Canadian Journal of Zoology

Occupancy, detectability and density of Crab-eating Cerdocyon thous in two protected areas of restinga habitats in

Journal: Canadian Journal of Zoology

Manuscript ID cjz-2018-0322.R2

Manuscript Type: Article

Date Submitted by the 07-May-2019 Author:

Complete List of Authors: Monteiro-Alves, Priscila; Universidade do Estado do Rio de Janeiro, Ecology Molino Helmer, Débora; Centro Universitário Espírito-Santense/FAESA, DepartmentDraft of Biology Ferreguetti, Átilla; Universidade do Estado do Rio de Janeiro, Ecology Pereira-Ribeiro, Juliane; Universidade do Estado do Rio de Janeiro, Rocha, Carlos Frederico; Universidade do Estado do Rio de Janeiro Bergallo, Helena; Universidade do Estado do Rio de Janeiro

Is your manuscript invited for consideration in a Special Not applicable (regular submission) Issue?:

Activity, Atlantic Forest, , Cerdocyon thous, Crab-eating Fox, Keyword: Random Encounter Models, Roadkill

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1 Occupancy, detectability and density of Crab-eating Fox Cerdocyon thous in two

2 protected areas of restinga habitats in Brazil

3

4 Priscila Stéfani Monteiro-Alves1, Débora Molino Helmer2, Atilla Colombo

5 Ferreguetti1*, Juliane Pereira-Ribeiro1, Carlos Frederico Duarte Rocha1 and Helena

6 Godoy Bergallo1

7

8 1Department of Ecology, Rio de Janeiro State University, Rua São Francisco Xavier, nº

9 524, Pavilhão Haroldo Lisboa da Cunha, 2º andar, sala 224. Bairro Maracanã, CEP:

10 20550-013. Rio de Janeiro, RJ, Brazil. Phone: 55(21)2334094

11 2Department of Biology, Centro Universitário Espírito-Santense/FAESA, Rua Anselmo

12 Serrat, nº 199, Bairro Ilha de MonteDraft Belo, CEP: 29053-250, Vitória, ES, Brazil, Phone:

13 55(27)33221158

14 * [email protected]

15

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16 Occupancy, detectability and density of Crab-eating Fox Cerdocyon thous in two

17 protected areas of restinga habitats in Brazil

18

19 PSMA, DMH, ACF, JPR, CFDR and HGB

20

21 Abstract

22 Crab-eating (Cerdocyon thous Linnaeus, 1766) are frequently recorded in lists of

23 communities. However, studies quantifying aspects of the species’ ecology are

24 uncommon in the literature. Thus, we aimed to quantify the density, activity, habitat

25 use, and potential threats of C. thous in two protected areas (PA) in Espírito Santo State,

26 Brazil. We used data derived camera traps and sand plots to model occupancy,

27 detectability, activity and, using RandomDraft Encounter Models (REMs), density and

28 abundance. We also estimate the species’ activity. Density of C. thous was 0.82 ind /

29 km2 with a total abundance of 119 individuals. We concluded that, in the PAs studied,

30 C. thous had bimodal, twilight-nocturnal activity patterns and was associated with water

31 sources. Although the species in the area has a relatively high density compared to that

32 from other areas in Brazil, it could be locally threatened by highway the road that

33 intercept the two PAs, promoting roadkill events and by domestic recorded in

34 these areas. Results presented herein can be a starting point to support future work in

35 the region and to make predictions regarding the management and conservation of Crab-

36 eating fox, a widely distributed species.

37

38 Key words: Activity, Atlantic Forest, Carnivora, Cerdocyon thous, Crab-eating Fox,

39 Random Encounter Models, Roadkill

40

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41 Introduction

42 Understanding the abundance, habitat associations, and activity pattern of a

43 species is essential for effective wildlife conservation planning (Cove et al. 2013; Rich

44 et al. 2014; Rovero et al. 2014). Each species is associated in different ways to

45 habitat structural features (Downes et al. 1998; Tews et al. 2004); for example, the

46 vegetation type (Trovati et al. 2002), the presence of water resources (Goulart et al.

47 2009) and elements that could represent some level of impact for the species, for

48 example, invasive species and roads (Carvalho et al. 2015; Lessa et al. 2016). Roads

49 have several negative impacts on animal populations, including habitat fragmentation

50 and reduction in habitat quality (Holderegger and Di Giulio 2010; Van Der Ree et al.

51 2015). Likewise, the introduction of invasive exotic species can pose as a major cause

52 of extinction of species, and in the caseDraft of the impacts imposed by interaction

53 with dogs is one of the most relevant factors for the loss of individuals of the native

54 fauna (Young et al. 2011; Hughes and MacDonald 2013). Thus, understanding which

55 factors determine the presence of a species in a region as threatened as the Atlantic

56 Forest contributes to the development of conservation measures to ensure their survival

57 in such areas (Jones 2001).

58 The Crab-eating Fox Cerdocyon thous Linnaeus 1766 is a medium-sized

59 mammal endemic to South America, having the most widespread geographical range

60 among neotropical canids (Courtnay and Maffei 2004; Lucherini 2015). In Brazil, this

61 species can be found in the Cerrado (savanna like vegetation), Pantanal (wetland),

62 Caatinga (semiarid region), Atlantic Forest, pastures, agriculture and in the Amazon

63 (Courtnay and Maffei 2004; Lucherini 2015). This broad distribution across multiple

64 habitat types is due to behavioral plasticity and a generalist diet (Delgado 2002; Rocha

65 et al. 2004; Pedó et al. 2006; Raíces and Bergallo 2010).

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66 The Crab-eating Fox is classified as Least Concern (LC) by the IUCN, mainly

67 because its wide distribution and its population being considered stable (Lucherini

68 2015). However, estimates of population size for this species are inaccurate and

69 outdated (Beisiegel et al. 2013). The Crab-eating Fox it is a species under some threats

70 related to roadkill risks (Rosa and Mauh 2004; Jorge et al. 2007; Cáceres et al. 2010),

71 persecution by humans (Beisiegel et al. 2013), confrontations with domestic dogs

72 (Lemos et al. 2011), and being infected by zoonoses (Carnieli et al. 2008). The species

73 is often cited in studies on the composition of the mammal community (Chiarello 1999,

74 Goulart et al. 2009, Delciellos 2016; Magioli et al. 2016) and in the evaluation of their

75 diet (Juarez and Marinho-Filho 2002; Gatti et al. 2006; Cazetta and Galetti 2009; Raíces

76 and Bergallo 2010) but studies evaluating the ecological aspects of C. thous are

77 uncommon in the literature (BeisiegelDraft et al. 2013).

78 Our study presents the first estimates of density and abundance of the species in

79 the two largest remnants of coastal sandy plains (hereafter restinga) in the State o

80 Espírito Santo, located in Brazil. By using occupancy and detectability modeling, we

81 explore the spatial distribution and habitat use of the species, from which we predicted

82 the direction of response to six covariates based on prior knowledge of C. thous ecology

83 (Beisiegel et al. 2013; Lucherini 2015). We tested the following hypothesis: 1)

84 occupancy probability would be higher in sites closest to water resources and human-

85 made trails; and 2) occupancy probability would be lower in sites closest to the road and

86 with the presence of domestic dogs. To better understand the ecological aspects, we also

87 tested if there were differences between the dry and rainy seasons in both the spatial and

88 temporal distribution and analyzed the possible impact of the Rodovia do Sol (ES-060

89 road) by quantifying roadkill events. We hypothesized that ES-060 road would be one

90 of the major threats for the species in the areas.

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92 Methods

93 Study Area

94 The study was carried out in two protected areas (hereafter PA), the Paulo César

95 Vinha State Park (PEPCV) and the Setiba Environmental Protection Area (EPA of

96 Setiba) located in the State of Espírito Santo, southeastern Brazil (Figure 1). The two

97 PAs are separated only by the Rodovia do Sol (ES-060 road) connecting the

98 municipalities of Guarapari and Vila Velha. The PEPCV (20º33'-20º38'S, 40º23'-

99 40º26'W) covers an area of approximately 1,500 ha, while the EPA of Setiba (20°32'-

100 20º39’S, 40º22’-40° 32'W) has approximately 12,960 hectares (IEMA 2016).

101 These two areas present four different habitats, usually following the succession

102 of beach vegetation zone (BVZ, closer to the sea, with herbaceous vegetation), shrub

103 vegetation zone (SVZ , with low shrubs,Draft usually cacti and bromeliads), open Clusia

104 formations (OCZ, composed of higher shrubs, high density of bromeliads and presence

105 of tree species, Clusia spp.) and restinga forests (RF, dominated by tree species)

106 (Oliveira et al. 2007; Oprea et al. 2009).

107

108 Data collection

109 Camera-trapping and sand-plots

110 Data were collected from February to October 2017. We placed a random grid

111 over a digital map of the Reserve and identified the sampling points by selecting grid

112 cells (i.e. each grid cell with 500 m2). We selected 35 sampling sites using a random

113 design distributed in 4 restinga vegetation types, 20 sites in the EPA of Setiba and 15 in

114 the PEPCV (Figure 1). We used the center point of each grid cell as the location for

115 sampling. This scheme was designed to model occupancy probability of the areas by the

116 Crab-eating Fox, as well as estimate density and abundance and to document its activity

117 pattern. 5 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 6 of 29

118 First, in each selected grid cell, we located randomly 1 camera trap per

119 grid cell. We did not use bait to attract the species. This approach resulted in a relatively

120 even distribution of points within the areas, while maintaining independence among

121 points, which were separated from one another by more than 500 m. At each site, we

122 installed 1 passive infrared Bushnell® camera trap horizontally pointing (Trophy Cam

123 HD Aggressor No Glow Trail Camera, Bushnell Outdoor Products, Overland Park,

124 Kansas) in picture function with no interval between pictures, affixed to tree trunks or

125 shrubs approximately 40–50 cm above the ground, for continuous surveying throughout

126 the study. The sensitivity was setting as high and no vegetation were removed in the

127 area. All stations were examined every 20-25 days to change batteries, when necessary.

128 Traps were programmed to operate for 24 h/d.

129 At each site, we also establishedDraft four sand plots of 1 m2 wide and 10 m apart

130 from each other. We checked for five consecutive days each month and the occurrence

131 of footprints in a site was considered a record of C. thous, independently of the number

132 of footprints and individuals that might produce the footprints. During each visit, the

133 surface of the sand was again smoothed to erase the previous footprints marks of in

134 order to allow recording new movement detections.

135

136 Covariate measurement

137 We used six covariates to model the occupancy and detection probabilities of the Crab-

138 eating Fox: distances from human settlements, water resources, human-made trails,

139 road, the presence of domestic dogs in each site and vegetation type. Data collection

140 methods for each covariate are described in the following paragraph.

141 We used QGIS software (QGIS Development Team 2017) and satellite imagery from

142 Google Earth (Google, Inc., Mountain View, CA) dated May 2016, to estimate four

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143 spatial covariates: distances from human settlements, water resources, human-made

144 trails, and road. We used the average distance to the center of each sampling site.

145 In each site we recorded the presence of domestic dogs using camera-trap and sand-plot.

146 We used the vegetation type in each site previously defined in the study area section.

147

148 Roadkills

149 Surveys were conducted along the Rodovia do Sol (Road ES-060) during daylight,

150 which has a total length of 67.5 km of extension. The EPA of Setiba is located between

151 the 27.9 km and 42 km and the PEPCV is situated between 29 km and 40 km of the

152 road-060. The highway has five subterranean passages for fauna. The local speed limit

153 is 60 or 80 km / h, depending on the road location. Samples were conducted on a

154 weekly basis, with intervals of two-4Draft days, between April and November 2017, totaling

155 28 weeks and 1250 km. Eight months of sampling were carried out from April to

156 November 2017. The two-person team traveled in the car at an average speed of 40 km /

157 hour. In addition, we searched a range of 10 m in the roadsides during every survey by

158 walking and surveying the entire road. Each carcass found was identified by the

159 observers who registered local geographic coordinates using a hand-held GPS with 5m

160 accuracy. The carcass was removed from the road to avoid double counting.

161

162 Data Analysis

163 First, we checked for spatial and temporal autocorrelation in the records of the Crab-

164 eating Fox for each site using Mantel tests (Oksanen et al. 2012). For the Mantel tests,

165 we calculated a spatial distance matrix using universal transverse Mercator-14

166 coordinates (in meters) taken at the center of each grid and a temporal distance matrix

167 using the months of sampling period. The Euclidean distance metric was used to

168 construct distance matrices for space and time. The Bray-Curtis distance metric was 7 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 8 of 29

169 used to a construct distance matrix of the records of Crab-eating Fox per site.

170 Significance of Mantel correlations was evaluated by a permutation test with 9,999

171 permutations. The analyses were performed in in R version 3.5.1

172 (http://cran.rproject.org, accessed 19 September 2018) with the package vegan version

173 2.0–4 for Mantel tests (Oksanen et al. 2012).

174 Obtaining estimates of C. thous density and abundances is a hard task since individual

175 recognition of C. thous is not easy using camera images and thus, we used a method

176 suggested by Rowcliffe et al. (2008) called random encounter model (REM). We used

177 the following REM equation to obtain density estimates from camera trap encounter

178 rates (Rowcliffe et al. 2008: equation 1):

푦 휋 179 퐷 = × Draft푡 푉푟(2 + 휃) 180 in which y is the number of independent photographic events, t is total camera survey

181 effort, V is average speed of animal movement, and r and θ are the radius and angle of

182 the camera trap detection zone, respectively. We defined an independent contact with a

183 camera as a Crab-eating fox entering and exiting the camera field of view. Therefore,

184 we considered consecutive photographic events of an individual remaining stationary in

185 front of a camera as the same event. For this specific analysis, we used data from the

186 period from April to August 2018 to assure the assumption of a closed population. We

187 defined this period based on the literature that shows no reproduction (Faria-Corrêa et

188 al. 2009) and we also did not record any cubs during this time-frame.

189 We carried out ex-situ field trials to determine the dimensions of each camera

190 detection zone. To estimate camera radius r, we approached each camera directly from

191 the front and on all fours 10 times and measured the distance from the camera to the

192 location at first trigger for each approach. For camera angle θ, we carried out 10 paired

193 approaches (1 from each side) perpendicular to the sensor beam at 5 m and recorded the

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194 location at first trigger. For each resulting location, we took a bearing using a compass

195 placed on a flat surface directly below the camera. We recorded detection angle as the

196 angle formed by the mean compass bearings taken on each side. We averaged values

197 across trials to obtain values r and θ. We also carried out a sensitivity analysis to

198 determine the effect of a 1% change in the value of r or u on estimated density. The

199 average speed of animal movement (V) was calculated as the mean of the summed the

200 distances using camera-trap records, per day as proposed by Rowcliffe et al. (2016).

201 This approach uses the speed at which an animal passes in front of a camera trap

202 measured by dividing the distance travelled by the duration of the sequence of pictures

203 obtained in the camera-trap. The distance travelled is the summed linear distance

204 between animal positions on the ground, which are identified by viewing images in the

205 field and reconstructing the movementDraft path on the ground within camera detection

206 zones relative to nearby landmarks such as trees and rocks. The total distance moved

207 between positions (d) was measured using a tape or hip chain. The duration of each

208 passage (t) is the difference between time stamps of the first and last images. Ideally all

209 sequences obtained should be processed in this way for analysis, however, when

210 individuals sometimes visibly responded to cameras, either by fleeing or by stopping to

211 investigate. Sequences showing such reactions was excluded from speed calculations,

212 leaving only those in which no reactions are visible, and in which unbiased speeds can

213 therefore reasonably be assumed. We carried out all analyses in R version 3.5.1

214 (http://cran.rproject.org, accessed 19 September 2018).

215 We used both the camera-trap and sand plots data to model occupancy and

216 detectability. We used a multi-season occupancy model that also estimates the

217 probability that an unoccupied site will be colonized (γ) and the probability that an

218 occupied site will suffer a local extinction (ε; MacKenzie et al. 2003). We defined

219 season according to the dry and rainy seasons in the study area, with a period of 9 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 10 of 29

220 approximately 50 days of sampling per season, totaling 10 occasions of 5 days for each

221 season. We left parameters for colonization (γ) and local extinction (ε) constant in all

222 models because we did not expect them to vary to any of the measured covariates,

223 which were very similar in the year that we conducted the study. We evaluated six

224 covariates that might influence occupancy and detection probabilities. This allowed us

225 to evaluate differences in habitat use as determined by a single covariate or using

226 additive models with a set of covariates, which would contribute to an improvement in

227 the model’s performance. We based conclusions on the ranking of the models and on

228 estimates of the effects of covariates on the parameters in the model.

229 We constructed our single-species, multi-season occupancy models using the

230 UNMARKED package in Program R (Fiske and Chandler, 2011). Top models were

231 selected using Akaike's InformationDraft Criterion adjusted for small sample size (AICc). All

232 models with a ΔAICc value < 2 were considered equivalent. We also used the weight

233 (AICcw) for each model, which corresponds to the amount of evidence in favor of a

234 given model, to choose the best model that we used for testing our hypotheses.

235 We used the Overlap package (Meredith and Ridout 2014) in R (R Core Team

236 2015), which was developed specifically for the visualization and analysis of activity

237 patterns from camera trap data. For this analysis, we grouped camera-trap species

238 records into 1 h time intervals, beginning at the hour mark (e.g. 09:00-09:59). We

239 defined the diurnal period as the time between 1 h after sunrise and 1 h before sunset,

240 and the nocturnal period as the time between 1 h before sunrise and 1 h after sunset.

241 Crepuscular periods, dawn and dusk were defined as the hour before and after sunrise

242 and sunset, respectively (Caravaggi et al. 2018). Activity pattern was constructed using

243 kernel density estimates which, in this context, describe the probability of a camera-trap

244 event occurring at any given time (Linkie and Ridout 2011). To test if the activity

245 period of the Crab-eating fox differs between the rainy and dry seasons, we performed 10 https://mc06.manuscriptcentral.com/cjz-pubs Page 11 of 29 Canadian Journal of Zoology

246 2-sample Kolmogorov–Smirnov tests to determine if the distributions of 2 sets of

247 activity patterns differed significantly.

248

249 Results

250 We recorded 82 independent events of Crab-eating fox across 713 camera trap

251 days during the five months used for the density analysis. Average speed during both

252 the 24-hour period was 0.21 km/hr (± 0.024). Following Rowcliffe et al. (2008)

253 estimation method, Crab-eating fox density was estimated at 0.82 (0.70 to0.94) ind/km2.

254 Considering the total area of the two protected areas (about 145 km2), we estimated an

255 abundance of 119 individuals (102 to 136 individuals).

256 We obtained 128 records of C. thous in 19 of 35 sites (0.54) in the dry season

257 (season 1) and the estimated mean occupancyDraft probability for this season was Ψ1 = 0.61

258 (SE = ± 0.05). In the rainy season (season 2), we obtained 98 records of C. thous at 15

259 of 35 sites (0.43) and the estimated mean occupancy probability was Ψt+1 = 0.50 (SE =

260 ± 0.054). Estimated detectability in our study was 0.42 ± 0.03. From the occupancy

261 models produced (Table 1 showing the 20 best models), occupancy was affected by four

262 covariates: 1) distance from water resources (water), which had a negative relationship,

263 in which occupancy by the crab-eating fox increased at lower distances (dropping Ψ =

264 0.76 to 0.19; Fig. 2A); 2) distance from the road (road), which had a positive

265 relationship in which occupancy increased at higher distances from the road (Ψ = 0.18

266 to 0.96; Fig. 2B); 3) distance from trails (trail), which predicted a negative relationship

267 with occupancy (dropping Ψ = 0.74 to 0.12; Fig. 2C); and 4) vegetation type (veg), with

268 higher occupancy in open vegetation areas and lower in forested areas (Ψ = 0.22 to 0.75

269 Fig. 2D).

270 Detectability was affected by two covariates: 1) distance from the road, which

271 had a positive relationship, increasing in sites with higher distances (p = 0.15 to 0.34; 11 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 12 of 29

272 Fig. 3A); and 2) domestic presence (dog), with higher detectability in the absence

273 of domestic dogs in the site (p = 0.55 to 0.24; Fig. 3B).

274 We recorded 6 roadkill events of individuals of Crab-eating fox, with 5

275 individuals (83.3%) of this total being recorded in the kilometers of the road where the

276 two PAs are intercepted. The only roadkill event found outside the intercepted area was

277 relatively close to the PAs, between the 43-44 km of the road.

278 We recorded a total of 182 Crab-eating fox activity records, being 78 in the rainy

279 season and 104, in the dry season. The crab-eating fox had a bimodal activity with two

280 periods of highest activity (peaks), one at night between 19:00 and 21:00 and the other,

281 in the morning between 04:00 and 06:00 (Fig. 4). The species differed in terms of

282 activity between seasons with overlap coefficients (0.62 +0.08). We found significant

283 differences in the distribution of activityDraft depending on season (KS test, D = 0.34; p =

284 0.02), being higher during the dry season.

285

286 Discussion

287 Density and abundance estimated for C. thous in the present study were similar

288 to other studies with similar vegetation type (Courtenay and Maffei 2004; Faria-Corrêa

289 et al. 2009). In a restinga area located in southern Brazil, it was estimated a similar

290 density with our study (0.78 ind / km2, Faria-Corrêa et al. 2009), while in Marajó Island,

291 a restinga located in the northern Brazil it was estimated a comparatively lower density

292 (0.55 ind / km2, Courtenay and Maffei 2004). This density estimated for C. thous in the

293 present study is the first one in the Atlantic Forest of Espírito Santo state.

294 We found that C. thous was more frequent during the dry season. This may be

295 related to a comparatively scarcity of resources in the dry season, which would make it

296 necessary for the species to move more within the camera trap array in the search for

297 food, increasing detection. Beisiegel et al. (2013) found a similar result in terms of 12 https://mc06.manuscriptcentral.com/cjz-pubs Page 13 of 29 Canadian Journal of Zoology

298 seasonal differences, with a higher frequency of detections in the dry season in the

299 Caatinga located in the northeastern, Brazil. Moreover, in the rainy season, there is

300 usually an increase in the availability of food resources, mainly in terms of the

301 abundance of and fruits (Motta-Junior et al. 1994), which could reduce the

302 search area for resources by the species producing a lower detection. In fact, most

303 studies on C. thous diet have found that arthropods, mainly insects, compose most of its

304 diet, together with fruits and seeds (Gatti et al. 2006; Cazetta and Galetti 2009; Raíces

305 and Bergallo 2010).

306 We found no effect in the occupancy and detectability regarding human

307 settlements, suggesting that the species could be adaptable to areas with human

308 interference. It is reported that the species is also adaptable to agriculture and habitats in

309 regeneration (Rocha et al. 2008; MagioliDraft et al. 2016). However, according to our results,

310 the Crab-eating fox preferred open and sparse habitats. We assume that this behavior is

311 due to the relatively homogeneous distribution of important food resources for the

312 species in open areas in restinga. In addition, we did not record the presence of potential

313 predators (i.e. large cats) for the species in the study area. It has been argued that top

314 predators such as large wildcats usually avoid exposure to open spaces (Trolle and Kery

315 2003). We also found that the Crab-eating fox occupancy was higher in areas near water

316 sources, which it has been shown that different species of mammals tend to be more

317 abundant near the water courses (Ferreguetti et al. 2017). In our study, the Crab-eating

318 fox also had a higher occupancy near the human made trails located in the study area

319 suggesting that the species uses human trails in the two PAs. This relationship could be

320 related to the fact that carnivore species are known to frequently use trails and roads to

321 move between areas (Dillon and Kelly 2007; Davis et al. 2011; Harmsen et al. 2011). In

322 a study carried out in an area of Atlantic Forest in the Santa Catarina State, southern

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323 Brazil, the Crab-eating fox had its habitat use strongly associated with wide trails

324 (Goulart et al. 2009).

325 We found a bimodal, twilight-nocturnal activity pattern for the species. Our

326 results are consistent with the knowledge about the species activity present in the

327 literature, showing that the Crab-eating fox has crepuscular and nocturnal habits (Juarez

328 and Marinho-Filho 2002; Delciellos 2016). The variance in species activity, this may be

329 related to the generalized diet and its ability to adapt to different environmental and

330 temporal conditions (Cazetta and Galetti 2009; Raíces and Bergallo 2010; Lucherini

331 2015). However, other studies have shown unimodal by C. thous. In southern Brazil, a

332 higher activity was recorded only between the period 6:00 pm and 6:00 am (Faria-

333 Corrêa et al. 2009), while a study conducted in northern Brazil, using radio-telemetry

334 techniques, estimated highest activityDraft only between 05:00 and 09:00 am (Maffei and

335 Taber 2003).

336 We found that the occupancy and detectability of C. thous was higher in areas

337 farther from the ES-060 road, which could suggest that the species avoid the road,

338 possibly because of the risk that road represent. In fact, in nine months of sampling, six

339 roadkill events were recorded. This species is usually one of the most frequent in studies

340 that evaluated roadkill (Turci and Bernarde 2009; Cáceres et al. 2010; Lemos et al.

341 2011; Beisiegel et al. 2013). In a study on the roadkill rate of mammals conducted in

342 Midwest Brazil, 43% of the records were of C. thous (Casella et al. 2006). As a result,

343 actions to mitigate the frequency of roadkill of C. thous and other should be

344 intensified. It is also important to promote seminars to educate drivers about not to

345 throw food on the road, as this tends to attract the fauna and cause roadkill.

346 The presence of the domestic dog also resulted in a lower detectability of the

347 Crab-eating fox. Domestic dogs could also represent a threat to this native canid species

348 in the study area. The literature has shown that domestic exotic animals can result in 14 https://mc06.manuscriptcentral.com/cjz-pubs Page 15 of 29 Canadian Journal of Zoology

349 serious impacts to native species. For example, the Crab-eating fox is often chased away

350 by domestic dogs and, when fleeing from them, tends to use burrows as a

351 hiding place to protect themselves (Lemos et al. 2011). In addition to that, the presence

352 of domestic dogs could represent other threats to the Crab-eating fox, such as disease

353 transmission (Deem et al. 2001). Therefore, the preservation of native species in PAs

354 (and other areas) should especially consider the control of exotic species such as the

355 domestic dog, in addition to conducting studies to evaluate their impact locally.

356 We concluded that, in the restinga area studied, C. thous had bimodal, twilight-

357 nocturnal activity patterns and was associated with water sources. Although the species

358 in the area has a relatively high density compared to that from other areas, it could be

359 threatened by the road that intercept the two PAs, promoting roadkill events and also by

360 domestic dogs recorded in these areas.Draft Results presented herein can be a starting point

361 to support future work in the region and to make predictions regarding the management

362 and conservation of Crab-eating fox, a widely distributed species. Furthermore, the

363 results can be used as a surrogate for other regions or biomes in which the species

364 occurs.

365

366 Acknowledgments

367 This study is part of the “Programa de Pesquisas em Biodiversidade da Mata

368 Atlântica (PPBio Mata Atlântica Network)" of Ministério de Ciência, Tecnologia,

369 Inovação e Comunicação (MCTIC) and was supported by Conselho Nacional de

370 Desenvolvimento Científico e Tecnológico (CNPq) (Process No. 457458/2012-7). The

371 authors benefitted from grants provided to HGB (process 307781/2014-3) and to CFDR

372 (302974/2015-6) from CNPq and through “Cientistas do Nosso Estado” Program from

373 FAPERJ to CFDR (process E-26/202.803.2018) and to HGB (process E-

374 26/201.267.2014). This study was conducted with the research license Process 15 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Page 16 of 29

375 76444341 - Authorization 003A-2017 provided by the "Instituto Estadual de Meio

376 Ambiente e Recursos Hídricos - IEMA". This study was financed in part by the

377 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) -

378 Finance Code 001.

379

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546 Figure captions

547 Figure 1. Two protected restinga areas (Paulo Cesar Vinha State Park and the Setiba

548 Environmental Protection Area) in Espírito Santo, Brazil. Map produced by the authors

549 themselves with original shapefile file.

550

551 Figure 2. Relationships between the occupancy rate and (A) the distance from water;

552 (B) distance from road; (C) distance from trails; and (D) vegetation type of the Crab-

553 eating fox Cerdocyon thous. OCZ= open clusia formations; SVZ= shrub vegetation

554 zone; BVZ= beach vegetation zone; and RF= restinga Forest. Data were estimated by

555 camera-trapping and sand plots at the Paulo Cesar Vinha State Park and the Setiba

556 Environmental Protection Area, Brazil, between February and October 2017.

557 Draft

558 Figure 3. Relationships between the detectability of Cerdocyon thous and (A) distance

559 between road and (B) presence of the domestic dog. Data were estimated by camera-

560 trapping and sand plots at the Paulo Cesar Vinha State Park and the Setiba

561 Environmental Protection Area, Brazil, between February and October 2017.

562

563 Figure 4. Circadian activity pattern of crab-eating Fox (Cerdocyon thous) in two

564 protected restinga areas in the Paulo Cesar Vinha State Park and the Setiba

565 Environmental Protection Area, Brazil, estimated by camera-trapping between March

566 and November 2017.

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Two protected restinga areas (Paulo Cesar Vinha State Park and the Setiba Environmental Protection Area) in Espírito Santo, Brazil. Map produced by the authors themselves with original shapefile file.

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Figure 2. Relationships between the occupancy rate and (A) the distance from water; (B) distance from road; (C) distance from trails; and (D) vegetation type of the Crab-eating fox Cerdocyon thous. OCZ= open clusia formations; SVZ= shrub vegetation zone; BVZ= beach vegetation zone; and RF= restinga Forest. Data were estimated by camera-trapping and sand plots at the Paulo Cesar Vinha State Park and the Setiba Environmental Protection Area, Brazil, between February and October 2017.

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Figure 3. Relationships between the detectability of Cerdocyon thous and (A) distance between road and (B) presence of the domestic dog. Data were estimated by camera-trapping and sand plots at the Paulo Cesar Vinha State Park and the Setiba Environmental Protection Area, Brazil, between February and October 2017.

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Figure 4. Circadian activity pattern of crab-eating Fox (Cerdocyon thous) in two protected restinga areas in the Paulo Cesar Vinha State Park and the Setiba Environmental Protection Area, Brazil, estimated by camera-trapping between March and November 2017.

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Table 1. Top 20 best multi-season occupancy and detectability models for the Crab-eating Fox Cerdocyon thous in the Paulo Cesar Vinha State Park and the Setiba Environmental Protection Area, Brazil, estimated by camera-trapping and sand-plots between March and November 2017, grouped in sampling intervals of 5 consecutive days. Covariates: distances from: trails (trail), water (water), road (road), and human settlements (human), vegetation type (veg), and domestic dog frequency (domestic_dog). Ψ = occupancy, p = detectability, AICcw = Akaike weight. Colonization (γ) and local extinction (ε) were constant in all models and are omitted from the models’ descriptions.

Model AICc ΔAICc AICcw nºparameters Ψ(veg;water;trail)p(road;domestic_dog) 315.12 0.00 0.20 8 Ψ(veg;water;road)p(road;domestic_dog) 315.18 0.06 0.20 8 Ψ(veg;trail;road)p(road;domestic_dog) 315.85 0.73 0.18 8 Ψ(water;trail;road)p(road;domestic_dog) Draft 316.42 1.30 0.17 8 Ψ(veg;water;trail;road)p(road;domestic_dog) 317.96 2.84 0.16 9 Ψ(veg;trail)p(road;domestic_dog) 318.32 3.20 0.05 7 Ψ(veg;road)p(road;domestic_dog) 318.96 3.84 0.02 7 Ψ(water;road)p(road;domestic_dog) 319.02 3.90 <0.01 7 Ψ(trail;road)p(road;domestic_dog) 320.41 5.29 <0.01 7 Ψ(water;trail)p(road;domestic_dog) 320.53 5.41 <0.01 7 Ψ(veg)p(road;domestic_dog) 321.36 6.24 <0.01 6 Ψ(water)p(road;domestic_dog) 321.68 6.56 <0.01 6 Ψ(.)p(road;domestic_dog) 321.92 6.80 <0.01 5 Ψ(road)p(road;domestic_dog) 322.03 6.91 <0.01 6 Ψ(trail)p(road;domestic_dog) 322.25 7.13 <0.01 6 Ψ(human;veg;water)p(road;domestic_dog) 324.36 9.24 <0.01 8 Ψ(human;veg;water;trail;road)p(road;domestic_dog) 325.02 9.90 <0.01 10 Ψ(human;water;trail;road)p(road;domestic_dog) 326.41 11.29 <0.01 9

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Ψ(human;veg;trail;road)p(road;domestic_dog) 326.92 11.80 <0.01 9 Ψ(human;trail;road)p(road;domestic_dog) 328.01 12.89 <0.01 8

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