1

1

PROCESSO FAPESP 2014/24921-0 2

3

Relatório Final 4

5

Seleção de habitat e padrões de movimento de onçapintada (Pantheraonca): 6

novosdesafiosnasanálises de informaçõesobtidasportelemetria GPS-Satélite 7

8

Outorgado: 9

Ronaldo Gonçalves Morato 10

Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros-ICMBio 11

12

Supervisores: 13

Peter Leimgruber 14

Justin M. Calabrese 15

ConservationEcology Center- SmithsonianConservationBiologyInstitute 16

17

18

19

20

21

22

23

24

25

ATIBAIA 26

Setembro/ 2016 27

28

2

Capítulo 1 29

30

Artigo publicado no periódico PlosOne 31

Morato RG, Stabach JA, Fleming CH, Calabrese JM, De Paula RC, Ferraz KMPM, et 32

al. (2016) Space Use and Movement of a Neotropical Top Predator: The Endangered 33

Jaguar. PLoS ONE 11(12): e0168176. doi:10.1371/journal. pone.0168176 34

35

Full Title: Space use and movement behavior of a Neotropical top predator: the 36

endangered jaguar 37

38

Short Title: Jaguar movement ecology 39

40

Morato RG1, 2, 3*, Stabach JA2, Fleming CH2, Calabrese JM2, De Paula RC1, 3, Ferraz 41

KMPM3, 4, Kantek DLZ5, Miyazaki SS5, Pereira TDC5, Araujo GR6, Paviolo A7, De 42

Angelo C7, Di Bitetti MS7, Cruz P7, Lima F8, 9, Cullen L8, Sana DA3, 10, Ramalho EE3, 43

11, Carvalho MM12, Soares, FHS12, Zimbres B13,Xavier MS14, Moraes, MDF14, 44

VogliottiA3,15, May Jr JA16, Haberfeld M16, Rampim L16, Sartorello L16, Ribeiro MC9, 45

Leimgruber P2 46

47

1 Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros, Instituto 48

Chico Mendes de Conservação da Biodiversidade, Atibaia, SP, Brazil. 49

2Conservation Ecology Center, Smithsonian Conservation Biology Institute, National 50

Zoological Park, Front Royal, VA, USA. 51

3 Instituto Pró-Carnívoros, Atibaia, SP, Brazil. 52

3

4 Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, 53

Piracicaba, Brazil. 54

5 Estação Ecológica Taiamã, Instituto Chico Mendes de Conservação da 55

Biodiversidade, Cáceres, MT, Brazil. 56

6Departamento de Medicina Veterinária, Universidade Federal de Viçosa, Viçosa, 57

MG, Brazil. 58

7Instituto de Biología Subtropical, Universidad Nacional de Misiones and CONICET, 59

Puerto Iguazú, Argentina. 60

8 IPÊ – Instituto de Pesquisas Ecológicas, Nazaré Paulista, SP, Brazil. 61

9 Laboratório de Ecologia Espacial e Conservação, Instituto de Biociências, 62

Univesidade Estadual de São Paulo, Rio Claro, SP, Brazil. 63

10 Programa de Pós-graduação em Ecologia, Universidade Federal do Rio Grande 64

do Sul, Porto Alegre, RS, Brazil. 65

11Instituto de DesenvolvimentoSustentávelMamirauá,Tefé, AM,Brazil. 66

12Instituto de Defesa e Preservação dos Felídeos Brasileiros, Corumbá de Goiás, 67

Go, Brazil. 68

13 Programa de Pós Graduação em Zoologia, Instituto de Ciências Biológicas, 69

Universidade de Brasília, DF, Brazil. 70

14Projeto Carnívoros do Iguaçu, Parque Nacional do Iguaçu, Instituto Chico Mendes 71

de Conservação da Biodiversidade, Foz do Iguaçu, PR, Brazil. 72

15 Universidade Federal da Integração Latino-Americana, Foz do Iguaçu, PR, Brazil. 73

16 Projeto Onçafari, Miranda, MS, Brazil. 74

4

*Corresponding author: 75

ronaldo.morato@icmbio.gov.br 76

Abstract 77

Accurately estimating home range and understanding movement behavior 78

can provide important information on ecological processes. Advances in data 79

collection and analysis have improved our ability to estimate home range and 80

movement parameters, both of which have the potential to impact species 81

conservation. Fitting continuous-time movement model to data and incorporating the 82

autocorrelated kernel density estimator (AKDE), we investigated range residency of 83

forty-four jaguars fit with GPS collars across five biomes in Brazil and Argentina. We 84

assessed home rangeand movement parameters of range resident animals and 85

compared AKDE estimates with kernel density estimates (KDE). We accounted for 86

differential space use and movement among individuals, sex, region, and habitat 87

quality. Thirty-three (80%) of collared jaguars were range resident. Home range 88

estimates using AKDE were 1.02 to 4.80 times larger than KDE estimates that did 89

not consider autocorrelation. Males exhibited larger home ranges, more directional 90

movement paths, and a trend towards larger distances traveled per day. Jaguars 91

with the largest home ranges occupied the Atlantic Forest, a biome with high levels 92

of deforestation and high human population density. Our results fill a gap in the 93

knowledge of the species’ ecologywith an aim towards better conservation of this 94

endangered/critically endangered carnivore – the top predator in the Neotropics. 95

96

97

Keywords: top predator conservation, home range, movement, Pantheraonca, ctmm 98

5

99

Introduction 100

Top predator as a model 101

In terrestrial environments, predators tend to restrict their movements within 102

defined areas to meet daily requirements. These animals are often considered range 103

residents [1]. Top predators, such as large-sized cats, are known to require large 104

areas [2]. Space use is likely to increase as habitat quality decreases, making this 105

group particularly vulnerable to habitat loss and fragmentation [3–5]. In addition, 106

movement parameters such as the total distance traveled per day and the tortuosity 107

of the movement path can increase in response to habitat fragmentation [6]. 108

Understanding animal movement and space use across dynamic landscapes is 109

critical for the establishment of effective conservation strategies [7], 110

includingthecreation/maintenanceofecologicalcorridorsdesignedtoguaranteethemove111

mentof focal species, improvingtheconnectivityof habitat patches 112

withinfragmentedlandscapes[8], andidentifyingpriorityareas for conservation[9]. 113

Accurately estimating home ranges and understanding animal movement behavior 114

provide information on ecological processes that can impact species conservation 115

[10,11]. 116

The jaguar (Pantheraonca) is widely distributed across a broad range of 117

habitats in the Americas [12]. Loss of habitat is the greatest threat for the species’ 118

long-term survival. The classification of the species as “near threatened” by the IUCN 119

[13]and endangered or critically endangered in Brazil and Argentina [14,15] reflects 120

that over 50% of the species’ natural habitat has been lost and converted to 121

anthropogenic land-uses in the past century [12]. 122

6

Jaguars are known to be range resident with young dispersing from their 123

natal area after becoming independent [16]. Several studies have estimated jaguar 124

home range across the species’ distributional range [17–20]. Nevertheless, no study 125

has accounted for the inherent autocorrelation structure of the movement data when 126

calculating jaguar home range estimates. Resulting home ranges are likely 127

underestimated [11]. Moreover, former studies lacked an empirical way to 128

characterize range residency. Thus, published results could include estimates of 129

“home range” for individuals that are not actually range resident but may be 130

dispersing or transient. It remains unclear if study-specific differences in jaguar home 131

range estimates are ecologically-based or the result of methodological artifacts [18]. 132

Surprisingly few studies have investigated jaguar movement to date[16,21–23], due 133

in part to the difficulty in locating and fitting individuals with monitoring devices. 134

Differential movement strategies have been reported between sexes, with males 135

moving greater distances and females being more restricted to home range center 136

points [21,24]. However, more detailed analyses, utilizing quickly evolving and 137

advanced analytical tools from movement ecology, are urgently needed on the 138

movements of jaguar across differing habitats, particularly across differing degrees of 139

habitat disturbance and fragmentation. 140

141

New perspectives on movement data analysis 142

The minimum convex polygon (MCP) and kernel density estimators (KDE) 143

are the most commonly used tools to estimate animal home ranges [11]. Both tools 144

have limitations that are often not acknowledged. MCPs, for example, lack any 145

underlying probabilistic model while KDEs are derived under the assumption of 146

independent and identically distributed data (IID), a process that assumes 147

uncorrelated positions and velocities and which cannot be recognized as a 148

7

movement model [11,25]. Incorporating autocorrelated data in conventional KDE 149

frameworks has the potential to underestimate the size of animal home ranges, 150

especially as the temporal frequency of positions collected increases [11] - a 151

situation common with modern GPS tracking devices. 152

New approaches such as fitting continuous-time stochastic movement models 153

to animal tracking data can account for inherent serial autocorrelation [26]. 154

Movement analyses using this method have a number of desirable properties, 155

including the ability to handle irregular sampling schedules (including gaps in the 156

data) and complex autocorrelation structures [25,27–29]. This approach includes 157

variogram analysis [27] and non-Markovian maximum likelihood estimation [28]. The 158

formerfacilitatesidentifyingimportantfeatures in the data (e.g., range residency), 159

whilethelatterallowsmodelsincorporatingthesefeaturestoberigorouslyfittothe data. 160

Once an appropriate continuous-time model has been selected and fit, 161

Autocorrelated Kernel Density Estimation (AKDE) then conditions on the fitted model 162

to allow accurate home range estimation even when data are strongly 163

autocorrelated[11]. Calabrese et al.(2016) [25] give a detailed account of using the 164

continuous-time movement modeling (ctmm) R package to perform this sequence of 165

analyses. 166

167

Jaguar motion in focus 168

We used the AKDE method to estimate home range, to evaluate path tortuosity, and 169

to quantify average distance traveled by jaguar across different regions of Brazil and 170

Argentina. Our first step involved characterizing the underlying movement behavior. 171

We expected adult jaguars to be range resident. Second, we expected larger home 172

range estimates than previously reported for the species, due to appropriately 173

accounting for the autocorrelation structure of the data [17,20,30–33]. We calculated 174

estimates of home range using both KDE and AKDE methods, demonstrating 175

8

ramifications. We also expected differential space use and movement between 176

individuals and sexes [18,23]. We predicted that jaguar inhabiting regions with poor 177

habitat quality (e.g., areas with high human presence and high levels of habitat loss) 178

to have larger home ranges. Animals throughout these regions should exhibit more 179

directional and persistent movement, with greater average distance traveled per day 180

[6]. 181

182

Material and Methods 183

184

Study area and data collection 185

We used GPS tracking to monitor 44 Jaguars from 1998 to 2016 and across 186

different habitats representing five biomes in Brazil and Argentina. Our dataset 187

represents the largest collection of jaguar movement data analyzed to date. Biomes 188

included the Amazon (4 males: 4 females), Atlantic Forest (6:6), Caatinga (2:0), 189

Cerrado (1:0), and Pantanal (9:12) (Fig. 1 and Table 1).190

9

Fig 1.Map of study areas in Brazil and on the borde r of Brazil and Argentina. 191

Source: mma.gov.br and wwf.org 192

10

Table 1 Extent and conservation status of remaining habitat in Brazil’s major biomes and a portion of Atlantic Forest in Argentina. Jaguars are 193

considered vulnerable in the Amazon and Pantanal, endangered in the Cerrado and critically endangered in the Atlantic Forest and 194

Caatinga[14,15]. 195

Amazon Atlantic Forest Caatinga Cerrado Pantanal

Biome area (km2)

4,196,943 1,110,182 844,453 2,036,448 150,355

Percentage of Brazil (%)

49.3 13.0 9.9 23.9 1.8

Biome remaining (%)

82.3 12.0 52.5 51.6 84.7

Protected Areas (%)

49.1 9.6 7.7 12.3 4.6

Mean habitant density per Km2

3 77 13 5 3

Mean livestock density per Km2

0.15 21.8 8.0 48 32.2

Jaguar Density (individuals per 100km2)

10.0 0.45-2.2 2.67 2.0 10.3

Source:http://siscom.ibama.gov.br/monitorabiomas/, [18,34–37] 196

11

197

The GPS collection schedules and time periods each animal was monitored 198

ranged from one position every half hour to one position every 24 hours. Estimated 199

ages of jaguars ranged from 18 months to 10 years, with the majority of jaguar (n = 200

41) being adults (> 3 years old). Two individuals (Esperança and Xango) were 201

monitored for two different periods. Monitoring periods ranged from 11 to 1,749 days 202

(mean = 183 days), while the number of recorded locations ranged from 53 to 10,989 203

(mean = 2,264). The total dataset consisted of 80,553 locations. Further details on fix 204

schedules, the number of days each animal was monitored, and the devices used to 205

monitor movement, are provided in S1 Table. All animals were captured following 206

standard protocols approved by the Instituto Chico Mendes de Conservação da 207

Biodiversidade - Ministério do MeioAmbiente - Brazil (ICMBio-SISBIO license 208

numbers: 30896-3, 46031-4, 36740-1, 44677-1,14202-4, 38006-1, 30053-1, 37867-209

1), the National Park Administration (NPA license 03/09), and Misiones Province 210

Government (ME license 119/2012) fromArgentina. 211

Collar fitting involved using trained dogs [38], box traps [30] and/or foot 212

snares [39] to facilitate animal capture. All individuals were anaesthetized with a 213

combination of tiletamine and zolazepam (10 mg kg-1), administered via an aluminum 214

dart fired from an air-powered rifle [38]. We examined each immobilized animal for 215

general body condition, determined its sex and age, collected its weight, and fit each 216

jaguar with a global positioning system collar. Animals were released at the site of 217

capture. All procedures followed guidelines approved by the American Society of 218

Mammologists[40]. 219

220

Home range and movement 221

12

We calculated variograms, fit movement models, and estimated home ranges 222

using the ctmm package [25,41] in the R environment for statistical computing [42]. 223

For each animal, we plotted the estimated semi-variance (function variogram)as a 224

function of time lag to visually inspect the autocorrelation structure of the location 225

data [27]. At zero to short time lags, a linear increase in the semi-variance 226

corresponds to uncorrelated velocity, suggesting movement models such as 227

Brownian motion (BM) or Ornstein-Uhlenbeck (OU). Upward curvature at these time 228

lags indicates velocity autocorrelation and suggests movement models such as 229

Integrated OU (IOU) or OU with foraging (OUF) [25,27]. Space use was investigated 230

by inspecting the behavior across longer time lags. Range residents are expected to 231

reach an asymptote on a timescale that roughly corresponds to the home-range 232

crossing time [25,27]. Individuals whose plotted semi-variance did not approach an 233

asymptote, however, were not assumed to be range resident [25]. These animals 234

were either not monitored for a long enough time period or did not exhibit behaviors 235

that meet the definition of a range resident and were removed from further analysis. 236

Models were fit via maximum likelihood (function ctmm.fit) [28] and ranked 237

based on AICc[43] (function ctmm.select) [25]. We estimated home range conditional 238

on the fitted, selected model for each individual using the akde function. OU models 239

are described by two parameters [i.e., home range crossing time (days) and variance 240

(km2)], while OUF models are described by three parameters [i.e., home range 241

crossing time (days), velocity autocorrelation timescale (h), and variance (km2)]. 242

These procedures resulted in estimates of the home range, home range crossing 243

time, velocity autocorrelation timescale and average distance traveled for each 244

individual if the selected model was OUF, or home range and home range crossing 245

time if the best model was OU. To show that KDE underestimates home range, we 246

calculated conventional KDEs (95%) for each animal, also fit in ctmm by passing a 247

13

fitted Independent Identically Distributed (IID) model (i.e., a model that, by definition, 248

ignores autocorrelation in the data) to the akde function [25]. 249

250

Statistical Analyses 251

To test our predictions that animal space use and movement (i.e., home 252

range, home range crossing time, velocity timescale and average distance traveled) 253

varied with gender and biome (i.e., Atlantic rainforest vs. Pantanal vs. Amazon), we 254

compared results using hierarchical Bayesian fixed-effect one-way ANOVAs [44,45]. 255

We tested for normality using Shapiro-Wilk tests [46] and by visually inspecting Q-Q 256

plots. Data were log transformed if dataset distributions did not meet model 257

assumptions. We estimated marginal posterior distributions of parameters using 258

Markov chain Monte Carlo (MCMC) methods. We fit a MCMC algorithm with 100,000 259

iterations and a 20% burn-in period (i.e., 20,000 iterations). We assessed 260

convergence by visually inspecting trace plots to ensure a reasonable exploration of 261

the parameter space and by confirming that the potential scale reduction factor was 262

<1.02 for each variable [47]. We calculated the probability (P) that the mean of one 263

group was greater than the other by sampling from each of the resulting posterior 264

distributions (10,000 iterations) [48]. We implemented all analyses in program R 265

using the rjags package [49], JAGS version 4.2.0. 266

We used human population (LandScan[50]) as a corollary of habitat quality 267

assuming that as human population density increases, habitat quality decreases 268

[51,52]. To assess the effect of human population (square root transformed) on home 269

range estimates, we fit linear regression models in a Bayesian framework. Models 270

were implemented in JAGS 4.2.0 [52] in the R programming language following 271

methods previously described [44,53]. We assessed model fit by calculating the 272

14

Bayesian p-value – the proportion of times when the replicated “ideal” dataset is 273

greater than the actual dataset [44,48]. Values close to 0.50 indicate a good model fit 274

(i.e., no difference between the two datasets). Model variability was displayed by 275

randomly sampling (10,000 times) from the posterior distributions of the alpha and 276

beta parameters. 277

278

Results 279

Range residency of jaguars 280

Thirty-three (33) individuals (15 males and 18 females) were determined to 281

be range resident after variogram inspection. Eight individuals (5 male, 3 female) 282

were assumed to be non-residents (Fig 2). Three individuals were monitored for too 283

short of a period (< 27 days) to determine movement behavior. Comparative 284

analyses across biomes excluded animals from the Caatinga and Cerrado since no 285

animals were considered residents (Caatinga) or due to a lack of an adequate 286

sample size (Cerrado). 287

Fig 2.(A)Variogram of a resident jaguar. Notice tha t the animal’s semi-variance 288

reaches an asymptote within a few days, roughly rep resenting the time to 289

cross its home range. The red line represents the f itted model and the red 290

shading represents the 95% CI. (B) A non-resident j aguar. Note the lack of a 291

clear asymptote despite the fact that the animal wa s monitored for a long 292

period (591 days). This lack of asymptote indicates that this animal is not 293

range resident and thus a home range analysis for t his individual is not 294

appropriate. For both A and B, the fraction of the variogram displayed is 65% 295

of the duration of each dataset. 296

15

297

Individual home range and movement of jaguar. 298

Comparison between AKDE and KDE 299

Home range and movement (home range crossing time, velocity timescale, 300

and average distance traveled) estimates varied between individuals (Table 2). 301

Home range estimates using AKDE were 1.0 to 4.8 times larger than estimates 302

obtained using KDE. Other than a few exceptions, AKDE 303

estimateswerelargerthanestimatespreviouslyreported (Table 3). 304

305

16

Table 2. Movement parameters and home range sizes for GPS-collared jaguar across Brazil and Argentina biomes. Home ranges were 306

estimated via 95% Kernel Density Estimates (KDE) and Autocorrelated Kernel Density Estimates (AKDE)1. 307

ID Sex/age (years)

Number of fixes/days

Home range crossing

time (day)

Velocity autocorrelation timescale (h)

Average distance traveled (km/day)

95%KDE (km2)2 AKDE (km2) (95% CI)

Amazon

Baden M/9 1,024/507 6.8 2.6 4.4 169.5 207.0 (168.8-249.3)

Caculao M/7 516/190 5.9 3.3 4.6 180.3 253.7 (187.7-329.6)

Confuso M/9 61/251 3.4 1.9 4.3 67.6 75.9 (39.3-124.5)

Coto F/7 501/154 9.7 2.5 2.3 53.0 85.5 (52.9-125.9)

Mamad* M/7 295/383 20.4 NA NA 174.3 309.7 (167.2-495.3)

Mamae F/11 784/333 4.8 0.9 4.2 43.7 49.4 (41.3-58.2)

Mudinha F/5 3,700/429 7.7 1.0 3.9 53.6 70.2 (58.1-83.4)

Atlantic Forest

Cassio M/6 159/159 1.5 NA NA 108.5 110.9 (92.1-131.4)

Denis M/5 797/370 4.5 0.9 15.4 414.9 502.1 (435.9-572.9)

Femea* F/5 211/139 4.3 NA NA 85.6 113.1 (85.2-145.0)

17

Gigi F/7 35/1,749 2.6 NA NA 233.5 246.2 (164.4 -344.3)

Livia* F/7 183/209 18.5 NA NA 230.4 718.6 (312.9-1290.1)

Taia* F/4 326/1,141 7.3 NA NA 183.2 250.7 (187.4-323.1)

Guacurari M/7 7,668/220 6.2 0.5 15.3 421.4 560.8 (431.7-706.6)

Naipi* F/2 53/119 2.5 NA NA 137.6 143.8 (98.8-197.0)

Yasirandi F/6 322/224 1.6 2.2 7.0 134.5 135.6 (117.0-155.5)

Zezao* M/8 156/171 2.1 NA NA 591.4 677.4 (550.7-817.1)

Cerrado

Xango 1 M/? 1,633/153 6.9 0.8 18.3 722.5 1,268.6 (831.9-1,795.8)

Xango 2 M/? 799/179 4.5 1.9 14.3 807.4 1,163.2 (904.8-1,453.6)

Pantanal

Anderson M/7 5,040/260 3.3 0.3 8.7 25.0 37.2 (32.1-47.2)

Caiman M/5 2,303/135 4.5 0.4 8.9 70.8 144.0 (78.9-144.0)

Dale M/7 4,705/252 9.3 0.3 6.7 58.4 91.9 (66.3-121.7)

Fera F/3 4,952/255 4.7 0.4 5.7 25.2 34.8 (30.2-39.7)

Milagre M/6 3,339/191 12.8 0.4 7.2 54.7 174.3 (105.0-261.0)

18

Selema F/6 2,817/126 4.2 0.4 5.8 23.7 37.8 (28.1-48.8)

Wendy* F/5 1,287/192 8.1 NA NA 27.4 52.1 (36.0-71.2)

Brutus M/5 1,256/76 3.6 0.5 15.6 193.2 277.7 (189.3-382.8)

Chuva F/10 741/73 0.9 0.3 13.9 31.5 35.9 (29.1-43.4)

Esperanca 1 F/7 842/53 1.1 0.2 15.4 31.1 39.7 (31.8-48.3)

Esperanca 2 F/10 2,232/126 1.8 0.2 12.5 31.2 36.9 (31.5-42.7)

Nati M/10 758/52 2.5 0.4 15.8 98.1 175.5 (113.6-259.7)

Nusa F/10 2,201/127 2.5 0.4 8.9 46.5 58.0 (47.5-69.7)

Teorema F/7 4,643/275 2.3 0.3 11.4 50.4 61.0 (54.9-67.4)

Troncha F/10 1,324/87 2.8 0.3 14.3 111.2 138.6 (102.2-180.3)

Vida F/5 398/33 0.6 0.3 16.4 15.3 24.7 (19.2-30.9)

1We used ctmm for AKDE home range estimation, following procedures by Fleming et al. (2015) [11]and Calabrese et al. (2016)[25]. For most 308

animals we were able to fit an Ornstein-Uhlenbeck Foraging (OUF) process model (Fleming et al 2014a, b) [27,28]to estimate home range 309

area. Home ranges for animals marked with * were based on an Ornstein-Uhlenbeck (OU) process model. 310

2Confidence intervals can be estimated for KDE using the ctmmpackage [25]. These data, however,were small and not included311

19

312

Table 3 Jaguar home range estimatesfromtheAmazon, Atlantic Forest, Cerrado,and Pantanal usingtheautocorrelationkerneldensityestimator 313

(AKDE), minimumconvexpolygon (MCP),orkerneldensityestimator (KDE). For AKDE, MCP,and KDE wedisplay themean, 314

minimum,andmaximumhome range values. For AKDE,wealsodisplay 95% confidenceintervals. 315

316

Biome Method Home range (km2) Mean home range (km2) Reference

Female Male

Amazon AKDE 49.4-309.7 68.4 (23.3-113.4) (n=3)

211.6 (52.9-370.2) (n=4)

Thisstudy

Atlantic Forest

AKDE 110.9-718.6 268.0 (223.1- 702.4) (n=5)

462.8 (71.9-853.7) (n=4)

Thisstudy

Atlantic Forest

MCP 100%

8.8-138 39.4 (n=2) 88.7 (n=4) [30]

Atlantic Forest

MCP 100%

43.8-177.7 87.3 (n=5) 102 (n=2) [31]

Atlantic Forest

KDE 85% 87-173 130 (n=2) 147 (n=1) [38]

Cerrado AKDE NA NA 1,268.6 (831.9-1,795.8) (n=1)

Thisstudy

Cerrado MCP 228-265 228 (n=1) 265.2 (n=2) [33]

20

80%

Pantanal AKDE 24.7-277.7 52.0 (28.7-75.2) (n=10)

144.3 (56.3-232.2) (n=6)

Thisstudy

Pantanal MCP 100%

25-90 32.3 (n=3) 90 (n=1) [16]

Pantanal MCP 100%

97.1-168.4 139.6 (n=4) 152.4 (n=1) [54]

Pantanal Kernel 95%

NA 38.2 (n=5) 67.4 (n=3) [19]

AdaptedfromAstete et al. (2007) [18]. 317

21

Sex differences on home range and movement paramete rs of 318

jaguars 319

Movement and home range size varied widely between male and female 320

jaguar. The probability that the home range size of males (range: 37.2 to 1,268.6 321

km2) was greater than females (range: 24.7 to 718.6 km2) was 0.97 (Fig 3A and 3B). 322

Males, almost exclusively, took longer to cross their home ranges (P= 0.99; Fig 3C 323

and 3D). Male movement paths, represented by velocity autocorrelation timescale, 324

were proportionally more directional (P= 0.94; Fig 3E and 3F), with a greater distance 325

traveled per day (P = 0.84; Fig 3G and 3H) when compared with female jaguar. All 326

data are summarized in Table 2. 327

Fig 3. Boxplot and Posterior Density Estimates for male and female home 328

range (log km 2) [A and B], home range crossing time (log days) [C and D], 329

velocity autocorrelation timescale (h) [E and F], a nd average distance traveled 330

(Km/day) [G and H]. Black line represents the diffe rence between the posterior 331

distribution of males and females, red represents t he posterior distribution of 332

females and blue represents the posterior distribut ion of males. 333

334

Differences in home range and movement across areas with 335

differing degrees of habitat loss and human populat ion 336

density 337

We observed differences in home range size between biomes (Fig. 4). The 338

probability that the home ranges of Atlantic Forest male jaguar were greater than 339

individuals from the Amazon or Pantanal was 0.87 and 0.98, respectively. Similarly, 340

the probability that the home range of Atlantic Forest female jaguar were greater than 341

22

individuals from the Amazon or Pantanal was 0.99 and 1.0, respectively. 342

Fig 4.Boxplot of home range (km 2) for males and female jaguar by biome. 343

Differential movements were observed across biomes. Female jaguar took 344

9.0 days (95% CI: 4.7-13.4), 7.4 days (95% CI: 1.3-9.4) and 2.3 days (95% CI: 0.1-345

6.4) to cross their home range in the Atlantic Forest, Amazon and Pantanal, 346

respectively. The probability that the velocity autocorrelation timescale of female 347

Amazon jaguar was greater than female Pantanal jaguar was 0.89. The inverse, 348

however, was observed in relation to daily distance traveled (Pantanal> Amazon). 349

The home range crossing time for male Amazon jaguar was greater than male jaguar 350

from the Pantanal or Atlantic Forest, although male jaguar from Pantanal took more 351

days to cross their home range than animals from the Atlantic Forest (Amazon 352

>Pantanal> Atlantic Forest). A similar result was found for the velocity autocorrelation 353

timescale. Average distance traveled was highest for jaguar inhabiting the Atlantic 354

Forest (Atlantic Forest >Pantanal> Amazon) (Table 4). 355

23

Table 4. Probability that the home range and movement parameter mean of male and female jaguars was different among locations (row vs 356

column). 357

Amazon Pantanal

Home

Range

(km2)

Home range

crossing time

(day)

Velocity

autocorrelation

timescale (h)

Average

Distance

traveled

(km/day)

Home

Range

(km2)

Home range

crossing time

(day)

Velocity

autocorrelation

timescale (h)

Average

Distance

traveled

(km/day)

Male

Amazon 0.85 0.85 0.99 0.02

Atlantic Forest

0.87 0.04 0.02 0.99 0.98 0.18 0.98 0.90

Female

Amazon 0.88 0.97 0.89 0.01

24

Atlantic Forest

0.99 0.66 NA NA 1.0 0.98 NA NA

NA- Not applicable, insufficient data358

25

Jaguar home ranges also increased in size with increasing human population 359

(Fig. 5). Bayesian p-value (0.495) indicates an adequate fit of the regression model 360

to the data. Males were most affected by human population size, represented by 361

increases in space use. The response of female jaguar was more restricted, with few 362

home ranges showing increases in relation to increases in human population. 363

Largest home ranges for male and female jaguar were observed across the Atlantic 364

Forest and Cerrado. 365

Fig 5 Jaguars’ home range estimates in relation to human population size 366

(square root transformed) across four study areas i n Brazil and Argentina. 367

Regression line is the species estimate from a line ar regression model 368

formulated in a Bayesian framework (Bayesian p-valu e=0.495). Error lines are 369

95% CI. 370

371

372

Discussion 373

The Autocorrelated Kernel Density Estimator isa recent analytical 374

development in movement ecology that removes the negative bias in home range 375

estimation by incorporating the autocorrelation structure inherent in most movement 376

datasets. This method yields better home range estimates, allowing movement 377

models to be fit to data with different temporal structures (e.g., irregular sampling 378

intervals, gaps, and short periods of data collection) [11]. Using this flexible 379

approach, we revealed important ecological processes in jaguar, including 380

heterogeneity in space use and movement owing to differences in individual, gender, 381

region, and habitat quality. 382

Our results provide support for differential movement behavior and space use 383

26

between individuals and sex. Additional differences were observed between regions 384

with differing degrees of human disturbance (i.e., population density), revealing 385

important aspects of jaguar ecology. Most large felids are broadly distributed and 386

inhabit different habitat types [2]. Habitat loss and increased human disturbance 387

have posed several threats to these species [2]. Our approach can help in providing 388

better information on the movement ecology of these species, resulting in an 389

important contribution to long-term conservation and management. 390

391

Jaguar residency 392

Several jaguars we tracked never established a home range. For juvenile 393

jaguars (< 3 years old), this may not be surprising [16]. But, we also discovered non-394

resident movement behavior in five adult jaguars (> 4 years old). Azevedo and 395

Murray (2007) [32] considered an animal ‘resident’ when it was observedtostay in 396

thesamearea for atleast 2 years. Such arbitrary classification may not be accurate. In 397

our study, we found a non-resident adult individual that had been monitored for 591 398

days (S1 Table). Consequently, time spent in an area is not adequate to estimate 399

home range and cannot explain how and why an individualuses space [55]. In 400

contrast, observing a clear asymptote in the variogram of an animal’s observed 401

movement track provides objective evidence of range residency [25,28]. 402

Recent research in movement ecology demonstrates that individuals of the 403

same species may exhibit different movement strategies under different 404

environmental conditions [56]. This seems to be well established for herbivores, such 405

as wildebeest, that can be migratory, nomadic or range resident [57,58]. Similar 406

behavioral and environmental plasticity have also been reported for carnivore 407

species such as lion [59], polar bear [60], wolverine [61], andwolf [62]. In our study, 408

we did not identify the underlying movement behavior of non-resident jaguars, 409

27

observed in jaguar collared in the Pantanal and the Caatinga biome. Non-resident 410

jaguar inhabiting the Pantanal exhibited a more directed linear movement path with 411

“short stops”. Non-resident jaguar inhabiting the Caatinga remained stationary for 412

long periods (2-3 months) before dispersing long distances (> 50 km) and returning 413

to their original location (data not shown). These movements could be described as 414

nomadic (as has been described for lions [59]) or potential migratory behavior. 415

Further investigation is required. 416

417

AKDE vs KDE 418

Our home range estimates using AKDE are larger than those reported in the 419

past and when compared with KDE estimates calculated on the same data (see 420

Tables 2 and 3). Although one might expect some variation in home range size when 421

monitoring different individuals [17,19,20,54], differences observed between previous 422

estimates are consistent and most likely represent the difference in how the 423

autocorrelation structure of the data was incorporated. Both MCP and KDE methods 424

ignore autocorrelation and have been proven to underestimate home range area 425

when used on autocorrelated tracking data [11,27]. AKDE accounts for 426

autocorrelation in the data and adjusts home range estimates accordingly (and with 427

appropriate confidence intervals) [32]. An accurate estimate of the home range can 428

result in vital insight into ecological processes[10]and provides a promising avenue 429

for further investigation. Most importantly, our estimates highlight that management 430

plans based on previouspublished results could severely underestimate the amount 431

of area required to adequately protect the species. 432

433

28

Males vsFemales 434

Ourfindingsthat male jaguar havelarger home ranges 435

thanfemalesisconsistentwithresultsfrompreviousstudiesof jaguar space 436

use[18,20,23]. A larger home range in terrestrial male carnivores has been 437

suggested to be bounded by the distribution of females and the need to increase 438

mating/reproductive opportunities [63]. Female home range size is known to be 439

shaped by the distribution of food availability, which is particularly important for 440

successful reproduction, including gestation and care of offspring [65]. Our findings 441

reinforce these observations, with female jaguar movement paths being 442

proportionally more tortuous (represented by smaller velocity autocorrelation 443

timescale)with smaller average distances traveled per day when compared with 444

males that are likely to take greater risks. 445

446

Differences in home range and movement across 447

areas with differing degrees of habitat loss and 448

human population density 449

450

Although differences in space use and movement have been reported for species 451

inhabiting different regions [56], this is the first study to report differential space use 452

and movement of jaguar across areas with different degrees of habitat loss and 453

human population density. Jaguars inhabiting the most disturbed biome, the Atlantic 454

Forest - with only 12% of habitat remaining and with high human population density, 455

resulted in large home range sizes and an increased average distance traveled. 456

29

Similarly, one jaguar inhabiting the Cerrado, a biome that has lost 50% of its natural 457

area [36], had the largest home range observed (1,268.6 km2). 458

We provide a broad overview of factors that can influence jaguar movement 459

decisions. At finer scales, several human activities such as livestock production, 460

poaching, and roads [52,64] influence jaguar space use and movement. Colchero et 461

al. (2011) [22] observed that human population density and roads have strong effects 462

on female jaguar movement decisions. Male jaguar, however, were less affected. In 463

our study, jaguar space use increased in areas with higher human population 464

presence and higher levels of habitat loss. While jaguar have been frequently 465

reported to prey on livestock [65], it is unclear if high livestock densities affect jaguar 466

movement decisions. Kanda (2015) [24], for example, observed that livestock did not 467

influence jaguar movement decisions. Instead, social interactions were reported to 468

be the main factor impacting jaguar movements. In our study, we were limited by the 469

resolution of the livestock density dataset (10km2) and therefore, did not evaluate the 470

response of jaguar to livestock. Incorporating a finer resolution dataset of livestock 471

density (< 1km2) combined with resource selection or step selection function analysis 472

[24] could be applied in future investigations (if available). 473

Movement distance is predicted to vary with spatial habitat pattern, increasing 474

across disturbed landscapes where risks increase [6]. As the Atlantic Forest and 475

Cerrado biomes continue to fragment, jaguars will likely have to travel longer 476

distances to locate mates and obtain resources. Increased home range size across 477

this lower quality habitat will most likely increase animals’ exposure to risk, including 478

increased vehicle collisions and poaching, and result in a situation often described as 479

an ecological trap [6]. 480

481

Conclusions 482

30

We compared home range size and movement behavior of jaguars monitored via 483

GPS collars across different temporal periods, unequal sampling intervals, and 484

varying autocorrelation structures. Two factors were critical for this achievement: 1) 485

the joint efforts of researchers working with the species across different sites in Brazil 486

and Argentina, resulting in the largest existing jaguar dataset with over 81,000 487

locations from 44 tracked individuals; and 2) the use of new analytical methods for 488

movement data.As hypothesized, we observed individual variability on space use 489

and movement, with male jaguars exhibiting larger home ranges, more directional 490

movements, and a higher probability of moving longer daily distances than females. 491

Jaguars inhabiting areas with higher human population size and higher levels of 492

habitat loss were also observed to have larger home rangesOur results fill a gap in 493

the knowledge of the species’ ecology and can contribute to long-term species 494

management and conservation. 495

496

Supporting Information 497

S1 TableList of the GPS collared jaguars with information on Biome, animal ID, sex 498

and estimated age (years), equipment used (tag brand and satellite system), 499

sampling protocol (time interval between locations), period of data collection, 500

coordinator and institution 501

502

Data Accessibility 503

Jaguars are listed as endangered or critically endangered in Brazil and Argentina. 504

They suffer high level of retaliation from ranchers, illegal hunting and tourism activity 505

31

across its distribution range [14]. Data, therefore, are highly sensitive. Raw data used 506

in analyses are available from the movebank database (https://www.movebank.org/) 507

for specific use and upon request to Ronaldo Gonçalves Morato 508

(ronaldo.morato@icmbio.gov.br), Agustin Paviolo (paviolo4@gmail.com), 509

EmilianoEsterciRamalho (eeramalho@gmail.com),Marina Motta Carvalho 510

(mmaribio@gmail.com), Mario Haberfeld (haberfeldm@gmail.com) and Fernando 511

Lima (pardalismitis@gmail.com). Contact the lead author for information. 512

513

Acknowledgements 514

We thank Antonio Carlos Csermak Junior, Apolonio N. S. Rodrigues, Cristina Gianni, 515

Dale Anderson, Jorge LuizPegoraro, Ivan Carlos Baptiston, Leanes Silva, Paulo 516

Roberto Amaral, Paulo Baptista,PollyanaMotinha, Rafael Garay, Raphael Xavier, 517

Rogério Silva de Jesus,Silvano Gianni, Tarcizio Paula, ThiagoLuczinski, 518

ValdomiroLemos,Wendy Debbas and the members and volunteers of 519

ProyectoYaguaretefor supporting on animal capture and monitoring. 520

Studies were funded by FAPESP (2013-10029-6), Cat Heaven Endangered 521

Species-Project Survival, Dallas World Aquarium,Orient Express Hotels do Brasil, 522

WWF Switzerland-Fundacion Vida Silvestre Argentina, Panthera Foundation and 523

Rufford Small Grant Foundation. RGM fellowship was funded by FAPESP (2014-524

24921-0). JMC and CHF were supported by a US National Science Foundation 525

Grant (ABI 1458748 to JMC). CNPq (Brazilian Government Research Council) 526

provided a research grant for MiltonCezarRibeiro (312045/2013-1), who also thanks 527

FAPESP (process 2013/50421-2) for their financial support. 528

529

32

Author Contributions 530

Conceptualization: RGM, JAS, PL. 531

Formal Analysis: RGM, JAS, PL, CHF, JMC. 532

Data Curation: RGM, AP, EER, MMC, MH, FL 533

Funding Acquisition: RGM, AP, CA, MSB, FL, LC, DAS, EER, MMC, MXS, MH, PL, 534

JMC 535

Investigation: RGM, RCP, KMPMF, DLZK, SSM, TDCP, GRA, AP, CA, MSB, PC, FL, 536

LC, DAS, EER, MMC, FHSS, BZ, MXS, MDFM, AV, JAM, MH, LR, LS, MCR. 537

Methodology: RGM, JAS, PL, CHF, JMC 538

Project Administration: RGM 539

Supervision: PL 540

Writing- Original Draft Preparation: RGM, JAS, CHF, JMC, RCP, KMPMF, DLZK, 541

SSM, TDCP, GRA, AP, CA, MSB, PC, FL, LC, DAS, EER, MMC, FHSS, BZ, MXS, 542

MDFM, AV, JAM, MH, LR, LS, MCR and PL 543

Writing- Review & Editing: RGM, JAS, MCR, PL 544

545

References 546

1. Chapman B, Hulthen K, Wellenreuther M, Hansson L-A, Nilsson JÅ, Bronmark 547

C. Patterns of animal migration. In: Hansson L-A, Akesson S, editors. Animal 548

movement across scales. 1st ed. Oxford: Oxford University Press; 2014. pp. 549

11–30. 550

2. Nowell K, Jackson P. Wild cats. Status Survey and Conservation Action Plan. 551

IUCN, Gland Switzerland. 1996; 110–113. doi:10.1023/A:1008907403806 552

33

3. Macdonald DW. The ecology of carnivore social behaviour. Nature. 1983;301: 553

379–384. 554

4. Crooks KR. Relative sensitivities of mammalian carnivores to habitat 555

fragmentation. Conserv Biol. 2002;16: 488–502. 556

5. Cardillo M, Purvis A, Sechrest W, Gittleman JL, Bielby J, Mace GM. Human 557

population density and extinction risk in the world’s carnivores. PLoS Biol. 558

2004; doi:10.1371/journal.pbio.0020197 559

6. Fahrig L. Non-optimal animal movement in human-altered landscapes. Funct 560

Ecol. 2007;21: 1003–1015. doi:10.1111/j.1365-2435.2007.01326.x 561

7. Nathan R. An emerging movement ecology paradigm. Proc Natl Acad Sci U S 562

A. 2008;105: 19050–19051. doi:10.1073/pnas.0808918105 563

8. Chetkiewicz C-LB, Boyce MS. Use of resource selection functions to identify 564

conservation corridors. J Appl Ecol. 2009;46: 1036–1047. doi:10.1111/j.1365-565

2664.2009.01686.x 566

9. Rodríguez-Soto C, Monroy-Vilchis O, Maiorano L, Boitani L, Faller JC, Briones 567

MÁ, et al. Predicting potential distribution of the jaguar (Panthera onca) in 568

Mexico: Identification of priority areas for conservation. Divers Distrib. 569

2011;17: 350–361. doi:10.1111/j.1472-4642.2010.00740.x 570

10. Horne JS, Garton EO. Selecting the best home range model: An information-571

theoretic approach. Ecology. 2006;87: 1146–1152. 572

11. Fleming CH, Fagan WF, Mueller T, Olson KA, Leimgruber P, Calabrese JM. 573

Rigorous home range estimation with movement data: a new autocorrelated 574

kernel density estimator. Ecology. 2015;96: 1182–1188. doi:10.1890/14-575

2010.1 576

12. Sanderson EW, Redford KH, Chetkiewicz C-LB, Medellin RA, Rabinowitz AR, 577

Robinson JG, et al. Planning to Save a Species: the Jaguar as a Model. 578

Conserv Biol. 2002;16: 1–15. 579

13. Caso A, Lopez-Gonzalez C, Payan E, Eizirik E, de Oliveira T, Leite-Pitman R, 580

et al. Panthera onca. In: The IUCN Red List of Threatened Species [Internet]. 581

2008 [cited 8 Dec 2015] p. e.T15953A5327466. Available: 582

http://dx.doi.org/10.2305/IUCN.UK.2008.RLTS.T15953A5327466.en. 583

14. Morato RG, Beisiegel BDM, Ramalho EE, Boulhosa LRP. Avaliação do risco 584

de extinção da onça-pintada Panthera onca (Linnaeus, 1758) no Brasil. 585

Biodiversidade Bras. 2013;3: 122–132. 586

15. G A, E C, De Angelo C, Di Bitetti MS, Lucherini M, Muzzachiodi N, et al. 587

Familia: Felidae. In: Ojeda R., Chillo V, Isenrath G, editors. Libro Rojo de 588

Mamiferos Amenazados de la Argentina. 1st ed. Mendoza: SAREM; 2012. pp. 589

92–101. 590

16. Schaller GB, Crawshaw PGJ. Movement Patterns of Jaguar. Biotropica. 591

34

1978;12: 161–168. doi:10.2307/2387967 592

17. Rabinowitz AR, Nottingham, Jr. BG. Ecology and behaviour of the Jaguar 593

(Panthera onca) in Belize, Central America. Journal of Zoology. 1986. pp. 594

149–159. 595

18. Astete S, Sollmann R, Silveira L. Comparative Ecology of Jaguars in Brazil. 596

CAT News. 2007; 9–14. 597

19. de Azevedo FCC, Murray DL. Spatial organization and food habits of jaguars 598

(Panthera onca) in a floodplain forest. Biol Conserv. 2007;137: 391–402. 599

doi:10.1016/j.biocon.2007.02.022 600

20. Cavalcanti SMC, Gese EM. Spatial ecology and social interactions of jaguars 601

(Panthera onca) in the southern Pantanal, Brazil. J Mammal. 2009;90: 935–602

945. doi:10.1644/08-MAMM-A-188.1 603

21. Conde DA, Colchero F, Zarza H, Christensen NL, Sexton JO, Manterola C, et 604

al. Sex matters: Modeling male and female habitat differences for jaguar 605

conservation. Biol Conserv. 2010;143: 1980–1988. 606

doi:10.1016/j.biocon.2010.04.049 607

22. Colchero F, Conde D a., Manterola C, Chávez C, Rivera a., Ceballos G. 608

Jaguars on the move: Modeling movement to mitigate fragmentation from road 609

expansion in the Mayan Forest. Anim Conserv. 2011;14: 158–166. 610

doi:10.1111/j.1469-1795.2010.00406.x 611

23. Sollmann R, Furtado MM, Gardner B, Hofer H, Jácomo ATA, Tôrres NM, et al. 612

Improving density estimates for elusive carnivores: Accounting for sex-specific 613

detection and movements using spatial capture–recapture models for 614

jaguars in central Brazil. Biol Conserv. 2011;144: 1017–1024. 615

doi:10.1016/j.biocon.2010.12.011 616

24. Kanda Z. Ecologia do movimento e dinâmica espaço-temporal da onça 617

pintada no Pantanal sul do Brasil. Universidade Estadual de São Paulo. 2015. 618

25. Calabrese JM, Fleming CH, Gurarie E. Ctmm : an R Package for Analyzing 619

Animal Relocation Data As a Continuous-Time Stochastic Process. Methods 620

Ecol Evol. 2016; doi:10.1111/2041-210X.12559 621

26. Fleming CH, SubaşI Y, Calabrese JM. Maximum-entropy description of animal 622

movement. Phys Rev E - Stat Nonlinear, Soft Matter Phys. 2015;91: 1–6. 623

doi:10.1103/PhysRevE.91.032107 624

27. Fleming CH, Calabrese JM, Mueller T, Olson K a, Leimgruber P, Fagan WF. 625

From fine-scale foraging to home ranges: a semivariance approach to 626

identifying movement modes across spatiotemporal scales. Am Nat. 2014;183: 627

E154-67. doi:10.1086/675504 628

28. Fleming CH, Calabrese JM, Mueller T, Olson KA, Leimgruber P, Fagan WF. 629

Non-Markovian maximum likelihood estimation of autocorrelated movement 630

35

processes. Methods Ecol Evol. 2014;5: 462–472. doi:10.1111/2041-631

210X.12176 632

29. Blackwell PG, Niu M, Lambert MS, Lapoint SD. Exact Bayesian inference for 633

animal movement in continuous time. Methods Ecol Evol. British Ecological 634

Society; 2016;7: 184–195. 635

30. Crawshaw Jr. PG. Comparative ecology of ocelot (Felis pardalis) and jaguar 636

(Panthera onca) in a protected subtropical forest in Brazil and Argentina. 637

University of Florida. 1995. 638

31. Cullen Jr. L, Abreu KC De, Sana D, Nava AFD. As onças-pintadas como 639

detetives da paisagem no corredor do Alto Paraná, Brasil. Nat e Conserv. 640

2005;3: 43–58. 641

32. de Azevedo FCC, Murray DL. Spatial organization and food habits of jaguars 642

(Panthera onca) in a floodplain forest. Biol Conserv. 2007;137: 391–402. 643

doi:10.1016/j.biocon.2007.02.022 644

33. Silveira L. Ecologia comparada e conservação da onça-pintada (Panthera 645

onca) e onça-parda (Puma concolor), no Cerrado e Pantanal [Internet]. 646

Universidade de Brasília. University of Brasilia. 2004. Available: 647

http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Ecologia+co648

mparada+e+conserva??o+da+on?a-pintada+(Panthera+onca)+e+on?a-649

parda+(Puma+concolor),+no+Cerrado+e+Pantanal#0 650

34. Ribeiro MC, Metzger JP, Martensen AC, Ponzoni FJ, Hirota MM. The Brazilian 651

Atlantic Forest: How much is left, and how is the remaining forest distributed? 652

Implications for conservation. Biol Conserv. Elsevier Ltd; 2009;142: 1141–653

1153. doi:10.1016/j.biocon.2009.02.021 654

35. Leal I, Silva J, Tabarelli M, Lacre Jr. T. Mudando o curso da conservação da 655

biodiversidade na Caatinga do Nordeste do Brasil. Megadiversidade. 2005;1: 656

139–146. 657

36. Machado RB, Neto MGP, Caldas EF, Gonçalves D a., Santos N a., Tabor K, 658

et al. Estimativas de perda da área do Cerrado brasileiro. … Int do Bras …. 659

2004; 1–23. doi:10.1590/S0104-42302007000600013 660

37. Robinson TP, William Wint GR, Conchedda G, Van Boeckel TP, Ercoli V, 661

Palamara E, et al. Mapping the global distribution of livestock. PLoS One. 662

2014;9. doi:10.1371/journal.pone.0096084 663

38. Morato RG, Conforti V a., Azevedo FC, Jacomo a. T a, Silveira L, Sana D, et 664

al. Comparative analyses of semen and endocrine characteristics of free-living 665

versus captive jaguars (Panthera onca). Reproduction. 2001;122: 745–751. 666

doi:10.1530/reprod/122.5.745 667

39. Balme GA, Slotow R, Hunter LTB. Impact of conservation interventions on the 668

dynamics and persistence of a persecuted leopard (Panthera pardus) 669

population. Biol Conserv. Elsevier Ltd; 2009;142: 2681–2690. 670

36

doi:10.1016/j.biocon.2009.06.020 671

40. Sikes R, Gannon W, Mammalogists TAC and UC of the AS of. Guidelines of 672

the American Society of Mammalogist for the use of wild mammals in 673

research. J Mammal. 2011;1: 235–253. 674

41. Fleming CH, Calabrese JM. ctmm: Continuous-Time Movement Modeling. R 675

package version 0.2.8. [Internet]. CRAN; 2015. Available: 676

http://cran.jellyfish.lol/web/packages/ctmm/index.html 677

42. R Development Core Team R. R: A Language and Environment for Statistical 678

Computing [Internet]. Team RDC, editor. R Foundation for Statistical 679

Computing. R Foundation for Statistical Computing; 2011. p. 409. 680

doi:10.1007/978-3-540-74686-7 681

43. Akaike H. A new look at the statistical model identification. IEEE Trans Autom 682

Control. 1974;19: 716–723. doi:10.1109/TAC.1974.1100705 683

44. Kéry M. Introduction to WinBUGS for Ecologists: A Bayesian approach to 684

regression, ANOVA, mixed models and related analyses. First Edit. Academic 685

Press; 2010. 686

45. McCarthy MA. Bayesian Methods for Ecology. Cambridge, New York, 687

Melbourne, Madrid, Cape Town, Singapore, Sau Paulo: Cambridge University 688

Press; 2007. 689

46. Royston P. The W test for normality. Appl Stat. 1982; 176–180. 690

47. Gelman A, Rubin D. Inference from iterative simulation using multiple 691

sequences. Stat Sci. 1992;7: 457–511. 692

48. Gelman a, Hill J. Data analysis using regression and multilevel/hierarchical 693

models. Policy Anal. 2007; 1–651. doi:10.2277/0521867061 694

49. Plummer M, Best N, Cowles K, Vines K. Coda: Output analysis and 695

diagnostics for MCMC. 2010. 696

50. Bright EA, Coleman PR, Rose AN, Urban ML. LandScan 2011. Digit dataset, 697

Oakridge Natl Lab Oakridge, TN, USA, web ornl gov/sci/landscan/index shtml. 698

2012; 699

51. Liu J, Taylor WW, Ouyang Z, Groop R, Tan Y, Zhang H. A framework for 700

evaluating the effects of human factors on wildlife habitat: The case of giant 701

pandas. Conserv Biol. 1999;13: 1360–1370. doi:10.1046/j.1523-702

1739.1999.98418.x 703

52. De Angelo C, Paviolo A, Wiegand T, Kanagaraj R, Di Bitetti MS. 704

Understanding species persistence for defining conservation actions: A 705

management landscape for jaguars in the Atlantic Forest. Biol Conserv. 706

2013;159: 422–433. doi:10.1016/j.biocon.2012.12.021 707

53. McCarthy M. Bayesian methods for ecology [Internet]. 1st ed. McCarthy MA, 708

37

editor. Chemistry & biodiversity. New York: Cambridge University Press; 2007. 709

doi:10.1017/CBO9780511802454 710

54. Crawshaw PG, Quigley HB. Jaguar spacing, activity and habitat use in a 711

seasonally flooded environment in Brazil. J Zool. 1991;223: 357–370. 712

Available: http://doi.wiley.com/10.1111/j.1469-7998.1991.tb04770.x 713

55. Powell RA, Mitchell MS. What is a home range? J Mammal. 2012;93: 948–714

958. doi:10.1644/11-MAMM-S-177.1 715

56. Singh NJNNJ, Börger L, Dettki H, Bunnefeld N, Ericsson G, Borger L, et al. 716

From migration to nomadism: Movement variability in a northern ungulate 717

across its latitudinal range. Ecol Appl. 2012;22: 2007–2020. doi:10.1890/12-718

0245.1 719

57. Boone RB, Thirgood SJ, Hopcraft JGC. Serengeti wildebeest migratory 720

patterns modeled from rainfall and new vegetation growth. Ecology. 2006;87: 721

1987–1994. 722

58. Stabach JA, Wittemyer G, Boone RB, Reid RS, J.S. Worden. Variation in 723

habitat selection by white-bearded wildebeest across different degrees of 724

human disturbance. Ecosphere. 2016; 725

59. Schaller GB. The Serengeti lion: A study of predator-prey relations [Internet]. 726

University of Chicago, Chicago, Illinois, USA. University of Chicago Press; 727

1976. doi:10.2307/1296618 728

60. Mauritzen M, Derocher AE, Oysten W. Space-use strategies of female polar 729

bears in a dynamic sea ice habitat. Can J Zool. 2001;79: 1704–1713. 730

61. May R, van Dijk J, Landa A, Andersen R, Andersen R. Spatio-temporal 731

ranging behaviour and its relevance to foraging strategies in wide-ranging 732

wolverines. Ecol Modell. 2010;221: 936–943. 733

doi:10.1016/j.ecolmodel.2009.12.024 734

62. Kittle AM, Anderson M, Avgar T, Baker JA, Brown GS, Hagens J, et al. Wolves 735

adapt territory size, not pack size to local habitat quality. J Anim Ecol. 2015;84: 736

1177–1186. doi:10.1111/1365-2656.12366 737

63. Mikael S. The mating tactics and spacing patterns of solitary carnivores. In: 738

Gittleman JL, editor. Carnivore behavior, ecology, and evolution. 1st ed. New 739

York: Cornell University Press; 1989. pp. 164–182. doi:10.1007/978-1-4757-740

4716-4_7 741

64. De Angelo C, Paviolo A, Di Bitetti M. Differential impact of landscape 742

transformation on pumas (Puma concolor) and jaguars (Panthera onca) in the 743

Upper Paraná Atlantic Forest. Divers Distrib. 2011;17: 422–436. 744

doi:10.1111/j.1472-4642.2011.00746.x 745

65. Carvalho E a. R, Zarco-González MM, Monroy-Vilchis O, Morato RG. 746

Modelling the risk of livestock depredation by jaguar in the Transamazon 747

38

Highway, Brazil. Basic Appl Ecol. Elsevier GmbH; 2015;16: 413–419. 748

doi:10.1016/j.baae.2015.03.005 749

750

39

751

752

40

7

53

75

4

01

23

4

0 2 4 6 8

A

Tim

e−lag (m

onths)

Semi−variance (square kilometers)

02

46

810

0 20 40 60 80

B

Tim

e−lag (m

onths)

Semi−variance (square kilometers)

755

756

41

757

758

42

43

759

760

44

Capítulo 2 761

762

Artigo em preparação para submissão ao periódico Ecography 763

764

Functional response and limiting factors in habitat selection by a top predator: the 765

jaguar as a model 766

767

Morato RG1, 2, 3*§, Stabach JA2*, Connette G2, Wittemyer G4, Calabrese JM2, De 768

Paula RC1, 3, Ferraz KMPM3, 5, Kantek DLZ6, Miyazaki SS6, Pereira TDC6, Araujo 769

GR7, Paviolo A8, De Angelo C8, Di Bitetti MS8, Cruz P8, Lima F9, 10, Cullen L9, Sana 770

DA3, Ramalho EE3, 11, Carvalho MM12, Soares, FHS12, Zimbres B13,Xavier MS14, 771

Moraes, MDF14, VogliottiA3,15, May Jr JA16, Haberfeld M16, Rampim L16, Sartorello L16, 772

Abade, LR17, Ribeiro MC10, Leimgruber P2 773

774

1 Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros, Instituto 775

Chico Mendes de Conservação da Biodiversidade, Atibaia, SP, Brazil. 776

2Conservation Ecology Center, Smithsonian Conservation Biology Institute, National 777

Zoological Park, Front Royal, VA, USA. 778

3 Instituto Pró-Carnívoros, Atibaia, SP, Brazil. 779

4 Department of Fish, Wildlife and Conservation Biology, Colorado State University, 780

CO, USA. 781

5 Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, 782

Piracicaba, Brazil. 783

45

6 Estação Ecológica Taiamã, Instituto Chico Mendes de Conservação da 784

Biodiversidade, Cáceres, MT, Brazil 785

7Departamento de Medicina Veterinária, Universidade Federal de Viçosa, Viçosa, 786

MG, Brazil 787

8Instituto de Biología Subtropical, Universidad Nacional de Misiones and CONICET, 788

Puerto Iguazú, Argentina. 789

9 IPÊ – Instituto de Pesquisas Ecológicas, Nazaré Paulista, SP, Brazil 790

10 Laboratório de Ecologia Espacial e Conservação, Instituto de Biociências, 791

Univesidade Estadual de São Paulo, Rio Claro, SP, Brazil. 792

11Instituto de DesenvolvimentoSustentávelMamirauá, Tefé, AM,Brazil. 793

12Instituto de Defesa e Preservação dos Felídeos Brasileiros, Corumbá de Goiás, 794

Go, Brazil. 795

13 Programa de Pós Graduação em Zoologia, Instituto de Ciências Biológicas, 796

Universidade de Brasília, DF, Brazil. 797

14Projeto Carnívoros do Iguaçu, Parque Nacional do Iguaçu, Instituto Chico Mendes 798

de Conservação da Biodiversidade, Foz do Iguaçu, PR, Brazil. 799

15 Universidade Federal da Integração Latino-Americana, Foz do Iguaçu, PR, Brazil. 800

16 Projeto Onçafari, Miranda, MS, Brazil 801

17University of Oxford, Oxford, UK 802

* These authors contributed equally to this work 803

§ Corresponding author: ronaldo.morato@icmbio.gov.br 804

805

46

1 Briefing 806

In this study, wefirst developed habitat selection models at the scale of the 807

home range (second order selection) that include factors jaguars are known or 808

suspected to respond to: 1) human presence(Conde et al. 2010, De Angelo et al. 809

2011); 2) livestock presence(Zarco-González et al. 2013, Carvalho et al. 2015); 810

3)forest coverage(De Angelo et al. 2011, Morato et al. 2014); 4)water(De Angelo et 811

al. 2011), and; 5) roads (Colchero et al. 2011, De Angelo et al. 2013). Second, we 812

developed habitat selection models within the home range (third and fourth order 813

selection)using the same habitat covariates. We had two main objectives: 1) to 814

quantify the spatial functional response, and; 2) to identify limiting factors at home 815

range and foraging scales. In both cases, we used a full model with individual and 816

study site as random effect to examine how the habitat selection coefficients 817

changed as a function of resource availability either at coarse or fine scale. Due to 818

limited data available we did not account for seasonal variation in the resource 819

availability or space use (Cavalcanti and Gese 2009). 820

Specifically, at home range scale; we expect a negative functional response 821

to human presence with jaguars avoiding contact with humans in areas with high 822

human population density, while showing indifference in areas with low human 823

population density. We also expect a negative functional response to water and 824

forest coverage for jaguars inhabiting arid areas, e.g., resource selection will 825

increase despite the low availability of these resources. Road is expected to exert a 826

negative response on jaguars. Finally, we expect a positive functional response to 827

livestock presence with higher encounter rates as a function of high livestock density. 828

At home range scale we expect human population density to be the limiting 829

factor for jaguars’ selection, while at fine scale (foraging) we expect jaguars’ 830

selection of forest coverage to be the most important. 831

47

2 Preliminary results 832

We confirmed that jaguars negatively respond to human presence while the 833

species, in general, selected areas with high percentage of forest coverage (Figure 1 834

and 2), however it is important to mention that response varied between individuals 835

and study site. We did not find any trend of species’ response to livestock density, 836

distance to roads and distance to rivers(Figure 3). In general, we observed a 837

functional response since changes in the landscape structure resultedin changes in 838

the use of specific habitat by the species. Also we observed differences between 839

individuals at the same study site. 840

At home range scale human population density is the main limiting factor for 841

the species’ habitat selection followed by forest coverage (Table 1). At foraging scale 842

forest coverage was identified as the main limiting factor (Figure 4). This result 843

reinforces the ideia that the hierarchy of a habitat selection for an individual of any 844

species should reflect the hierarchy of factors limiting an individual’s fitness 845

48

Table 1 Parameter coefficients of the variables analyzed for each study site at home range scale. Mean, standard deviation, 95% credible intervals, proportion of the posterior that has the same signal of the mean (probability), individuals’ variance and study sites’ variance.

Parameters Iguacu/

Iguazu

Morro do Diabo Invinhema Caatinga Cerrado Amazonia Taiama Caiman

Population density

Mean -8.4 -8.4 -8.7 -10.4 -8.7 -8.8 -11.0 -8.2

Standard Deviation 4.4 4.5 5.0 8.3 5.3 4.2 11.0 4.4

Credible intervals

-17.1 -0.5

-17 - -0.2 -20 - -0.5 -32 - -1.3 -20 - -0.1 -17.8 - -1.2 -42.8 -

-1.5 -17.1 –

0.01

Probability 0.98 0.97 0.98 0.98 0.98 0.99 0.99 0.98

Individuals’ variance 295.3

Study sites’ variance 33.5

Livestock density

Mean 0.91 0.98 0.90 0.86 0.90 3.3 1.0 1.1

Standard Deviation 5.1 5.2 5.3 5.2 5.4 6.1 5.0 5.1

Credible intervals

-10 – 12

-9.9 – 12 -9.9 – 12.2 -11 – 12 -10 – 12 -9.2 – 15.9 -9.7 –

12.1 -9.5 – 11.9

49

Probability 0.50 0.50 0.45 0.60 0.51 0.73 0.48 0.61

Individuals’ variance 913.4

Study sites’ variance 13.9

Distance to Rivers

Mean -1.2 -1.1 -0.6 -0.8 -0.18 -0.23 -7.4 -1.1

Standard Deviation 0.8 0.9 1.0 1.2 1.6 0.7 0.7 0.6

Credible intervals

-3 – 0.5

-2.9 – 0.7 -2.6 – 1.5 -3.2 – 1.7 -3.3 – 3.0 -1.6 – 1.1 -8.8 - -

6.0 -2.3 –

0.2

Probability 0.93 0.88 0.71 0.73 0.55 0.63 1.0 0.95

Individuals’ variance 3.6

Study sites’ variance 0.14

Distance to Roads

Mean -1.9 -0.2 0.50 -0.50 -0.02 0.37 0.15 -0.93

Standard Deviation 3.1 2.2 2.8 2.5 2.8 3.2 2.0 2.0

Credible -10 – -4.4 -3.9 – 7.9 -5.7 – 0.2 4.9 – 6.7 -6.7 – 6.0 -3.5 – -5.5 –

50

intervals 2.5 – 4.9 4.9 2.8

Probability 0.74 0.60 0.50 0.90 0.57 0.60 0.48 0.68

Individuals’ variance 78.2

Study sites’ variance 6.7

Forest Coverage

Mean 1.2 0.3 -0.3 -0.45 0.72 1.0 0.53 0.3

Standard Deviation 0.2 0.2 0.2 0.3 0.3 0.2 0.1 0.1

Credible intervals

0.1 – 1.7

-0.1 – 0.7 -0.7 – 0.1 -1.1 – 0.2 0.08 – 1.4 0.7-1.4 0.27 –

0.79 0.01-0.54

Probability 1.0 0.90 0.90 0.90 0.98 1.0 0.99 0.98

Individuals’ variance 0.14

Study sites’ variance 0.54

51

Figure 1 Relative probability of use in relation to human population density at each study site. Iguaçu National Park (PNI), Morro do Diabo State Park (MDSP), Invinhema State Park (ISP), Caatinga (CA), Cerrado (CE), Mamirauá Sustainable Reserve (MSR), Taiama Ecological Station (TES) and Caiman Farm (CF).

Figure 2 Relative probability of use in relation to forest coverage at each study site. Iguaçu National Park (PNI), Morro do Diabo State Park (MDSP), Invinhema State Park (ISP), Caatinga (CA), Cerrado (CE), Mamirauá Sustainable Reserve (MSR), Taiama Ecological Station (TES) and Caiman Farm (CF).

Figure 3 Functional responses in habitat selection (home range scale) for jaguars. Selection coefficients were estimated for each individual using a discrete choice analysis with individual and study site as random effect. Iguaçu National Park (PNI), Morro do Diabo State Park (MDSP), Invinhema State Park (ISP), Caatinga (CA), Cerrado (CE), Mamirauá Sustainable Reserve (MSR), Taiama Ecological Station (TES) and Caiman Farm (CF).

Figue 4 Response in habitat selection for jaguars at foraging scale. Selection coefficients were estimated for each individual using a discrete choice analysis with individual and study site as random effect. Iguaçu National Park (PNI), Morro do Diabo State Park (MDSP), Invinhema State Park (ISP), Caatinga (CA), Cerrado (CE), Mamirauá Sustainable Reserve (MSR), Taiama Ecological Station (TES) and Caiman Farm (CF).

52

0 10 20 30 40

0.0

0.2

0.4

0.6

0.8

1.0

Human population density

Rel

ativ

e pr

obab

ility

of u

sePNI

MDSP

ISP

CA

CE

MSR

TES

CF

53

0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

1.0

Forest Coverage

Rel

ativ

e pr

obab

ility

of u

se

PNI

MDSP

ISP

CA

CE

MSR

TES

CF

54

0 10 20 30 40

−100

−50

0

50

PNI MDSP ISP CA CE MSR TES CF

Human Population Density

0 10 20 30 40

−200

−100

0

100

200

PNI MDSP ISP CA CE MSR TES CF

Livestock Density

0 10 20 30 40

−30

−20

−10

0

10

PNI MDSP ISP CA CE MSR TES CF

Distance to Rivers

0 10 20 30 40

−40

−20

0

20

PNI MDSP ISP CA CE MSR TES CF

Distance to Roads

0 10 20 30 40

−4

−2

0

2

4

6

PNI MDSP ISP CA CE MSR TES CF

Forest Coverage

Individual Jaguars_Home Range_Random Effects

55

0 10 20 30 40

−0.4

−0.2

0.0

0.2

0.4

PNI MDSP ISP CA CE MSR TES CF

Human Population Density

0 10 20 30 40

−1.0

−0.5

0.0

0.5

1.0

PNI MDSP ISP CA CE MSR TES CF

Livestock Density

0 10 20 30 40

−3

−2

−1

0

1

PNI MDSP ISP CA CE MSR TES CF

Distance to Rivers

0 10 20 30 40

−2

−1

0

1

2

PNI MDSP ISP CA CE MSR TES CF

Distance to Roads

0 10 20 30 40

−1

0

1

2

PNI MDSP ISP CA CE MSR TES CF

Forest Coverage

Individual Jaguars_Foraging_Random Effects

56

Capítulo 3

Resumos de Congresso:

VII Seminário de Pesquisa e VII Encontro de Iniciação Científica do ICMBio

12-15 de setembro de 2016

Apresentação de Poster:

1) Ela é grande o suficiente? Importância da amplia ção da Estação Ecológica Taiamã e efetivação de corredores de dispersão para a manutenção de uma população mínima viável de onças pintadas ( Pantheraonca).

Ronaldo Gonçalves Morato1 (ronaldo.morato@icmbio.gov.br), Daniel Luiz Zanela Kantek2 (daniel.kantek@icmbio.gov.br), Rogerio Cunha de Paula1 (rogerio.paula@icmbio.gov.br), Selma S Myizaki2 (selma.onuma@icmbio.gov.br), ThadeuDeLuque Pereira2 (thadeu.pereira@icmbio.gov.br), Gediendeson Riberio Araújo3 (gediendson@gmail.com).

1-Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros(ICMBio), Estrada Municipal HisaichiTakebayashi, 8600 - Bairro da Usina, CEP: 12.952-011, Atibaia, SP, 2-Estação Ecológica Taiamã, Cáceres-MT, 3- Departamento de Medicina Veterinária, Universidade Federal de Viçosa, Viçosa-MG.

2) Padrões de Movimentação da Onça Pintada ( Patheraonca) no Parque Nacional da Serra da Capivara - PI.

Danieli Ribeiro, Julia Emi de Faria Oshima1, Milton Cezar Ribeiro1, Gediendson Ribeiro de Araujo2, Silvia Neri Godoy3, Rogerio Cunha de Paula3, Ronaldo Gonçalves Morato3

1-Laboratório de Ecologia Espacial e Conservação, Departamento de Ecologia, Instituto de Biociências, Universidade Estadual Paulista “Júlio de Mesquita Filho” (UNESP), Campus Rio Claro, Av. 24-A, 1515, CEP 13506-900, Rio Claro, SP.2-Departamento de Medicina Veterinária, Universidade Federal de Viçosa, Viçosa, MG. 3-Centro Nacional de Pesquisa e Conservação de Mamíferos Carnívoros(ICMBio), Estrada Municipal HisaichiTakebayashi, 8600 - Bairro da Usina, CEP: 12.952-011, Atibaia, SP.

Apresentação de Oral: 1o colocado: melhor trabalho de iniciação científica

3) Seleção de habitat de Pantheraonca (Linnaeus, 1758) em uma paisagem heterogênea no Pantanal sul matogrossense

57

Patrícia Kalaf Alves dos Santos1, Julia Emi de Faria Oshima1, Milton Cezar Ribeiro1, Claudia Zukeran Kanda1, Mario Haberfeld2, Lilian Rampin2, Leonardo Sartorello2, Rogerio Cunha de Paula3, Silvia Neri Godoy3,Ronaldo Gonçalves Morato3

1-Laboratório de Ecologia Espacial e Conservação, Departamento de Ecologia, Instituto de Biociências, Universidade Estadual Paulista “Júlio de Mesquita Filho” (UNESP), Campus Rio Claro, Av. 24-A, 1515, CEP 13506-900, Rio Claro, SP. 2-Projeto Onçafari, Refúgio Ecológico Caiman. 3-Centro Nacional De Pesquisa e Conservação De Mamíferos Carnívoros (ICMBio), Estrada Municipal HisaichiTakebayashi, 8600 - Bairro da Usina, CEP: 12.952-011, Atibaia, SP.

Resumo de Congresso:

23rd The WildlifeSocietyConference

October 17-20 2016

Oral presentation:

1) Differences in Jaguar Movementand Home Range SizeAccrossVariedLandscapes in Braziland Argentina.

Morato RG1, 2, 3*, Stabach JA2, Leimgruber P2, Fleming CH2, Calabrese JM2, De Paula RC1, 3, Kantek D4, Onuma S4, De Luque T4, Araujo G5, Paviolo A6, De Angelo C6, Di Bitetti M6, Cruz P6, Ramalho EE3, 7, Carvalho MM8, Xavier MS9, May Jr J10, Haberfeld M10, Rampim L10, Sartorello L10, Sana DA3, Lima F11, Cullen L11, Ferraz, KMPM12

1CENAP-ICMBio, 2CEC-SCBI, 3Instituto Pró-Carnívoros, 4Estação EcológicaTaiamã-ICMBio, 5UFV, 6IBS-CONICET, 7Instituto Mamirauá, 8NEX, 9Parque Nacional do Iguaçu-ICMBio, 10Projeto Onçafari, 11IPE, 12ESALQ-USP.