Camera traps for evaluating ungulate densities and interspecific interactions in the Beaver Hills region of Alberta
Bibliographic record
Abstract
Interspecific interactions and species-habitat interactions are primary drivers in shaping distributions of wildlife populations across variable landscapes. The aspen parkland is a highly productive and heterogenous ecosystem characterized by a mosaic of habitat types maintained by disturbance that supports diverse ungulate assemblages. Elk Island National Park (EINP) and Cooking Lake – Blackfoot Provincial Recreation Area (BPRA) are two fenced natural areas in the Beaver Hills region of central Alberta where aspen parkland is being conserved. These areas maintain high densities of native ungulates including elk (Cervus elaphus), moose (Alces alces), white-tailed deer (Odocoileus virginianus), and mule deer (O. hemionus), with plains bison (Bison bison bison) and wood bison (B. bison athabascae) also inhabiting EINP. My first objective was to use camera traps to estimate ungulate densities in EINP and BPRA, and to evaluate cameras as a viable alternative to aerial ungulate surveys (AUS). I applied the time-in-front-of-the-camera (TIFC) method and compared TIFC density estimates to AUS densities, identifying strengths and weaknesses for both approaches per species. We collected data from 43 cameras in EINP between December 2016 and October 2020, and 23 cameras in BPRA from April 2019 to August 2020. I estimated yearly densities in the north and south sections of EINP (2017-2019), and in BPRA (2019). Moose had the lowest discrepancy between approaches, bison TIFC density estimates were lower than AUS densities, and elk TIFC density estimates were higher the AUS densities. I was also able to provide deer TIFC density estimates in EINP and BPRA in the absence of aerial survey data. Overall, I found that the TIFC and AUS approaches were complementary, where the AUS performed better for species in open habitats, while TIFC performed better for surveying species in closed habitats. My second objective was to evaluate spatiotemporal overlap and interspecific interactions between bison and elk in EINP. Bison and elk have overlapping habitat use and diet and are the primary focus of ungulate management in EINP. I examined distributions of both species in relation to season, landscape characteristics, anthropogenic features, and heterospecifics using generalized linear models (GLMs). I then examined seasonal daily activity patterns of bison and elk and calculated the degree of overlap. The spatial analysis revealed that bison counts were positively associated with higher proportions of open habitats across seasons and in areas farther from water in summer and fall but had no associations with distance to water in winter. Bison removal year was a significant predictor variable for bison counts in winter when the bison roundup takes place. Spatial modeling revealed that elk avoided areas with high linear feature density across seasons. During fall and winter, I observed higher elk counts associated with bison presence. Temporal activity patterns revealed that elk were crepuscular in all three seasons, but bison activity patterns varied with diurnal activity being more common in the summer, crepuscular activity in winter, and intermediate activity patterns in the fall. Coefficients of overlap between elk and bison were high in all three seasons with the greatest difference in daily activity patterns in summer and the highest overlap in winter when both species showed strong crepuscular activity. Despite the fenced perimeter in EINP resulting in high ungulate densities, limited dispersal, and low predation, our data show similar patterns of habitat use and interactions between bison and elk to those in other systems. Spatiotemporal partitioning between bison and elk does not appear necessary to coexist in the aspen parkland. Future research should focus on ungulate impacts to vegetation in relation to fluctuating ungulate densities and interspecific interactions. Continued monitoring and active management are necessary to protect the ecological integrity of these natural areas for years to come.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".