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Record W6988058609

Wildfire Refugia Within a Boreal Shield Peatland and Rock Barrens Landscape: Identification, Drivers, and Ecohydrological Indicators

2022· dissertation· en· W6988058609 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBorealPeatTaigaHabitatFire regimeFire ecologyClimate changeDisturbance (geology)
DOInot available

Abstract

fetched live from OpenAlex

Fire refugia, defined as unburned, functionally intact patches of habitat within a fire footprint, play an important role in post-fire recovery and landscape resilience to fires. Increased fire activity in the Canadian boreal forest due to climate change highlights the need to properly identify and manage wildfire refugia to protect the natural resilience of boreal ecosystems. While previous fire refugia research has focused on western Canada, we present the first characterization of fire refugia, with a focus on peatland fire refugia, in Ontario. We use remotely sensed multispectral imagery and stereo-derived DEM data from the 2018 Parry Sound 33 wildfire in the Ontario Boreal Shield to determine the primary drivers of fire refugia formation on this landscape, and to develop a model to predict the occurrence of potential fire refugia based on these drivers. We found that the Normalized Difference Moisture Index (NDMI) and the Topographic Position Index (TPI, 200m radius neighbourhood) had the strongest control on wildfire refugia probability in the model, with a combined relative influence of 63.8%. Additionally, wildfire refugia tended to form in peat-filled depressions, valleys, and forested areas within the study area, whereas drier, open rock barrens were most susceptible to fire. Overall, the model had a high predictive accuracy, with a cross-validated AUC of 0.88, and a sensitivity of 81.2%. We conclude that local scale topography and simple flow accumulation models can act as a powerful tool in predicting fire refugia occurrence in this landscape. In the second part of this study, we examined the in-situ indicators of peatland fire refugia occurrence. We conducted vegetation surveys at eight peatland fire refugia and eight reference sites representative of the range of wetland types found on this landscape. We found that the peatland fire refugia had a significantly different understorey vegetation composition when compared to the reference sites. Environmental factors within the peatland fire refugia which significantly influenced this separation included median peat depth, pH, and specific conductance (SpC); where peatland fire refugia were deeper and had a lower pH and SpC when compared to the reference sites. While no vascular indicator species were identified within the peatland fire refugia, there were two bryophyte indicator species: Sphagnum rubellum and Sphagnum magellanicum which were significantly associated with the peatland fire refugia. We conclude that understorey vegetation composition, indicator species presence, peat depth, pH and SpC could be useful when distinguishing peatlands with a high refugia probability, however, further research is needed to understand how this may vary geographically and in response to top-down controls, such as fire weather. Overall, the preliminary characterization of fire refugia in the Ontario Boreal Shield will provide a basis for the identification and mapping of fire refugia within this ecozone for applications in conservation, restoration, and fire and land management.

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)0.001
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 score0.604
Threshold uncertainty score0.796

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
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 head0.181
Teacher spread0.177 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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