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Using capture-recapture models in wildlife camera-trapping monitoring and the study case
... Infrared-triggered camera technology can make up for the above-mentioned disadvantages [19,20]. Cameras cause minimal disturbance to animals and their habitat, can be used continuously for long periods of time, can withstand harsh weather conditions, and are relatively cheap [19,21,22]. ...
The Asian elephant ( Elephas maximus ) population in Nangunhe National Nature Reserve in China represents a unique evolutionary branch that has been isolated for more than twenty years from neighboring populations in Myanmar. The scarcity of information on population structure, sex ratio, and body condition makes it difficult to develop effective conservation measures for this elephant population. Twelve individuals were identified from 3,860 valid elephant images obtained from February to June 2018 (5,942 sampling effort nights) at 52 camera sites. Three adult females, three adult males, one subadult male, two juvenile females, two juvenile males and one male calf were identified. The ratio of adult females to adult males was 1:1, and the ratio of reproductive ability was 1:0.67, indicating the scarcity of reproductive females as an important limiting factor to population growth. A population density of 5.32 ± 1.56 elephants/100 km ² was estimated using Spatially Explicit Capture Recapture (SECR) models. The health condition of this elephant population was assessed using an 11-point scale of Body Condition Scoring (BCS). The average BCS was 5.75 (n = 12, range 2–9), with adult females scoring lower than adult males. This isolated population is extremely small and has an inverted pyramid age structure and therefore is at a high risk of extinction. We propose three plans to improve the survival of this population: improving the quality and quantity of food resources, removing fencing and establishing corridors between the east and wet parts of Nangunhe reserve.
Political borders and natural boundaries of wildlife populations seldom coincide, often to the detriment of conservation objectives. Transnational monitoring of endangered carnivores is rare, but is necessary for accurate population monitoring and coordinated conservation policies. We investigate the benefits of collaboratively monitoring the abundance and survival of the critically endangered Amur leopard, which occurs as a single transboundary population across China and Russia. Country‐specific results overestimated abundance and were generally less precise compared to integrated monitoring estimates; the global population was similar in both years: 84 (70–108, 95% confidence interval). Uncertainty in country‐specific annual survival estimates were approximately twice the integrated estimates of 0.82 (0.69–0.91, 95% confidence limits). This collaborative effort provided a better understanding of Amur leopard population dynamics, represented a first step in building trust, and lead to cooperative agreements to coordinate conservation policies.
The future of the critically endangered Amur leopard (Panthera pardus orientalis) is at a crucial point, and effective conservation strategies implemented within its primary historical range in Northeast China may determine the fate of this species. However, when a conservation plan was first developed for the species, scarce information on the leopards' status existed. To illustrate regional conservation challenges, we focused on the Hunchun Nature Reserve and the surrounding area along the China-Russia border, a potential stronghold for Amur leopard conservation. We conducted large-scale data analysis with a field camera-trapping network to present the first population estimates for this species using a spatially explicit capture–recapture approach. We then used a zero-inflated regression model to analyze the relationship of leopards with major prey species and anthropogen-ic disturbances. Our results indicate that leopards are returning to China, but most of them are part of a " border population " or are transient; their numbers are far too few to establish a healthy population. The spatial counts of leopards were noticeably high in areas with high prey richness and areas far from settlements and roads. Areas with few prey species and high human and cattle use exhibited a greater probability of " excess absences " of leopards. Mitigating human disturbances by progressively minimizing cattle and human impacts on the forest should be pursued along with habitat expansion for large ungulates, whose presence is essential for leopard occupancy. This study provides crucial information to support Chinese government recovery efforts and for refining conservation practices in human-dominated landscapes to ensure the long-term survival of this species.
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site.
Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g., camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology. This article is protected by copyright. All rights reserved.
Non-invasive techniques have long been used to estimate wildlife population abundance and density. However, recent technological breakthroughs have facilitated non-invasive estimation of the proportion of animal populations with certain diseases. Giraffes Giraffa camelopardalis are increasingly becoming recognized as a species of conservation concern with decreasing population trajectories across their range in Africa. Diseases may be an important component impacting giraffe population declines, and the emerging ?Giraffe Skin Disease? (GSD), characterized by the appearance of wrinkled skin and alopecic lesions on the limbs, neck, and chest of infected giraffe, may hinder movement causing increased susceptibility to predation. We examined the prevalence of GSD in Tanzania's Ruaha National Park over a 4-month period in 2015, using photographic capture?recapture surveys via road-based transects. We divided the study area into five circuitous survey units, each approximately 100 km in length ($\bar x$ = 99.22 km, SD = 3.72), and surveyed for giraffes for four months. From these surveys, we developed a database of spatially-explicit giraffe photographs. We processed these photos for individual identification and fitted spatial capture?recapture models to predict the spatial configuration of giraffe abundance and GSD prevalence within the study area. Our results indicated that >86% of the giraffe population showed signs of GSD and that the disease was more prevalent in the northern and north-eastern portion of Ruaha National Park. Synthesis and applications. Our research shows that data from non-invasive surveys can be used in spatial capture?recapture (SCR) models to estimate the proportion of a population affected by a visible disease. Researchers and conservationists can use SCR models to better examine the variation in parameters associated with these populations such as sex and age class, movement, and encounter rate, which may be linked to the prevalence of the disease, while incorporating broad spatial and temporal dimensions of the population in such areas. We discuss the implications of this research for conservation of threatened species with an emphasis on disease ecology and vulnerability to predations, and more broadly, for wildlife conservation. This article is protected by copyright. All rights reserved.
As an apex predator the Amur tiger (Panthera tigris altaica Temminck, 1844) could play a pivotal role in maintaining the integrity of forest ecosystems in Northeast Asia. Due to habitat loss and harvest over the past century, tigers rapidly declined in China and are now restricted to the Russian Far East and bordering habitat in nearby China. To facilitate restoration of the tiger in its historical range, reliable estimates of population size are essential to assess effectiveness of conservation interventions. Here we used camera trap data collected in Hunchun National Nature Reserve from April to June of 2013 and 2014 to estimate tiger density and abundance using both maximum likelihood and Bayesian spatially explicit capture-recapture (SECR) methods. A minimum of eight individuals were detected in both sample periods and the documentation of marking behavior and reproduction suggests the presence of a resident population. Using Bayesian SECR modeling within the 11,400 km(2) state space, density estimates were 0.33 and 0.40 individuals/100 km(2) in 2013 and 2014, respectively, corresponding to an estimated abundance of 38 and 45 animals for this transboundary Sino-Russia population. In a maximum likelihood framework, we estimated densities of 0.30 and 0.24 individuals/100 km(2) corresponding to abundances of 34 and 27, in 2013 and 2014, respectively. These density estimates are comparable to other published estimates for resident Amur tiger populations in the Russian Far East. This study reveals promising signs of tiger recovery in Northeast China, and demonstrates the importance of connectivity between the Russian and Chinese populations for recovering tigers in Northeast China. This article is protected by copyright. All rights reserved.
Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.
Wildlife managers need reliable methods to estimate large carnivore densities and population trends; yet large carnivores are elusive, difficult to detect, and occur at low densities making traditionalapproaches intractable. Recent advances in spatial capture-recapture (SCR) models have provided new approaches for monitoring trends in wildlife abundance and these methods are particularly applicable tolarge carnivores. We applied SCR models in a Bayesian framework to estimate mountain lion densities in the Bitterroot Mountains of west central Montana. We incorporate an existing resource selection function (RSF) as a density covariate to account for heterogeneity in habitat use across the study area and include data collected from harvested lions. We identify individuals through DNA samples collected by (1) biopsydarting mountain lions detected in systematic surveys of the study area, (2) opportunistically collecting hair and scat samples, and (3) sampling all harvested mountain lions. We included 80 DNA samplescollected from 62 individuals in the analysis. Including information on predicted habitat use as a covariate on the distribution of activity centers reduced the median estimated density by 44%, the standard deviationby 7%, and the width of 95% credible intervals by 10% as compared to standard SCR models. Within the two management units of interest, we estimated a median mountain lion density of 4.5 mountain lions/100km2 (95% CI=2.9, 7.7) and 5.2 mountain lions/100 km2 (95% CI=3.4, 9.1). Including harvested individuals (dead recovery) did not create a significant bias in the detection process by introducing individuals thatcould not be detected after removal. However, the dead recovery component of the model did have a substantial effect on results by increasing sample size. The ability to account for heterogeneity in habitatuse provides a useful extension to SCR models, and will enhance the ability of wildlife managers to reliably and economically estimate density of wildlife populations, particularly large carnivores.
Context
The Amur tiger and leopard, once roaming over the Eurasian continent, are now endangered and confined to the Sikhote-Alin Mountains, Russia—a landscape that has been increasingly fragmented due to human activities. The ultimate fate of these big cats depends on whether they can resettle in their previous main historical range in NE China. Recent sightings of these animals along the China–Russia border have aroused new hope, but direct evidence is lacking.
Objectives
The main objectives of our study were (1) to determine the abundance and spatiotemporal patterns of tigers, leopards, and primary prey; (2) to investigate factors influencing the resettlement of the two big cats; and (3) to propose a landscape-scale conservation plan to secure the long-term sustainability of the Amur tiger and leopard.
Methods
We monitored the two felids, their prey, and human activities, with 380 camera-trap stations, for a total of 175,127 trap days and over an area of 6000 km2 in NE China. We used the constraint line method to characterize cattle grazing and human influences on tigers, leopards, and their prey species.
Results
Our results show that, unexpectedly, at least 26 tigers and 42 leopards are present within China, which are confined primarily to a narrow area along the border with Russia. We have further identified that cattle grazing and human disturbances are the key hurdles to the dispersal of the tigers and leopards farther into China where suitable habitat is potentially available.
Conclusions
Amur tigers and leopards are returning to China, indeed, but their long-term resettlement is not likely without active and timely conservation efforts on landscape and regional scales. To overcome the hurdles to the resettlement of tigers and leopards in China, we propose a “Tiger and Leopard Resettlement Program” that will engage the government, local communities, and researchers, so that the long-term sustainability of the Amur tigers and leopards can be ensured.
The Andean cat (Leopardus jacobita) is one of the most endangered, yet least known, felids. Although the Andean cat is considered at risk of extinction, rigorous quantitative population studies are lacking. Because physical observations of the Andean cat are difficult to make in the wild, we used a camera-trapping array to photo-capture individuals. The survey was conducted in northwestern Argentina at an elevation of approximately 4,200 m during October-December 2006 and April-June 2007. In each year we deployed 22 pairs of camera traps, which were strategically placed. To estimate detection probability and density we applied models for spatial capture-recapture using a Bayesian framework. Estimated densities were 0.07 and 0.12 individual/km2 for 2006 and 2007, respectively. Mean baseline detection probability was estimated at 0.07. By comparison, densities of the Pampas cat (Leopardus colocolo), another poorly known felid that shares its habitat with the Andean cat, were estimated at 0.74–0.79 individual/km2 in the same study area for 2006 and 2007, and its detection probability was estimated at 0.02. Despite having greater detectability, the Andean cat is rarer in the study region than the Pampas cat. Properly accounting for the detection probability is important in making reliable estimates of density, a key parameter in conservation and management decisions for any species.
Recently the Sunda clouded leopard Neofelis diardi was recognized as a separate species distinct from the clouded leopard Neofelis nebulosa of mainland Asia. Both species are categorized as Vulnerable on the IUCN Red List. Little is known about the newly identified species and, in particular, information from forests outside protected areas is scarce. Here we present one of the first density estimates calculated with spatial capture–recapture models using camera-trap data. In two commercial forest reserves in Sabah (both certified for their sustainable management practices) the density of the Sunda clouded leopard was estimated to be c. 1 per 100 km2 (0.84±SE 0.42 and 1.04±SE 0.58). The presence of the Sunda clouded leopard in such forests is encouraging for its conservation but additional studies from other areas, including protected forests, are needed to compare and evaluate these densities.
The Amazonian moist forest, which covers most
of French Guiana, is one of the core habitats for the lowland
tapir Tapirus terrestris. Tapirs are hunted in French Guiana,
although a law introduced in 2011 restricts hunting to one
animal per person per hunting trip. We carried out cameratrap
surveys in the Nouragues Nature Reserve for 4 years,
with the goal of estimating tapir densities in undisturbed
conditions and determining sustainable harvest levels
for tapirs in French Guiana. We analysed our data with a
Bayesian spatially explicit capture–recapture model, with
parameter sharing across surveys to improve estimates, and
used the model to calculate derived parameters such as
maximum sustainable harvest levels. Density estimates for
all four surveys were similar and the model indicated a
difference in encounter rates for the two camera models
used but no difference in encounter rates or home range
sizes for males and females or between years. Based on the
calculated density of 0.32 tapir km−2 we estimated sustainable
harvest levels at 0.009 tapir km−2. Comparing this value
to hunting surveys from 11 sites between 1999 and 2006, we
found that hunting levels were unsustainable in at least
seven villages. We conclude that even the new restrictive
hunting law will not prevent overhunting of tapirs in certain
areas and thus stronger regulations are needed. However,
because of the remoteness of tapir habitat in many parts of
French Guiana tapirs are not immediately threatened in the
country as a whole.
Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture-recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size (N ˆ) and effective sampling area (A ˆ) are used to calculate density (D ˆ 5 N ˆ/A ˆ); and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets ( n 5 11) were developed using nine rodent species from capture-recapture live-trapping on both trapping grids and trapping webs in four replicate 4.2-ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional ''saturation'' trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the com- putation of true densities. Density estimates ( D ˆ) were calculated using program CAPTURE for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities (D) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this ''blind'' test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DIS- TANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web-based uniform and half-normal models in program DISTANCE, and the grid-based models Mb and Mbh in program CAPTURE (with Aadjusted by species-specific full mean maximum distance moved (MMDM) values). These models produced accurate Dvalues (with 95% confidence intervals that included the true D values) and exhibited acceptable bias but poor precision. However, in linear regression analyses comparing each model's Dvalues to the true D values over the range of observed test densities, only the web-based uniform model exhibited a regression slope near 1.0; all other models showed substantial slope deviations, indicating biased estimates at higher or lower density values. In addition, the grid-based Danalyses using full MMDM values for Warea adjustments required a number of theoretical assumptions of uncertain validity, and we therefore viewed their empirical successes with caution. Finally, density estimates from the independent analysts were highly variable, but estimates from web-based approaches had smaller mean square errors and better achieved confidence-interval coverage of D than did grid-based
Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture--recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on "ecological distance," i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture-recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture-recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.
Across their range jaguars Panthera onca are important conservation icons for several reasons: their important role in ecosystems as top carnivores, their cultural and economic value, and their potential conflicts with livestock. However, jaguars have historically been difficult to monitor. This paper outlines the first application of a systematic camera trapping methodology for abundance estimation of jaguars. The methodology was initially developed to estimate tiger abundance in India. We used a grid of camera traps deployed for 2 months, identified individual animals from their pelage patterns, and estimated population abundance using capture-recapture statistical models. We applied this methodology in a total of five study sites in the Mayan rainforest of Belize, the Chaco dry forest of Bolivia, and the Amazonian rainforest of Bolivia. Densities were 2.411 observed animals, 16capturesrecaptures2,280 trap nights, and sample areas of 107–458 km2. The sampling technique will be used to continue long-term monitoring of jaguar populations at the same sites, to compare with further sites, and to develop population models. This method is currently the only systematic population survey technique for jaguars, and has the potential to be applied to other species with individually recognizable markings.
Assessing the conservation status of species of concern is greatly aided by unbiased estimates of population size. Population size is one of the primary parameters determining urgency of conservation action, and it provides baseline data against which to measure progress toward recovery. Asiatic black bears (Ursus thibetanus) and sun bears (Helarctos malayanus) are vulnerable to extinction, but no statistically rigorous population density estimates exist for wild bears of either species. We used a camera-based approach to estimate density of these sympatric bear species. First, we tested a technique to photograph bear chest marks using 3 camera traps mounted on trees facing each other in a triangular arrangement with bait in the center. Second, we developed criteria to identify individual sun bears and black bears based on chest-mark patterns and tested the level of congruence among 5 independent observers using a set of 234 photographs. Finally, we camera-trapped wild bears at 2 study areas (Khlong E-Tow, 33 km², and Khlong Samor-Pun, 40 km²) in Khao Yai National Park, Thailand, and used chest marks to identify individual bears and thereby derive capture histories for bears of each species. Average congruence among observers' identifications of individual bears was 78.4% for black bear and 92.9% for sun bear across sites. At Khlong E-Tow, we recorded 13 black bears (8 M, 4 F, 1 unknown sex) and 8 sun bears (1 M, 5 F, 2 unknown sex). At Khlong Samor-Pun, we recorded 10 black bears (6 M, 4 F) and 6 sun bears (4 M, 2 F). We used a spatially explicit capture-recapture method, resulting in density estimates of 8.0 (SE = 3.04) and 5.8 (SE = 2.31) black bears per 100 km² and 5.9 (SE = 3.07) and 4.3 (SE = 2.32) sun bears per 100 km² for each study area, respectively. Our camera trap design and chest-mark identification criteria can be used to estimate density of sun bears and black bears, enhancing knowledge of the conservation status of these threatened and little-known bear species.
Camera trapping has recently been introduced as an unbiased and practical method for monitoring tiger abundance. In a high density area in the Royal Bardia National Park in lowland Nepal, we tested this method by trapping very intensively within a 25 km 2 area to determine the true number of animals in that area. We then tested the effect of study design by sub-sampling the data set using varying distances between trap stations and by reducing the number of trapping nights at each station. We compared these numbers with the density estimates generated by the capture–recapture models of the program CAPTURE. Both distance between traps and trapping duration greatly influenced the results. For example, increasing the inter-trap distance from 1 to 2.1 km and reducing the trapping duration per station from 15 to 10 nights reduced the number of tigers captured by 25%. A significant decrease in trapping rates during successive 5-night periods suggested that our tigers became trap-shy, probably because of the photo flash and because they detected the camera traps from cues from impression pads 50 m from the traps. A significant behavioural response was also confirmed by the program CAPTURE. The best capture–recapture model selected by the computer program (M bh) gave precise estimates from data collected by the initial 1 km spacing of traps. However, when we omitted data from half the number of traps, thus decreasing the sampling effort to a more realistic level for monitoring purposes, the program CAPTURE underestimated the true number of tigers. Most probably, this was due to a combination of trap shyness and the way the study was designed. Within larger protected areas, total count from intensive, stratified subsampling is suggested as a complementary technique to the capture–recapture method, since it circumvents the problem of trap shyness.
We used capture-recapture analyses to esti-mate the density of a tiger Panthera tigris population in the tropical forests of Huai Kha Khaeng Wildlife Sanctuary, Thailand, from photographic capture histories of 15 distinct individuals. The closure test results (z 5 0.39, P 5 0.65) provided some evidence in support of the demographic closure assumption. Fit of eight plausible closed models to the data indicated more support for model M h , which incorporates individual heterogeneity in capture probabil-ities. This model generated an average capture probability ^ p 5 0.42 and an abundance estimate of b Nð b SE½ b NÞ 5 19 (9.65) tigers. The sampled area of b AðWÞð b SE½ b AðWÞÞ 5 477.2 (58.24) km 2 yielded a density estimate of b Dð b SE½ b DÞ 5 3.98 (0.51) tigers per 100 km 2 . Huai Kha Khaeng Wildlife Sanctuary could therefore hold 113 tigers and the entire Western Forest Complex c. 720 tigers. Although based on field protocols that constrained us to use sub-optimal analyses, this estimated tiger density is comparable to tiger densities in Indian reserves that support moderate prey abundances. However, tiger densities in well-protected Indian reserves with high prey abundances are three times higher. If given adequate protection we believe that the Western Forest Complex of Thailand could potentially harbour .2,000 wild tigers, highlight-ing its importance for global tiger conservation. The monitoring approaches we recommend here would be useful for managing this tiger population.
We examine the abundance and distribution of Sumatran tigers (Panthera tigris sumatrae) and nine prey species in Bukit Barisan Selatan National Park on Sumatra, Indonesia. Our study is the first to demonstrate that the relative abundance of tigers and their prey, as measured by camera traps, is directly related to independently derived estimates of densities for these species. The tiger population in the park is estimated at 40–43 individuals. Results indicate that illegal hunting of prey and tigers, measured as a function of human density within 10 km of the park, is primarily responsible for observed patterns of abundance, and that habitat loss is an increasingly serious problem. Abundance of tigers, two mouse deer (Tragulus spp.), pigs (Sus scrofa) and Sambar deer (Cervus unicolor) was more than four times higher in areas with low human population density, while densities of red muntjac (Muntiacus muntjac) and pigtail macaques (Macaca nemestrina) were twice as high. Malay tapir (Tapirus indicus) and argus pheasant (Argusianus argus), species infrequently hunted, had higher indices of relative abundance in areas with high human density. Edge effects associated with park boundaries were not a significant factor in abundance of tigers or prey once human density was considered. Tigers in Bukit Barisan Selatan National Park, and probably other protected areas throughout Sumatra, are in imminent danger of extinction unless trends in hunting and deforestation are reversed.
The understanding of the dynamics of animal populations and of related ecological and evolutionary issues frequently depends on a direct analysis of life history parameters. For instance, examination of trade-offs between reproduction and survival usually rely on individually marked animals, for which the exact time of death is most often unknown, because marked individuals cannot be followed closely through time. Thus, the quantitative analysis of survival studies and experiments must be based on capture-recapture (or resign ting) models which consider, besides the parameters of primary interest, recapture or resighting rates that are nuisance parameters. Capture-recapture models oriented to estimation of survival rates are the result of a recent change in emphasis from earlier approaches in which population size was the most important parameter, survival rates having been first introduced as nuisance parameters. This emphasis on survival rates in capture-recapture models developed rapidly in the 1980s and used as a basic structure the Cormack-Jolly-Seber survival model applied to an homogeneous group of animals, with various kinds of constraints on the model parameters. These approaches are conditional on first captures; hence they do not attempt to model the initial capture of unmarked animals as functions of population abundance in addition to survival and capture probabilities. This paper synthesizes, using a common framework, these recent developments together with new ones, with an emphasis on flexibility in modeling, model selection, and the analysis of multiple data sets. The effects on survival and capture rates of time, age, and categorical variables characterizing the individuals (e.g., sex) can be considered, as well as interactions between such effects. This "analysis of variance" philosophy emphasizes the structure of the survival and capture process rather than the technical characteristics of any particular model. The flexible array of models encompassed in this synthesis uses a common notation. As a result of the great level of flexibility and relevance achieved, the focus is changed from fitting a particular model to model building and model selection. The following procedure is recommended: (1) start from a global model compatible with the biology of the species studied and with the design of the study, and assess its fit; (2) select a more parsimonious model using Akaike's Information Criterion to limit the number of formal tests; (3) test for the most important biological questions by comparing this model with neighboring ones using likelihood ratio tests; and (4) obtain maximum likelihood estimates of model parameters with estimates of precision. Computer software is critical, as few of the models now available have parameter estimators that are in closed form. A comprehensive table of existing computer software is provided. We used RELEASE for data summary and goodness-of-fit tests and SURGE for iterative model fitting and the computation of likelihood ratio tests. Five increasingly complex examples are given to illustrate the theory. The first, using two data sets on the European Dipper (Cinclus cinclus), tests for sex-specific parameters, explores a model with time-dependent survival rates, and finally uses a priori information to model survival allowing for an environmental variable. The second uses data on two colonies of the Swift (Apus apus), and shows how interaction terms can be modeled and assessed and how survival and recapture rates sometimes partly counterbalance each other. The third shows complex variation in survival rates across sexes and age classes in the roe deer (Capreolus capreolus), with a test of density dependence in annual survival rates. The fourth is an example of experimental density manipulation using the common lizard (Lacerta vivipara). The last example attempts to examine a large and complex data set on the Greater Flamingo (Phoenicopterus ruber), where parameters are age specific, survival is a function of an environmental variable, and an age × year interaction term is important. Heterogeneity seems present in this example and cannot be adequately modeled with existing theory. The discussion presents a summary of the paradigm we recommend and details issues in model selection and design, and foreseeable future developments.
Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors approach is practical - it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package.
Accurate information on the density and abundance of animal populations is essential for understanding species' ecology and for conservation planning, but is difficult to obtain. The endangered orangutan (Pongo spp.) is an example; due to its elusive behavior and low densities, researchers have relied on methods that convert nest counts to orangutan densities and require substantial effort for reliable results. Camera trapping and spatial capture–recapture (SCR) models could provide an alternative but have not been used for primates. We compared density estimates calculated using the two methods for orangutans in the Wehea Forest, East Kalimantan, Indonesia. Camera trapping/SCR modeling produced a density estimate of 0.16 ± 0.09–0.29 indiv/km 2 , and nest counts produced a density estimate of 1.05 ± 0.18–6.01 indiv/km 2. The large confidence interval of the nest count estimate is probably due to high variance in nest encounter rates, indicating the need for larger sample size and the substantial effort required to produce reliable results using this method. The SCR estimate produced a very low density estimate and had a narrower but still fairly wide confidence interval. This was likely due to unmodeled heterogeneity and small sample size, specifically a low number of individual captures and recaptures. We propose methodological fixes that could address these issues and improve precision. A comparison of the overall costs and benefits of the two methods suggests that camera trapping/SCR modeling can potentially be a useful tool for assessing the densities of orangutans and other elusive primates, and warrant further investigation to determine broad applicability and methodological adjustments needed.
Automated photography of tigers Panthera tigris for purely illustrative purposes was pioneered by British forester Fred Champion (1927, 1933) in India in the early part of the Twentieth Century. However, it was McDougal (1977) in Nepal who first used camera traps, equipped with single-lens reflex cameras activated by pressure pads, to identify individual tigers and study their social and predatory behaviors. These attempts involved a small number of expensive, cumbersome camera traps, and were not, in any formal sense, directed at “sampling” tiger populations.
Survival is often estimated from capture–recapture data using Cormack–Jolly–Seber (CJS) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. Consequently, apparent survival is lower than true survival unless study area fidelity equals one. Underestimation of true survival from capture–recapture data is a main limitation of the method.We develop a spatial version of the CJS model that allows estimation of true survival. Besides the information about whether a specific individual was encountered at a given occasion, it is often recorded where the encounter occurred. Thus, information is available about the fraction of dispersal that occurs within the study area, and we use it to model dispersal and estimate true survival. Our model is formulated hierarchically and consists of survival, dispersal and observation submodels, assuming that encounters are possible anywhere within a study area.In a simulation study, our new spatial CJS model produced accurate estimates of true survival and dispersal behaviour for various sizes and shapes of the study area, even if emigration is substantial. However, when the information about dispersal is scarce due to low survival, low recapture probabilities and high emigration, the estimators are positively biased. Moreover, survival estimates are sensitive to the assumed dispersal kernel.We applied the spatial CJS model to a data set of adult red-backed shrikes (Lanius collurio). Apparent survival of males (c. 0·5) estimated with the CJS model was larger than in females (c. 0·4), but the application of the spatial CJS model revealed that both sexes had similar survival probabilities (c. 0·6). The mean breeding dispersal distance in females was c. 700 m, while males dispersed only c. 250 m between years.Spatial CJS models enable study of dispersal and survival independent of study design constraints such as imperfect detection and size of the study area provided that some of the dispersing individuals remain in the study area. We discuss possible extensions of our model: alternative dispersal models and the inclusion of covariates and of a habitat suitability map.
Neotropical felids such as the ocelot (Leopardus pardalis) are secretive, and it is difficult to estimate their populations using conventional methods such as radiotelemetry or sign
surveys. We show that recognition of individual ocelots from camera-trapping photographs is possible, and we use camera-trapping
results combined with closed population capture-recapture models to estimate density of ocelots in the Brazilian Pantanal.
We estimated the area from which animals were camera trapped at 17.71 km2. A model with constant capture probability yielded an estimate of 10 independent ocelots in our study area, which translates
to a density of 2.82 independent individuals for every 5 km2 (SE 1.00).
Owing to habitat conversion and conflict with humans, many carnivores are of conservation concern. Because of their elusive nature, camera trapping is a standard tool for studying carnivores. In many vertebrates, sex-specific differences in movements – and therefore detection by cameras – are likely. We used camera trapping data and spatially explicit sex-specific capture–recapture models to estimate jaguar density in Emas National Park in the central Brazilian Cerrado grassland, an ecological hotspot of international importance. Our spatially explicit model considered differences in movements and trap encounter rate between genders and the location of camera traps (on/off road). We compared results with estimates from a sex-specific non-spatial capture–recapture model. The spatial model estimated a density of 0.29 jaguars 100km−2 and showed that males moved larger distances and had higher trap encounter rates than females. Encounter rates with off-road traps were one tenth of those for on-road traps. In the non-spatial model, males had a higher capture probability than females; density was estimated at 0.62 individuals 100km−2. The non-spatial model likely overestimated density because it did not adequately account for animal movements. The spatial model probably underestimated density because it assumed a uniform distribution of jaguars within and outside the reserve. Overall, the spatial model is preferable because it explicitly considers animal movements and allows incorporating site-specific and individual covariates. With both methods, jaguar density was lower than reported from most other study sites. For rare species such as grassland jaguars, spatially explicit capture–recapture models present an important advance for informed conservation planning.
The endangered Asian tapir (Tapirus indicus) is threatened by large-scale habitat loss, forest fragmentation and increased hunting pressure. Conservation planning for this species, however, is hampered by a severe paucity of information on its ecology and population status. We present the first Asian tapir population density estimate from a camera trapping study targeting tigers in a selectively logged forest within Peninsular Malaysia using a spatially explicit capture-recapture maximum likelihood based framework. With a trap effort of 2496 nights, 17 individuals were identified corresponding to a density (standard error) estimate of 9.49 (2.55) adult tapirs/100 km(2) . Although our results include several caveats, we believe that our density estimate still serves as an important baseline to facilitate the monitoring of tapir population trends in Peninsular Malaysia. Our study also highlights the potential of extracting vital ecological and population information for other cryptic individually identifiable animals from tiger-centric studies, especially with the use of a spatially explicit capture-recapture maximum likelihood based framework.
Classical closed-population capture–recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture–recapture models that accommodate the spatial attribute inherent in capture–recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km2 area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000 km2 (95% Bayesian CI: 5.9–15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies. © 2011 The Wildlife Society.
Summary • Estimating animal density using capture–recapture data from arrays of detection devices such as camera traps has been problematic due to the movement of individuals and heterogeneity in capture probability among them induced by differential exposure to trapping. • We develop a spatial capture–recapture model for estimating density from camera-trapping data which contains explicit models for the spatial point process governing the distribution of individuals and their exposure to and detection by traps. • We adopt a Bayesian approach to analysis of the hierarchical model using the technique of data augmentation. • The model is applied to photographic capture–recapture data on tigers Panthera tigris in Nagarahole reserve, India. Using this model, we estimate the density of tigers to be 14·3 animals per 100 km2 during 2004. • Synthesis and applications. Our modelling framework largely overcomes several weaknesses in conventional approaches to the estimation of animal density from trap arrays. It effectively deals with key problems such as individual heterogeneity in capture probabilities, movement of traps, presence of potential ‘holes’ in the array and ad hoc estimation of sample area. The formulation, thus, greatly enhances flexibility in the conduct of field surveys as well as in the analysis of data, from studies that may involve physical, photographic or DNA-based ‘captures’ of individual animals.
The jaguar (Panthera onca) is the largest feline in the Americas and third largest world-wide, smaller in size only to the tiger (P. tigris) and lion (P. leo). Yet, in comparison, relatively few studies on jaguar population densities have been conducted and baseline data for management purposes are needed. Camera trapping and capture–recapture sampling methods were used to estimate the size of a jaguar population in the Pantanal’s open wet grassland habitat, an important area for the long-term survival of the species. This study is the first jaguar population estimate conducted in co-operation with a GPS-telemetry study providing an important opportunity for comparing different methods of density estimation. An accessible area within a 460 km2 privately-owned ranch was sampled with equal effort during the dry seasons of 2003 and 2004. Thirty-one and twenty-five individual jaguars were identified in 2003 and 2004, respectively. Estimates of jaguar abundance were generated by program CAPTURE. Density estimates were produced according to different methods used to calculate the effectively sampled areas which ranged from 274 to 568 km2. For 2003, the currently-used mean maximum distance moved (MMDM) method produced a density of 10.3 jaguars/100 km2, while GPS-telemetry-based calculations produced a mean density of 6.6 jaguars/100 km2. For 2004, the MMDM method produced an estimate of 11.7 jaguars/100 km2 while GPS-telemetry calculations produced a density of 6.7 jaguars/100 km2. Our results suggest that the widely-used MMDM method used to calculate effectively sampled areas is significantly under-reflecting maximum distances moved by jaguars and their range-use and, thereby, considerably inflating cat density estimates. This overestimation could place a population in a difficult situation by lengthening the time taken to initiate protection measures because of underestimating the risk to that population.
We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.
Estimating the abundance of Nepal's largest population of tigers Panthera tigris
- J B Karki
- B Pandav
- S R Jnawali
- R Shrestha
- Nmb Pradhan
- B R Lamichane
- P Khanal
- N Subedi
- Y V Jhala
Karki JB, Pandav B, Jnawali SR, Shrestha R, Pradhan NMB,
Lamichane BR, Khanal P, Subedi N, Jhala YV (2015)
Estimating the abundance of Nepal's largest population of
tigers Panthera tigris. Oryx, 49, 150-156.
Estimating puma densities from camera trapping across three study sites
- M J Kelly
- A J Noss
- Di Bitetti
- M S Maffei
- L Arispe
- R L Paviolo
- De Angelo
- Di Blanco
Kelly MJ, Noss AJ, Di Bitetti MS, Maffei L, Arispe RL,
Paviolo A, De Angelo CD, Di Blanco YE (2008) Estimating
puma densities from camera trapping across three study
sites: Bolivia, Argentina, and Belize. Journal of Mammalogy, 89, 408-418.
Shooting rate of Catopuma temminckii by auto-induction infrared camera and estimation of population density in Changqing Nature Reserve
- A L Wu
- P Chen
- X F Zhang
Wu AL, Chen P, Zhang XF (2014) Shooting rate of Catopuma
temminckii by auto-induction infrared camera and estimation
of population density in Changqing Nature Reserve. Shaanxi
Forest Science and Technology, (1), 22-24. (in Chinese with
English abstract) [武阿莉, 陈鹏, 张晓峰 (2014) 长青保
护区自动感应红外相机金猫拍摄率与种群密度. 陕西林
业科技, (1), 22-24.]
Conservation implications of drastic
- L F Zhu
- X J Zhan
- H Wu
- S N Zhang
- T Meng
- M W Bruford
- F W Wei
Zhu LF, Zhan XJ, Wu H, Zhang SN, Meng T, Bruford MW,
Wei FW (2010) Conservation implications of drastic