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Record W4391945832 · doi:10.1111/rec.14121

The relative effects of artificial shrubs on animal community assembly

2024· article· en· W4391945832 on OpenAlexafffund
Mario Zuliani, Nargol Ghazian, Suzanne E. MacDonald, Christopher J. Lortie

Bibliographic record

VenueRestoration Ecology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsEcologyShrubVertebrateForagingHabitatBiologySpecies richnessEcosystemAbundance (ecology)Relative species abundanceEcosystem engineer

Abstract

fetched live from OpenAlex

Facilitative associations between the foundational shrub species Ephedra californica and local vertebrate species can drive positive interactions within desert ecosystems that influence diversity and assembly processes. These foundational shrubs can contribute to the structural heterogeneity of ecosystems for plants and animals including variation in temperature profiles, refuge from predation, and habitat for foraging. Artificial structures can also influence fine‐scale ecological and micro‐environmental dynamics. We tested the hypothesis that artificial shrubs (mimics) positively influence desert vertebrate association through facilitative interactions, similar to foundational shrub species. Mimics were deployed at four distinct sites within the central deserts of Southern California. A combination of camera traps and temperature pendants were utilized to measure the association patterns of vertebrate species and the microclimatic variation at mimic, open, and shrubs. A total of 21 species were observed in this study. Mimics had a significantly higher vertebrate abundance and richness than open microsites and functioned similarly to shrubs. These findings suggest that mimics can be utilized as a stop‐gap replacement for foundational shrub species as they can act as a novel fine‐scale habitat for many desert vertebrate species.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.490

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 studies0.0010.000
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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