Ecology documents and interprets the abundance and distribution of organisms. Ecoinformatics addresses this challenge by analyzing databases of observational data. Ecoinformatics of insects has high scientific and applied importance, as insects are abundant, speciose, and involved in many ecosystem functions. They also crucially impact human well-being, and human activities dramatically affect insect demography and phenology. Hazards, such as pollinator declines, outbreaks of agricultural pests and the spread insect-borne diseases, raise an urgent need to develop ecoinformatics strategies for their study. Yet, insect databases are mostly focused on a small number of pest species, as data acquisition is labor-intensive and requires taxonomical expertise. Thus, despite decades of research, we have only a qualitative notion regarding fundamental questions of insect ecology, and only limited knowledge about the spatio-temporal distribution of insects. We describe a novel high throughput cost-effective approach for monitoring flying insects as an enabling step towards “big data” entomology. The proposed approach combines “high tech” deep learning with “low tech” sticky traps that sample flying insects in diverse locations. As a proof of concept we considered three recent insect invaders of Israel’s forest ecosystem: two hemipteran pests of eucalypts and a parasitoid wasp that attacks one of them. We developed software, based on deep learning, to identify the three species in images of sticky traps from Eucalyptus forests. These image processing tasks are quite difficult as the insects are small (<5 mm) and stick to the traps in random poses. The resulting deep learning model discriminated the three focal organisms from one another, as well as from other elements such as leaves and other insects, with high precision. We used the model to compare the abundances of these species among six sites, and validated the results by manually counting insects on the traps. Having demonstrated the power of the proposed approach, we started a more ambitious study that monitors these insects at larger spatial and temporal scales. We aim at building an ecoinformatics repository for trap images and generating data-driven models of the populations’ dynamics and morphological traits.

中文翻译：

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昆虫生态信息学的高通量数据采集和深度学习

生态学记录并解释了生物的丰富和分布。生态信息学通过分析观测数据的数据库来应对这一挑战。昆虫的生态信息学具有很高的科学和应用重要性，因为昆虫种类繁多，种类繁多，并且参与了许多生态系统功能。它们还至关重要地影响着人类的福祉，人类的活动也极大地影响着昆虫的人口统计学和物候学。诸如传粉媒介数量下降，农业害虫暴发和昆虫传播疾病传播等危害，迫切需要开发生态信息学策略进行研究。但是，由于数据采集需要大量劳动并且需要生物分类学专业知识，因此昆虫数据库主要集中在少数害虫种类上。因此，尽管进行了数十年的研究，我们对昆虫生态学的基本问题只有定性的概念，对昆虫的时空分布只有有限的知识。我们描述了一种新颖的高通量，具有成本效益的方法来监测飞行中的昆虫，这是迈向“大数据”昆虫学的一个可行步骤。提议的方法将“高科技”深度学习与“低技术”粘性陷阱结合在一起，该陷阱可以在不同位置对飞行的昆虫进行采样。作为概念的证明，我们考虑了以色列森林生态系统中最近出现的三个昆虫入侵者：两种桉树类的半足类害虫和一种攻击其中一种的寄生蜂。我们基于深度学习开发了软件，以从桉树林的粘性陷阱中识别出这三种物种。这些图像处理任务非常困难，因为昆虫很小（< 5毫米）并随意摆放陷阱。最终的深度学习模型将三种焦点生物彼此之间以及其他元素（如树叶和其他昆虫）进行了高精度区分。我们使用该模型比较了六个地点中这些物种的丰度，并通过手动计算陷阱上的昆虫来验证结果。在展示了所提出的方法的强大功能之后，我们开始了一项更具雄心的研究，即在更大的时空尺度上监视这些昆虫。我们的目标是建立一个生态信息学信息库，用于捕获图像，并生成种群动态和形态特征的数据驱动模型。精度高。我们使用该模型比较了六个地点中这些物种的丰度，并通过手动计算陷阱上的昆虫来验证结果。在展示了所提出的方法的强大功能之后，我们开始了一项更具雄心的研究，即在更大的时空尺度上监视这些昆虫。我们的目标是建立一个生态信息学信息库，用于捕获图像，并生成种群动态和形态特征的数据驱动模型。精度高。我们使用该模型比较了六个地点中这些物种的丰度，并通过手动计算陷阱上的昆虫来验证结果。在展示了所提出的方法的强大功能之后，我们开始了一项更具雄心的研究，即在更大的时空尺度上监视这些昆虫。我们的目标是建立一个生态信息学信息库，用于捕获图像，并生成种群动态和形态特征的数据驱动模型。