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Record W6959474090 · doi:10.7939/r3-qhcz-vf97

Regional-scale hydrologic settings buffer black spruce regeneration in the presence of post-fire droughts

2024· dissertation· en· W6959474090 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2024
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsPeatBlack spruceBorealTaigaClimate changeHydrology (agriculture)Water tableWater content

Abstract

fetched live from OpenAlex

Climate change is increasing the frequency of droughts and wildfires, reducing tree recruitment, and altering post-fire species composition. In Canada’s western boreal forests, postfire recruitment, particularly of drought-intolerant coniferous species like black spruce, has declined in recent decades to the benefit of early-successional species like jack pine and trembling aspen. Groundwater supplied to forests via adjacent peatlands may help to resist such reductions in recruitment and compositional shifts, particularly during droughts. The degree to which peatlands buffer adjacent forests from drought-induced regeneration failure may therefore depend on topographic position and soil texture, factors that govern groundwater connectivity. I examined how these topoedaphic factors influence upland tree regeneration from post-fire drought, defined in this study as the post-fire climate moisture deficit across sampled fires. Since higher-positioned peatlands (bogs, poor fens) are mostly fed by precipitation, they are more vulnerable to drought compared to fen-like peatlands at lower topographic positions that are fed by groundwater. I therefore hypothesized that regenerating forest density, growth, and composition at lower topographic positions would be buffered from post-fire drought by water sources from the adjacent fen across a range of soil textures. Specifically, I predicted that tree density, volume, and proportions of black spruce should decline with high topographic positions, favoring instead jack pine and aspen following post-fire drought. I tested this prediction by measuring 58 post-fire upland forest stands ranging from 5 – 20-years old that experienced wet or dry post-fire weather. Study sites spanned local (relative to adjacent peatland) and regional topographic position (relative to a regional low) gradients. I used generalized linear mixed effects models to test interactions between these local and regional topographic positions, soil texture, and post-fire climate. I found significant reductions in regenerating black spruceiii proportions at high regional topographic positions across fine- and coarse-textured soils with post-fire drought. Total regeneration (stem density), tree volume (basal area), and species of jack pine and aspen showed no correlations with post-fire drought. This study highlights that hydrologically well-connected areas of Alberta’s boreal forest may act as refugia from drought and fire for drought-intolerant black spruce, and that more predominant upland jack pine and aspen species appeared to be resilient under the current fire regime. Larger scale ecohydrological dynamics therefore interact with forest regeneration and should be considered to identify areas that may resist altered post-fire trajectories.

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)0.001
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 score0.985
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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