The effects of disturbance on behaviour, habitat use and energy of spring staging snow geese
Bibliographic record
Abstract
Summary For many species, human‐induced disturbances can severely influence an individual's pay‐off; however, energy‐cost variations from different disturbance types have rarely been reported. We evaluated the dynamic behavioural responses of staging greater snow geese Anser caerulescens atlanticus to different types of disturbance in southern Quebec, Canada, between 1997 and 2000. We specifically considered the impact of a unique measure, a spring conservation hunt implemented in agricultural habitats in 1999. We tracked 237 radio‐tagged females for 2764 h and recorded 697 take‐offs following fortuitous disturbance, scaring and hunting in three regions characterized by different habitats. Geese used cornfields in south‐western Quebec, Scirpus marshes and hayfields in the upper St Lawrence estuary, and Spartina marshes and hayfields in the lower estuary. Overall, disturbance levels increased in the upper and lower estuary during years with hunting, mostly through an increase in hunting and scaring activities. The probability of geese returning to a refuge after disturbance in agricultural habitats increased in years with hunting except in the corn‐growing region. The short‐term energy gain of geese resuming feeding after disturbance was less than before disturbance, and this difference was greater in years with hunting. Distances flown after disturbance decreased with flock size and were longer after scaring and hunting than after fortuitous disturbances in the Scirpus region. Overall, habitat use varied among years and associated estimated energy gain decreased markedly in both years with hunting in the Spartina and corn‐growing region, but did not change in the Scirpus region. Changes in behaviour due to disturbance, and especially those associated with hunting, probably contributed to the reduced body condition of staging greater snow geese during years with hunting. Synthesis and applications . From a methodological viewpoint, we highlight the importance of tracking the behaviour of individual animals after disturbance to properly evaluate its impact. From a conservation perspective, we provide empirical arguments to limit the hunting of breeding waterfowl during their prenuptial migration in order to facilitate their fattening and forthcoming reproduction. From a management standpoint, we suggest that a side‐effect of disturbance induced by spring hunting to control overabundant populations may be reduced fattening and breeding output among birds that survive. Together, these data emphasize further the importance of measuring the direct and indirect effects of disturbance rather than assuming effects from the incidence of the disturbance alone.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".