Skip to content
Publicly Available Published by De Gruyter February 24, 2018

Space use and movement of jaguar (Panthera onca) in western Paraguay

Roy T. McBride and Jeffrey J. Thompson ORCID logo
From the journal Mammalia

Abstract

Home range and core area size were estimated for jaguar (Panthera onca) in western Paraguay in the Dry Chaco, Humid Chaco and Pantanal using an autocorrelated kernel density estimator. Mean home range size was 818 km2 (95% confidence interval: 425–1981) in the Dry Chaco and 237 km2 (95% confidence interval: 90–427) in the Humid Chaco/Pantanal. Core areas, defined as the home range area where use was equal to expected use, was consistent across sexes and systems represented on average by the 59% home range isopleth (range: 56–64%). Males had a higher probability of larger home ranges and more directional and greater daily movements than females collectively and within systems. The large home ranges in the Dry Chaco are attributable to the relatively low productivity of that semi-arid ecosystem and high heterogeneity in resource distribution while larger than expected home ranges in the Humid Chaco/Pantanal compared to home range estimates from the Brazilian Pantanal may be due to differences in geomorphology and hydrological cycle. The large home ranges of jaguars in western Paraguay and a low proportional area of protected areas in the region demonstrate the importance of private ranchland for the long-term conservation of the species.

Introduction

Globally, apex predators, and the maintenance of their functional roles, are severely threatened due to anthropogenic pressures, particularly associated with large spatial needs to access sufficient prey to meet metabolic requirements and persecution (Ripple et al. 2014). Habitat conversion and degradation and over hunting of prey species increase spatial requirements of apex predators, increasing conflict with humans and affecting social behavior, dispersal and habitat use (Macdonald 1983, Crooks 2002, Cardillo et al. 2004, Ripple et al. 2014). Consequently, an understanding of the space use and movement ecology of apex predators is key to effective conservation decision making for these species.

The jaguar (Panthera onca) is the largest feline in the Americas, distributed from the southwestern United States to northern Argentina, although it presently occupies <50% of its original range, and <80% of the range outside of Amazonia, due to habitat loss and persecution (Sanderson et al. 2002, Zeller 2007, de la Torre et al. 2017). Given the contraction of the species’ distribution, range-wide conservation efforts have focused upon maintaining connectivity among key populations throughout the species’ range (Sanderson et al. 2002, Rabinowitz and Zeller 2010), however, an effective implementation of this management approach is partly dependent upon a thorough understanding of the spatial and movement ecology of jaguars.

For a big cat the jaguar is relatively understudied (Brodie 2009), and although multiple studies have estimated jaguar home range size (Schaller and Crawshaw 1980, Rabinowitz and Nottingham 1986, Crawshaw and Quigley 1991, Crawshaw 1995, Scognamillo et al. 2002, Crawshaw et al. 2004, Silveira 2004, Cullen 2006, Azevedo and Murray 2007, Cavalcanti and Gese 2009, Tobler et al. 2013, Morato et al. 2016) and movements (Conde et al. 2010, Colchero et al. 2011, Sollmann et al. 2011, Morato et al. 2016), there is still relatively little known about the species’ spatial and movement ecology. Since anthropogenic factors drive jaguar occurrence throughout its range by determining habitat availability and quality (Zeller et al. 2011, Petracca et al. 2014a,b, Thompson and Martínez Martí 2015) this conspicuous knowledge gap on how jaguars perceive and use the landscape is of concern as it limits managers’ ability to quantifiably design and manage conservation landscapes for the jaguar.

Range-wide, the jaguar is considered near threatened (Quigley et al. 2017), however, at the austral limit of its distribution the species is considered critically endangered in Argentina and endangered in Brazil and Paraguay. Although multiple studies have investigated space use by jaguar in Brazil and Argentina (Schaller and Crawshaw 1980, Crawshaw and Quigley 1991, Crawshaw 1995, Crawshaw et al. 2004, Silveira 2004, Cullen 2006, Azevedo and Murray 2007, Cavalcanti and Gese 2009, Morato et al. 2016) there has been no such research on the species in Paraguay despite a recognized need in the face of a rapid constriction in the species’ distribution in relation to a country-wide expansion of the agricultural sector (Secretaría del Ambiente et al. 2016) which has resulted in some of the highest rates of deforestation in the world (Hansen et al. 2013).

Given the status of the jaguar in Paraguay, the lack of information on the spatial and movement ecology of the species is of concern within the context of continued habitat loss, the maintenance of in-country and trans-boundary connectivity of populations, and their implications for the range-wide conservation of the jaguar. Consequently, GPS-based telemetry was used to study space use and movements of jaguars in western Paraguay in the Dry Chaco, Humid Chaco and Pantanal, the region with the largest jaguar population in the country. Moreover, developing methodologies were employed to determine home range residency and account for autocorrelation in the data (Fleming et al. 2014, 2015, Calabrese et al. 2016), allowing for rigorous comparisons with estimates from other research employing the same methodologies (Morato et al. 2016), which is a recognized need for jaguar spatial ecology (Gonzalez-Borrajo et al. 2017).

Based upon carnivore ecology in general, and jaguar ecology specifically, it was expected that male home range size and movement rates would be greater than those of females (Sandel 1989, Cavalcanti and Gese 2009, Conde et al. 2010, Sollmann et al. 2011, Morato et al. 2016, Gonzalez-Borrajo et al. 2017) and that jaguars in the Dry Chaco would exhibit larger home ranges, higher movement rates, and more directional movement compared to those in the more productive habitats of the Humid Chaco and Pantanal (Sandel 1989, Fahrig 2007, Gutiérrez-González et al. 2012). Also, when compared to other sites (Morato et al. 2016) it was expected that estimates from the Humid Chaco and Pantanal would be similar to those from the Brazilian Pantanal, while estimates from the Dry Chaco would be larger than those from more humid systems but possibly similar to jaguars from the Brazilian Cerrado due to biotic and abiotic similarities between systems. Apart from constituting an important contribution towards the conservation of jaguars within Paraguay, placing the results into a comparative context with research from neighboring countries will facilitate the efficacy of trans-boundary conservation efforts, with important implications for range-wide conservation strategies for jaguar.

Materials and methods

Study area

The study was conducted in three ecosystems in western Paraguay; Dry Chaco, Humid Chaco and Pantanal (Figure 1). The Dry Chaco is comprised of xeric forest, savannas and grasslands and the Humid Chaco and Pantanal are a mosaic of seasonally flooded grasslands, palm savanna and xerophilic woodlands on higher ground (Olson et al. 2001, Mereles et al. 2013). The delineations between the Humid Chaco and Pantanal differ (Olson et al. 2001, Mereles et al. 2013), however, for this study the similarities between systems and among study sites in those systems make this discrepancy moot and consequently the Humid Chaco and Pantanal were treated as a single system in the analysis.

Figure 1: Map showing the location of Paraguay in South America, the distribution of the Dry and Humid Chaco and Pantanal in western Paraguay (Olson et al. 2001) and study areas where jaguar movements were monitored.

Figure 1:

Map showing the location of Paraguay in South America, the distribution of the Dry and Humid Chaco and Pantanal in western Paraguay (Olson et al. 2001) and study areas where jaguar movements were monitored.

The western half of Paraguay is generally semi-arid with a pronounced east-west gradient in precipitation and humidity which divides the Chaco into the Humid Chaco with precipitation approximately >1000 mm/year and the Dry Chaco with precipitation <1000 mm/year (Olson et al. 2001). The Pantanal is also subjected to this east-west precipitation gradient; however, it and the Humid Chaco are also strongly effect by the hydrological cycles of the Rio Paraguay (Mereles et al. 2013).

In the Humid Chaco the study area was conducted on Estancia Aurora, a 30,000 ha cattle ranch in the north of the department of Villa Hayes and in the Pantanal on the 65,000 ha ranch Estancia Fortín Patria and on a 80,000 ha section of the ranch Estancia Leda. In the central Dry Chaco, research was undertaken on the 50,000 ha Faro Moro ranch and more northerly in the 720,000 ha Defensores del Chaco National Park and the neighboring 269,000 ha of ranchland of the former consortium Grupo Chovoreca.

Jaguar captures

Jaguars were captured using trained hounds to tree or bay jaguars which were then anesthetized using a weight-dependent dose of a mix of ketamine hydrochloride and xylazine hydrochloride injected by a dart shot from a tranquilizer gun (McBride and McBride 2007). Capture methods followed American Society of Mammalogy protocols (Sikes 2016) and in >60 captures and recaptures of jaguar and puma over the study period there were no deaths or noticeable injury to animals.

From 2002 to 2009 jaguars were fitted with Telonics Generation II, data store-on-board, GPS collars (Telonics, Mesa, AZ, USA) which were set to record locations at 4 h intervals. Starting in 2009 Northstar GPS collars (D-cell, Northstar, King George, VA, USA) programmed to record locations at 4 h intervals were used and beginning in 2012 Telonics Generation III GPS collars (Telonics, Mesa, AZ, USA) were used which were set to record locations daily every 2 h from 1800 to 0600 h.

Home range estimation

To estimate home ranges continuous-time stochastic movement models were fit to the telemetry data, incorporating variogram analysis of semi-variance in locations in relation to time lags to inspect the autocorrelation structure in the data over time and to account for variable sampling intervals (Fleming et al. 2014). Starting values derived from semi-variance functions were used for maximum likelihood model fitting with model selection based upon Akaike Information Criteria, adjusted for small sample size (AICc), and model weights (Fleming et al. 2014, 2015, Calabrese et al. 2016). If the best fitting model displayed range residency by an individual the model was used to estimate a home range using autocorrelated kernel density estimation (AKDE; Fleming et al. 2015).

Movement models tested were a random search model (Brownian motion) with uncorrelated velocities and no limits to space use, a random search model with constrained space use (Ornstein–Uhlenbeck, OU), and Ornstein–Uhlenbeck motion with foraging (OUF) which is the OU process with correlated velocities (Fleming et al. 2014, Calabrese et al. 2016). All these models account for autocorrelation in positions, while the OUF model accounts for autocorrelation in velocities and the OU and OUF models include range residency (home range). Consequently, the OU and OUF models produce estimates of home range size and home range crossing time, while the OUF model additionally estimates the velocity autocorrelation time scale (a measure of path sinuosity) and mean distance traveled per day (Fleming et al. 2014, Calabrese et al. 2016).

If individuals exhibited residency in their movements home range areas were estimated using AKDE based upon the best fitting model. When applied to serially autocorrelated data AKDE more accurately estimates home range compared to traditional kernel density estimation (KDE; Worton 1989) as KDE assumes independent identically distributed data (IID), which when violated, can result in greatly underestimated home range size (Fleming et al. 2015).

Semi-variogram analysis, model selection and AKDE were undertaken using the ctmm package (Calabrese et al. 2016) in R 3.3.2 (R Development Core Team 2010). Data collected with an irregular sampling schedule starting in 2012 were accounted for using the dt argument within the variogram function in the ctmm package (Calabrese et al. 2016). Additionally, for comparison with home range estimates from previous research using traditional home range estimators, both the 95% KDE home ranges under a model assuming IID and 95% minimum convex polygons (MCP) home ranges (Burt 1943) were estimated using the using the ctmm (Calabrese et al. 2016) and adehabitatHR (Calenge 2006) packages in R, respectively (Supplementary Table 1).

Core area estimation

Core areas of AKDE home ranges were estimated as the area encompassed within the isopleth where the proportional use of the estimated home range is equal to the predicted probability of use (Seaman and Powell 1990, Bingham and Noon 1997, Vander Wal and Rodgers 2012). This was determined by fitting an exponential curve to the isopleths of the AKDE home range of each individual at 10% increments from 10 to 90%, and at the 95 and 99% isopleths, in relation to the proportional area of the home range that each of those isopleths encompassed based upon the area of the 99% home range estimate. The threshold where proportional home range size begins to increase at a rate greater than the probability of use (slope=1; Seaman and Powell 1990, Bingham and Noon 1997, Vander Wal and Rodgers 2012) was determined to define the isopleth that represented the core area boundary.

Statistical analyses

For the statistical analysis jaguars from the Humid Chaco and the Pantanal were combined into a single group since the characteristics of the system are highly similar, with the delineation between the two systems debatable (Olson et al. 2001, Mereles et al. 2013), and consequently jaguars from those systems are subjected to similar ecological and anthropogenic drivers. Additionally, only individuals that exhibited residency in their movement behavior and space use [demonstrated by their variograms reaching an asymptote at approximately their home range crossing time (Fleming et al. 2014, Calabrese et al. 2016)] were included in the comparative analysis of differences between sexes and ecosystems.

A fixed-effect one-way analysis of variance (ANOVA) in a Bayesian modeling framework was used to test for differences in estimates of home range size, home range crossing time, directionality in movement (velocity autocorrelation time scale) and mean daily distance traveled between sexes (systems combined), between systems (sexes combined), between sexes within a system, and between same sexes between systems. Normality in the data was tested using the Shapiro-Wilk test and log-transforming the data when their distribution did not meet assumptions of normality.

The analysis was undertaken in R 3.2.2. (R Development Core Team 2010) using WinBUGS (Lunn et al. 2000) and the R2WinBUGS package (Sturtz et al. 2005). WinBUGS was run with three chains for 100,000 iterations and a 20,000 iteration burn-in period; confirming convergence by a scale reduction factor ≤1.1 and visual inspection of trace plots for lack of autocorrelation (Gelman and Hill 2007). Differences between groups were tested by taking 10,000 random samples from posterior distributions for each group of interest, comparing the proportional frequency (probability, p) that posterior estimates of parameters were different for males than females overall and within systems, different for all individuals between systems, and different between same sexes between the Dry Chaco and the Humid Chaco/Pantanal. The closer p is to 0 or 1 the greater the probability that the groups are different, while there is no difference between groups when p=0.5.

Results

Jaguar captures and data collection

From June 2002 to June 2014 35 jaguars were captured and collared, of which 19 individuals provided sufficient data for analysis; seven in the Dry Chaco (five males, two females), nine in the Humid Chaco (three males, six females) and three in the Pantanal (one male, two females) with estimated ages between 2 and 10 years (Table 1). Collars collected data between 52 and 439 days, obtaining from 148 to 3462 locations (Table 1).

Table 1:

Sex, age, sample characteristics and estimated movement parameters, AKDE home range, core area and core area isopleths for study jaguars in the Paraguayan Dry Chaco, Humid Chaco and Pantanal.

IDSex/age (year)Number of fixes/daysVelocity autocorrelation timescale (h)Home range crossing time (days)Average distance traveled (km/day)Home range (km2) (95% CI)Core (km2)Core area isopleths (%)
Dry Chaco
 DC1M/51094/3761.18.028.82143 (1558–2820)50459
 DC3M/2722/3631.811.57.9421 (288–580)10763
 DC4M/5620/821.93.515.0550 (349–797)18258
 DC6M/71387/3932.24.819.31063 (822–1335)32957
 DC7M/53462/4391.42.717.1445 (381–515)8564
 DC5F/61610/3861.111.511.8591 (411–805)17859
 DC2F/8921/3791.79.59.7511 (363–683)17656
Humid Chaco/Pantanal
 Pan2F/21694/3751.14.37.971 (58–85)2460
 HC5F/4593/2420.51.220.992 (75–110)2361
 HC4F/3288/2661.56.29.3270 (187–369)8657
 HC8F/1980/1700.210.213.7121 (71–183)3258
 HC7F/61668/3240.19.222.3246 (172–332)7357
 HC9F/6928/3620.29.713.9118 (83–159)3359
 Pan3M/6727/1921.43.416.6428 (320–550)13457
 HC3M/4983/1431.44.415.0424 (290–584)13856
 HC6M/10660/1330.95.513.4341 (216–494)9160
 Pan1F/41695/366NA3.5NA550 (349–797)2160
 HC1M/6148/88NA5.9NA958 (534–1505)28358
 HC2F/6280/54NA5.7NA73 (35–125)2257

Home range, core area and movement parameter estimates

Best fitting models for the movement of jaguars were either the OU or OUF models with 16 individuals demonstrating residency (Table 1). Estimated home range sizes varied between 86 and 2909 km2 and core areas between 21 and 504 km2. Core areas were represented by a consistent proportion of the home range; ranging between the 56 and 64% isopleths (Table 1).

Male and female mean home range size were 727 km2 (95% confidence interval (CI): 355–1954) and 255 km2 (95% CI: 90–578), respectively and 818 km2 (95% CI: 425–1981) and 237 km2 (95% CI: 90–427) for jaguars in the Dry Chaco and Humid Chaco/Pantanal, respectively. In the Dry Chaco mean home range size for males was 925 km2 (95% CI: 424–2035) and 551 km2 (95% CI: 513–590) for females, while in the Humid Chaco/Pantanal the mean home range was 398 km2 (95% CI: 345–427) and 156 km2 (95% CI: 90–267) for males and females, respectively (Figure 2).

Figure 2: Home range and movement parameters of male and female jaguars in the Dry Chaco and Humid Chaco/Pantanal.Boxes show the median and 25 and 75% percentiles with whiskers representing the data range within 1.5 times the distance of the box.

Figure 2:

Home range and movement parameters of male and female jaguars in the Dry Chaco and Humid Chaco/Pantanal.

Boxes show the median and 25 and 75% percentiles with whiskers representing the data range within 1.5 times the distance of the box.

Males demonstrated larger home ranges (p=0.99), higher daily movement (p=0.84), greater directionality in movement (velocity autocorrelation time scale) (p=0.84) and lower home range crossing times (p=0.9) (Table 2). Between systems, home ranges were larger (p=1), movements more directional (p=0.99) and home range crossing times greater (p=0.77) in the Dry Chaco, while daily travel distance was similar between systems but with a slightly higher probability of being larger in the Dry Chaco (p=0.61).

Table 2:

Probabilities based upon posterior distributions, that home range and movement parameters are different between sex and ecosystem, between sexes within systems, and between same sexes between systems.

Home range (km2)Home range crossing time (days)Velocity autocorrelation timescale (h)Average distance traveled (km/day)
Dry Chaco male>Dry Chaco female0.710.080.710.89
Humid Chaco/Pantanal male>Humid Chaco/Pantanal female0.990.170.950.52
Dry Chaco male>Humid Chaco/Pantanal male0.910.750.860.72
Dry Chaco female>Humid Chaco/Pantanal female0.990.890.960.23
All Dry Chaco>All Humid Chaco/Pantanal10.770.990.61
Male>Female0.990.100.990.84

Between systems males in the Dry Chaco had higher probabilities to have larger home ranges (p=0.91), higher home range crossing time (p=0.75), greater directionality in movement (p=0.86), and greater daily travel distances (p=0.72) (Table 2), although values for all parameters were more variable in males from the Dry Chaco (Figure 2). A similar pattern was evident between females in both systems for home range size (p=0.99), home range crossing time (p=0.89) and directionality in movement (p=0.96) which were greater for females in the Dry Chaco, however, females in the Dry Chaco had lower daily movements (p=0.77) than those in the Humid Chaco/Pantanal (Table 2).

Discussion

These are the first estimates of movement parameters and home range and core area for jaguar in the Dry Chaco, Humid Chaco, and Paraguayan Pantanal, which furthermore take advantage of developing methods to empirically test for home range residency and account for autocorrelation in telemetry data when estimating space use (Fleming et al. 2014, 2015, Calabrese et al. 2016). The results include the largest home range estimates recorded for jaguar (Dry Chaco) and, as expected, jaguars in the more productive Humid Chaco/Pantanal had smaller home ranges, lower movement rates and had less directionality in movements compared to jaguars in the Dry Chaco. Also, consistent with previous research males had larger home ranges, higher movement rates and more directional movements than females overall and within systems.

Overall and between systems male home ranges were larger than females which was expected (Cavalcanti and Gese 2009, Sollmann et al. 2011, Morato et al. 2016, Gonzalez-Borrajo et al. 2017) as female home range size is driven to maximize food availability and reproductive success while minimizing metabolic costs, which consequently results in home range sizes that are at an optimal minimum (Sandel 1989, Sunquist and Sunquist 1989). Conversely, male home ranges are driven by food availability and a need to maximize contact with receptive females which leads to males maximizing home range size towards optimizing reproductive opportunities constrained by their metabolic limits (Sandel 1989, Sunquist and Sunquist 1989). This relationship is further supported by the estimated movement parameters which showed that males traveled farther, faster, and more directionally than females in response to the need to cover and maintain their larger home ranges.

Consistent with expectations home range sizes of jaguars in the Dry Chaco were larger than in the Humid Chaco and Pantanal, overall and between sexes within systems where male home ranges were greater than females. The larger home ranges in the Dry Chaco are attributable to the lower productivity of that semi-arid ecosystem, more heterogeneously distributed prey and water, and negative effects of anthropogenic factors (i.e. deforestation; Fahrig 2007, Gutiérrez-González et al. 2012).

Home range estimates from the Dry Chaco for both males and females are considerably larger than other estimates from this study and Morato et al. (2016), although estimates of male home range size from the Dry Chaco (mean: 925 km2, 95% CI: 424–2035) are consistent with the estimate for a single male from the Brazilian Cerrado (1269 km2), a semi-arid ecosystem with environmental and land use similarities to the Gran Chaco. Morato et al. (2016) demonstrated that increasing home range size of jaguars was associated with lower habitat quality, which is consistent with the very large home ranges from the Dry Chaco which, although larger, were closest in size to Morato et al.’s (2016) home ranges in the Atlantic forest which they considered to be of the lowest habitat quality of their study areas.

Home range sizes from the Humid Chaco/Pantanal were expected to be similar to estimates from the Brazilian Pantanal, however, estimates were 59 and 112% larger for males and females, respectively, than home ranges reported for the Brazilian Pantanal; falling between estimates from the Amazon and Atlantic forest, although most similar to jaguars from the Amazon (Morato et al. 2016). These differences may be related to differences in the geomorphology of the two regions and its interaction with local hydrological cycles.

The Paraguayan Pantanal and the study area in the Humid Chaco have less forest area and a relatively greater area of inundated land during a large portion of the year compared to the Pantanal study areas of Morato et al. (2016) in Brazil. Consequently, the reduced forest area, with smaller and more isolated forest patches during annual flooding, could drive the comparatively larger home ranges observed in the Paraguayan Pantanal and Humid Chaco, although reduced jaguar densities resulting from persecution may also play a role in liberating available space and permitting greater space use.

Differences in the mean movement parameters were evident between jaguars in the Humid Chaco/Pantanal and in the Brazilian Pantanal whereby movements were more directional in the Humid Chaco/Pantanal, although still relatively sinuous but most similar to jaguars in the Atlantic forest, while daily movements were very similar to those in the Amazon. Jaguars in the Dry Chaco had high movement rates and directionality in movement, similar to individuals from the Amazon in seasonally flooded forests (Morato et al. 2016).

These similarities are possibly responses to movements among sporadically distributed critical resources (prey, water, mates) despite the large differences in ecosystem characteristics. Conversely, although daily movement rate of jaguars in the Humid Chaco/Pantanal were similar to those in the Dry Chaco and Amazon, the relatively low directionality demonstrated by jaguars in the Humid Chaco/Pantanal suggests that, although jaguars are covering relatively large areas, movements are in response to more homogenously distributed resources within home ranges.

Core area size was highly similar across systems and sexes, encompassed on average by the 59% home range isopleth (95% CI: 56–64%), which represented on average 29% (95% CI: 21–34%) of total home range area. This indicates that despite home range size, sex, or system jaguars are most intensively using about a third of their home range area. Additionally, the results suggest a cautious interpretation of arbitrarily defined core area delimitations (Powell 2012), however, it is also recognized that with AKDE home ranges the median or mean area are justifiable measures of central tendency (Fleming and Calabrese 2017).

In light of the extensive deforestation that is occurring in the Dry Chaco of western Paraguay, the large home ranges observed in this system, which are consistent with the estimated low density of jaguar in the Bolivian Dry Chaco (Noss et al. 2012), are of concern as they demonstrate the large forested area that jaguars in the Dry Chaco require. In the Humid Chaco/Pantanal spatial requirement of jaguars were greater than expected based on estimates from the Brazilian Pantanal, which suggests lower than expected densities in these systems in Paraguay and cautions against extrapolating population parameter estimates from other regions within the Pantanal to the Rio Paraguay flood plain in Paraguay.

In both the Dry Chaco and the Humid Chaco/Pantanal there may be an important effect on space use caused by reduced jaguar densities from persecution which is pervasive throughout western Paraguay, illustrated by the confirmation that >50% of the study animals were killed due to persecution. Persecution is common throughout the range of the jaguar, however, its practice and magnitude is not equivocal geographically and consequently how the removal of individuals may impact space use, and subsequently comparisons among ecosystems and regions, needs to be considered and is of interest for future research.

The large spatial requirements of jaguars in western Paraguay, particularly in the Dry Chaco, indicate that the protected areas of the region, which represent <5% of the total regional area, are likely insufficient to maintain a viable regional population, especially in light of the level of persecution on private lands. This highlights an urgent need to mitigate jaguar-human conflict in the region by actively including the livestock production sector in the conservation decision making process. Furthermore, given continuing deforestation, conservation initiatives need to take into account the large spatial needs of jaguar in western Paraguay by recognizing and incorporating the role of private lands in the long-term conservation of the species in Paraguay and in maintaining trans-boundary connectivity among populations.

Acknowledgments

The authors thank DVM Sybil Zavala, Cougar McBride and Caleb McBride for assistance in the field and the many sportsmen and conservationists who contributed to supporting this work. This research was conducted under the permission of the Secretariat of the Environment (SEAM) of Paraguay. JJT was supported by the Consejo Nacional de Ciencia y Tecnología of Paraguay (CONACYT).

References

Azevedo, F.C.C. and D.L. Murray. 2007. Spatial organization and food habits of jaguars (Panthera onca) in a floodplain forest. Biol. Conserv. 137: 391–401.10.1016/j.biocon.2007.02.022Search in Google Scholar

Bingham, B.B. and B.R. Noon. 1997. Mitigation of habitat “take”: application to habitat conservation planning. Conserv. Biol. 11: 127–139.10.1046/j.1523-1739.1997.95331.xSearch in Google Scholar

Brodie, J.F. 2009. Is research effort allocated efficiently for conservation? Felidae as a global case study. Biodivers. Conserv. 18: 2927–2939.10.1007/s10531-009-9617-3Search in Google Scholar

Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. J. Mammal. 24: 346–352.10.2307/1374834Search in Google Scholar

Calabrese, J.M., C.H. Fleming and E. Gurarie. 2016. Ctmm: an R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7: 1124–1132.10.1111/2041-210X.12559Search in Google Scholar

Calenge, C. 2006. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197: 516–519.10.1016/j.ecolmodel.2006.03.017Search in Google Scholar

Cardillo, M., A. Purvis, W. Sechrest, J.L. Gittleman, J. Bielby and G.M. Mace. 2004. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2: e197.10.1371/journal.pbio.0020197Search in Google Scholar PubMed PubMed Central

Cavalcanti, S.M.C. and E.M. Gese. 2009. Spatial ecology and social interactions of jaguars (Panthera onca) in the southern Pantanal, Brazil. J. Mammal. 90: 935–945.10.1644/08-MAMM-A-188.1Search in Google Scholar

Colchero, F., D. Conde, C. Manterola, C. Chávez, A. Rivera and G. Ceballos. 2011. Jaguars on the move: modeling movement to mitigate fragmentation from road expansion in the Mayan Forest. Anim. Conserv. 14: 158–166.10.1111/j.1469-1795.2010.00406.xSearch in Google Scholar

Conde, D.A., F. Colchero, H. Zarza, N.L. Christensen, J.O. Sexton, C. Manterola, C. Chávez, A. Rivera, D. Azuara and G. Ceballos. 2010. Sex matters: modeling male and female habitat differences for jaguar conservation. Biol. Conserv. 143: 1980–1988.10.1016/j.biocon.2010.04.049Search in Google Scholar

Crawshaw, Jr., P.G. 1995. Comparative ecology of ocelot (Felis pardalis) and jaguar (Panthera onca) in a protected subtropical forest in Brazil and Argentina. PhD Dissertation, University of Florida. Gainsville, Florida, USA.Search in Google Scholar

Crawshaw, Jr., P.G. and H.B. Quigley. 1991. Jaguar spacing, activity, and habitat use in a seasonally flooded environment in Brazil. J. Zool. 223: 357–370.10.1111/j.1469-7998.1991.tb04770.xSearch in Google Scholar

Crawshaw, Jr., P.G., J.K. Mahler, C. Indrusiak, S.M.C. Cavalcanti, M.R.P. Leite-Pitman and K. M. Silvius. 2004. Ecology and conservation of the jaguar (Panthera onca) in Iguaçu National Park, Brazil. In: (K.M. Silvius, R. E. Bodmer and J.M.V. Fragoso, eds.) People in nature: wildlife conservation in South and Central America. Columbia University Press, New York. pp. 286–296.Search in Google Scholar

Crooks, K.R. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16: 488–502.10.1046/j.1523-1739.2002.00386.xSearch in Google Scholar

Cullen, Jr., L. 2006. Jaguar as landscape detectives for the conservation in the Atlantic Forest of Brazil. PhD Dissertation. University of Kent, UK.Search in Google Scholar

de la Torre, J.A., J.F. González-Maya, H. Zarza, G. Ceballos and R.A. Medellín. 2017. The jaguar’s spots are darker than they appear: assessing the global conservation status of the jaguar Panthera onca. Oryx. 1–16. doi:10.1017/S0030605316001046.10.1017/S0030605316001046Search in Google Scholar

Fahrig, L. 2007. Non-optimal animal movement in human-altered landscapes. Funct. Ecol. 21: 1003–1015.10.1111/j.1365-2435.2007.01326.xSearch in Google Scholar

Fleming, C.H. and J.M. Calabrese. 2017. A new kernel density estimator for accurate home-range and species-range area estimation. Methods Ecol. Evol. 8: 571–579.10.1111/2041-210X.12673Search in Google Scholar

Fleming, C.H., J.M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber and W.F. Fagan. 2014. From fine-scale foraging to home ranges: a semivariance approach to identifying movement modes across spatiotemporal scales. Am. Nat. 183: E154–E167.10.1086/675504Search in Google Scholar PubMed

Fleming, C.H., W.F. Fagan, T. Mueller, K.A. Olson, P. Leimgruber and J.M. Calabrese. 2015. Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator. Ecology 96: 1182–1188.10.1890/14-2010.1Search in Google Scholar PubMed

Gelman, A. and J. Hill. 2007. Data analysis using regression and multilevel hierarchical models. Cambridge University Press. New York, USA.10.1017/CBO9780511790942Search in Google Scholar

Gonzalez-Borrajo, N., J.V. López-Bao and F. Palomares. 2017. Spatial ecology of jaguars, pumas, and ocelots: a review of the state of knowledge. Mammal. Rev. 47: 1365–2907.10.1111/mam.12081Search in Google Scholar

Gutiérrez-González, C.E., M.Á. Gómez-Ramírez and C.A. López-González. 2012. Estimation of the density of the near threatened jaguar Panthera onca in Sonora, Mexico, using camera trapping and an open population model. Oryx. 46: 431–437.10.1017/S003060531100041XSearch in Google Scholar

Hansen, M.C., P.V. Potapov, R. Moore, M. Hancher, S.A. Turubanova, A. Tyukavina, D. Thau, S.V. Stehman, S.J. Goetz, T.R. Loveland and A. Kommareddy. 2013. High-resolution global maps of 21st-century forest cover change. Science 342: 850–853.10.1126/science.1244693Search in Google Scholar

Lunn, D.J., A. Thomas, N. Best and D. Spiegelhalter. 2000. WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10: 325–337.10.1023/A:1008929526011Search in Google Scholar

Macdonald, D.W. 1983. The ecology of carnivore social behaviour. Nature 301: 379–384.10.1038/301379a0Search in Google Scholar

McBride, Jr., R.T. and R.T. McBride. 2007. Safe and selective capture technique for jaguars in the Paraguayan Chaco. Southwest Nat. 52: 570–577.10.1894/0038-4909(2007)52[570:SASCTF]2.0.CO;2Search in Google Scholar

Mereles, F., J.L. Cartes, R.P. Clay, P. Cacciali, C. Paradeda, O. Rodas and A. Yanosky. 2013. Análisis cualitativo para la definición de las ecorregiones de Paraguay occidental. Paraquaria Natural 1: 12–20.Search in Google Scholar

Morato, R.G., J.A. Stabach, C.H. Fleming, J.M. Calabrese, R.C. De Paula, K.M. Ferraz, D.L. Kantek, S.S. Miyazaki, T.D. Pereira, G.R. Araujo, A. Paviolo, C. De Angelo, M.S. Di Bitetti, P. Cruz, F. Lima, L. Cullen, D.A. Sana, E.E. Ramalho, M.M. Carvalho, F.H.S. Soares, B. Zimbres, M.X. Silva, M.D.F. Moraes, A. Vogliotti, J.A. May, Jr., M. Haberfeld, L. Rampim, L. Sartorello, M.C. Ribeiro and P. Leimgruber. 2016. Space use and movement of a neotropical top predator: the endangered jaguar. PLoS One 11: p.e0168176.10.1371/journal.pone.0168176Search in Google Scholar

Noss, A.J., B. Gardner, L. Maffei, E. Cuéllar, R. Montaño, A. Romero-Muñoz, R. Sollman and A.F. O’Connell. 2012. Comparison of density estimation methods for mammal populations with camera traps in the Kaa-Iya del Gran Chaco landscape. Anim. Conserv. 15: 527–535.10.1111/j.1469-1795.2012.00545.xSearch in Google Scholar

Olson, D.M., E. Dinerstein, E.D. Wikramanayake, N.D. Burgess, G.V. Powell, E.C. Underwood, J.A. D’amico, I. Itoua, H.E. Strand, J.C. Morrison and C.J. Loucks. 2001. Terrestrial ecoregions of the world: a new map of life on earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51: 933–938.10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2Search in Google Scholar

Petracca, L.S., S. Hernández-Potosme, L. Obando-Sampson, R. Salom-Pérez, H. Quigley and H.S. Robinson. 2014a. Agricultural encroachment and lack of enforcement threaten connectivity of range-wide jaguar (Panthera onca) corridor. J. Nat. Conserv. 22: 436–444.10.1016/j.jnc.2014.04.002Search in Google Scholar

Petracca, L.S., O.E. Ramírez-Bravo and L. Hernández-Santín. 2014b. Occupancy estimation of jaguar Panthera onca to assess the value of east-central Mexico as a jaguar corridor. Oryx. 48: 133–140.10.1017/S0030605313000069Search in Google Scholar

Powell, R.A. 2012. Movements, home ranges, activity, and dispersal. In: (L. Boitani and R.A. Powell, eds.) Carnivore ecology and conservation: a handbook of techniques. Oxford University Press, London, United Kingdom. pp. 188–217.10.1093/acprof:oso/9780199558520.003.0009Search in Google Scholar

Quigley, H., R. Foster, L. Petracca, E. Payan, R. Salom and B. Harmsen. 2017. Panthera onca. The IUCN Red List of Threatened Species 2017: e.T15953A50658693. http://dx.doi.org/10.2305/IUCN.UK.2017-3.RLTS.T15953A50658693.en.10.2305/IUCN.UK.2017-3.RLTS.T15953A50658693.enSearch in Google Scholar

Rabinowitz, A.R. and B.G. Nottingham. 1986. Ecology and behavior of the jaguar (Panthera onca) in Belize, Central America. J. Zool. 210: 149–159.10.1111/j.1469-7998.1986.tb03627.xSearch in Google Scholar

Rabinowitz, A. and K.A. Zeller. 2010. A range-wide model of landscape connectivity and conservation for the jaguar, Panthera onca. Biol. Conserv. 143: 939–945.10.1016/j.biocon.2010.01.002Search in Google Scholar

R Development Core Team. 2010. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Search in Google Scholar

Ripple, W.J., J.A. Estes, R.L. Beschta, C.C. Wilmers, E.G., Ritchie, M. Hebblewhite, J. Berger, B. Elmhagen, M. Letnic, M.P. Nelson and O.J. Schmitz. 2014. Status and ecological effects of the world’s largest carnivores. Science 343: 1241484.10.1126/science.1241484Search in Google Scholar PubMed

Sandel, M. 1989. The mating tactics and spacing patterns of solitary carnivores, In: (J.L. Gittleman, ed.) Carnivore behavior, ecology, and evolution. Cornell University Press, New York. pp. 164–182.Search in Google Scholar

Sanderson, E.W., K.H. Redford, C.B. Chetkiewicz, R.A. Medellin, A.R. Rabinowitz, J.G. Robinson and A.B. Taber. 2002. Planning to save a species: the jaguar as a model. Conserv. Biol. 16: 58–71.10.1046/j.1523-1739.2002.00352.xSearch in Google Scholar

Schaller, G.B. and P.G. Crawshaw, Jr. 1980. Movement patterns of jaguar. Biotropica 12: 161–168.10.2307/2387967Search in Google Scholar

Scognamillo, D., I. Maxit, M. Sunquist and L. Farrell. 2002. Ecología del jaguar y el problema de la depredación de ganado en un hato de los llanos venezolanos. In: (R. Medellin, C. Equihua, C.L.B. Chetkiewicz, P.G. Crawshaw, Jr., A. Rabinowitz, K.H. Redford, J.G. Robinson, E.W. Sanderson and A.B. Taber, eds.) El jaguar en el nuevo milenio. Universidad Nacional Autónoma de México and Wildlife Conservation Society, Distrito Federal, México. pp. 139–150.Search in Google Scholar

Seaman, D.E. and R.A. Powell. 1990. Identifying patterns and intensity of home range use. Bears: their biology and management Vol. 8, A Selection of Papers from the Eighth International Conference on Bear Research and Management. Victoria, British Columbia, Canada, February 1989. International Association of Bear Research and Management. pp. 243–249.Search in Google Scholar

Secretaría del Ambiente, Wildlife Conservation Society Paraguay and Itaipu Binacional. 2016. Plan de Manejo de la Panthera onca, Paraguay 2017–2026. First ed., Asunción, Paraguay.Search in Google Scholar

Sikes, R.S. 2016. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mammal. 97: 663–688.10.1093/jmammal/gyw078Search in Google Scholar PubMed PubMed Central

Silveira, L. 2004. Ecologia Comparada e Conservação da Onça-pintada (Panthera onca) e Onça-parda (Puma concolor), no Cerrado e Pantanal. PhD Dissertation, University of Brasilia, Brasilia, Brasil.Search in Google Scholar

Sollmann, R., M.M. Furtado, B. Gardner, H. Hofer, A.T. Jácomo, N.M. Tôrres and L. Silveira. 2011. Improving density estimates for elusive carnivores: accounting for sex-specific detection and movements using spatial capture–recapture models for jaguars in central Brazil. Biol. Conserv. 144: 1017–1024.10.1016/j.biocon.2010.12.011Search in Google Scholar

Sturtz, S., U. Ligges and A. Gelman. 2005. R2WinBUGS: a Package for running WinBUGS from R. J. Stat. Soft. 12: 1–16.10.18637/jss.v012.i03Search in Google Scholar

Sunquist, M.E. and F.C. Sunquist. 1989. Ecological constraints on predation by large felids. In: (J.L. Gittleman, ed.) Carnivore behavior, ecology, and evolution. Cornell University Press, Ithaca, New York. pp. 283–301.10.1007/978-1-4757-4716-4_11Search in Google Scholar

Thompson, J.J. and C. Martínez Martí. 2015. Patterns and determinants of jaguar (Panthera onca) occurence in habitat corridors at the southwestern extent of the species range. In: (C. Martínez Martí, ed.) Cats, Cores and Corridors: a survey to assess the status of Jaguars and their habitat in the southernmost part of their range. Panthera, New York. pp. 26–40.Search in Google Scholar

Tobler, M.W., S.E. Carrillo-Percastegui, A.Z. Hartley and G.V. Powell. 2013. High jaguar densities and large population sizes in the core habitat of the southwestern Amazon. Biol. Conserv. 159: 375–381.10.1016/j.biocon.2012.12.012Search in Google Scholar

Vander Wal, E. and A.R. Rodgers. 2012. An individual-based quantitative approach for delineating core areas of animal space use. Ecol. Model. 224: 48–53.10.1016/j.ecolmodel.2011.10.006Search in Google Scholar

Worton, B.J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70: 164–168.10.2307/1938423Search in Google Scholar

Zeller, K.A. 2007. Jaguars in the New Millennium Data Set Update: The State of the Jaguar in 2006. Wildlife Conservation Society, Bronx, New York.Search in Google Scholar

Zeller, K.A., S. Nijhawan, R. Salom-Pérez, S.H. Potosme and J.E. Hines. 2011. Integrating occupancy modeling and interview data for corridor identification: a case study for jaguars in Nicaragua. Biol. Conserv. 144: 892–901.10.1016/j.biocon.2010.12.003Search in Google Scholar


Supplementary Material:

The online version of this article offers supplementary material (https://doi.org/10.1515/mammalia-2017-0040).


Received: 2017-04-12
Accepted: 2018-01-05
Published Online: 2018-02-24
Published in Print: 2018-11-27

©2018 Walter de Gruyter GmbH, Berlin/Boston