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Record W7066872376

Invasive species removal and changing fire regimes in a ləkʷəŋən Garry oak ecosystem

2022· dissertation· en· W7066872376 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemStewardship (theology)Invasive speciesVegetation (pathology)Restoration ecologyNovel ecosystemNational parkIntroduced speciesEcosystem services
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines restoration of Garry oak ecosystems in Southwestern British Columbia, Canada. Before the arrival of European settlers, Coast Salish peoples practiced intensive stewardship and cultivation practices that heavily shaped Garry oak ecosystems. These long-standing stewardship practices are responsible for the abundance of culturally important plants found in Garry oak ecosystems today. In addition to their cultural value to Coast Salish peoples, Garry oak ecosystems also support unique biodiversity, including numerous at-risk species. These ecosystems and the values they embody came under threat with the arrival of European settlers, who introduced non-native plants and excluded Coast Salish peoples and their stewardship practices from these ecosystems. Today, Garry oak ecosystems have been reduced to a fraction of their pre-colonial distribution and remaining patches are typically heavily invaded by both native and non-native plants. Their cultural and biological values coupled with ongoing degradation has motivated both Indigenous and non-Indigenous land managers to implement restoration programs in Garry oak ecosystems. To inform future restoration efforts, this thesis examines ecological impacts of a long-term restoration program and a wildfire in a lək̓ʷəŋən Garry oak ecosystem at Mill Hill Regional Park near Langford, British Columbia. In Project 1, vegetation responses to a 13-year invasive species removal program were quantified to determine if native plant populations were successfully bolstered by the removal efforts. In Project 2, impacts of an unintended wildfire on the relative cover of native and non-native plants were examined. This attempted to explore potential ecosystem shifts that may occur as wildfires increase in frequency and severity as predicted by climate models. In Project 1, the greatest change observed after invasive species removal was an increase in other introduced species, while increases in native species were not statistically significant. In Project 2, introduced Anthoxanthum odoratum was facilitated by fire while native Camassia spp. were reduced by it. Taken together, these results demonstrate the complexity of restoring Indigenously managed ecosystems where multiple introduced species have existed for long periods. Invasive species, specifically Anthoxanthum odoratum, showed greater responses to removal efforts and wildfire than native species. Intensive, long-term restoration programs that utilize multiple tools, including low-intensity fire, invasive removal, herbicide, and seeding of native species appear necessary to bolster native species without unintentional facilitation of introduced species. Coast Salish peoples and stewardship practices were integral in maintaining these ecosystems before the arrival of European settlers and should play a key role in their restoration today, though traditional practices will likely need adapted to account for environmental changes caused by colonization. Furthermore, to avoid continuing the cultural damage that began with colonization, it is vital that Coast Salish First Nations lead or be directly involved in restoration of these ecosystems, which continue to hold irreplaceable cultural value.

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 score0.276
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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
Published2022
Admission routes1
Has abstractyes

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