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One century of carbon dynamics in the eastern Canadian boreal forest under various management strategies and climate change projections

2024· article· en· W4402859063 on OpenAlexaffabout
Abderrahmane Ameray, Xavier Cavard, Dominic Cyr, Osvaldo Valeria, Miguel Montoro Girona, Yves Bergeron

Bibliographic record

VenueEcological Modelling · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsEnvironment and Climate Change CanadaUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
Fundersnot available
KeywordsTaigaClimate changeBorealEcologyEnvironmental scienceCarbon fibersForest managementPhysical geographyGeographyCarbon cycleClimatologyAgroforestryForestryEcosystemBiologyGeologyMathematics

Abstract

fetched live from OpenAlex

• Forest management and climate change are the main drivers of carbon dynamics in the eastern Canadian boreal forest. • Spruce budworm outbreaks can potentially switch the Eastern Quebec boreal forest from a carbon sink into a carbon source. • -Fire may increase carbon emissions from Central and Western Quebec boreal forests, mainly under RCP4.5 and RCP8.5. • Clearcut-based scenarios may enhance carbon emission from decomposition by increasing the abundance of broadleaf species, which decompose faster than coniferous. • Partial cutting–based scenarios mimic conservation scenarios by increasing total ecosystem carbon and may accumulate more carbon than conservation and clear-cut-based scenarios. Partial cutting has lower canopy removal intensities than clearcutting and has been proposed as an alternative harvesting approach to enhance ecosystem services, including carbon sequestration and storage. However, the ideal partial cutting/clearcutting proportion that should be applied to managed areas of the eastern Canadian boreal forest to enhance long-term carbon sequestration and storage at the landscape scale remains uncertain. Our study projected carbon dynamics over 100 years (2010–2110) under a portfolio of management strategies and future climate scenarios within three boreal forest management units in Quebec, Canada, distributed along an east–west gradient. To model future carbon dynamics, we used LANDIS-II, its Forest Carbon Succession extension, and several extensions that account for natural disturbances in the boreal forest (wind, fire, spruce budworm). We simulated the effects of several management strategies on carbon dynamics, including a business-as-usual strategy (clearcutting applied to more than 95 % of the annually managed area), and compared these projections against a no-harvest natural dynamics scenario. We projected an overall increase in total ecosystem carbon storage, mostly because of increased productivity and broadleaf presence under limited climate change. The drier Western region under climate scenario RCP8.5 was an exception, as stocks decreased after 2090 because of the direct negative effects of extreme climate change on coniferous species’ productivity. Under the natural dynamic scenario, our simulations suggest that the Quebec Forest in the Central and Western regions may act as a carbon sink, despite high fire-related carbon emissions, particularly under RCP4.5 and RCP8.5 Conversely, the eastern region periodically switched from carbon sink to source following SBW outbreaks, thus being a weak sink over the simulation period. Applying partial cutting to over 50 % of the managed forest area effectively mitigated the negative impacts of climate change on carbon balance, reducing differences in stand composition and carbon storage between naturally dynamic forests and those managed for timber. In contrast, clearcutting-based scenarios, including the business-as-usual approach, substantially reduced total ecosystem carbon storage— by approximately double (10 tC ha −1 yr −1 ) compared to partial cutting scenarios (<5 tC ha −1 yr −1 ). Clearcutting led to higher heterotrophic respiration due to the proliferation of fast-decomposing broadleaves, resulting in lower carbon accumulation compared to partial cuts. Our findings underscore the importance of balancing canopy removal intensities to increase carbon sequestration and storage while preserving other ecosystem qualities under climate change.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.795

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)0.0000.000

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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2024
Admission routes2
Has abstractyes

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