Population Density Estimates Research Articles

Camera-trapping estimates of the relative population density of Sympetrum dragonflies: application to multihabitat users in agricultural landscapes.

Although camera trapping has been effectively used for wildlife monitoring, its application to multihabitat insects (i.e., insects requiring terrestrial and aquatic ecosystems) is limited. Among such insects, perching dragonflies of the genus Sympetrum (darter dragonflies) are agroenvironmental indicators that substantially contribute to agricultural biodiversity. To examine whether custom-developed camera traps for perching dragonflies can be used to assess the relative population density of darter dragonflies, camera trapping, a line-transect survey of mature adult dragonflies, and a line-transect survey of exuviae were conducted for three years in rice paddy fields in Japan. The detection frequency of camera traps in autumn was significantly correlated with the density index of mature adults recorded during the transect surveys in the same season for both Sympetrum infuscatum and other darter species. In analyses of camera-detection frequency in autumn and exuviae in early summer, a significant correlation was observed between the camera-detection frequency of mature adults and the exuviae-density index in the following year for S. infuscatum; however, a similar correlation was not observed for other darter species. These results suggest that terrestrial camera trapping has the potential to be effective for monitoring the relative density of multihabitat users such as S. infuscatum, which shows frequent perching behavior and relatively short-distance dispersal.

Seasonal Differences in the Encounter Rate of the Fat-Tailed Dwarf Lemur (Cheirogaleus medius) in the Transitional Forests of Northwest Madagascar: Implications for Reliable Population Density Assessment

Primate encounter rates often vary throughout the year due to seasonal differences in activity, ecology, and behaviour. One notably extreme behaviour is continuous hibernation. Although a rare adaptation in primates, the dwarf lemurs of Madagascar (genus Cheirogaleus) enter obligate hibernation each year during the dry season, after spending the wet season consuming high-energy foods. Whilst seasonal changes in activity in some Cheirogaleus populations are well-known, many species remain little-studied, and there is no specific information on their encounter rates, nor when they enter and emerge from hibernation. This uncertainty critically affects reliable calculation of population density estimates for these highly threatened lemurs. In this study, we assessed how encounter rates of the fat-tailed dwarf lemur (C. medius) vary seasonally in the transitional forests of the Sahamalaza-Iles Radama National Park, northwest Madagascar, during a 4-year period. We established a system of line transects (N = 60) throughout our study area, on which we conducted distance sampling of C. medius. We then used our distance sampling data to calculate encounter rate and population density data. We found encounter rates of C. medius to be significantly higher during the wet season compared with the dry season. Furthermore, encounter rates of C. medius were particularly low from May–August. These results provide some evidence to suggest the time period that C. medius hibernate in Sahamalaza-Iles Radama National Park. These findings underpin the importance of careful study design when surveying threatened species with seasonal differences in activity, such as those that hibernate. This study also demonstrates the importance of species-specific behavioural data for accurate population density assessment, which is required to inform conservation action.

Not Just Pictures: Utility of Camera Trapping in the Context of African Swine Fever and Wild Boar Management

African swine fever (ASF) is a highly contagious disease affecting all suids and wild boar (Sus scrofa). Since 2007, ASF has spread to more than 30 countries in Europe and Asian regions, and the most recent outbreak has been in mainland Italy (reported on January 2022). When the genotype II of the ASF virus infects a population, a mortality rate close to 90% is usually reported. This drop in wild boar abundance produces a cascade effect in the entire ecosystem. In this context, effective monitoring tools for deriving management parameters are a priority aspect, and the utility of camera trapping could have been overlooked. Here, sampling the infected area in north Italy, we showed the utility of camera traps in the context of ASF infection. Specifically, we used 43 camera traps randomly distributed to (i) estimate movement parameters and population density of wild boar, roe deer (Capreolus capreolus), and wolf (Canis lupus); (ii) quantify wild boar recruitment; and (iii) assess whether the human restriction rules are being met. On the first spring after the outbreak detection, our results for wild boar indicated a density of 0.27 ind·km−2 ± 0.11 (standard error, SE), a daily activity level of 0.49 ± 0.07 (i.e., 11.76 h·day−1), a daily distance travelled of 9.07 ± 1.80 km·day−1, a litter size of 1.72 piglets·group−1, and a 72% of pregnant females. Despite human outdoor activities being restricted in the infected zone, we recorded human presence in 19 camera traps. The wide range of parameters estimated from the camera trap data, together with some intrinsic and practical advantages of this tool, allows us to conclude that camera traps are well positioned to be a reference approach to monitor populations affected by ASF. The population-specific parameters are of prime importance for optimizing ASF control efforts.

The Population Study of Maleo Bird (Macrocephalon maleo) in The Nesting Ground of Saluki Village, Lore Lindu National Park

Sulawesi Island is an island that has a diversity of flora and fauna, some of which are endemic animals. One of the protected endemic fauna species is the maleo bird. Maleo was designated as a protected animal because the number of populations in the wild was decreasing, and its conservation status was in the endangered category. Concrete steps that could be taken to increase the maleo population were through various activities, including conducting ongoing and periodic studies of the maleo population. In particular, this study aims to examine the current population of the maleo bird in the nesting ground of Saluki Village, Lore Lindu National Park. In this study, the estimation of maleo population density uses two approaches. The first approach was direct observation of the maleo population outside the nesting habitat using the transect method. The second approach was using the active nest count approach at nesting sites. Based on the results, it was found that in the egg-laying locations, there were 32 active nests and 31 inactive nests. Based on these results, the maleo population density in the egg-laying locations was 29.7 individuals/Ha. While outside the egg-laying habitat, there were eight maleo individuals consisting of four juvenile individuals and four young adults (sub-adults). These results showed that the population density outside the egg-laying habitat was 1.7 individuals/Ha. Four maleo predators were found at the study site, namely Varanus salvator, Spilornis rufipectus, Ictinaetus malayensis and Falco sp. In addition, there was one type of potential maleo predator, Macaca tongkeana, with as many as seven individuals. Two potential factors could disturb the maleo population: the laying location factor and the dense undergrowth factor.

Assessment of the accuracy of counting large ungulate species (red deer Cervus elaphus) with UAV‐mounted thermal infrared cameras during night flights

Unmanned aerial vehicles (UAVs) are increasingly used in wildlife surveying, including estimation of population densities. It is essential that we evaluate and test new survey methods to guide optimal sampling strategies. This study aimed to assess the accuracy of using a UAV‐mounted thermal infrared (TIR) camera to count red deer Cervus elaphus populations, and how this was influenced by flight season, height and velocity, in order to help guide future census design. We flew 57 flights across a captive population of red deer in a 13 ha deer park enclosure of semi‐natural habitat, representative of the species' range in northern Germany. Flights and image assessments were performed with no prior knowledge of actual population size. Accuracy was quantified by comparing real population size (known only to deer park staff) and independently estimated population sizes from UAV TIR images. Accuracy was significantly influenced by ecological season (early and late winter, spring and early summer) and height. Across all seasons, lower flights (100 m) performed better than higher ones (120 m), with lower flights in early winter and early summer being on average accurate to within 1% of actual population counts. For the season where we had the largest range of temperatures between flights (late winter) we found that accuracy was highest when temperatures were lowest. Flights were also able to identify all five stags (defined as a male deer ≥ 2 years old) present in early summer, but not in spring. Deer appeared to avoid the landing/take‐off area, but there were no noted behavioural responses to drones flying over animals when at constant height and velocity during surveys. Our results indicate that UAV‐mounted TIR camera have the potential to accurately count populations of large ungulate species, but that flight season, height and potentially temperature need to be taken into account to maximise accuracy. This approach has the potential to be scaled up to more accurately estimate densities of wild populations compared to existing approaches.

Gliders for passive acoustic monitoring of the oceanic environment

Ocean gliders are quiet, buoyancy-driven, long-endurance, profiling autonomous platforms. Gliders therefore possess unique advantages as platforms for Passive Acoustic Monitoring (PAM) of the marine environment. In this paper, we review available glider platforms and passive acoustic monitoring systems, and explore current and potential uses of passive acoustic monitoring-equipped gliders for the study of physical oceanography, biology, ecology and for regulatory purposes. We evaluate limiting factors for passive acoustic monitoring glider surveys, such as platform-generated and flow noise, weight, size and energy constraints, profiling ability and slow movement. Based on data from 34 passive acoustic monitoring glider missions, it was found that <13% of the time spent at sea was unsuitable for passive acoustic monitoring measurements, either because of surface communications or glider manoeuvre, leaving the remainder available for subsequent analysis. To facilitate the broader use of passive acoustic monitoring gliders, we document best practices and include workarounds for the typical challenges of a passive acoustic monitoring glider mission. Three research priorities are also identified to improve future passive acoustic monitoring glider observations: 1) Technological developments to improve sensor integration and preserve glider endurance; 2) improved sampling methods and statistical analysis techniques to perform population density estimation from passive acoustic monitoring glider observations; and 3) calibration of the passive acoustic monitoring glider to record absolute noise levels, for anthropogenic noise monitoring. It is hoped this methodological review will assist glider users to broaden the observational capability of their instruments, and help researchers in related fields to deploy passive acoustic monitoring gliders in their studies.

An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data

AbstractNon‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions. We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space‐use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois (Rupicapra rupicapra), red deer (Cervus elaphus), and wild boar (Sus scrofa). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of unaccounted individual heterogeneity in space use. The degree to which abundance estimates were underestimated depended mostly on the amount of variation in space use and detectability among age classes. This bias was also accompanied by a reduction in precision and coverage probability of the SCR estimators. We discuss the implications of these findings, possible approaches to identify problematic violations in available data sets (goodness‐of‐fit tests), and potential further developments of SCR models to ensure reliable abundance estimates for ungulate populations from NGS data.

Robust Facility Location of Container Clinics: A South African Application

There is a lack of dynamic facility location models for developing countries that consider the changes in the problem environment over time, such as patient population and population migration. Therefore, this paper focuses on using optimization and goal programming to locate health care facilities in an uncertain environment using multiple possible future urban development senarios. To achieve this, a robust multi-objective facility location model is developed and used to determine locations for container clinic deployment over multiple years in selected communities in South Africa. A synthetic population and urban growth simulation model are used to estimate population density and distribution from 2018 to 2030 for three development senarios. The results from the urban growth simulation model are then used as input into the facility location model to locate facilities whilst considering the three future development scenarios. Results of the model indicate that the robust model can be used to find locations that provide a relatively good solution to all considered development scenarios, providing key role players with quantitative decision support during network design under uncertainty. An accessibility analysis investigates the impact of the prescribed accessibility percentage on model results and a budget analysis evaluates the impact of a case that includes a budget constraint. From these two analyses it is illustrated that the model is sensitive to changes in parameters and that the model can be used by key stakeholders to combine network design and urban development planning for improved decision making.

Density of Axis Deer in Texas: Management Implications for Native White-tailed Deer and Associated Habitats

Abstract Axis deer Axis axis have been widely introduced to new geographic ranges, and in the United States, free-ranging axis deer have become well established in the Edwards Plateau ecoregion as well as other portions of Texas. However, no estimates of axis deer population density nor size have been conducted since 1994. It is hypothesized that axis deer on the Edwards Plateau are potentially competing with native white-tailed deer Odocoileus virginianus for food, space, and habitat resources, and causing damage to important riparian habitats. Our goal was to estimate regional densities of axis deer and white-tailed deer, and provide insight about the potential impacts axis deer may have on native wildlife and their habitats. Estimated using distance sampling techniques in 2018 and 2019, average axis deer density was 19.7 (95% CI: 14.1–25.6) axis deer/km2 compared with 23.0 (95% CI: 18.2–27.5) white-tailed deer/km2, and axis deer densities ranged from 16.9 to 171.0/km2 among eight different land cover types in Kimble County, Texas, with a county-wide estimate of 61,078 (95% CI: 30,407–100,369) axis deer. Axis deer densities were greatest in riparian land cover types, and they selected for two riparian land cover types and upland grasslands. Axis deer population estimates clearly indicate their population size has increased substantially since introduction to Texas in the 1930s. Population management of axis deer is warranted to limit impacts to native wildlife from potential habitat usurpation, or damage to riparian vegetation communities, soil, and water quality.

Environmental Impact Assessment of Civil Engineering Project on the Distribution of Gorillas and Chimpanzees in Deng Deng National Park Cameroon

Civil engineering projects have many impacts in environment sustainability. The purpose of this research is to evaluate the human consequences of civil engineering building activities on the natural habitats of gorillas and chimps in the Deng Deng National Park, in particular, and Cameroon in general. An indirect census based on single-pass nest counts was used to estimate ape density at Deng Deng National Park. Data were collected at the various field trips during 3 months. The line transect was the most efficient and reliable census method. It has been used in many studies concerning the estimation of ape population density. This method consisted of making observations along the centre line of the transect. The principle was based on the assumption that the probability of detecting a nest decreases. The identification of consequences entailed listing all important environmental components that were likely to be affected. According to the study's findings, the number of nid sites and population density of great crested newts rose in the Deng Deng National Park to 80 sites, 391 nids, and 0.80 ind/km2. The Lom Pangar hydroelectric dam has had significant ripple effects on the distribution of great apes in general and particularly on the density of chimps. The study finds that, despite habitat fragmentation, the population of large singes has increased significantly in the Deng Deng National Park.

Camera‐trapping density estimates suggest critically low population sizes for the Wet Tropics subspecies of the spotted‐tailed quoll (Dasyurus maculatus gracilis)

AbstractAccurate estimates of distribution and population density are critical for the management of threatened species. This is particularly pertinent for mammalian predators, whose generally low population density, elusive nature, and large home range requirements make it difficult to detect declines. We aimed to refine population estimates of the northern spotted‐tailed quoll (Dasyurus maculatus gracilis) in the Wet Tropics bioregion, to estimate the total number of adults, the likely size of subpopulations across the known distribution of the subspecies, and its associated conservation status. We performed targeted upland camera‐trapping surveys from June 2017 to May 2019. To calculate population densities, we used a combination of the number of individuals identified from each survey and the mean maximum distance moved from three life history stages. We then extrapolated these estimates to modelled suitable habitat areas, refined by the camera‐trapping surveys. Population sizes for the six defined subpopulations were estimated, and ranged from approximately 5 to 105 individuals. The total population was estimated to be 221 individuals. This total population estimate, and the estimates for each of the subpopulations, are lower than previous published estimates and are cause for concern. Given the low population estimates presented here and unresolved threats driving declines in some subpopulations, we suggest elevation of this subspecies to Critically Endangered under the EPBC Act.

Counting the Capital's cats: Estimating drivers of abundance of free-roaming cats with a novel hierarchical model.

Free-roaming cats are a conservation concern in many areas but identifying their impacts and developing mitigation strategies requires a robust understanding of their distribution and density patterns. Urban and residential areas may be especially relevant in this process because free-roaming cats are abundant in these anthropogenic landscapes. Here, we estimate the occupancy and density of free-roaming cats in Washington D.C. and relate these metrics to known landscape and social factors. We conducted an extended camera trap survey of public and private spaces across D.C. and analyzed data collected from 1483 camera deployments from 2018 to 2020. We estimated citywide cat distribution by fitting hierarchical occupancy models and further estimated cat abundance using a novel random thinning spatial capture-recapture model that allows for the use of photos that can and cannot be identified to individual. Within this model, we utilized individual covariates that provided identity exclusions between photos of unidentifiable cats with inconsistent coat patterns, thus increasing the precision of abundance estimates. This combined model also allowed for unbiased estimation of density when animals cannot be identified to individual at the same rate as for free-roaming cats whose identifiability depended on their coat characteristics. Cat occupancy and abundance declined with increasing distance from residential areas, an effect that was more pronounced in wealthier neighborhoods. There was noteworthy absence of cats detected in larger public spaces and forests. Realized densities ranged from 0.02 to 1.75 cats/ha in sampled areas, resulting in a district-wide estimate of ~7296 free-roaming cats. Ninety percent of cat detections lacked collars and nearly 35% of known individuals were ear-tipped, indicative of district Trap-Neuter-Return (TNR) programs. These results suggest that we mainly sampled and estimated the unowned cat subpopulation, such that indoor/outdoor housecats were not well represented. The precise estimation of cat population densities is difficult due to the varied behavior of subpopulations within free-roaming cat populations (housecats, stray and feral cats), but our methods provide a first step in establishing citywide baselines to inform data-driven management plans for free-roaming cats in urban environments.

Urbanization and physical activity in the global Prospective Urban and Rural Epidemiology study

Urbanization may influence physical activity (PA) levels, although little evidence is available for low- and middle- income countries where urbanization is occurring fastest. We evaluated associations between urbanization and total PA, as well as work-, leisure-, home-, and transport-specific PA, for 138,206 adults living in 698 communities across 22 countries within the Prospective Urban and Rural Epidemiology (PURE) study. The 1-week long-form International PA Questionnaire was administered at baseline (2003–2015). We used satellite-derived population density and impervious surface area estimates to quantify baseline urbanization levels for study communities, as well as change measures for 5- and 10-years prior to PA surveys. We used generalized linear mixed effects models to examine associations between urbanization measures and PA levels, controlling for individual, household and community factors. Higher community baseline levels of population density (− 12.4% per IQR, 95% CI − 16.0, − 8.7) and impervious surface area (− 29.2% per IQR, 95% CI − 37.5, − 19.7), as well as the rate of change in 5-year population density (− 17.2% per IQR, 95% CI − 25.7, − 7.7), were associated with lower total PA levels. Important differences in the associations between urbanization and PA were observed between PA domains, country-income levels, urban/rural status, and sex. These findings provide new information on the complex associations between urbanization and PA.

Black bear density and habitat use variation at the Sierra Nevada‐Great Basin Desert transition

AbstractIn the first 2 decades of the twenty‐first century, American black bear (Ursus americanus) populations rebounded with range expansions into areas where the species was previously extirpated. While there are a number of factors that limit range expansion, habitat quality and availability are among the most important. Such factors may be particularly important in western Nevada, USA, at the transition zone of the Sierra Nevada and the Great Basin Desert. We deployed a multi‐faceted data collection system including motion‐sensitive cameras, noninvasive hair sampling and genotyping, and global positioning system (GPS) tracking. We analyzed data using spatial capture‐recapture to estimate population density and dynamic occupancy models to estimate habitat use. Black bear habitat use and density were substantially higher in the Sierra Nevada than the Great Basin Desert and had strong positive relationships with the presence of conifer land cover in the transition zone. The average black bear density was >4 times higher in the mixed‐conifer forests of the Sierra Nevada (12.4 bears/100 km2) than in desert mountain ranges with piñon (Pinus monophylla)‐juniper (Juniperus spp.) woodland (2.7 bears/100 km2). The low‐elevation shrub and grassland portions of the study area had even lower estimated black bear density (0.6 bears/100 km2) and probability of use (0.03, 95% CI = 0.00–0.09). Across these spatially variable configurations in black bear density, we estimated the population size to be 418 individuals (95% CI = 239–740). Declining density towards the range edge, coupled with a relatively stable range of black bears in Nevada observed since 2000, suggests that further species range expansion into the western Great Basin may be limited by habitat quality and availability.

Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features.

We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a server, thus raising privacy concerns. Here, we propose a Federated KDE framework for estimating the user population density, which not only keeps location data on the devices but also provides probabilistic privacy guarantees against a malicious server that tries to infer users' location. Our approach Federated random Fourier feature (RFF) KDE leverages a random feature representation of the KDE solution, in which each user's information is irreversibly projected onto a small number of spatially delocalized basis functions, making precise localization impossible while still allowing population density estimation. We evaluate our method on both synthetic and real-world datasets, and we show that it achieves a better utility (estimation performance)-vs-privacy (distance between inferred and true locations) tradeoff, compared to state-of-the-art baselines (e.g., GeoInd). We also vary the number of basis functions per user, to further improve the privacy-utility trade-off, and we provide analytical bounds on localization as a function of areal unit size and kernel bandwidth.

Mammal population density estimation using camera traps based on a random encounter model: theoretical basis and practical recommendations

Estimating the population density of mammals has long been one of the problematic tasks of both fundamental population ecology and practical programmes for their conservation and management. The majority of methods for population density estimation using camera traps are focused on individually marked species. This review paper presents the theoretical and practical foundations of a method, Random Encounter Model (REM), used for estimating the population density of unmarked mammal species using camera traps. Based on an extensive analysis of the literature and our personal practical experience, we discussed the theory and practice for the application of this method, as well as its strengths and weaknesses. In this method, if we know parameters of the effective detection zone of a camera trap (radius and angle), and the length of the day range, it is possible to correct the trapping rate (i.e. the number of independent trap events per total number of camera traps-nights) in order to calculate the population density of species. The effective detection zone of a camera trap is determined through modelling using computer vision algorithms. The mammal day range is calculated based on its activity level and travel speed, taking into account behavioural patterns based on machine learning models. For REM, a random or systematic design for the camera trap placements should be used. If camera traps are installed against trails or roads, appropriate correction factors must be applied. The effectiveness and reliability of REM has been confirmed by many independent population density estimates, including capture-recapture analyses, visual transect counts, and scat counts. To date, the implementation of REM and its extensions is presented in the R programming environment. It has been established that the main difficulties in the use of the REM are technical imperfections of the camera traps themselves, the relatively large required number of their stations (at least 50 or more), as well as long calibration work. For all these difficulties, possible solutions are proposed. In conclusion, practical recommendations are provided for the use of REM in studies in Protected Areas.

A Machine Learning Method for Building Height Estimation Based on Sentinel-2 Bi-Temporal Images

Building height information is essential for many applications such as urban planning and population density estimation. The building shadow length varies according to seasons, which is shown as different digital number values in multi-temporal images. Thus, the bi-temporal satellite remote sensing images of Sentinel-2 are used to estimate the buildings height in this study. An area of 15 km × 15 km in Beijing, China is taken as the study area. By preprocessing the data, the remaining pixels are split into two parts: 70% as the training data set and the rest as the testing data set. Then, one classification model and three regression models are proposed with using Random Forest (RF) method. Based on the testing data, it shows that the accuracy rate of the classification model has reached 98.4% with the kappa coefficient of 0.93. And the regression models' root-mean-square error (RMSE) is 0.61 floor for 1–6 floors group, 0.41 floor for 7–12 floor group, and 0.98 floor for above 12 floor group. The final RMSE is 1.62 floor with RF models. In general, this study shows the feasibility of using satellite mid-resolution optical image to estimate the building height and provides an important reference for regional building height estimation in the future.

Estimating brown bear population density and abundance using camera traps in the Central Forest State Nature Reserve (west of European Russia)

This paper presents the results of estimating the population density and abundance of Ursus arctos (hereinafter – brown bear) in the Southern Forestry of the Central Forest State Nature Biosphere Reserve (CFNR), West of European Russia, in 2021 based on the Random Encounter Model (REM) based upon data obtained from camera traps. Methods for obtaining parameters necessary for building a model are demonstrated. A total of 7970 camera trap nights were worked out at 46 stations, and 502 independent trap events were obtained. The average relative abundance index (RAI) was 6.28 ± 1.59. The total average brown bear population density was 0.086 ± 0.034 individuals per 1 km2. The approximate estimated abundance was 18.98 ± 7.54 individuals. The coefficient of variation was 38%. Population density estimates had a pronounced seasonal dynamics. The minimum value was recorded for the period from 24 June to 23 July (individuals feeding on meadows and ants outside the CFNR core area), and the maximum for the period from 24 July to 22 August (brown bears feeding by berries in the CFNR core area). We found a strong significant correlation between brown bear population density and its relative abundance index (r = 0.81, p < 0.05). It was found that with an increase in the sampling period duration, the estimate of the population density noticeably decreases (r = -0.53, p < 0.05). Parameters of the average travel speed and activity level are a subject to the greatest variability, which determines the significant variability of the day range. In general, the method of population density estimation using REM is highly promising to carry out the brown bear population size estimation in forests and mountain forests, where visual estimations are difficult or impossible.

Urban Leopards of National Capital: Estimating Density and Occupancy of Leopard (Panthera pardus) in Asola Bhatti Wildlife Sanctuary, New Delhi, India

Studies, in recent past have revealed that obligate carnivores are adapting to human dominated landscapes. Leopards, amongst other large carnivores are highly adaptable and can survive in a range of environments. Although any sporadic sightings of leopards have occurred in New Delhi. However, any reliable population and occupancy estimation was lacking from the region, whereas understanding of population density estimation is important in providing local and regional level conservation and management of the species. Our study is conducted to better understand leopard status and population density estimation in Asola Bhatti Wildlife Sanctuary of New Delhi. During 588 camera trap nights, we detected 8 individual leopards. Based on spatial capture-recapture framework the leopard density inside the sanctuary was estimated at 4.5 ± 0.019 leopards/100km² despite very high anthropogenic pressure. Occupancy modelling based on Inverse Distance Weighted Interpolation showed species distribution across the sanctuary which was highest in the areas adjoining densely populated region of the city from where it was observed that large number of vehicle and people cross the sanctuary daily to for their work. The moderate leopard density in this small protected area with high human population pressure can be due to the absence of any other large carnivore, less competition with co-predators because of their low numbers (i.e., hyenas), high availability of prey base and inclusive protected area management. We encourage further research to explore the landscape connectivity between the neighboring suitable leopard habitats like Sariska Tiger Reserve in Rajasthan, via Aravalli hill range of South Haryana till Asola Bhatti Wildlife Sanctuary of New Delhi for understanding wildlife corridor connectivity in the Northern Aravalli Range at landscape level

Population status and density estimate of Leopard Panthera pardus fusca in dry thorn forests of southern India

The study aims to assess the population status and density of the Leopard (Panthera pardus fusca) in the Erode Forest Division, Tamil Nadu. The Erode Forest division has numerous villages within its boundaries and is exposed to associated biotic pressures. In order to improve the management practices of the wildlife in the area and to enhance its conservation, it is relevant to assess its status. In 2018 and 2020, we estimated the population density of the leopard in the entire forest division using 2 km2 sample grids and the camera trapping method. In each grid, we deployed a pair of camera traps, the cumulative number of trap-nights being 22983 (10732 trap nights in 2018 and 12251 in 2020). We analysed the obtained data using spatially explicit capture-recapture models (SECR). The surveys yielded 198 images of the leopard in 2018 and 272 images in 2020, of which 48 and 44 leopard individuals were identified in the respective years based on the rosette pattern. The density estimates were 5.16 (SE = ±0.89) and 4.00 (SE ± 0.72) individuals/100 km2 in 2018 and 2020, respectively. We found that in some regions overlapping with the human-use area, leopard densities were high, indicating that the species successfully exploits areas near human habitation and highlighting its high potential for interaction with humans. This baseline estimate and insights will help prioritize management actions, strengthen large mammal conservation beyond the boundaries of protected areas (PA), and plan human-wildlife conflict mitigation measures to enable the persistence of large carnivores in multi-use forests.