Competition reverses the response of shrub seedling mortality and growth along a soil moisture gradient
Bibliographic record
Abstract
Abstract Predicting species responses to climate change involves understanding both the direct effects of environmental change, as well as indirect effects mediated by altered interspecific interactions. Indirect effects may be particularly important for understanding native species responses in systems invaded by highly competitive exotic species. For instance, Mediterranean climate regions are predicted to experience more frequent drought, and are increasingly invaded by exotic annual plants. For native shrubs in these regions, seedling establishment is episodic, and associated with high rainfall years. However, exotic annual plants also often increase in abundance with high rainfall, suggesting competition from exotic annual species could alter the relationship between rainfall and shrub seedling establishment. Theories such as the stress gradient hypothesis predict competition intensity should increase with resource supply, but there have been few evaluations of competitive interactions across experimental gradients of soil moisture availability. Here we examined how competition from an exotic annual influenced native shrub establishment, across an experimental soil moisture gradient. Seedlings of two native shrub species ( Encelia californica and Eriogonum fasciculatum ) were grown with and without an exotic grass competitor ( Avena fatua ) across eight water availability levels, and monitored for growth and survival. These species are common and abundant in the Mediterranean climate region of coastal Southern California, where climate change projections include long‐term drought for the coming decades. Without competition, shrub seedlings achieved higher growth and survival at high water availability levels. However, when grown in competition with the exotic grass, shrub seedlings had higher growth and/or survival under relatively dry conditions, suggesting competition can modify and even reverse species responses to changing rainfall patterns, compared to predictions made in the absence of competitors. The exotic grass strongly reduced soil nitrate and water availability when it was planted with the native shrub seedlings, and the exotic grass responded with positive but saturating growth with increasing levels of water addition. Synthesis . This experiment demonstrates that competition from invasive species can alter native species responses to climate change; consistent with ecological theories predicting a positive association between the supply of limiting resources and the intensity of competition.
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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 | 0.001 | 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 | 0.001 |
| 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 | 0.000 |
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 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".