Soil biodiversity knowledge and use worldwide: Results from a global survey
Bibliographic record
Abstract
Soil biodiversity is a major component of global biodiversity, but remains poorly characterized in many locations, and is under threat mainly due to land use change and intensification. Detailed assessments of soil biodiversity and a better knowledge of the ecology and distribution of soil organisms worldwide are needed to address threats to soil function, and potential impacts on ecosystem service delivery. A worldwide expert survey was conducted in March 2022 to identify who is doing what, where and how, as well as the main gaps, pitfalls and opportunities across existing national initiatives and research. The questions addressed microbes, fauna and their activity in soils, community and functional assessments, inventories, mapping and monitoring activities, ecosystem services, applications, threats to soil biodiversity, education and communication activities, and public policies related to soil biodiversity. Over 2,000 responses were received, from >1,350 institutions and 135 countries, mainly from experts in research and academia. Respondents worked mostly with soil microbes, focusing primarily on bacteria (85%) and fungi (79%) and less on Archaea, Algae, soil viruses and lichens. Most applied genomic or molecular techniques, as well as activity and process measurements. Soil fauna was less studied overall, with few respondents active in taxonomy (19-34% depending on the taxon). Fifty countries reported inventories, and 48 had monitoring programs, though most (>65%) covered only microbes and fewer (<50%) addressed fauna taxa. A wide variety of methods were used to assess soil fauna and they were widely used as bioindicators. The survey highlighted the lack of studies on the valuation of multiple ecosystem services provided by soil biota, and the poor knowledge on public policies regarding soils and its biodiversity. We identified a need for harmonized global-scale sampling and measuring protocols that are integrated into conventional soil surveys and soil health assessments, as well as approaches that consider multiple taxonomic groups, to provide key information to support policy agendas aimed at soil conservation and sustainability and to propose a design for a Global Soil Biodiversity Observatory.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".