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Record W2966195469 · doi:10.1139/cjfr-2018-0543

Influence of spawning salmon on tree-ring width, isotopic nitrogen, and total nitrogen in old-growth Sitka spruce from coastal British Columbia

2019· article· en· W2966195469 on OpenAlexafffundvenueabout
T. E. Reimchen, Estelle Arbellay

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of Victoria
FundersHakai InstituteDavid Suzuki FoundationTula Foundation
KeywordsNutrientDendrochronologyNitrogenEnvironmental scienceδ15NProductivityChinook windOncorhynchusNutrient cycleFisheryEcologyBiologyForestryFish <Actinopterygii>δ13CGeographyStable isotope ratioChemistry

Abstract

fetched live from OpenAlex

Coastal watersheds of the North Pacific benefit immensely from bear-mediated uploading of salmon nutrients, which increases aquatic and terrestrial productivity. To quantify the influence of spawning salmon on tree-ring signatures, we analyzed 543 rings from the heartwood of 13 old-growth Sitka spruce (Picea sitchensis (Bong.) Carrière) trees from five geographically separated watersheds in coastal British Columbia. In comparison with adjacent control trees, those receiving salmon nutrients (salmon trees) have rings that are, on average, 1.5 mm wider, 4.5‰ more enriched in isotopic nitrogen, and 0.021% more elevated in total nitrogen (P &lt; 0.001, Mann–Whitney–Wilcoxon test). In this study, salmon nutrients enhance average stem growth by 19%. Furthermore, salmon trees show that increases in tree-ring width and nitrogen values lag sporadic, high salmon runs by 0 to 5 years. Using differences between control and salmon trees from the same site, our results collectively indicate that tree-ring width, isotopic nitrogen, and total nitrogen are valid, complementary tools for investigating historic, annual fluctuations in salmon abundance in coastal watersheds. We recommend their use in future, tree ring based reconstructions of past nutrient cycling over decadal to centennial time scales.

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)0.001
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 score0.161
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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

Citations8
Published2019
Admission routes4
Has abstractyes

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