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Record W3118385256 · doi:10.3390/f12010093

Functional Ecology of Forest, Heath, and Shrub Savannah Alternate States in Eastern Canada

2021· article· en· W3118385256 on OpenAlexaffabout
Colin St. James, Azim U. Mallik

Bibliographic record

VenueForests · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsLakehead University
Fundersnot available
KeywordsSpecies evennessSpecies richnessEcologyShrubAlpha diversityNiche differentiationGamma diversitySpecies diversityInterspecific competitionBeta diversityNichePlant communityBiologyGeography

Abstract

fetched live from OpenAlex

In eastern Canada, alternation of wildfire regime due to fire suppression creates alternate vegetation states converting black spruce forest to heath and shrub savannah (SS). We compared the taxonomic diversity (TD) and functional diversity (FD) of post-fire forest, heath, and SS alternate states to determine if community FD can explain their persistence. We hypothesized that (i) species diversity (TD and FD) would be the highest in forest followed by SS and heath due to decreased interspecific competition and niche differentiation, (ii) differences between TD and FD indices would be greater in communities with high TD in forest due to high trait differentiation and richness, and (iii) changes in community trait values would indicate niche limitations and resource availability. We conducted this study in Terra Nova National Park, Newfoundland, Canada. We calculated functional dispersion (alpha FD), functional pairwise dissimilarity (beta FD), Shannon’s diversity (alpha TD), and Bray–Curtis dissimilarity (beta TD) from species cover. We used five functional traits (specific root length, specific leaf area, leaf dry matter content, height, and seed mass) related to nutrient acquisition, productivity, and growth. We found lower beta diversity in forest than heath and SS; forest also had higher species diversity and greater breadth in niche space utilization. SS was functionally similar to heath but lower than forest in functional dispersion and functional divergence. It had the highest functional richness and evenness. There was no difference in functional evenness between forest and heath. Functional beta diversity was the highest in forest, and did not differ between heath and SS. Resource acquisition and availability was the greatest in forest and the lowest in heath. We suspect that this might be due to forest having the highest functional trait turnover and niche utilization. We conclude that alternate vegetation states originating from alterations to the natural fire regime negatively impact ecosystem function.

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 score0.935
Threshold uncertainty score0.945

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 science0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.202 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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