Effects of spruce bark beetle infestations on grizzly bear forage
Bibliographic record
Abstract
A study in the Kluane Region that ran from 1992 to 1997 found that the grizzly bear, Ursus arctos, population may be decreasing by 3% annually. The study also found that cubs of the year mortality rate was among the highest in North American (McCann, 1998). Where a population may be declining, the stability and availability of resources within these bears' ranges is critical. Consecutive warm winters and warm and dry summers have resulted in epidemic levels of spruce beetle, Denroctonus rufipennis, consequently reshaping the structure of over 300,000 hectares of northern boreal forest in the southwest Yukon. This study was conducted to determine how structural changes in the forest resulting from the spruce beetle infestation have changed forage availability for grizzly bears. In July and August of 2008, I ran 51 vegetation transects in and around Haines Junction, Yukon. I measured vegetation characteristics at approximately four plots on each transect. At each plot I measured forest characteristics such as the health of spruce trees, ground cover, abundance of grizzly bear forage species, and abundance of berries. I used zero-inflated negative binomial models for total berries collected in sample plots, and negative binomial models when analyzing productivity response variables for individual short shrub species as well as mean short shrub and mean herb cover. My data analysis largely showed that explanatory variables designed to reflect spruce beetle infestation in sample plots were rarely correlated with productivity response variables. However, I was able to determine that variables, such as slope or aspect (i.e. variables not related to spruce beetle infestation), affected productivity. The impact of spruce beetle infestation on productivity of understory plant species may force grizzly bears to adapt to a changing ecosystem. Understanding this relationship will assist management efforts for the Kluane grizzly bears in the future.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".