A systematic review of the attractant-decoy and repellent-plant hypotheses: do plants with heterospecific neighbours escape herbivory?
Bibliographic record
Abstract
This systematic review highlights the relative support and implications of the attractant-decoy and repellent-plant hypotheses, discussing important linkages between these theories and the opportunity for novel integration into ecological and applied research. An extensive systematic review of the current literature on the attractant-decoy and repellent-plant hypotheses was done to describe the following attributes of the research to date: (i) the geographic extent (country and biome) of studies on this topic, (ii) the scope of experimental designs used, (iii) the level of support for these hypotheses with respect to the breadth of ecological niches tested, (iv) the level of support for these hypotheses with respect to the classes of herbivores examined and, lastly, (v) the ecological impact or purpose of these studies. Herein, we summarize important research gaps in the empirical literature on this topic and identify novel opportunities for critical linkages between ecological and applied theories. A total of 37% of experiments testing these two associated hypotheses were done in North America, frequently in either temperate broadleaf (26% of studies) or taiga ecosystems (15% of studies). The majority of these studies involved experimental manipulations such as removing and transplanting vegetation and either tracked or excluded mammalian herbivores. Ecological implications were primarily examined (59% of studies), but there were also implications described for agriculture and commercial forestry in 22% of studies. The repellent-plant hypothesis was well supported in many ecological systems, particularly for mammalian herbivores, but the attractant-decoy hypothesis has been less frequently tested, thereby representing an important research gap. Insect herbivores were under-represented in all categories except in applied contexts such as commercial forestry and agriculture. There is a clear need for studies to connect these two ecological hypotheses with the management of agriculture and restoration efforts in many ecosystems. Research on the co-evolution and facilitation between palatable and unpalatable plants also represents another novel area of future study.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".