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Record W6998716633

Annual Cycle Phenology and Winter Habitat Selection of White-Winged Scoters in Eastern North America

2018· article· en· W6998716633 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Media Literacy Education · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatRange (aeronautics)PhenologyAnnual cycleAbiotic componentEstuaryOverwinteringSan Joaquin
DOInot available

Abstract

fetched live from OpenAlex

Concern over declining populations of several North American sea duck species has led to research addressing how environmental and anthropogenic factors in various stages of the annual cycle affect survival, habitat use, site fidelity, and migratory strategies. Southern New England provides key wintering habitat for White-winged Scoters (Melanitta fusca). This area has also pioneered the development of offshore wind energy in North America. I deployed implanted satellite transmitters in 52 adult female White-winged Scoters captured during the wintering period in southern New England, and on a molting area in the St. Lawrence River estuary in Quebec between 2015 and 2016. I used winter movement data to determine winter arrival and departure dates, total length of stay, home ranges, and site fidelity for scoters wintering in southern New England. Scoters spent over half of the annual cycle on the wintering grounds and demonstrated a high degree of inter-annual site fidelity to composite core-use areas. Sizes of individual 50% core-use home ranges were variable (x̅ = 868 km2; range = 32 to 4,220 km2) and individual 95% utilization distributions ranged widely (x̅ = 4,388 km2; range = 272 to 18,235 km2). More than half of all tagged birds occupied two or more discrete core-use areas that were up to 400 km apart. I combined these home range estimates with biotic and abiotic habitat data to calculate resource selection functions to model predicted relative probability of use for White-winged Scoters throughout the southern New England study area. Scoters selected for areas with lower salinity, lower sea surface temperature, higher chlorophyll-a concentrations, and higher hard-bottom substrate probability. Resource selection function models classified 18,649 km2 (23%) of the study area as high probability of use, which included or immediately bordered ~420 km2 of proposed Wind Energy Area lease blocks. Important habitats and key environmental characteristics identified by this study should be carefully considered when siting and developing future offshore wind energy areas.\nUnderstanding full annual cycle movements of long-distance migrants is essential for delineating populations, assessing connectivity, evaluating crossover effects between life stages, and informing management strategies for vulnerable or declining species. In a complementary second study, I used the same 52 satellite-tagged female White-winged Scoters to document annual cycle phenology, delineate migration routes, identify primary areas used during winter, stopover, breeding, and molt, and to assess the strength of migratory connectivity and spatial population structure. Most scoters wintered along the Atlantic coast from Nova Scotia to southern New England, with some on Lake Ontario. Scoters followed four migration routes to breeding areas from Quebec to the Northwest Territories. Principal post-breeding molting areas were in James Bay and the St. Lawrence River estuary. Migration phenology was synchronous regardless of winter or breeding origin. Cluster analyses delineated two primary breeding areas, one molting, and one wintering area. Scoters demonstrated overall weak to moderate connectivity among life stages, with molting to wintering connectivity the strongest. Thus, White-winged Scoters that winter in eastern North America appear to constitute a single continuous population.

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 score0.063
Threshold uncertainty score0.528

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 communication0.0000.001
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 head0.006
GPT teacher head0.257
Teacher spread0.252 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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