Biodiversity recovery during rainforest reforestation as indicated by rapid assessment of epigaeic ants in tropical and subtropical Australia
Bibliographic record
Abstract
Abstract There is growing interest in the potential for reforestation to assist the recovery of rainforest biodiversity. There is also a need to identify taxonomically tractable groups for use as cost‐effective indicators when monitoring the status of biodiversity within reforested sites. Insects are an important component of terrestrial biodiversity but often require considerable resources to sample at species level. Ant genera and generic‐based functional groups have been suggested as possible indicators of environmental disturbance. Here we ask to what extent the development of biodiversity is indicated by epigaeic ant genera and functional groups, across different types of reforestation in tropical and subtropical Australia. In each region, we used pitfall traps to sample the ants in replicate sites of: unmanaged regrowth, monoculture and mixed species plantations and ‘ecological restoration’ plantings, together with reference sites in pasture and rainforest. We recorded 35 epigaeic ant genera (and 4623 individuals) from 50 tropical sites, and 39 genera (and 9904 individuals) from 54 subtropical sites, with 47 genera overall. Community composition of both genera and functional groups differed between pasture and rainforest, although many genera were widespread in both. Reforested sites were intermediate between pasture and rainforest in both regions, and showed a gradient associated with decreasing grass and increasing tree and litter cover. Older monoculture plantations and ecological restoration plantings had the most rainforest‐like ant assemblages, and mixed‐species cabinet timber plots the least, of the reforested sites. We conclude that ground‐active ant genera and functional groups sampled in rapid surveys by pitfall‐trapping showed only a modest ability to discriminate among different types of reforestation. Species‐level identification, perhaps together with expanded sampling effort, could be more informative, but would require resourcing beyond the scope of rapid assessments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".