Population Distribution and Seasonal Resource Selection by Elk (Cervus elaphus) in Central Ontario
Bibliographic record
Abstract
Understanding population structure and resource selection are essential for wildlife management and conservation. I assessed the population structure and resource selection of elk (Cervus elaphus) in central Ontario. I used fuzzy and hierarchical cluster analyses to elucidate elk population structure based on spatial data collected from 41 radio-collared elk. I assessed impacts of habitat quality on space use using the minimum convex polygon (MCP) and fixed kernel methods. I evaluated resource selection by elk in winter, spring, summer, and fall, from December 2011 to August 2013, using resource selection functions (RSF’s). I used a generalized linear mixed model (GLMM) to evaluate resource selection functions and used Akaike information criterion (AICc ) to select the best model of 20 candidate models constructed a priori. Models included parameters representing resources known to be important to elk: elevation, aspect, slope, distance to roads and water, and habitat. Both fuzzy and hard clustering indicated that elk in Central Ontario occur in a metapopulation that includes 5 subpopulations. The largest cluster consisted of a core group of 22 radio-collared elk located in Burwash with several satellite subpopulations spread along a 50km long north-south axis and a small subpopulation to the west located in Worthington. Survival rates among subpopulations were similar ranging from 0.71 to 0.83; however, anthropogenic causes of mortality were predominant only in the Burwash subpopulation. Space use and density of elk differed between core and satellite subpopulations. Resource selection by elk differed by time of day and season. In all seasons, elk selected open habitats at night and more forested areas during the day. Elk avoided areas close to roads in spring, but selected them in winter at night. Elk selected higher elevations in winter and for south facing slopes in spring and fall. Elk displayed strong crepuscular activity patterns in all seasons; however, movements were limited in winter. Understanding population structure is important in order to develop appropriate management plans. My results support the conclusion that population structure can be reliably assessed using spatial data. Resource selection is a dynamic process that changes with seasons, as well as animal activity across the diel period. Resource selection should include time of day in order to obtain a complete picture of resources important to a particular species and to support the conservation of habitats used for various animal activities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".