The increase in camera trapping studies over the past decade has resulted in data on prey species being obtained opportunistically while camera trapping for carnivores. These data provide valuable information on those prey species that may not otherwise have been obtained (e.g. O'Brien et al. 2003; Khorozyan et al. 2008; Stein et al. 2008). This can be particularly useful for rare and secretive animals that may otherwise be difficult to observe (Datta et al. 2008; Rovero & Marshall 2009; Tobler et al. 2009).
The use of a relative abundance index (RAI) based on raw count data obtained from camera trapping capture rate (number of camera days/independent photograph) and/or the inverse of that (number of independent photographs/100 trap days) is one option and has been discussed by several authors in recent years (Carbone et al. 2001; Carbone et al. 2002; Jennelle et al. 2002; Williams et al. 2002). The method has been found to provide a reliable index of true density for tigers and prey (O'Brien et al. 2003) and for Harvey‟s duiker (Cephalophus harveyi) (Rovero & Marshall 2009). It has also been used as a straightforward index of relative
abundance (Treves et al. 2010). Relative abundance indices were calculated for eight free- ranging game species and, as an additional food source for predators in the area exists in the form of wildlife stock on game farms, data on these species obtained during CS2, undertaken primarily on two game farms, are also presented. Data and analysis on captures of carnivore species are detailed in Chapter 5 and data and analysis of activity patterns for both carnivore and prey species are presented in Chapter 7.
Another alternative for estimating the availability of prey is that of using estimates of occupancy as a surrogate for abundance (Conroy & Carroll 2009). Occupancy in this context is used, in broad terms, to mean that a species is present (Conroy & Carroll 2009) and more specifically, can be defined as the proportion of a sampled area that is occupied by that species (MacKenzie
et al. 2006). There has been considerable activity around this subject in the literature of late
and models have been developed to make such occupancy estimations (e.g. MacKenzie et al. 2002; MacKenzie & Nichols 2004; MacKenzie et al. 2006). The strategy can be particularly useful where the effort and/or expense required to make direct estimations of abundance are not possible and has been used to provide occupancy estimates in a number of studies across a range of taxa including tigers (Linkie et al. 2006), brown hyaena (Thorn et al. 2009), sun bears
(Helarctos malayanus) (Linkie et al. 2007), five species of ungulate in the Amazonian basin
(Tobler et al. 2009) and golden eagles (Aquila chrysaetos)(Martin et al. 2009).
The models used for this type of analysis are based on a detection history for the species concerned, such that if a site were surveyed six times it would give a matrix of the kind 001010
45 indicating that the species had been detected on the third and fifth sampling occasions.
However, detection of any individual of a species is unlikely to be perfect for any number of reasons and that heterogeneity of detection is a key factor here. Detection histories compiled for sites comprising 1‟s and 0‟s do not provide information on individuals that may occur at a site but are not detected, in other words giving a „false negative‟ (MacKenzie et al. 2002). Occupancy itself should be positively correlated with abundance as with increasing abundance occupancy may also increase (MacKenzie & Nichols 2004). Of course, as MacKenzie et al. (2006) point out, while occupancy and abundance are clearly related they are not the same thing. But Royle & Nichols (2003) take this further and make the case that probably the most important source of heterogeneity in detection probability is variation in abundance. To
elaborate, where abundance varies between sites, probability of detection depends not only on the probability of detection for that species, but on the abundance of that species at any given site. In theory this linkage allows for estimations of the distribution of site-specific abundance where adequate provision has been made to characterise the distribution of detection
probability (Royle & Nichols 2003). A model based on this assumption (the Royle-Nichols (RN) model) is available in the program PRESENCE 3.0 (MacKenzie 2010). In contrast to the other models available in the program, which suppose that probability of detection varies by individual and that that probability is a random value in a mixture distribution, the RN model places the mixture distribution on abundance (with site detection being a binomial and
abundance a Poisson distribution) (MacKenzie et al. 2006). Population closure is a requirement of the model and in PRESENCE it is also assumed that individuals are distributed spatially according to a Poisson distribution which has a single parameter,
(„lambda‟), the mean. MacKenzie et al. (2006) elucidate that the appeal of the model is that given the assumptions that abundance follows a Poisson process and individual detectability is independent, it could be reasonable to view
as density, from which the abundance of animals at a site can be derived (MacKenzie et al. 2006 p. 140). However, they continue to caution that as such assumptions are unlikely to hold in most situations, abundance estimates could therefore be viewed purely as a random effect leading to variation in detection probability. In such instances the model can be made to resemble others in the program where the mixture is placed on probability of detection.To examine the perceptions of stakeholders, farmers who participated in the questionnaire survey were asked to estimate the abundance on their farms of several naturally occurring prey species. The species, which included some not previously mentioned, were kudu, springbok, duiker, steenbok, warthog, hares (scrub (Lepus saxatilis) and spring (Pedetes capensis)) and guineafowl (Numida meleagris). Livestock (n = 15) and game farmers (n = 5) were asked to say whether they thought these species were absent, rare, common or very common (Figure 4.3). As kudu and springbok cannot traverse game fences these two species were omitted from
46 the questionnaire survey for game farmers. Information gained during informal interviews was also utilised. Further details on the methodologies used are presented in section 2.2.