Natural disturbance allows multiple anuran taxa to persist in a dynamic wetland complex
Bibliographic record
Abstract
Abstract The maintenance of biological diversity is frequently enhanced in a heterogenous landscape by some level of disturbance. Thus, when a landscape becomes stabilized and homogenized through the spread of an invasive plant species, there may be consequences for native biodiversity, particularly for those biotas that depend on the pre‐existing, natural disturbance regime of that landscape. At Long Point, Ontario, Canada, a sandspit in Lake Erie, the community of anuran amphibians experienced the co‐incidence of 2 major disturbance events, 1 anthropogenic and 1 natural: an intervention to remove the invasive form of the common reed (Phragmites australis australis) and a spate of extensive dune washouts caused by high water levels and storms. As a result of the unequal distribution of disturbance in the landscape, different areas were affected by only the dune washouts, only the common reed treatment, both disturbances, or neither disturbance. From May to August 2021, we explored how the disturbances affected the resident frog and toad species habitat associations in the 4 different disturbance conditions in the landscape. By means of minnow traps, acoustic surveys, and visual surveys, we determined that the dune washouts had a greater influence on resident anurans than the common reed treatment. Ranid frogs (Ranidae) tended to inhabit non‐washout sites, whereas Fowler's toads (Anaxyrus fowleri) congregated in the newly formed, sand flats and shallow, de‐vegetated pools resulting from the washouts. Neither ranid frogs nor Fowler's toads demonstrated avoidance of the sites affected by the herbicide treatment and mechanical rollover of the common reed. This evidence of species sorting, which enables multiple species to persist in a dynamic and heterogeneous landscape, suggests that wildlife management in aid of threatened species recovery may find success by encouraging natural disturbance regimes in dynamic landscapes. Future management endeavors should consider refraining from complete dune stabilization in these landscapes to allow the natural washouts to occur at a higher frequency, while ensuring the continued effectiveness of the invasive common reed eradication.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".