Where does environmental stochasticity most influence population dynamics? An assessment along a regional core‐periphery gradient for prairie breeding ducks
Bibliographic record
Abstract
Abstract Aim Geographical disparities in the effect of the environment on population dynamics have been shown to follow a core‐periphery gradient when peripheral populations are less abundant and occur in marginal habitat. Whether the gradient in environmental influence occurs in the absence of a gradient in abundance is not known. We tested whether duck populations on the periphery of their main breeding region were more strongly affected by environmental stochasticity and less abundant than at the core. Location The Prairie Pothole Region of central United States and Canada Methods We used the North American Waterfowl Breeding Population and Habitat Survey to model the dynamics of 10 duck species at 1059 sites spanning 1961–2012. We used the North American Breeding Bird Survey to measure abundance at the same sites, averaged over the same time span. We used structural equation models to characterize relationships among a site's distance from the regional centre, a site's abundance, and the degree to which a site's ducks were affected by environmental stochasticity. Results Environmental stochasticity never strongly contributed to population dynamics in the region's core. However, we did not find a linear increase in environmental stochasticity with increasing distance from the core. For seven species, we found that environmental stochasticty was stronger in sites of lower abundance. For only two species did average abundance decline from the region's core to periphery. Main conclusions Variability in the magnitude of environmental stochasticity follows coarse spatial differences in wetland productivity. Yet, among peripheral sites, the contribution of the environment to population dynamics varies, possibly reflecting the importance of the local environment and dispersal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".