The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions

C H A P T E R

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The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions Ezequiel Fabiano*, Lorraine K. Boast**, Angela K. Fuller†,a, Chris Sutherland‡ *University of Namibia, Katima Mulilo, Namibia **Cheetah Conservation Botswana, Gaborone, Botswana † Cornell University, Ithaca, NY, United States ‡ University of Massachusetts-Amherst, Amherst, MA, United States

Remote camera trapping and associated advances in ecological statistics (i.e., capture–­ recapture [C–R] modeling) provide effective and efficient means to study wild animal populations (O’Connell et al., 2011). Photographic C–R has been applied across a diverse range of taxa to investigate many types of ecological questions, for example, to assess the impacts of land-use on species diversity (Kauffman et al., 2007), to estimate and assess trends in species distribution, abundance, and density (e.g., tigers Panthera tigris; Karanth et al., 2006), and to examine species’ behavior and social interactions (e.g., Eurasian lynx Lynx lynx; Vogt et al., 2014). Camera trapping provides a noninvasive approach especially useful for studying low-density and elusive species, such as

the cheetah (Acinonyx jubatus). In addition, cheetahs are individually recognizable from photographs by their unique spot patterns, which remain unaltered throughout the lifetime of an individual (Caro and Durant, 1991; Chapter 32), providing natural marks that can be used to estimate abundance using C–R methods (O’Connell et al., 2011). Here, we provide a brief overview of previous applications of camera trapping of cheetahs, focusing on spatial capture–recapture (SCR) approaches to estimate cheetah abundance and density. We discuss the challenges faced when surveying cheetahs using camera traps and provide some suggestions that can improve the success of future cheetah camera trapping studies.

a

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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Cheetahs: Biology and Conservation http://dx.doi.org/10.1016/B978-0-12-804088-1.00029-0 Copyright © 2018 Elsevier Inc. All rights reserved.

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29.  The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions

SURVEYING CHEETAHS: APPLICATIONS OF CAMERA TRAPPING The most common application of camera trap surveys in cheetah research has been to estimate abundance and density using C–R methods. These studies have been conducted in a variety of environments, ranging from the hyperarid Saharan desert in Algeria to thornbush and woodland savannah habitats in farmland/ Botswana, Kenya, Namibia, and South Africa (Table 29.1). In addition to abundance and density, camera trapping has been used to investigate other aspects of cheetah ecology, such as confirming cheetah presence in remote habitats of the Sahara desert (Sillero-Zubiri et al., 2015), assessing annual survival on farmlands (Cheetah Conservation Fund, unpublished data), evaluating temporal activity patterns (Belbachir et al., 2014; Fabiano, 2013), investigating the role of scent marking posts for social communication (Fig. 29.1) (Fabiano, 2013; Marnewick et al., 2006), and monitoring distribution patterns (Andresen et al., 2014).

ESTIMATING CHEETAH ABUNDANCE AND DENSITY WITH CAMERA TRAPS Conducting camera trap surveys to estimate abundance or density requires that multiple cameras are placed throughout a study area. The number of cameras deployed, their exact location, the distance between cameras, and the duration of study should be determined by the specific ecological question of interest, the ecology of the species, knowledge gained through pilot or previous surveys, and the availability of resources. To capture both flanks of an individual for individual identification, cameras are often, although not always (e.g., Brassine and Parker, 2015), placed opposite each other in pairs. Camera placement depends strongly

on the movement patterns of the species; ideal locations are those areas that are likely to be utilized by all classes of individuals (i.e., males and females of all ages). Typically for cheetahs, cameras are placed along roads or trails or at scent marking sites. To obtain quality photographs of the entire body of a passing cheetah, and to maximize the likelihood of individual identification, cameras should be placed at approximately shoulder height of an adult cheetah (e.g., 75 cm; Boast et al., 2013), and to reflect daily activity patterns, should be operational for 24 h a day (Cozzi et al., 2012). Individual identification has been manually performed (e.g., Marnewick et al., 2008), but spot recognition software is available (Kelly, 2001). It is well understood that when surveying a population, not all individuals are likely to be encountered (i.e., surveys rarely result in a complete census). As such, statistical models are required to estimate the unobserved proportion of the population and thus the total population size. When a population is sampled repeatedly and individuals are identifiable by natural markings or otherwise, closed population C–R models can be used to analyze the resulting capture histories to estimate detectability and abundance (Royle et al., 2014; Williams et al., 2001). Capture histories represent a temporal sequence of individual encounters over multiple sampling occasions, which are commonly (though not a requirement) defined as a single day in camera trap studies. The period of time over which a survey is conducted is assumed to be closed (i.e., no additions or losses to the population), and depends on the biology, movement, and life history of the focal species. For large carnivores this period has typically been approximately 90 days (Hedges et al., 2016) including for cheetahs (Table 29.1). One criticism of traditional C–R approaches is that they are nonspatial, that is, capture histories indicate only the occasion during which an individual was detected and ignore the location of the detection (Chapter 1 in Royle et al., 2014).

5.  Techniques and analyses

Marnewick et al. (2008)

Marker et al. (2008)

O’Brien and Kinnaird (2011)

Fabiano (2013)a

Belbachir et al. (2014)

Fenced reserve/ South Africa (n = 1)c

Conservancy/ Namibia (n = 1)d

Conservancy/ Kenya (n = 1)d

Conservancy/ Namibia (n = 10)d

Unfenced Botswana protected farmland/ area/Algeria Botswana (n = 2) (n = 1)d

Fenced reserve/ Botswana (n = 2)c

Survey area size (km2)

240

277

200

341 ± 41

2551

475

240

Study duration (days)

30

90

84

90 ± 0

60; 90

84

90; 130

No. of cameras stations 12

13

21

16 ± 1

40

26

60; 60

Space between camera stations (km ± SD)

—

17.0 ± 9.2

1.3 ± 0

17.0 ± 9.2

10.0 ± 0

4.3 ± 0.8

3.7; 3.1

Survey designe

Static

Static

Block

Static

Static

Block

Block

Statistical analysis methodf

Nonspatial

Nonspatial

Nonspatial

Nonspatial; B-SCR

Nonspatial

B-SCR

B-SCR

No. of camera trap nights

120

1170

1764

1377 ± 120

1862; 3367

1063

2660; 3750

No. of days to first cheetah capture

3

1

—

38 ± 28

—

6

9

Days until all identified 9 individuals are detected

84

—

45 ± 20

35; 82

45

—

No. of adult cheetahs detected (male: female)

5:0

11:0

3:0

32:7

4:0; 3:0

4:1

5:2; 5:2

No. of independent captures of adult cheetahs

12

72

4

332 ± 231

15; 17

17

18; 31

Survey Study site/country design (number of surveys)

Brassine and Parker (2015)b

Estimating cheetah abundance and density with camera traps

5.  Techniques and analyses

Results

Boast et al. (2015)



TABLE 29.1 A Summary of the Study Design and Calculated Estimates of Cheetah Abundance and Density in Camera Trapping Studies

(Continued)

417

Marker et al. (2008)

O’Brien and Kinnaird (2011)

Capture probability ± SD (95% HPD)

0.17

0.29

Baseline encounterg ± SD (95% HPD)

—

Spatial scale (mean ± SD (km, 95% HPD) FMMDM (km) 2

Fabiano (2013)a

Belbachir et al. (2014)

Boast et al. (2015)

Brassine and Parker (2015)b

0.04

0.32 ± 0.21 (0.12 – 0.78)

0.20; 0.21

NA

NA

—

NA

0.34 ± 0.33 (0.09 – 1.03)

—

0.04 ± 0.03 0.03 ± 0.03 (0.002 – 0.091) (0.01 – 0.07); 0.01 ± 0.01 (0.00 – 0.02)

—

—

NA

5.03 ± 1.70 (1.63 – 7.26)

—

8.83 (±13.55, 3.60 – 15.49)

5.10 (±1.02, 3.27 – 7.14); 6.29 (±1.57, 3.78 – 9.48)

—

12.4

0

9.67 ± 4.7

44.9; 44.9

11.9

28.0

Density/1000 km  ± SD NA (95% CI/HPD)h

9.4 ± 2.1

22.5

Nonspatial 6.0 ± 4.0 (1.0 – 15.0); B-SCR 11.0 ± 4.0 (1.0 – 19.0)

0.3; 0.2

g

3.2 (0.40 – 7.7)

6.1 ± 1.8 (3.0 – 9.0) 5.8 ± 2.0 (2.4 – 9.0)

Measures of precision are the standard deviation (SD), the 95% confidence intervals (95% CI) or highest posterior distribution (95% HPD). a Average of individual surveys (mean ± SD, where applicable). b Results presented are those based on the 90 and 130 trapping days with cameras placed at focal points. c Fenced but does not restrict cheetah movement. d Unfenced cattle and wildlife conservancy. e Block (cameras are relocated to a new survey block which is part of the total area to be surveyed) and static (1 survey block, cameras are not relocated). f Nonspatial, density estimation determined by adding a buffer around the camera trapping grid [based on the full mean maximum distance moved (FMMDM) of individuals captured at different camera stations]; B-SCR, Bayesian Spatial Capture Recapture. g Baseline encounter = probability of detection if the camera trap was exactly at the activity center of an individual. h The assumption that all individuals have independence movements was only upheld in Boast et al. (2015).

29.  The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions

5.  Techniques and analyses

Marnewick et al. (2008)

418

TABLE 29.1 A Summary of the Study Design and Calculated Estimates of Cheetah Abundance and Density in Camera Trapping Studies (cont.)



Estimating cheetah abundance and density with camera traps

FIGURE 29.1  A male cheetah scent marking a tree by spraying urine, captured by a remote camera trap in northcentral Namibia. Source: Cheetah Conservation Fund.

Estimating absolute density becomes problematic in nonspatial C–R methods because individuals may move outside the area delimited by a camera trap array inducing a form of individual heterogeneity in detectability which makes it difficult to define precisely the effective area being sampled by an array of cameras. Various methods have been proposed to calculate this area in nonspatial C–R (Karanth and Nichols, 2002; Wilson and Anderson, 1985a,b). These methods include creating buffers around each camera station or around the minimum convex polygon of the outer camera locations. Buffer size can be based on the mean maximum distance moved (MMDM) of individuals captured at different locations, the half MMDM, or using the mean home range radius (HRR) calculated from a representative sample of collared cheetahs from the same area and time. The full MMDM and the HRR are considered the most accurate for estimating density using nonspatial closed population models in large carnivores (Sharma et al., 2010), including cheetahs (Marker et al., 2008). Although these methods are commonly applied they are ad hoc, and fail

419

to explicitly model species movement parameters during density estimation. In addition, nonspatial C–R fails to explicitly accommodate the individual encounter heterogeneity (i.e., patterns of detection that vary by individual) due to the between–individual variability in trap exposure. It is logical to assume that individuals with activity centers (home range centers) closer to a camera trap will have more detections than that of an individual located farther from the same camera trap. Because the density of camera traps will be lowest at the edges of a trapping array, individuals that spend most of their time on the periphery or off the trapping array will have a lower overall detection probability than those located within the center. Failure to account for detection heterogeneity can result in biased estimates of abundance (Otis et al., 1978). A preferable alternative to nonspatial C–R is SCR (Borchers and Efford, 2008; Efford, 2004; Royle et al., 2009a). The major advance of SCR models is the inclusion of a spatially explicit encounter model that relates the detection of individuals to the distance between an individual’s activity center and a camera trap, and a spatial point process model that describes the distribution of activity centers within a prescribed area. Key to estimating the spatial scale of detection (i.e., movement range of an individual) are the unique spatial locations where individuals are detected, that is, spatial encounter histories that acknowledge when, and importantly where, individuals were encountered. The objective in SCR is to estimate the number of unobserved individuals and thus the total number of individuals (activity centers) within the prescribed area, providing an estimate of absolute density. Thus, SCR directly addresses the two major concerns of nonspatial C–R models: individual heterogeneity in detectability due to the juxtaposition of individuals and traps (Efford et al., 2009; Royle et al., 2009b), and the explicit definition of the sampled area included as part of the model.

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29.  The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions

TABLE 29.2 A Review of Core Nonspatial and Spatial Capture Recapture (SCR) Model Assumptions and an Indication of Potential Camera Study Design Aspects That can Reduce the Degree of Bias in Estimates Resultant When These Assumptions are Violated Aspect of camera study design to consider to reduce risk of violating assumption

Assumptions

Nonspatial models

SCR model

  1. All individuals are correctly identified from photographs

✓

✓

Use multiple individual observers to identify individuals

  2. No unmodeled variation in the probability of detection

✓

✓

Adequate camera trap spacing, density and placement of cameras at focal points that maximize both sex detection

  3. The population is demographically closed

✓

✓

Length of study, time of year survey conducted (e.g., avoid birth peaks)

  4. The population is geographically closed

✓

✓

Survey areas include multiple individuals’ complete home ranges. Avoid sampling when dispersal is likely

  5. Each capture is an independent event

✓

✓

Include covariates (e.g., social groups); or by inflating variance (e.g., c-hat, an estimate of overdispersion); further analysis needed

  6. All individual's home ranges are circular

✓

  7. Distribution of individuals follows a Poisson or Binomial distribution

✓

Apply, if necessary and data permits, the nonstationary home range SCR model using ecological distances

  8. Individuals have independent activity centers

—

✓

Apply, if data allow, SCR models that allow for dependence between locations

  9. Home ranges are fixed during the survey period

—

✓

Conduct surveys within a single season; apply Markovian SCR models or include a resource selection function as a density covariate

10. Probability of detection declines with distance of the activity center from the trap in a Euclidean manner

—

✓

Apply the nonstationary home range SCR model using ecological distances

11. Areas around cameras is homogenous in terms of habitat suitability

✓

✓

Selection of nonsuitable habitat in SCR models, large study area

Adapted from Boast L., 2014.

5.  Techniques and analyses



Challenges faced when surveying cheetahs using camera traps

SCR models can be analyzed using maximum likelihood methods (Borchers and Efford, 2008; Efford, 2004) or Bayesian methods (Royle et al., 2014). Both approaches are equally suitable (Gerber and Parmenter, 2015), and the choice of analysis depends on the study objectives (e.g., Fuller et al., 2016; Royle et al., 2014). Sutherland and Royle (2016) provide an overview of the software available to conduct either analysis. Additional core assumptions for nonspatial and SCR models are presented in Table 29.2.

CHALLENGES FACED WHEN SURVEYING CHEETAHS USING CAMERA TRAPS Although the application of camera traps has some distinct advantages for studying elusive species, such as the cheetah (see section, Surveying Cheetahs: Applications of Camera Trapping), the method is not without challenges. The primary challenge is that cheetahs occur naturally at low density throughout their range, making it difficult to obtain sufficient sample sizes (i.e., number of unique individuals and spatial recaptures) to ensure precise density estimates. Previous cheetah surveys have captured between 3 and 11 adult cheetahs (median = 5; Table 29.1). These sample sizes are less than the 30 individuals with 20 recaptures recommended for SCR by Tobler and Powell (2013). These low sample sizes contributed to the typically low precision in density estimates in all previous cheetah studies (Table 29.1). In an attempt to increase sample sizes, cheetah surveys have often placed camera stations at scent marking sites. However, marking sites are predominately used by territorial males (­Chapter 9). This camera placement can, therefore, result in a substantial sex bias toward males (in particular territorial males) as observed in all previous cheetah camera surveys (i.e., 56 males compared to 10 females;

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­Table  29.1). Small sample sizes limit the ability to estimate sex-­specific detection probabilities. A consequence of this limitation is that the averaged estimates of detectability will be weighted toward the more frequently observed sex. Nonetheless, despite low sample sizes of females, estimation of a sex-specific spatial scale parameter is still possible, provided females are detected at multiple locations. Indeed, accounting for sex is vital if intersex overall detection probabilities differ largely (Efford and Mowat, 2014; Sollmann et al., 2011). An additional challenge when surveying cheetahs with cameras is the sociality of males (i.e., male cheetahs can occur alone or in coalitions of two or more individuals), which violates the assumption that all captures are independent and that individuals have single, independent activity centers (Table 29.2). This violation tends to inflate the apparent precision of density estimates (Head et al., 2013; Efford, personal communication). Head et al. (2013) accounted for this violation by including a categorical social group covariate (solitary vs. group individuals) when working with group living species (i.e., chimpanzees Pan troglodytes troglodytes, gorillas Gorilla gorilla gorilla, and forest elephants Loxodonta cyclotisii). Development of models that account for social grouping and efforts to increase the sample size of cheetahs to enable the inclusion of covariates are thus needed. In low-density, wide-ranging species, a large sample size is difficult to achieve, and few published camera trap surveys of felids have reached the general recommendations on sample size (Foster and Harmsen, 2012). However, efforts can be made to optimize cheetah camera trap survey design to maximize sample size while maintaining model assumptions (Table 29.2) using accrued knowledge from previous cheetah camera surveys and research conducted on cheetah behavior and space use (Chapters 8 and 9).

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29.  The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions

LESSONS LEARNED TO MAXIMIZE CHEETAH DETECTION AND OPTIMIZE CHEETAH CAMERA SURVEYS

and Parker, 2015; O’Brien and Kinnaird, 2011). Although this targeted placement of cameras may induce heterogeneity in capture probabilities (e.g., due to differential use of scent marking sites between sexes), this approach is often used in camera survey studies of large carniTiming of Camera Trap Study vores (e.g., Sharma et al., 2010). We suggest that To date, no peer-reviewed camera trap study ­local knowledge of the study area and informahas compared cheetah detection rates between tion from previous studies on space-use and beseasons. However, a long-term camera trapping havior, particularly of female and nonterritorial study, involving five surveys in winter (June–­ male cheetahs, be utilized to select camera staOctober) and summer (September–April), in tion locations. Additional considerations include north-central Namibia showed no significant adaptive sampling approaches that represent difference in cheetah detection rates between two-phase sampling, such as combining random seasons (captures = 849 in summer and 970 sampling followed by targeted sampling in areas in winter, U = 17.5, P = 0.29) (Fabiano, 2013). of documented animal presence, based on ­explicit However, females showed a trend for increased criteria, such as counts of individuals detected in capture probability at scent marking sites in phase-one sampling (Conroy et al., 2008). the summer (10 out of 15 capture events; Fabiano, 2013). We encourage that sampling be conducted across seasons in other cheetah popula- Maximize Survey Area Size With tions, as density and detectability may be driven Appropriate Camera Spacing by seasonal factors. When designing a camera trap survey, it is imThe median study duration of previous portant to consider the balance between spatial cheetah camera surveys was 90 days (range coverage to increase the number of individuals 30–130 days; Table 29.1). Extending the survey encountered, trap density to increase the number length increases the opportunity of capture as of spatial recaptures of individuals, and attempts was observed in a cheetah survey in Botswana to sample all habitat types (Foster and Harm(e.g., up to 130 days; Brassine and Parker, 2015) sen, 2012; Royle et al., 2014). Cheetahs do not use and in a leopard survey in China (up to 123 days; space homogeneously (Broekhouis and GopalasHedges et al., 2016). However, increased preci- wamy, 2016); therefore, surveys should sample sion in density estimates due to higher recapture the various habitat types available in proportion rates may be countered by the inclusion of tran- to their occurrence in the landscape of interest. sient individuals, potentially inflating density Camera spacing and the size of the survey estimates (Larrucea et al., 2007). SCR models, area are often constrained by practical considhowever, can be robust to the inclusion of tran- erations (e.g., the number of available cameras, sient individuals (Royle et al., 2016), therefore personnel, and logistics). The median survey longer survey lengths should be considered. area size in previous studies was 277 km2 (range

Selection of Camera Placement Cheetah captures are greater when cameras are selectively placed at sites associated with known cheetah presence (e.g., scent marking sites), as opposed to random placement (Brassine

200–2551 km2; Table 29.1). Therefore, study areas were generally smaller than a cheetah home range size, which is reported to vary between 125 and 2161 km2 depending on sex and habitat (Chapter 8). To maximize survey area size, it has been suggested that cameras be placed up to a home range radius apart without introducing

5.  Techniques and analyses



423

Conclusions

bias (Sollmann et al., 2012; Sun et al., 2014). The home range radius used should be that of the smaller ranging sex (Kelly et al., 2013). Based on cheetah home range estimates, intercamera distance could be between 7 and 26 km, depending on habitat. These values would fall within the MMDM of cheetahs in previous camera trap surveys (9.7–45.0 km; Table 29.1). Hence, information on cheetah movements, gathered at the camera survey site (or if unavailable from a population occurring under similar natural systems), should be used to inform and determine camera trap spacing. An additional way to increase survey area size is to use only one camera at each station, as implemented by Brassine and Parker (2015). Although recently developed models exist to account for partial identity of individuals (i.e., photographing only one flank) in nonspatial (McClintock, 2015) and spatial (Augustine et al., 2016) C–R models, pairs of cameras are preferred to minimize the risk of data loss (e.g., due to camera malfunction). Alternatively, survey designs in which pairs of cameras are rotated within a larger sampling area, known as a block design, can increase spatial coverage without sacrificing the double camera deployment (Karanth and Nichols, 2002). A variation on the block design recommends placing cameras in clusters of two to four, with at least two clusters per home range (Sun et al., 2014). The advantage is that clusters of cameras can be spread farther apart from regular camera placement, increasing the area sampled and hence the number of individuals exposed to cameras stations (Sun et al., 2014). Design considerations and associated parameter sensitivity to trap spacing and array configuration are provided by Efford and Fewster (2013), in Chapter 10 in Royle et al. (2014), Sollmann et al. (2012), and Sun et al. (2014).

Data analysis SCR methods offer a powerful statistical framework for estimating density. These

models allow ecologists and conservation biologists to explicitly investigate hypotheses about species’ spatial ecology (Royle et al., 2014, 2017), which is vital to the conservation of species and not available using nonspatial C–R models. Previous camera studies of cheetahs have used the R package SPACECAP (Bayesian analysis; Gopalaswamy et al., 2012); however, the maximum likelihood estimation R packages secr (Efford, 2015) and oSCR (Sutherland et al., 2016) are also applicable. Camera trap survey design is an important consideration to minimize the risks of violating model assumptions (Table 29.2), which continues to be an active area of research. Pilot surveys and computer simulations can be informative about design considerations, such as number of cameras, distance between camera stations, and trapping array size. For example, the R package secrdesign (Efford, 2016) can be used to assess the potential bias and predicted precision of density estimates under specific survey designs (Chapter 10 in Royle et al., 2014).

CONCLUSIONS Surveying cheetahs to estimate abundance and density is particularly challenging because the species occurs at low densities across large areas, commonly resulting in small sample sizes. Nevertheless, monitoring cheetahs with camera traps and analyzing the resulting data using SCR methods provide a promising solution for studying the spatial ecology of cheetahs while simultaneously estimating absolute density, and ultimately improving the conservation of this highly threatened species. Researchers are encouraged to perform simulations prior to conducting camera trapping surveys to determine sampling effort and trap spacing. The conservation, management, and ultimate survival of cheetahs can benefit greatly from well-conceived camera trap studies combined with analytical tools as has been described in this chapter.

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29.  The Use of Remote Camera Trapping to Study Cheetahs: Past Reflections and Future Directions

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5.  Techniques and analyses