Timing matters: phenological constraints and predation shape Arctic community structure
Bibliographic record
Abstract
Summary Top-down and bottom-up controls of animal populations are key elements of niche and coexistence theories, but there is still little empirical evidence on how these forces determine species distribution and community assemblies. In Arctic ecosystems, spring snowmelt sets the timing and duration of the snow-free period, thereby controlling food availability, while predation often imposes additional constraints on prey species. The relative importance of these abiotic and biotic filters on distribution is also susceptible to vary with body size. Using 10 years of high-resolution data on all major members of an Arctic vertebrate community and their shared predator, we tested how snowmelt timing interacts with predation to shape species occurrence and community structure. Species occurrence declined with later snowmelt dates, with larger-bodied species being particularly constrained by short snow-free periods. Predation further modulated species occurrence, with responses varying according to body mass. Our findings highlight the combined influence of the phenology of food availability and predation as important filters shaping local community structure. Building on species contrasted responses, we propose a conceptual framework for how phenological constraints and predation jointly shape community assembly in highly seasonal environments.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".