Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species

Denys, Christiane; Jacquet, François; Kadjo, Blaise; Missoup, Alain Didier; Aniskine, Vladimir; Bellocq, Joëlle Goüy de; Soropogui, Barré; Douno, Mory; Sylla, Massamba; Nicolas, Violaine; Lalis, Aude; Monadjem, Ara

doi:10.5252/zoosystema2021v43a30

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…This form of divergent morphological evolution could explain the fact that both the largest and the smallest species, C. theresae and C. grandiceps (also representing the extremes of the morphospace) occupy roughly the same geographic area. It is also of note that these two species occupy different habitats, C. theresae being almost exclusively limited to open landscapes such as savannas, fields and fallows, and the larger C. grandiceps being an exclusive forest dweller 15 , 26 . Habitat preferences play a key role in facilitating the co-occurrence of morphologically similar species 27 .…”

Section: Discussionmentioning

confidence: 99%

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Incongruences between morphology and molecular phylogeny provide an insight into the diversification of the Crocidura poensis species complex

Voet

Denys

Colyn

et al. 2022

Sci Rep

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Untangling the factors of morphological evolution has long held a central role in the study of evolutionary biology. Extant speciose clades that have only recently diverged are ideal study subjects, as they allow the examination of rapid morphological variation in a phylogenetic context, providing insights into a clade’s evolution. Here, we focus on skull morphological variability in a widely distributed shrew species complex, the Crocidura poensis species complex. The relative effects of taxonomy, size, geography, climate and habitat on skull form were tested, as well as the presence of a phylogenetic signal. Taxonomy was the best predictor of skull size and shape, but surprisingly both size and shape exhibited no significant phylogenetic signal. This paper describes one of the few cases within a mammal clade where morphological evolution does not match the phylogeny. The second strongest predictor for shape variation was size, emphasizing that allometry can represent an easily accessed source of morphological variability within complexes of cryptic species. Taking into account species relatedness, habitat preferences, geographical distribution and differences in skull form, our results lean in favor of a parapatric speciation model within this complex of species, where divergence occurred along an ecological gradient, rather than a geographic barrier.

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Section: Discussionmentioning

confidence: 99%

“…Contrary to its exclusive forest-dwelling sister species, some C. cf. grandiceps specimens were captured in open landscapes 26 . This apparent morphological convergence between C. cf.…”

Section: Discussionmentioning

confidence: 99%

Incongruences between morphology and molecular phylogeny provide an insight into the diversification of the Crocidura poensis species complex

Voet

Denys

Colyn

et al. 2022

Sci Rep

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“…This work shows the existence of significant differences in the craniodental shapes of the three species of shrews. These differences are related to the different ecological niches that these three species occupy 4 . For example, C. malayana and C. monticola are both insectivores but they live in different types of forests.…”

Section: Introductionmentioning

confidence: 99%

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Pillay,

Pathmanathan,

Dabo-Niang

et al. 2023

Preprint

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This work proposes a functional data analysis (FDA) approach for morphometrics in classifying three shrew species (S. murinus, C. monticola and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 90 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear models) using predicted PC scores obtained from both methods (combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species. Overall, the generalised linear models (GLM) was the most accurate (95.4% accuracy) among the four classification models.

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Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Pillay,

Pathmanathan,

Dabo-Niang

et al. 2024

Sci Rep

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This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

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Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species

Cited by 3 publications

References 53 publications

Incongruences between morphology and molecular phylogeny provide an insight into the diversification of the Crocidura poensis species complex

Incongruences between morphology and molecular phylogeny provide an insight into the diversification of the Crocidura poensis species complex

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

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