African Wild Dog Habitat Use Modelling Using Telemetry ... - BioOne

African wild dog habitat use modelling using telemetry data and citizen scientist sightings: are the results comparable? 1,2 Tafadzwa Shumba *(

§

1,3

) , Robert A. Montgomery ( 1,2

1

Gregory S. A. Rasmussen & David W. Macdonald ( 1

), )

Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Tubney House, Tubney, Abingdon OX13 5QL, U.K. 2 3

Painted Dog Research Trust, P.O. Box 285, Victoria Falls, Zimbabwe

Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, U.S.A. Received 17 May 2017. To authors for revision 11 August 2017. Accepted 22 October 2017

Quantifying landscape characteristics that wildlife select is essential for conservation and management action. Models that map wildlife resource selection tend to be informed by telemetry technology which is costly to acquire/maintain and potentially risky to deploy. Therefore, there is value in pursuing alternative data collection protocols, such as citizen scientist approaches to ascertain whether they can reveal results comparable to those derived from telemetry studies. The conservation of African wild dogs (Lycaon pictus) presents an interesting case study to examine this topic. The species is rare and wideranging, hence data collection is both challenging and costly. They are, however, a groupliving species with unique and conspicuous coat markings, making them potentially well-suited to citizen science data collection strategies. Here, we fitted resource selection functions (RSFs) built from Global Position System (GPS) telemetry data, and from citizen scientist data, collected in and around Hwange National Park, Zimbabwe. We assessed comparability of these RSFs by evaluating the relative importance of parameters, parameter coefficients (direction and magnitude of effect), and the spatial predictions of relative probability of use by African wild dogs. The most important predictors in both models were proportion of woodland and bushland, the number of habitat types, and distance to waterhole. Furthermore, spatial predictions from both models displayed a high degree of overlap (r = 0.74), indicating similarities in selected and avoided habitat patches. Our analysis demonstrates that sufficient citizen science data can be a valuable alternative to telemetry data for African wild dogs. We thus encourage the collection and use of citizen science data for similar analyses, particularly when funding is limited. Our work also highlights areas in and around Hwange National Park with the highest probability of being used by African wild dogs, which is where conservation efforts should be intensified. Keywords: citizen science, Global Positioning System, habitat selection, Lycaon pictus, resource selection function.

INTRODUCTION Wildlife habitat selection research seeks to quantify the landscape characteristics that organisms rely on for subsistence, reproduction, and survival (Acosta Jamett & Simonetti, 2004; Arthur, Garner, Manly & McDonald, 1996; MacKenzie et al., 2006). Ecological inquiry on this topic has expanded rapidly over the last 25 years with both technologi*To whom correspondence should be addressed. E-mail: [email protected]

cal and quantitative advancements (Hebblewhite & Haydon, 2010). In light of this development, less quantitatively rigorous techniques have been more intensely scrutinized. For instance, data collected in non-systematic formats such as those derived from citizen scientists have been criticized for being opportunistic, having presence-only data with low resolution, and being spatially and/or temporally auto-correlated (Elith & Leathwick, 2007; Stockwell & Peterson, 2002; Yackulic et al., 2013). Furthermore, citizen scientists are often perceived as unreliable due to their difficulties in effectively

African Journal of Wildlife Research 48(1): 013002 (2018) ISSN 2410-7220 [Print], ISSN 2410-8200 [Online] — DOI: https://doi.org/10.3957/056.048.013002

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African Journal of Wildlife Research Vol. 48, No. 1, 2018

identifying species (McKelvey, Aubry & Schwartz, 2008). Despite these limitations, citizen science data have been effectively used in both marine (Eastman, Hidalgo-Ruza, Macaya-Caquilpána, Nuñeza & Thiel, 2014; Richardson, Wood, Neil, Nowacek & Moore, 2012) and terrestrial ecosystems (Andreka, Linn, Perrin & Maddock, 1999; McCaffrey, 2005; Swanson, Kosmala, Lintott & Packer, 2016). These and other studies show that data from citizen scientists can be useful for evaluating a variety of questions across the broad field of animal ecology and are particularly applicable to scenarios where systematic surveys are either impractical or expensive to conduct (Cohn, 2008; Dickinson et al., 2012; Thorson, Scheuerell, Semmens & Pattengill-Semmens, 2014). Additionally, involving the public in data-hungry projects is important to cover extensive ground in less time (Buesching et al., 2015), and providing baseline data to guide management action (Ward et al., 2015). Also, involving the society increases their understanding of environmental issues which is essential in conservation and environmental protection globally (Bonney et al., 2009; Reed, 2008; Silvertown, 2009). Global Positioning System (GPS) telemetry has facilitated the collection of animal location data with impressive precision (Thurfjell, Ciuti & Boyce, 2014). The growth of this technology has encouraged subsequent statistical development to model wildlife resource selection from these data (Montgomery & Roloff, 2017). Consequently, resource selection functions (RSFs), resource selection probability functions (RSPF), and resource utilization functions have been developed (RUFs; Johnson, Nielsen, Merrill, McDonald & Boyce, 2006; Lele, 2009; Marzluff, Millspaugh, Hurvitz & Handcock, 2004; Millspaugh et al., 2006). These models document the selection of resources which have important implications in the ecology, survival and fecundity of wildlife (Boyce, 2006; Roever, Boyce & Stenhouse, 2010). However, these telemetry systems, which remotely return spatially-explicit locations, at pre-set intervals, can be prohibitively expensive (Haines, Grassman Jr, Tewes & Janecka, 2006; Hebblewhite & Haydon, 2010). In order to fit collars, telemetry requires study animals to be immobilized using anaesthetic drugs, which requires special permits and often veterinarian support. These techniques are therefore costly and as immobilization of wildlife may occasionally affect subject animals through capture stress and

myopathy (Arnemo et al., 2006; Cattet, Boulanger, Stenhouse, Powell & Reynolds-Hogland, 2008; Ponjoan et al., 2008), the technology comes with inherent risks. Citizen scientists, such as tourists in safari vehicles, on the other hand, can passively record spatially-explicit data important in understanding the ecology of any study species. Thus, with a minimum cost to the researcher, citizen science can provide an affordable mechanism to survey large areas in and around protected areas (Thorson et al., 2014). That being said, this technique is not without its own set of risks, as persistent tourism has been found to have negative fitness consequences on certain animals (Ciuti et al., 2012). African wild dogs (Lycaon pictus) are one species for which citizen science data could be particularly useful. The species is conspicuous due to their relatively large pack sizes (ranging on average between eight to 13 individuals) and unique coat markings (Creel & Creel, 2002; Frame, Malcolm, Frame & van Lawick, 1979; Maddock & Mills, 1994). Also, they tend to use road networks for travelling (Abrahms et al., 2015), making them available to citizen scientists (who tend to be confined to vehicles). Furthermore, although collaring of African wild dogs rarely results in chronic stress or death (Creel, 1997; Creel, Creel & Monfort, 1997; De Villiers et al., 1995; Woodroffe, 2001), there are concerns of deleterious effects of collaring with subsequent recommendations that the practice should be avoided where possible (Arnemo et al., 2006; Burrows, Hofer & East, 1995). This makes alternative methods such as citizen science particularly important for habitat use studies especially if the species in question is endangered and the reasons for collaring can be easily substituted. Zimbabwe is one of the few countries that still contains a viable population of African wild dogs (Rasmussen, 1997; Woodroffe, Ginsberg & Macdonald, 1997; Woodroffe, McNutt & Mills, 2004). The species is red listed as endangered by the International Union for Conservation of Nature (IUCN) with a declining population trend (IUCN/ SSC, 2007; Macdonald & Sillero-Zubiri, 2004). Because African wild dogs are rare and wide-ranging (IUCN/SSC, 2013; McNutt et al., 2008) studying them is inherently difficult and expensive. Consequently, financial constraints can ensure that the landscape parameters this species requires to persist continue to be less understood. In some cases, this reality has forced wildlife

Shumba et al.: African wild dog habitat use modelling

managers to make decisions based on uncertain or incomplete information (Harvey & Platenberg 2009; Frey 2006, Elith & Leathwick 2007). Here, we investigated the efficacy of citizen science data in modelling habitat use by African wild dogs in and around Hwange National Park, Zimbabwe. To do so, we fitted intensity of use RSFs built from GPS telemetry data, as is conventionally done, and from citizen scientist data. We then compared the outputs of these models by the relative importance of parameters, the parameter coefficients (direction and magnitude of effect), and the spatial predictions of relative probability of use by African wild dogs. This type of study is valuable for African wild dog conservation as it can show how different research techniques can help to quantify optimum habitat characteristics to prioritize for protection. Whatever the data source or analysis used, an improved understanding of habitat use and selection is important for effective conservation of any species globally (Johnson, Seip & Boyce, 2004). METHODS

Study area We conducted this research in and around 2 Hwange National Park, which covers, 17 500 km

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in western Zimbabwe (Fig. 1). African wild dogs in this system use habitat within the park as well as outside the park. The habitat bordering the park includes communal land for crop cultivation and livestock ranching as well as private concessions for photographic and hunting safaris. The landscape is semi-arid, with seasonal rainfall occurring from November to March. The mean annual rainfall is 632 mm, though there is substantial variation across the years ranging from 324 to 1160 mm (Chamaillé-Jammes, Fritz & Murindagomo, 2006). Because of the erratic rainfall, Zimbabwe Parks and Wildlife Management Authority (ZPWMA), artificially provisions water in the park during the dry season. The vegetation is mainly woodland savanna dominated by African teak (Baikiaea plurijuga), acacia (Acacia spp.), mopane (Colophospermum mopane), bush willows (Combretum spp.) and silver cluster-leaf (Terminalia sericea; (Rodgers, 1993). The area has a broad spectrum of prey species which includes impala (Aepyceros melampus), kudu (Tragelaphus strepsiceros), bushbuck (Tragelaphus scriptus), and duiker (Sylvicapra grimmia), amongst others. Lions (Panthera leo) and spotted hyaenas (Crocuta crocuta) are the main competitors of African wild dogs in the study area (Rasmussen, Gusset,

Fig. 1. Study area in and around Hwange National Park, Zimbabwe showing the 4 km2 grids from which GPS telemetry and citizen science data were available to model habitat use by African wild dogs based on the intensity of use represented by the number of times a grid was used. The results were then extrapolated to other areas in the study area were no records were available.

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Courchamp & Macdonald, 2008), despite the area also having important populations of other carnivores such as leopard (Panthera pardus).

Data collection We collected GPS telemetry and citizen scientist data from 2009 to 2014, as part of a long-term ongoing study on the ecology and conservation of African wild dogs. Licences to acquire drugs and immobilize African wild dogs by free-range darting for research purposes were provided and ethically approved by the Medicines Control Authority of Zimbabwe (MCAZ) and ZPWMA. We deployed Sirtrack GPS/Argos collars (http://www.sirtrack. co.nz/) across five African wild dog packs and monitored their movement throughout the duration of the study, despite a total of 19 packs being recorded in the area over the study period. We defined a pack as a set of African wild dogs with at least one adult of each sex (Somers, Graf, Szykman, Slotow & Gusset, 2008). To maximize the spatial coverage for habitat analysis, packs with parapatric territories were selected for collaring. At least one individual per study pack was immobilized and fitted with a collar. In cases when the individual with the collar died, we recovered the collar and put it on another individual in the same pack. On other occasions when the collar failed, a new collar was deployed on a new individual in the same pack. In all cases, the alpha female was avoided for collaring, mainly because if anything goes wrong during the anaesthesia, pack integrity would be compromised. Moreover, one of the drugs used (ketamine), is known to cross the placenta (Jalanka & Roeken, 1990), hence not appropriate to the alpha female if suspected to be pregnant or in oestrus. For procedures, protocol and further details see Rasmussen (2009) and Rasmussen & Macdonald (2012). The collars were programmed to try and connect with the remote satellite every 100 minutes in order to get a fix, this was successful on 91% of the occasions during the study. For factors which affect the acquisition of GPS fixes see D’eon & Delprate (2005) and Lewis, Rachlow, Garton & Vierling (2007). During the study, the average number of satellites acquired for each fix was 7.14, which enabled most fixes to be accurately determined on a three dimensional scale, i.e. with horizontal coordinates and elevation. To collect citizen scientist data, we collaborated with tourists at local lodges, picnic sites, and ZPWMA offices. We provided research protocols

and sighting sheets to willing citizen scientists. When African wild dogs were sighted, the citizen scientist would fill in the sighting sheet, which included columns of information on date, time, GPS position, count and demography of the pack observed, indication whether photographs were taken, and an e-mail address. We then reconnected with the citizen scientists via e-mail, for clarification and to obtain photographs, which we used to identify the pack and the individuals based on their unique coat markings. When photographs were not available, we used the GPS location, the number and demography of the observed animals to determine the actual pack detected. All sightings used in this study were verified either by photographs, interviews or follow-ups. Sightings which lacked photographs and/or satisfactory information were thus excluded. Although, the resultant sighting reports contained data collected across the whole National Park, for this analysis, we only used verified citizen scientist sightings of the packs which were GPS-collared. This was done to enable direct comparisons with our GPS telemetry data which was the main objective of this study.

Environmental covariates We considered a total of 11 environmental covariates believed to influence habitat use by African wild dogs. We measured the proportions of 2 these variables in each of 4 km grid squares across the study area. We established this resolution based on known movement parameters of 2 African wild dogs. For example, a 4 km resolution resembles the average distance African wild dogs can travel during a single hunting bout which is about 1–2 km (Fuller & Kat, 1990; Hubel et al., 2016) and was also justified given the total distance they can travel within a day (Fuller & Kat, 1990; Jackson, McNutt & Apps, 2012). Landsat 8 Operational Land Imager (OLI) sensor, satellite images (2013–2014) validated by vegetation indices and the Landsat Vegetation Continuous Field (LVCF) were used to develop the study area vegetation structure map, with which the proportion of; grassland, bushland, woodland and woodland evergreen across each 4 km2 grid cell were determined. Using the vegetation structure map, we then calculated habitat configuration metrics which included the number of different vegetation classes in each grid (nhabtypes) and the total number of vegetation patches in each grid ( npatches ). We also considered three edge


characteristics (habitat where two different habitat types intersect) as covariates. These were edge density (i.e. amount of edge in m/ha), between grassland and woodland (edge1), between grassland and woodland evergreen ( edge2 ), and between grassland and bushland (edge3 ). Such attributes are likely to affect African wild dog habitat use because of their influence on prey distribution and availability or the corresponding impact on sympatric large carnivores (Millspaugh, Rittenhouse, Montgomery, Matthews & Slotow, 2015; Trinkel, Van Niekerk, Fleischman, Ferguson & Slotow, 2007). Finally, we mapped the distance to nearest waterhole (distance to waterhole) and processed a terrain ruggedness index (terrain ruggedness), which represents a ruggedness value for each cell in the raster as a function of the eight nearest neighbourhood cells (Riley, 1999), using the Digital Elevation Model (DEM). Table 1 shows a description of the considered variables and the respective methods used in their extraction, using Quantum GIS 2.8 (QGIS Development Team, 2015).

Model development We fitted intensity of use RSFs (Nielson & Sawyer, 2013) to the African wild dog location data derived from the GPS telemetry and citizen science. The structure of these RSF models were identical. In both cases, we modelled the response variable (the total number of African wild dog loca2 tions recorded in each used 4 km grid), as a function of the environmental covariates (Millspaugh, Rittenhouse, Montgomery, Matthews & Slotow,

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2015). Disproportionately selected areas were therefore based on the intensity of use indicated by the number of times a grid was used (Broman, Litvaitis, Elingwood, Tate & Reed, 2014). The intensity of use RSFs ensured quantification of use on the continuum that ranged from one to the maximum recorded across the used grid cells (Nielson & Sawyer, 2013). Using predictive modelling of habitat use, we extrapolated these results to other areas in the study area where no records were available. To ensure our variables were at a similar scale, we standardized them to a mean of zero and standard deviation of one and then included the natural logarithm of the total number of observed locations as an offset variable (Nielson and Sawyer, 2013). All models were fit using generalized linear models in the lme4 package (Bates, Machler, Bolker & Walker, 2015; Bates, Maechler, Bolker & Walker, 2015), using R software (R Core Development Team, 2014).

Model diagnostics and model selection When conducting our model diagnostics, we first tested our environmental covariates for collinearity. In cases when collinearity was observed (i.e. | r | > 0.70) we retained the more influential covariate based on relative importance and significance in the global model. We then ran the global model with all possible combinations featuring non-collinear covariates and assessed goodness of fit using the chi-square test, which 2 compares the residual deviance to the c distribution (Dalgaard, 2008). However, because none of the best models in

Table 1. Environmental variables used to assess African wild dog habitat use in and around Hwange National Park, Zimbabwe (2009–2014). Each variable was represented as a raster and values were extracted at a resolution of 4 km2 resolution, using Quantum GIS 2.8. Variable

Description

Extraction

grassland woodland bushland woodland evergreen nhabtypes npatches edge1 edge2 edge3 distance to waterhole terrain ruggedness

Proportion of grassland habitat in each grid Proportion of woodland habitat in each grid Proportion of bushland habitat in each grid Proportion of woodland evergreen habitat in each grid Number of vegetation classes in each grid Number of habitat patches in each grid Amount of edge between grassland and woodland habitats Amount of edge between grassland and woodland evergreen habitats Amount of edge between grassland and bushland habitats Distance to nearest waterhole Terrain ruggedness index based on the Digital Elevation Model (DEM)

Zonal statistics Zonal statistics Zonal statistics Zonal statistics Zonal statistics Zonal statistics Zonal statistics Zonal statistics Zonal statistics Zonal statistics Raster terrain analysis and Zonal statistics

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both cases had an AICc weight greater than 0.9 (Grueber, Nakagawa, Laws & Jamieson, 2011), we used the MuMIn package in R (Barton, 2012) for conditional model averaging to obtain parameter coefficients. In both cases, models from the global model with cumulative AICc weights £0.95 were used (Barton, 2012). We then used the coefficients to develop predictive raster maps of relative probability of habitat use by African wild dogs in and around Hwange National Park. The resultant predictive maps were then compared for degree of similarity and overlap using a regression analysis in QGIS 2.8 with the grass plugin (QGIS Development Team, 2015). RESULTS We obtained a total of 5236 GPS telemetry fixes from the five African wild dog packs that we monitored. These locations occurred in 377 grids out of 2 a total possible of 4375 grids (4 km ), thus covering 8.6% of the total available grids (Fig. 1). From citizen scientists we obtained a total of 512 verified sightings, which covered a total of 154 grids, i.e. 3.5% of the total available grid cells (Fig 1). Based on our acceptable correlation of (| r | > 0.70), we retained six out of the eleven covariates in our final modelling. We thus retained woodland, bushland, distance to waterhole, terrain ruggedness, edge1 and nhabtypes, while grassland, woodland evergreen, npatches, edge2 and edge3 were removed. The chi-square test for the residual deviance showed a good global model fit for both the GPS 2 telemetry data (c = 4.512, d.f. = 8, P = 0.808), and 2 the citizen science data (c = 6.031, d.f. = 8, P = 0.648). The top model for explaining habitat use using GPS telemetry data had four variables, namely

nhabtypes, woodland, distance to waterhole and bushland. This model had an AICc weight of 0.19 (Table 2). The estimated parameter coefficients from the global model after model averaging (Table 3) indicated a positive selection for woodland, nhabtypes and terrain ruggedness, while distance to waterhole, bushland and edge1 had a negative effect on habitat selection by African wild dogs. African wild dogs thus selected areas with a high proportion of woodland habitat, a high number of habitat classes and rugged terrain but avoided areas with a high proportion of bushland, habitat close to waterholes and edges between grassland and woodland habitats. Nevertheless, bushland, nhabtypes, distance to waterhole and woodland were the four most important variables in explaining habitat selection based on the data. Using citizen science data, the top model had two variables nhabtypes and distance to waterhole. The model had an AICc weight of 0.14, while the model with nhabtypes, distance to waterhole and woodland was the second ranked with an AICc weight of 0.12 and a delta AICc of 0.34 (Table 4). Similarly to the GPS data, a model averaging approach was used to get parameter coefficients. Results indicated that distance to waterhole, nhabtypes, woodland and bushland were the four most important variables. The above were also the most important variables in the telemetry model, despite being ranked differently (Table 3). There were also similarities in the direction and magnitude of effect for all variables included (Table 3). The only variable that differed between the models was terrain ruggedness which positively impacted African wild dog habitat use in the GPS telemetry model but negatively affected habitat use in the citizen scientist model.

Table 2. Ranking of the GPS telemetry data resource selection function (RSF) models with DAICc £ 2, explaining African wild dog habitat use based on AICc, with the number of model parameters (K), delta AICc (DAICc) Akaike weights (wi) and cumulative AICc weights (Cum wi) and the null intercept only model. Model parameters

K

AICc

DAICc

wi

Cum wi

nhabtypes + woodland + distance to waterhole + bushland nhabtypes + woodland + distance to waterhole + bushland + terrain ruggedness nhabtypes + woodland + distance to waterhole + bushland + edge1 nhabtypes + bushland + distance to waterhole nhabtypes + bushland + distance to waterhole + terrain ruggedness nhabtypes + woodland + distance to waterhole + bushland + terrain 1 ruggedness + edge Intercept only model

6 7

1038.28 1039.00

0.00 0.73

0.19 0.13

0.19 0.32

7 5 6 8

1039.08 1039.65 1039.66 1040.06

0.80 1.38 1.38 1.78

0.13 0.10 0.10 0.08

0.45 0.55 0.65 0.73

2

1058.72

20.44

0.00

0.95


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Table 3. Conditional model averaging coefficients for parameters in the global model using models with a cumulative AICc weight £0.95, for both GPS telemetry and citizen science data. The rank order reflects the relative importance (RI) of each variable in the global model and the respective beta values, the standard errors and P-values. GPS telemetry model

Citizen scientist data model

Variable

RI (rank)

$

S.E.

P

RI (rank)

$

S.E.

P

Intercept nhabtypes bushland distance to waterhole woodland terrain ruggedness edge1

0.98 (1) 0.95 (2) 0.95 (3) 0.66 (4) 0.41 (5) 0.38 (6)

–8.539 0.202 –0.202 –0.424 0.129 0.255 –0.140

0.168 0.072 0.074 0.171 0.072 0.219 0.133

0.0001 0.005 0.006 0.013 0.074 0.246 0.290

0.87 (2) 0.4 (4) 1 (1) 0.4 (3) 0.27 (5) 0.26 (6)

–7.574 0.265 –0.134 –1.155 0.122 –0.297 –0.032

0.426 0.114 0.120 0.384 0.105 0.540 0.201

0.0001 0.020 0.265 0.003 0.243 0.582 0.873

Table 4. Ranking of the citizen scientist data resource selection function (RSF) models with DAICc £ 2, explaining African wild dog habitat use based on AICc with the number of model parameters (K), delta AICc (DAICc), Akaike weights (wi) and cumulative AICc weights (Cum wi) and the null, intercept only. Model parameters

K

AICc

DAICc

wi

Cum wi

nhabtypes + distance to waterhole nhabtypes + distance to waterhole + woodland nhabtypes + distance to waterhole + bushland nhabtypes + distance to waterhole + woodland + bushland nhabtypes + distance to waterhole + terrain ruggedness Intercept only model

4 5 5 6 5 2

425.11 425.45 425.69 426.91 427.11 434.43

0.00 0.34 0.58 1.79 2.00 9.32

0.14 0.12 0.11 0.06 0.05 0.00

0.14 0.26 0.37 0.43 0.48 0.95

However, in both cases, the parameter was nonsignificant (Table 3). Using parameter coefficients from model averaged results for both the GPS telemetry and citizen scientist models (Table 3), we modelled the predictive probability of habitat use in and around Hwange National Park (Fig. 2). The two predictions showed a high degree of overlap (r = 0.74), indicating a high level of similarity in both selected and avoided habitat patches across the landscape. DISCUSSION Citizen science has been used in a number of studies to understand the ecology of wildlife (Andreka et al., 1999; Buesching, Newman & Macdonald, 2014; Buesching et al., 2015; McCaffrey, 2005). However, to date only a few have compared citizen science data to more advanced remotely sensed data such as from GPS telemetry in modelling the habitat use of species of conservation concern (Broman et al., 2014; Quinn, 1995). Here, we documented that citizen science data produced models with comparable parameters,

comparable coefficients, whose resultant predictions greatly overlapped (r = 0.74), with the ones derived from GPS telemetry data (Fig. 2). In both models the proportion of woodland, proportion of bushland, the number of habitat types, and the distance to waterhole were the most important variables, despite different orders of importance. The proportion of woodland and the number of habitat types in each grid had a positive effect on habitat selection, while distance to waterhole and the proportion of bushland had a negative effect in both models (Table 3). These results indicate that citizen science data, for certain species and certain research questions, can be a functional surrogate for more expensive and intrusive data collection approaches. Citizen science data that derives from vehiclebased surveys are importantly biased to detection of species near roads (Broman et al., 2014; Quinn, 1995). This is an important constraint when deciding whether or not citizen scientist data can be applied to species or systems of interest. We support that African wild dogs are well-suited to this type of data collection given that their habitat

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Fig. 2. Predicted spatial relative probability of habitat use by African wild dogs in and around Hwange National Park, Zimbabwe based on intensity of use resource selection functions (RSF) fit (a) using GPS telemetry data and (b) using citizen scientist data collected between 2009 and 2014.

use corresponds to habitat near roads. They have been shown to use roads a lot especially for travelling between habitat patches (Abrahms et al., 2015) and for territorial marking (Parker, 2010), hence in areas with reasonable accessibility and road network, citizen science will be a practical alternative. Their unique coat markings and large pack sizes also makes them conspicuous and perfect for citizen science data collection approaches (Maddock & Mills, 1994). Besides, there are elements of citizen scientist data which GPS telemetry cannot solely provide. Such information includes pack size, sex ratios, number of adults and pups and photographs. Telemetry data retains point-based estimates of the location of a collared animal at some pre-set interval (Haines et al., 2006; Hebblewhite & Haydon, 2010). The technology does not yield further essential information about the status of the group and such information cannot be obtained, without making efforts to physically locate the collared individual and or the pack. For a group-living species such as the African wild dog, the pack is more important

than the individual (Angulo, Rasmussen, Macdonald & Courchamp, 2013; Courchamp & Macdonald, 2001), hence information about the pack, which comes through citizen scientists can be more valuable than GPS positions of one individual. Additionally, the use of citizen science gives enthusiastic non-scientists a chance to contribute to conservation work. This engages them in a participatory way that helps in increasing the global awareness of the plight of endangered species and consequently their conservation (Reed, 2008). Habitat use and selection studies are devised in such a way that they can provide inference on one or more of the four orders of selection (Johnson, 1980). The desired scale of a study is a major determinant of the best method to be used for data collection (Broman et al., 2014). Telemetry data are well-suited for describing patterns of animal resource selection across all orders, while citizen science data are more capable of describing selection at the second (placement of home ranges) or third (within home range selection)


orders (Boyce, 2006; Broman et al., 2014; Johnson et al., 2005). We therefore appreciate the importance of telemetry data in modelling habitat use and selection patterns at a finer scale (Hebblewhite & Haydon, 2010). The technology is, however, very expensive. The average market price of a single GPS collar used in this study was (US$2500), and after including the cost of fuel to find the dogs and collar them, drugs and salaries for field staff, the technology costs thousands of dollars. Besides, the technology also has its limitations and sources of bias through missed location fixes (D’eon & Delprate, 2005) and location error due to environmental factors (Lewis et al., 2007). Additionally, risks associated with the process of immobilization and handling of study animals in order to put collars (Arnemo et al., 2006; Cattet et al., 2008; Ponjoan et al., 2008), should not be overlooked, even though there are no reports of anaesthesia influencing mortality or chronic stress among African wild dogs (Creel et al., 1997; De Villiers et al., 1995). Citizen scientist data on the other hand, are cheap and relatively easy to obtain. The technique, however, comes at a cost, both to the individuals and the environment. In other studies, disturbances by tourist were shown to negatively influence wildlife populations and behaviour, for example humans were shown to greatly trigger vigilance and reduced foraging by elk (Cervus canadensis) more than natural predators did (Ciuti et al., 2012). We therefore acknowledge that citizen science might lead to animal disturbance. Among African wild dogs this is probably a major concern only when they are followed off-road to their denning sites, which can, however, be dealt with by proper law enforcement. Nevertheless, citizen science remains a valuable technique to study the ecology of endangered and long ranging species especially when funds to conduct rigorous systematic surveys are limited (Ward et al., 2015). Its efficacy and precision in modelling resource use and selection will, however, depend on the study, the species, the manner in which the data were collected, thoroughness in validating the data and use of clear and well described protocols, the question to be answered, the landscape characteristics, the relative availability, accessibility and road network within the different habitat types considered (Quinn, 1995). Every animal has certain requirements for survival (MacKenzie et al., 2006) and different approaches have been developed to characterize,

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model and understand how species use space and resources (Manly, McDonald, Thomas, McDonald & Erickson, 2002). In this study, the proportion of woodland and number of habitat types were the major positive predictors of habitat use, whilst habitat close to waterholes and bushland were avoided. African wild dogs will for example disproportionately use woodland habitat because of their pursuit type of hunting (Creel & Creel, 2002; Reich, 1981). Their main prey includes kudu, impala and bushbuck, which have all been shown to inhabit these vegetated environments (Hayward, O’Brien, Hofmeyr & Kerley, 2006). Through using woodland habitat, African wild dogs are able to maximize their prey encounter rate and significantly reduce their chase distance since they can get close enough to prey before starting the chase (Rasmussen et al., 2008). They also positively selected rugged terrain, which is likely to increase their hunting efficiency, with other studies indicating that they prefer such habitat for denning (Jackson et al., 2014). There are, however, two likely explanations for African wild dogs to avoid areas close to waterholes, which was apparent in both the GPS and citizen science data model results. First, open habitats are apparent around waterholes in Hwange National Park due to heavy browsing by elephants (Loxodonta africana) and other mega herbivores resulting in the piosphere effect, in which there is severe transformation and degradation of vegetation around waterholes (Landman, Schoeman, Hall-Martin & Kerley, 2012; Mukwashi, Gandiwa & Kativhu, 2013). This makes areas around waterholes too open for African wild dogs to energetically cost-effectively use for hunting. Secondly, lions in Hwange have been shown to hunt mostly close to waterholes (Davidson et al., 2013), hence African wild dogs might thus avoid such areas because of the presence of lions. In conclusion, our study has shown that citizen science data can provide ecological information about African wild dog habitat use results comparable to what GPS telemetry data can. Minimizing sources of bias and ensuring the use of clear and well-described protocols for capturing data will certainly yield better results. We therefore recommend the use of citizen science data as an alternative or complement to telemetry data particularly when funds are limited, coupled with an urgent need for such information to guide conservation and management action.

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ACKNOWLEDGEMENTS The authors would like to thank the Director General of the Zimbabwe Parks and Wildlife Management Authority for providing the permit to conduct research on African wild dogs in and around Hwange National Park. Oxford University Department of Zoology, Wildlife Conservation Research Unit is also acknowledged for financial and material support during the time the project was undertaken. We would also like to thank Eduardo Moraes Arraut for creating and sharing the Hwange vegetation structure map used for this project. We would also like to thank Tatenda Muchopa for helping with data cleaning and GIS advice. Finally, thanks to three anonymous reviewers for helpful comments on the previous version of this manuscript. §

ORCID iDs T. Shumba R.A. Montgomery D.W. Macdonald

orcid.org/0000-0002-2558-1089 orcid.org/0000-0001-5894-0589 orcid.org/0000-0002-5731-6482

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