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Record W2126628174

Tree spatial pattern within the forest-tundra ecotone: a comparison of sites across Canada 1

2011· article· en· W2126628174 on OpenAlexaboutno aff
Karen A. Harper, Ryan K. Danby, Danielle L. De Fields, Keith P. Lewis, Andrew J. Trant, Brian M. Starzomski, Rodney Arthur Savidge, Luise Hermanutz

Bibliographic record

VenueSaint Mary's University Institutional Repository (Saint Mary's University) · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsEcotoneTundraGeographyForestryTaigaPhysical geographyTree lineSpatial ecologyEcologyClimate changeLarchTransectEcosystemShrubBiology
DOInot available

Abstract

fetched live from OpenAlex

Although many studies have focused on factors influencing treeline advance with climate change, less consider- ation has been given to potential changes in tree spatial pattern across the forest-tundra ecotone. We investigated trends in spatial pattern across the forest-tundra ecotone and geographical variation in the Yukon, Manitoba, and Labrador, Canada. Tree cover was measured in contiguous quadrats along transects up to 100 m long located in Forest, Ecotone, and Tundra sections across the forest-tundra transition. Spatial patterns were analyzed using new local variance to estimate patch size and wavelet analysis to determine the scale and amount of aggregation. Compared with the Forest, tree cover in the Eco- tone was less aggregated at most sites, with fewer smaller patches of trees. We found evidence that shorter trees may be clumped at some sites, perhaps due to shelter from the wind, and we found little support for regular spacing that would in- dicate competition. With climate change, trees in the Ecotone will likely become more aggregated as patches enlarge and new patches establish. However, results were site-specific, varying with aspect and the presence of krummholz (stunted trees); therefore, strategies for adaptation of communities to climate change in Canada's subarctic forest would need to re- flect these differences. Resume´ : Alors que plusieurs etudes ont mis l'accent sur les facteurs qui influencent la progression de la limite des arbres due aux changements climatiques, peu d'attention a eteaccordee aux changements potentiels du patron de repartition spa- tiale des arbres dans l'ecotone entre la foret et la toundra. Nous avons etudieles changements de la repartition spatiale dans l'ecotone entre la foret et la toundra de meme que sa variation geographique au Yukon, au Manitoba et au Labrador, au Canada. Le couvert forestier a etemesuredans des quadrats contigus etablis le long de transects pouvant atteindre 100 m de longueur et situes dans les sections de foret, d'ecotone et de toundra dans la zone de transition entre la forete t la toundra. Les patrons spatiaux ont eteanalyses avec les methodes de la nouvelle variance locale pour estimer la taille des olots de foret et avec l'analyse par ondelettes pour determiner l'echelle et le degred'agregation. Comparativement ala foret, le couvert forestier de l'ecotone etait moins groupedans la plupart des stations, ce qui s'est traduit par des olots de foret plus petits et moins nombreux. Nous avons trouvedes preuves que les petits arbres peuvent etre regroupes dans cer- taines stations, probablement pour se proteger du vent, et nous avons trouvepeu de signes d'espacement regulier qui au- raient indiquela presence de competition. Avec les changements climatiques, les arbres etablis dans l'ecotone se regrouperont probablement davantage par l'agrandissement des olots et par l'etablissement de nouveaux o ˆlots. Cependant, ces resultats sont specifiques aux stations puisqu'ils varient selon leur orientation et en fonction de la presence de krumm- holz (arbres prostres). Ainsi, les strategies d'adaptation des communautes aux changements climatiques dans la foret su- barctique canadienne devraient refleter ces differences. (Traduit par la Redaction)

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.196
Teacher spread0.177 · 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.

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

Citations23
Published2011
Admission routes1
Has abstractyes

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