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Record W3176537895 · doi:10.1139/as-2021-0003

Remotely sensed trends in vegetation productivity and phenology during population decline of the Bathurst caribou (<i>Rangifer tarandus groenlandicus</i>) herd

2021· article· en· W3176537895 on OpenAlexaffvenue
Katherine D. Dearborn, Ryan K. Danby

Bibliographic record

VenueArctic Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of WinnipegQueen's University
Fundersnot available
KeywordsTundraEcotoneVegetation (pathology)Climate changeEcologyTaigaEnhanced vegetation indexPhenologyPhysical geographyGrowing seasonGeographyProductivityRange (aeronautics)PopulationEnvironmental scienceModerate-resolution imaging spectroradiometerEcosystemNormalized Difference Vegetation IndexBiologyHabitatVegetation IndexDemographySatellite

Abstract

fetched live from OpenAlex

The Bathurst caribou (Rangifer tarandus groenlandicus (Borowski, 1780)) herd declined from ∼349 000 animals in 1996 to ∼8200 in 2018. Climate-driven changes to tundra and boreal vegetation is one hypothesis for the decline. We modelled and mapped annual productivity and phenology across the herd’s range using enhanced vegetation index (EVI) data derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) time series spanning 2000–2017. Maximum annual EVI, growing season length, and time-integrated EVI increased significantly on 16%, 18%, and 49% of the core annual range, respectively. Trends toward longer growing seasons were driven entirely by earlier spring green-up and, along with time-integrated EVI, were most prevalent in tundra regions. Trends in forested regions were overwhelmingly related to the influence of forest fires, which burned more than half of the range below the forest–tundra ecotone since 1965. These trends suggest that climate-driven changes in production and phenology have occurred in the tundra and forest–tundra portions of the range and could have contributed to the recent herd decline. However, the trends may also be a result of herd decline itself, given the loss of this large herbivore from the landscape. Elucidating cause and effect will require comprehensive analysis of interactions between climatic variables, herd dynamics, and vegetation change, complemented by targeted field investigations.

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 score0.023
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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