Refining the stress‐gradient hypothesis for competition and facilitation in plant communities
Bibliographic record
Abstract
Summary The stress‐gradient hypothesis (SGH) predicts that the frequency of facilitative and competitive interactions will vary inversely across abiotic stress gradients, with facilitation being more common in conditions of high abiotic stress relative to more benign abiotic conditions. With notable exceptions, most tests of the SGH have studied the interaction between a single pair or a few pairs of species, and thus have evaluated shifts in the magnitude and direction of pair‐wise interactions along stress gradients, rather than shifts in the general frequency of interactions. The SGH has been supported by numerous studies in many ecosystems, has provided a crucial foundation for studying the interplay between facilitation and competition in plant communities, and has a high heuristic value. However, recent empirical research indicates that factors like the variation among species and the nature of the stress gradient studied add complexity not considered in the SGH, creating an opportunity to extend the SGH's general conceptual framework. We suggest that one approach for extending the SGH framework is to differentiate between the original idea of how ‘common’ interactions might be along stress gradients and the ubiquitous empirical approach of studying shifts in the strength of pair‐wise interactions. Furthermore, by explicitly considering the life history of the interacting species (relative tolerance to stress vs. competitive ability) and the characteristics of the stress factor (resource vs. non‐resource) we may be able to greatly refine specific predictions relevant to the SGH. We propose that the general pattern predicted by the SGH would hold more frequently for some combinations of life histories and stress factor, particularly when the benefactor and beneficiary species are mostly competitive and stress‐tolerant, respectively. However, we also predict that other combinations are likely to yield different results. For example, the effect of neighbours can be negative at both ends of the stress gradient when both interacting species have similar ‘competitive’ or ‘stress‐tolerant’ life histories and the abiotic stress gradient is driven by a resource (e.g. water). Synthesis. The extension of the SGH presented here provides specific and testable hypotheses to foster research and helps to reconcile potential discrepancies among previous studies. It represents an important step in incorporating the complexity and species‐specificity of potential outcomes into models and theories addressing how plant–plant interactions change along stress gradients.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".