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Record W3159172204 · doi:10.1002/ecs2.3481

Predicting patterns of terrestrial lichen biomass recovery following boreal wildfires

2021· article· en· W3159172204 on OpenAlexafffundabout
Ruth Greuel, Geneviève É. Degré‐Timmons, Jennifer L. Baltzer, Jill F. Johnstone, Eliot J. B. McIntire, Nicola J. Day, Sarah J. Hart, Philip D. McLoughlin, Fiona K. A. Schmiegelow, Merritt R. Turetsky, Alexandre Truchon‐Savard, Mario D. van Telgen, Steven G. Cumming

Bibliographic record

VenueEcosphere · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsUniversity of GuelphUniversity of AlbertaUniversity of SaskatchewanUniversité LavalNatural Resources CanadaCanadian Forest ServiceWilfrid Laurier University
FundersEnvironment and Climate Change CanadaUniversity of TorontoWestern Economic Diversification CanadaPolar Knowledge CanadaUniversity of ManitobaW. Garfield Weston FoundationCamecoUniversity of Saskatchewan
KeywordsLichenTaigaCladoniaBorealEcologyBiomass (ecology)Environmental sciencePhysical geographyBiologyGeography

Abstract

fetched live from OpenAlex

Abstract Increased fire activity due to climate change may impact the successional dynamics of boreal forests, with important consequences for caribou habitat. Early successional forests have been shown to support lower quantities of caribou forage lichens, but geographic variation in, and controls on, the rates of lichen recovery has been largely unexplored. In this study, we sampled across a broad region in northwestern Canada to compare lichen biomass accumulation in ecoprovinces, including the Saskatchewan Boreal Shield, the Northwest Territories Taiga Shield, and Northwest Territories Taiga Plains, divided into North and South. We focused on the most valuable Cladonia species for boreal and barren‐ground caribou: Cladonia mitis and C. arbuscula , C. rangiferina and C. stygia , and C. stellaris and C. uncialis . We developed new allometric equations to estimate lichen biomass from field measurements of lichen cover and height; allometries were consistent among ecoprovinces, suggesting generalizability. We then used estimates of lichen biomass to quantify patterns of lichen recovery in different stand types, ecoprovinces, and with time following stand‐replacing fire. We used a hurdle model to account both for the heterogeneous nature of lichen presence (zero inflation) and for the range of abundance in stands where lichen was present. The first component of the hurdle model, a generalized linear model, identified stand age, stand type, and ecoprovince as significant predictors of lichen presence. With a logistic growth model, a measure of lichen recovery (time to 50% asymptotic value) varied from 28 to 73 yr, dependent on stand type and ecoprovince. The combined predictions of the hurdle model suggest the most rapid recovery of lichen biomass across our study region occurred in jack pine in the Boreal Shield (30 yr), while stands located in the Taiga Plains (North and South) required a longer recovery period (approximately 75 yr). These results provide a basis for estimating future caribou habitat that encompasses some of the large variation in fire effects on lichen abundance and vegetation types across the range of boreal and barren‐ground caribou in North America.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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.

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
Published2021
Admission routes3
Has abstractyes

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