Reading Check: What Distinguishes One Zebra Population From Another Zebra Population?

Abstruse

Methods that accurately estimate animal abundance or density are crucial for wildlife management. Although numerous techniques are available, in that location have been few comparisons of the precision and cost-effectiveness of unlike approaches. We assess the precision and price of three methods for estimating densities of the Endangered Grevy'southward zebra Equus grevyi. We compare distance sampling and photographic capture–recapture, and a new technique, the random come across model (REM) that uses photographic camera-trap encounter rates to estimate density. All three methods provide comparable density estimates for Grevy'south zebra and are preferable to the common practice of raw counts. Photographic capture–recapture is the most precise and line-transect distance sampling the least precise. Line transects and photographic capture–recapture surveys are toll-effective in the get-go year and REM is about cost-effective in the long-term. The methods used here for Grevy'due south zebra may exist practical to other rangeland ungulates. Nosotros advise that for single species monitoring programmes in which individuals can exist identified, photographic capture–recapture surveys may exist the preferred method for estimating wildlife abundances. When encounter rates are low, distance sampling lacks the precision of the other methods but its cost advantage may go far advisable for long-term or multi-species monitoring programmes. The REM is an efficient and precise method of estimating densities merely has loftier initial equipment costs. We believe REM has the potential to work well for many species but it requires independent estimates of animal movements and grouping size.

Introduction

Developing methods that accurately approximate the affluence or density of beast populations is of key importance for management of threatened species. Complete population counts are resources intensive, rarely feasible considering of logistical constraints, and have poor theoretical support (Cochran, Reference Cochran1977; Lancia et al., Reference Lancia, Nichols, Pollock and Bookhout1994). The preferred arroyo is to sample the population. Typically, sampling is cheaper and provides measures of precision for estimated parameters (Williams et al., Reference Williams, Nichols and Conroy2002).

When using a sampling approach one must first distinguish betwixt the observed or detected number of animals and the true population abundance. The central concept is of detection probability; i.eastward. only a fraction of all animals present are typically detected during a survey. If we gauge this fraction we can estimate the true population abundance. Estimating detection probability unremarkably requires collecting ancillary data during the course of a survey (Lancia et al., Reference Lancia, Nichols, Pollock and Bookhout1994; MacKenzie et al., Reference MacKenzie, Nichols, Lachman, Droege, Andrew Royle and Langtimm2002). Several sampling methods have been developed that guess detection probability and population affluence (eastward.g. line transect sampling, point count surveys, variable circular plots, and capture–recapture).

Given the several sampling techniques available it can be a challenge to make up one's mind the most appropriate method. The choice typically depends on field logistics, the ecology of the study species and available resources (Lancia et al., Reference Lancia, Nichols, Pollock and Bookhout1994). For example, species that are difficult to observe directly may require camera trapping or rails surveys (Silveira et al., Reference Silveira, Jacomo and Diniz-Filho2003; Lyra-Jorge et al., Reference Lyra-Jorge, Ciocheti, Pivello and Meirelles2008). For highly visible animals in relatively open up habitat, directly count-based methods such as distance sampling are frequently appropriate. When animals are not readily observable, capture–recapture techniques are preferable. These require that animals exist individually recognizable (e.g. by tags, natural markings, or Dna identification). For animals without individual markings, distance sampling (Buckland et al., Reference Buckland, Anderson and Burnham2001) or a random encounter model method (REM; Rowcliffe et al., Reference Rowcliffe, Field, Turvey and Carbone2008) may be appropriate to judge density.

Distance sampling is widely used for estimating abundances of wild animals (Buckland et al., Reference Buckland, Anderson and Burnham2001). For large-bodied, group-living mammals line transects are ordinarily used (Calambokidis & Barlow, Reference Calambokidis and Barlow2004; Ogutu et al., Reference Ogutu, Bhola, Piepho and Reid2006; Buckland et al., Reference Buckland, Plumptre, Thomas and Rexstad2010), and are suitable for large, highly visible ungulates in African savannahs (Ogutu et al., Reference Ogutu, Bhola, Piepho and Reid2006). Observers motion along one or more than lines and count groups of animals detected and calculate perpendicular distances to groups (Buckland et al., Reference Buckland, Anderson and Burnham2001). Because not all groups may be detected count data are adjusted co-ordinate to the probability of detection (Thomas et al., Reference Thomas, Buckland, Burnham, Anderson, Laake, Borchers, Strindberg, El Shaarawi and Piegorsch2002), which is directly related to the observed distribution of distances of groups from the line. This distribution may be modelled using various functions. These functions, and consequently the precision of the resulting density estimates, may be improved through a number of fundamental functions and adjustment terms (Thomas et al., Reference Thomas, Buckland, Burnham, Anderson, Laake, Borchers, Strindberg, El Shaarawi and Piegorsch2002) from which the best-fitting model can be chosen (Burnham & Anderson, Reference Burnham and Anderson2002). For a review, run into Buckland et al. (Reference Buckland, Anderson and Burnham2001).

When individuals can be recognized or marked, capture–recapture methods can be used to model population parameters (Otis et al., Reference Otis, Burnham, White and Anderson1978; Amstrup et al., Reference Amstrup, McDonald and Manly2005). For species with natural markings, individuals may be identified from photographs (taken past an observer or automated cameras). Photographic identification is unremarkably used for capture–recapture modelling of individually identifiable marine species (Forcada & Aguilar, Reference Forcada and Aguilar2000; Calambokidis & Barlow, Reference Calambokidis and Barlow2004). Camera traps are typically employed for cryptic or nocturnal species that are not hands observed (O'Brien et al., Reference O'Brien, Kinnaird and Wibisono2003), such as tigers Panthera tigris and leopards Panthera pardus (Karanth, Reference Karanth1995; O'Brien, Reference O'Brien, O'Connell, Nichols and Karanth2011). For photographic sampling techniques animals are identified on the beginning sighting and then again by re-sighting (Karanth & Nichols, Reference Karanth and Nichols1998; Smith et al., Reference Smith, Allen, Clapham, Hammond, Katona and Larsen1999), and full capture histories are developed for all identified individuals. Most contempo capture–recapture models employ a maximum likelihood framework, assuasive resource allotment of model weights, and tin contain model averaging to obtain a final estimate (Chao & Huggins, Reference Chao, Huggins, Amstrup, McDonald and Manly2005).

When individuals are not uniquely identifiable different approaches are needed to derive population estimates from camera-trap information. Nether sure circumstances camera-trap encounter indices may give authentic estimates of relative affluence (O'Brien et al., Reference O'Brien, Kinnaird and Wibisono2003) although in that location is poor theoretical support for a full general meet rate–abundance relationship (Jennelle et al., Reference Jennelle, Runge and MacKenzie2002). Photographic camera-trap methods require boosted assumptions to chronicle relative abundance to density (Royle & Nichols, Reference Royle and Nichols2003). In some cases a calibrated relative affluence can be a reliable indicator of density (O'Brien et al., Reference O'Brien, Kinnaird and Wibisono2003). To eliminate the need for calibration a new method for reliably estimating densities using camera traps, without the demand for uniquely marked individuals, has been proposed. The REM (Rowcliffe et al., Reference Rowcliffe, Field, Turvey and Carbone2008) estimates density past modelling the underlying procedure by which animals encounter camera traps (Hutchinson & Waser, Reference Hutchinson and Waser2007). Past incorporating mean group size and speed of movement, run into rates tin can be modelled and unbiased density estimates derived. This method has not still been widely tested (just run into Rovero & Marshall, Reference Rovero and Marshall2009; Manzo et al., Reference Manzo, Bartolommei, Rowcliffe and Cozzolino2011).

Here we examine the precision and cost of altitude sampling, REM and photographic capture–recapture surveys for estimating population density and abundance of an ungulate, Grevy's zebra Equus grevyi, in a savannah habitat. The kickoff two methods were used as function of a broader study on wildlife–livestock interactions (TGO and MFK); the latter was focused on estimating affluence of Grevy's zebra (VHZ and SRS).

Grevy's zebra is categorized as Endangered on the IUCN Red List, with < 3,000 remaining (Moehlman et al., Reference Moehlman, Rubenstein and Kebede2008). The species occurs mainly in Republic of kenya, with a small number of isolated populations in Ethiopia. It is a territorial species that associates in unstable herds (Sundaresan et al., Reference Sundaresan, Fischhoff, Dushoff and Rubenstein2007). Reproductively active adult males defend territories (commonly 6–12 km²) whereas female herds and bachelor males range more than widely (Ginsberg, Reference Ginsberg1987). Individuals are easily recognized, past sight or calculator identification software, from their unique stripe patterns (Hiby, Reference Hiby2010).

Currently, most monitoring of Grevy's zebra is by raw counts from the ground or air. Typically, aeriform total counts are employed to judge their populations (Parker et al., Reference Parker, Sundaresan, Chege and Brien2011) but these counts lack estimates of precision and detectability. Some researchers have begun to use marking–recapture methods to generate population estimates of Grevy'southward zebra (Nelson & Williams, Reference Nelson and Williams2000). Given the low overall numbers of the species and its express range, accurate abundance estimates that incorporate precision measures are important for management and conservation.

We use Grevy's zebra equally a model to explore how the three sampling methods would perform for a diversity of big mammal species in savannah habitats. Grevy's zebra may serve every bit a proficient model for three reasons. Firstly, they have home ranges similar in area to many other savannah ungulates. Secondly, they are easily observed during the day (for distance sampling and photographic transect approaches) just are besides active at night (for photographic camera trapping). Thirdly, they possess unique stripe patterns, enabling identifications of individuals.

Report area

Surveys were carried out at the 200 km² Mpala Ranch and Conservancy, which is characterized by semi-arid Acacia bushland/grassland, in the Laikipia District of central Kenya; this expanse supports 1 of the greatest concentrations of Grevy's zebra in the country. The property, like others in the region, is unfenced and animals are complimentary to travel across property boundaries. Mpala hosts a variety of wild ungulate species, including mutual zebra Equus quagga, besides as express numbers of domestic cattle, sheep, camels and donkeys (c. 12 livestock units km⁻²).

Hateful annual rainfall at Mpala is 594 mm in the s, where the majority of Grevy'due south zebra are constitute, but is highly variable, with droughts becoming more than frequent (Franz, Reference Franz2007). The Salvation is broadly divided into two habitats based on soil type: red sandy loam soils and dirt-rich, volcanic-derived soils ('black cotton'). Grevy'due south zebra are institute in much lower densities across black cotton soils, so we restricted our studies to cherry-red soil habitats and the transition zones between these soil types.

Methods

Distance sampling

We conducted line transect surveys for Grevy'south zebras in June 2008, January and June 2009, and January and June 2010 (Fig. 1). We used 53 transects in June 2008 totalling 98.seven km and 54 transects totalling 100.2 km thereafter. Transects were laid out systematically along roads and surveys were conducted from vehicles during 06.30–10.00 and sixteen.30–xviii.thirty. Two observers positioned on the roof of the vehicle at c. three m pinnacle recorded all groups of Grevy'south zebra every bit part of a multi-species written report. We measured observer-to-animal distances using laser rangefinders and transect bearing and angle between animal and observer with digital compasses. Perpendicular altitude to the transect was calculated using the angle between the transect and the fauna and radial altitude from observer to animal.

Fig. one Mpala Ranch and Conservancy showing the areas covered for each of the three survey methods. Points and transects in the black cotton wool soil habitat that were not used in the analyses are not shown. The shaded rectangle on the inset shows the location of Mpala in Kenya.

It is usually inadvisable to use roads or trails for line transect surveys (Anderson, Reference Anderson2001; Buckland et al., Reference Buckland, Anderson and Burnham2001) because animals may use roads preferentially or because roads may constitute unrepresentative habitat. This is non the instance for our line transect surveys because (one) animal trails traverse the habitat in a dense web and so that roads do not correspond special access that is restricted elsewhere, (2) vehicle employ of these roads is minimal, reducing the likelihood that animals volition avoid the roads, (3) animals are generally habituated to vehicles, and (iv) upon examination of detection curves, we did non notice any evidence of heaping (allure) or gaps (avoidance) in the vicinity of roads.

Nosotros analysed the June 2008 and June 2010 line transect surveys (43 and 44 transects, respectively) on cherry soils and transition zones to guess Grevy'due south zebra density using Altitude v. half-dozen.0 (Thomas et al., Reference Thomas, Buckland, Rexstad, Laake, Strindberg and Hedley2010). Nosotros used data from all v surveys to augment the estimation of the detection function and mail-stratified by the June 2008 and 2010 Grevy'southward zebra survey vs all other surveys of Grevy's zebra combined. We analysed the data every bit exact perpendicular distances to zebra groups (Table i). We evaluated one-half-normal, hazard, and uniform models with cosine and unproblematic polynomial adjustments and chose the final model based on a minimum Akaike information criterion (Burnham & Anderson, Reference Burnham and Anderson2002). The June 2008 density judge was compared to the REM estimate based on 2008 camera-trap survey data, and the June 2010 estimate was compared to the June 2010 photographic capture–recapture surveys.

Table 1 Results of the June 2008 and 2010 line transect surveys for Grevy's zebra Equus grevyi. Density estimates are presented equally the number of individuals km^−two ± SE, with the coefficient of variation (CV). Affluence estimates are presented with 95% conviction intervals (CI).

Photographic capture–recapture surveys

We used standardized photographic surveys along roads to estimate abundance. To reduce the survey expanse to a manageable size we divided the study expanse into three routes: southern, central and northern. Each section was surveyed five times on sequent days. The due south was surveyed during 8–12 June, key during 14–eighteen June, and n during 6–10 July 2010. A fixed route forth roads and tracks, designed to minimize retracing paths on a given solar day, was driven during 07.00–thirteen.00 while searching for Grevy's zebra. On each of v survey days roads were driven in a different order. When Grevy's zebras were encountered, as many individuals as possible were photographed using a digital SLR photographic camera. We also recorded sex, age and reproductive status. Individuals were subsequently identified by their stripe patterns using automated photo-identification software (Hiby, Reference Hiby2010). Private capture histories were generated from these data and capture histories for all individuals were pooled in a unmarried capture history file. Only iii individuals were sighted in multiple survey areas and their capture histories in the 2 areas were combined additively into a single history (eastward.thousand. 10110 and 00001 combine to go 10111). Nosotros assume these combined histories take little effect on the overall population modelling given our large sample size. Affluence estimates from photographic surveys were generated using standard closed population models (Otis et al., Reference Otis, Burnham, White and Anderson1978). We tested for population closure using the standard Otis examination (1978). Population size estimates were obtained with Marker v. 6.1 (White & Burnham, Reference White and Burnham1999). We estimated the area surveyed using a minimum convex polygon around the combined survey routes, subtracting the area of an next holding that cuts into the Mpala Conservancy and that was not surveyed. An estimated area of 156 kmⁱⁱ was sampled and used to summate densities.

Random meet model

The REM is a method for estimating densities with photographs from photographic camera traps, without the need for uniquely marked animals (Rowcliffe et al., Reference Rowcliffe, Field, Turvey and Carbone2008). The REM estimates density by modelling the underlying process past which animals encounter camera traps (Hutchinson & Waser, Reference Hutchinson and Waser2007). By incorporating average grouping size (g) and average speed of movement (v), see rates can be modelled and unbiased density estimates (D) can be derived (Equation 1). Camera-related parameters include trapping rate (y/t) and trap detection zone, for which we measure detection distance (r) and angle (θ).

(1) $$gD = \displaystyle{y \over t}\displaystyle{\pi \over {vr(2 + \theta )}}$$

Camera traps were set betwixt 8 January and 12 April 2008. Nosotros divided the ranch into 2 km^two blocks, located the centroid of each cake (north = 97 blocks) using ArcView v. 3.two (ESRI, Redlands, USA) and designated these points every bit potential trap sites (Fig. 1). Actual trap points were located at an 'optimal' location (the UTM coordinates of which were determined with a global positioning system, GPS), within a 50 m radius of the centre signal. Optimal photographic camera-trap placement was subjective and typically included a road or active game trail. Two Deercam film cameras (Non-Typical Inc., Park Falls, USA) were mounted on posts at each point to photograph both sides of passing animals. Sampling took place sequentially in iii blocks of 25 points, and one block of 22 points for a total of 97 points. Ten of these points were in black cotton soils and were excluded from analyses. Each point was active for 21–23 days and cameras were checked on days 5, 10 and 15, with films and batteries beingness inverse as needed. Each photo was stamped with a time and engagement that facilitated assigning photographic events to sampling intervals. Cameras were prepare with a 30 second filibuster betwixt photographs to avoid repeated triggering by the aforementioned animals lingering in front of the photographic camera.

We then used REM to derive density, which was advisable in this case because nosotros did not have a sufficient number of photographs suitable for identification of individuals. To calculate encounter rates betwixt Grevy's zebras and photographic camera traps we combined sequential photographs into a unmarried run across when information technology was clear that a single group was moving through the field of view (ordinarily a series in < v minute bridge, maximum 19 minutes). Grevy's zebra speed and clustering patterns were estimated independently. Speed (v) was derived from hourly GPS collar information from seven developed zebras collared on the Mpala Conservancy, nerveless for one–9 months beginning in June 2007 (D.I. Rubenstein & South.R. Sundaresan, unpubl. data). Mean grouping size (g) was calculated from driving censuses conducted on Mpala during 2007–2008. Camera-related parameters r (12 m) and θ (0.175 radians) were taken from Rowcliffe et al. (Reference Rowcliffe, Field, Turvey and Carbone2008) because nosotros used the same model and placement of Deercam photographic camera traps. We estimated variance in encounter rates (y/t) by bootstrapping, resampling photographic camera locations with replacement x,000 times, as recommended by Rowcliffe et al. (Reference Rowcliffe, Field, Turvey and Carbone2008). Overall variance of density estimates was computed using the delta method (Seber, Reference Seber1982; J.M. Rowcliffe, pers. comm.).

Cost comparison

Nosotros evaluated the cost effectiveness and efficiency of each method using start-up costs (equipment purchases) and the cost of transportation and human resources.

Results

Distance sampling

We observed 51 Grevy'southward zebras in 9 groups during the June 2008 line transect surveys on red and transition soils. In June 2010 we detected 33 Grevy's zebras in three groups. We used data from all v surveys from June 2008 to June 2010 (n = twoscore groups) to improve the detection function. We then post-stratified by survey flow to estimate densities for each menstruum. We only study results for 2008 and 2010 surveys for comparing with the REM and photographic capture–recapture methods, respectively. The all-time-fitting model was a run a risk rate model with two cosine terms and the arithmetic mean for grouping size. We estimated densities of one.82 ± SE 1.12 individuals km⁻ⁱⁱ in 2008 and 1.21 ± SE 0.768 individuals km⁻ⁱⁱ in 2010, and thus population estimates of 313 and 207 Grevy'due south zebras in the study expanse in 2008 and 2010, respectively (Table i).

Photographic capture–recapture surveys

During 15 days of photographic capture–recapture surveys we were able to photograph the majority of Grevy'southward zebras sighted. In 36 of twoscore sightings (xc%) we photographed at to the lowest degree one Grevy'due south zebra in each grouping. The average proportion of individuals in a grouping photographed and successfully identified within a sighting was high ( $\bar 10$ = 98.5%). Foals were excluded from our analyses because their movements are not independent of their mothers. In total 103 developed individuals were identified.

Otis' test (1978) supported the closure assumption (Z = two.75, P = 0.997). For both the standard closed capture and the Huggins linear logit estimates a fourth dimension-based/heterogeneity model was the best fit, yielding similar results (M _th,Otis: $\hat North$ = 145 ± SE xv.1; One thousand _th,Huggins: $\hat N$ = 147 ± SE 15.5). Virtually of the models produced comparable estimates with similar conviction intervals (Tabular array 2) suggesting that the prepare of closed models is a good fit for our photographic sighting information. Density estimates were based on a sampling surface area of 156 kmⁱⁱ ( $\hat D_{{\rm Otis}} $ = 0.93 ± SE 0.10 and $\hat D_{{\rm Huggins}} $ = 0.94 ± SE 0.10). These results are lower than the line transect estimates for 2010 but had overlapping confidence intervals, and are not significantly different (Fig. 2).

Fig. 2 Comparison of the 2008 and 2010 density estimates for Grevy's zebra Equus grevyi for the three sampling techniques: line transects, random encounter model (REM) and photographic capture–recapture (Photograph C–R). Figures are presented as individuals per km with SE confined.

Table two Estimated abundance and capture probabilities of adult Grevy's zebras from the June and July 2010 photographic capture–recapture surveys under different closed population models generated with MARK. Time varying models with heterogeneity, shown in bold, were chosen based on Akaike information criterion (AIC) values. All models use a logit link function. Nosotros ran a basic behavioural model only no combinations thereof.

Random encounter model

From photographic camera-trapping data we encountered 40 carve up Grevy's zebra groups at 87 photographic camera points over an average of 19.4 trap days, yielding an encounter rate of 0.0236 encounters per mean solar day (Table 3). Bootstrapping the come across rate gave a standard error of 0.0076. The velocity calculated from hourly zebra GPS locations using collar information was 0.34 km h⁻¹ (n = 12,141 hourly locations, SE = 0.004) or 8.16 km day^−ane. Group size, as adamant from independent counts during route surveys in Mpala, was iv.58 individuals per group (n = 224, SE = 0.316). Camera-related parameters (Rowcliffe et al., Reference Rowcliffe, Field, Turvey and Carbone2008) were assumed to have no variance. The resulting density estimate was 0.346 groups km⁻² (SE = 0.190) or 1.58 Grevy's zebra km^−two (SE = 0.42). The REM density estimate is slightly lower than the line transect judge for 2008 merely has overlapping confidence intervals and is not significantly different (Fig. 2).

Tabular array three Summary of the variables required to calculate density of Grevy'due south zebra from photographic camera-trapping rates using Rowcliffe's random run across model (Rowcliffe et al., Reference Rowcliffe, Field, Turvey and Carbone2008). The mean value for each variable is presented ± SE, with the coefficient of variation (CV).

Cost comparing

Because all three methods produce comparable results, choosing the nearly cost-efficient method will depend on the implementation cost relative to the cost of improving precision. Bones equipment for line transect surveys includes two sets of binoculars, laser rangefinders, and digital compasses (USD 1,750; Tabular array four). Photographic capture–recapture simply requires a digital camera and lens (USD 700). Camera-trap projects take the highest initial investment in camera traps (30 traps at USD 200 each), batteries and memory cards (USD 6,860 total). For density estimates using camera traps we also required GPS collars (USD iii,000 each) to collect movement data and boosted field costs to collect grouping size data. Person days in the field were greatest for photographic capture–recapture and less for camera trapping and line transects, which were similar. Information processing was most intensive for photographic capture–recapture and least intensive for distance sampling (Tabular array iv). The running cost is similar for line transect surveys (USD 1,160) and camera-trap surveys (USD 712 using 30 camera units) and lower compared to photographic capture–recapture (USD 2,045). Most of the difference in running cost is because of the time required for postal service-processing, which may be substantial for camera traps and photographic capture–recapture. Both methods require processing photographs of animals prior to information analysis and the fourth dimension taken for this is related to the number of photographs taken.

Tabular array four Resources comparison for the 3 sampling methods. The toll of vehicles and GPS units were common to all projects and are non presented here. Notation that two vehicles were used in distance sampling whereas unmarried vehicles were used for the other techniques. All methods covered a survey area of c. 170 km².

Discussion

Our results show that line transect surveys, photographic capture–recapture surveys and the REM methods provide coinciding estimates of the density and abundance of Grevy'southward zebra. Any one of these methods is preferable to the common practise of attempting to conduct complete censuses of populations. If nosotros had used only sighting information (i.e. untransformed counts from transects or counts of individuals identified from photographs) from line transect and photographic capture–recapture surveys we would have captured only c. 16 and 71% of the estimated Mpala population, respectively. Our results point that census information may atomic number 82 to spurious conclusions nearly Grevy'due south zebra populations, and that any sampling methods that incorporate measures of detection probability and precision are preferable (Williams et al., Reference Williams, Nichols and Conroy2002).

For brusque-term surveys over small areas we recommend using photographic capture–recapture techniques to yield the about precise abundance estimates of hands observable, individually identifiable species. Distance sampling, although somewhat imprecise in this detail example, is an affordable method for assessing densities of species that are directly observable, and may be desirable when monitoring multiple species simultaneously or comparison across sites. Our written report suggests that REM may be an accurate and appropriate method for difficult-to-observe and unmarked species being monitored over time.

In full general, the photographic capture–recapture method is highly precise when abundance is used every bit the monitoring metric in a relatively pocket-size area and when recapture rates are high. This method has been used for closed population estimates of Grevy's zebra in northern Kenya (Nelson, Reference Nelson2003). In a study of Arabian oryx Oryx leucoryx, an ungulate of similar size, detection cues and densities, mark–resighting was more precise than distance sampling (Seddon et al., Reference Seddon, Ismail, Shobrak, Ostrowski and Magin2003). Photographic capture–recapture also proved to be the most precise (CV 10.4%) of the three survey techniques in our study simply is the about expensive method considering of the substantial costs of data collection and processing. One advantage of this technique is that field methods are simple and start-up costs are depression.

Although the field component of photographic surveys is rapid and cost-effective for curt-term surveys, the identification of animals is fourth dimension-consuming and requires either comprehensive knowledge of individual markings or the utilise of an impartial, automated identification system. If advisable software is non available, identification by eye may be expensive in terms of both time and cost, and may be unreliable. This tin be controlled through careful preparation of technicians involved in information processing but is much easier when automated identification software is used (Hiby et al., Reference Hiby, Lovell, Patil, Kumar, Gopalaswamy and Karanth2009).

Photographic capture–recapture requires that individuals are photographed at adequately shut range, which may prove difficult in bush habitat or in areas where animals accept get shy of humans. In this case, bias tin arise because some individuals in the population are uncatchable. We do not believe in that location is any such bias in our population of Grevy's zebra equally we photographed the majority of animals encountered. More than more often than not, it is possible that using heterogeneity models (Thousand _h) may be able to business relationship for unequal catchability in other species.

Using the photographic capture–recapture method to monitor density, equally opposed to abundance, may be problematic considering we do non have an objective method for estimating the sampling expanse. In our report it is impossible to decide if the small-scale difference between the 2010 density estimates from line transects (1.2 km⁻²) vs the capture–recapture method (0.93 km⁻ⁱⁱ) is all-time explained past true variation or by our ad hoc interpretation of survey area for our photographic surveys. The photographic method does not generate movement data that can be used in a number of ad hoc density estimation procedures (Wilson & Anderson, Reference Wilson and Anderson1985; Jett & Nichols, Reference Jett and Nichols1987; Parmenter et al., Reference Parmenter, Yates, Anderson, Burnham, Dunnum and Franklin2003). We estimated the surface area of coverage based on a minimum convex polygon only information technology is possible that we overestimated the coverage, resulting in an underestimation of density. Given the uncertainty in estimating the surface area sampled we recommend that the photographic method be used with abundance equally the state variable of interest.

The utility of photographic capture–recapture surveys can be extended past targeted, long-term monitoring, which can provide estimates of population vital rates. This method would likewise facilitate interpretation of survival when using open up population models and Pollock'due south robust blueprint (Pollock, Reference Pollock1982) practical to information collected over several seasons (Karanth et al., Reference Karanth, Nichols, Kumar and Hines2006).

Distance-based sampling surveys yielded the least precise (CV 61–64%) density estimates, fifty-fifty with the use of coincident data to broaden the detection part. This is a common problem when distance-based sampling methods are applied to rare species that occur in widely dispersed groups (Otto & Pollock, Reference Otto and Pollock1990). Low meet rates, with many transects having null encounters, can boss the variance in density; in our report > 70% of overall variance was because of variance in come across rate. Large variation in group sizes (1–16 in this study) tin can also inflate the variance of density. In this study variation in group size has less of an effect than the variance in encounter rate. Low come across rates are difficult to improve through changes in sampling design equally using more transects does non guarantee an increase in the encounter rate.

Altitude sampling is based on the idealized scenario of animals being distributed in space co-ordinate to a stochastic process with charge per unit parameter D (density). Transects are placed at random or systematically to ensure that objects in the survey strip are uniformly distributed in relation to distance from the transect. Although nosotros were restricted to using roads we believe that our methods see the qualification of a systematic sample with a randomized starting point and that the open up landscape allowed us to sample almost all potential habitats. In many areas occupied by Grevy's zebra the development of systematic survey designs may exist feasible. Distance sampling rests on four basic assumptions: (1) animals on the transect are always detected, (2) animal locations are always measured to the point where the brute was first detected, (3) distances to the animals and angles between the beast and the transect are measured exactly, and (4) groups are counted accurately (Buckland et al., Reference Buckland, Anderson and Burnham2001). Grevy's zebras are large conspicuous animals, and then the offset supposition is met. Accurate measurements are possible using laser rangefinders and digital compasses, and this helps to minimize heaping of observations on the transect line and besides improves estimation of perpendicular distances. Recording grouping sizes accurately can be problematic in some bush habitats and intendance must be exercised to ensure that group counts are as complete as possible.

However, in that location are several benefits of using density estimates from distance sampling equally a monitoring metric. Firstly, start-upwardly costs are depression and it is relatively cheap to process data considering recognition of individuals is non required. Secondly, line transect surveys may exist used to judge density for several large ungulates simultaneously. In this example, the lack of precision for rare species may be outweighed if in that location is a need to derive estimates for more than common species. Thirdly, density estimates are comparable between sites whereas abundance estimates cannot exist compared directly betwixt sites. We recommend the use of distance-based density estimates as a state variable when between-site comparisons are important and when multi-species monitoring methods are needed.

Our density estimate from REM was consequent with the estimates from altitude-based sampling and was more precise (CV 26.3%). In contrast, when testing REM for wood ungulates Rovero & Marshall (Reference Rovero and Marshall2009) found that it yielded college estimates than line transect counts. Although the REM estimate was more than precise than for distance sampling information technology may be untenable to generate accurate speed-of-motion data in wide-ranging species; GPS collar data for this study were obtained from a separate study. Additionally, grouping size of Grevy'south zebra is highly variable because of the fluid nature of their social construction, and required an assessment from previous surveys. The high start-up cost of REM may brand it impractical for short-term studies or projects with limited funding. Photographic camera malfunction and theft may also pose pregnant obstacles. A large number of cameras are required for ungulates occurring at low to medium densities but fewer cameras or trapping days may be viable for mutual species. In this report, all the same, cameras were not placed specifically to capture Grevy's zebra, as this was part of a multi-species study. Variation in trapping charge per unit and precision could be reduced with a more targeted trapping scheme. This method is best for shy animals, provided that unbiased, independent estimates of speed and group size tin can be generated.

Cost may exist a limiting factor in the selection of a field survey technique. The REM has the highest equipment beginning-upwardly costs simply because it requires little field time it is cheapest in the long term. The photographic capture–recapture method had the lowest start-up costs, only loftier operation costs considering of the amount of time in the field and the time to process photographs. The line transect method is intermediate for both starting time-upwardly costs and field time, and data analysis is quickest.

In conclusion, all three methods are suitable for estimating Grevy'due south zebra densities and are preferable to complete census counts. Based on our study we can make some wider recommendations for the monitoring of other similar ungulate species. For small or enclosed properties, photographic capture–recapture surveys may exist the all-time choice for estimating wildlife abundances provided the substantial operating costs are affordable. Loftier-precision methods may exist required when monitoring is role of a management action that is probable to result in subtle abundance changes over time. For multi-species programmes altitude sampling or REM are preferred every bit they may provide sufficient precision and information can easily exist gathered for several species simultaneously. Distance sampling lacks the precision of the other two methods especially when run into rates are low just it is appropriate for hands observable or mutual species, for animals without private markings or for long-term studies. The REM is an efficient and precise method of estimating densities merely has high initial costs. We believe REM, although relatively new, holds promise for estimating density of many wild fauna species.

Acknowledgements

We thank J. Marcus Rowcliffe for word and advice on estimating variance associated with REM. For help in the field we thank Francis Lomojo, Hussein Mohamed, Frankline Otiende and Allison Williams. This work was funded by Panthera, Wildlife Conservation Society, Leslie Scott, Denver Zoological Foundation and Princeton University. For supporting the authors during this study we thank Princeton Academy (VHZ), Denver Zoological Foundation (SRS), Wild animals Conservation Social club (TGO) and the Mpala Wild animals Foundation (MFK).

Biographical sketches

Victoria H. Zilch's research interests focus on the ecology and conservation of terrestrial vertebrates. Siva R. Sundaresan conducts research on big mammalian ungulates and carnivores. He directs Denver Zoological Foundation's conservation programme in Kenya, which includes research on Grevy's zebra. Timothy One thousand. O'Brien has conducted research on primates, birds and terrestrial mammals, and is peculiarly interested in the development of accurate and precise monitoring metrics. He is currently a senior scientist and statistician at the Wildlife Conservation Society. Margaret F. Kinnaird has conducted research on a broad variety of topics, including human–elephant conflict, parrot trade, forest dynamics, not-timber forest product apply, and hornbill and primate conservation. She currently serves every bit the Executive Manager of the Mpala Research Eye where her research interests include how different livestock management regimes influence wildlife affluence and multifariousness.

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