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Record W3049610380 · doi:10.1111/fwb.13600

Diversity of diatoms, benthic macroinvertebrates, and fish varies in response to different environmental correlates in Arctic rivers across North America

2020· article· en· W3049610380 on OpenAlexaffabout
Jennifer Lento, Sarah M. Laske, Isabelle Lavoie, Daniel Bogan, Robert B. Brua, Stéphane Campeau, K. S. Chin, Joseph M. Culp, Brianna Levenstein, Michael Power, Émilie Saulnier‐Talbot, Rebecca Shaftel, Heidi K. Swanson, Matthew S. Whitman, Christian E. Zimmerman

Bibliographic record

VenueFreshwater Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversité LavalCenter for Northern StudiesImpactWilfrid Laurier UniversityGovernment of Northwest TerritoriesUniversity of New BrunswickUniversité du Québec à Trois-RivièresUniversity of WaterlooEnvironment and Climate Change CanadaInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDiatomBenthic zoneEcologyBiodiversitySpecies richnessArcticBeta diversityGlacial periodAlpha diversityInvertebrateEnvironmental scienceGeographyOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Climate change poses a significant threat to Arctic freshwater biodiversity, but impacts depend upon the strength of organism response to climate‐related drivers. Currently, there is insufficient knowledge about Arctic freshwater biodiversity patterns to guide assessment, prediction, and management of biodiversity change. As part of the Circumpolar Biodiversity Monitoring Program's first freshwater assessment, we evaluated diversity of diatoms, benthic macroinvertebrates, and fish in North American Arctic rivers. Alpha diversity was assessed in relation to temperature, water chemistry, bedrock geology, and glaciation history to identify important environmental correlates. Biotic composition was compared among groups to evaluate response to environmental gradients. Macroinvertebrate α‐diversity declined strongly with increasing latitude from 48°N to 82°N, whereas diatom and fish diversity peaked around 70°N without a clear latitudinal decline. Macroinvertebrate diversity was significantly positively related to air temperature. Diatom diversity was related to bedrock geology and temperature, whereas fish diversity was related to glaciation history. Fish and macroinvertebrate assemblages differed between sites in western Canada, where invertebrate composition was more variable, and Alaska, where fish composition was more variable. In sites with both diatom and macroinvertebrate data, diatom composition was distinct in Alaska, where richness was highest in former glacial refugia. Macroinvertebrate composition was distinct in lowest‐latitude eastern and high‐latitude western Canadian sites where temperature was highest. Temperature, precipitation, geology, calcium, and substrate size were important environmental correlates for diatoms and macroinvertebrates, although the relative importance of each correlate differed. Diatom taxa were most strongly associated with water chemistry, whereas benthic invertebrate composition related most strongly to precipitation and temperature. This large‐scale study provides the most substantial integration and analysis of river diatom, macroinvertebrate, and fish data from the North American Arctic to date. Findings suggest that macroinvertebrates will show the strongest response to climate‐related shifts in temperature, whereas diatoms and fish are more likely to respond to climate‐induced shifts in nutrients and hydraulic connectivity. However, significant gaps in data coverage limited our ability to reliably evaluate spatial patterns and detect change. These gaps could be reduced by improving collaborative efforts between the U.S.A. and Canada to harmonise future monitoring.

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)0.000
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 score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.199
Teacher spread0.188 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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