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Record W3088026608 · doi:10.18122/td/1724/boisestate

Phenological Mismatch is Correlated with Fitness Outcomes and Adaptive Behavior in a Generalist Avian Predator Distributed Across North America

2020· dissertation· en· W3088026608 on OpenAlexaboutno aff
Kathleen R. Callery

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPhenologyNest (protein structural motif)BiologyGeneralist and specialist speciesEcologyKestrelReproductive successPredationBroodProductivityAbundance (ecology)DemographyPopulationHabitat

Abstract

fetched live from OpenAlex

Climate-driven advances in the start of spring may result in a phenological mismatch between peak-prey abundance and the breeding season of secondary consumers. Phenological mismatch has been well-studied in insectivorous birds for which reproductive productivity is strongly linked to caterpillar abundance. The effects of mismatch on the productivity of dietary generalists, that forage on several types of prey, are less well-understood. Further, few studies have addressed questions about the effects of mismatch on survival, an important component of fitness that can be affected by breeding in sub-optimal conditions. We examined the relationship between phenological mismatch and fitness for a widespread generalist raptor, the American kestrel (Falco sparverius). In the first chapter, we collected productivity data from nest observations across the contiguous US and southern Canada and quantified phenological mismatch on each nest as the difference in days between the start of spring and clutch initiation. Then, we examined the relationship between mismatch, location, and productivity. Also, we investigated whether incubation behavior leading to hatching-asynchrony was related to phenological mismatch. Kestrels that laid eggs after the start of spring had fewer nestlings and higher rates of nest failure compared to kestrels that laid eggs before the start of spring. The strength of the mismatch effect depended on location. In the northeast, the number of fledglings per brood and rates of nest success were high for pairs nesting before the start of spring, but the effect of phenological mismatch was strongest here, with rapid declines in nest success associated with mismatch. Whereas, in the xi southwest, early-laying pairs had lower productivity and success relative to the northeast, but the effects of phenological mismatch were not as strong as the northeast. The effect of location is likely related to climatic constraints on the growing season and the time window for kestrel breeding that are becoming stronger in the northeast and weaker in the southwest. The timing of male incubation behavior was associated with hatching asynchrony, and males breeding after the start of spring were more likely to initiate incubation early as opposed to males breeding before the spring index date, suggesting that hatching asynchrony is a possible mechanism to cope with phenological mismatch. In the second chapter, we investigated the relationships between phenological mismatch and survival using mark-and-recapture data from two distinct, long-term study sites in Idaho and New Jersey where kestrel exhibit difference migration strategies. We created a multistate mark-recapture models to estimate the annual survival of adult (afterhatch- year) and juvenile (hatch-year or yearling) kestrels. For the multistate framework, we categorized the phenological mismatch of nests at each site “earlier” or “later” relative to the yearly median difference in days between clutch initiation date and the start-of-spring date, which was estimated at each nest box location. In addition, we included covariates for nesting success, sex, and minimum winter temperature anomaly in our survival models. Mismatch was associated with the survival of kestrels that produced young; however, the direction of this effect differed between populations. In Idaho, successful kestrels had higher survival when they bred “earlier” rather than “later.” In New Jersey, successful kestrels had higher survival when they bred “later” rather than “earlier." Differences in survival between sites may reflect differences in seasonality, climate change patterns, or consequences of migration strategies. For partially migrant xii populations (i.e, Idaho kestrels), mismatch may rapidly drive directional selection for birds to breed earlier by favoring survival and productivity, but for fully migrant populations (i.e., New Jersey) that have a limited window of time to reproduce, mismatch may create trade-offs between reproduction and survival. Mismatch did not affect the survival of adult birds with failed nests, and there was no difference in survival between hatch-year birds produced from “earlier” or “later” nests. In Idaho, males had higher survival rates than females and warmer winter temperatures positively correlated with survival in all age and sex classes. In New Jersey, sex and winter temperature did not explain survival. In sum, we found negative consequences of phenological mismatch on the fitness of American kestrels, generalist predator. For both productivity and survival, the effect of mismatch was more severe for kestrels in the northeast, where the breeding season is shorter and kestrels more migratory when compared to the west. These results demonstrate that duration of breeding season is an important factor to consider when assessing vulnerability to climate change, and that a generalist diet does not ensure resilience to phenological mismatch.

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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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