Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania
Abstract Background Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constra...
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ftdatacite:10.6084/m9.figshare.c.5112431 2023-05-15T15:50:14+02:00 Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania MacLeod, Norman Horwitz, Liora Kolska 2020 https://dx.doi.org/10.6084/m9.figshare.c.5112431 https://springernature.figshare.com/collections/Machine-learning_strategies_for_testing_patterns_of_morphological_variation_in_small_samples_sexual_dimorphism_in_gray_wolf_Canis_lupus_crania/5112431 unknown figshare https://dx.doi.org/10.1186/s12915-020-00832-1 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Physiology FOS Biological sciences 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Sociology FOS Sociology 19999 Mathematical Sciences not elsewhere classified FOS Mathematics 111714 Mental Health FOS Health sciences Collection article 2020 ftdatacite https://doi.org/10.6084/m9.figshare.c.5112431 https://doi.org/10.1186/s12915-020-00832-1 2021-11-05T12:55:41Z Abstract Background Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures. Results Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sex-mediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shape-based sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers. Conclusion Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses. Article in Journal/Newspaper Canis lupus gray wolf DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Physiology FOS Biological sciences 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Sociology FOS Sociology 19999 Mathematical Sciences not elsewhere classified FOS Mathematics 111714 Mental Health FOS Health sciences |
spellingShingle |
Physiology FOS Biological sciences 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Sociology FOS Sociology 19999 Mathematical Sciences not elsewhere classified FOS Mathematics 111714 Mental Health FOS Health sciences MacLeod, Norman Horwitz, Liora Kolska Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania |
topic_facet |
Physiology FOS Biological sciences 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Sociology FOS Sociology 19999 Mathematical Sciences not elsewhere classified FOS Mathematics 111714 Mental Health FOS Health sciences |
description |
Abstract Background Studies of mammalian sexual dimorphism have traditionally involved the measurement of selected dimensions of particular skeletal elements and use of single data-analysis procedures. Consequently, such studies have been limited by a variety of both practical and conceptual constraints. To compare and contrast what might be gained from a more exploratory, multifactorial approach to the quantitative assessment of form-variation, images of a small sample of modern Israeli gray wolf (Canis lupus) crania were analyzed via elliptical Fourier analysis of cranial outlines, a Naïve Bayes machine-learning approach to the analysis of these same outline data, and a deep-learning analysis of whole images in which all aspects of these cranial morphologies were represented. The statistical significance and stability of each discriminant result were tested using bootstrap and jackknife procedures. Results Our results reveal no evidence for statistically significant sexual size dimorphism, but significant sex-mediated shape dimorphism. These are consistent with the findings of prior wolf sexual dimorphism studies and extend these studies by identifying new aspects of dimorphic variation. Additionally, our results suggest that shape-based sexual dimorphism in the C. lupus cranial complex may be more widespread morphologically than had been appreciated by previous researchers. Conclusion Our results suggest that size and shape dimorphism can be detected in small samples and may be dissociated in mammalian morphologies. This result is particularly noteworthy in that it implies there may be a need to refine allometric hypothesis tests that seek to account for phenotypic sexual dimorphism. The methods we employed in this investigation are fully generalizable and can be applied to a wide range of biological materials and could facilitate the rapid evaluation of a diverse array of morphological/phenomic hypotheses. |
format |
Article in Journal/Newspaper |
author |
MacLeod, Norman Horwitz, Liora Kolska |
author_facet |
MacLeod, Norman Horwitz, Liora Kolska |
author_sort |
MacLeod, Norman |
title |
Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania |
title_short |
Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania |
title_full |
Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania |
title_fullStr |
Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania |
title_full_unstemmed |
Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania |
title_sort |
machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (canis lupus) crania |
publisher |
figshare |
publishDate |
2020 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.5112431 https://springernature.figshare.com/collections/Machine-learning_strategies_for_testing_patterns_of_morphological_variation_in_small_samples_sexual_dimorphism_in_gray_wolf_Canis_lupus_crania/5112431 |
genre |
Canis lupus gray wolf |
genre_facet |
Canis lupus gray wolf |
op_relation |
https://dx.doi.org/10.1186/s12915-020-00832-1 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.5112431 https://doi.org/10.1186/s12915-020-00832-1 |
_version_ |
1766385207374512128 |