{"title":"Effectiveness of Malaise trap and sweep net sampling in sawfly research (Hymenoptera: Symphyta)","authors":"","doi":"10.1007/s11756-024-01651-3","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Malaise traps and sweep nets are commonly used to study sawfly faunas, seasonality and communities. Here we analyse a large dataset obtained with these methods in Slovakia and Hungary over the last two and a half decades. The dataset included collections from twenty-one sites, each covering the entire growing season, eleven of which were obtained with the Malaise trap and ten with the sweep net. We conclude that both methods are suitable for faunistic studies of sawflies (Hymenoptera, Symphyta), although they may lead to certain biased results for some Symphyta groups. Special attention should be paid to Siricidae, Orussidae, Xiphidriidae and Cimbicidae, which were only weakly recorded with both methods. Argidae, Blennocampinae, Dolerinae and Tenthredininae were underrepresented in the Malaise trap samples and Allantinae in the sweep net samples. Both methods gave equally good results in measuring species richness in an one-year study. Ideally, they should be used together as they complement each other well. The net method has a great advantage in determining the exact population density. In contrast, the Malaise trap collections were often heavily dominated by only a few species, with males being preferentially trapped. Use of Malasie trap should be preferred for the study of seasonal flight activity.</p>","PeriodicalId":8978,"journal":{"name":"Biologia","volume":"29 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biologia","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s11756-024-01651-3","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOLOGY","Score":null,"Total":0}
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Abstract
Malaise traps and sweep nets are commonly used to study sawfly faunas, seasonality and communities. Here we analyse a large dataset obtained with these methods in Slovakia and Hungary over the last two and a half decades. The dataset included collections from twenty-one sites, each covering the entire growing season, eleven of which were obtained with the Malaise trap and ten with the sweep net. We conclude that both methods are suitable for faunistic studies of sawflies (Hymenoptera, Symphyta), although they may lead to certain biased results for some Symphyta groups. Special attention should be paid to Siricidae, Orussidae, Xiphidriidae and Cimbicidae, which were only weakly recorded with both methods. Argidae, Blennocampinae, Dolerinae and Tenthredininae were underrepresented in the Malaise trap samples and Allantinae in the sweep net samples. Both methods gave equally good results in measuring species richness in an one-year study. Ideally, they should be used together as they complement each other well. The net method has a great advantage in determining the exact population density. In contrast, the Malaise trap collections were often heavily dominated by only a few species, with males being preferentially trapped. Use of Malasie trap should be preferred for the study of seasonal flight activity.
期刊介绍：
Established in 1946, Biologia publishes high-quality research papers in the fields of microbial, plant and animal sciences. Microbial sciences papers span all aspects of Bacteria, Archaea and microbial Eucarya including biochemistry, cellular and molecular biology, genomics, proteomics and bioinformatics. Plant sciences topics include fundamental research in taxonomy, geobotany, genetics and all fields of experimental botany including cellular, whole-plant and community physiology. Zoology coverage includes animal systematics and taxonomy, morphology, ecology and physiology from cellular to molecular level.
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