Partitioning net interactions among plants along altitudinal gradients to study community responses to climate change
Bibliographic record
Abstract
Summary Altitudinal gradients provide a useful space‐for‐time substitution to examine the capacity for plant competition and facilitation to mediate responses to climate change. Decomposing net interactions into their facilitative and competitive components, and quantifying the performance of plants with and without neighbours along altitudinal gradients, may prove particularly informative in understanding the mechanisms behind plant responses to environmental change. To decouple the inherent responses of species to climate from the responses of plant–plant interactions to climate, we conducted a meta‐analysis. Using data from 16 alpine experiments, we tested if changes in net interactions along altitudinal gradients were due to a change in the performance of target species without neighbours (i.e. environmental severity effects only) or with neighbours (neighbour trait mediated effects). There was a global shift from competition to facilitation with increasing altitude driven by both environmental severity and neighbour trait effects. However, this global pattern was strongly influenced by the high number of studies in mesic climates and driven by competition at low altitude in temperate climates (neighbour trait effect), and facilitation at high altitude in arctic and temperate climates (environmental severity effect). In Mediterranean systems, there was no significant effect of competition, and facilitation increased with decreasing altitude. Changes in facilitation with altitude could not unambiguously be attributed to either neighbour trait effects or environmental severity effects, probably because of the opposing stress gradients of cold and aridity in dry environments. Partitioning net interactions along altitudinal gradients led to the prediction that climate change should decrease the importance of facilitation in mesic alpine communities, which might in turn exacerbate the negative effects of climate change in these regions. In xeric climates, the importance of facilitation by drought‐tolerant species should increase at low altitudes which should mitigate the negative effect of climate change. However, the importance of facilitation by cold‐tolerant species at high altitudes may decrease and exacerbate the effects of climate change.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".