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Record W4399586442 · doi:10.1098/rspb.2024.0399

Lemming population fluctuations around the Arctic

2024· article· en· W4399586442 on OpenAlexaff
Charles J. Krebs

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArcticThe arcticGeographyPopulationOceanographyEnvironmental scienceClimatologyGeologyDemographySociology

Abstract

fetched live from OpenAlex

When Charles Elton, over 100 years ago at Oxford, began to collate natural history data about fluctuations in lemming populations in the Arctic, he discovered population cycles, 3-to 4-year fluctuations in density that were well known to Arctic peoples but not to scientists.Elton's classic 1924 paper describing this pattern in the Journal of Experimental Biology stimulated a medley of Arctic biologists around the North Polar regions to gather quantitative data on these cycles.The culmination of much of this research is published now in the Proceedings of the Royal Society, Series B and demonstrates how the lemming cycle has worked its way into ordinary language.Buried in this natural history was an important change in our world view which stated that nature was in a state of balance, the equilibrium view of the world's ecosystems.No one today would believe in a natural balance which rested on the idea that nature will take care of itself and heal from any disturbance imposed by humans.We live in a non-equilibrial world, where change is expected, and part of this change in world view is due to lemmings, the small rodents of the northern tundra.The rapidity of climate change in recent years brought out the view that cycles in these rodents would disappear with drastic consequences for northern food chains because lemmings are a keystone species for many species of predators.Weasels and arctic foxes are the obvious mammalian predators, but lemmings are eaten by grizzly bears, wolves, wolverine, arctic ground squirrels, snowy owls, long-tailed jaegers and a variety of predacious birds.Losing lemming cycles would cause a collapse of this food chain.Gauthier et al.[1] reviewed the evidence from 24 time series over the past 19-91 years and concluded that lemming cycles are not perfectly cyclic in the mathematical sense but have an average period of 3.7 years in 55% of the 649 site-years of data available.Cycles can be lost for several years causing worries among ecologists that cycles were disappearing but in all cases returned to a 3-to 4-year period.Figure 1 illustrates this cyclic pattern with data from one of the 24 time series used in Gauthier et al. [1].These observations trigger two broad questions: what causes these lemming cycles?and why might they disappear for a few years but then come back?The causes of lemming cycles have stimulated much study and speculation.Elton suggested in 1924 that oscillations in climate in the Arctic might be a general cause producing synchrony in lemming numbers over large areas of the Arctic.But more data [1] have shown that synchrony is not absolute but regional, and the climate variables we can easily measure do not correlate with the time when cycles lapsed during the last four decades.What is true of the past however is not necessarily going to continue into the future, so continuous monitoring is required to discern this catastrophic possibility of ecosystem collapse from the predators that rely on lemmings as the base of the Arctic food chain [2].The lemming cycle has always been suspected to have something to do with the Arctic winter; so much research has attempted to see if changes in snow depth, winter temperatures, rain-on-snow events and rapid melting in mid-winter could be involved in high mortality.The difficulty ecologists face is winter-what exactly is going on under the snow?We have good

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 score0.006
Threshold uncertainty score0.802

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 studies0.0010.002
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 head0.032
GPT teacher head0.273
Teacher spread0.241 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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