Large Mammal Movement: Differences in Primary and Branch Logging Road Use in Algonquin Provincial Park, Ontario
Bibliographic record
Abstract
There is an expansive network of roads in Algonquin Provincial Park (APP) to \nfacilitate forestry resource extraction. This leaves a research need for examining how the \nlogging road network in APP affects the large mammals, and what local-level and \nlandscape-level variables influence that use. Local-level data was collected directly at \nobservation points, and landscape-level data was produced from ArcGIS for 40km2, \n80km2, and 130km2 buffer areas. The objective of my study was to look at the use of \nprimary and branch logging roads by five large mammal species in APP, and determine if \nlandscape-level variables had an influence on the level of movement and utilization. The \nfive species included moose, white-tailed deer, American black bear, eastern wolf and \ncoyote. My null hypothesis (H0) states that there will be no difference in large mammal \nuse between the primary and branch logging roads within APP and that local- and \nlandscape-level variables will have no influence on them; my alternative hypothesis (H1) \nstates that there will be less large mammal activity on the primary logging roads, more \nlarge mammal movement on the branch logging roads and local- and landscape-level \nvariables will influence this use. Tracking was done by vehicle on six transects across \nthe park for three repeated surveys where species identification and local-level variables \nwere recorded. Landscape-level variables were acquired through GIS analysis in the lab. \nBased on the results from the local-level data, branch and primary logging road use \ndiffered in composition, though no significance was found between the use by large \nmammals for these two types of road. Through generalized linear models, specific \ncombinations of landscape-level variables did influence large mammal movement on the \nprimary and branch logging roads within three habitat range scales (130km2, 80km2, and \n40km2). The most significance was seen at the buffer of 40km2 on the branch logging \nroads, with the variables road density (p < 0.01), percent forest cover (p = 0.04) and \ntopographic ruggedness (p < 0.01) all having a strong impact on large mammal \nmovement. The only significant findings for primary logging roads were also at the \n40km2 scale with percent forest cover (p = 0.03) and percent water cover (p = 0.02) \nhaving an impact on large mammal movement. Overall, the landscape variables had \ngreater influence on branch logging roads that may be explained by the quality of the \nsurrounding habitat, as well as greater influence at smaller buffer scales. Further research \nand monitoring of the large mammals in APP is recommended to expand on this \npreliminary study. Greater understanding of the local- and landscape-level variables at \ndiffering habitat ranges will assist in understanding these large mammal movements and \nprovide data to base logging road management on. As large mammals are wide-ranging \nspecies, my study informs APP that their logging road network does not seem to hinder \nthe movements of this group of animals. Overall, the large mammals in APP did not \nhave any significant difference in their use of primary and branch logging roads of APP. \nFurther research has the potential to give greater understanding of the impacts of the \nlogging road network on the five large mammal species studied in APP. There is also the \npotential for useful management strategies to emerge for large mammals in this park, and \nhow to incorporate human activities within their habitat while maintaining sustainable \npopulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".