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Record W2770083475 · doi:10.1128/aem.02061-17

Limited Effects of Variable-Retention Harvesting on Fungal Communities Decomposing Fine Roots in Coastal Temperate Rainforests

2017· article· en· W2770083475 on OpenAlexafffundabout
Timothy J. Philpott, Jason S. Barker, Cindy E. Prescott, S. J. Grayston

Bibliographic record

VenueApplied and Environmental Microbiology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsDecomposerBiologyTemperate rainforestLitterEcologyBotanyPostharvestChronosequenceGuildEcological successionEcosystem

Abstract

fetched live from OpenAlex

ABSTRACT Fine root litter is the principal source of carbon stored in forest soils and a dominant source of carbon for fungal decomposers. Differences in decomposer capacity between fungal species may be important determinants of fine-root decomposition rates. Variable-retention harvesting (VRH) provides refuge for ectomycorrhizal fungi, but its influence on fine-root decomposers is unknown, as are the effects of functional shifts in these fungal communities on carbon cycling. We compared fungal communities decomposing fine roots (in litter bags) under VRH, clear-cut, and uncut stands at two sites (6 and 13 years postharvest) and two decay stages (43 days and 1 year after burial) in Douglas fir forests in coastal British Columbia, Canada. Fungal species and guilds were identified from decomposed fine roots using high-throughput sequencing. Variable retention had short-term effects on β-diversity; harvest treatment modified the fungal community composition at the 6-year-postharvest site, but not at the 13-year-postharvest site. Ericoid and ectomycorrhizal guilds were not more abundant under VRH, but stand age significantly structured species composition. Guild composition varied by decay stage, with ruderal species later replaced by saprotrophs and ectomycorrhizae. Ectomycorrhizal abundance on decomposing fine roots may partially explain why fine roots typically decompose more slowly than surface litter. Our results indicate that stand age structures fine-root decomposers but that decay stage is more important in structuring the fungal community than shifts caused by harvesting. The rapid postharvest recovery of fungal communities decomposing fine roots suggests resiliency within this community, at least in these young regenerating stands in coastal British Columbia. IMPORTANCE Globally, fine roots are a dominant source of carbon in forest soils, yet the fungi that decompose this material and that drive the sequestration or respiration of this carbon remain largely uncharacterized. Fungi vary in their capacity to decompose plant litter, suggesting that fungal community composition is an important determinant of decomposition rates. Variable-retention harvesting is a forestry practice that modifies fungal communities by providing refuge for ectomycorrhizal fungi. We evaluated the effects of variable retention and clear-cut harvesting on fungal communities decomposing fine roots at two sites (6 and 13 years postharvest), at two decay stages (43 days and 1 year), and in uncut stands in temperate rainforests. Harvesting impacts on fungal community composition were detected only after 6 years after harvest. We suggest that fungal community composition may be an important factor that reduces fine-root decomposition rates relative to those of above-ground plant litter, which has important consequences for forest carbon cycling.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.335

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations20
Published2017
Admission routes3
Has abstractyes

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