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Record W2803695173 · doi:10.1002/ecs2.2271

Predator effects link ecological communities: kelp created by sea otters provides an unexpected subsidy to bald eagles

2018· article· en· W2803695173 on OpenAlexafffund
Erin U. Foster, Sara Wickham, Jane C. Watson

Bibliographic record

VenueEcosphere · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsVancouver Island UniversityTula FoundationUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaHakai InstituteTula Foundation
KeywordsKelpKelp forestEcologyIntertidal zonePredationFisheryOtterBiologyGeography

Abstract

fetched live from OpenAlex

Abstract Ecological communities are best studied at the landscape level, where linkages among communities are considered. Such linkages are often driven by increases in primary production caused by apex predators limiting herbivores. In this note, we describe a novel linkage among sea otters ( Enhydra lutris ), the long‐lived woody kelp ( Pterygophora californica ), and bald eagles ( Haliaeetus leucocephalus ). While counting sea otters in an isolated group of rocky islets on the Central Coast of British Columbia, we found an eagle nest composed of mostly Pterygophora stalks. Pterygophora recruits rapidly after sea otters arrive in an area and limit sea urchins; the subsequent pulsed Pterygophora recruitment results in narrow age‐class cohorts that senesce en masse after about 20 yr. When the woody stipes wash ashore, they degrade slowly and persist as beach wrack for years. These windrows of woody kelp are common on the BC coast where sea otters have re‐established. We demonstrate how this subsidy can be used by bald eagles, and predict that as sea otters recover across their range in BC , further effects of Pterygophora to intertidal, supralittoral, and terrestrial communities will be observed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.002

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2018
Admission routes2
Has abstractyes

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