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Article

Human–Puma Conflict in the Dry Chaco: Species’ Occupancy and Ranchers’ Perception Before and After the Creation of a Protected Area

by
Fernando R. Barri
1,2,*,
Thiago Costa
3,4,5,
Jessica Manzano-García
3,6 and
Flavio Cappa
7,8
1
Instituto de Diversidad y Ecología Animal (CONICET-UNC), Córdoba PC 5000, Argentina
2
Facultad de Ciencias Exactas Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba PC 5000, Argentina
3
Instituto de Antropología de Córdoba (CONICET-UNC), Córdoba PC 5000, Argentina
4
Departamento de Antropología, Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba PC 5000, Argentina
5
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Ministério da Ciência, Tecnologia e Inovação, Brasília PC 70.070-010, Brasil
6
Parque de la Biodiversidad de Córdoba, Córdoba PC 5000, Argentina
7
Centro de Investigaciones de la Geósfera y Biósfera (CONICET—UNSJ), San Juan PC 5402, Argentina
8
Dpto Biología-Facultad de Ciencias Exactas, Físicas y Naturales (UNSJ), San Juan PC 5402, Argentina
*
Author to whom correspondence should be addressed.
Conservation 2025, 5(4), 78; https://doi.org/10.3390/conservation5040078 (registering DOI)
Submission received: 3 October 2025 / Revised: 13 November 2025 / Accepted: 16 November 2025 / Published: 3 December 2025
(This article belongs to the Special Issue Conservation Biology, Management of Natural Resources, and Protected Areas Policies)
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Abstract

Although the creation of protected areas helps to protect biodiversity, it does not necessarily contribute to the reduction in some conflicts, such as livestock predation by large carnivores. We evaluated the presence of puma (Puma concolor) in a large ranch converted into a National Park and in surrounding rancher areas in the Dry Chaco of Argentina. Two years after livestock removal from the protected area, puma occupancy was associated with wild prey richness, which was greater in the park than in the neighboring ranches, and was negatively related to livestock presence. We also evaluated ranchers’ perceptions of puma presence and their tolerance to livestock predation. Ranchers showed a negative perception of puma presence and a low tolerance to livestock predation. Therefore, this study suggests that, while the creation of a protected area can improve both predator and prey densities, successful mitigation of human–predator conflict requires further strategies and interventions, like economic compensation and environmental education programs.

1. Introduction

Human–carnivore conflict arises from the competition between large carnivores and humans for space and resources [1]. Understanding the human dimensions associated with this conflict is critical for developing effective mitigation strategies for this important conservation issue [2]. Carnivore populations have been drastically reduced due to the combined effects of hunting, habitat loss, and the decrease in wild prey [3,4]. The presence of large carnivores generates conflicts that usually involve several stakeholders and specific environmental, social, and economic contexts [5]. In the case of wild big cats, the conflict generated due to livestock predation mainly depends on human tolerance to economic losses; however, in general, ranchers’ perceptions are not consistent with actual predator abundance [6,7,8]. On the other hand, livestock predation increases when prey populations decline [9]; on the contrary, if habitat and prey are protected, predators will thrive, even under low levels of poaching [10]. In that sense, several studies indicate that big cat densities increase in protected areas, e.g., [11,12,13]. However, the creation of a protected area does not necessarily help avoid the conflict generated by livestock predation in the neighboring areas, e.g., [14,15,16].
In South America, human–big wild cat conflicts due to livestock predation have a long history [17]. Unfortunately, lethal control is the most common method used by ranchers to avoid the risk of economic losses [18,19]. In the Chaco ecoregion in northern Argentina, lethal control led to the extinction of the jaguar (Panthera onca) [20] and a reduction in the puma (Puma concolor) population [21]. Moreover, even though the economic damage caused by puma predation is poorly known [22], human–puma conflicts are likely to increase due to the extensive conversion of Chaco forest into livestock production systems [23]. In turn, the effectiveness of the creation of protected areas as a strategy to reduce this conflict has still not been evaluated in depth. For instance, ranchers surrounding protected areas usually complain about the presence of predators [24,25]; this situation is particularly remarkable in the southern Chaco region [26].
The puma is the fourth largest wild cat in the world and the most widespread native terrestrial mammal of the Americas [27]. Although it is not in danger of extinction, its populations are declining in their distribution range [28]. It is an adaptable and opportunistic predator of nocturnal habits, with a diet composed mostly of wild mammals [29]. Even though ranchers believe that puma prefers domestic livestock, different studies have shown that the species prefers wild prey, with domestic livestock representing a smaller proportion of its diet, e.g., [19,30,31]. In turn, a review demonstrates that consumption of medium-sized wild species by puma is negatively correlated with consumption of domestic species [32], indicating that the greater the amount of wild prey available, the lower the need of pumas to prey on livestock. Puma predation on livestock depends on many factors other than the loss of wild prey, including innate and learned behavior, the health and status of individuals, and livestock husbandry practices [9,10].
Human perception of wild fauna varies with social, historical, and ecological contexts [33,34]. It is known that when governments do not help ranchers face economic losses due to predation of livestock, ranchers’ tolerance is significantly reduced, and their perception of predator density or the number of attacks on livestock increases [8,24,35]. In rural areas with extensive livestock farming, human attitudes toward pumas is generally negative. In particular, ranchers consider predation to be the main cause of economic losses, and therefore hunt pumas to protect livestock [23,36]. In the Dry Chaco, extensive livestock ranching is the main productive activity, and ranchers do not receive any type of economic compensation for the losses caused by puma predation [26]. For this reason, even though local communities largely depend on the wild fauna for their well-being, some species, such as the puma, are negatively perceived and frequently hunted [37,38].
Therefore, addressing human–large carnivore conflict requires an interdisciplinary approach [39,40,41]. In addition, taking the necessary actions to reduce conflict at the local scale requires understanding of the level of tolerance among ranchers and the relationship between space use by predators and anthropogenic and environmental variables [42]. Accordingly, we analyzed human–puma conflict in a region of the Argentinian Dry Chaco, where a protected area was created in 2018. We evaluated the natural and anthropogenic factors that explain the puma occupancy in the area, both before the creation of the park, and inside and outside the park after its creation and subsequent removal of livestock. We also analyzed ranchers’ perceptions of the puma presence in the area, including tolerance toward puma predation, the control methods used to reduce predation on livestock, and whether they considered that the creation of the park could reduce livestock predation. We hypothesized that puma occupancy would be positively associated with the creation of the park and negatively associated with surrounding ranches; we also hypothesized that ranchers would not perceive that the protected area could help reduce livestock predation conflict due to the historical lack of public policies to support rancher development in the region. Based on our findings, we discuss strategies for mitigating this human–puma conflict.

2. Methodology

2.1. Study Area

This study was conducted in the southeastern portion of the Dry Chaco region in Argentina, characterized by a subtropical xerophytic forest [43]. The climate is subtropical semi-arid; rainfall is the lowest of the entire Chaco ecoregion (from 300 to 500 mm annually, concentrated in summer) and average annual temperature is close to 20 °C, with absolute maximums of 42 °C [44], without significant rainfall variations during the study period (https://mapascordoba.gob.ar/viewer/mapa/488, accessed on 17 April 2024). These climatic characteristics imply an extreme aridity that limits productive human activities. The main activity in the region is extensive livestock farming, mainly cattle, as well as goat and horses to a lesser extent, with an average of one livestock unit every 20 hectares [45]. Despite climatic limitations, it is an area of significant biocultural heritage, whereas local communities’ knowledge and practices are completely entangled with their socioecological context [26].
The area is located in the Argentine province of Córdoba in the departments of Minas and Cruz del Eje, two predominantly rural districts. The Minas department, where most of our work was conducted, is characterized by dispersed housing. The age of the population is 34 years on average, with a predominance of males (6.1 males to 1 female) [26]. Between the 1980s and 1990s, men were devoted to the extraction of timber, especially white quebracho (Aspidosperma quebracho-blanco), which was mainly used for producing charcoal [37]. Currently, livelihoods depend on goat and cattle raising in ranches [26].
To protect the biological and cultural diversity of the region, the Traslasierra National Park (hereinafter, the “park”) was created in 2018 in a former livestock ranch of approximately 100,000 hectares, located in the northwest of Córdoba province, Argentina (Figure 1). Before the park’s creation, extensive livestock rearing was carried out in the area, and in 2020, the approximately 6000 livestock units present were removed to guarantee the park’s conservation objectives. However, the creation of the park raised concerns among surrounding ranchers about the possible increase in puma predation on their livestock (pers. obs.).

2.2. Ecological Sampling Design

We deployed camera traps in both the park and the neighboring ranches to estimate the occupancy of the puma, its wild prey (Table 1, Figure 2), such as brownish brocket deer (Subulo gouazoubira), collared peccary (Dicotyles tajacu), and plains viscacha (Lagostomus maximus), and livestock (Bos taurus) [46]. The first sampling was conducted in the park in 2018, before livestock removal (10 camera traps). Subsequent samplings were conducted after livestock were removed (2020), both inside the park and in six adjacent ranches (ranging in area between 700 ha and 18,000 ha), in 2021 (6 and 7 cameras inside and outside the park, respectively) and 2022 (8 and 9 cameras inside and outside the park, respectively. The camera traps were set up at an average distance of 3983 m from each other and at a height of 50 cm from the ground on trails used by both livestock and wild animals. Camera placement was based both on puma density in the northern Dry Chaco (<1 ind/100 km2, [21]); low primary productivity and animal density were associated with the driest characteristics of the southern Dry Chaco [44]. Each camera remained active on average for 58 nights, mostly in spring, and cameras that were damaged or active for less than 30 days were discarded. Total sampling effort was 2337 camera-trap nights, which resulted in 7743 independent records of animals: 74 records of puma (4 in 2018, 50 in 2021, and 20 in 2022), 1318 records of wild prey, and 2769 records of livestock. The remaining records were not relevant to our research.
We downloaded the photos from the cameras and removed the false triggers. Then we labeled the photos with the name of the species that appeared on them using the software Digikam 8.7.0. We used the library camtrapR [47] in R software to create the record table, which presents all species records ordered by camera, hour, and date. In addition, we identified all the “puestos” (ranchers’ dwellings) and water sources available in the study area. With these georeferenced points, we measured the Euclidean distances between camera trap points and the nearest “puesto” and water sources. These data were used as explanatory variables of the detection or non-detection of puma presence. We selected these variables, as well as wild prey and livestock presence, because they best explain the distribution of the species [21,29,32] and because the main demographic and environmental variables (such as human population density and forest cover) are homogeneous across the region [43,44].

2.3. Ecological Data Analysis

To assess whether anthropogenic variables (protected area—park; unprotected area—ranches with livestock presence) or environmental variables (water availability and presence of wild prey) affect puma occupancy, we used camera trap data collected in 2018, 2021, and 2022. Detection histories were generated using the camtrap R 2.3.1 package [47] with an occasion length of 8 days, which maximized detection rates. The detection matrix was then used to fit single-season occupancy models within a Bayesian framework using the rstand and ubms packages [48]. No variables potentially affecting the detection component of the models were identified. Additionally, we tested models including trap effort (number of active days) as a detection covariate, but they did not perform better than the null model. Therefore, we assumed a constant detection probability across occasions and focused our analysis on ecological covariates with an influence on occupancy.
The common explanatory variables for the occupancy component across the three periods were distance to the nearest “puesto” (D_ranch), distance to the closest water source (D_water), and q0 and q1 for potential wild prey [49]. To determine the latter variables, we used the effective number of species by estimating Hill numbers. This analysis uses indices with different levels of sensitivity to the relative abundance of species [50]. q0 corresponds to species richness without considering the abundance of individuals, whereas q1 is analogous to the exponential of Shannon’s index, with all species being included with a weight proportional to their abundance in the community [49,51]. For the 2021 and 2022 sampling periods, we also included the variables protection category of the area (P_NP), livestock occupancy (Occu_C), and livestock density (Dens_C). Livestock occupancy (Occu_C) was estimated using camera trap data and was analyzed following the same procedure as that used for puma occupancy, with single-season occupancy models within a Bayesian framework using the detection and occupancy component as constant. Livestock density (Dens_C) was obtained directly from local livestock owners based on the number of animals reported per ranch. All continuous variables were standardized (mean = 0, SD = 1) prior to modeling.
We fitted six (2018) and ten (2021 and 2022) candidate models (Table 2). Model comparison was performed using the Leave-One-Out Information Criterion (LOOIC), and model support was evaluated based on relative weights. To evaluate the model robustness and the reliability of the parameter estimates, we examined trace and posterior density plots for all fitted models using the bayesplot package. All models showed excellent convergence (R-hat ≈ 1) and unimodal well-mixed posterior distributions, indicating that the results were stable and not driven by prior assumptions (Supplementary Materials). Additionally, to assess potential spatial autocorrelation in model residuals, we calculated Moran’s I statistic using the spdep package. Geographic coordinates of camera stations were transformed to UTM projection, and spatial neighborhood matrices were built using a 10 km distance threshold. Moran’s I tests were non-significant (p > 0.4) across all years, indicating that residuals were spatially independent and that unaccounted spatial structure did not bias model inference.
To assess the intensity of spatial use by puma across camera stations, we modeled the number of independent detections of Puma concolor at each site separately for each sampling year (2018, 2021, and 2022). Count data were analyzed using generalized linear models (GLMs) assuming Poisson or negative binomial distributions, depending on overdispersion diagnostics. For each year, the predictor variables included were the same as in the occupancy models. Covariates were standardized (mean = 0, SD = 1) before modeling. Competing models were compared using AICc, and model averaging was applied when multiple models received similar support. Effect sizes and confidence intervals were extracted for the most influential predictors. Goodness-of-fit was assessed via residual plots and Pearson dispersion statistics. Additionally, significant effects were visualized using partial effect plots.
To assess the temporal variation in the frequency of puma records across years, as a proxy for abundance—measured as the number of independent records per camera station—we conducted pairwise proportion tests. Specifically, we compared the proportion of records between years using the prop.test function in R, setting the alternative hypothesis as “less” and a 95% confidence level. This test allowed us to assess whether the proportion of records in a year was significantly lower than in the subsequent year. We compared 2018 vs. 2021, 2018 vs. 2022, and 2021 vs. 2022. The Yates continuity correction was not applied in order to avoid an overly conservative result due to small sample sizes.
All analyses were performed in R software (R Core Team 2022) using additional packages such as camtrapR 2.3.0 [47], unmarked 1.5.0 [52], vegan 2.6.8 [53], MASS 7.3-60.2 [54], and MuMIn 1.48.11 [55].

2.4. Sociological Surveys

We conducted participant observation and both open and semi-structured interviews following the dynamic interactive cycle characteristic of anthropological methodology [37,38]. In April 2023, we invited ranchers from the area surrounding the park (in approximately 15 km2), where livestock density ranged from 0.03 to 0.07 heads per hectare during the study period, to participate in a workshop (attending more than 30 ranchers). Key local actors (the mayor of the nearest town, local butcher, park ranger, among others) contacted the ranchers, and then we visited them personally at their ranches. The average surface of the ranches was 2500 ha; most of the ranchers were men between 30 and 60 years old, with at least primary education level and belonging to the lower or middle economic class. We informed ranchers about the project, and the ethical and methodological requirements used to conduct it [56]. After hearing ranchers’ perspectives on livestock predation by pumas, we asked whether they would be interested in participating in a survey intended to inform the analysis of the conflict. In the following months, we contacted those who agreed to participate and had the necessary time to respond the interview (20 ranchers, with an average of two hours per interview), which was more than 75% of the ranchers surrounding the park. The first question we asked them was whether they agreed to the anonymous use of the information they provided for our study. In all cases, ranchers expressed their agreement.

2.5. Sociological Analysis

Firstly, open, extensive, and in-depth interviews [57] were conducted with 20 ranchers. Ranchers’ most common or outstanding opinions were recorded during two hours with the aim to know their qualitative assessment of the park creation and how they consider it influences the puma–livestock conflict. Secondly, semi-structured surveys [23,58] were conducted with the same ranchers to obtain quantitative values, based on a specific questionnaire on the puma conflict. We asked ranchers what they considered the most important cause of livestock loss; the response was scored using a scale of increasing importance from 1 to 5. Also we asked the percentage (from 0 to 100%) of tolerance towards the loss of livestock due to predation by puma, and the control methods used to avoid predation and their effectiveness (very, moderately, or slightly effective).

3. Results

3.1. Puma Occupancy

In 2018 and 2021, puma occupancy patterns were weakly explained by the evaluated covariates. In both years, the null model received the highest or comparable support (ELPD weights = 0.21 and 0.14, respectively; Table 2), indicating a lack of clear environmental or anthropogenic drivers of puma space use. Although some variables, such as distance to water (95% CrI = 0.58–1.73), distance to human settlements (95% CrI = 0.29–1.60), and wild prey richness (95% CrI = 0.54–1.52) in 2018, or distance to ranches (95% CrI = 0.78–1.24) and protection status (95% CrI = 0.78–1.36) in 2021, showed positive trends, their explanatory power was low and uncertain. In contrast, in 2022—two years after livestock removal—the two best-supported models included wild prey richness (q0) and cattle occupancy (Occu_C) (ELPD weights = 0.19 and 0.14, respectively; Table 2). The intercept-only (null) model also fell within the ΔELPD < 2 range (ΔELPD = 1.75), indicating that these covariates only provided a moderate improvement in model fit. The coefficients are reported on the logit scale; back-transformed estimates indicate that puma occupancy probability increased from approximately 0.37 at low to 0.94 at high wild prey richness, and decreased from about 0.92 to 0.48 across the gradient of cattle occupancy. Although both effects were credible (95% CrI excluding zero; Table 2), the proximity of the null model suggests that these associations should be interpreted cautiously.
The percentage of occupied sites per year showed a significant increase in 2021 (70%) compared to 2018 (23%) (test of difference between two proportions, p < 0.05). No difference was detected between 2021 and 2022 (test of difference between two proportions: p = 0.32), although there was a small increase in 2022 (70% vs. 73% of occupied sites).

3.2. Intensity of Spatial Use by Puma

Significant differences in the number of puma records were detected between 2018 and 2021 (GLMBinom_Neg Z = −3.19; p = 0.001), as well as between 2021 and 2022 (GLMBinom_Neg Z = −2.38; p = 0.017), with 2021 having the highest number of records.
For 2018, a positive relationship was found between the intensity of spatial use by puma and q1 (Table 3). In 2021, the best model was the null model. In 2022, the three best models (ΔAICc < 2) contained one of the following variables (in decreasing order of importance): q0, livestock occupancy, and livestock density. The relationship between the intensity of spatial use and q0 was positive, indicating that a higher richness of wild prey increased the number of puma records, as observed for occupancy (Table 4). On the other hand, the relationship with the other two variables was negative, indicating that the number of puma records decreased with the increasing number of sites occupied by livestock and increasing livestock density (Figure 3).

3.3. Ranchers’ Perceptions of Puma Predation

During the interviews, ranchers were skeptical or negative about the contribution of the park’s creation to the reduction in puma predation on livestock. Indeed, most ranchers considered that the park’s creation would increase predation, since they thought that the park was a site “where the puma can hide to prevent being hunted and to be able to reproduce”. While, in general, ranchers acknowledge the value of preserving biodiversity, they are reluctant to accept conservation measures that jeopardize their economic well-being. Some of the most outstanding sentences that summarize the ranchers’ opinions about the historical human–puma conflict and the park creation are as follows: “We know that pumas are good for the woodland, and we don’t want to kill them for no reason, but they do great damage to us, that’s why we have to hunt them when they are “cebados” (local term referring to a puma individual who regularly attacks livestock); “in the past, puma were hunted and nothing happened, now the government tell us it is forbidden … but no one compensates us for our financial losses when they kill our livestock!”; “If they want a park there, there is no problem, but they should keep all the pumas inside, they should put up a high fence and keep all pumas in”; and “we distrust measures like the creation of the park because we never receive support from the State, now it turns out that the puma has more rights than us”.
According to ranchers’ perceptions, predation by puma was the most important cause of livestock loss, followed by drought, livestock theft, livestock accidents, and disease (Table 5). The level of tolerance towards the loss of livestock due to predation by puma varied from 0% (45% of the interviewees) to 1% (33% of the interviewees) to 5% (22% of the interviewees) (Table 5). Among the control methods to avoid puma predation, puma hunting was valued as very effective, surveillance and guard dogs as moderately effective, and other methods, like livestock enclosure, as slightly effective (Table 5).
The perceived average loss of livestock in the ranches adjacent to the park due to puma predation before the park’s creation (2018) was 10% (±2) and increased to nearly 25% (±5) when livestock were removed from the park (2020). In the first and second years after livestock removal (2021 and 2022), livestock loss was 12% (±3), showing a tendency to return to the values prior to the creation of the park (Table 5). In all cases, ranchers responded negatively as to whether the creation of the park could reduce predation by puma. They also considered hunting to be a good measure to mitigate livestock loss, that the State should bear the costs of puma conservation, and that they were willing to adopt new strategies to avoid predation as long as those strategies did not involve modifying their modes of production.

4. Discussion

We observed that the occupancy and intensity of spatial use by pumas before the creation of the park (2018) or the year following livestock removal (2021) was not explained by any of the variables analyzed. However, in the second year following livestock removal (2022), puma occupancy may have been positively associated with wild prey richness and negatively associated with cattle occupancy. These relationships coincided with an increasing tendency for prey richness and a decreasing tendency for cattle occupancy in the park. In this sense, the frequency of puma records was negatively associated with livestock presence. In other words, pumas preferred to inhabit the park, where they found a greater variety of wild prey over time. They also avoided the ranches, probably because wild prey richness was low and also because there they are frequently persecuted [37]. Nevertheless, this situation did not reduce the conflict due to livestock predation; much on the contrary, the negative perception of ranchers was maintained or even increased with the creation of the park. This ranchers’ negative perception of protected areas has also been observed in other regions of the world where human–big cat conflicts are constant, e.g., [15,59,60]. This situation could also be explained by the source–sink dynamic in large carnivores, since, even in contexts of intensive hunting, as long as they have areas where they are protected and can reproduce, they will maintain stable populations at a landscape scale [61]. Yet, the survival of top predators due to the creation of protected areas is an exception, so it is even more important to analyze the socio-ecological context they area involved (whit included prey recovery, local governance, economic incentives to avoid hunting, among others) to modify the aspects that cause the decline of their populations [62].
The negative puma occupancy with respect to livestock occupancy found in this work agrees with a previously reported increase in puma presence with increasing ranch size and distance to the nearest town, or with a decrease with human presence [21,22]. Moreover, Cocimano et al. [36] reported direct economic costs as well as indirect and hidden costs due to livestock loss by puma predation, and Nanni et al. [23] established that retaliatory hunting was common among respondents who experienced livestock predation. Furthermore, ranchers tend to increase their negative perception when they do not receive help from the government to apply mitigation measures or economic compensation for the loss of livestock due to predation [24]. In that sense, we expected that, in our study area, one of the poorest in the region [45], ranchers would be less tolerant to the loss of livestock due to puma predation because they do not receive help from the government to tackle this conflict.
Although we were not able to confirm the levels of livestock loss by puma predation mentioned by the ranchers, the coincidence in the estimations made by all of them suggests that there was indeed an increase in predation the year after livestock removal from the park. This increase may be explained by the lack of livestock availability in the park, which forced the pumas accustomed to preying on livestock to displace to the neighboring ranches. In turn, the fact that, two years after livestock removal, predation percentages dropped to values of the years prior to livestock removal from the park could be related to the hunting of these individuals by ranchers, a common practice in the region when livestock losses increase [37]. Other variables that, in general, also could be explained in this aspect are the inter-annual variation in prey abundance, ranching/puma persecution practices, or marked climate changes [25], even though no differences were observed in these aspects in our study during those two analyzed years. However, due to social data not being spatially paired with ecological data, the result of this study should be taken cautiously, while further interdisciplinary studies need to be complemented, including ranchers who were unable to participate in the interviews, to better understand the human–puma conflict in the study area.
On the other hand, the effectiveness of other interventions for livestock protection from puma predation (e.g., flashing lights to deter predators) was found to be variable and often low [62]. In the case of the study area, mitigation methods to prevent puma predation on livestock have not been tested yet, and each rancher uses different strategies according to their knowledge and economic resources [37]. In addition, due to the environmental conditions prevailing in the region, extensive farming is necessary to sustain livestock productivity; in this situation, applying predation mitigation methods becomes more difficult. Further work with ranchers will be necessary to evaluate the most effective strategies for mitigating puma predation (such as increasing livestock corral confinement during the calving season and subsidizing animal feed during this period). For this reason, the government must become involved, considering both the ecological and social contexts [39,42] and the importance of engaging the community in conflict research and management [63] to avoid lethal control [64].
According to the occupancy results obtained in this study, we suggest that hunting the natural prey of pumas should be avoided in the ranches surrounding the park, since prey availability may limit predation on livestock [32,65]. However, when livestock dominate the landscape, the presence of wild animals is generally reduced [66]. Therefore, to keep wild prey assemblages intact, it will be necessary to raise awareness among ranchers of the need to avoid practices that could reduce the presence of wildlife. Furthermore, to increase ranchers’ level of tolerance, they should receive information about puma behavior and the different strategies that can be applied to avoid the conflict [6,8].
Although creating protected areas contributes to the reduction in the defaunation process occurring in the Dry Chaco ecoregion [67], the conflict generated by puma livestock predation will probably continue, particularly in areas where ranchers strongly believe that pumas pose a risk to their livestock [26]. At the same time, nearly 98% of safe puma habitat patches and movement areas were found to occur outside protected areas [68]. To date, the creation of the Traslasierra National Park has not settled the human–puma conflict in the southern portion of the Argentinian Dry Chaco, even though we observed that pumas preferred the protected area with a greater presence of wild prey and avoided areas with livestock. Hence, a deep understanding of the factors involved in ranchers’ perception is necessary [35]. Perceptions are not always limited to fears or unfounded beliefs, as previously proposed [69]; rather, they are also related to other factors, like economic constraints or lack of support to local production. On the other hand, while guidelines on how to tackle human–wild fauna conflicts offer a useful framework for their solution [25], managing the coexistence of large carnivores and ranchers requires case-specific strategies [70].
Human–puma conflict in the Dry Chaco will not only require actions to protect the species, but also the understanding of the community needs and support to improve the life quality of the community. Thus, the government should, on the one hand, apply economic measures to compensate for the losses due to livestock predation and help ranchers apply mitigation measures that have been fairly successful (foxlights night predator, electrical wiring, among others). On the other hand, the government should develop environmental education programs adapted to the cultural context of the local communities, with the aim to achieve a more harmonious coexistence between humans and wild fauna in the south of the Dry Chaco.

5. Conclusions

This study suggests that, while the creation of protected areas is useful for protecting wildlife, it is not enough to reduce human–puma conflict in the southern Argentinian Dry Chaco. The mismatch between ranchers’ perceptions and puma presence is influenced by a long history of human–puma conflict in the region, mainly based on the economic losses to livestock production in one of the poorest communities of the region. Local people, particularly from ranches surrounding the park, should become involved in the decision-making process about puma conservation. Indeed, because ranchers consider that conserving the biodiversity in the region is not their responsibility, the government should support them through funds and knowledge in the development of different strategies other than lethal control to reduce livestock predation and increase the abundance and distribution of puma wild prey. Moreover, institutional–technical and/or economic support is necessary to increase the ranchers’ level of tolerance. Hence, different strategies, such as training and subsidizing ranchers, in addition to creating protected areas, should be developed to allow for the coexistence of livestock production and wildlife conservation over time.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/conservation5040078/s1, Trace plots (top panels) and posterior density distributions (bottom panels) for the intercept parameters of the state model (βstate) and the detection model (β_det).

Author Contributions

F.R.B. and F.C. designed the ecological study and performed the statistical analyses, T.C. and J.M.-G. designed the social study and data interpretation. F.R.B., T.C. and J.M.-G. carried out the fieldwork. All authors have read and agreed to the published version of the manuscript.

Funding

This study was carried out with funding from the National Scientific and Technical Research Council of Argentina (PIP 2021-2023) and the Secretariat of Science and Technology of the National University of Córdoba (PIDTA 2020-2023).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina (protocol code RESOL—2021- 1639—APN-DIR#CONICET and date of approval 21 September 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to are not yet available on CONICET or SECyT institutional repositories.

Acknowledgments

The National Parks Administration authorized the study in Traslasierra National Park. Gabriel Boaglio and Pablo Martín Contreras contributed to the fieldwork. We especially thank the ranchers who allowed us to conduct sampling in their fields and contributed to the interviews.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (A) Study area including the Traslasierra National Park and surrounding area, with details of the location of the camera traps, “puestos” (ranchers’ dwellings) and water sources, (B) Dry Chaco ecoregion, and (C) camera-trap photo of a puma.
Figure 1. (A) Study area including the Traslasierra National Park and surrounding area, with details of the location of the camera traps, “puestos” (ranchers’ dwellings) and water sources, (B) Dry Chaco ecoregion, and (C) camera-trap photo of a puma.
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Figure 2. Common wild puma prey in the study area: (A) Chunga burmeisteri, (B) Tolypeutes matacus, (C) Catagonus wagneri, (D) Dolichotis patagonum, (E) Mazama gouazoubira, (F) Lama guanicoe.
Figure 2. Common wild puma prey in the study area: (A) Chunga burmeisteri, (B) Tolypeutes matacus, (C) Catagonus wagneri, (D) Dolichotis patagonum, (E) Mazama gouazoubira, (F) Lama guanicoe.
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Figure 3. Effect of explanatory variables: (a): native prey richness, (b): sites occupied by livestock in relation to the number of puma records, (c) sites occupied by livestock and (d) density of livestock in relation to the number of puma records. The shaded area represents the 95% confidence intervals for each variable.
Figure 3. Effect of explanatory variables: (a): native prey richness, (b): sites occupied by livestock in relation to the number of puma records, (c) sites occupied by livestock and (d) density of livestock in relation to the number of puma records. The shaded area represents the 95% confidence intervals for each variable.
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Table 1. Wild prey species were included to estimate the q0 and q1 variables.
Table 1. Wild prey species were included to estimate the q0 and q1 variables.
Animal ClassScientific NamesCommon Name
BirdChunga burmeisteriChuña
BirdNothoprocta cinerascensInambú montaraz
BirdEudromia elegansMartineta
BirdRhea americanaÑandú
MammalDolichotis salinicolaConejo del palo
MammalMazama gouazoubiraCorzuela
MammalLama guanicoeGuanaco
MammalSus scrofaJabalí *
MammalLepus europaeusLiebre europea *
MammalDolichotis patagonumMara
MammalCatagonus wagneriPecarí quimelero
MammalPecari tajacuPecarí de collar
MammalChaetophractus sp.Quirquincho
MammalMicrocavia jayatCuis
MammalTolypeutes matacusMataco bola
MammalLagostomus maximusVizcacha
ReptileSalvator rufescensIguana
ReptileChelonoidis chilensisTortuga
* exotic species.
Table 2. Ranking of the best-supported and null models for occupancy term (ψ) in Bayesian occupancy models for cougar (Puma concolor).
Table 2. Ranking of the best-supported and null models for occupancy term (ψ) in Bayesian occupancy models for cougar (Puma concolor).
MODELSELPDkΔ ELPDSE ELPDElpd WeightR_HatMin_n_eff
occupancy term (ψ)
2018
~1~1−16.192.35000.21132,622.40
~1~dist_ranch−16.242.950.050.560.210.9940,679.15
~1~dist_wat−16.503.10.30.840.18139,900.26
~1~dist_ranch + dist_water + q0 + q1−16.834.590.640.780.15142,510.69
~1~q1−16.903.710.711.030.15138,727.71
~1~q0−17.643.961.450.80.1136,574.42
2021
~1~1−59.193.3000.140.9935,091.20
~1~P_NP−59.333.920.130.890.13136,367.59
~1~dist_ranch−59.454.50.251.540.12133,788.92
~1~occu_C−59.634.250.431.130.110.9927,212.99
~1~dens_C−59.684.30.451.10.11138,844.84
~1~dens_C + occu_C−59.774.410.531.310.11134,264.89
~1~q1−59.794.340.580.640.10.9930,754.88
~1~q0−60.244.331.030.380.08134,961.03
~1~dist_wat−60.334.461.120.330.080.9936,960.88
2022
~1~q0−47.422.44000.19135,327.34
~1~occu_C−48.052.060.632.070.14134,713.27
~1~dens_C−48.502.81.081.640.11133,917.52
~1~P_PN−48.562.341.131.780.11135,033.11
~1~dens_C + Occu_C−48.563.21.141.970.11137,588.45
~1~1−49.1621.741.730.08129,468.81
~1~dist_ranch + dist_wat + q0 + q1 + dens_C + occu_C + P_NP−49.195.961.761.410.08138,774.09
ELPD: expected predictive accuracy; K: number of parameters; ΔELPD: difference in expected predictive accuracy between any model and the best model; SE ELPD: standard error of the difference in predictive accuracy; Elpd weight: weight of the model based on ELPD; indicates relative support among candidate models; R_hat: convergence diagnostic (Gelman–Rubin statistic) for MCMC chains; values close to 1 indicate good convergence; Min_n_eff: minimum effective sample size across all parameters; higher values indicate more reliable estimates; dist_ranch: distance to the nearest ranch; dist_water: distance to the nearest water source; q0: Hill’s number corresponds to native prey richness; q1: Hill’s number corresponds to native prey richness with a weight proportional to prey abundance; P_NP: protection situation of the area (protected and unprotected); dens_C: density of livestock (ind/ha); occu_C: livestock occupancy values.
Table 3. First five models (Poisson distribution) of space use intensity by Puma concolor in relation to environmental variables for each sampling year. 2018: before the park creation; 2021 and 2022: one and two years after livestock removal from the park, respectively. The chosen models are shaded gray.
Table 3. First five models (Poisson distribution) of space use intensity by Puma concolor in relation to environmental variables for each sampling year. 2018: before the park creation; 2021 and 2022: one and two years after livestock removal from the park, respectively. The chosen models are shaded gray.
ModelskAICcΔAICcwiCum_wi
2018 (unprotected sites)
q1216.820.000.680.68
q0220.343.520.120.80
D_ranch220.353.520.120.92
NULL121.414.590.070.99
D_water224.537.710.011.00
2021 (6 protected and 7 unprotected sites)
NULL268.300.000.300.30
D_ranch368.880.580.220.52
Occu_C370.742.430.090.61
P_NP370.742.430.090.70
Dens_C370.742.430.090.79
2022 (8 protected and 9 unprotected sites)
q0250.220.000.310.31
Occu_C250.250.030.310.62
Dens_C251.441.220.170.79
P_NP252.312.090.110.90
NULL254.023.810.050.95
D_ranch: distance to the nearest ranch; D_water: distance to the nearest water source; q0: Hill’s number corresponds to native prey richness; q1: Hill’s number corresponds to native prey richness with a weight proportional to prey abundance; P_NP: protection situation of the area (protected and unprotected); Dens_C: density of livestock (ind/ha); Occu_C: livestock occupancy values.
Table 4. Parameter estimates (±SE) and 95% confidence interval limits (CL) for explanatory variables describing the occupancy of Puma concolor in relation to environmental variables for 2018 and 2022. The year 2021 is not included because the NULL model was the best model.
Table 4. Parameter estimates (±SE) and 95% confidence interval limits (CL) for explanatory variables describing the occupancy of Puma concolor in relation to environmental variables for 2018 and 2022. The year 2021 is not included because the NULL model was the best model.
Explanatory
Variable		Parameter
Estimate ± SE		CL
LowerUpper
2018
Intercept−1.83 ± 0.89−4.26−0.50
q11.26 ± 0.490.382.45
2022
Intercept−0.00 ± 0.26−0.570.46
q00.59 ± 0.240.131.08
Intercept0.58 ± 0.250.041.03
Occu_C−1.27 ± 0.56−2.52−0.27
Intercept0.01 ± 0.26−0.560.48
Dens_C−0.59 ± 0.29−1.22−0.08
q0: Hill number corresponding to native prey richness. q1: Hill number corresponding to native prey richness, with a weight proportional to prey abundance. Dens_C: density of livestock (ind/ha), Occu_C: livestock occupancy values.
Table 5. Importance of livestock mortality causes and percentage of loss due to puma predation estimated by ranchers in the study area.
Table 5. Importance of livestock mortality causes and percentage of loss due to puma predation estimated by ranchers in the study area.
Causes of Livestock LossPuma PredationDroughtTheftAccidentDisease
Importance (scale from 1 to 5)53111
Average % of livestock loss before park creation105111
Average % of livestock loss the year after livestock removal from the park (2020)255111
Average % of livestock loss after the first year of livestock removal from the park (2021/22)125111
Average % of tolerance of livestock loss1.355011
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MDPI and ACS Style

Barri, F.R.; Costa, T.; Manzano-García, J.; Cappa, F. Human–Puma Conflict in the Dry Chaco: Species’ Occupancy and Ranchers’ Perception Before and After the Creation of a Protected Area. Conservation 2025, 5, 78. https://doi.org/10.3390/conservation5040078

AMA Style

Barri FR, Costa T, Manzano-García J, Cappa F. Human–Puma Conflict in the Dry Chaco: Species’ Occupancy and Ranchers’ Perception Before and After the Creation of a Protected Area. Conservation. 2025; 5(4):78. https://doi.org/10.3390/conservation5040078

Chicago/Turabian Style

Barri, Fernando R., Thiago Costa, Jessica Manzano-García, and Flavio Cappa. 2025. "Human–Puma Conflict in the Dry Chaco: Species’ Occupancy and Ranchers’ Perception Before and After the Creation of a Protected Area" Conservation 5, no. 4: 78. https://doi.org/10.3390/conservation5040078

APA Style

Barri, F. R., Costa, T., Manzano-García, J., & Cappa, F. (2025). Human–Puma Conflict in the Dry Chaco: Species’ Occupancy and Ranchers’ Perception Before and After the Creation of a Protected Area. Conservation, 5(4), 78. https://doi.org/10.3390/conservation5040078

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