Bibliographic record
Abstract
All organisms must find and consume resources to live, and the strategies an organism uses when foraging can have significant impacts on their fitness. Models assuming optimality in foraging behavior, and which quantitatively account for the costs, benefits, and biological constraints of foraging, are common in the animal literature. Plant ecologists on the other hand have rarely adopted an explicit framework of optimality with respect to plant root foraging. Here, we show with a simple experiment that the marginal value theorem (MVT), one of the most classic models of animal foraging behavior, can provide novel insights into the root foraging behavior of plants. We also discuss existing data in the literature, which has not usually been linked to MVT to provide further support for the benefits of an optimal foraging framework for plants. As predicted by MVT, plants invest more time and effort into highly enriched patches than they do to low-enriched patches. On the basis of this congruency, and the recent calls for new directions in the plant foraging literature, we suggest plant ecologists should work toward a more explicit treatment of the idea of optimality in studies of plant root foraging. Such an approach is advantageous because it forces a quantitative treatment of the assumptions being made and the constraints on the system. While we believe significant insight can be gained from the use of preexisting models of animal foraging, ultimately plant ecologists will have to develop taxa-specific models that account for the unique biology of plants.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".