It takes a village to grow a tree: most tree species benefit from \ndissimilar neighbors
Bibliographic record
Abstract
Scientific consensus is that diverse tree species positively impact forest productivity, especially when species are functionally dissimilar. Under the complementarity hypothesis, differences in species traits reduce competition among neighboring tree species. However, while this relationship has been extensively studied at the community level, there is a lack of understanding regarding how individuals of different species specifically respond to a functionally dissimilar neighborhood. In this study, we used permanent plots from Quebec, Canada, and 19 focal tree species to test whether: (1) tree growth response to neighborhood dissimilarity varies with their identity and competition intensity, and (2) focal tree species’ traits explain their response to neighborhood dissimilarity. We demonstrate that: tree growth is primarily influenced by competition, species identity, and their interactions, but that dissimilarity, alone and in interaction with the main drivers of tree growth, explains an additional 1.8% of the variation in species growth. Within this context, (1) most species’ respond positively to neighborhood dissimilarity, with magnitude being species and competition dependent, and (2) focal tree traits partly explain these dependencies, with shade-intolerant species benefiting most from dissimilar neighbors under high competition. Our study provides empirical support for the complementarity hypothesis, emphasizing the small but consistent positive effect of functional dissimilarity on tree growth in local neighborhoods. Our findings identify the species with the highest potential of benefiting from dissimilar neighbors but also demonstrate that the positive effect of neighborhood dissimilarity is not limited to a select few species with specific traits; rather, it is observed across a diverse range of species. The cumulative growth responses of individuals to functionally dissimilar neighbors may help explain the commonly observed higher productivity in more diverse communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".