Rapid initial recovery and long‐term persistence of a bee community in a former landfill
Bibliographic record
Abstract
Abstract The effects of habitat restoration are usually studied using cross‐sectional comparisons of species assemblages among sites of various ages or disturbance levels. Longitudinal studies, however, are necessary for detecting long‐term responses to habitat restoration and for understanding annual demographic variation. To investigate the time course of bee community restoration in sites previously made uninhabitable by anthropogenic disturbance, we studied a former landfill site for 10 years from initial revegetation in 2003 until 2013, comparing two restored sites with three nearby, undisturbed control sites. We used permutational multivariate analysis of variance and generalised additive mixed models to investigate how bee abundance and species richness varied over time (years), between seasons and between restoration levels. Landfill restoration and the creation of foraging and nesting habitat resulted in rapid and persistent occupation by bees, suggesting that efforts to restore bee communities can be successful when a source of colonists exists nearby. Based on earlier studies, we predicted that in restored sites there would be an initial rapid increase in bee abundance and species richness, followed by a decline to a stable intermediate level. This prediction was supported as bee abundance and species richness in restored sites increased until 2006/2007 and subsequently declined. In control sites, there were significant declines in abundance and species richness over time, despite a lack of anthropogenic disturbance. Possible contributors are changing weather patterns, especially severe droughts; plant community succession resulting in loss of bare ground for nesting sites; and increasing suburbanisation of the surrounding landscape.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".