Factors Governing the Distribution and Abundance of Arctic Ground Squirrels
Bibliographic record
Abstract
Extinction is the most extreme future state for any wildlife population. In Canada’s northern montane boreal regions, the disappearance of any small herbivore will have consequences for the way energy flows between trophic levels. Arctic ground squirrels (Urocitellus parryii plesius) were once so plentiful as to be responsible for approximately one-quarter of the energy flow at the herbivore level (Boonstra et al., 2001). However, population dynamics of this species over the past decade serve as a potent example of how northern regions may now be in dramatic flux. The Arctic ground squirrel is the largest ground squirrel in North America and has the most northerly distribution (Fig. 1). It lives throughout the montane boreal, alpine, and tundra regions and hibernates from September to mid-April (Naughton, 2012). In the Yukon, it is an important seasonal food source for many predators, including lynx (Lynx canadensis), coyote (Canis latrans), red fox (Vulpes vulpes), wolf (Canis lupus), black bear (Ursus americanus), grizzly bear (Ursus arctos), wolverine (Gulo gulo), Red-tailed Hawk (Buteo jamaicensis), Northern Goshawk (Accipiter gentilis), and Golden Eagle (Aquila chrysaetos). Arctic ground squirrels are also hunted by Yukon First Nations as a traditional source of food. Population fluctuations of Arctic ground squirrels therefore affect the food supply available to a wide list of predators. For almost three decades (1973 – 99), ground squirrel populations in the boreal forests of the Kluane region (SW Yukon) cycled in a predictable manner (Werner et al., 2015b) in concert with the snowshoe hare (Lepus americanus; Boutin et al., 1995). This 9 – 10 year cycle was stable up until 2000. In that year, populations crashed, and they have not, as yet, recovered. This once important species declined from 17% of the regional herbivore biomass to nearly zero. Most boreal forest populations (~95%) are now extinct, as are a large fraction (~65%) of nearby meadow populations. I am now conducting experiments and surveys designed to clarify the likely causes and consequences of these dramatic changes. The measures described below are intended to address a number of specific research projects.
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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 | 0.000 | 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".