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Record W4300812930 · doi:10.26443/msurj.v12i1.37

Are species largely redundant? Testing the reliability of increasingly complex trait-based classifications in understanding Canadian Arctic ecosystems

2017· article· en· W4300812930 on OpenAlexafffundabout
Gabriel Yahya Haage

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFunctional groupTraitBiologyEcologyTaxonomic rankEcosystemArcticTaxonComputer science

Abstract

fetched live from OpenAlex

Background: In recent years, some ecologists have advocated the use of functional groups instead of direct species in linking site composition to the environment. They could potentially reveal connections between distant sites and aid in the formation of widely-applicable environmental policies. Several studies have compared the efficiency of using functional groups, in which species are grouped based on functional traits, like feeding method or size, to using species directly. However, few have looked at the effect of varying the complexity of functional groups when compared to species data. This study compares functional group classes of varying complexity, with complexity defined as the number of traits considered, to species data. The hypothesis that more complex functional group classes, compared to less complex classes, tend to approach the results obtained when using taxonomy, is tested. Methods: In testing this hypothesis, this study uses site composition data from aquatic floor (benthic) ecosystems in the Canadian Arctic. Four functional traits were considered important to describe these species: Bioturbation (sediment disturbance), body size, feeding habit and mobility. These traits were used to segregate species into functional groups of varying complexity, with complexity level determined by the number of traits (out of four) being used. Four environmental characteristics were considered for each site: Chlorophyll a, phaeopigments, depth and salinity. In order to test how similar functional group data is to species data, we sought to determine whether the same environmental variables were important in explaining site composition. This was determined by BIO-ENV analyses and Spearman Rank correlations. Mantel permutation tests then determined whether the correlations were significant. Results: While all levels of complexity, from one to four functional traits, showed some significant correlations (Spearman Rank ≥0.5, p≤ 0.05) between site composition and environmental variables, there was no general trend suggesting functional group complexity correlates with greater similarity to taxonomic data. For presence/absence data, all functional results, regardless of complexity, pinpointed only phaeopigments as important, while presence/absence species data also included chlorophyll a and depth. All results with strong and significant correlations (r≥0.5 p≤ 0.05), regardless of data type or complexity, maintained a measure of food supply (Chlorophyll a or phaeopigments), demonstrating its importance in determining ecosystem composition at these sites. Limitations: Potential improvements include measuring traits directly from the organisms, considering more environmental variables and increasing the number of functional traits considered. Which traits are considered also vary with each study. Conclusions: The hypothesis was not validated by the results. When pinpointing the most complex functional group class (the most important variable), rather than a less complex class, it was not guaranteed that the chosen variables would be the same as species data. Some classes of less complexity showed greater similarity to full species data. Some outcomes, like the presence/absence results, also imply certain species redundancies in the ecosystem, particularly regarding depth. These results have implications for the concept of functional redundancies in ecosystems, an important point in developing widely applicable environmental policies.

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)0.012
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.004
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.258
GPT teacher head0.354
Teacher spread0.097 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2017
Admission routes3
Has abstractyes

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