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...

Full description

Bibliographic Details
Published in:BMC Biology
Main Authors: MacLeod, Norman, Kolska Horwitz, Liora
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2020
Subjects:
Online Access:http://dx.doi.org/10.1186/s12915-020-00832-1
https://link.springer.com/content/pdf/10.1186/s12915-020-00832-1.pdf
https://link.springer.com/article/10.1186/s12915-020-00832-1/fulltext.html
id crspringernat:10.1186/s12915-020-00832-1
record_format openpolar
spelling crspringernat:10.1186/s12915-020-00832-1 2023-05-15T15:50:07+02:00 Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania MacLeod, Norman Kolska Horwitz, Liora 2020 http://dx.doi.org/10.1186/s12915-020-00832-1 https://link.springer.com/content/pdf/10.1186/s12915-020-00832-1.pdf https://link.springer.com/article/10.1186/s12915-020-00832-1/fulltext.html en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY BMC Biology volume 18, issue 1 ISSN 1741-7007 Cell Biology Developmental Biology Plant Science General Agricultural and Biological Sciences General Biochemistry, Genetics and Molecular Biology Physiology Ecology, Evolution, Behavior and Systematics Structural Biology Biotechnology journal-article 2020 crspringernat https://doi.org/10.1186/s12915-020-00832-1 2022-01-04T16:34:30Z 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 Springer Nature (via Crossref) BMC Biology 18 1
institution Open Polar
collection Springer Nature (via Crossref)
op_collection_id crspringernat
language English
topic Cell Biology
Developmental Biology
Plant Science
General Agricultural and Biological Sciences
General Biochemistry, Genetics and Molecular Biology
Physiology
Ecology, Evolution, Behavior and Systematics
Structural Biology
Biotechnology
spellingShingle Cell Biology
Developmental Biology
Plant Science
General Agricultural and Biological Sciences
General Biochemistry, Genetics and Molecular Biology
Physiology
Ecology, Evolution, Behavior and Systematics
Structural Biology
Biotechnology
MacLeod, Norman
Kolska Horwitz, Liora
Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania
topic_facet Cell Biology
Developmental Biology
Plant Science
General Agricultural and Biological Sciences
General Biochemistry, Genetics and Molecular Biology
Physiology
Ecology, Evolution, Behavior and Systematics
Structural Biology
Biotechnology
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
Kolska Horwitz, Liora
author_facet MacLeod, Norman
Kolska Horwitz, Liora
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 Springer Science and Business Media LLC
publishDate 2020
url http://dx.doi.org/10.1186/s12915-020-00832-1
https://link.springer.com/content/pdf/10.1186/s12915-020-00832-1.pdf
https://link.springer.com/article/10.1186/s12915-020-00832-1/fulltext.html
genre Canis lupus
gray wolf
genre_facet Canis lupus
gray wolf
op_source BMC Biology
volume 18, issue 1
ISSN 1741-7007
op_rights https://creativecommons.org/licenses/by/4.0
https://creativecommons.org/licenses/by/4.0
op_rightsnorm CC-BY
op_doi https://doi.org/10.1186/s12915-020-00832-1
container_title BMC Biology
container_volume 18
container_issue 1
_version_ 1766385107868844032