Cross‐ecosystem effects of a large terrestrial herbivore on stream ecosystem functioning
Bibliographic record
Abstract
Large terrestrial consumers have direct and indirect effects on forest ecosystem function, but few studies have investigated the impacts of terrestrial consumers on freshwater ecosystems. In the Cape Breton Highlands of Nova Scotia, browsing by hyper‐abundant moose following a spruce budworm outbreak has transformed boreal forest into grasslands. We conducted a field study to investigate the potential for cross‐ecosystem effects of hyper‐abundant moose following budworm outbreak on small boreal stream ecosystem structure and function. With our field study, we tested the prediction that watersheds with higher levels of moose‐mediated grasslands in their sub‐basin would have higher stream temperatures, total nitrogen, electrical conductivity, periphyton biomass and macroinvertebrate abundances. While our data supported several of our predictions pertaining to moose impacts on the abiotic variables (i.e. temperature range, total nitrogen, electrical conductivity) we found evidence of variable moose impacts on the benthic community. Specifically, we observed lower relative abundance of predatory invertebrates in streams with high moose impacts compared to streams with low moose impacts in their watersheds but no evidence of moose impacts on the relative abundance of shredders, filterers, gatherers, and grazers. This empirical study fills a key gap in our understanding of spatial ecosystem ecology by providing insight into the effects of large terrestrial consumers across ecosystem boundaries with potential implications for landscape‐scale management of hyper‐abundant ungulates. Given the broad availability and improvement in remote sensing technology, the novel integration of remote sensing and field studies employed here may provide a roadmap for future studies of meta‐ecosystem dynamics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".