This is the peer reviewed version of the following article: Potter, L.C., Brady, C.J and Murphy, B.P (2018) Accuracy of identifications of mammal species from camera trap images: A northern Australian case study. Austral Ecology, Vol. 44, Iss. 3, Pp 473-483, which has been published in final form at https://doi.org/10.1111/aec.12681. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Title: Accuracy of identifications of mammal species from camera trap images: a northern Australian case study Author details: Larissa C. Potter Corresponding author; [email protected]; (mobile) 0477 808 361; Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT, 0909, Australia Christopher J. Brady Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT, 0909, Australia Brett P. Murphy NESP Threatened Species Recovery Hub, Charles Darwin University, Darwin, NT, 0909, Australia Running title: Accurate mammal identifications from camera traps Acknowledgements Research was supported by Tiwi Plantation Corporation, Plantation Management Partners, The Tiwi Land Council and The Australian Research Council (DE130100434). We would like to thank Ian Radford, Anna Miller, Rebecca Diete, William Ross and Hugh Davies for providing camera trap images. We are grateful to everyone who participated in our survey. This study was conducted with Human Ethics approval from Charles Darwin University (H17001). Author Manuscript This article is protected by copyright. All rights reserved 1 2 MISS LARISSA CARMEL POTTER (Orcid ID : 0000-0002-6244-9941) 3 4 5 Article type : Original Article 6 7 8 Accuracy of identifications of mammal species from camera trap images: a northern 9 Australian case study 10 Larissa C. Potter1,2 11 Christopher J. Brady2 12 Brett P. Murphy2,3 13 14 Abstract 15 Camera traps are a powerful and increasingly popular tool for mammal research, but like all 16 survey methods, they have limitations. Identifying animal species from images is a critical 17 component of camera trap studies, yet while researchers recognise constraints with 18 experimental design or camera technology, image misidentification is still not well 19 understood. We evaluated the effects of a speies attriutes od ass ad 20 distinctiveness) and individual observer variables (experience and confidence) on the 21 accuracy of mammal identifications from camera trap images. We conducted an internet- 22 based survey containing 20 questions about observer experience and 60 camera trap 23 images to identify. Images were sourced from surveys in northern Australia and included 25 24 species, ranging in body mass from the Delicate mouse (Pseudomys delicatulus, 10 g) to the 25 Agile wallaby (Macropus agilis, >10 kg). There was a weak relationship between accuracy of 26 mammal identifications and observer experience. Accuracy was highest (100%) for 27 distinctive species (e.g. Short-beaked echidna [Tachyglossus aculeatus]) and lowest (36%) 28 for superficially non-distinctive mammals (e.g. rodents like the Pale field-rat [Rattus 29 tunneyi]). There wasAuthor Manuscript a positive relationship between accuracy of identifications and body 30 mass. Participant confidence was highest for large and distinctive mammals, but was not 31 related to participant experience level. Identifications made with greater confidence were 32 more likely to be accurate. Unreliability in identifications of mammal species is a significant This article is protected by copyright. All rights reserved 33 limitation to camera trap studies, particularly where small mammals are the focus, or where 34 similar-looking species co-occur. Integration of camera traps with conventional survey 35 techniques (e.g. live-trapping), use of a reference library or computer-automated programs 36 are likely to aid positive identifications, while employing a confidence rating system and/or 37 multiple observers may lead to collection of more robust data. Although our study focussed 38 on Australian species, our findings apply to camera trap studies globally. 39 40 Key words: camera trap, northern Australia, species identification, small mammal, wildlife 41 survey 42 43 Introduction 44 Over the last three decades, the number of camera trap studies for detecting mammals has 45 risen dramatically (Meek et al. 2015). This is partly a response to increased availability and 46 affordability of commercial devices (Meek et al. 2015; Tobler et al. 2008), but also a result of 47 advantages of camera traps over other sampling methods (De Bondi et al. 2010). However, 48 like all survey methods, camera traps have inherent limitations, and it is crucial they are 49 understood and acknowledged (Claridge and Paull 2014; Meek et al. 2015b; Meek et al. 50 2014). Currently, our understanding of the constraints of camera trapping is limited, 51 particularly how these constraints affect our capacity to obtain unbiased and ecologically 52 meaningful data (Burton et al. 2015; Meek et al. 2015b; Newey et al. 2015). This is 53 particularly important given the increasing use of camera traps to aid management and 54 conservation decisions (Burns et al. 2018; Comer et al. 2018; Jenks et al. 2011). 55 56 In a review of the Australian camera trap literature between 1991 and 2003, Meek et al. 57 (2015b) found few studies acknowledging or discussing impacts of camera trap limitations 58 on the outcome of results. When mentioned, researchers tend to focus on limitations of 59 cameras themselves, including false triggers, battery life and reliability (Glen et al. 2013; 60 Moseby and Read 2014), or experimental design elements like camera orientation (De Bondi 61 et al. 2010; Smith andAuthor Manuscript Coulson 2012), detection power (Nelson et al. 2014) and comparisons 62 to other sampling methods (Ballard et al. 2014; Swan et al. 2014b). However, Meek et al. 63 (2015b) classified the pitfalls of camera trapping into three broad categories: cameras, 64 animals and observers. While the limitations of camera technology are increasingly This article is protected by copyright. All rights reserved 65 recognised, the effect of speies attriutes ad hua ability are still not well understood. 66 Although camera technology is automated, the identification of images is generally done 67 manually, and is strongly influenced by human ability (Ballard et al. 2014; Burns et al. 2018; 68 Vernes et al. 2014). For example, in their examination of misidentification of small rodents 69 in Victoria, Burns et al. (2018) found that accuracy in species identifications was species- 70 specific and conditional on image type (white-flash vs. infrared), but the relationship 71 between accuracy and experience was complicated, with the conclusion that species 72 identification appears to be an innate skill. 73 74 Species identification from camera trap images is potentially difficult, introduces inherent 75 error and may be biased by observer skill and experience (Dundas et al. 2014; Meek et al. 76 2013). Difficulty with species identification may also be affected by the presence of 77 superficially similar, sympatric species (Claridge et al. 2010; Meek et al. 2013; Oliveira- 78 Santos et al. 2010). For example, Meek and Vernes (2016) remarked on the difficulty in 79 discriminating between eight sympatric rodents from the family Muridae, while Claridge et 80 al. (2010) reported difficulty distinguishing small and superficially similar marsupials from 81 the genus Antechinus (family Dasyuridae). 82 83 Due to the growing importance of camera trap survey data for conservation and 84 management, it is imperative to understand factors which may affect accuracy of mammal 85 identifications. The aim of this study was to investigate the effect of two species-level 86 attributes, animal body mass and superficial distinctiveness (i.e. size, body shape, pelage 87 colouration or patterning), and two observer-level attributes, experience and confidence, on 88 accuracy of identification of mammal species from camera trap images. We predicted that 89 accuracy and observer confidence in identifications would be lowest for small, non- 90 distinctive species, in line with personal experience and the literature (Claridge et al. 2010; 91 Meek and Vernes 2016). Additionally, we expected that more experienced observers would 92 demonstrate higher accuracy and confidence levels. 93 Author Manuscript 94 Materials and methods 95 Collection of camera trap images This article is protected by copyright. All rights reserved 96 Sixty camera trap images of 25 native terrestrial mammal species (Table 1) were collated 97 from six research projects across northern Australia, including coastal regions of the 98 Northern Territory (NT) and the Kimberley region, Western Australia (WA). Individuals were 99 identified to species level by researchers involved in each project (Corey et al. 2013; Davies 100 et al. 2017; Diete et al. 2017) using image sequences, local knowledge, and confirmation 101 from trap records. 102 103 Survey design 104 To assess accuracy of mammal identifications by wildlife scientists and enthusiasts, an 105 internet-based survey was developed using the website SurveyMonkeyTM 106 (www.surveymonkey.com). Respondents were canvassed through Twitter 107 (www.twitter.com), LinkedIn (www.linkedin.com; Australian Ecologists and Environmental 108 Professionals page) and the Facebook (www.facebook.com) groups: Australian Mammal 109 Society, Australian Mammal Identification