Effects of land use and soil properties on taxon richness and abundance of soil assemblages
Bibliographic record
Abstract
Abstract Land‐use change and habitat degradation are among the biggest drivers of aboveground biodiversity worldwide but their effects on soil biodiversity are less well known, despite the importance of soil organisms in developing soil structure, nutrient cycling and water drainage. Combining a global compilation of biodiversity data from soil assemblages collated as part of the PREDICTS project with global data on soil characteristics, we modelled how taxon richness and total abundance of soil organisms have responded to land use. We also estimated the global Biodiversity Intactness Index (BII)—the average abundance and compositional similarity of taxa that remain in an area, compared to a minimally impacted baseline, for soil biodiversity. This is the first time the BII has been calculated for soil biodiversity. Relative to undisturbed vegetation, soil organism total abundance and taxon richness were reduced in all land uses except pasture. Soil properties mediated the response of soil biota, but not in a consistent way across land uses. The global soil BII in cropland is, on average, a third of that originally present. However, in grazed sites the decline is less severe. The BII of secondary vegetation depends on age, with sites with younger growth showing a lower BII than mature vegetation. We conclude that land‐use change has reduced local soil biodiversity worldwide, and this further supports the proposition that soil biota should be considered explicitly when using global models to estimate the state of biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".