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Changes in bird communities by planting non-native spruce in coastal birch forests of northern Norway

2002· article· en· W2543931694 on OpenAlexvenueno aff
Vera Helene Hausner, Nigel G. Yoccoz, Karl‐Birger Strann, Rolf A. Ims

Bibliographic record

VenueEcoscience · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersNorsk institutt for naturforskningNorges Forskningsråd
KeywordsEcotoneSpecies richnessEcologyHabitatAbundance (ecology)Betula pendulaGeographyForest managementBiologyBotany

Abstract

fetched live from OpenAlex

Coastal birch forests in northern Norway have over the last 50 years gradually been replaced with non-native spruce plantations. To investigate possible changes in bird communities, we compared species richness and composition in six forest types (mature spruce plantations, ecotones, mixed forests, and three birch forest types) in two regions over two years. In the southern region, birch forests tended to host a higher number of species than spruce plantations, but species composition differed between forest types. The species composition in rich birch forests was particularly distinguished from spruce plantations, but also from the less productive birch habitats. Furthermore, there were dissimilarities in species richness and composition between regions. Ground nesters and species nesting on their northern margins occurred most frequently in the southern region, whereas cavity-nesters were most abundant in the northern region. Some species declined in abundance from 1998 to 1999, whereas three species shifted their main distribution from birch forests to mixed forests/ecotones. Possible factors underlying these results are discussed. We recommend that researchers and managers pay attention to habitat qualities within forest types (i.e., other than tree species composition in overstory) as well as to regional variations in species assemblages when addressing outcomes of management practices.

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 score0.794
Threshold uncertainty score0.409

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 studies0.0000.001
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 head0.016
GPT teacher head0.227
Teacher spread0.212 · 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

Citations25
Published2002
Admission routes1
Has abstractyes

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