The significance of partial migration for food web and ecosystem dynamics
Bibliographic record
Abstract
Migration is ubiquitous and can strongly shape food webs and ecosystems. Less familiar, however, is that the majority of life cycle, seasonal and diel migrations in nature are partial migrations: only a fraction of the population migrates while the other individuals remain in their resident ecosystem. Here, we demonstrate different impacts of partial migration rendering it fundamental to our understanding of the significance of migration for food web and ecosystem dynamics. First, partial migration affects the spatiotemporal distribution of individuals and the food web and ecosystem-level processes they drive differently than expected under full migration. Second, whether an individual migrates or not is regularly correlated with morphological, physiological, and/or behavioural traits that shape its food-web and ecosystem-level impacts. Third, food web and ecosystem dynamics can drive the fraction of the population migrating, enabling the potential for feedbacks between the causes and consequences of migration within and across ecosystems. These impacts, individually and in combination, can yield unintuitive effects of migration and drive the dynamics, diversity and functions of ecosystems. By presenting the first full integration of partial migration and trophic (meta-)community and (meta-)ecosystem ecology, we provide a roadmap for studying how migration affects and is affected by ecosystem dynamics in a changing 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".