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Record W3109309468 · doi:10.1111/jfb.14635

Determining the appropriate pretreatment procedures and the utility of liver tissue for bulk stable isotope (<scp>δ<sup>13</sup>C</scp> and <scp>δ<sup>15</sup>N</scp>) studies in sharks

2020· article· en· W3109309468 on OpenAlexaff
K. Blue Pahl, David J. Yurkowski, Sabine P. Wintner, Geremy Cliff, Matthew L. Dicken, Nigel E. Hussey

Bibliographic record

VenueJournal of Fish Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsFisheries and Oceans CanadaUniversity of Windsor
Fundersnot available
KeywordsCarchariasBiologyUreaExtraction (chemistry)Liver tissueCarcharhinusIsotopeChloroformIsotope analysisStable isotope ratioZoologyBiochemistryAnimal scienceEcologyChromatographyEndocrinologyChemistry

Abstract

fetched live from OpenAlex

Abstract Stable‐isotope analysis (SIA) provides a valuable tool to address complex questions pertaining to elasmobranch ecology. Liver, a metabolically active, high turnover tissue (~166 days for 95% turnover), has the potential to reveal novel insights into recent feeding/movement behaviours of this diverse group. To date, limited work has used this tissue, but ecological application of SIA in liver requires consideration of tissue preparation techniques given the potential for high concentrations of urea and lipid that could bias δ13C and δ15N values (i.e., result in artificially lower δ13C and δ15N values). Here we investigated the effectiveness of (a) deionized water washing (WW) for urea removal from liver tissue and (b) chloroform‐methanol for extraction of lipids from this lipid rich tissue. We then (a) established C:N thresholds for deriving ecologically relevant liver isotopic values given complications of removing all lipid and (b) undertook a preliminary comparison of δ13C values between tissue pairs (muscle and liver) to test if observed isotopic differences correlated with known movement behaviour. Tests were conducted on four large shark species: the dusky (DUS, Carcharhinus obscurus), sand tiger (RAG, Carcharias taurus), scalloped hammerhead (SCA, Sphyrna lewini) and white shark (GRE, Carcharodon carcharias). There was no significant difference in δ15N values between lipid‐extracted (LE) liver and lipid‐extracted/water washed (WW) treatments, however, WW resulted in significant increases in %N, δ13C and %C. Following lipid extraction (repeated three times), some samples were still biased by lipids. Our species‐specific “C:N thresholds” provide a method to derive ecologically viable isotope data given the complexities of this lipid rich tissue (C:N thresholds of 4.0, 3.6, 4.7 and 3.9 for DUS, RAG, SCA and GRE liverLEWW tissue, respectively). The preliminary comparison of C:N threshold corrected liver and muscle δ13C values corresponded with movement/habitat behaviours for each shark; minor differences in δ13C values were observed for known regional movements of DUS and RAG (δ13CDiffs = 0.24 ± 0.99‰ and 0.57 ± 0.38‰, respectively), while SCA and GRE showed greater differences (1.24 ± 0.63‰ and 1.08 ± 0.71‰, respectively) correlated to large‐scale movements between temperate/tropical and pelagic/coastal environments. These data provide an approach for the successful application of liver δ13C and δ15N values to examine elasmobranch ecology.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.272
Teacher spread0.244 · 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 designBench or experimental
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

Citations9
Published2020
Admission routes1
Has abstractyes

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