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Record W2047898262 · doi:10.1139/b01-015

Ecological effects of the non-native <i>Pinus nigra</i> on sand dune communities

2001· article· en· W2047898262 on OpenAlexvenueno aff
Lissa M. Leege, Peter G. Murphy

Bibliographic record

VenueCanadian Journal of Botany · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersMichigan Department of Natural Resources
KeywordsVegetation (pathology)Sand dune stabilizationEcologyHabitatSpecies richnessWoody plantEcological successionIntroduced speciesNative plantAbiotic componentPinus <genus>Plant communityEnvironmental scienceGeographyBiologyBotany

Abstract

fetched live from OpenAlex

Owing to their successional nature, sand dunes provide an opportunity to examine the effects of non-native species introduced into multiple habitats. We investigated the biotic and abiotic effects of non-native Pinus nigra in four habitats on the dunes of the eastern shore of Lake Michigan. The 26 000 pines were planted in foredunes, forest edges, wetpannes, and inland blowouts as a stabilization measure in 1956–1972, and in 1995 the surviving trees ranged in stand density from 274–1176 trees per hectare. Pinus nigra stands were associated with reduced cover of dune vegetation except in forest edges, and with depressed species richness only in wetpanne sites. Higher densities of woody stems occurred in P. nigra stands at the edge of native forest than in sites lacking P. nigra, suggesting that pines accelerate succession to a woody community. Pinus nigra stands were associated with lower light levels than native stands of comparable or greater stand densities (Pinus banksiana in wetpannes and Populus deltoides in foredunes). In addition, P. nigra sites were drier than P. banksiana sites in wetpannes. The non-native pines may have modified the four dune habitats and appear to be functionally different from stands of native trees.Key words: functional equivalency, non-native species, Pinus nigra, plant invasion, sand dunes.

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)0.000
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 score0.110
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations38
Published2001
Admission routes1
Has abstractyes

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