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Record W2184731734 · doi:10.5038/2074-1235.42.1.1061

A First Population Assessment of Black Oystercatcher Haematopus Bachmani in California

2014· article· en· W2184731734 on OpenAlexaboutno aff
A. J. Weinstein, L.K. Trocki, R. R. LeValley, Robert H. Doster, Trisha Distler, Kim Krieger

Bibliographic record

VenueMarine ornithology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
FundersNational Park ServiceU.S. Fish and Wildlife ServiceCalifornia Department of Parks and RecreationMarisla Foundation
KeywordsSeabirdEcologyGeographyHabitatPopulationRange (aeronautics)BiodiversitySurvivorship curveIntertidal zoneReproductive successFisheryBiologyDemography

Abstract

fetched live from OpenAlex

A first population assessment of Black Oystercatcher Haematopus bachmani in California.Marine Ornithology 42: 49-56.Black Oystercatcher Haematopus bachmani is considered vulnerable to decline owing to small global population size, low reproductive success and complete dependence on rocky intertidal shorelines that are impacted by human use and rising sea levels.In response to poor baseline knowledge of the population of the species in California, during 2011 we undertook the first targeted survey measuring distribution and abundance.For the mainland, we used a standardized protocol developed specifically for detecting Black Oystercatchers during the early breeding season, when pair fidelity to breeding territories is highest and movement is lowest.For the Channel Islands, government biologists used a standardized seabird monitoring protocol adapted to detect Black Oystercatchers.For the Farallones, data are taken from the literature.On the mainland, 164 observers participated in the survey in 12 of the state's 15 coastal counties.Observers surveyed approximately 9% of the mainland California coast, equalling approximately 18% of the state's mainland suitable habitat, defined below.A total of 1 160 Black Oystercatchers were detected in this subset of habitat, more than the previous estimate for the entire state (<1 000 individuals).Average density of individuals in mainland surveyed areas was 3.14 birds/km; 135 nests were positively identified, and average nest density was 0.4 nests/km surveyed.On the Northern Channel Islands, approximately 20% the total coastline (66 km), and 20% of the suitable habitat (58 km) of San Miguel, Santa Rosa, Santa Cruz, Anacapa and Santa Barbara islands was surveyed.A total of 176 adult or sub-adult Black Oystercatchers were detected.Average density of individuals in surveyed areas was 2.7 individuals/km, comparable to mainland densities.In areas thoroughly surveyed, observers found densities of nesting territories comparable with those in Alaska and British Columbia, considered the core of the species' range.Based on observed densities in surveyed areas, and including estimates from the literature and more recent observations of a population of 60 at the Farallones, we conservatively estimate a total mainland and Farallones population between 3 971 and 5 213 and a Northern Channel Islands population between 779 and 854, for a total statewide population between 4 749 and 6 067.Our results indicate that California is a critical rather than peripheral part of the Black Oystercatcher range.This result, plus ongoing threats, emphasizes the need to monitor Black Oystercatcher population trends and to identify and protect the most important habitats for Black Oystercatchers in California.The Black Oystercatcher appears highly amenable to citizen science monitoring, particularly at smaller spatial scales, owing to its life history characteristics and charismatic appeal to the public.

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 score0.264
Threshold uncertainty score0.525

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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