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Record W4386649563 · doi:10.1101/2023.09.07.556613

Indigenous Knowledge as a sole data source in habitat selection functions

2023· preprint· en· W4386649563 on OpenAlexafffund
Rowenna Gryba, Andrew VonDuyke, Henry P. Huntington, Billy Adams, Justin Gatten, Qaiyyan Harcharek, Robert Sarren, Greg H. R. Henry, Marie Auger‐Méthé

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of CanadaNorth Pacific Research BoardAssociation of Canadian Universities for Northern Studies
KeywordsHabitatIndigenousGeographySubsistence agricultureTraditional knowledgeDocumentationWildlifeEcologyEnvironmental resource managementBiologyAgricultureComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

While Indigenous Knowledge (IK) contains a wealth of information on the behaviour and habitat use of species, it is rarely included in the species-habitat models frequently used by ‘Western’ species management authorities. As decisions from these authorities can limit access to species that are important culturally and for subsistence, exclusion of IK in conservation and management frameworks can negatively impact both species and Indigenous communities. In partnership with Iñupiat hunters, we developed methods to statistically characterize IK of species-habitat relationships and developed models that rely solely on IK to identify species habitat use and important areas. We provide methods for different types of IK documentation and for dynamic habitat types (e.g., ice concentration). We apply the method to ringed seals (natchiq in Iñupiaq) in Alaskan waters, a stock for which the designated critical habitat has been debated in part due to minimal inclusion of IK. Our work demonstrates how IK of species-habitat relationships, with the inclusion of dynamic habitat types, expands on existing mapping approaches and provides another method to identify species habitat use and important areas. The results of this work provide a straightforward and meaningful approach to include IK in species management, especially through co-management processes. “Agencies have a traditional way they do science and including Indigenous Knowledge is less traditional.” - Taqulik Hepa, subsistence hunter and Director, North Slope Borough Department of Wildlife Management Statement of Positionality This study and the conversion and application of Indigenous Knowledge (IK) for habitat use models was initiated through discussions with the North Slope Borough Department of Wildlife Management (DWM). The DWM is an agency of the regional municipal government representing eight primarily Iñupiat subsistence communities in Northern Alaska. One of the goals of the DWM is to “assure participation by Borough residents in the management of wildlife and fish… so that residents can continue to practice traditional methods of subsistence harvest of wildlife resources in perpetuity” (1). Additionally, this project was presented to the Ice Seal Committee (ISC) for review, input, and approval. The ISC is an Alaskan Native organization with representatives from five regions that cover ice-associated seal ranges and “was established to help preserve and enhance ice seal habitat; protect and enhance Alaska Native culture, traditions-particularly activities associated with the subsistence use of ice seals” (2). Both the DWM and the ISC have mandates to manage ice-associated seals considering both IK and ‘Western’ scientific knowledge (1, 2), and this study was developed to meet those mandates. Iñupiat hunters from Utqiaġvik, Alaska (Figure 1) were collaborators on this project, five of whom are co-authors (B. Adams, B. Frantz, J. Gatten, Q. Harcharek, and R. Sarren), while the other hunter chose to remain anonymous for this publication. The other authors are not Indigenous: R. Gryba was a PhD candidate at the University of British Columbia, M. Auger-Méthé and G. Henry are professors at the University of British Columbia, A. Von Duyke is a researcher at the DWM, and H. Huntington is an independent social scientist. Significance Statement Indigenous Knowledge (IK) is an extensive source of information of species habitat use and behavior, but is still rarely included in statistical methods used for species conservation and management. Because current conservation practices are frequently still rooted in ‘Western’ practices many Indigenous organizations are looking for ways for IK to be better included and considered. We worked with Iñupiat hunters to develop a new statistical approach to characterize IK and use it as a sole data source in habitat models. This work expands on mapping approaches, that are valuable, but cannot be applied to dynamic habitat types (e.g., ice concentration). This work shows how IK can be meaningfully included in modelling and be considered in current approaches for species management.

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)0.012
metaresearch head score (Gemma)0.056
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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.065
GPT teacher head0.339
Teacher spread0.275 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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