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Record W4402911105 · doi:10.24124/2024/59547

Habitat and community ecology of Canada lynx across intensively managed forest landscapes

2024· dissertation· en· W4402911105 on OpenAlexaboutno aff
Shannon Michael Crowley

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyGeographyHabitatCommunityBiology

Abstract

fetched live from OpenAlex

,In the subboreal forests of central British Columbia, large-scale and rapid timber harvest has resulted in fundamental changes to the age distribution of forests. Canada lynx (Lynx canadensis) is a habitat and prey specialist on snowshoe hare (Lepus americanus) that is likely sensitive to broad-scale habitat change. It is unclear how large-scale timber harvest may influence community dynamics, including the much studied lynx-hare predator-prey cycle. The density and associated population cycles of Canada lynx differ substantially between northern and southern populations with few studies of the population or habitat ecology of lynx inhabiting southern boreal forests. My research addressed gaps in our understanding of the population, community, and habitat ecology of Canada lynx in subboreal forests of British Columbia. Specifically, I investigated the ecological factors influencing the habitat use, community interactions, and survey methods of Canada lynx during two contrasting periods of cyclic lynx and hare abundance and across a landscape with widespread and rapid forest harvesting. In Chapter 2, I compared habitat selection at two different movement scales using GPS-collared lynx and American marten as well as camera data collected during two contrasting periods of prey abundance. I found that camera traps, in general, reflected the habitat use of GPS-collared lynx and marten. Mid-level and top-level vegetation cover were important predictors of habitat use for both marten and lynx, but with opposite directional influences. Lynx and marten demonstrated differential use of habitat defined by forest age and structure suggesting that each species would serve as a unique indicator of forest condition and change. My objective in Chapter 3 was to determine if a combination of camera traps, abundance estimates, and behavioural cues could be used to monitor cyclic population trends. I found that lynx behaviours and relative abundance were strongly correlated. Consistent with my predictions, years with higher lynx and hare abundance were characterized by increases in cheek-rubbing, scentmarking, and grouping behaviours. Population indices and estimates, in combination with behavioural observations for lynx, provided insights into the ecological drivers of population trends. In Chapter 4, I used camera traps to investigate the influence of sympatric carnivores (coyote, fisher, wolverine) and prey (snowshoe hare, red squirrel) on the habitat use and cooccurrence of Canada lynx. I found that lynx occurrences mirrored the cyclic change in hare abundance, while the number of sympatric mustelid species and the occurrences of each species increased during the low period. The co-occurrence of lynx with other sympatric carnivores increased at a time of prey scarcity suggesting predator populations in subboreal forests may be in a dynamic state of habitat overlap dependent on cyclic prey abundance. In total, my research provides new insights on the habitat, behavioural, and community ecology of lynx found in subboreal forests that are experiencing rapid change. The ecology of lynx in that system is a dynamic response to not only change in forest structure, but also the abundance of their primary prey, snowshoe hare. Also, my results provide guidance on the appropriate application and possible biases of a range of methods for monitoring the distribution and abundance of lynx.

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)0.000
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 score0.140
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.213
Teacher spread0.200 · 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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