What evidence exists on the effects of competition on trees’ responses to climate change? A systematic map protocol
Bibliographic record
Abstract
Abstract Background Projections of climate change impacts upon forests are likely inaccurate if based on the premise that only climate controls tree growth. Species interactions control growth, but most research has ignored these effects on how trees respond to climate change. Climate change is inducing natural species selection. However, this selection does not occur at the community level. Species selection starts with competition amongst individual trees. Competition is an individual-to-individual antagonistic interaction that, if severe, can constrain the presence of trees within a particular environment. Thus, climate change impacts individual tree selection within forests. Projecting climate change impacts on forests should account for the effects of climate on tree growth and the effects of competition. The inclusion of competition can increase the predictive power of simulations. Methods We propose a protocol to systematically map the available literature on climate change impacts on forests and produce a comprehensive list of methods applied to measure competition and model the competition effects on tree growth responses to climate change. This systematic map is not limited to any country or continent or specific tree species or forest type. The scope of the search focuses on time (when the evidence was published), location (geographic location of the evidence) and research design (competition indices and modelling methods). We will evaluate articles at three levels: title, abstract and full text. We will conduct a full-text assessment on all articles that pass a screening at the title and abstract stages. We will report the extracted evidence in a narrative synthesis to summarize the evidence’s trends and report knowledge gaps.
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 | 0.090 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.027 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.107 | 0.020 |
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".