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Record W7106036859 · doi:10.7939/83101

Avian responses to forest disturbances in the Canadian Rockies

2025· dissertation· en· W7106036859 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsDisturbance (geology)Generalist and specialist speciesVegetation (pathology)Mountain pine beetleHabitatEcosystemClimate changeForest dynamicsForest ecology

Abstract

fetched live from OpenAlex

Disturbance regimes in the Canadian Rockies have changed substantially over the past century. Historically frequent, low intensity, patchy fires have been replaced by increasingly large, severe, and stand-replacing events, while the mountain pine beetle (MPB) outbreak has affected millions of hectares of forest since 1999. These shifts—driven by settler colonial fire suppression and anthropogenic climate change—have altered forest structure and composition, with uncertain consequences for wildlife. In particular, it remains unclear how bird communities—widely used indicators of ecosystem change— are responding to novel combinations of disturbance type, severity, extent, and recovery trajectories. My thesis investigates how birds respond to two broad-scale forest disturbances in the Canadian Rockies: high-severity wildfire and post-MPB outbreak management. Using data from autonomous recording units (ARUs), I analyzed avian species richness, community composition, and species-specific responses to disturbance types across different temporal and spatial scales. In Chapter 2, I assessed bird community responses to the 2017 Kenow Wildfire in Waterton Lakes National Park, using ARU data from 337 locations collected between 2018 and 2022. Elevation was the dominant predictor of avian community structure, but wildfire modified these patterns by amplifying species losses at mid-elevations, where bird communities may be more sensitive to disturbance. At lower elevations, fire effects were less pronounced—possibly due to generalist species or faster vegetation recovery—while high-elevation communities appeared relatively unaffected, potentially due to sparser pre-fire vegetation conditions. Species-level models showed varied responses, with mid-elevation old-growth forest specialists exhibiting the strongest declines. In Chapter 3, I examined bird communities across post-MPB-attacked forests in two regions that experienced temporally distinct outbreaks: a more recent outbreak in Jasper National Park (Jasper) and an earlier, historic outbreak in the Southern Rockies. Using ARU data collected in 2023 and 2024, I compared bird communities across three post-MPB-attack disturbance types: burned, harvested, and left-standing (n = 293). In Jasper, bird communities differed most clearly by disturbance type, whereas in the Southern Rockies, communities converged over time. Indicator species analysis revealed stronger and more distinct species associations in Jasper, while communities in the Southern Rockies showed possible legacy effects of wildfire. Together, these findings highlight how avian responses to disturbance are shaped by cumulative disturbance, elevation, and time, but also by the spatial configuration and scale of disturbance. Across both chapters, bird communities responded most strongly in landscapes where disturbance was spatially distinct, or applied in patchy mosaics. This research contributes to a growing body of work emphasizing the importance of heterogeneity and scale in shaping post-disturbance recovery, and supports forest management approaches that incorporate spatial and temporal variation to sustain biodiversity in dynamic mountain systems.

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 score0.012
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
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 head0.173
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
Published2025
Admission routes1
Has abstractyes

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