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The effects of climate change, urbanisation and agriculture are changing theway insects occupy habitats. Some species may utilise anthropogenicmicrohabitat features for their existence, either because they prefer them tonatural features, or because of no choice. Other species are dependent onnatural microhabitats. Identifying and analysing these insects' use of naturaland anthropogenic microhabitats is important to assess their responses to achanging environment, for improving pollination and managing invasive pests.Traditional studies of insect microhabitat use can now be supplemented bymachine learning-based insect image analysis. Typically, research has focusedon automatic insect classification, but valuable data in image backgrounds hasbeen ignored. In this research, we analysed the image backgrounds available onthe ALA database to determine their microhabitats. We analysed themicrohabitats of three insect species common across Australia: Drone flies,European honeybees and European wasps. Image backgrounds were classified asnatural or anthropogenic microhabitats using computer vision and machinelearning tools benchmarked against a manual classification algorithm. We foundflies and honeybees in natural microhabitats, confirming their need for naturalhavens within cities. Wasps were commonly seen in anthropogenic microhabitats.Results show these insects are well adapted to survive in cities. Management ofthis invasive pest requires a thoughtful reduction of their access tohuman-provided resources. The assessment of insect image backgrounds isinstructive to document the use of microhabitats by insects. The method offersinsight that is increasingly vital for biodiversity management as urbanisationcontinues to encroach on natural ecosystems and we must consciously provideresources within built environments to maintain insect biodiversity and manageinvasive pests.

Image background assessment as a novel technique for insect microhabitat identification