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Record W2064695360 · doi:10.2193/2007-591

Managing for Elevated Yield of Moose in Interior Alaska

2009· article· en· W2064695360 on OpenAlexaboutno aff
Rodney D. Boertje, Mark A. Keech, Donald D. Young, Kalin A. Kellie, C. Tom Seaton

Bibliographic record

VenueJournal of Wildlife Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsUrsusCanisHunting seasonPopulationPredationGrizzly BearsPopulation densityGeographyCullingBiologyAnimal scienceEcologyDemographyHerd

Abstract

fetched live from OpenAlex

ABSTRACT Given recent actions to increase sustained yield of moose ( Alces alces ) in Alaska, USA, we examined factors affecting yield and moose demographics and discussed related management. Prior studies concluded that yield and density of moose remain low in much of Interior Alaska and Yukon, Canada, despite high moose reproductive rates, because of predation from lightly harvested grizzly ( Ursus arctos ) and black bear ( U. americanus ) and wolf ( Canis lupus ) populations. Our study area, Game Management Unit (GMU) 20A, was also in Interior Alaska, but we describe elevated yield and density of moose. Prior to our study, a wolf control program (1976–1982) helped reverse a decline in the moose population. Subsequent to 1975, moose numbers continued a 28‐year, 7‐fold increase through the initial 8 years of our study (λ B1 = 1.05 during 1996–2004, peak density = 1,299 moose/1,000 km 2 ). During these initial 8 hunting seasons, reported harvest was composed primarily of males ( = 88%). Total harvest averaged 5% of the prehunt population and 57 moose/1,000 km 2 , the highest sustained harvest‐density recorded in Interior Alaska for similar‐sized areas. In contrast, sustained total harvests of <10 moose/1,000 km 2 existed among low‐density, predator‐limited moose populations in Interior Alaska (≤417 moose/1,000 km 2 ). During the final 3 years of our study (2004–2006), moose numbers declined (λ B2 = 0.96) as intended using liberal harvests of female and male moose ( = 47%) that averaged 7% of the prehunt population and 97 moose/1,000 km 2 . We intentionally reduced high densities in the central half of GMU 20A (up to 1,741 moose/1,000 km 2 in Nov) because moose were reproducing at the lowest rate measured among wild, noninsular North American populations. Calf survival was uniquely high in GMU 20A compared with 7 similar radiocollaring studies in Alaska and Yukon. Low predation was the proximate factor that allowed moose in GMU 20A to increase in density and sustain elevated yields. Bears killed only 9% of the modeled postcalving moose population annually in GMU 20A during 1996–2004, in contrast to 18–27% in 3 studies of low‐density moose populations. Thus, outside GMU 20A, higher bear predation rates can create challenges for those desiring rapid increases in sustained yield of moose. Wolves killed 8–15% of the 4 postcalving moose populations annually (10% in GMU 20A), hunters killed 2–6%, and other factors killed 1–6%. Annually during the increase phase in GMU 20A, calf moose constituted 75% of the predator‐killed moose and predators killed 4 times more moose than hunters killed. Wolf predation on calves remained largely additive at the high moose densities studied in GMU 20A. Sustainable harvest‐densities of moose can be increased several‐fold in most areas of Interior Alaska where moose density and moose: predator ratios are lower than in GMU 20A and nutritional status is higher. Steps include 1) reducing predation sufficient to allow the moose population to grow, and 2) initiating harvest of female moose to halt population growth and maximize harvest after density‐dependent moose nutritional indices reach or approach the thresholds we previously published.

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 score0.251
Threshold uncertainty score0.313

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 head0.013
GPT teacher head0.236
Teacher spread0.224 · 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

Citations53
Published2009
Admission routes1
Has abstractyes

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