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Record W2978473274 · doi:10.1111/fwb.13405

Circumpolar patterns of Arctic freshwater fish biodiversity: A baseline for monitoring

2019· article· en· W2978473274 on OpenAlexaff
Sarah M. Laske, Per‐Arne Amundsen, Kirsten Christoffersen, Jaakko Erkinaro, Brian Hayden, Jani Heino, Kerstin Holmgren, Kimmo K. Kahilainen, Jennifer Lento, Panu Orell, Johan Östergren, Michael Power, Р. Р. Рафиков, Atso Romakkaniemi, Martin‐A. Svenning, Heidi K. Swanson, Matthew S. Whitman, Christian E. Zimmerman

Bibliographic record

VenueFreshwater Biology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of WaterlooUniversity of New Brunswick
FundersNorges ForskningsrådNaturvårdsverketMinistry of Science and Higher Education of the Russian FederationAcademy of Finland
KeywordsSpecies richnessBiodiversityArcticCircumpolar starSalvelinusEcologyBeta diversityEcoregionAlpha diversityFreshwater ecosystemLatitudeGeographyGamma diversitySpecies diversityFreshwater fishEcosystemBiologyFisheryOceanographyTroutFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract Climate change, biological invasions, and anthropogenic disturbance pose a threat to the biodiversity and function of Arctic freshwater ecosystems. Understanding potential changes in fish species distribution and richness is necessary, given the great importance of fish to the function of freshwater ecosystems and as a resource to humans. However, information gaps limit large‐scale studies and our ability to determine patterns and trends in space and time. This study takes the first step in determining circumpolar patterns of fish species richness and composition, which provides a baseline to improve both monitoring and conservation of Arctic freshwater biodiversity. Information on species presence/absence was gathered from the Circumpolar Biodiversity Monitoring Program's Freshwater Database and used to examine patterns of freshwater fish γ‐, α‐, and β‐diversity across 234° of longitude in the Arctic. The metrics of diversity provided information on species richness and composition across hydrobasins, ecoregions, and Arctic zones. Circumpolar patterns of fish species biodiversity varied with latitude, isolation, and coarse ecoregion characteristics; patterns were consistent with historic and contemporary barriers to colonisation and environmental characteristics. Gamma‐diversity was lower in the high Arctic compared to lower latitude zones, but α‐diversity did not decrease with increasing latitude below 71°N, reflecting glacial history. Alpha‐diversity was reduced to a single species, Arctic charr Salvelinus alpinus , in ecoregions above 71°N, where γ‐diversity was the lowest. Beta‐diversity indicated little variation in the composition and richness of species across the High Arctic; at lower latitudes, ecoregions contained more species, although species composition turned over across large spatial extents. In an analysis of five ecoregions in the circumpolar Arctic, physical isolation, and ecoregion area and topography were identified as strong drivers of γ‐, α‐, and β‐diversity. Physical isolation reduced the γ‐ and α‐diversity, and changes in β‐diversity between adjacent locations were due mainly to losses in species richness, rather than due to differences in species composition. Heterogeneity of habitats, environmental gradients, and geographic distance probably contributed to patterns of fish dissimilarity within and across ecoregions. This study presents the first analysis of large‐scale patterns of freshwater fish biodiversity in the circumpolar Arctic. However, information gaps in space, time, and among taxonomic groups remain. Future inclusion of extensive archive and new data will allow future studies to test for changes and drivers of the observed patterns of biodiversity. This is important given the potential impacts of ongoing and accelerating climate change, land use, and biotic exchange on Arctic fish biodiversity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.997

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)0.0040.001

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 head0.218
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 teacher head, not a consensus.

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

Citations29
Published2019
Admission routes1
Has abstractyes

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