Reviews and How to Wear the Pathera Pardus Scarfe

• Loading metrics

Open Access

Peer-reviewed

Research Article

Spatial variation in leopard (Panthera pardus) site use across a slope of anthropogenic pressure in Tanzania's Ruaha mural

• Leandro Abade,
• Jeremy Cusack,
• Remington J. Moll,
• Paolo Strampelli,
• Amy J. Dickman,
• David W. Macdonald,
• Robert A. Montgomery

ten

• Published: Oct 10, 2018
• https://doi.org/10.1371/journal.pone.0204370

Figures

Abstract

Understanding large carnivore occurrence patterns in anthropogenic landscapes next to protected areas is central to developing actions for species conservation in an increasingly human-dominated world. Amidst large carnivores, leopards (Panthera pardus) are the well-nigh widely distributed felid. Leopards occupying anthropogenic landscapes frequently come into conflict with humans, which often results in leopard mortality. Leopards' use of anthropogenic landscapes, and their frequent interest with conflict, make them an insightful species for understanding the determinants of carnivore occurrence across homo-dominated habitats. We evaluated the spatial variation in leopard site apply across a multiple-utilize landscape in Tanzania'southward Ruaha mural. Our written report region encompassed i) Ruaha National Park, where homo activities were restricted and sport hunting was prohibited; ii) the Pawaga-Idodi Wild animals Management Area, where wildlife sport hunting, wild animals poaching, and illegal pastoralism all occurred at relatively depression levels; and iii) surrounding village lands where carnivores and other wild fauna were oftentimes exposed to human-carnivore conflict related-killings and agricultural habitat conversion and development. We investigated leopard occurrence beyond the study region via an all-encompassing camera trapping network. We estimated site use as a function of environmental (i.e. habitat and anthropogenic) variables using occupancy models inside a Bayesian framework. Nosotros observed a steady refuse in leopard site use with downgrading protected area status from the national park to the Wild fauna Management Expanse and hamlet lands. Our findings suggest that human-related activities such as increased livestock presence and proximity to homo households exerted stronger influence than prey availability on leopard site utilize, and were the major limiting factors of leopard distribution across the slope of human force per unit area, particularly in the village lands outside Ruaha National Park. Overall, our written report provides valuable data about the determinants of spatial distribution of leopards in human-dominated landscapes that can help inform conservation strategies in the borderlands next to protected areas.

Citation: Abade L, Cusack J, Moll RJ, Strampelli P, Dickman AJ, Macdonald DW, et al. (2018) Spatial variation in leopard (Panthera pardus) site use across a gradient of anthropogenic pressure in Tanzania's Ruaha landscape. PLoS ONE 13(ten): e0204370. https://doi.org/x.1371/periodical.pone.0204370

Editor: Marco Festa-Bianchet, Université de Sherbrooke, CANADA

Received: May 28, 2018; Accepted: September five, 2018; Published: Oct x, 2018

Copyright: © 2018 Abade et al. This is an open up access commodity distributed under the terms of the Creative Eatables Attribution License, which permits unrestricted apply, distribution, and reproduction in whatsoever medium, provided the original author and source are credited.

Data Availability: The leopard occurrence information used for the model can be found freely bachelor at https://github.com/labade/GitHub/tree/master/leop_occu_data.

Funding: L.A. was supported by a CNPq-Brasil scholarship (215981/2013-viii), and grants by the Rufford Foundation (Rufford Small Grants), Cleveland Metroparks Zoo/ Cleveland Zoological Order, IdeaWild, Lady Margaret Hall, WildCRU, and Ruaha Carnivore Project. J.C. was supported by the UK Natural Environs Research Council, R.J.One thousand. was supported past an National Science Foundation Graduate Research Fellowship. The funders had no role in written report design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors accept declared that no competing interests exist.

ane. Introduction

Equally apex predators, large-bodied mammals of the order Carnivora can exert important influence on regulation of trophic interactions and the maintenance of ecosystem functions [1, 2]. Large carnivores, besides their intrinsic value as species [three], are besides of import revenue-generators for a multimillion-dollar ecotourism and sport hunting manufacture that contributes to national economies likewise as the conservation and management of wildlife and wilderness, peculiarly in Africa [iv, 5]. Despite clear ecological, economical, and intrinsic value, big carnivore populations are threatened globally, with 24 of the remaining 31 species documented to be declining [ane]. Such population losses are attributable to habitat conversion, human persecution, casualty depletion, unsustainable hunting, and exploitation for body parts [i, 6, vii]. Human population growth and urbanization around protected areas, especially in the sub-Saharan African countries [8, 9], present imminent challenges for carnivore conservation. For example, mortality is college forth the boundaries of protected areas where large carnivores risk being killed preventatively or in retaliation to predation events that can cause substantial fiscal loss to people's livelihoods [10–12]. The habitats associated with this human being-carnivore interface can function equally population sinks, whereby the high human-induced large carnivore offtake can "drain" populations from the bordering protected areas and compromise population persistence [eleven, 12]. However, equally big carnivores are oft wide-ranging and maintain large home ranges [1, 13], they ordinarily rely on these peripheral human-dominated lands around protected areas [xi, 14, 15] that can provide important habitats for these species. For case, 68% of the most suitable habitats for leopards in South Africa have been estimated to occur outside national parks and protected areas, in areas of human being occupation and subject to habitat conversion [16]. Thus, these homo-dominated habitats can be essential to the conservation of large carnivore populations [10, 11, 17, 18]. Accordingly, determining the extent to which large carnivores tin occupy areas of increasing human being force per unit area, such as those represented by human encroachment of wildlands and agro-pastoralism, is of major importance for their conservation.

Among large carnivores, leopards (Panthera pardus) are the most widespread felid species, occupying the most diverse habitat types including deserts, forests, and savannahs [xix]. Leopards' behavioural flexibility and dietary plasticity facilitates their successful occupation of highly modified and heavily disturbed human-dominated landscapes, given adequate human tolerance to their presence in such habitats [20, 21]. For instance, even in densely populated areas (400 people/km²) leopards tin alive alongside people by mostly feeding on livestock and domestic dogs, and finding refuge in crops and agricultural lands [20, 21]. Despite such ecological plasticity, leopards are threatened by rampant habitat destruction and fragmentation, prey depletion induced past bushmeat poaching and overgrazing, unsustainable harvest past sport hunting and to attend demands for trunk parts, and disharmonize-related bloodshed [19, 22, 23]. Equally a result, leopard populations have experienced >xxx% global range contraction in the past xx years. In Africa, leopards accept lost 48–67% of their historical distribution, with the nearly pronounced reductions in northern and western Africa [19]. The species is expected to undergo further population decline across its overall Sub-Saharan African range given the observed high charge per unit of prey depletion [22] and habitat loss induced by increasing homo population in the next 50 years [23].

Tanzania is one of the almost important countries for leopard conservation in Africa, where its vast array of national parks and game reserves protects substantial portions of the leopard'southward extant range [19]. Leopards correspond an important economic nugget for Tanzania, as the species is amongst the nearly exported trophy species; in 2008 hunting of leopards and other mammalian megafauna contributed to a USD 56.3 million revenue for hunting operators and governments [iv]. Despite the ecological and economic importance of leopards, the electric current lack of empirical field data on leopard environmental hinders the development of effective conservation strategies designed to protect the species in Tanzania [24, 25].

In this study, we investigated the factors affecting the probability of leopard site use at the interface of protected and unprotected habitat in southern Tanzania's Ruaha landscape. Our study expanse encompassed the eastern portions of Ruaha National Park, the adjacent semi-protected Pawaga-Idodi Wildlife Direction Area (WMA), and unprotected village lands. Specifically, we assessed spatial variation in leopard site use in response to (i) anthropogenic disturbance, as indicated by distance to households and livestock number, (two) the availability of primary prey species, and (iii) proximity to water sources. Documenting the factors associated with carnivore site use is central to prioritising conservation efforts for these species. The methods and framework presented in this report provide a timely and useful tool that is going to go ever more than of import in increasingly man-modified protected to unprotected habitat interfaces.

2. Material and methods

Ethics statement

Data collection was based on the use of camera traps, a non-invasive method that does not involve contact with the study species, nor interfere with their natural behaviour. Fieldwork was carried out under research permit no. TWRI/TST/65/VOL.VII/85/146 to LA, issued by the Tanzania Wild fauna Research Institute (TAWIRI) and the Committee for Inquiry and Engineering (COSTECH).

The Ruaha landscape

We conducted our report in southern Tanzania across the Ruaha landscape (Fig 1). The Ruaha landscape spans over 50 000 km^two and supports substantial populations of large carnivores. For this reason, the landscape has been listed by the Tanzania Wildlife Research Institute every bit a priority for carnivore research and conservation [25]. Ruaha National Park is one of the largest national parks in Africa, spanning over 20 226 km². Bays hunting of wildlife is prohibited within the park and in the hamlet lands, but is permitted in limited sections of the WMA. In the village lands, large carnivores are exposed to anthropogenic disturbance, including intense human being-carnivore conflict, bushmeat snaring, and indiscriminate poisoning. The village lands are inhabited by over lx 000 people divided amongst 21 villages [26]. The predominant livelihood is agropastoralism [27]. Donkeys, goats, and cattle are the most commonly kept domestic livestock. Although no official numbers for livestock affluence are available for this area, the overall Iringa region, inside which Ruaha National Park sits, contains a fifth of Tanzania'south full domestic animals, with > 620 000 livestock and > one.5 meg poultry [27]. Attitudes towards large carnivores among hamlet members tend to be negative, motivated by both existent and perceived carnivore depredation of livestock [28]. Consequently, big carnivores experience high rates of human-induced mortality in this landscape. From 2010–2016, 100 lions and other big carnivores were killed by people. Given the intense conflict and mortality rates, and the paucity of information about the spatial distribution of large carnivores in these areas [24, 25], information technology is imperative to improve understanding of the ecological and anthropogenic factors influencing big carnivore occurrence in these areas.

Fig 1. Spatial distribution of the camera-trap stations (red shaded circles) across the Ruaha landscape.

one–11 represents sampling areas: 1. Mdonya; 2. Kwihala; 3. Msembe; 4. Mwagusi; 5. Lunda-Ilolo; 6. Pawaga; seven. Lunda; viii. Idodi; ix. Malinzanga; x. Nyamahana; 11. Magosi. The yellowish shaded circles correspond the number of contained detections of leopards (Panthera pardus) at each photographic camera-trap station (> 5 minutes between detection).

https://doi.org/x.1371/journal.pone.0204370.g001

The climate of the region is semi-arid to arid, with an average annual precipitation of 500 mm, and a bimodal rainy season from Dec to Jan and March to April [29]. The vegetation cover is a mosaic of semi-arid savannahs and northerly Zambesian miombo woodlands [xxx]. The hamlet lands are primarily covered by agricultural fields (mostly rice and maize crops), and livestock grazing areas. The Greater Ruaha River is the main water source in the written report area, especially during the dry flavour. This river provides central resources for wildlife, attracting species towards the park borders with the WMA and hamlet land.

Leopard data

To document leopard site use, we deployed 127 not-baited, remotely triggered, single photographic camera-trap stations (CTs) that sampled 11 areas across the Ruaha landscape during the dry seasons of 2014 and 2015. In 2014, we placed 42 Reconyx HC500 CTs forth animal trails, and sampled the Msembe expanse, virtually the park headquarters, where at that place is low anthropogenic pressure [31]. In 2015, we used 85 Bushnell Scoutguard CTs and extended sampling to other 10 areas, including four sampling areas in RNP, ii in the WMA, and 4 in the village lands (Fig 1). Nosotros used a pseudostratified method for deploying our CTs, ensuring a minimum one.5–2 km distance betwixt stations, and 15–20 km distance between sampling areas whenever possible. The sampling areas were distributed beyond a range of distances from the edge of the national park (0–10 km; 10–20 km; >30 km) to enable examining potential spatial variation in leopard occurrence (Fig 1). We gear up the CTs facing animal trails when the pre-defined GPS coordinates were constitute within 5 meters from the nearest open up path showing signs of animal use. We adopted this design and so equally to increase detection of more elusive species [32]. All the CTs were placed in trees or poles at a acme of 0.3–0.5 meters off the basis. Nosotros visited the CTs every 30–fifty days to retrieve data and service the traps. Though certain regions of the national park were inaccessible (especially in the road-less southern sections), our CTs placement intended to capture substantial habitat heterogeneity observed across the landscape.

We pooled the overall information and analysed it in a single-season framework, as previous studies conducted in the central areas of the Ruaha National Park take institute large carnivores to have similar site use patterns across the dry out seasons of 2014 and 2015 [31]. Nosotros collapsed the temporal extent of the sampling into vii days bin intervals, across a 32-calendar week survey (~210 days) flow. This timeframe has been chosen to ensure continued sampling through the whole dry flavour. Given the long duration of our survey across the whole dry seasons, we were unable to meet the population closure assumption of the occupancy model [33–35]. However, such assumption can exist relaxed when changes in the population of interested are assumed to happen randomly during the survey menses [34], which may the instance with our extended sampling period. The relaxation of the population closure assumption requires changing the interpretation of the occupancy parameter from true occupancy to proportion of site used by the species, which originally was our main involvement. Thus, in this study, site use equates to the probability that a given site was used during the overall survey menses, rather than the probability of continuous site occupation [35].

The leopard occurrence information used for the model can be institute freely available at https://github.com/labade/GitHub/tree/principal/leop_occu_data.

Environmental covariates

We modelled leopard site use every bit a function of five environmental covariates known to influence leopard habitat pick (Table 1) [36–40]. We calculated the distance to Neat Ruaha River and distance to household covariates as rasters at a resolution of 500 one thousand (S1 Fig). We generated the rasters in QGIS 2.6.0 [41] from freely available geoprocessed satellite imagery and data nerveless past University of Oxford's Wildlife Conservation Research Unit of measurement, Ruaha Carnivore Project. We developed a primary wild prey availability covariate for leopards. To do and so, we calculated a temporal take hold of-per unit endeavour (CPUE) index of prey availability for each CTs based on the number of independent records for the main five leopard prey species [42]. Casualty species included bushbuck (Tragelaphus scriptus), mutual duiker (Sylvicapra grimmia), greater kudu (Tragelaphus strepsiceros), impala (Aepyceros melampus), and warthog (Phacochoerus africanus). We calculated the CPUE by multiplying the number of independent events at each CTs past the species average mass, divided by the CTs sampling effort, and standardised per 100 camera trap days [xiv]. Casualty mass was based on standard reference guides [43]. The CPUE alphabetize is often used in the fisheries industry to assess stock abundance, and provide data for monitoring the furnishings of harvesting on populations [44, 45].The concept backside CPUE is that the size of the take hold of from a population should increase when population density or effort increases [46]. Thus, in principle, CPUE could serve as an affluence alphabetize, and be used to notice variation in numbers as in affluence itself. The concept has been used in the studies of carnivore live trapping [47], bushmeat harvesting and poaching [48, 49], and to estimate prey biomass in camera-trapping and occupancy studies [50]. We as well calculated a livestock presence covariate past summing the total independent livestock detections at each CTs. Livestock species included cattle, goats, and donkeys. We pooled these species because the objective was to assess the overall disturbance potential of livestock grazing on leopard site use, irrespective of the livestock species. Nosotros considered contained detection events for leopard, casualty and livestock as those with > v minutes between records [14].

Table 1. Covariates and corresponding expected influence on the estimates of leopard site apply and detection in the Ruaha landscape, southern Tanzania, during the dry out seasons of 2014–2015.

Ψ: Estimated probability of site use; p: probability of detection, given site utilise. CPUE: grab-per unit try index of casualty availability for each camera-trap station based on the number of independent records for the main five leopard casualty species [42] photographed during the survey.

https://doi.org/10.1371/periodical.pone.0204370.t001

Given that trail types have been found to influence on probability of carnivore detection in this study region [31], nosotros evaluated the effect of trail blazon [animal trails (AT); no-trails (NT); human-made roads (RD)] on leopard detection probability.

Prior to model fitting, we standardized (z-score) all covariates [51], and assessed predictor collinearity using Pearson correlation and variance inflation factor tests. All the covariates used in the models were those minimally correlated (Pearson <0.seven, VIF <3 [52]; S1 and S2 Tables.).

Model analyses and averaging

We used temporally replicated surveys (i.due east. weeks) to gauge the latent, unobserved probability of site use of each CT, Z _i , where Z _i = 1 if site i is occupied and 0 otherwise. Nosotros used the replicate surveys to estimate detection probability, p _i,j , where p _i,j is the probability that leopards are detected at site i during replicate j, given use of that site (i.e., Z _i = 1) [33, 53]. We fit the model with a random intercept at the level of each of the eleven areas sampled in the study [54, 55] to minimise potential spatial autocorrelation among model residuals (S2 Fig). Our final model to judge leopard site utilize was implemented as follows: (one) where Ψ _i represents the probability of leopard site use at the i ^thursday CT, α _{surface area} represents a random intercept indexed by sampling area with estimated hyperparameters μ (mean) and τ² (variance), and α _1,2…five represent the influence of associated covariates at the i ^th CT (Tabular array 1).

The final detection model was implemented equally follows: (2) where p _i,j represents the probability of detection at the i ^th CT during survey j given that a site is used (i.eastward., Z _i = 1), β ₀ is the intercept, and β _k represents the effect of the m ^th trail type (m = iii) on leopard detection at each CT, with fauna trail (AT) equally the reference category.

Nosotros implemented and analysed the models using a Bayesian framework and Markov concatenation Monte Carlo (MCMC) simulations in R v.2.13.0 [56] and JAGS [57] through the parcel 'R2jags' [58]. We estimated the degree of back up for the effect of each covariate on site use through the Bayesian inclusion parameter due west_c [59], which had a Bernoulli distribution and an uninformative prior probability of 0.5. The posterior probability of due west_c corresponds to the estimated probability of any given covariate ('C') to be included in the best model of a gear up of 2^C candidate models [fourteen, 55, 60]. We calculated model-averaged estimates for the covariate coefficients over the global models from MCMC posterior histories, as described by Royle & Dorazio [60]. Nosotros used uninformative uniform priors for all covariates and implemented the models using three bondage of 500 000 iterations each, discarding the first 50 000 equally fire-in, and thinned the posterior bondage by 10. We assessed the model convergence by ensuring R-hat values for all parameters was <ane.1 [61].

3. Results

We recorded a total of 232 independent leopard events over 12 987 camera-trap days at 42 of the 127 CTs (33%). We recorded 197 leopard detections at 36 out of 77 CTs in the national park, 35 detections at 6 out of xvi CTs in WMA, and no detections at the 35 CTs installed in the village lands, despite the consistent sampling attempt in this area (Fig 1; Table 2).

Nosotros recorded a total of viii 120 contained detections of the principal prey of leopards (Figs 2 and 3). We observed spatial variation in the number of primary prey detections, with a total of 5 766 independent casualty records in the national park, 2 116 in the WMA, and 238 in the village lands (Fig iii). Nosotros registered 2 811 independent events of livestock in 32 out of 35 village land CTs.

Fig 2. Contained detections of the chief leopard prey species at each camera-trap station.

A. Bushbuck (Tragelaphus scriptus); B. Mutual duiker (Sylvicapra grimmia); C. Greater kudu (Tragelaphus strepsiceros); D. Impala (Aepyceros melampus); East. Warthog (Phacochoerus africanus); F. Livestock. 1–xi represents sampling areas: 1. Mdonya; 2. Kwihala; 3. Msembe; four. Mwagusi; 5. Lunda-Ilolo; 6. Pawaga; vii. Lunda; 8. Idodi; 9. Malinzanga; 10. Nyamahana; 11. Magosi.

https://doi.org/10.1371/journal.pone.0204370.g002

Fig 3. Variation in prey detection across the gradient of anthropogenic pressure in the Ruaha landscape.

Independent events (> 5 min interval between detection). Bushbuck (Tragelaphus scriptus); Common duiker (Sylvicapra grimmia); Greater kudu (Tragelaphus strepsiceros); Impala (Aepyceros melampus); Warthog (Phacochoerus africanus).

https://doi.org/x.1371/journal.pone.0204370.g003

We establish a significantly potent negative relationship between the probability of leopard site apply and habitats that were closer to households. Similarly, we observed a negative, admitting highly variable and not-meaning, influence of increased livestock presence on leopard site use. Additionally, nosotros found no evidence for a relationship betwixt prey availability, distance to the Smashing Ruaha River, and the probability of leopard site utilize (Tabular array 3; Fig 4). The relatively loftier Bayesian inclusion parameter values (wc−Table 3) for both proximity to households and livestock presence, in comparing to prey availability, suggest that leopard site use was primarily influenced by lower levels of anthropogenic pressure level than prey availability during the survey. Finally, we found a lack of outcome of trail blazon on detection probability (Table 3).

Fig iv. Predicted association of the covariates to the probability of site use of leopards (Panthera pardus).

The solid line represents the posterior hateful, and the calorie-free grey lines represent the estimated uncertainty based on a random posterior sample of 150–200 iterations. Occupancy probability = site use.

https://doi.org/10.1371/journal.pone.0204370.g004

Tabular array iii. Posterior means, standard deviations (S.D.), 95% apparent intervals (C.I.), and Bayesian inclusion parameters (due west_c) of leopard site use models fit to camera-trap data from the Ruaha landscape, southern Tanzania, during the dry out seasons of 2014–2015.

https://doi.org/10.1371/journal.pone.0204370.t003

iv. Give-and-take

Our findings suggest that man-related activities such as increased livestock presence and proximity to human households exerted stronger influence than casualty availability on leopard site use, and were the major limiting factors of leopard distribution across the slope of human pressure, peculiarly in the village lands outside Ruaha National Park. Leopards take been shown to adapt to heavily disturbed anthropogenic environments, occurring in areas with loftier human densities and of low wild prey density [xix, 62, 63]. Importantly, our results suggest that such adaptations to human-pressure and threats may be context-specific [64]. It is crucial to highlight that the express leopard site utilize observed exterior the national park should not be interpreted as a result of the covariates considered in this study in isolation, but besides as a consequence of the underlying loftier persecution and human induced mortality of large carnivores in the written report site [28, 65]. The combination of these factors is likely limiting leopard occurrence exterior the protected area in the Ruaha landscape.

Determinants of leopard site utilize

The lack of leopard detections in the village lands suggests low population densities for the species in the unprotected areas surrounding Ruaha National Park. This expanse has undergone rapid conversion of habitats due to intense homo and livestock inroad [66, 67], intense disharmonize and loftier human-induced carnivore killing [28, 65], with all these factors likely contributing towards creating a difficult border for leopard populations in these non-protected areas. These results are like to those presented by Henschel et al. [68] and Ramesh et al. [69], that institute leopard use of habitat and abundance to be negatively influenced past areas with high human activity or increased bushmeat poaching. The negative influence of livestock presence on leopard site utilize could advise a potential risk-avoidance strategy targeted at areas of intense human exposure. Large carnivores have been plant to change and adjust spatiotemporal behaviour and dwelling range in areas of intense herding activities to minimise exposure to human herders and livestock [70, 71]. Alternatively, intense livestock herding could be associated with overgrazing and potential displacement of wild casualty beyond the village lands, although our analyses showed fiddling support for this hypothesis. It is noteworthy that the lack of leopard detections in hamlet lands should non exist understood equally the absenteeism of the species in these areas. Leopards undoubtedly utilise these hamlet lands, as indicated by reported livestock depredations, and the corresponding number of leopard killings in this area [28]. We admit that the precise mechanistic connections between low-levels of leopard detections in the village lands and the diversity of sources of anthropogenic pressure are elusive. In addition, our sampling strategy might have influenced our ability to capture habitat heterogeneity for detail covariates (e.m. livestock presence and distance to households), which could exist limiting detection probability, and the models to precisely guess the effect of such covariates on leopard habitat use. The limitations of camera-traps to but survey relatively small areas, associated with the likely low leopard densities and detection probability in village lands, means that broader survey across the whole mural, and the use of complementary methods such every bit spoor tracks could render a more precise estimate of the variables influencing leopard site use in these areas of human occupation. Furthermore, due to lack of available data, we did not account for leopard movement blueprint and habitation-range variation beyond the landscape and between seasons, which may take contributed to limit our site use estimates. These factors have been recently shown to substantially influence on species detection and site occupancy estimates from camera-trapping studies [72, 73]. Thus, further work based on camera-trapping should, whenever possible, incorporate motion data to improve site utilise and occupancy estimates. Despite these limitations, our results are the first to investigate the environmental determinants of leopard site apply across the gradient of anthropogenic pressure in the Ruaha mural, and provide much needed information to help furthering our agreement of the furnishings of homo activities on limiting leopard spatial distribution across one of the nigh important large carnivore strongholds in East Africa.

The observed weak association betwixt leopard site use and main prey availability (Table 3; Fig 4) provided an interesting insight into leopard ecology in this landscape. Prey availability is a known determinant of site use, spatial distribution, and population density of leopards [15, 38, 74] and other carnivores [22, 75, 76]. In fact, recent studies have shown that areas of increased leopard population density were linked to loftier abundance of medium-sized wild prey [15, 69]. I explanation of the observed weak relationship is that leopards could exist relying on smaller prey species than those considered in this study, especially outside Ruaha National Park, as a potential response to larger casualty scarcity. Leopards are known to shift and rely on pocket-sized-sized casualty species (<20 kg) in areas of increased bushmeat hunting and intense competition with humans for limited food resources [42, 68, 77], similar to those of the village lands effectually the national park. The depression prey detection across village lands, where they are exposed to intense bushmeat poaching [78], could help to approve such hypothesis (Figs ii and iii). Even though nosotros plant weak association between leopard site use and prey availability, information technology is nonetheless important to highlight that casualty depletion could still pose a serious threat to leopards locally. Prey depletion is one of the primary limiting factors to leopard occurrence and population density across their extant range, and potentially more detrimental to their survival than direct human-induced killings [15, 19, 69].

Implications for leopard conservation

Our results highlight the importance of protected areas on the conservation of wide-ranging large carnivores such as the leopard. Large protected areas such as Ruaha National Park are key in protecting important habitats for leopards and other big carnivores [79, fourscore] against the increasing homo pressure observed in village lands surrounding protected areas across Africa [viii, 9, 66]. Our findings suggest that intense man activities, likely coupled with underlying high levels of human-induced carnivore bloodshed due to conflict [28, 36, 81], represent key-limiting factors to leopard spatial distribution in the human-dominated non-protected areas around Ruaha National Park. Similar results have been found elsewhere in Africa, where the spatial distribution and population density of leopards [15], every bit well every bit of other large carnivores such equally lions [82] and other smaller carnivores [xiv, 83] have been express past increased human being and livestock encroachment, pastoralism, conflict and human-mediated mortality in anthropogenic landscapes surrounding protected areas. If leopards are to be successfully conserved in such areas of human occupation, information technology is vital to address the threats imposed past people and livestock immediately adjacent to protected areas.

In the context of this written report, one much-needed strategy is the mitigation of carnivore-related conflict with people [28, 65, 81]. Increasing people'south awareness and access to constructive actions to reduce the perceived run a risk originating from carnivore presence could help to increase tolerance and improve attitudes towards leopards and other large carnivores locally [84]. For example, systematic widespread improvement of husbandry practices using predator-proof bomas [81, 85], and prevention of human-carnivore conflict could lead to a substantial reduction in leopard and other large carnivore mortality, and contribute to conservation of these species in the village lands [65]. Additionally, developing strategies to reduce the associated costs of big carnivores' presence while increasing the tangible benefits of having these species in the hamlet lands could assistance to promote their conservation [65, 86, 87]. For instance, the provisions of veterinary medicines, health care, and didactics associated with large carnivore presence equally role of a community-based conservation approach in some of the villages around Ruaha National Park resulted in 80% reject of large carnivore killing, although those initiatives currently operate across less than half of the village country [65].

On a mural level, concerted efforts to develop integrated management strategies and adaptive livestock and wildlife foraging systems could aid limit the impact of livestock on rangeland habitats and wild animals [88]. Guaranteed access to optimum foraging sites past livestock, and the implementation of planned grazing strategies–which consists of establishing several grazing paddocks that enable livestock rotation based on forage growth charge per unit—across rangelands could aid minimising competition with wildlife, prey depletion, habitat degradation due to overgrazing, and ultimately promote wildlife conservation [88, 89]. Withal, these strategies tin be difficult to implement in areas where livestock owners can exist highly nomadic and transient, as is the case in the vicinity of Ruaha National Park. Finally, we emphasize that strategies aimed at conserving leopards and other large carnivores within human-dominated lands should be implemented in collaboration with local communities, given that these local communities will bear the costs of co-existing with these species, and ultimately exist responsible for deciding upon their conservation [90].

Supporting data

S1 Fig. Ready of covariates hypothesised to influence site apply by leopards (Panthera pardus) across the Ruaha landscape, southern Tanzania, during our surveys in the dry seasons of 2014–2015.

A. Altitude to the Great Ruaha River; B. Distance to households. Primary prey availability (CPUE), livestock presence and trail type not represented here.

https://doi.org/10.1371/journal.pone.0204370.s001

(TIF)

S2 Fig. Spline correlograms for the leopard (Panthera pardus) occupancy models.

Spline correlograms from a generalized linear model (A) and a generalized linear mixed model that included a random intercept at the CT level (B) showing a reduction in spatial autocorrelation. Distance betwixt paired sample locations in kilometres (Km).

https://doi.org/ten.1371/journal.pone.0204370.s002

(TIF)

Acknowledgments

L.A. thanks TAWIRI/TANAPA, RCP staff, Christos Astaras, Paul Johnson, and Nuno Faria.

References

1. one. Ripple WJ, Estes JA, Beschta RL, Wilmers CC, Ritchie EG, Hebblewhite M, et al. Status and ecological effects of the world's largest carnivores. Science. 2014;343(6167). pmid:24408439
   • View Article
   • PubMed/NCBI
   • Google Scholar
2. 2. Allen BL, Allen LR, Andrén H, Ballard G, Boitani L, Engeman RM, et al. Tin we salvage large carnivores without losing large carnivore scientific discipline? Food Webs. 2017;12:64–75.
   • View Commodity
   • Google Scholar
3. 3. Woodroffe R, Redpath SM. When the hunter becomes the hunted. Science. 2015;348(6241):1312–4. pmid:26089495
   • View Article
   • PubMed/NCBI
   • Google Scholar
4. four. Di Minin Eastward, Leader-Williams N, Bradshaw CJA. Banning trophy hunting will exacerbate biodiversity loss. Trends in Ecology & Evolution. 2016;31(2):99–102. http://dx.doi.org/10.1016/j.tree.2015.12.006
   • View Article
   • Google Scholar
5. v. Creel S, Thou'Soka J, Dröge E, Rosenblatt E, Becker MS, Matandiko Due west, et al. Assessing the sustainability of African lion trophy hunting, with recommendations for policy. Ecological Applications. 2016;26(7):2347–57. pmid:27755732
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. half dozen. Macdonald DW. Fauna behaviour and its role in carnivore conservation: examples of 7 deadly threats. Animate being Behaviour. 2016;120:197–209. PubMed PMID: WOS:000385375900022.
   • View Commodity
   • Google Scholar
7. 7. Ripple WJ, Abernethy K, Betts MG, Chapron G, Dirzo R, Galetti One thousand, et al. Bushmeat hunting and extinction risk to the earth's mammals. R Soc Open up Sci. 2016;3(10):160498. pmid:27853564; PubMed Central PMCID: PMCPMC5098989.
   • View Article
   • PubMed/NCBI
   • Google Scholar
8. 8. Wittemyer G, Elsen P, Bean WT, Burton ACO, Brashares JS. Accelerated human population growth at protected area edges. Science. 2008;321(5885):123–6. PubMed PMID: WOS:000257320800052. pmid:18599788
   • View Article
   • PubMed/NCBI
   • Google Scholar
9. 9. Newmark WD. Isolation of African protected areas. Frontiers in Ecology and the Environs. 2008;six(six):321–8. PubMed PMID: WOS:000258249200018.
   • View Article
   • Google Scholar
10. 10. Woodroffe R, Ginsberg JR. Edge Effects and the Extinction of Populations Inside Protected Areas. Science. 1998;280(5372):2126–8. pmid:9641920
    • View Article
    • PubMed/NCBI
    • Google Scholar
11. 11. Loveridge AJ, Hemson One thousand, Davidson Z, Macdonald D. African lions on the edge: reserve boundaries equally "bonny sinks". In: Macdonald DW, Loveridge AJ, editors. Biology and conservation of Wild Felids. New York: Oxford University Press; 2010. p. 283–304.
12. 12. Loveridge AJ, Valeix M, Elliot NB, Macdonald DW. The landscape of anthropogenic mortality: how African lions respond to spatial variation in chance. Journal of Applied Ecology. 2016;54(three):815–25.
    • View Article
    • Google Scholar
13. 13. Treves A, Karanth KU. Human-carnivore conflict and perspectives on carnivore management worldwide. Conservation Biology. 2003;17(6):1491–9.
    • View Commodity
    • Google Scholar
14. 14. Burton AC, Sam MK, Balangtaa C, Brashares JS. Hierarchical multi-species modeling of carnivore responses to hunting, habitat and prey in a Due west African protected area. Plos One. 2012;7(5). doi: ARTN e38007 PubMed PMID: WOS:000305353400068. pmid:22666433
    • View Article
    • PubMed/NCBI
    • Google Scholar
15. 15. Rosenblatt Due east, Creel South, Becker MS, Merkle J, Mwape H, Schuette P, et al. Effects of a protection slope on carnivore density and survival: an example with leopards in the Luangwa valley, Zambia. Environmental and Evolution. 2016;6(eleven):3772–85. PubMed PMID: WOS:000377043200026. pmid:27231529
    • View Article
    • PubMed/NCBI
    • Google Scholar
16. xvi. Swanepoel LH, Lindsey P, Somers MJ, Van Hoven W, Dalerum F. Extent and fragmentation of suitable leopard habitat in S outh A frica. Animal Conservation. 2013;sixteen(one):41–l.
    • View Article
    • Google Scholar
17. 17. Pitman RT, Swanepoel LH, Hunter L, Slotow R, Balme GA. The importance of refugia, ecological traps and scale for large carnivore management. Biodiversity and Conservation. 2015;24(8):1975–87.
    • View Commodity
    • Google Scholar
18. 18. van der Meer E, Fritz H, Blinston P, Rasmussen GSA. Ecological trap in the buffer zone of a protected area: effects of indirect anthropogenic mortality on the African wild domestic dog Lycaon pictus. Oryx. 2014;48(two):285–93. PubMed PMID: WOS:000337969700020.
    • View Article
    • Google Scholar
19. xix. Jacobson AP, Gerngross P, Lemeris JR, Schoonover RF, Anco C, Breitenmoser-Wursten C, et al. Leopard (Panthera pardus) condition, distribution, and the enquiry efforts across its range. Peerj. 2016;4. doi: ARTN e1974 PubMed PMID: WOS:000376488700001. pmid:27168983
    • View Article
    • PubMed/NCBI
    • Google Scholar
20. 20. Athreya V, Odden Yard, Linnell JDC, Krishnaswamy J, Karanth KU. A true cat amidst the dogs: leopard Panthera pardus nutrition in a human-dominated mural in western Maharashtra, Bharat. Oryx. 2016;50(one):156–62.
    • View Commodity
    • Google Scholar
21. 21. Braczkowski AR, O'Bryan CJ, Stringer MJ, Watson JE, Possingham HP, Beyer HL. Leopards provide public health benefits in Mumbai, India. Frontiers in Ecology and the Environment. 2018;16(3):176–82.
    • View Article
    • Google Scholar
22. 22. Wolf C, Ripple WJ. Prey depletion as a threat to the world's large carnivores. Roy Soc Open Sci. 2016;3(viii). doi: ARTN 160252 PubMed PMID: WOS:000384411000016. pmid:27853599
    • View Article
    • PubMed/NCBI
    • Google Scholar
23. 23. Stein AB, Athreya Five, Gerngross P, Balme G, Henschel P, Karanth U, et al. Panthera pardus (errata version published in 2016). The IUCN Red Listing of Threatened Species 2016, 2016.
    • View Article
    • Google Scholar
24. 24. TAWIRI. Proceedings of the first Tanzania panthera leo and leopard conservation activeness programme workshop. Arusha, Tanzania: Tanzania Wild fauna Enquiry Establish, 2007.
25. 25. TAWIRI. Tanzania carnivore conservation activeness program. Arusha: 2009.
26. 26. Green KE, Adams WM. Dark-green grabbing and the dynamics of local-level appointment with neoliberalization in Tanzania's wild fauna management areas. The Journal of Peasant Studies. 2015;42(ane):97–117.
    • View Article
    • Google Scholar
27. 27. NBS. Iringa region social-economic profile. Dar Es Salaam: Ministry building of Finance, National Bureau of Statistics, Iringa Regional Secretariat, 2013.
28. 28. Dickman AJ, Hazzah L, Carbone C, Durant SM. Carnivores, culture and 'contagious disharmonize': Multiple factors influence perceived problems with carnivores in Tanzania's Ruaha landscape. Biological Conservation. 2014;178(0):nineteen–27. http://dx.doi.org/10.1016/j.biocon.2014.07.011.
    • View Article
    • Google Scholar
29. 29. Walsh MT. The Evolution of Customs Wildlife Management in Tanzania: Lessons from the Ruaha Ecosystem. Mweka,Tanzania: African Wildlife Management in the New Millennium; 2000.
30. 30. Sosovele H, Ngwale JJ. Socio-economical root causes of the loss of biodiversity in the Ruaha catchment area. 2002.
    • View Article
    • Google Scholar
31. 31. Cusack JJ, Dickman AJ, Rowcliffe JM, Carbone C, Macdonald DW, Coulson T. Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities. PLoS Ane. 2015;10(5): e0126373. pmid:25950183
    • View Article
    • PubMed/NCBI
    • Google Scholar
32. 32. Wearn OR, Rowcliffe JM, Carbone C, Bernard H, Ewers RM. Assessing the status of wild felids in a highly-disturbed commercial forest reserve in Borneo and the implications for camera trap survey design. PLoS One. 2013;8(11):e77598. pmid:24223717
    • View Article
    • PubMed/NCBI
    • Google Scholar
33. 33. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Andrew Royle J, Langtimm CA. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 2002;83:2248–55.
    • View Article
    • Google Scholar
34. 34. MacKenzie DI, Nichols JD. Occupancy as a surrogate for affluence interpretation. Animal biodiversity and conservation. 2004;27(1):461–7.
    • View Commodity
    • Google Scholar
35. 35. MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL, Hines JE. Occupancy interpretation and modeling. Inferring patterns and dynamics of species occurrence: Elsevier Inc.; 2006.
36. 36. Abade L, Macdonald DW, Dickman AJ. Using mural and bioclimatic features to predict the distribution of lions, leopards and spotted hyaenas in Tanzania's Ruaha landscape. PLoS ONE. 2014;9(5):e96261. pmid:24797792
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37. Bailey TN. The African leopard: ecology and behavior of a solitary felid. Caldwell, New Bailiwick of jersey: Blackburn Press; 2005.
38. 38. Balme Chiliad, Hunter L, Slotow R. Feeding habitat selection by hunting leopards Panthera pardus in a woodland savanna: prey catchability versus abundance. Animal Behaviour. 2007;74(3):589–98.
    • View Article
    • Google Scholar
39. 39. Gavashelishvili A, Lukarevskiy V. Modelling the habitat requirements of leopard Panthera pardus in west and central Asia. Journal of Applied Ecology. 2008;45(2):579–88.
    • View Article
    • Google Scholar
40. 40. Pitman RT, Swanepoel LH, Ramsay PM. Predictive modelling of leopard predation using contextual Global Positioning Organization cluster analysis. Journal of Zoology. 2012;288(3):222–30.
    • View Commodity
    • Google Scholar
41. 41. QGIS.
42. 42. Hayward MW, Henschel P, O'Brien J, Hofmeyr M, Balme G, Kerley GIH. Prey preferences of the leopard (Panthera pardus). Periodical of Zoology. 2006;270(two):298–313.
    • View Article
    • Google Scholar
43. 43. Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann Grand, Taranukha D, et al. Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res. 2013;41(Database upshot):D1027–33. pmid:23193293; PubMed Cardinal PMCID: PMCPMC3531213.
    • View Article
    • PubMed/NCBI
    • Google Scholar
44. 44. Quinn TJ, Deriso RB. Quantitative fish dynamics: Oxford Academy Press; 1999.
45. 45. Hilborn R, Walters CJ. Quantitative fisheries stock cess: option, dynamics and dubiety: Springer Science & Business organisation Media; 2013.
46. 46. Seber GA. A review of estimating animal abundance II. International Statistical Review/Revue Internationale de Statistique. 1992:129–66.
    • View Article
    • Google Scholar
47. 47. Gese EM. Monitoring of terrestrial carnivore populations. 2001.
    • View Article
    • Google Scholar
48. 48. Burn RW, Underwood FM, Blanc J. Global trends and factors associated with the illegal killing of elephants: a hierarchical Bayesian analysis of carcass see data. PLoS one. 2011;6(9):e24165. pmid:21912670
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
49. 49. Keane A, Jones JP, Milner‐Gulland East. Encounter data in resource management and ecology: pitfalls and possibilities. Journal of Applied Ecology. 2011;48(5):1164–73.
    • View Article
    • Google Scholar
50. l. Burton AC, Sam MK, Balangtaa C, Brashares JS. Hierarchical multi-species modeling of carnivore responses to hunting, habitat and prey in a West African protected area. PloS one. 2012;vii(v):e38007. pmid:22666433
    • View Article
    • PubMed/NCBI
    • Google Scholar
51. 51. Long RA, Donovan TM, MacKay P, Zielinski WJ, Buzas JS. Predicting carnivore occurrence with noninvasive surveys and occupancy modeling. Landscape Ecol. 2011;26(3):327–40. PubMed PMID: ISI:000288808100003.
    • View Article
    • Google Scholar
52. 52. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods in Environmental and Evolution. 2010;ane(1):iii–14.
    • View Article
    • Google Scholar
53. 53. Tyre AJ, Tenhumberg B, Field SA, Niejalke D, Parris K, Possingham HP. Improving precision and reducing bias in biological surveys: Estimating false-negative error rates. Ecological Applications. 2003;13(6):1790–801. PubMed PMID: WOS:000187616400022.
    • View Article
    • Google Scholar
54. 54. Rhodes JR, McAlpine CA, Zuur AF, Smith GM, Ieno EN. GLMM applied on the spatial distribution of koalas in a fragmented landscape. In: Zuur AF, editor. Mixed effects models and extensions in ecology with R. New York: Springer; 2009. p. 469–92.
55. 55. Moll RJ, Kilshaw K, Montgomery RA, Abade 50, Campbell RD, Harrington LA, et al. Clarifying habitat niche width using broad-scale, hierarchical occupancy models: a instance study with a recovering mesocarnivore. Journal of Zoology. 2016;300(3):177–85.
    • View Commodity
    • Google Scholar
56. 56. R Core Team. R: A language and environment for statistical calculating. two.15.1 ed. Vienna, Austria: R Foundation for Statistical Computing; 2012.
57. 57. Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the tertiary International Workshop on Distributed Statistical Computing; March 20–22; Vienna, Austria2003.
58. 58. Su YS, Yajima M. R2jags: a package for running jags from R. 2012.
    • View Commodity
    • Google Scholar
59. 59. Kuo L, Mallick B. Variable selection for regression models. Sankhyā: The Indian Journal of Statistics. 1998;sixty(Series B):65–81.
    • View Article
    • Google Scholar
60. lx. Royle JA, Dorazio RM. Hierarchical modeling and inference in ecology: The assay of data from populations, metapopulations and communities. San Diego, California: Academic Press; 2008.
61. 61. Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Gelman A, editor. New York: Cambridge University Printing; 2007.
62. 62. Athreya V, Odden M, Linnell JDC, Krishnaswamy J, Karanth U. Large cats in our backyards: persistence of large carnivores in a human dominated mural in India. PLoS ONE. 2013;8(3):e57872. pmid:23483933
    • View Article
    • PubMed/NCBI
    • Google Scholar
63. 63. Athreya V, Srivathsa A, Puri M, Karanth KK, Kumar NS, Karanth KU. Spotted in the news: using media reports to examine leopard distribution, depredation, and management practices exterior protected areas in Southern India. PLoS One. 2015;10(11). doi: ARTN e0142647 PubMed PMID: WOS:000364430700149. pmid:26556229
    • View Article
    • PubMed/NCBI
    • Google Scholar
64. 64. Alexander JS, Shi Thousand, Tallents LA, Riordan P. On the high trail: examining determinants of site use past the Endangered snowfall leopard Panthera uncia in Qilianshan, Prc. Oryx. 2016;50(ii):231–8. PubMed PMID: WOS:000372525900014.
    • View Article
    • Google Scholar
65. 65. Dickman AJ. Big carnivores and conflict in Tanzania's Ruaha mural. In: Redpath SM, Gutiérrez RJ, Wood KA, Evely A, Immature JC, editors. Conflicts in Conservation: Navigating towards solutions: Cambridge University Press; 2015. p. 30–ii.
66. 66. Lankford B, Tumbo S, Rajabu 1000. H2o competition, variability and river bowl governance: a critical analysis of the Great Ruaha River, Tanzania. In: Molle F, wester P, editors. River basin trajectories: societies, environment and development. London, UK: CABI International; 2009. p. 171–95.
67. 67. Wenban-Smith H. Population growth, internal migration and urbanisation in Tanzania, 1967–2012: Stage 2 (Final Report). International Growth Centre, 2015.
    • View Article
    • Google Scholar
68. 68. Henschel P, Hunter LTB, Coad L, Abernethy KA, Mühlenberg Yard. Leopard prey choice in the Congo Bowl rainforest suggests exploitative competition with human bushmeat hunters. Journal of Zoology. 2011;285(i):eleven–xx.
    • View Commodity
    • Google Scholar
69. 69. Ramesh T, Kalle R, Rosenlund H, Downs CT. Low leopard populations in protected areas of Maputaland: a consequence of poaching, habitat condition, abundance of prey, and a pinnacle predator. Ecology and Development. 2017;7(half-dozen):1964–73. pmid:28331603
    • View Article
    • PubMed/NCBI
    • Google Scholar
70. 70. Kolowski JM, Holekamp KE. Ecological and anthropogenic influences on space utilize past spotted hyaenas. Journal of Zoology. 2009;277(i):23–36.
    • View Commodity
    • Google Scholar
71. 71. Oriol-Cotterill A, Macdonald DW, Valeix M, Ekwanga S, Frank LG. Spatiotemporal patterns of panthera leo space employ in a human-dominated landscape. Creature Behaviour. 2015;101:27–39. http://dx.doi.org/ten.1016/j.anbehav.2014.eleven.020.
    • View Commodity
    • Google Scholar
72. 72. Stewart Fe, Fisher JT, Burton Air-conditioning, Volpe JP. Species occurrence data reverberate the magnitude of animal movements better than the proximity of animal space use. Ecosphere. 2018;9(2).
    • View Article
    • Google Scholar
73. 73. Neilson EW, Avgar T, Burton Air conditioning, Broadley M, Boutin Southward. Brute motion affects interpretation of occupancy models from camera‐trap surveys of unmarked animals. Ecosphere. 2018;9(1).
    • View Article
    • Google Scholar
74. 74. Balme GA, Batchelor A, Britz ND, Seymour 1000, Grover G, Hes L, et al. Reproductive success of female person leopards Panthera pardus: the importance of top-down processes. Mammal Review. 2013;43(3):221–37. PubMed PMID: ISI:000319982700006.
    • View Article
    • Google Scholar
75. 75. Hayward MW, O'Brien J, Kerley GIH. Conveying capacity of big African predators: Predictions and tests. Biological Conservation. 2007;139(1–ii):219–29.
    • View Article
    • Google Scholar
76. 76. Carbone C, Gittleman JL. A common rule for the scaling of carnivore density. Science. 2002;295(5563):2273–6. PubMed PMID: ISI:000174561700048. pmid:11910114
    • View Article
    • PubMed/NCBI
    • Google Scholar
77. 77. Pitman RT, Mulvaney J, Ramsay PM, Jooste E, Swanepoel LH. Global Positioning System-located kills and faecal samples: a comparison of leopard dietary estimates. Journal of Zoology. 2014;292(1):eighteen–24. PubMed PMID: WOS:000328679000003.
    • View Article
    • Google Scholar
78. 78. Knapp East, Peace N, Bechtel Fifty. Poachers and poverty: assessing objective and subjective measures of poverty among illegal hunters outside Ruaha National Park, Tanzania. Conservation and Social club. 2017;15(ane):24–32.
    • View Commodity
    • Google Scholar
79. 79. Lindsey PA, Petracca LS, Funston PJ, Bauer H, Dickman A, Everatt K, et al. The performance of African protected areas for lions and their casualty. Biological Conservation. 2017;209:137–49. PubMed PMID: WOS:000404308600017.
    • View Article
    • Google Scholar
80. eighty. Di Minin E, Slotow R, Hunter LTB, Montesino Pouzols F, Toivonen T, Verburg PH, et al. Global priorities for national carnivore conservation under land utilise alter. Scientific Reports. 2016;6:23814. PubMed PMID: PMC4817124. pmid:27034197
    • View Article
    • PubMed/NCBI
    • Google Scholar
81. 81. Abade L, Macdonald DW, Dickman AJ. Assessing the relative importance of mural and husbandry factors in determining large carnivore depredation risk in Tanzania'due south Ruaha mural. Biological Conservation. 2014;180(0):241–viii. http://dx.doi.org/ten.1016/j.biocon.2014.10.005.
    • View Article
    • Google Scholar
82. 82. Everatt KT, Andresen L, Somers MJ. The influence of prey, pastoralism and poaching on the hierarchical use of habitat by an apex predator. African Journal of Wild animals Research. 2015;45(two):187–96.
    • View Article
    • Google Scholar
83. 83. Rich LN, Miller DAW, Robinson HS, McNutt JW, Kelly MJ. Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal customs. Journal of Applied Ecology. 2016;53(four):1225–35.
    • View Commodity
    • Google Scholar
84. 84. Bruskotter JT, Wilson RS. Determining where the wild things will be: using psychological theory to notice tolerance for large carnivores. Conservation Letters. 2014;vii(3):158–65. PubMed PMID: WOS:000337590000003.
    • View Article
    • Google Scholar
85. 85. Lichtenfeld LL, Trout C, Kisimir EL. Bear witness-based conservation: predator-proof bomas protect livestock and lions. Biodiversity and Conservation. 2015;24(iii):483–91.
    • View Article
    • Google Scholar
86. 86. Hemson Grand, Maclennan South, Mills K, Johnson P, Macdonald D. Community, lions, livestock and money: A spatial and social analysis of attitudes to wildlife and the conservation value of tourism in a human-carnivore conflict in Botswana. Biological Conservation. 2009;142(eleven):2718–25.
    • View Article
    • Google Scholar
87. 87. Blackburn South, Hopcraft JGC, Ogutu JO, Matthiopoulos J, Frank L. Human–wild animals conflict, benefit sharing and the survival of lions in pastoralist customs-based conservancies. Periodical of Practical Ecology. 2016;53(four):1195–205.
    • View Article
    • Google Scholar
88. 88. Fynn RWS, Augustine DJ, Skin MJS, de Garine-Wichatitsky M. Strategic direction of livestock to meliorate biodiversity conservation in African savannahs: a conceptual basis for wildlife–livestock coexistence. Journal of Applied Ecology. 2016;53(2):388–97.
    • View Article
    • Google Scholar
89. 89. Odadi WO, Fargione J, Rubenstein DI. Vegetation, wild fauna and livestock responses to planned grazing management in an Arican pastoral landscape. Land Degradation & Development. 2017;28(vii):2030–viii.
    • View Article
    • Google Scholar
90. 90. Dolrenry Due south, Hazzah L, Frank G. Conservation and monitoring of a persecuted African lion population by Maasai warriors. Conservation Biological science. 2016;thirty(3):467–75. pmid:27111059
    • View Article
    • PubMed/NCBI
    • Google Scholar

bellthingety.blogspot.com

Source: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0204370

Share this post