Macrophytes promote aquatic insect conservation in artificial ponds
Bibliographic record
Abstract
Abstract Farmland ponds promote regional aquatic biodiversity; however, optimally managing these ponds requires knowledge on how the biodiversity differs between ponds across catchments with different land uses. This study investigated the response of macrophytes, both those in the pond and on the bank, as well as dragonfly and water beetle species richness, diversity and composition in artificial ponds, to numerous environmental variables in catchments dominated by three land uses: protected areas (as reference sites), sugarcane‐dominated and forestry‐dominated landscapes, across two seasons. There was a strong association between insect species richness and vegetation cover, particularly sedges ( Cyperus spp.) in spring and bulrushes ( Typha capensis ) in summer. There was also a positive response between insect species richness and sedges up to 70% cover, and bulrushes up to 40% cover, after which there was a decrease. Furthermore, the protected area ponds contained more rare and specialist insect species. Pond size was not a major variable for biodiversity overall. Although there was generally little congruence in composition among the three taxa, dragonflies were a good surrogate for water beetles and floating macrophytes. Well‐vegetated ponds, both in the water and along the margin, are the most suited to promoting biodiversity, as long as the vegetation is not too dense, and not dominated by one plant species. Artificial ponds in production landscapes have great potential for conserving aquatic species, irrespective of land use, as long as there is management for macrophyte density and heterogeneity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".