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Record W1232149504

Status of moose populations and challenges to moose management in Fennoscandia.

2003· article· en· W1232149504 on OpenAlexvenueno aff
Sten Lavsund, Tuire Nygrén, Erling J. Solberg

Bibliographic record

VenueAlces · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEcological successionPopulation densityHabitatPopulationEcologyPopulation declinePhysical geographyBiologyDemography
DOInot available

Abstract

fetched live from OpenAlex

In the Fennoscandian countries, Norway, Sweden, and Finland, moose (Alces alces) populations began to increase rapidly in the 1960s and have since then been among the most productive and heavily harvested moose populations in the world. At the start of the 20th century, the total annual harvest was < 10,000 moose, whereas in 2000, the annual kill reached about 200,000. The winter population was estimated to be about 500,000. In Sweden and Finland, the highest harvest numbers (and presumably population density) were recorded in the first half of the 1980s and in Finland again in the late 1990s and during the beginning of the 2000s. In Norway, the 1990s was the decade of the highest harvest numbers. The current regional moose density during winter varies from < 0.2 to about 2 moose/km2 within Fennoscandia. Locally, the density may far exceed this level in typical wintering areas (e.g., 5-6 moose/km2). In general, the current densities are lower in the north than in the south and higher in Norway and Sweden than in Finland. The strong increase in harvest and the present high densities are explained by several factors. First, modern forestry clear-cutting practices have provided Fennoscandian moose with prime habitats in the form of early succession stages. Accordingly, the current carrying capacity is likely to be relatively high compared to the situation 50-100 years ago. The current trend, however, is towards less activity in the forest and a decreasing proportion of forests found at an early successional stage. This may increase the food limitation already seen in several populations; i.e., in all three countries, body mass and recruitment rates have been found to decrease with increasing density. Second, the introduction of sex and age-specific harvesting in the early 1970s has increased the general productivity of the populations. By focusing the harvest on calves, yearlings, and adult males, the proportion of productive females, the mean age of females, and the annual recruitment rate have increased. Simultaneously, the proportion and mean age of males have decreased, and in some populations, this has been associated with delayed parturition dates and lower fecundity; i.e., due to inadequate number of males for timely reproduction. Third, mortality other than hunting is low, and only near the eastern border of Finland with Russia has predation by wolves and bears had a notable effect on productivity figures. This situation is about to change with increasing populations of large carnivores in all of Fennoscandia during the 1990s. The management principles have been quite similar within Fennoscandia, although differences in legislation have resulted in national and regional differences in management performance. In general, moose managers take advantage of data collected by hunters during the hunting season (e.g., hunting statistics, number, sex, and age of moose observed) to monitor population development and determine hunting quotas. Moreover, in all three countries, the issues of traffic accidents and damage to forestry and agriculture play a central role in moose management and discussions concerning optimum population sizes. ALCES VOL. 39: 109-130 (2003)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.167
Threshold uncertainty score0.331

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.262
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations184
Published2003
Admission routes1
Has abstractyes

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