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Record W23193590 · doi:10.1002/bip.22076

Ecological relationships among partial harvesting, vegetation, snowshoe hares, and Canada lynx in Maine

2006· article· en· W23193590 on OpenAlexaboutno aff
Laura F. Robinson

Bibliographic record

VenueBiopolymers · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersU.S. Fish and Wildlife ServiceNational Council for Air and Stream ImprovementMaine Agricultural and Forest Experiment Station
KeywordsSnowshoe hareEcologyVegetation (pathology)GeographyEnvironmental sciencePredationBiology

Abstract

fetched live from OpenAlex

Understanding the ecological factors affecting habitat use by the Canada lynx (Lynx canadensis) and its primary prey, the snowshoe hare (Lepus americanus), could help formulate conservation strategies for this carnivore, which is federally listed as threatened and occurs in only four regions of the U.S.A. I measured vegetation characteristics and snowshoe hare densities in 15 regenerating conifer clearcuts and 21 partially harvested stands in northern Maine during the leaf-off seasons, 2005 and 2006; and the leaf-on season, 2005. Regenerating clearcut stands had been harvested between 1974 and 1985 and were subsequently treated with an aerial application of herbicide between 1982 and 1997. Partially harvested stands were last harvested between 1985 and 2004 and included selection harvests, shelterwood harvests, and overstory removal harvests. Vegetation characteristics varied widely across partially harvested stands. This variance can be described by two principal components associated with the conifer composition and understory density within these stands. Snowshoe hare densities also varied widely in partially harvested stands: 0.26-1.65 hares/ha for the combined 2005-2006 leaf-off seasons. All 21 partially harvested stands had lower hare densities than the mean hare density for regenerating conifer clearcuts (2.10 hares/ha, SE=0.22) during these two years. I modeled the relationship of individual vegetation variables to hare densities across the 36 stands surveyed using an information theoretic approach. Hare density during the leaf-off season was positively associated with conifer stem densigy and basal area removed was negatively related to the density of logs in the stand. These three variables explained 67% of the variance in observed hare densities; however, conifer stem density was the single variable that was most strongly related to hare densities. I used GIS modeling to evaluate the relationships between lynx occurrence/non-detection and hare density, bobcat occurrence, fisher harvest density, maximum snow depth, and elevation at the geographic range- and the home range-scales in Maine. At the geographic-scale, lynx occurrence was associated with: 1) areas of higher hare density, and 2) absence of bobcats. Within the geographic range of lynx, simulated home ranges centered on lynx occurrences were associated with: 1) higher hare densities, 2) absence of bobcats, and 3) an interaction between hare density and bobcat occurrence, compared to surveyed areas without lynx detections. Only two surveys detected both bobcats and lynx, but these data suggest geographic- and home range-scale allopatry between these two species. At the geographic scale, the area of land in regenerating clearcuts was positively associated with lynx occurrence, likely as a result of the high hare densities supported by regenerating clearcuts. Annual clearcutting in Maine has been decreasing since the early 1990's and this trend may result in less regenerating forest on the landscape in the future, which might have long-term negative consequences if the objective is to maintain or increase current population levels of Canada lynx in Maine.

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)0.000
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 score0.587
Threshold uncertainty score0.832

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.185
Teacher spread0.174 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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