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Record W4403258603 · doi:10.1016/j.foreco.2024.122319

Microclimate drives growth of hair lichens in boreal forest canopies after partial cutting

2024· article· en· W4403258603 on OpenAlexaff
Per‐Anders Esseen, Darwyn Coxson

Bibliographic record

VenueForest Ecology and Management · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsUniversity of Northern British Columbia
FundersSveriges LantbruksuniversitetSvenska Forskningsrådet Formas
KeywordsLichenMicroclimateTaigaEnvironmental scienceEcologyBorealCanopyGeographyBiology

Abstract

fetched live from OpenAlex

Hair lichens in the genera Alectoria and Bryoria dominate old-growth circumboreal coniferous forests and have important ecosystem functions, particularly for reindeer and caribou. These lichens are sensitive to changes in climate and are unable to maintain a high standing crop in industrial forestry based on clear-cutting, highlighting the need of management models based on continuous cover forestry. We examined how dry mass (DM) growth and CO 2 exchange in hair lichens depended on the balance between growth (carbon gain from photosynthesis) and losses (both carbon loss from respiration and mass loss from fragmentation). Partial cutting trials were conducted in a Picea abies -dominated forest by three levels of basal area (BA) removal (0 %; 33 %; 67 %), with five 80 m × 80 m plots per level. We compared two species with similar functional traits but with different cortical pigments , the pale Alectoria sarmentosa and the dark Bryoria fremontii . Lichens were transplanted within the lower canopy using net cages over a 1-year period to evaluate net growth, loss by thallus fragmentation and gross growth. Canopy openness and transmitted radiation during the growing season were estimated from hemispherical photographs. Canopy temperature, relative humidity , and photosynthetic photon flux density were monitored, with microclimate data subsequently used to model net CO 2 exchange using previously published response matrices describing net photosynthetic and respiratory activity. Net DM growth of A. sarmentosa was higher than in B. fremontii , and increased with level of BA removal, being twice as high in the 67 % BA removal as in the control. In contrast, B. fremontii responded weakly to partial cutting due to high rates of thallus fragmentation. However, gross growth of both species increased with canopy openness and transmitted radiation. The modelled net assimilation showed large seasonal variation, with the largest difference among levels of BA removal in autumn. The estimated DM growth agreed well with observed gross growth in A. sarmentosa but was underestimated in B. fremontii . Modelling of CO 2 exchange can provide a mechanistic understanding of how hair lichens respond to partial cutting and climate change . The response of hair lichens to microclimate in partial cuts depends on the trade-off between growth and losses. Results suggest that the faster lichen growth on residual trees in the one-third removal partial cuts compensated to a significant degree for the loss of lichen mass by the removal of host trees.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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
Published2024
Admission routes1
Has abstractyes

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