Dynamic energy budget model for a bumble bee colony: Predicting the spatial distribution and dynamics of colonies across multiple seasons
Bibliographic record
Abstract
Abstract Bumble bees are important pollinators of many crops around the world. In recent decades, agricultural intensification has resulted in significant declines in bumble bee populations and the pollination services they provide. Empirical studies have shown that this trend can be reversed, however, by enhancing the agricultural landscape with natural habitat, such as adding wildflower patches adjacent to crops. Despite the empirical evidence, the mechanisms behind these positive effects are not fully understood, and the specific characteristics of the enhanced natural habitat that would maximize benefits are unclear at this time. Theoretical studies, in the form of mathematical models, have proven useful in elucidating the underlying mechanisms and determining the optimal natural habitat configurations. Existing models, however, generally focus only on particular aspects of bumble bee behaviour; some models are accurate at describing population dynamics, while others are accurate at describing their spatial distribution. In this work, we build a unique model coupling population dynamics, using a whole-colony Dynamic Energy Budget (DEB) approach, to a spatial distribution model based on the maximum energy principle. This coupling gives valuable new insights into the effects of spatial arrangements on population dynamics, and vice-versa. With our model, we answer questions such as when, how much, or what type of wildflower patches should be planted to maximize crop pollination services and minimize bee decline. We find that planting wildflowers that bloom before and after crop bloom is crucial to achieve high pollination services and preserving wild pollinator populations. We also find that small quantities of natural habitat are needed when the crop is nutritionally rich, but higher quantities are most beneficial when the crop is nutritionally deficient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".