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Record W4409225813 · doi:10.31219/osf.io/cdfsw_v1

Checklist of the avian diversity of Alaska

2025· preprint· en· W4409225813 on OpenAlexaboutno aff
Jack J. Withrow, Daniel Gibson, Kevin Winker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistDiversity (politics)GeographyPolitical scienceBiologyPaleontology

Abstract

fetched live from OpenAlex

More than just a state, Alaska constitutes the entire northwestern extent of North America. Alaska is a vast area (586,412 mi2/1,518,800 km2 of land), spanning nearly 60 degrees of longitude and 20 degrees of latitude, with roughly ~45,000 mi/72,000 km of coastline. The area considered here includes surrounding waters within the U. S. 200-nautical-mile exclusive economic zone (see Gibson and Withrow 2015) and thus represents an area of over 2,000,000 mi2/~5,000,000 km2. It represents the eastern half of Beringia, a pivotally important area for the exchange of New and Old World avifaunas and for high-latitude avian endemism (Winker et al. 2023). Spanning a wide swath of the northern reaches of the Pacific Ocean, Alaska hosts breeding migrants from all seven continents, including tens of millions of birds from Asia (Winker and Gibson 2010). There are three main intrinsic drivers of Alaska’s avian diversity: Its geographic size and position on the globe, the diversity of its aquatic and terrestrial habitats, and its dynamic history of climatic and habitat fluctuations. This region’s high latitude causes extreme annual seasonality, making migration a predominant life-history strategy among the state’s birds. With migration, especially long-distance migration, comes enhanced dispersal, increasing the likelihood of vagrancy and colonization. Diverse contemporary habitat types occur in extensive expanses of nearshore and offshore marine waters; marine, brackish, and freshwater littoral zones; freshwater wetlands, rivers, lakes, and streams; the variety of heath and tundra types that dominate the Aleutian and Bering Sea islands, much of western and northern Alaska, and alpine areas; and meadows, shrubs, taiga (boreal forest), and temperate rainforests (e.g., Gabrielson and Lincoln 1959; Kessel 1979, 1998; Audubon Alaska 2014). Because of past glacial cycles, the availability and distribution of these habitats has changed throughout the Pleistocene, but the lack of major glaciation throughout much of Beringia caused the region as a whole to be a large glacial refugium within which smaller refugia also existed for some birds (Winker et al. 2023). This long-term history generated high levels of regional endemism (for this latitude) among species and subspecies of birds (ibid). In addition, southeast Alaska extends into the Pacific Northwest refugium complex, adding further to the state’s avian diversity (Shafer et al. 2010, Winker et al. 2023). The historic climatic dynamism of the Pleistocene continues today and into the future with global warming, which is occurring about four times faster in Arctic regions than elsewhere (Previdi et al. 2021, Rantanen et al. 2022). Alaska’s extensive Arctic environments (see U.S. Congress 1984) are thus undergoing rapid changes, affecting avian occurrences and distributions in multiple ways (e.g., Marcot et al. 2015, Smith et al. 2019, Renner et al. 2024). Importantly, many changes will not be due to habitat shifts. Among migratory birds in this region, extensive movements are made in eastern and western directions, such that the time available to reproduce, more than habitat, dictates range limits. As growing seasons lengthen, these range limits can change rapidly (see Benson and Winker 2015, Winker and Gibson 2018). Together, these drivers explain not only the current diversity of Alaska birds, but also why we expect this list to continue to grow over time. The starting point for this list is AOU (1998) and supplements through Chesser et al. (2023) for phylogenetic sequence and the limits of families, genera, and species. We no longer follow this list, because the American Ornithological Society decided to use avian nomenclature as a tool of social activism. This leaves us free to retain long-used names and to occasionally disagree over interpretations of the scientific evidence for some taxonomic changes. These differences can be found in the NOTES sections of the affected taxa and are summarized in the Appendix. For subspecies, the starting point is Gibson and Withrow (2015) and the two subsequent reports of the Alaska Checklist Committee (Gibson et al. 2018, 2023). The Alaska Checklist Committee (ACC) has been instrumental in collating, assessing, and publishing new records of Alaska birds. Designation of status (rare, casual, accidental; see Key to occurrence, below) at the species level follows this committee’s Checklist of Alaska Birds, 31st edition (Gibson et al. 2025). Changes from these starting points are based on published evidence and are referenced and explained (except for changes to status, which can generally be found in the ACC reports). The breeding status for all taxa is also given (see Key).We consider subspecies to be populations or groups of populations that breed in a portion of the species’ range that have a diagnosably different phenotype from other subspecies (in presumably heritable traits). Subspecies have the potential for gene flow between them, and diagnosability generally follows the 75% Rule (Patten and Unitt 2002, Winker and Haig 2010). We recognize that some of the subspecies recognized here might not meet stringent contemporary standards of diagnosability, but until they are examined in more detail and those results are published, we maintain the historical perspective as a working hypothesis. Subspecies in brackets are those not represented by an archived specimen and/or where the identity is an inference (usually geographic). Our ability to find differences among populations, especially in genetics, has grown immensely, but some traits (e.g., neutral genetic differences, vocalizations in taxa where they are learned) are not as indicative of the long-term, adaptive changes that subspecies nomenclature attempts to capture. For brevity, we do not give distributional statements, those being available elsewhere (e.g., AOU 1957, 1998; Vaurie 1959, 1965; Dickinson and Remsen 2013; Dickinson and Christidis 2014; del Hoyo and Collar 2014, 2016; Gibson and Withrow 2015; etc.). Full citations to the authorities for taxa listed here can be obtained, for example, from A. P. Peterson’s website (Zoonomen.net), D. Lepage’s and Birds Canada website Avibase (avibase.bsc-eoc.org), and in Lynx Edicions Handbook of the Birds of the World series.Alaska’s avian checklist has grown at a remarkably steady average of 3.5 species per year since the mid-1900s and shows no sign of reaching an asymptote. Gabrielson and Lincoln (1959) discussed 311 species of Alaska birds, and that number grew steadily through Kessel and Gibson (1978: 381), Gibson and Kessel (1992: 436), Gibson and Kessel (1997: 448), Gibson et al. (2003: 468), Gibson et al. (2008: 485), Gibson et al. (2013: 499), Gibson et al. (2018: 521), and Gibson et al. (2023: 541). This checklist of Alaska’s birds now includes 548 species and an additional 119 subspecies. Of these 548 species, 55 are rare, 159 are casual, and 85 are accidental; 234 species regularly breed within the state (“B”; with an additional 75 regularly breeding subspecies). An additional 38 species have at one time or another bred within the state (“b”), and 8 probably have (“?”), but are not here considered a normal part of the nesting avifauna.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.030
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.008

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 head0.210
Teacher spread0.199 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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