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Record W4400056424 · doi:10.3389/fmars.2024.1336455

Drone-based photogrammetry reveals differences in humpback whale body condition and mass across North Atlantic foraging grounds

2024· article· en· W4400056424 on OpenAlexafffund
Chelsi Napoli, Nathan Hirtle, Julia E. F. Stepanuk, Fredrik Christiansen, Eleanor I. Heywood, Thomas Grove, Alyssa Stoller, Flordespina Dodds, Maria Glarou, Marianne H. Rasmussen, Gina L. Lonati, Kimberley T. A. Davies, Simone K. A. Videsen, Malene Simon, Tenna Boye, Ann M. Zoidis, Sean K. Todd, Lesley H. Thorne

Bibliographic record

VenueFrontiers in Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of New Brunswick
FundersHáskóli ÍslandsNational Marine Fisheries ServiceDalhousie UniversityNew York State Department of Environmental Conservation
KeywordsHumpback whaleForagingFisheryDronePhotogrammetryGeographyOceanographyAerial imageryWhaleBiologyEcologyGeologyRemote sensing

Abstract

fetched live from OpenAlex

Baleen whales are key consumers in marine ecosystems and can serve as ecosystem sentinels. Body condition, defined as an individual’s energy stores relative to its structural size, can provide a useful proxy for health in baleen whales. As capital breeders, important life history events in baleen whales such as seasonal migrations and reproduction depend on having sufficient energy stores. Spatiotemporal variability of body condition of baleen whales can reflect differences in energy accumulated during the foraging season. Here we assess and compare the body condition and mass of humpback whales ( Megaptera novaeangliae) across four different foraging areas from the West Indies distinct population segment in the Northwest Atlantic. Morphometric measurements of humpback whales were obtained using unoccupied aerial systems (UAS, or drones) from the New York Bight, the Gulf of Maine, Iceland, and Greenland. Uncertainty in morphometric estimates was incorporated and propagated using a bootstrapping approach. Measurements were used to estimate body volume and calculate a body condition index (BCI) for each individual whale. Since body mass is a key parameter for understanding animal physiology and bioenergetics, we further compared whale body mass to body size between foraging areas by converting body volume to body mass using estimates of tissue density from tagging studies. BCI showed significant differences between foraging areas with a large effect size (ANCOVA: mean η 2 = 0.168; all p< 0.001) when incorporating day of year and year as covariates. Humpback whales in the Gulf of Maine showed significantly higher BCI than those in the New York Bight, Iceland, and Greenland. Standardized Major Axis (SMA) regressions comparing log-log relationships of both body volume and body mass, respectively, to total length reinforced these results. Humpback whales in the Gulf of Maine showed significantly higher elevation in the SMAs than those in the other study regions (p<0.001), implying that humpback whales foraging in the Gulf of Maine accumulated greater energy reserves for a given body size. Estimates of body mass indicate that for a given body length, humpback whales in the Gulf of Maine have an 18% greater body mass than those in the New York Bight, Iceland, or Greenland. Regional differences in prey availability or anthropogenic threats could contribute to the observed patterns in body condition. Our findings highlight the importance of regional environmental factors to the nutritional health of baleen whales.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.012
GPT teacher head0.252
Teacher spread0.241 · 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

Citations22
Published2024
Admission routes2
Has abstractyes

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