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Record W4396538100 · doi:10.24124/2024/59482

Moose responses to anthropogenic disturbance across a range of spatial scales: Diet, habitat use, and movement

2024· dissertation· en· W4396538100 on OpenAlexaboutno aff
Lisa Jeanne Koetke

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyGeneralist and specialist speciesAbundance (ecology)Range (aeronautics)HabitatDisturbance (geology)ForbPredationGeographyPopulationHerbivoreBiodiversitySpatial ecologyBiologyEnvironmental scienceGrassland

Abstract

fetched live from OpenAlex

Habitat loss and climate change are two of the leading causes of the global decline of biodiversity. Declines in the abundance of moose (Alces americanus) in British Columbia, Canada, in the 2000s were hypothesized to result from an interaction between a severe climate-induced insect outbreak and resulting increases in salvage harvest of affected forests. I investigated the behavioral and distributional responses of moose to forest harvesting disturbance across a range of spatial scales and tested the use of N-mixture models and camera trap data to estimate population abundance. At a fine spatial scale, I used microhistological analysis of moose fecal samples to assess the effects of logging on the diet of moose. In areas with greater intensities of forest harvesting, moose consumed fewer forbs, shrubs, and fir trees, and their diet was more diverse. These dietary responses were consistent with the Niche Expansion Hypothesis, which predicted that a generalist herbivore would eat a greater diversity of plants to compensate for decreased availability or quality of preferred forage. I used LiDAR and GPScollar data to test hypotheses that explained the use of horizontal and vertical cover by moose. Risk of predation and hunting (Direct Mortality Hypothesis) was the primary factor that influenced the use of cover. Moose used different forest structures, ranging from open to closed, depending on the threat (predation or hunting) and their response was modulated by maternal status. At a coarser spatial scale, I assessed a suite of hypotheses concerning the causes of partial migration, effects of migration on habitat used, and fitness effects of migration. Wildfire disturbance in the winter range was the primary driver of migration and most migratory moose experienced less wildfire disturbance after leaving their winter range. Migrants displaying specific movement tactics (e.g., distance and timing of migratory movements) experienced increased probability of parturition and neonate survival. Migration exposed moose to increased risk of predation but residents were more vulnerable to health-related causes of mortality. While migration provided some moose with fitness benefits, it did not fully mitigate the amount of wildfire disturbance in the summer range, particularly after severe wildfire seasons. Finally, I tested the sensitivity of estimates of population abundance produced by N-mixture models parameterized with camera trap data to ecological conditions, spatial scale of covariates, the potential for temporally non-independent detections, and model choice based on parsimony. Nmixture models produced accurate and reasonably precise estimates of abundance of moose and were robust to model formulation, the spatial scale of associated covariates, and the criteria used to define independent detection. However, I recommend avoiding measures of parsimony for selecting the model or models to generate a population estimate. In total, the results of my dissertation suggest that land management and forest harvest should maintain forest communities that vary in structure and composition. In particular, large-scale disturbance can alter the diet of moose, expose maternal moose to increased risk of mortality, and, in the case of wildfire, it could lead to a decline in migratory behaviors and populations.,

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.275
Teacher spread0.262 · 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
Published2024
Admission routes1
Has abstractyes

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