestrial biodiversity and at least a quarter of all biodiversity on Earth. Yet, research into broad, generalizable spatial and temporal patterns of soil biota has been limited compared to aboveground systems due to complexities of the soil system. We review the literature and identify key considerations necessary to expand soil macroecology beyond the recent surge of global maps of soil taxa, so that we can gain greater insight into the mechanisms and processes shaping soil biodiversity. We focus primarily on three groups of soil taxa (earthworms, mycorrhizal fungi and soil bacteria) that represent a range of body sizes and ecologies, and, therefore, interact with their environment at different spatial scales. Results The complexities of soil, including fine‐scale heterogeneity, 3‐D habitat structure, difficulties with taxonomic delimitation, and the wide‐ranging ecologies of its inhabitants, require the classical macroecological toolbox to be expanded to consider novel sampling, molecular identification, functional approaches, environmental variables, and modelling techniques. Main conclusions Soil provides a complex system within which to apply macroecological research, yet, it is this property that itself makes soil macroecology a field ripe for innovative methodologies and approaches. To achieve this, soil‐specific data, spatio‐temporal, biotic, and abiotic considerations are necessary at all stages of research, from sampling design to statistical analyses. Insights into whole ecosystems and new approaches to link genes, functions and diversity across spatial and temporal scales, alongside methodologies already applied in aboveground macroecology, invasion ecology and aquatic ecology, will facilitate the investigation of macroecological processes in soil biota, which is key to understanding the link between biodiversity and ecosystem functioning in terrestrial ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".