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Camera trapping of wildlife has been practiced since the early 20th century (Chapman, 1927), but in the last 20 years or so, camera traps have become readily available and much more affordable. As a result, they are becoming a mainstream tool in conservation and ecology, with uses ranging from simple species inventories (e.g. Silveira, Jacomo & Diniz, 2003), the discovery of new species (e.g. Rovero et al., 2008), through abundance estimation (e.g. Karanth, 1995), conservation assessments (e.g. Kinnaird et al., 2003; Linkie et al., 2006), population dynamics (e.g. Karanth et al., 2006) and forest ecology (e.g. Beck & Terborgh, 2002; Weckel, Giuliano & Silver, 2006; Kitamura et al., 2008). This explosion in camera trap use is reflected in 50% annual growth over the past decade in the number of published papers that either directly address camera trapping methods or use them as a research tool (Fig. 1).

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Figure 1.  Annual publications investigating or using camera trapping methods, extracted by Web of Science topic search on ‘camera trap‘. The 2008 figure is projected pro rata based on publications appearing in the first 2 months of the year. No publications before 1993 were returned.

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In this context, Tobler et al. have produced a useful template for obtaining unbiased species richness estimates from camera trap data. As they point out, a major benefit of this approach is that it can piggy back on studies that are targeted at specific species. Up to now, the single most common use of camera traps has been to estimate the abundance of cat species, relying on individual recognition from coat patterns (e.g. Karanth & Nichols, 1998; Maffei, Cuellar & Noss, 2004; Trolle & Kery, 2005). Methods that extend to a wider range of terrestrial species (including the many species that cannot be individually recognized) greatly increase the value of camera trap studies and techniques, and if used retrospectively could make use of a vast amount of data that has already been collected.

However, Tobler et al. raise some questions that deserve further exploration. First, given that targeted studies usually focus on very elusive species, they need to maximize capture rate. This is often done by using baits or lures, or by focusing trap placement in areas frequently used by the target species. For large carnivores, this generally means placement on dirt roads or large game trails. This kind of selective placement has been shown to influence capture rates of some species (Trolle & Kery, 2005; Weckel et al., 2006), but despite the fact that Tobler et al.'s study was primarily aimed at jaguars, the results suggest that this factor need not influence species richness estimates. While this is encouraging, they do suggest that some forms of directed placement may violate the assumptions of the diversity models they recommend. If this is so, it will be important to explore further the survey designs and environmental conditions under which the available species richness estimators break down.

While Tobler et al. focus on species richness estimates from camera trap data, they also show an interesting relationship between species body mass and the probability of triggering cameras. From this they conclude that trap rates cannot be used as an index to compare relative abundance across species. They rightly point out that there are factors other than abundance that influence trapping rate, particularly animal movement rates, body size and patterns of habitat use. Given these confounding factors, it would of course be wrong to assume that differences in trapping rates directly reflect differences in abundance, either within or between species. However, we believe that it is possible in principle to control for these confounding variables and so extract the underlying abundance signal in trapping rate data. For example, the distance within which animals are detected is easy to measure from the photographs themselves, enabling detectabilities to be estimated for specific species and surveys. Measuring animal movement rates present more of a challenge, but assuming continuing improvements in technology and analytical methods, we believe that it will soon become possible to estimate species- and survey-specific movement rates from camera data. These ideas clearly need more work, however, we have taken the first step down this road with the development of a model that quantifies the theoretical relationship between trapping rate and animal density, controlling for key confounding variables (Rowcliffe et al., in press).

Finally, the current flowering of camera trap applications and methodologies, as exemplified by Tobler et al., is leading to an enormous expansion in the number of sites where camera traps are used. With the development of new schemes such as Tropical Ecology Assessment and Monitoring (Martins, Sanderson & Silva-Junior, 2007), the Wildlife Picture Index (http://www.wildlifepictureindex.org) and Tigers Forever (http://www.tigersforever.org), the pace of growth is likely increase for the foreseeable future. At the same time, although there are now well over 100 published papers using camera traps, we are aware of several long-standing studies that have never been fully analysed or published in easily accessible form. Given this impressive but uncoordinated growth in camera trapping studies, there is enormous future potential, but also a need for greater integration and consensus. Particularly one useful step would be a global data facility for camera trapping studies, such as those that are happening in some other areas of ecological research, such as Movebank (http://www.movebank.org), which aims to bring together wildlife tracking data. While we do not underestimate the sensitivities in such centralized data holding schemes, the gains to ecological and conservation science through improved integration would be huge.

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