Wenbo Deng, Xinxing Luo, Simon Y. W. Ho, Shuran Liao, Zongqing Wang, Yanli Che
Cockroaches are an ecologically and economically important insect group, but some fundamental aspects of their evolutionary history remain unresolved. In particular, there are outstanding questions about some of the deeper relationships among cockroach families. As a group transferred from Blaberoidea Saussure to Blattoidea Latreille, the evolutionary history of the family Anaplectidae Walker requires re-evaluation. In our study, we infer the phylogeny of Blattoidea based on the mitochondrial genomes of 28 outgroup taxa and 67 ingroup taxa, including 25 newly sequenced blattoid species mainly from the families Anaplectidae and Blattidae Latreille. Our results indicate that Blattoidea is the sister group of the remaining Blattodea Brunner von Wattenwyl and that Blattoidea can be divided into three main clades: Blattidae + Tryonicidae McKittrick & Mackerras, Lamproblattidae McKittrick + Anaplectidae and Termitoidae Latreille + Cryptocercidae Handlirsch. Our analyses provide robust support for previously uncertain hypotheses. The sister group of Termitoidae + Cryptocercidae (Xylophagodea Engel) is inferred to constitute the rest of Blattoidea, for the first time. Within Blattidae, Hebardina Bey-Bienko is placed as the sister lineage to the rest of Blattidae. The subfamily Archiblattinae is polyphyletic, Blattinae is paraphyletic and Polyzosteriinae is monophyletic (Macrocercinae Roth not included); the genus Periplaneta Burmrister is polyphyletic. Based on the results of our phylogenetic analyses, we have revised these taxa. A new subfamily, Hebardininae subfam.nov., is proposed in Blattidae. Archiblattinae and Shelfordella Adelung are synonymized with Blattinae and Periplaneta, respectively: Archiblattinae Kirby syn.nov. and Shelfordella Adelung syn.nov. Our inferred divergence times indicate that Blattoidea emerged in the Late Triassic, with six families in Blattoidea diverging in the Middle and Late Jurassic. We suggest that the divergences among lineages of Asian Blattidae and Anaplectidae were driven by the uplift of the Himalayas and deglaciation during the Quaternary, leading to the present-day distributions of these taxa.
{"title":"Inclusion of rare taxa from Blattidae and Anaplectidae improves phylogenetic resolution in the cockroach superfamily Blattoidea","authors":"Wenbo Deng, Xinxing Luo, Simon Y. W. Ho, Shuran Liao, Zongqing Wang, Yanli Che","doi":"10.1111/syen.12560","DOIUrl":"10.1111/syen.12560","url":null,"abstract":"<p>Cockroaches are an ecologically and economically important insect group, but some fundamental aspects of their evolutionary history remain unresolved. In particular, there are outstanding questions about some of the deeper relationships among cockroach families. As a group transferred from Blaberoidea Saussure to Blattoidea Latreille, the evolutionary history of the family Anaplectidae Walker requires re-evaluation. In our study, we infer the phylogeny of Blattoidea based on the mitochondrial genomes of 28 outgroup taxa and 67 ingroup taxa, including 25 newly sequenced blattoid species mainly from the families Anaplectidae and Blattidae Latreille. Our results indicate that Blattoidea is the sister group of the remaining Blattodea Brunner von Wattenwyl and that Blattoidea can be divided into three main clades: Blattidae + Tryonicidae McKittrick & Mackerras, Lamproblattidae McKittrick + Anaplectidae and Termitoidae Latreille + Cryptocercidae Handlirsch. Our analyses provide robust support for previously uncertain hypotheses. The sister group of Termitoidae + Cryptocercidae (Xylophagodea Engel) is inferred to constitute the rest of Blattoidea, for the first time. Within Blattidae, <i>Hebardina</i> Bey-Bienko is placed as the sister lineage to the rest of Blattidae. The subfamily Archiblattinae is polyphyletic, Blattinae is paraphyletic and Polyzosteriinae is monophyletic (Macrocercinae Roth not included); the genus <i>Periplaneta</i> Burmrister is polyphyletic. Based on the results of our phylogenetic analyses, we have revised these taxa. A new subfamily, Hebardininae <b>subfam.nov.</b>, is proposed in Blattidae. Archiblattinae and <i>Shelfordella</i> Adelung are synonymized with Blattinae and <i>Periplaneta</i>, respectively: Archiblattinae Kirby <b>syn.nov.</b> and <i>Shelfordella</i> Adelung <b>syn.nov.</b> Our inferred divergence times indicate that Blattoidea emerged in the Late Triassic, with six families in Blattoidea diverging in the Middle and Late Jurassic. We suggest that the divergences among lineages of Asian Blattidae and Anaplectidae were driven by the uplift of the Himalayas and deglaciation during the Quaternary, leading to the present-day distributions of these taxa.</p>","PeriodicalId":22126,"journal":{"name":"Systematic Entomology","volume":"48 1","pages":"23-39"},"PeriodicalIF":4.8,"publicationDate":"2023-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/syen.12560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42589056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tomochika Fujisawa, Víctor Noguerales, Emmanouil Meramveliotakis, Anna Papadopoulou, Alfried P. Vogler
Complex bulk samples of insects from biodiversity surveys present a challenge for taxonomic identification, which could be overcome by high-throughput imaging combined with machine learning for rapid classification of specimens. These procedures require that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. However, such transfer learning may be problematic for the study of new samples not previously encountered in an image set, for example, from unexplored ecosystems, and require methods of domain adaptation that reduce the differences in the feature distribution of the source and target domains (training and test sets). We assessed the efficiency of domain adaptation for family-level classification of bulk samples of Coleoptera, as a critical first step in the characterization of biodiversity samples. Neural network models trained with images from a global database of Coleoptera were applied to a biodiversity sample from understudied forests in Cyprus as the target. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images, and on dataset complexity. The accuracy of between-datasets predictions (across disparate source–target pairs that do not share any species or genera) was at most 82% and depended greatly on the standardization of the imaging procedure. An algorithm for domain adaptation, domain adversarial training of neural networks (DANN), significantly improved the prediction performance of models trained by non-standardized, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, but the imaging conditions and classification algorithms need careful consideration.
{"title":"Image-based taxonomic classification of bulk insect biodiversity samples using deep learning and domain adaptation","authors":"Tomochika Fujisawa, Víctor Noguerales, Emmanouil Meramveliotakis, Anna Papadopoulou, Alfried P. Vogler","doi":"10.1111/syen.12583","DOIUrl":"10.1111/syen.12583","url":null,"abstract":"<p>Complex bulk samples of insects from biodiversity surveys present a challenge for taxonomic identification, which could be overcome by high-throughput imaging combined with machine learning for rapid classification of specimens. These procedures require that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. However, such transfer learning may be problematic for the study of new samples not previously encountered in an image set, for example, from unexplored ecosystems, and require methods of domain adaptation that reduce the differences in the feature distribution of the source and target domains (training and test sets). We assessed the efficiency of domain adaptation for family-level classification of bulk samples of Coleoptera, as a critical first step in the characterization of biodiversity samples. Neural network models trained with images from a global database of Coleoptera were applied to a biodiversity sample from understudied forests in Cyprus as the target. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images, and on dataset complexity. The accuracy of between-datasets predictions (across disparate source–target pairs that do not share any species or genera) was at most 82% and depended greatly on the standardization of the imaging procedure. An algorithm for domain adaptation, domain adversarial training of neural networks (DANN), significantly improved the prediction performance of models trained by non-standardized, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, but the imaging conditions and classification algorithms need careful consideration.</p>","PeriodicalId":22126,"journal":{"name":"Systematic Entomology","volume":"48 3","pages":"387-401"},"PeriodicalIF":4.8,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/syen.12583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41419891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}