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Record W2073322961 · doi:10.1093/aob/mcv015

Substantial variation in leaf senescence times among 1360 temperate woody plant species: implications for phenology and ecosystem processes

2015· article· en· W2073322961 on OpenAlexaff
Zoe A. Panchen, Richard B. Primack, Amanda S. Gallinat, Birgit Nordt, Albert‐Dieter Stevens, Yanjun Du, Robert T. Fahey

Bibliographic record

VenueAnnals of Botany · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiologyPhenologySenescenceEcosystemTemperate climateDeciduousGrowing seasonTemperate deciduous forestBotanySeasonalityAbscissionEcology

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Autumn leaf senescence marks the end of the growing season in temperate ecosystems. Its timing influences a number of ecosystem processes, including carbon, water and nutrient cycling. Climate change is altering leaf senescence phenology and, as those changes continue, it will affect individual woody plants, species and ecosystems. In contrast to spring leaf out times, however, leaf senescence times remain relatively understudied. Variation in the phenology of leaf senescence among species and locations is still poorly understood. METHODS: Leaf senescence phenology of 1360 deciduous plant species at six temperate botanical gardens in Asia, North America and Europe was recorded in 2012 and 2013. This large data set was used to explore ecological and phylogenetic factors associated with variation in leaf senescence. KEY RESULTS: Leaf senescence dates among species varied by 3 months on average across the six locations. Plant species tended to undergo leaf senescence in the same order in the autumns of both years at each location, but the order of senescence was only weakly correlated across sites. Leaf senescence times were not related to spring leaf out times, were not evolutionarily conserved and were only minimally influenced by growth habit, wood anatomy and percentage colour change or leaf drop. These weak patterns of leaf senescence timing contrast with much stronger leaf out patterns from a previous study. CONCLUSIONS: The results suggest that, in contrast to the broader temperature effects that determine leaf out times, leaf senescence times are probably determined by a larger or different suite of local environmental effects, including temperature, soil moisture, frost and wind. Determining the importance of these factors for a wide range of species represents the next challenge for understanding how climate change is affecting the end of the growing season and associated ecosystem processes.

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)0.001
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.291
Teacher spread0.246 · 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

Citations95
Published2015
Admission routes1
Has abstractyes

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