世界上第一个蟋蟀科(Scolytinae)寄主植物数据集:杂类(第 2 部分)。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-12 DOI:10.1038/s41597-024-04087-1
Matteo Marchioro, Laura Besana, Michele Rossini, Davide Vallotto, Enrico Ruzzier, Giacomo Ortis, Isabel Martinez-Sañudo, Massimo Faccoli
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引用次数: 0

摘要

鞘翅目蟋蟀科(Scolytinae)是最成功的入侵物种类群之一。它们通常通过活体植物、木材和木质包装材料在各大洲之间迁移。这些昆虫对经济和生态造成的破坏,既有在寄主植物组织内钻蛀的直接作用,也有某些物种携带的病原体。为了通过规划有效的监测战略来防止引入潜在有害的新物种,了解记录在案的寄主植物至关重要。本研究的第三篇论文提供了一份完整的寄主植物清单,其中包括隶属于 16 个部族的 2 193 种蝼蛄寄主植物的经济分类:这些物种分属以下 16 个部落:Amphiscolytini、Bothrosternini、Carphodicticini、Chaetophloeini、Crypturgini、Diamerini、Dryocoetini、Hexacolini、Hylesinini、Hyorrhynchini、Hypoborini、Micracidini、Phloeotribini、Phrixosomatini、Scolytini 和 Scolytoplatypodini。
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The first host plant dataset of Curculionidae Scolytinae of the world: miscellaneous tribes (Part 2).

Scolytinae (Coleoptera: Curculionidae) are one of the most successful groups of invasive species. They are generally moved among continents transported within live plants, timber and wood-packaging materials. The damages caused by these insects, of economic and ecological relevance, can be caused both by the direct action of burrowing within host plant tissues and by the pathogens carried by some species. In order to prevent the introduction of new and potentially harmful species by planning effective monitoring strategies, the knowledge of recorded host plants is essential. This third contribution provides a complete list of host plants, with economic categorization, for 2,193 scolytine species, belonging to 16 tribes: Amphiscolytini, Bothrosternini, Carphodicticini, Chaetophloeini, Crypturgini, Diamerini, Dryocoetini, Hexacolini, Hylesinini, Hyorrhynchini, Hypoborini, Micracidini, Phloeotribini, Phrixosomatini, Scolytini, and Scolytoplatypodini.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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