Extreme precipitation reduces reproductive output of an endangered raptor
Bibliographic record
Abstract
Summary The frequency and intensity of extreme weather has increased in North America against a backdrop of anthropogenic land change. Few studies have examined how wildlife is affected by extreme weather, and none have examined whether any resulting effects are contingent upon the degree of anthropogenic landscape change. Using an 8‐year study in Canada (2003–2010), we examined how nest survival of burrowing owls Athene cunicularia varied in relation to weather, vegetation and soil type around the nest. Using a 3‐year (1992, 1993 and 1996) feeding experiment, we examined whether food limitation also causes owlet mortality during inclement weather. Lastly, we examined how productivity (i.e. annual fledgling output) between 2003 and 2010 varied as a function of breeding season precipitation anomalies. Using this relationship, we estimated how productivity has changed in response to breeding season precipitation anomalies from 1960 to 2012. During extreme precipitation events, nest survival decreased because of flooding. When burrow flooding did not occur, the youngest owlets in broods that were not food‐supplemented had the lowest survival rates when there was precipitation, yet almost all food‐supplemented owlets survived bouts of inclement weather. Accordingly, annual productivity from 2003 to 2010 varied inversely with breeding season precipitation anomalies, and we estimated that mean annual productivity decreased by 12% from 1960 to 2012. Synthesis and applications . Extreme rainfall during the breeding season reduced reproductive success of burrowing owls. Given that many raptors experience food limitation during extreme rainfall, large‐scale habitat management to increase the abundance and availability of prey may allow these species to better withstand acute food shortages. In light of predicted increases in the frequency and intensity of extreme weather, supplemental feeding could be used in triage situations for burrowing owl management and has the potential to be an effective short‐term conservation measure for other raptors. Protecting or reclaiming pastures in uplands that are less prone to flooding would further buffer burrowing owls and other ground‐nesting species from extreme precipitation. These actions should mitigate the negative effects of extreme rainfall in the short term; however, long‐term persistence of many species will become increasingly uncertain, as climate change scenarios predict an increase in the frequency and intensity of extreme weather.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".