Low leopard populations in protected areas of Maputaland: a consequence of poaching,habitat condition,abundance of prey,and a top predator

Identifying the primary causes affecting population densities and distribution of flagship species are necessary in developing sustainable management strategies for large carnivore conservation. We modeled drivers of spatial density of the common leopard (Panthera pardus) using a spatially explicit capture–recapture—Bayesian approach to understand their population dynamics in the Maputaland Conservation Unit, South Africa. We camera‐trapped leopards in four protected areas (PAs) of varying sizes and disturbance levels covering 198 camera stations. Ours is the first study to explore the effects of poaching level, abundance of prey species (small, medium, and large), competitors (lion Panthera leo and spotted hyenas Crocuta crocuta), and habitat on the spatial distribution of common leopard density. Twenty‐six male and 41 female leopards were individually identified and estimated leopard density ranged from 1.6 ± 0.62/100 km² (smallest PA—Ndumo) to 8.4 ± 1.03/100 km² (largest PA—western shores). Although dry forest thickets and plantation habitats largely represented the western shores, the plantation areas had extremely low leopard density compared to native forest. We found that leopard density increased in areas when low poaching levels/no poaching was recorded in dry forest thickets and with high abundance of medium‐sized prey, but decreased with increasing abundance of lion. Because local leopard populations are vulnerable to extinction, particularly in smaller PAs, the long‐term sustainability of leopard populations depend on developing appropriate management strategies that consider a combination of multiple factors to maintain their optimal habitats.     

Assessing Estimators of Snow Leopard Abundance

Abstract: The secretive nature of snow leopards (Uncia uncia) makes them difficult to monitor, yet conservation efforts require accurate and precise methods to estimate abundance. We assessed accuracy of Snow Leopard Information Management System (SLIMS) sign surveys by comparing them with 4 methods for estimating snow leopard abundance: predator:prey biomass ratios, capture-recapture density estimation, photo-capture rate, and individual identification through genetic analysis. We recorded snow leopard sign during standardized surveys in the SaryChat Zapovednik, the Jangart hunting reserve, and the Tomur Strictly Protected Area, in the Tien Shan Mountains of Kyrgyzstan and China. During June-December 2005, adjusted sign averaged 46.3 (SaryChat), 94.6 (Jangart), and 150.8 (Tomur) occurrences/km. We used counts of ibex (Capra ibex) and argali (Ovis ammon) to estimate available prey biomass and subsequent potential snow leopard densities of 8.7 (SaryChat), 1.0 (Jangart), and 1.1 (Tomur) snow leopards/100 km². Photo capture-recapture density estimates were 0.15 (n = 1 identified individual/1 photo), 0.87 (n = 4/13), and 0.74 (n = 5/6) individuals/100 km² in SaryChat, Jangart, and Tomur, respectively. Photo-capture rates (photos/100 trap-nights) were 0.09 (SaryChat), 0.93 (Jangart), and 2.37 (Tomur). Genetic analysis of snow leopard fecal samples provided minimum population sizes of 3 (SaryChat), 5 (Jangart), and 9 (Tomur) snow leopards. These results suggest SLIMS sign surveys may be affected by observer bias and environmental variance. However, when such bias and variation are accounted for, sign surveys indicate relative abundances similar to photo rates and genetic individual identification results. Density or abundance estimates based on capture-recapture or ungulate biomass did not agree with other indices of abundance. Confidence in estimated densities, or even detection of significant changes in abundance of snow leopard, will require more effort and better documentation.     

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km²) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km² (95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km² for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

Population density of striped hyenas in relation to habitat in a semi-arid landscape,western India

We used camera trapping in conjunction with a spatial explicit capture–recapture model to estimate striped hyena (Hyaena hyaena) density and occupancy models to investigate factors affecting striped hyena detection probabilities in Ranthambhore Tiger Reserve (RTR), Rajasthan, India. A sampling effort of 4,450 trap days/nights over 75 days yield 68 photo captures of 21 unique striped hyenas (based on individual markings and visual identification); the estimated striped hyena density was 5.49?±?1.27 individuals/100 km². Results of our occupancy model suggested that a rugged terrain is an important factor that influences striped hyena detection probability. Correlation with striped hyena detection with human settlement provides evidence of social tolerance of striped hyena towards humans, and more occurrence of resources allowed coexistence of hyena in a human-dominated landscape. This elasticity (inhabited areas close to humans) demonstrated by striped hyenas is an exception among carnivore communities living in this semi-arid habitat.     

Calibrating a camera trap–based biased mark–recapture sampling design to survey the leopard population in the N'wanetsi concession,Kruger National Park,South Africa

Estimating large carnivore abundance can be challenging. A biased leopard (Panthera pardus) population survey was conducted in the N'wanetsi concession in the Kruger National Park (KNP), South Africa, using motion‐sensitive camera traps from April to August 2008. Survey effort included 88 trapping occasions and 586 trap days. The survey yielded 24 leopard photographs, comprising fourteen adults of eleven males and three females. The capture rate was determined to be 24.4 trap days per leopard. Estimates of population abundance stabilized at approximately 500 trap days. Precision of population estimates began to stabilize after 378 trap days. We estimated that there were nineteen leopards in an area of 150 km². Leopard density was estimated at 12.7 leopards per 100 km². We explore the possibility of employing the methods used in this study to survey the leopard population in the KNP and surrounding areas.     

Sampling bias in snow leopard population estimation studies

Accurate assessments of the status of threatened species and their conservation planning require reliable estimation of their global populations and robust monitoring of local population trends. We assessed the adequacy and suitability of studies in reliably estimating the global snow leopard (Panthera uncia) population. We compiled a dataset of all the peer-reviewed published literature on snow leopard population estimation. Metadata analysis showed estimates of snow leopard density to be a negative exponential function of area, suggesting that study areas have generally been too small for accurate density estimation, and sampling has often been biased towards the best habitats. Published studies are restricted to six of the 12 range countries, covering only 0.3–0.9% of the presumed global range of the species. Re-sampling of camera trap data from a relatively large study site (c.1684 km²) showed that small-sized study areas together with a bias towards good quality habitats in existing studies may have overestimated densities by up to five times. We conclude that current information is biased and inadequate for generating a reliable global population estimate of snow leopards. To develop a rigorous and useful baseline and to avoid pitfalls, there is an urgent need for (a) refinement of sampling and analytical protocols for population estimation of snow leopards (b) agreement and coordinated use of standardized sampling protocols amongst researchers and governments across the range, and (c) sampling larger and under-represented areas of the snow leopard's global range.     

Scent Lure Effect on Camera-Trap Based Leopard Density Estimates

Density estimates for large carnivores derived from camera surveys often have wide confidence intervals due to low detection rates. Such estimates are of limited value to authorities, which require precise population estimates to inform conservation strategies. Using lures can potentially increase detection, improving the precision of estimates. However, by altering the spatio-temporal patterning of individuals across the camera array, lures may violate closure, a fundamental assumption of capture-recapture. Here, we test the effect of scent lures on the precision and veracity of density estimates derived from camera-trap surveys of a protected African leopard population. We undertook two surveys (a ‘control’ and ‘treatment’ survey) on Phinda Game Reserve, South Africa. Survey design remained consistent except a scent lure was applied at camera-trap stations during the treatment survey. Lures did not affect the maximum movement distances (p = 0.96) or temporal activity of female (p = 0.12) or male leopards (p = 0.79), and the assumption of geographic closure was met for both surveys (p >0.05). The numbers of photographic captures were also similar for control and treatment surveys (p = 0.90). Accordingly, density estimates were comparable between surveys (although estimates derived using non-spatial methods (7.28–9.28 leopards/100km²) were considerably higher than estimates from spatially-explicit methods (3.40–3.65 leopards/100km²). The precision of estimates from the control and treatment surveys, were also comparable and this applied to both non-spatial and spatial methods of estimation. Our findings suggest that at least in the context of leopard research in productive habitats, the use of lures is not warranted.     

Density of tiger and leopard in a tropical deciduous forest of Mudumalai Tiger Reserve,southern India,as estimated using photographic capture–recapture sampling

Density of tiger Panthera tigris and leopard Panthera pardus was estimated using photographic capture–recapture sampling in a tropical deciduous forest of Mudumalai Tiger Reserve, southern India, from November 2008 to February 2009. A total of 2,000 camera trap nights for 100 days yielded 19 tigers and 29 leopards within an intensive sampling area of 107 km². Population size of tiger from closed population estimator model M_b Zippin was 19 tigers (SE = ±0.9) and for leopards M_h Jackknife estimated 53 (SE = ±11) individuals. Spatially explicit maximum likelihood and Bayesian model estimates were 8.31 (SE = ±2.73) and 8.9 (SE = ±2.56) per 100 km² for tigers and 13.17 (SE = ±3.15) and 13.01 (SE = ±2.31) per 100 km² for leopards, respectively. Tiger density for MMDM models ranged from 6.07 (SE = ±1.74) to 9.72 (SE = ±2.94) per 100 km² and leopard density ranged from 13.41 (SE = ±2.67) to 28.91 (SE = ±7.22) per 100 km². Spatially explicit models were more appropriate as they handle information at capture locations in a more specific manner than some generalizations assumed in the classical approach. Results revealed high density of tiger and leopard in Mudumalai which is unusual for other high density tiger areas. The tiger population in Mudumalai is a part of the largest population at present in India and a source for the surrounding Reserved Forest.     

No silver bullet? Snow leopard prey selection in Mt. Kangchenjunga,Nepal

In this study, we investigated the impact of domestic and wild prey availability on snow leopard prey preference in the Kangchenjunga Conservation Area of eastern Nepal—a region where small domestic livestock are absent and small wild ungulate prey are present. We took a comprehensive approach that combined fecal genetic sampling, macro‐ and microscopic analyses of snow leopard diets, and direct observation of blue sheep and livestock in the KCA. Out of the collected 88 putative snow leopard scat samples from 140 transects (290 km) in 27 (4 × 4 km²) sampling grid cells, 73 (83%) were confirmed to be from snow leopard. The genetic analysis accounted for 19 individual snow leopards (10 males and 9 females), with a mean population size estimate of 24 (95% CI: 19–29) and an average density of 3.9 snow leopards/100 km² within 609 km². The total available prey biomass of blue sheep and yak was estimated at 355,236 kg (505 kg yak/km² and 78 kg blue sheep/km²). From the available prey biomass, we estimated snow leopards consumed 7% annually, which comprised wild prey (49%), domestic livestock (45%), and 6% unidentified items. The estimated 47,736 kg blue sheep biomass gives a snow leopard‐to‐blue sheep ratio of 1:59 on a weight basis. The high preference of snow leopard to domestic livestock appears to be influenced by a much smaller available biomass of wild prey than in other regions of Nepal (e.g., 78 kg/km² in the KCA compared with a range of 200–300 kg/km² in other regions of Nepal). Along with livestock insurance scheme improvement, there needs to be a focus on improved livestock guarding, predator‐proof corrals as well as engaging and educating local people to be citizen scientists on the importance of snow leopard conservation, involving them in long‐term monitoring programs and promotion of ecotourism.     

Estimating abundances,densities, and interspecific associations in a carnivore community

Estimating population abundances, densities, and interspecific interactions are common goals in wildlife management. Camera traps have been used to estimate the abundance and density of a single species, and are useful for carnivores that occur at low densities. Spatial capture–recapture (SCR) models can be used to estimate abundance and density from a camera trap array when all, some, or no individuals in the population can be uniquely identified. These SCR models also estimate locations of individual activity centers, the spatial patterning of which could provide important information about interspecific interactions. We used SCR models to estimate abundances, densities, and activity centers of each of 3 carnivore species (i.e., dingo [Canis familiaris], red fox [Vulpes vulpes], and feral cat) using photographs from 1 camera trap array in southeastern Australia during September to November 2015. Some dingoes and feral cats were uniquely identifiable and therefore, we used a spatial mark–resight model for these species. We could not uniquely identify fox individuals, however, so we used a spatial unmarked (SUN) model for this species. Our estimated dingo density was 0.06/km². The fox (0.25/km²) and feral cat (0.16/km²) densities are within the ranges previously reported for these species in Australia. We obtained a relatively imprecise fox density estimate because we did not have detections of uniquely identifiable individuals; hence, the SUN model should be used as a last resort. We next modeled spatial dependence among the estimated activity centers for the 3 species using a spatial pair correlation function for a marked point process. Consistent with our expectations, the activity centers of dingoes and foxes were strongly negatively associated at distances of <1,000 m. Foxes and feral cats were also negatively associated at distances of <1,500 m. Surprisingly, dingoes and feral cats were positively associated at distances of >500 m, with no association evident at distances of <500 m. Our study extends the inferences that can be made from using a camera trap array and SCR methods to include spatial patterning and interspecific interactions, and provides new insights into the carnivore community of dingoes, foxes, and feral cats in southeastern Australia. © 2019 The Authors. The Journal of Wildlife Management Published by Wiley Periodicals, Inc.     

Big Cats in Our Backyards: Persistence of Large Carnivores in a Human Dominated Landscape in India

Protected areas are extremely important for the long term viability of biodiversity in a densely populated country like India where land is a scarce resource. However, protected areas cover only 5% of the land area in India and in the case of large carnivores that range widely, human use landscapes will function as important habitats required for gene flow to occur between protected areas. In this study, we used photographic capture recapture analysis to assess the density of large carnivores in a human-dominated agricultural landscape with density >300 people/km² in western Maharashtra, India. We found evidence of a wide suite of wild carnivores inhabiting a cropland landscape devoid of wilderness and wild herbivore prey. Furthermore, the large carnivores; leopard (Panthera pardus) and striped hyaena (Hyaena hyaena) occurred at relatively high density of 4.8±1.2 (sd) adults/100 km² and 5.03±1.3 (sd) adults/100 km² respectively. This situation has never been reported before where 10 large carnivores/100 km² are sharing space with dense human populations in a completely modified landscape. Human attacks by leopards were rare despite a potentially volatile situation considering that the leopard has been involved in serious conflict, including human deaths in adjoining areas. The results of our work push the frontiers of our understanding of the adaptability of both, humans and wildlife to each other’s presence. The results also highlight the urgent need to shift from a PA centric to a landscape level conservation approach, where issues are more complex, and the potential for conflict is also very high. It also highlights the need for a serious rethink of conservation policy, law and practice where the current management focus is restricted to wildlife inside Protected Areas.     

One or two cameras per station? Monitoring jaguars and other mammals in the Amazon

Camera trapping has become a popular technique to monitor carnivore populations due to its usefulness in estimating abundance. Nevertheless, there are a number of problems associated with study design which are motivating researchers to search for a compromise that ensures improvement of precision while being cost-effective. We have used data from a capture?Crecapture study in a forested area in central Brazil to evaluate the effectiveness of using one versus two cameras per trapping station for determining jaguar (Panthera onca) density and capture rates of several other mammals. The capture rate for the jaguar and other species recorded with only one camera was lower than that with two cameras. The number of jaguars identified using photos from one camera ranged between six and seven animals, but reached ten individuals when two-camera sets were used where pictures of both flanks could be positively individualized. These differences, combined with different estimates of effective sampled area size, resulted in jaguar densities estimates ranging from 2.18 to 5.40 and 3.99?individuals/100?km² when one and two cameras were used per station, respectively (using the half-MMDM and Heterogeneity model). Based on our results, we recommend the use of two cameras per station for jaguar density monitoring to ensure reasonable levels of reliability and accuracy of estimates despite a small sample size.     

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy‐deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate‐only model shows that only 11.64% (17,190 km²) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km² (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate‐only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.     

The First Estimates of Marbled Cat Pardofelis marmorata Population Density from Bornean Primary and Selectively Logged Forest

The marbled cat Pardofelis marmorata is a poorly known wild cat that has a broad distribution across much of the Indomalayan ecorealm. This felid is thought to exist at low population densities throughout its range, yet no estimates of its abundance exist, hampering assessment of its conservation status. To investigate the distribution and abundance of marbled cats we conducted intensive, felid-focused camera trap surveys of eight forest areas and two oil palm plantations in Sabah, Malaysian Borneo. Study sites were broadly representative of the range of habitat types and the gradient of anthropogenic disturbance and fragmentation present in contemporary Sabah. We recorded marbled cats from all forest study areas apart from a small, relatively isolated forest patch, although photographic detection frequency varied greatly between areas. No marbled cats were recorded within the plantations, but a single individual was recorded walking along the forest/plantation boundary. We collected sufficient numbers of marbled cat photographic captures at three study areas to permit density estimation based on spatially explicit capture-recapture analyses. Estimates of population density from the primary, lowland Danum Valley Conservation Area and primary upland, Tawau Hills Park, were 19.57 (SD: 8.36) and 7.10 (SD: 1.90) individuals per 100 km², respectively, and the selectively logged, lowland Tabin Wildlife Reserve yielded an estimated density of 10.45 (SD: 3.38) individuals per 100 km². The low detection frequencies recorded in our other survey sites and from published studies elsewhere in its range, and the absence of previous density estimates for this felid suggest that our density estimates may be from the higher end of their abundance spectrum. We provide recommendations for future marbled cat survey approaches.     

Density and population structure of the jaguar (<Emphasis Type="Italic">Panthera onca</Emphasis>) in a protected area of Los Llanos,Venezuela, from 1 year of camera trap monitoring

Density is crucial for understanding large carnivore ecology and conservation, but estimating it has proven methodologically difficult. We conducted 1 year of camera trapping to estimate jaguar (Panthera onca) density and population structure in the Los Llanos region of Venezuela on the Hato Piñero ranch, where hunting is prohibited and livestock are excluded from half of ranch lands. We identified 42 different jaguars and determined their sex, age class, and reproductive status. We estimated adult jaguar densities with spatial capture-recapture models, using sex/reproductive state and session as covariates. Models without temporal variation received more support than models that allowed variation between sessions. Males, reproductive females, and nonreproductive females differed in their density, baseline detectability, and movement. The best estimate of total adult jaguar population density was 4.44 individuals/100 km². Based on reproductive female density and mean number of offspring per female, we estimated cub density at 3.23 individuals/100 km² and an overall density of 7.67 jaguars/100 km². Estimated jaguar population structure was 21% males, 11% nonreproductive females, 26% reproductive females, and 42% cubs. We conclude that extending the sampling period to 1 year increases the detectability of females and cubs and makes density estimates more robust as compared to the more common short studies. Our results demonstrate that the Venezuelan Llanos represent important jaguar habitat, and further, they emphasize the importance of protected areas and hunting restrictions for carnivore conservation.     

Integrating aspects of ecology and predictive modelling: implications for the conservation of the leopard cat (Prionailurus bengalensis) in the Eastern Himalaya

An understanding of species ecology is vital for effective conservation, particularly if the species forms an important constituent of the lesser mammal guild and regulates small mammal and bird populations. As the ecological role of the leopard cat (Prionailurus bengalensis) in the intricate eastern Himalayan habitats is not known, we assessed the site occupancy, detection probability and activity pattern of leopard cats in Khangchendzonga Biosphere Reserve, India, based on sign surveys and camera trapping. The estimated site occupancy was 0.352?±?0.061 and detection probability was 0.143?±?0.0484. Occupancy modelling indicated low elevation, high rodent abundance and tree cover as best predictors for the occupancy of leopard cat. Diet based on analysed scats revealed murids as the most dominant prey (89.2 %). Information based on photographic captures indicated that the leopard cat exhibited a nocturnal activity pattern (peak activity between 0200–0300 hours), which coincided with its principal prey (revealed through diet analysis), but mainly contradicted with other sympatric competitors, hence indicating a temporal partitioning of resources among them. Ecological niche factor analysis indicated that the leopard cat exhibits high global marginality (1.32) and low global tolerance (0.275). The habitat suitability map for leopard cats showed majority of the habitat as unsuitable (1,959.44 km²) and predicted only 164.54 km² areas of lower temperate forests as moderate to highly suitable. As highly suitable habitats of the leopard cat are in close proximity to villages, conflict issues are a major threat and therefore need to be addressed in conservation program for this felid.     

红外相机技术在鼠类密度估算中的应用

2009年5月至2011年7月,在古田山国家自然保护区24 hm2(600 m×400 m)样地中用分层随机抽样法布置20台红外相机,监测样地内的鼠类密度.利用红外相机技术,引用物理学中气体分子碰撞率原理,在不对鼠类进行个体识别的情况下,估算样地内鼠类密度.结果表明,以此估算的样地内鼠类密度D3与标志重捕法估算的鼠类密度D之间不存在显著差异(P＞0.05),两者契合程度高,说明此模型具有相当高的精确性.而在较高的相机分布密度(0.83台/hm2)下得出的鼠类密度季节消长状况也验证了该模型的可靠程度.     

Big cats at large: Density,structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality

Human impact is near pervasive across the planet and studies of wildlife populations free of anthropogenic mortality are increasingly scarce. This is particularly true for large carnivores that often compete with and, in turn, are killed by humans. Accordingly, the densities at which carnivore populations occur naturally, and their role in shaping and/or being shaped by natural processes, are frequently unknown. We undertook a camera-trap survey in the Sabi Sand Game Reserve (SSGR), South Africa, to examine the density, structure and spatio-temporal patterns of a leopard Panthera pardus population largely unaffected by anthropogenic mortality. Estimated population density based on spatial capture–recapture models was 11.8 ± 2.6 leopards/100 km². This is likely close to the upper density limit attainable by leopards, and can be attributed to high levels of protection (particularly, an absence of detrimental edge effects) and optimal habitat (in terms of prey availability and cover for hunting) within the SSGR. Although our spatio-temporal analyses indicated that leopard space use was modulated primarily by “bottom-up” forces, the population appeared to be self-regulating and at a threshold that is unlikely to change, irrespective of increases in prey abundance. Our study provides unique insight into a naturally-functioning carnivore population at its ecological carrying capacity. Such insight can potentially be used to assess the health of other leopard populations, inform conservation targets, and anticipate the outcomes of population recovery attempts.     

Leopard density in post-conflict landscape,Cambodia: Evidence from spatially explicit capture–recapture

Effective conservation of large carnivores requires reliable estimates of population density, often obtained through capture–recapture analysis, in order to prioritize investments and assess conservation intervention effectiveness. Recent statistical advances and development of user-friendly software for spatially explicit capture–recapture (SECR) circumvent the difficulties in estimating effective survey area, and hence density, from capture–recapture data. We conducted a camera-trapping study on leopards (Panthera pardus) in Mondulkiri Protected Forest, Cambodia. We compared density estimates using SECR with those obtained from conventional approaches in which the effective survey area is estimated using a boundary strip width based on observed animal movements. Density estimates from Chao heterogeneity models (3.8 ± SE 1.9 individuals/100 km²) and Pledger heterogeneity models and models accounting for gender-specific capture and recapture rates (model-averaged density 3.9 ± SE 2.9 individuals/100 km²) were similar to those from SECR in program DENSITY (3.6 ± SE 1.0/100 km²) but higher than estimates from Jack-knife heterogeneity models (2.9 ± SE 0.9 individuals/100 km²). Capture probabilities differed between male and female leopards probably resulting from differences in the use of human-made trails between sexes. Given that there are a number of biologically plausible reasons to expect gender-specific variation in capture probabilities of large carnivores, we recommend exploratory analysis of data using models in which gender can be included as a covariate affecting capture probabilities particularly given the demographic importance of breeding females for population recovery of threatened carnivores. © 2011 The Wildlife Society.     

How varying pest and trap densities affect Tribolium castaneum capture in pheromone traps

The red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), is an important insect pest in food processing facilities. Pheromone trapping is frequently used to monitor red flour beetle populations in structures; however, the optimal trap density and the relationship between trap captures and beetle density is not known. Two experiments were performed concurrently in environmentally controlled 30‐m² walk‐in chambers to determine the relationship between aggregation pheromone trap captures of red flour beetles and beetle and trap number. In one experiment, beetle density was kept constant at 200 individuals per chamber while trap number was varied from 1 to 8, and in the other experiment trap number remained constant at one per chamber while beetle density varied from 20 to 800 individuals. Results indicated that approximately one out of 23 red flour beetles were captured in a trap. Number of beetles captured in traps increased significantly as beetle density increased; however, the proportion of beetles captured remained consistent across beetle densities with a mean of 4.7 ± 0.6% of individuals captured. Trap captures varied significantly with trap placement within experimental chambers, indicating that subtle differences in the trapping environment can influence trap captures. Data suggested that trap densities of 0.07–0.10 m^?2 (2–3 traps per chamber) would maximize trap capture, whereas a trap density of 0.13 m^?2 (four traps per chamber) would maximize the predictive ability of a trapping equation estimating beetle density from trap captures. Results provide information needed to more thoroughly explore how environmental factors might influence red flour beetle trap capture in the absence of changes in beetle density. Further understanding of these relationships will allow for more accurate assessments of absolute beetle density from pheromone trap capture data.