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Masting in whitebark pine ( <i>Pinus albicaulis</i> ) depletes stored nutrients

2012· article· en· W2146703973 on OpenAlexaff
Anna Sala, Kelly A. Hopping, Eliot J. B. McIntire, Sylvain Delzon, Elizabeth E. Crone

Bibliographic record

VenueNew Phytologist · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversité Laval
FundersRocky Mountain Research StationU.S. Forest ServiceNational Science Foundation
KeywordsNutrientBiologyMast (botany)ShootPhotosynthesisBotanyEcologyAgronomy

Abstract

fetched live from OpenAlex

• In masting trees, synchronized, heavy reproductive events are thought to deplete stored resources and to impose a replenishment period before subsequent masting. However, direct evidence of resource depletion in wild, masting trees is very rare. Here, we examined the timing and magnitude (local vs individual-level) of stored nutrient depletion after a heavy mast event in Pinus albicaulis. • In 2005, the mast year, we compared seasonal changes in leaf and sapwood nitrogen (N) and phosphorus (P) concentrations and leaf photosynthetic rates in cone-bearing branches, branches that never produced cones, and branches with experimentally removed cones. We also compared nutrient concentrations in cone branches and branches that had never had cones between 2005 and 2006, and measured tree ring width and new shoot growth during 2005. • During the mast year, N or P depletion occurred only in tissue fractions of reproductive branches, where photosynthetic rates were reduced. However, by the end of the following year, nutrients were depleted in all branches, indicating individual-level resource depletion. New shoot and radial growth were not affected by masting. • We provide direct evidence that mast events in wild trees deplete stored nutrients. Our results highlight the importance of evaluating reproductive costs over time and at the individual level.

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 score0.009
Threshold uncertainty score0.915

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)0.0010.001

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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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

Citations142
Published2012
Admission routes1
Has abstractyes

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