Weather‐mediated decline in prey delivery rates causes food‐limitation in a top avian predator
Bibliographic record
Abstract
Inclement weather can negatively affect breeding birds directly by exposure to factors such as severe temperature and rainfall, or indirectly by reducing food supply. During a three‐year study of Arctic peregrine falcons Falco peregrinus tundrius breeding in Nunavut, Canada, we estimated annual prey density at a biologically relevant scale (i.e. the home range of breeding pairs), and examined the manner in which prey density and within‐season weather conditions influenced occupancy of breeding sites, egg‐laying, hatch rate, prey delivery rates and growth and survivability of nestlings. The first two summers of our study (2010–2011) were warm and dry, while the third summer (2012) was cool and wet, and was preceded by a severe spring rain event. We found that occupancy of breeding sites was consistently high. As a proportion of the number of eggs laid, hatch rate did not change among years, but the number of eggs laid per occupied site declined in the third year of the study. In the first two years of the study, the number of nestlings per occupied sited was high, but declined in the third year. Total prey density at the home range scale was similar in 2010 and 2012, while the highest prey density was recorded in 2011. Total prey delivery rates to nestlings and nestling growth rates were significantly lower in 2012, which received more precipitation than 2010 and 2011. Nestling growth rates were similar in 2010 and 2011, but were markedly different in 2012; for both sexes the period of rapid growth was of shorter duration in 2012 and asymptotic weights were lower. This research contributes to the growing body of evidence that indicates severe rain events and ongoing periods of wet weather can reduce reproductive output of Arctic‐breeding raptors regardless of whether it occurs during laying, incubation or brood rearing.
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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".