The pholcid spiders of Tunisia (Araneae: Pholcidae): new species, new records, and deep COI divergence in Holocnemus reini

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  The pholcid spiders of Tunisia (Araneae: Pholcidae): new species, new records, and deep COI divergence in Holocnemus reini Abstract Tunisia features a rapid transition between Mediterranean and Saharan biogeographical zones, resulting in a high degree of environmental heterogeneity. Despite this ecological diversity, Tunisian spiders have received limited attention compared to those of neighboring Maghreb countries. Here we combine the results of recent collections in Tunisia focusing on Pholcidae with a comprehensive review of previously published information, resulting in a total of six genera and eleven species. We present first records of the genera Micropholcus Deeleman-Reinhold & Prinsen, 1987 and Spermophorides Wunderlich, 1992 for Tunisia, represented by four new species: M. kahinae Huber sp. nov., M. echebbii Huber sp. nov., S. nabilae Huber & Kmira sp. nov., S. bchirae Huber sp. nov.; in addition, we redescribe S. huberti (Senglet, 1973) from Tunisian specimens,...

YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images

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YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images


Abstract

Develoment of image recognition AI algorithms for flower-visiting arthropods has the potential to revolutionize the way we monitor pollinators. Ecologists need light-weight models that can be deployed in a field setting and can classify with high accuracy. We tested the performance of three deep learning light-weight models, YOLOv5nano, YOLOv5small, and YOLOv7tiny, at object recognition and classification in real time on eight groups of flower-visiting arthropods using open-source image data. These eight groups contained four orders of insects that are known to perform the majority of pollination services in Europe (Hymenoptera, Diptera, Coleoptera, Lepidoptera) as well as other arthropod groups that can be seen on flowers but are not typically considered pollinators (e.g., spiders-Araneae). All three models had high accuracy, ranging from 93 to 97%. Intersection over union (IoU) depended on the relative area of the bounding box, and the models performed best when a single arthropod comprised a large portion of the image and worst when multiple small arthropods were together in a single image. The model could accurately distinguish flies in the family Syrphidae from the Hymenoptera that they are known to mimic. These results reveal the capability of existing YOLO models to contribute to pollination monitoring.

Stark, T., Ştefan, V., Wurm, M. et al. YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images. Sci Rep 13, 16364 (2023). https://doi.org/10.1038/s41598-023-43482-3