Testing hypotheses of trophic level interactions: a boreal forest ecosystem
Bibliographic record
Abstract
Models of community organization involve variations of the top‐down (predator control) or bottom‐up (nutrient limitation) hypotheses. Verbal models, however, can be interpreted in different ways leading to confusion. Therefore, we predict from first principles the range of possible trophic level interactions, and define mathematically the instantaneous effects of experimental perturbations. Some of these interactions are logically and biologically unfeasible. The remaining set of 27 feasible models is based on an initial assumption, for simplicity, of linear interactions between trophic levels. Many more complex and non‐linear models are logically feasible but, for parsimony, simple ones are tested first. We use an experiment in the boreal forest of Canada to test predictions of instantaneous changes to trophic levels and distinguish between competing models. Seven different perturbations systematically removed each trophic level or, for some levels, supplemented them. The predictions resulting from the perturbations were concerned with the direction of change in biomass in the other levels. The direct effects of each perturbation produced strong top‐down and bottom‐up changes in biomass. At both the vegetation and herbivore levels top‐down was stronger than bottom‐up despite some compensatory growth stimulated by herbivory. The combination of experiments produced results consistent with two‐way (reciprocal) interactions at each level. Indirect effects on one or two levels removed from the perturbation were either very weak or undetectable. Top‐down effects were strong when direct but attenuated quickly. Bottom‐up effects were less strong but persisted as indirect effects to higher levels. Although the ‘pure reciprocal’ model best fits our results for the boreal forest system different models may apply to different ecosystems around the world.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".