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Inexpensive Monitoring of Flying Insect Activity and Abundance Using bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457487; this version posted August 25, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Inexpensive monitoring of flying insect activity and 2 abundance using wildlife cameras ∗1,2 2 1 3 Jesse R A Wallace , Therese Reber , Brendan Beaton , David Dreyer2, and Eric J Warrant1,2 1Research School of Biology, Australian National University 2Lund Vision Group, Department of Biology, Lund University, Sweden 4 Abstract 5 1. The ability to measure flying insect activity and abundance is important for ecologists, 6 conservationists and agronomists alike. However, existing methods are laborious and 7 produce data with low temporal resolution (e.g. trapping and direct observation), or are 8 expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar 9 entomology). 10 2. We propose a method called “camfi” for long-term non-invasive monitoring of the activity 11 and abundance of low-flying insects using images obtained from inexpensive wildlife cameras, 12 which retail for under USD$100 and are simple to operate. We show that in certain 13 circumstances, this method facilitates measurement of wingbeat frequency, a diagnostic 14 parameter for species identification. To increase usefulness of our method for very large 15 monitoring programs, we have developed and implemented a tool for automatic detection 16 and annotation of flying insect targets based on the popular Mask R-CNN framework. 17 This tool can be trained to detect and annotate insects in a few hours, taking advantage of 18 transfer learning. 19 3. We demonstrate the utility of the method by measuring activity levels and wingbeat 20 frequencies in Australian Bogong moths Agrotis infusa in the Snowy Mountains of New 21 South Wales, and find that these moths have a mean wingbeat frequency of 48.6 Hz 22 (SE = 1.4), undertake dusk flights in large numbers, and that the intensity of their dusk 23 flights is modulated by daily weather factors. Validation of our tool for automatic image 24 annotation gives baseline performance metrics for comparisons with future annotation 25 models. The tool performs well on our test set, and produces annotations which can be 26 easily modified by hand if required. Training completed in less than 2 h on a single machine, 27 and inference took on average 1.15 s per image on a laptop. 28 4. Our method will prove invaluable for ongoing efforts to understand the behaviour and 29 ecology of the iconic Bogong moth, and can easily be adapted to other flying insects. The 30 method is particularly suited to studies on low-flying insects in remote areas, and is suitable 31 for very large-scale monitoring programs, or programs with relatively low budgets. ∗[email protected] Running title: Camera-based monitoring of flying insects Key words: Bogong moth, camfi, insect flight behaviour, insect monitoring, object detection, trail camera, wildlife camera, wingbeat frequency 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457487; this version posted August 25, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 32 1 Introduction 33 The ability to measure flying insect activity and abundance is important for ecologists, conservationists 34 and agronomists alike. Traditionally, this is done using tedious and invasive methods including nets 35 (e.g. Drake & Farrow, 1985), window traps (e.g. Knuff et al., 2019), light traps (e.g. Beck et al., 36 2006; Infusino et al., 2017), and pheromone traps (e.g. Athanassiou et al., 2004; Laurent & Frérot, 37 2007), with the latter being favoured by agronomists for its specificity. The WWII development of 38 radar led to the introduction of radar ornithology (Eastwood, 1967; Gauthreaux Jr & Belser, 2003), 39 and ultimately radar entomology (Drake & Reynolds, 2012; Riley, 1989), which facilitated non-invasive 40 remote sensing of insects flying up to a couple of kilometres above the ground, and became extremely 41 important for understanding the scale and dynamics of insect migration (Chapman et al., 2011). More 42 recently, entomological lidar has been introduced, which benefits from a number of advantages over 43 radar, in particular the ability to measure insects flying close to the ground, without suffering from 44 ground clutter (Brydegaard et al., 2017; Brydegaard & Jansson, 2019). However, both entomological 45 radar and entomological lidar systems are relatively large (requiring vehicle access to study sites), 46 bespoke, expensive, and require expertise to operate, reducing their utility and accessibility to field 47 biologists. 48 We propose a method for long-term non-invasive monitoring of the activity and abundance of low- 49 flying insects using inexpensive wildlife cameras, which retail for under USD$100 and are simple to 50 operate. We show that in certain circumstances, this method facilitates the measurement of wingbeat 51 frequency, a diagnostic parameter for species identification. We demonstrate the utility of the method 52 by measuring activity levels and wingbeat frequencies in Australian Bogong moths Agrotis infusa 53 that were photographed flying across a boulder field near Cabramurra in the Snowy Mountains of 54 New South Wales. The Bogong moth is an important source of energy and nutrients in the fragile 55 Australian alpine ecosystem (Green, 2011), and is a model species for studying directed nocturnal 56 insect migration and navigation (Adden et al., 2020; Dreyer et al., 2018; Warrant et al., 2016). A 2 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457487; this version posted August 25, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 57 dramatic drop in the population of Bogong moths has been observed in recent years (Green et al., 2021; 58 Mansergh et al., 2019), adding it to the growing list of known invertebrate species whose populations 59 are declining (Sánchez-Bayo & Wyckhuys, 2019). The present method will prove invaluable for 60 ongoing efforts to understand the behaviour and ecology, and monitor the population of this iconic 61 species. Our method can easily be adapted to other flying insects, and is particularly suited to 62 large-scale monitoring programs with limited resources. 63 2 Methods 64 The methods outlined below summarise the method of using wildlife cameras for monitoring flying 65 insects, and detail specific methods employed in this study. With the exception of the camera set-up 66 (in section 2.1 and section 2.2), and the manual image annotation (section 2.3), each of the methods 67 described below have been automated in our freely available software. A full practical step-by-step 68 guide for using this method along with complete documentation of the latest version of the code 69 is provided at https://camfi.readthedocs.io/. A PDF version of the documentation is provided at 70 https://camfi.readthedocs.io/_/downloads/en/latest/pdf/. 71 2.1 Measurement of rolling shutter line rate 72 For the purposes of measuring the wingbeat frequency of the moths in the images captured by the 73 wildlife cameras, it is important to know the line rate of the cameras’ rolling shutters. This was 74 measured by mounting one of the cameras so that its lens pointed at a rotating white line (in this 75 case, a strip of paper taped to a cardboard tube attached to the blades of a small electric fan), 76 ensuring that the centre of the white line and its centre of rotation were coincident. The apparatus 77 for measuring the rolling shutter line rate of the cameras is shown in Fig. 1. 78 The exact rotational velocity of the line was measured by synchronising a strobe light from a smart 3 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457487; this version posted August 25, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1000 b 1250 1500 A CB A c A 1750 C 2000 B Pixel row Pixel 2250 B 2500 2750 a a 1000 A 1250 1500 A 1750 C 2000 B Pixel row Pixel A CB d 2250 e 2500 B 2750 Figure 1: Measurement of rolling shutter line rate of the wildlife cameras used in this study. Left: Apparatus for measuring rolling shutter line rate. a. Camera supports (chairs). b. Camera mount (long chopsticks with duct tape). c. Camera to be measured. d. Rotor motor (desk fan with front cover removed). e. Rotor (cardboard tube) with white reference line (paper masked with black duct tape). Right: Two rolling shutter line rate measurement images, with reference annotations marked, and angles ∠A1CB1 and ∠A2CB2 shown. Using the coordinates of these reference annotations, and if the rotational velocity of the white reference line is known, the rolling shutter line rate of the camera can be calculated using equation 1. 4 bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457487; this version posted August 25, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 79 phone application (Strobily, 2019) to the period of rotation of the line. Synchronisation is achieved 80 when the line appears stationary under the strobe. 81 A photograph was taken using the camera and the corners of the motion blur traced by the rotation 82 line were marked using the free and open-source VGG Image Annotator (VIA) (Dutta & Zisserman, 83 2019) (VIA is a simple and standalone manual annotation software for image, audio and video, and is 84 available from https://www.robots.ox.ac.uk/~vgg/software/via/).
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