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Purpose : Thirty-one species of arboreal tiger beetles are known from Sri Lanka of which 25 species are endemic. However, their habitat types are poorly documented and the available records are far outdated. Therefore, a survey of tiger beetles was carried out to determine their present occurrence with emphasis on agricultural habitat types. Research Method : Forty-six locations of the country, covering eighteen districts, all provinces, representing a majority of bioclimatic zones except those in Montane Sri Lanka were surveyed for arboreal tiger beetles. Sampling was conducted using the visual encounter method. Collected beetles were identified using taxonomic keys and descriptions. Findings : Eight species of arboreal tiger beetles were collected from the survey. Majority of the species (06) were collected from crop cultivations of coconut and also from tea, fruit farms, betel leaf, cinnamon and pepper. Four species of Derocrania and two species of Tricondyla were recorded from the cultivations and all had fused elytra and hence unable to fly. Derocrania scitiscabra was the dominant arboreal tiger beetle species in the crop cultivations. Originality/ Value : The study documents hitherto unrecorded habitat types for a poorly documented important beetle group of Sri Lanka. It further provides information for future research on the possibility of using arboreal tiger beetles as bio-control agents of insect pests of agricultural crops.

Automated identification of insects is a tough task where many challenges like data limitation, imbalanced data count, and background noise needs to be overcome for better performance. This paper describes such an image dataset which consists of a limited, imbalanced number of images regarding six genera of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of image collection is at a high level as the images were taken from different sources, angles and on different scales. Thus, the salient regions of the images have a large variation. Therefore, one of the main intentions in this process was to get an idea about the image dataset while comparing different unique patterns and features in images. The dataset was evaluated on different classification algorithms including deep learning models based on different approaches to provide a benchmark. The dynamic nature of the dataset poses a challenge to the image classification algorithms. However transfer learning models using softmax classifier performed well on current dataset. The tiger beetle classification can be challenging even to a trained human eye, therefore, this dataset opens a new avenue for the classification algorithms to develop, to identify features which human eyes have not identified.