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Record W4296039039 · doi:10.1002/ece3.9244

Ecoregion and community structure influences on the foliar elemental niche of balsam fir (<i>Abies balsamea</i> (L.) Mill.) and white birch (<i>Betula papyrifera</i> Marshall)

2022· article· en· W4296039039 on OpenAlexafffund
Travis Heckford, Shawn Leroux, Eric Vander Wal, Matteo Rizzuto, Juliana Balluffi‐Fry, Isabella C. Richmond, Yolanda F. Wiersma

Bibliographic record

VenueEcology and Evolution · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMemorial University of NewfoundlandGovernment of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandMitacsCanada Foundation for InnovationDepartment of Natural Resources, Government of Newfoundland and Labrador
KeywordsAbies balsameaEcoregionBetula platyphyllaNicheBalsamBiologyBetulaceaeBotanyEcologyCunninghamia

Abstract

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Abstract Changes in foliar elemental niche properties, defined by axes of carbon (C), nitrogen (N), and phosphorus (P) concentrations, reflect how species allocate resources under different environmental conditions. For instance, elemental niches may differ in response to large‐scale latitudinal temperature and precipitation regimes that occur between ecoregions and small‐scale differences in nutrient dynamics based on species co‐occurrences at a community level. At a species level, we compared foliar elemental niche hypervolumes for balsam fir (Abies balsamea (L.) Mill.) and white birch (Betula papyrifera Marshall) between a northern and southern ecoregion. At a community level, we grouped our focal species using plot data into conspecific (i.e., only one focal species is present) and heterospecific groups (i.e., both focal species are present) and compared their foliar elemental concentrations under these community conditions across, within, and between these ecoregions. Between ecoregions at the species and community level, we expected niche hypervolumes to be different and driven by regional biophysical effects on foliar N and P concentrations. At the community level, we expected niche hypervolume displacement and expansion patterns for fir and birch, respectively—patterns that reflect their resource strategy. At the species level, foliar elemental niche hypervolumes between ecoregions differed significantly for fir (F = 14.591, p‐value = .001) and birch (F = 75.998, p‐value = .001) with higher foliar N and P in the northern ecoregion. At the community level, across ecoregions, the foliar elemental niche hypervolume of birch differed significantly between heterospecific and conspecific groups (F = 4.075, p‐value = .021) but not for fir. However, both species displayed niche expansion patterns, indicated by niche hypervolume increases of 35.49% for fir and 68.92% for birch. Within the northern ecoregion, heterospecific conditions elicited niche expansion responses, indicated by niche hypervolume increases for fir of 29.04% and birch of 66.48%. In the southern ecoregion, we observed a contraction response for birch (niche hypervolume decreased by 3.66%) and no changes for fir niche hypervolume. Conspecific niche hypervolume comparisons between ecoregions yielded significant differences for fir and birch (F = 7.581, p‐value = .005 and F = 8.038, p‐value = .001) as did heterospecific comparisons (F = 6.943, p‐value = .004, and F = 68.702, p‐value = .001, respectively). Our results suggest species may exhibit biogeographical specific elemental niches—driven by biophysical differences such as those used to describe ecoregion characteristics. We also demonstrate how a species resource strategy may inform niche shift patterns in response to different community settings. Our study highlights how biogeographical differences may influence foliar elemental traits and how this may link to concepts of ecosystem and landscape functionality.

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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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