Biosolids application increases grasshopper abundance in the short term in a northern Canadian grassland
Bibliographic record
Abstract
Degraded grasslands are common worldwide, often due to overgrazing by livestock; such degradation often reduces plant growth and water quality, while increasing soil erosion, wildfires, and invasive species. Recent restoration efforts have used organic amendments to increase soil nutrients, improve water retention, and increase forage production. Biosolids, the stabilised and pathogen-treated remains from wastewater treatment plants, have strong impacts on soil nutrients and plant growth, but there is very little known about impacts on higher trophic levels. We worked on northern grasslands in British Columbia, Canada, to test whether biosolids applications changed grasshopper abundances, body sizes, or species richness. We used hoop transects to measure density and timed net samples to determine richness and evenness. There were significantly higher (~3.8×) grasshopper densities at sites where biosolids were applied 1–2 years before sampling compared with control sites or sites where biosolids were applied in the year of sampling. Tibia lengths of grasshoppers varied with treatment, species, and sex, but there was no clear signature of biosolids leading to bigger body sizes. There were no significant differences in species richness or equitability in relation to the year of the biosolids application. Collectively, our results show that biosolids have large impacts on grasshopper densities, but no clear impact on community structure or body size. Because grasshoppers can be dominant insect herbivores and are critical prey for many birds and mammals, our results suggest biosolids could be an important tool in the context of site restoration or efforts to improve populations of insectivorous vertebrates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".