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Record W4387056329 · doi:10.3389/fevo.2023.1187222

Long-term monitoring in the boreal forest reveals high spatio-temporal variability among primary ecosystem constituents

2023· article· en· W4387056329 on OpenAlexaffabout
Charles J. Krebs, Stan Boutin, Rudy Boonstra, Dennis L. Murray, Thomas S. Jung, Mark O’Donoghue, Benjamin Gilbert, Piia M. Kukka, Shawn D. Taylor, Terry K. Morgan, Ryan Drummond, A. R. E. Sinclair, Alice J. Kenney

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsYukon UniversityUniversity of British ColumbiaThe Scarborough HospitalYukon Department of EnvironmentUniversity of TorontoTrent UniversityUniversity of Alberta
Fundersnot available
KeywordsTaigaEcologyVulpesCanisPeromyscusAbundance (ecology)BorealGeographyBiologyPredation

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.228
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2023
Admission routes2
Has abstractyes

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