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Record W2756115097 · doi:10.1080/11956860.2017.1374322

Breeding success and productivity of urban and rural Eurasian sparrowhawks <i>Accipiter nisus</i> in Scotland

2017· article· en· W2756115097 on OpenAlexvenueno aff
Michael Thornton, Ian B. Todd, Staffan Roos

Bibliographic record

VenueEcoscience · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsAccipiterNest (protein structural motif)Brood parasiteAccipitridaeEcologyBroodPopulationBreeding bird surveyAvian clutch sizeBiologyGeneralist and specialist speciesGeographyPredationHabitatZoologyDemographyReproductionParasitism

Abstract

fetched live from OpenAlex

The conversion of rural habitat into built-up areas often affects animal species negatively. However, some generalist bird species and raptors relying on avian prey have colonised urban environments. Surprisingly, no study has compared the breeding biology of urban and rural populations of a very common old-world raptor, the Eurasian sparrowhawk (Accipiter nisus). Here, we compare the territory occupancy rate, breeding success and productivity (i.e., the number of fledglings) over four years (2009–2012) of an urban and a rural sparrowhawk population in Scotland. Our results showed that urban sparrowhawk territories were occupied significantly more frequently (mean % years occupied ± se: 66.8 ± 5.9%) than rural territories (42.8 ± 4.7%). Clutch size, brood size and the number of fledglings produced did not differ between the populations. However, the breeding success was significantly higher in the urban (annual mean ± se: 97.2 ± 2.3% nests successful) than in the rural population (80.5 ± 6.6%) because of a higher nest desertion rate at the egg and chick stages in the rural population. Our study suggests that warm weather in July may have more negative effects on rural sparrowhawks compared to urban sparrowhawks. The mechanism behind the difference is unknown and requires further work.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 teacher head, 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

Citations18
Published2017
Admission routes1
Has abstractyes

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