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Record W7024024249

A Primary Productivity Hypothesis for Disturbance-Mediated Apparent Competition for Boreal Caribou in Canada

2021· dissertation· en· W7024024249 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWoodland caribouBorealUngulatePredationTaigaCompetition (biology)Context (archaeology)ProductivityPredator
DOInot available

Abstract

fetched live from OpenAlex

The most widely reported threat to populations of boreal and mountain woodland caribou (Rangifer tarandus caribou) involves what has come to be known as disturbance-mediated apparent competition (DMAC). Here, anthropogenic and natural disturbances that increase the abundance of deciduous-browsing cervids (e.g., moose [Alces alces] and white-tailed deer [Odocoileus virginianus]) are thought to promote predator (especially wolf [Canis lupus]) numbers, in turn heightening predation risk to caribou. We know most about the hypothesis of DMAC as it relates to caribou where the species is under threat by industry; i.e., from relatively productive southern boreal and mountain systems where landscapes are highly managed and multiple species of predators and ungulate prey interact with caribou. Yet almost 2/3 of extant boreal caribou range occurs in poorly productive, wildfire-dominated areas where caribou compete with only one ungulate species (moose) in the context of DMAC. In Ch. 2, using data specific to the Saskatchewan Boreal Shield, I tested for evidence of DMAC with data specific to an area of previously known low primary productivity. I found that the successional dynamics after fire of the low-productivity boreal shield did not allow for flushes in deciduous browse, meaning moose density could not increase and resulting in no evidence for DMAC in this system. To test predictions consistent with DMAC, in Ch. 3, I examined the relationship between net primary productivity (NPP) with calf recruitment and adult female survival at a national scale. I accounted for variables influencing DMAC, including metrics of large mammal richness, alternative prey biomass, and predator biomass. While geographic site played an important role, NPP was the most important variable in beta regressions, visually influenced PCA dimensionality in the dataset, and was a primary causal factor for reduced caribou survival and recruitment in Structural Equation Models (SEM). The results indicate that NPP and anthropogenic disturbance act as an impetus for DMAC, where the phenomenon is unlikely to occur in low-productivity areas. Overall, I postulate that the DMAC phenomenon is dependent on NPP, or energy in the system, where burned areas of low NPP may not create the conditions necessary for DMAC to occur. Understanding what factors influence where DMAC occurs and at what scale will be critical for determining effective conservation strategies for local caribou range-planning and Canada’s federal Recovery Strategy for boreal caribou.

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)0.002
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 score0.115
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.145
Teacher spread0.139 · 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
Published2021
Admission routes1
Has abstractyes

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