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Record W7067116456

Life history, morphometric and habitat use variation in arctic charr (Salvelinus alpinus) populations of southern Baffin Island, Nunavut, Canada

2008· dissertation· en· W7067116456 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2008
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsnot available
Fundersnot available
KeywordsOtolithFish finArcticFish measurementHabitatRange (aeronautics)Fish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

Arctic charr (Salvelinus alpinus) are known to be phenotypically plastic throughout their range and exist as land-locked, lake-resident and migrant morphotypes.The focus of my study was to compare two morphs of charr (initially classified as smali- and large-mature) that co-exist seasonally in three open lake systems of Baffin Island, Nunavut, Canada.Specifically I identifred differences between the two morphotypes within each lake studied via morphological comparisons and habitat use (Chapter 2) and life history characters (Chapter 3).Results indicate that maximum otolith Sr level and the range of otolith Sr are statistically significant in lakes LH001 and PG082 between small-maturing and large-maturing groups.No statistical significance was observed for the maximum otolith Sr level and the range of otolith Sr in lake PG015.A component of each charr population within lakes LH001 and PG082 is migratory (large-maturing charr) whereas another component is lake-resident (small-maturing chan).In lake PGO15 there is evidence to suggest that small-mature charr are utilising inter-tidal habitats.Four morphological characters were identif,red as different in lakes LH001 and PG082 between small-maturing and undeveloped groups (immature and small resting individuals): eye diameter, pectoral fin length, pelvic fin length and upper jaw length.Four morphological characters were identified as different in lake PG0i5: eye diameter, pectoral fin length, pelvic fin length and fork depth.Univariate tests of the morphological characters showed statistically signif,rcant differences between the maturity types with the exception of eye diameter in lake PGO15 and upper jaw length and pelvic fin in iake LH001.The clear morphological variation observed between small-maturing and undeveloped fish in all three lakes of the study suggests ecological niche specialisation.Within all3 lakes 1l studied small-mature charr appear to have body form and structural characteristics that suggest lake-residency whereas large-mature charr have body form and structural characters that suggest anadromy.The lake-resident charr had larger fin lengths which are functionally better for manoeuvring in complex habitats (i.e.darting between rocks).The anadromous charr had short fin lengths which are functionally better for swimming long distances.Life history characters were variable amongst the two morphs firrther referred to as lake-resident and anadromous morphs.Anadromous charr within all lakes of the study had a mean size at maturity that ranged from 532 mm to 641mm and mean age of maturity that ranged from i0 to 13 years.In comparison, lake-resident charr had a mean size that ranged from 184 mm to 202 mm and a mean age at maturity that ranged from 8 to 9 years.The relationship between body size and fecundity or egg diameter did not differ between the two morphs.The von Bertalanffy growth model indicated that overall growth of both lake-resident and anadromous char was significantly different in all three lakes studied.Specifically, lake-resident charr initially had growth rates that were double that of anadromous charr.The results of our study parallel what is observed in general salmonid life histories but differ from some current Ernopean studies of Arctic charr growth.These studies suggest that juvenile stage lake-resident charr growth is slow and juvenile stage migrant charr growth is fast. ACKNO\ilLEDGEMENTSThanks to my advisor Darren Gillis for his abilities to point

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.865

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)0.0000.000

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 head0.021
GPT teacher head0.205
Teacher spread0.184 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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