Long-term monitoring in the boreal forest reveals high spatio-temporal variability among primary ecosystem constituents
Bibliographic record
Abstract
The boreal forest, the world’s largest terrestrial biome, is undergoing dramatic changes owing to anthropogenic stressors, including those of climate change. To track terrestrial ecosystem changes through space and time, robust monitoring programs are needed that survey a variety of ecosystem constituents. We monitored white spruce ( Picea glauca ) cone crops, berry ( Empetrum nigrum, Shepherdia canadensis ) production, above-ground mushroom abundance, and the abundance of small mammals ( Clethrionomys rutilus , Peromyscus maniculatus ), North American red squirrels ( Tamiascirus hudsonicus ), snowshoe hares ( Lepus americanus ), and carnivores ( Lynx canadensis , Canis latrans , Vulpes vulpes , Martes americana , Mustela erminea ) across 5 sites in the Yukon, Canada. Monitoring began in 1973 at Lhù’ààn Mân’ (Kluane Lake) and additional protocols were added until a complete sequence was fixed in 2005 at all 5 sites and continued until 2022. White spruce cone counts show mast years at 3–7-year intervals. Ground berries and soapberry counts were highly variable among sites and counts did not correlate among sites or between years for different species. Red-backed voles showed clear 3–4-year cycles at Kluane and probably at the Mayo and Watson Lake sites, but showed only annual cycles in Whitehorse and Faro. Snowshoe hares fluctuated in 9–10-year cycles in a travelling wave, peaking one year earlier at Watson Lake but in synchrony at all other sites, with no clear sign of peak density changing or cyclic attenuation over the last 50 years. Red squirrel numbers at Kluane exhibit marked inter-year variability, driven mainly by episodic white spruce cone crops and predation from Canada lynx and coyotes as hare densities undergo cyclic decline. Snow track counts to index mammalian predators have been conducted on our Kluane and Mayo sites, indicating that lynx numbers rise and fall with a 1–2-year lag at these two sites, tracking the hare cycle. Coyotes and lynx at Kluane peak together following the hare cycle, but coyote counts are also depressed during deep snow years. To summarize, we noted considerable inter-site variability in the population dynamics of many boreal forest ecosystem constituents, but the keystone species (snowshoe hare, Canada lynx) exhibit remarkably similar population trends across the region. We continue to monitor wildlife abundance, cone crops, berry production, and mushroom biomass to determine changes associated with increasing temperature and fluctuating rainfall. The Yukon boreal forest is changing as climate shifts, but the changes are slow, variable across sites, taxa specific, and of uncertain predictability.
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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 | 0.001 | 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".