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Record W7000670282

Geographic variation in skull morphology of the wolf (Canis lupus) in relation to prey size across North America

2021· dissertation· en· W7000670282 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2021
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSkullGeographic variationPredationPrincipal component analysisMorphology (biology)SpiderCentipede
DOInot available

Abstract

fetched live from OpenAlex

The wolf (Canis lupus) is the largest member of the dog family, the most widely distributed nonhuman land mammal, and one of the most studied creatures on Earth. Vast amounts of knowledge about wolf ecology, life history, anatomy, and physiology have been gathered over the decades, and research into the taxonomic status of its many geographically diverse populations, based largely on cranial and body morphology, has received much attention. However, the relationship the wolf shares with its prey, specifically in terms of how the morphology of its skull is affected by, or related to, its prey in a naturally selective context, has received far less attention. In my attempt to determine the character of this relationship and its likely causes, I employ in this study a multivariate statistical approach using a total of 23 linear measurements taken from the crania, teeth, and jaws/mandibles of 583 museum-curated North American wolves (333 males, 250 females), harvested between 1894 and 2009 from 17 geographic regions. Specimen harvest locations include the arctic, northwestern, and central provinces/territories of Canada; mainland Alaska-Yukon, and the Alexander Archipelago; northern and south-central Ontario, and New Mexico. Only adult skulls were sampled, and specimens were determined to be adults by examining the closure of the basisphenoid suture, presence of mature dentition, and developed postorbital processes and sagittal crests. A geographically varying index of mean pooled prey weight (MPPW) was developed through literature review against which variables reflecting skull size and shape would be compared. Wolves are sexually dimorphic, with males appearing significantly larger on average than females in all linear skull measurements. A 5-Factor Principal Component Analysis (PCA) revealed that PC 1 accounted for ~76.5% of the observable variation in male and female data, and that the overall size of any skull, male or female, is best represented primarily by its condylobasal and jaw lengths (CBL, JL) and secondarily by its zygomatic width (Zyg B). A factor value (F1 Score) associated with PC 1 was ascribed to skulls, and this value equates with its greatest length (GSL; premaxilla to sagittal crest). The largest specimens hailed from mainland Alaska-Yukon, the Rocky Mountains, Wood Buffalo National Park, and central Saskatchewan. Moderately sized skulls were from arctic and northwestern Canada, the Alexander Archipelago, and northern Ontario. The smallest skulls were from south-central Ontario and New Mexico. Linear regression indicated significantly positive relationships for wolves of both sexes between MPPW and F1 Score, GSL, and Zyg B. When 17 sampling regions are reduced to 13, by combining geographically contiguous regions which display non-differences in average skull size, biome/climate characteristics, and prey base assemblages, these relationships are strengthened. These relationships, especially MPPW and Zyg B, are also stronger for males, suggesting that males are better adapted than females for hunting and killing large prey. Skull size is also significantly positively related to prey species richness, suggesting that greater availabilities of food help to maximize growth potentials in wolves. This may be why Alexander Archipelago wolves, which seasonally prey upon salmon, are larger than predicted given a diet consisting primarily of small deer. It may also help explain why Rocky Mountain wolves, living in a region inhabited by 6 or 7 large prey species, are the largest on average of any other region. For 13 sampling regions, I calculated the average bite forces (BFs) of wolves at the mandibular canine (BFC) and the first mandibular molar (BFM) using 3D digital modelling techniques and the ‘dry skull method’ developed by Thomason (1991). BFs were significantly positively related to MPPW, and this relationship was stronger for females. This may be due to greater natural variance in males; it may also suggest that females are adapted to process flesh with greater efficiency before ingestion, possibly a consequence of nutritional requirements associated with reproduction. BFs are most strongly associated with the size of the jaw muscles, which are intricately linked to Zyg B. The relationships between BFs and Zyg B is stronger than the relationships between MPPW and BFs, indicating that prey size only indirectly influences bite force via skull morphology. This study determines the presence of links between prey size, prey availability, and skull size in wolves, and is the first to distinguish between sex and among geographic regions in estimating their bite forces. This study would benefit from (1) additional skulls from specific regions and (2) data on regional prey densities to compare against skull sizes. Future research may involve genetic analyses to examine if regional differences in wolf skull morphology are also reflected by genotype, and isotope analyses to better characterize the regional diets of geographically diverse wolf populations.

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)0.001
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 score0.941
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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