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Record W7084054459 · doi:10.6084/m9.figshare.28566530

Data and code for "Impacts of Tropical Cyclones on Northwest Atlantic Seabirds: Insights from a Category 1 hurricane"

2025· other· en· W7084054459 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicCultural and Social Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSeabirdLandfallTropical cycloneAtlantic hurricanePhenology

Abstract

fetched live from OpenAlex

<b>About this repository</b>This repository contains *data* and *code* associated with the following publication:<br><br>Tori V. Burt, Robert J. Blackmore, Sydney M. Collins, Kyle J. N. d’Entremont, Christopher R. E. Ward, Josh Cunningham, Cerren Richards, Fiona Le Taro, Sabina I. Wilhelm, Amanda E. Bates, Stephanie Avery-Gomm, William A. Montevecchi. 2025. <b>Impacts of Tropical Cyclones on Northwest Atlantic Seabirds: Insights from a Category 1 hurricane. </b><i>bioRxiv. </i><br><b>Abstract</b>Tropical cyclones are annual occurrences in the western North Atlantic Ocean, where many seabird species are vulnerable to the environmental factors associated with extreme weather events. We summarize the history of tropical cyclones in Newfoundland, Canada, which hosts globally significant populations of seabirds. We examine the interactions that historical tropical cyclones have had with breeding seabirds by plotting the temporal association of Category 1 hurricanes with the breeding phenology of colonial seabirds in Newfoundland and identifying which major colonies have fallen within the pathways of these hurricanes. As a case study, we explore how Hurricane Larry (2021) coincided with increased stranding and mortality of Northern Gannets and Leach’s Storm-Petrels. The breeding seasons of Northern Gannets and Leach’s Storm-Petrels overlapped with all Category 1 hurricanes that have made landfall with Newfoundland from 1851 to 2024, with the central pathways of at least one hurricane passing over each of the six large Leach’s Storm-Petrel colonies and one of the three Northern Gannet colonies. For Northern Gannets, a notable stranding and mortality event occurred with a minimum count of 146 stranded and 130 dead from September 13 to 24, 2021. For Leach’s Storm-Petrels, a minor stranding and mortality event occurred with a count of 19 stranded and 16 dead from September 10 to 14, 2021, which was significantly higher than strandings and deaths reported during this period in 2020, 2022, 2023, and 2024. As global climate change drives a shift in the timing, frequency, severity, and attributes of tropical cyclones, we raise the concern that the impacts of tropical cyclones on breeding seabirds may worsen.<b>Data</b>This repository summarizes breeding phenology information for eight seabird species breeding in Newfoundland. This repository also contains a dataset of colony information for six large (&gt; 10,000 pairs) Leach's Storm-Petrel colonies and three Northern Gannet colonies in Newfoundland as of 2021. See the manuscript above for details. The details of each variable are described in the ReadMe file.<br>Feb_23_BreedingPhenology_hurricane.csvlesp_noga_large_colonies.csvThis repository also contains two collated datasets of reported on-land strandings and mortalities of Northern Gannets and Leach’s Storm-Petrels in eastern Newfoundland. See manuscript above for details. The details of each variable are described in the ReadMe file.<br>Gannet_Summary_HL_2024_11_26.csvHL_Lesp_Bdv_Cpaws_Mar_11_2025.csvThis repository also contains the subset of the dataset (INSERT LINK) of information on all Category 1 hurricanes that have made landfall with Newfoundland from 1851 to 2024, including "best tracks."<br>NL_hurricanes_cat1_best_tracks.csv<b>Code</b>The code required to reproduce Figure 1 (see manuscript above) is in Breeding_phenology.Rmd (input file is Feb_23_BreedingPhenology_hurricane.csv).The code required to reproduce Figure 2 (see manuscript above) is in Colony_risk_assessment.Rmd (input files are lesp_noga_large_colonies.csv and NL_hurricanes_cat1_best_tracks.csv).The code required to reproduce Figure 3 (see manuscript above) is in Hurricane_Larry_colonies.Rmd (input files are lesp_noga_large_colonies.csv and Hurricane Larry "best track" and wind radii (https://www.nhc.noaa.gov/data/tcr/index.php?season=2021&amp;basin=atl).The code required to reproduce Figures 4 and 5 (see manuscript above) are in NOGA_stranding_map (input files are lesp_noga_large_colonies.csv and Gannet_Summary_HL_2024_11_26.csv) and LESP_stranding_map (input files are lesp_noga_large_colonies.csv and HL_Lesp_Bdv_Cpaws_Feb_24_2025.csv), respectively. Shapefile for Hurricane Larry "best track" and wind radii can be found at https://www.nhc.noaa.gov/data/tcr/index.php?season=2021&amp;basin=atl.The code required to reproduce Figure 6 (see manuscript above) is in HL_stranding_comparison (input file is HL_Lesp_Bdv_Cpaws_Mar_11_2025.csv).<b>Software Requirements</b>Scripts are written for R version 4.2.2. (2022-10-31 ucrt). See scripts and the publication for packages and software citations.<b>Terms of use:</b>Anyone can share this material, provided it remains unaltered in any way, this is not done for commercial purposes, and the original authors are credited and cited (Attribution CC-BY-NC-ND).

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.168
Threshold uncertainty score0.988

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)0.0130.000

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 head0.048
GPT teacher head0.260
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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