Systematic Biology
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Morphology-Based Identification of <i>Bemisia tabaci</i> Cryptic Species Puparia via Embedded Group-Contrast Convolution Neural Network Analysis Abstract
The Bemisia tabaci species complex is a group of tropical–subtropical hemipterans, some species of which have achieved global distribution over the past 150 years. Several species are regarded currently as among the world’s most pernicious agricultural pests, causing a variety of damage types via direct feeding and plant-disease transmission. Long considered a single variable species, genetic, molecular and reproductive compatibility analyses have revealed that this “species” is actually a complex of between 24 and 48 morphologically cryptic species. However, determinations of which populations represent distinct species have been hampered by a failure to integrate genetic/molecular and morphological species–diagnoses. This, in turn, has limited the success of outbreak-control and eradication programs. Previous morphological investigations, based on traditional and geometric morphometric procedures, have had limited success in identifying genetic/molecular species from patterns of morphological variation in puparia. As an alternative, our investigation focused on exploring the use of a deep-learning convolution neural network (CNN) trained on puparial images and based on an embedded, group-contrast training protocol as a means of searching for consistent differences in puparial morphology. Fifteen molecular species were selected for analysis, all of which had been identified via DNA barcoding and confirmed using more extensive molecular characterizations and crossing experiments. Results demonstrate that all 15 species can be discriminated successfully based on differences in puparium morphology alone. This level of discrimination was achieved for laboratory populations reared on both hairy-leaved and glabrous-leaved host plants. Moreover, cross-tabulation tests confirmed the generality and stability of the CNN discriminant system trained on both ecophenotypic variants. The ability to identify B. tabaci species quickly and accurately from puparial images has the potential to address many long-standing problems in B. tabaci taxonomy and systematics as well as playing a vital role in ongoing pest-management efforts. [Aleyrodidae; entomology; Hemiptera; machine learning; morphometrics; pest control; systematics; taxonomy; whiteflies.]
Systematic Biology - Tập 71 Số 5 - Trang 1095-1109 - 2022
Comparing Bootstrap and Posterior Probability Values in the Four-Taxon Case
Systematic Biology - Tập 52 Số 4 - Trang 477-487 - 2003
An Empirical Test of Bootstrapping as a Method for Assessing Confidence in Phylogenetic Analysis
Systematic Biology - Tập 42 Số 2 - Trang 182-192 - 1993
Evidence for Time Dependency of Molecular Rate Estimates
Systematic Biology - Tập 56 Số 3 - Trang 515-522 - 2007
Phylogenetic Relationships in the Genus Mus, Based on Paternally, Maternally, and Biparentally Inherited Characters
Systematic Biology - Tập 51 Số 3 - Trang 410-431 - 2002
Identifying Conflicting Signal in a Multigene Analysis Reveals a Highly Resolved Tree: The Phylogeny of Rodentia (Mammalia)
Systematic Biology - Tập 52 Số 5 - Trang 604-617 - 2003
Bias in Phylogenetic Estimation and Its Relevance to the Choice between Parsimony and Likelihood Methods
Systematic Biology - Tập 50 Số 4 - Trang 525-539 - 2001
Phylogenetic Relationships, Divergence Time Estimation, and Global Biogeographic Patterns of Calopterygoid Damselflies (Odonata, Zygoptera) Inferred from Ribosomal DNA Sequences
Systematic Biology - Tập 54 Số 3 - Trang 347-362 - 2005
Reconstruction of Organismal and Gene Phylogenies from Data on Multigene Families: Concerted Evolution, Homoplasy, and Confidence
Systematic Biology - Tập 41 Số 1 - Trang 4-17 - 1992
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