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.
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 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 | 0.000 | 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) | 0.001 | 0.001 |
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".