Does this photo make my range look big?
Article first published online: 23 JUL 2010
© 2010 The Authors. Journal compilation © 2010 The Zoological Society of London
Volume 13, Issue 4, pages 347–349, August 2010
How to Cite
Dobson, A. and Nowak, K. (2010), Does this photo make my range look big?. Animal Conservation, 13: 347–349. doi: 10.1111/j.1469-1795.2010.00381.x
- Issue published online: 23 JUL 2010
- Article first published online: 23 JUL 2010
We have a friend with a wonderful garden in rural Appalachia. He's both fascinated by nature and a confirmed camera addict. He decided to set some cameras at different points around his ‘back-yard’ along the edge of Black Roshannon State Park in Central Pennsylvania. He was amazed by what the cameras detected: bob-cats, coyotes, black bears, as well as a huge abundance of turkeys and white-tailed deer that he'd seen but only partially realized were so common (Fig. 1).
Similar things happened to Mark Hixon at the University of Oregon when he put underwater remote cameras on coral reefs. The technology to take photographs underwater has improved hugely over the last few years, but the catch with photography is that the photographer always has an influence on who's in the picture. This becomes truly apparent when the camera is left on the edge of a reef and set to automatically take pictures when something new ‘interrupts’ its field of view, or breaks an infra-red beam. In the case of coral reefs, predators suddenly appear in great abundance, massively underlining the impression you get when scuba diving that something large and predatory is watching you! Mark Hixon's photos provide strong evidence that a significant abundance and diversity of large piscivorous fish is still present in areas where they are not often sighted.
Could this rapidly developing and increasingly available technology be used for quantifying the diversity and abundance of rare mammals in tropical forests? And can it do so on large enough spatial scales to produce indices of abundance that meet the requirements for long-term monitoring of biodiversity (Balmford, Bennum et al., 2005; Balmford, Crane et al., 2005; Dobson, 2005). The article by O'Brien and colleagues on page 335 of this issue illustrates that camera traps may provide an incredibly powerful new tool for monitoring biological diversity – the wildlife picture index. They show that networks of camera traps can be readily coordinated using a number of subtle statistical tools to provide non-invasive and highly balanced methods for long-term monitoring of rare species in tropical forests.
A number of other recent studies provide increasing support for the use of remote cameras as major tools for monitoring biodiversity, they illustrate the power of these methods to detect and quantify the communities of vertebrate species that other techniques often fail to detect. Heat-activated camera traps have been widely used to inventory the occurrence and conservation status of elusive and threatened mammals (Rovero & De Luca, 2007; Tobler, Carrillo-Percastegui et al., 2008) and to study their activity and resource partitioning (Bowkett, Rovero & Marshall, 2008; Tobler, Carrillo-Percastegui & Powell, 2009). They have been used to estimate density using mark–recapture models for species with distinguishable individuals (e.g. Silveira et al., 2009). When individuals cannot be distinguished, trapping rates have recently been calibrated with independent indices of animal abundance (Rowcliffe et al., 2008; Rovero & Marshall, 2009). The use of camera traps is non-invasive, cost-effective, facilitates the standardization of survey methods across sites and enables the rapid assessment of biodiversity in remote areas. Moreover, having images of rare species makes it easier to communicate the importance of sites and set conservation priorities.
A significant number of rare and secretive carnivores have effectively been rediscovered with camera traps: in Iran, the largest remaining group of Asiatic cheetahs was photographed in 2005; in the Amazon rainforest, the short-eared dog or zorro, the only species in genus Atelocynus has recently been caught on camera after its re-discovery in 1990; in the Udzungwa Mountain forests of Tanzania the rare Jackson's mongoose, African's least known carnivore, has been photographed marking a first record of the species in Tanzania (De Luca & Rovero, 2006).
The Tanzania Mammal Atlas Project has combined intensive camera trapping surveys with sighting data to generate checklists of species for Tanzania's major national parks, to update species distribution maps, and to develop national conservation action plans for its mammals (e.g. Pettorelli et al., 2009). One of the more notable findings include a tree pangolin photographed in Minziro, one of the three patches of forest representing the Guinea-Congo biome in Tanzania, potentially a Sokoke dog mongoose (known primarily from Sokoke Forest north of Mombasa, Kenya) caught on camera trap east of Mkomazi National Park, and finding that the shy and nocturnal bushy-tailed mongoose is much more common in Tanzania than had been previously thought.
In the Eastern Arc Mountains of Tanzania, camera traps have enabled several important – and unusual – discoveries about carnivores, small antelopes and elephant shrews. In the Udzungwa Mountains, for example, an Abbot's duiker was photographed with a Tanzanian torrent frog in its mouth (Rovero, Jones & Sanderson, 2005) and recently, a new species of giant elephant shrew – the grey-faced sengi – was discovered (Rovero et al., 2008). Camera traps are increasingly used to measure activity in dispersal corridors; this provides important information on how well the landscape is connected for different species (Perault & Lomolino, 2000; Hilty & Merenlender, 2004) as well as how well artificial connections such as underpasses help reconnect natural dispersal routes (Ford, Clevenger et al., 2009).
Are there any downsides to the use of camera traps for monitoring biodiversity? As is almost inevitably the case with any study of biodiversity, there is almost no baseline data with which to compare the observed camera trap data and early estimates of population density were fraught by potential statistical biases (Jennelle, Runge et al., 2002). A number of studies have shown that camera traps can be calibrated against other non-invasive methods of monitoring, such as quantifying tracks and other signs of activity (Silveira, Jacomo et al., 2003); they produce survey results that are consistent, significantly reduce ambiguities in species identification and quickly recoup the initial start-up costs. The baseline problem is still hard to deal with, particularly as the rapid advances in camera technology will be accompanied by reductions in costs of equipment. Thus increasingly widespread use of camera traps will inevitably lead to both apparent range expansions for some species and even ‘recoveries from extinction’ for others. Ultimately, statistical techniques need to be developed that allow increases in sampling rate to not distort the true underlying trends in the data. This could presumably be readily achieved by sub-sampling the larger grid at a matching intensity to the earliest grid used to survey the habitat. Importantly, this increase in detection means that any observed decrease in abundance on a camera trapping grid; or any apparent extinction, will have to be taken seriously as it is likely to reflect a very real decrease in abundance. Thus camera traps could serve as early warning systems for losses of biodiversity as well as powerful methods for monitoring changes in abundance diversity and the appearance of invasive species and even poachers. Just as a picture is often said to be worth a thousand words, a network of pictures, may be worth hundreds of thousands of words when it comes to conveying to the broader public how rates of biodiversity are changing at alarming rates. The next challenge is to find ways to utilize other rapidly developing technological methods such as cell phones to monitor biodiversity in consistent ways over broad geographical areas.
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