Cool as a moose: How can browsing counteract climate warming effects across boreal forest ecosystems?
Bibliographic record
Abstract
Herbivory has potential to modify vegetation responses to climatic changes. However, climate and herbivory also affect each other, and rarely work in isolation from other ecological factors, such as plant-plant competition. Thus, it is challenging to predict the extent to which herbivory can counteract, amplify, or interact with climate impacts on ecosystems. Here, we investigate how moose modify climatic responses of boreal trees by using experimental exclosures on two continents and modeling complex causal pathways including several climatic factors, multiple tree species, competition, tree height, time, food availability, and herbivore presence, density, and browsing intensity. We show that moose can counteract, that is, "cool down" positive temperature responses of trees, but that this effect varies between species depending on moose foraging preferences. Growth of preferred deciduous trees was strongly affected by moose, whereas growth of less preferred conifers was mostly driven by climate and tree height. In addition, moose changed temperature responses of rowan in Norway and balsam fir in Canada, by making fir more responsive to temperature but decreasing the strength of the temperature response of rowan. Snow protected trees from browsing, and therefore moose "cooling power" might increase should a warming climate result in decreased snow cover. Furthermore, we found evidence of indirect effects of moose via plant-plant competition: By constraining growth of competing trees, moose can contribute positively to the growth of other trees. Our study shows that in boreal forests, herbivory cooling power is highly context dependent, and in order to understand its potential to prevent changes induced by warming climate, species differences, snow, competition, and climate effects on browsing need to be considered.
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) | 0.000 | 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; a candidate call from one teacher head, 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".