396 E. K. Madsen and F. Broekhuis
TABLE 1 Environmental covariates hypothesized to influence the probability of habitat use, including the effect for each species. Category and prediction
Source
Human disturbance All species will avoid human disturbance, with leopards Panthera pardus showing the lowest level of avoidance
Proportion of fenced area Elephants Loxodonta africana will avoid areas with a high proportion of fencing
Habitat type preference Cheetahs Acinonyx jubatus will select for open habitat but all other species will select for semi-closed
Protected areas Cheetahs, elephants, lions Panthera leo & wild dogs Lycaon pictus will all have a preference for sites closer to protected areas; the effect will not be as strong for hyaenas Crocuta crocuta and leopards
Distance to rivers Elephants, leopards, lions & wild dogs will all select areas close to rivers
of untrained individuals increases the chance that false posi- tive detections will occur through, for example, misidentifi- cation or false reporting, resulting in an overestimation of occupancy (Royle & Link, 2006; Petracca et al., 2018). This can be accounted for by focusing on easily recognizable spe- cies (Miller et al., 2011) and by using models that account for false positives (Royle & Link, 2006). Another factor that needs to be taken into account is de-
tection probability. For example, presence/absence data may be biased towards habitats, such as open plains, where there is a high chance that an animal is detected. If this is not ac- counted for it may be impossible to correctly predict the areas that are most suitable for wildlife (Pulliam, 1988). The detection probability may also be influenced by the amount of time that a person spends outside, which is likely to vary with occupation (Turvey et al., 2015). Some studies have used the proportion of the year or other continuous covariates to account for effort (Zeller et al., 2011), however this is not possible if the interviewee is constantly resident in their area. If this is the case then categorical variables can be used. Additionally, assuming that non-detection equates to absence could result in a negative bias in occupancy esti- mates (MacKenzie et al., 2002, 2003). Imperfect detection can be accounted for by repeating surveys in each site, facili- tating the calculation of detection probability using the de- tection history (MacKenzie et al., 2002). Failure to account for imperfect detection can lead to unreliable results and thus to ill-informed conservation decisions (MacKenzie et al., 2002, 2004). Both detection probability and imperfect detection can be accounted for using site-occupancy model- ling (Pillay et al., 2011). Thesemodels have been expanded to account for false positives (Royle & Link, 2006)and can
Galanti et al. (2006), Athreya et al. (2013), Schuette et al. (2013), Loveridge et al. (2017)
Thouless & Sakwa (1995), Loarie et al. (2009)
Creel & Creel (1998), Carbone et al. (2005), Hopcraft et al. (2005), Galanti et al. (2006), Balme et al. (2007, 2017a,b), Bissett & Bernard (2007), Kolowski & Holekamp (2009), Athreya et al. (2013), Broekhuis et al. (2013)
Woodroffe & Ginsberg (1998), Galanti et al. (2006), Kolowski & Holekamp (2009), Athreya et al. (2013), Schuette et al. (2013), Loveridge et al. (2017), Klaassen & Broekhuis (2018)
Hopcraft et al. (2005), Balme et al. (2007; 2017a; 2017b), De Knegt et al. (2011), Cozzi (2012)
therefore be used to provide robust results on species presence and distribution from interview data (Petracca et al., 2018). Here we use interview data and false positive occupancy
modelling to identify areas of high wildlife use outside the protected areas of the MaasaiMara, Kenya, to highlight pri- ority locations for the potential expansion of conservancies. Basing management decisions on a single species may be unreliable because species show variation in behavioural plasticity when faced with threats (Woodroffe, 2000)so we used amulti-species approach focused on six largemammals: cheetahs Acinonyx jubatus, elephants Loxodonta africana, spotted hyaenas Crocuta crocuta, leopards Panthera pardus, lions Panthera leo and wild dogs Lycaon pictus.Wefocused on five carnivores because they can have wide-ranging, key- stone ecological effects, and the protection of intact carnivore guilds is therefore of particular importance (Ripple et al., 2014; Wolf & Ripple, 2017). Carnivores are also sensitive to human disturbance (Woodroffe, 2000),which is significantwhen set- ting aside areas for protection in a human-dominated land- scape such as the Maasai Mara. Elephants were included because they require large home ranges and are important ecosystemengineers (De Knegt et al., 2011). As the six species have different ecological requirements, we hypothesize that their distributions will differ. Our predictions, based on key landscape variables, are summarized in Table 1.
Study area
The study was conducted in the Maasai Mara in south-west Kenya. Datawere collected around theMaasaiMara National Reserve and the adjacent wildlife conservancies, hereafter
Oryx, 2020, 54(3), 395–404 © 2018 Fauna & Flora International doi:10.1017/S0030605318000297
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