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Record W3122670236 · doi:10.1038/s41598-021-81583-z

Foraging niche partitioning in sympatric seabird populations

2021· article· en· W3122670236 on OpenAlexaffabout
Christina Petalas, Thomas Lazarus, Raphaël A. Lavoie, Kyle H. Elliott, Mélanie F. Guigueno

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsEnvironment and Climate Change CanadaMcGill University
Fundersnot available
KeywordsSeabirdSympatric speciationForagingNicheNiche differentiationEcologyBiologyZoologyEvolutionary biologyPredation

Abstract

fetched live from OpenAlex

Sympatric species must sufficiently differentiate aspects of their ecological niche to alleviate complete interspecific competition and stably coexist within the same area. Seabirds provide a unique opportunity to understand patterns of niche segregation among coexisting species because they form large multi-species colonies of breeding aggregations with seemingly overlapping diets and foraging areas. Recent biologging tools have revealed that colonial seabirds can differentiate components of their foraging strategies. Specifically, small, diving birds with high wing-loading may have small foraging radii compared with larger or non-diving birds. In the Gulf of St-Lawrence in Canada, we investigated whether and how niche differentiation occurs in four incubating seabird species breeding sympatrically using GPS-tracking and direct field observations of prey items carried by adults to chicks: the Atlantic puffin (Fratercula arctica), razorbill (Alca torda), common murre (Uria aalge), and black-legged kittiwake (Rissa tridactyla). Although there was overlap at foraging hotspots, all species differentiated in either diet (prey species, size and number) or foraging range. Whereas puffins and razorbills consumed multiple smaller prey items that were readily available closer to the colony, murres selected larger more diverse prey that were accessible due to their deeper diving capability. Kittiwakes compensated for their surface foraging by having a large foraging range, including foraging largely at a specific distant hotspot. These foraging habitat specialisations may alleviate high interspecific competition allowing for their coexistence, providing insight on multispecies colonial living.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.269
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 teacher head, not a consensus.

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

Citations46
Published2021
Admission routes2
Has abstractyes

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