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Record W2952290980 · doi:10.82308/35405

Intra- and interspecific phenotypic variation in mammals and its effect on biodiversity under climate warming

2018· article· en· W2952290980 on OpenAlexaboutno aff
Kirsten Crandall

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsBergmann's ruleLatitudeIntraspecific competitionInterspecific competitionEcologyRange (aeronautics)BiodiversityBiologyClimate changePhenologyExtinction (optical mineralogy)Variation (astronomy)Global warmingTemperate climateGeography

Abstract

fetched live from OpenAlex

Global average temperature is rapidly increasing and is currently the warmest it has been in the Northern Hemisphere over the past centuries, driving species to local extirpation or extinction, to shift their distributional range or to adapt locally to the warming climate. A given species' sensitivity to changes in its environment may be related with patterns and amount of variation in its phenotype. As such, a decrease in body size has been proposed as the third universal ecological response to global warming, following distributional shifts and changes in phenology. Across their geographic ranges, mammals are thought to vary in body size following Bergmann's rule, which predicts that populations within a species found at higher latitudes, or colder climates, are larger in size compared to populations occurring farther south in warmer temperatures. While Bergmann's rule is well supported by empirical data among mammals, there is more and more evidence for exceptions to the rule, with species either decreasing in size with latitude or displaying no apparent relation between latitude and size. The patterns of variation in various morphological traits (skull length, skull width, tooth row length, and body mass) of 17 small and midsize mammalian hosts of Lyme disease were analyzed to determine if body size variation occurred in a predictable manner through space and time at an interspecific and intraspecific level. Results suggest little evidence of a generalizable pattern supporting Bergmann's rule within and among species at both a broad spatial and temporal scale. The effect of latitude or time on each of the morphological traits studied were highly variable leading to three types of responses: increases in size, decreases in size, or no changes in size across space and time. Overall, size trends were detected more often in space than through time, as size variation in space was studied over a significantly larger temperature gradient than the recent change in temperature that occurred over the past 120 years. Additionally, large-bodied species were not more likely to conform to Bergmann's rule than small-bodied species, in contrast to what was previously reported in the literature; in fact, this study showed that small mammals were found to vary more in size with latitude or time than midsize mammals. Contrary to predictions, size trends related to cranial measurements were detected as more likely to conform to Bergmann's rule than body mass, indicating the importance of simultaneously comparing metrics in studies on body size variation across species' ranges. However, body mass was found to have increased amounts of trait variability compared to cranial measurements; along a latitudinal gradient, the direction and magnitude of the variability depended on the size category of the species. Climate change is expected to cause the mammalian hosts of Lyme disease to expand their geographic ranges northward, facilitating the establishment of the bacteria and tick populations in southern Canada. For the mammalian hosts of Lyme disease, studies on phenotypic variation can help determine which host species have an increased sensitivity to climate and should be integrated into future species distribution models to increase the model's predictive power. More accurate projections of a host species' future distributional shifts into southern Canada will help determine the human populations most at-risk for Lyme disease in the future.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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