Book reviews

The automated camera-trap has been used to remotely observe animals in various habitats since the middle of the 20th century (Kucera and Barrett 2011). Camera-trapping was initially a complimentary monitoring tool rather than a primary survey method (e.g. used in addition to track or scat surveys), but this changed in the 1990s when conservation scientists successfully used cameras to detect and estimate the population size of cryptic tigers (Panthera tigris) in India (Karanth 1995; Trolliet et al. 2014). Since then, camera-trap technology has rapidly increased in sophistication (e.g. improved image resolution, time-lapse photography, and advanced night settings; McCallum 2013), and become more user-friendly and affordable, which has allowed a greater number of field-studies to embrace cameratrap technology (Trolliet et al. 2014). Consequently, type ‘camera trap’ into any internet search engine and one will be inundated with peer-reviewed publications, reports and advertisements for the technologywhich is rapidly changing the nature of field experiments in conservation science and wildlife management. In response to the popularity of camera traps for research, the First International Camera Trapping Colloquiumwas hosted in Sydney, Australia, in 2012. It was at this forum that wildlife researchers and managers gathered to present findings and discuss the benefits and constraints of using camera traps on multiple taxa in diverse ecosystems. Meek and Flemings’ ‘Camera Trapping: Wildlife Management and Research’ is a collation of 32 of the research papers, plus a review, presented at the Colloquium. While this is not the first book to explore the use of camera traps by researchers (e.g. O’Connell et al. 2011), Meek and Flemings’ text is the first book to use the results of a survey of researchers and managers to identify common topics of interest, and use these topics to structure the text. Australian and several international papers are grouped under four broad topics. Camera trapping for animal monitoring: case studies – The first section presents 10 studies from the International Colloquium which exemplify the current scope of camera-trap research. The studies are representative of the types of logistical, social and technological challenges facing researchers attempting to evaluate standard and novel hypotheses. A good example is that of Sangay et al. (p. 87), who honestly recount the challenges of monitoring mammal diversity using traditional methods in the Himalayan Kingdom of Bhutan. Like other studies, camera traps have enabled researchers to circumvent the logistical challenges of traditional surveys and generate baseline data for many mammal species, including confirming the occurrence of charismatic mammals such as the national animal, the Bhutan takin (Budorcas taxicolor whitei). The research in Bhutan also illustrates how camera data can have positive social outcomes; with information on the activitybudgets of tigers (Panthera tigris) and their main prey, sambar deer (Rusa unicolor), being used to educate graziers on how to modify farming practices to protect livestock, graziers and tigers alike. Camera technology, constraints and pitfalls – This section includes papers which highlight the benefits and perils associated with using camera traps in wildlife studies. There are several examples of how choice of camera models can alter accuracy of survey data (e.g. detecting different mammal sizes, Urlus et al. p. 111), and particular settings and placement of different models are discussed. Hradsky (p. 181) carried out a study on the relationship between canopy cover and the swamp wallaby (Wallabia bicolor), to establish whether removal of the yarra burgan shrub (Kunzea leptospermoides) from a forest reserve would impact wallaby populations. The presence of wallabies at all 42 of Hradsky’s trap sites in Victoria, made relationships with habitat variables difficult to test. However, grouping camera images into four diel periods, revealed that wallabies were actually using yarra burgan as important habitat and modifying use with time of day. This study exemplifies how the strength of ecological relationships can be misinterpreted or overlooked if camera data are not broken down and analysed in a variety of ways (i.e. at coarse and fine scales). Survey design – Inappropriate survey design is a common issue for those beginning or replicating camera-trap studies (Rovero et al. 2013; Burton et al. 2015). Speakers at the International Colloquium repeatedly linked weak survey design with failure to achieve research outcomes, and acknowledged that time and resources were consequently wasted– commodities that few wildlife researchers can afford to misuse. There are several papers in section 3 which exemplify how clever adaptation of survey design can improve surveillance of ‘tricky’ taxa (e.g. arboreal mammals, reptiles). However, it is the paper by Jansen et al. (p. 263) which should pique the interest of readers. Tropical Ecology Assessment and Monitoring is a network of camera traps across 16 disturbed tropical sites on three continents, which monitor large–medium animal communities for change as a result of anthropogenic disturbances. Systematically deployed cameras gather data which are compared across site and year, and analysed using tailored software. Data are uploaded and made available to the wider scientific community for discussion. TEAM is an example of how camera traps are being used to upscale remotemonitoring ofwildlife populations, to collaboratively answer ‘bigger’ ecology questions. Data management and analyses –Data from any monitoring method are only as good as their analysis and interpretation. Any tools which simplify the collation and analysis of camera-trap data, are therefore worthy of researchers’ consideration. This section introduces readers to the types of techniques being used by International Colloquium delegates to streamline the entry, organisation and interpretation of camera data. For instance, Sanderson and Harris (p. 283) provide a step-by-step guide to managing camera data without the need to enter it manually; and Flazon et al. (p. 299) develop an algorithm model to automate the analysis of images. Tucked in at the end of Part 4, is Meek CSIRO PUBLISHING