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Record W4206633154 · doi:10.1101/2022.01.14.476385

Phenology effects on productivity and hatching-asynchrony of American kestrels ( <i>Falco sparverius</i> ) across a continent

2022· preprint· en· W4206633154 on OpenAlexaboutno aff
Kathleen R. Callery, Sarah E. Schulwitz, Anjolene R. Hunt, Jason M. Winiarski, Christopher J. W. McClure, Richard A. Fischer, Julie A. Heath

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersStrategic Environmental Research and Development ProgramU.S. Department of Defense
KeywordsPhenologyNest (protein structural motif)ProductivityBroodSeasonal breederIncubationEcologyBiologyHatchingNesting seasonPredationGeography

Abstract

fetched live from OpenAlex

Abstract Climate-driven advances in spring can result in phenological mismatch between brood rearing and prey availability and consequently cause decreased productivity in birds. How consequences of mismatch vary across species’ ranges, and how individual behavior can mitigate mismatch effects is less studied. We quantified the relationship between phenological mismatch, productivity, and behavioral adaptations of American kestrels ( Falco sparverius ) across their breeding range in the United States and southern Canada. We obtained phenology and productivity data using nest observations from long term nest box monitoring, remote trail cameras, and community-scientist based programs. We collected data on parental incubation behavior and hatch asynchrony using trail cameras in nest boxes. Kestrels that laid eggs after the start of spring had higher rates of nest failure and fewer nestlings than earlier nesters, and effects of mismatch on productivity were most severe in the Northeast. In contrast, kestrels in the Southwest experienced a more gradual decline in productivity with seasonal mismatch. We attribute the effect of location to the growing season and temporal nesting windows (duration of nesting season). Specifically, resource availability in the Northeast is narrow and highly peaked during the breeding season, potentially resulting in shorter nesting windows. Conversely, resource curves may be more prolonged and dampened in the Southwest, and growing seasons are becoming longer with climate change, potentially resulting in longer nesting windows. We found that the onset of male incubation was negatively associated with lay date. Males from breeding pairs that laid eggs after the start of spring began incubation sooner than males from breeding pairs that laid before the start of spring. Early-onset male incubation was positively associated with hatching asynchrony, creating increased age variation in developing young. In sum, we demonstrate that American kestrels are vulnerable to phenological mismatch, and that this vulnerability varies across space. Northeastern populations could be more vulnerable to mismatch consequences, which may be one factor contributing to declines of kestrels in this region. Also, we demonstrate early onset of incubation as a potential adaptive behavior to advance average hatch date and spread out offspring demands, but it is unknown how impactful this will be in mitigating the fitness consequences of phenology mismatch. Highlights Climate-driven phenological mismatch is a growing conservation concern. We studied phenology and productivity of an avian predator across North America. American kestrels nesting after the start of spring had lower productivity. Later nesting kestrels altered incubation to create hatching asynchrony. Mismatch effects were strongest in the Northeast and may contribute to population declines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.225
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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