Recreational boating, landscape configuration, and local habitat structure as drivers of odonate community composition in an island setting
Bibliographic record
Abstract
Abstract Anthropogenic impacts to aquatic and terrestrial ecosystems are ubiquitous. Among these, local impacts to freshwater coastal wetlands from recreational boating are potentially severe. We determine the relative contribution of natural factors (local habitat structure and landscape configuration) and estimated impact from anthropogenic factors (i.e. pressure from recreational boating) to odonate community composition. Odonate adults and exuviae were sampled from 17 islands within the 30 000 islands of the Georgian Bay Region of Lake Huron (Ontario, Canada). These islands experience a gradient of boating pressure from four marinas. The magnitude of impacts due to anthropogenic factors was estimated by marina dock space, proximity to marked boating channels, and proximity to a major highway. Redundancy analyses and variance partitioning were utilised to quantify the relative influence of local habitat structure, landscape configuration, and anthropogenic pressures on the distribution of 18 odonate species. Our results show that local habitat structure, landscape configuration, and boating pressures influence odonate community composition. Overall variance in the species composition explained was 36.5% for adults (25.3% landscape configuration and habitat structure, 6.0% boating pressure, 5.2% shared) and 21.9% for exuviae (13.2% landscape configuration and habitat structure, 6.9% boating pressure, 1.8% shared). We found that communities of adults and larvae (sampled as exuviae) are influenced by different factors. Overall, we find evidence that odonate community composition is affected by boating pressures. This stresses the need to consider not only global‐scale human disturbances in conservation planning but also localised effects which differentially impact major life stages.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".