Photographic Capture Recapture Research Articles

Brown bear body patches are temporally stable and represent a unique individual visual signature

Patches of color may be used to communicate to conspecifics, mainly in species showing uniform coloration, and may (a) help individuals maintain visual contact, such as between mothers and their young; (b) function as signals of subordination or to frighten rivals; (c) warn conspecifics of approaching predators; and/or (d) signal reproductive condition, health, or genetic quality to potential mates. Intraspecific communication represents one of the major evolutionary forces responsible for the coloration of body parts, but the meaning of many of these signals is still unclear. One of the first steps to understanding whether fur marks have a role in social communication is to understand whether such body patches are stable over time (i.e., whether they represent a unique visual signature for every individual). During the period 1999–2021, we recorded yearly pictures of 7 female (mean no. of monitoring years per bear = 13.6, standard deviation [SD] = 4.6; range = 9–22 yr) and 6 male (mean no. of monitoring years per bear = 9.3, SD = 4.3; range = 5–15 yr) brown bears (Ursus arctos) in the Cantabrian Mountains (NW Spain). We show that body mark shapes are stable over time and, because of their uniqueness, might represent a distinctive signature of individuals. Brown bear body marks may act as multicomponent signals, where different features of a given mark may inform about different aspects of the bearer or act as back-ups. For example, a quality-signaling capacity does not preclude the same mark from being used in other functions at the same time, such as individual recognition. Noninvasive techniques helpful for identifying individuals have been developed for estimating population size, reproductive rates, and the survival of several carnivore species. Fur marks that are stable over time can thus be useful in field research (e.g., body marks that are persistent and do not vary over time are an important tool in longitudinal photographic capture–recapture studies).

Estimating roe deer density using motion‐sensitive cameras in Switzerland

AbstractWildlife conservation and management need accurate methods for population survey and monitoring. Absolute counts of roe deer populations (Capreolus capreolus) are not possible, but the rapid advancement of motion‐sensitive camera technologies and new analytical approaches might potentially lead to more precise estimates at lower costs compared to traditional survey methods. We applied spatially explicit photographic capture–recapture models (SCR) in the Lake Geneva basin, Switzerland, from 25 April to 20 September 2018 to estimate roe deer densities in a pilot survey. We investigated the effect of survey duration and camera density on male roe deer density estimates to select the sampling design that produced density estimates with sufficient accuracy and precision at lower costs (i.e., material, fieldwork, data processing, and analyses). Males could be identified based on their antlers, which allowed us to apply SCR to estimate their density. Because females could not be identified individually, we inferred the overall roe deer density (adult and sub‐adult roe deer) based on the sex ratio estimated from motion‐sensitive camera photos. According to the results of sub‐sampling simulations and by taking into account the financial costs associated with fieldwork and analyses, we conclude that 20 motion‐sensitive cameras set over 20 nights (i.e., the 20/20 method) is a good compromise to provide reliable estimates of male roe deer density. Furthermore, studies estimating overall roe deer density using SCR and sex ratio estimates should be conducted from mid‐August to the end of October just after rutting season and the peak of yearling dispersal, when the movement rates of males and females, and hence their detection probabilities, are similar and when males are still carrying their antlers. This approach was successfully applied in 4 selected study areas with contrasting roe deer management regimes, resulting in overall roe deer density estimates ranging from 3.9 ± 1.3 (SE) deer/km2 forest to 22.5 ± 6.1 deer/km2 forest. Our study provides a valuable and cost‐effective approach using photographic SCR methodology and sex ratio information to calculate roe deer density estimates that can be used in management measures such as defining hunting quotas.

Revisiting animal photo‐identification using deep metric learning and network analysis

Abstract An increasing number of ecological monitoring programmes rely on photographic capture–recapture of individuals to study distribution, demography and abundance of species. Photo‐identification of individuals can sometimes be done using idiosyncratic coat or skin patterns, instead of using tags or loggers. However, when performed manually, the task of going through photographs is tedious and rapidly becomes too time‐consuming as the number of pictures grows. Computer vision techniques are an appealing and unavoidable help to tackle this apparently simple task in the big‐data era. In this context, we propose to revisit animal re‐identification using image similarity networks and metric learning with convolutional neural networks (CNNs), taking the giraffe as a working example. We first developed an end‐to‐end pipeline to retrieve a comprehensive set of re‐identified giraffes from about 4,000 raw photographs. To do so, we combined CNN‐based object detection, SIFT pattern matching and image similarity networks. We then quantified the performance of deep metric learning to retrieve the identity of known individuals, and to detect unknown individuals never seen in the previous years of monitoring. After a data augmentation procedure, the re‐identification performance of the CNN reached a Top‐1 accuracy of about 90%, despite the very small number of images per individual in the training dataset. While the complete pipeline succeeded in re‐identifying known individuals, it slightly under‐performed with unknown individuals. Fully based on open‐source software packages, our work paves the way for further attempts to build automatic pipelines for re‐identification of individual animals, not only in giraffes but also in other species.

Parameter affecting the I3S algorithm reliability: how does correcting for body curvature affect individual recognition?

Abstract ContextIn recent years, multiple computer algorithms, which allow us to perform photographic capture–recapture analysis, have been developed. Their massive application, also in wildlife demographic and ecological studies, is largely due to the fact that these tools are non-invasive and non-expensive. To maximise the performance of these programs, it is essential to have a good photo-standardisation so as to avoid bias in the results. A lot of ‘non-standardised’ photos are not usable for capture–mark–recapture (CMR) analysis, entailing the loss of potentially exploitable data. AimsNo study has accurately investigated the effect of the corporal bending of an animal on the performance of the interactive individual identification system (I3S) algorithm. For this reason, we assessed the effect of this photographic standardisation parameter (PSP) on the reliability of this algorithm. MethodsWe assessed the effect of the body position of Triturus cristatus between capture and recapture photos on the error rates of a group of standardised pictures, performing a generalised linear model analysis. We have also evaluated the effect of image correction (i.e. straightening of newts’ bodies) on the error rates (expressed by false rejection rates, FRRs) of the first (standardised) photo-group (G1) and of a non-standardised photo-group (G2). To perform this, we used I3S-Pattern+ for the photo-matching analysis and I3S-Straighten for the correction of the pictures. Key resultsThe difference of body angles between capture and recapture pictures had a significantly increased error rates in G1. Digital correction of body bending reduced the error rates. For the pictures where corporal bending was not digitally corrected, the top 20 FRRs were 0.38 and 0.33 for G1 and G2 respectively. For corrected (straightened) pictures, the top 20 FRRs were 0.026 and 0.15. ConclusionsOur findings showed a high impact of newt corporal bending and photographic treatment on the I3S algorithm reliability. ImplicationsWe provide some recommendations to avoid or minimise the effects of this PSP and improve photo-standardisation during and after CMR studies of species of Urodela. In this way, pictures that would be unusable in photo-matching software under current practice could become usable, increasing the available data to conduct a survey.

Photographic Capture-Recapture for Free-Roaming Dog Population Estimation: Is It Possible to Optimize the Dog Photo-Identification?

It is important to estimate the population of free-roaming dogs because this information helps with planning and developing appropriate dog population control strategies as well as with epidemiological surveillance services. The photographic capture-recapture method has been used to estimate animal populations, including free-roaming dogs.

Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies.

Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state‐of‐the‐art photo‐matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo‐matching algorithms; Wild‐ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel‐based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates.

Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources

Estimating animal distributions and abundances over large regions is of primary interest in ecology and conservation. Specifically, integrating data from reliable but expensive surveys conducted at smaller scales with cost-effective but less reliable data generated from surveys at wider scales remains a central challenge in statistical ecology. In this study, we use a Bayesian smoothing technique based on a conditionally autoregressive (CAR) prior distribution and Bayesian regression to address this problem. We illustrate the utility of our proposed methodology by integrating (i) abundance estimates of tigers in wildlife reserves from intensive photographic capture–recapture methods, and (ii) estimates of tiger habitat occupancy from indirect sign surveys, conducted over a wider region. We also investigate whether the random effects which represent the spatial association due to the CAR structure have any confounding effect on the fixed effects of the regression coefficients.

Examining disease prevalence for species of conservation concern using non‐invasive spatial capture–recapture techniques

Summary 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 ( = 99·22 km, SD = 3·72), and surveyed for giraffes for 4 months. From these surveys, we developed a data base of spatially explicit giraffe photographs. We processed these photographs 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, 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.

Survival estimation of a cryptic antelope via photographic capture-recapture

Survival estimation of a cryptic antelope via photographic capture-recapture

The Use of Camera Traps for Monitoring the Population of Long-Tailed Gorals

The long-tailed goral (Nemorhaedus caudatus) is a rare montane ungulate species with a patchy distribution. In the Sikhote-Alin Reserve, gorals occupy the northern part of their range, concentrated primarily in a small coastal area (6.4km2) in Abrek Urochishe. Our pilot study tested the feasibility of individual photo-identification of gorals and population size estimation using the capture–recapture method. We used 10 camera traps spaced 0.6–2km apart on coastal slopes to monitor the gorals. Four additional cameras were placed at the Reserve boundaries, mainly for law enforcement purposes, such as documenting trespassers. Between June 1 and December 31, 2013, we collected nearly 3000 photographs of gorals, 500 photographs of other wildlife, and 12 images of illegal activities within the Reserve. The total sampling effort was 1870 camera days. Photo data showed that goral horns are reliable biometric identifiers, distinguishable by size, shape, pattern, and the number of rings. The proportion of individually identified gorals in our photos was 0.64 (SE=0.05). Most individuals (45) were marked (i.e., first detected on camera) in the fall; therefore, preliminary estimates of the goral population size were made between October 11 and December 20, 2013. A closure test confirmed that the population was, in fact, closed (z=−2.670, P=0.004). The best-fit closed population multiple recapture model for our data was the heterogeneity model Mh (programme CAPTURE), which assumes an unequal capture probability (χ2=112.19; d.f.=9; P=0.000). The average goral capture probability was 0.16, and the corresponding population size was estimated at 90 individuals (SE=6.91; 95% CI: 77–125 individuals). The average goral population density in a 3.5km2 effective sampled area (56% of the entire plot area) was 25 individuals/km2 (SE=5.62). Extrapolation to locations that lacked data suggests that Abrek Urochishe supports a goral population of 160 individuals. Our results demonstrate that camera trap data can be used for photographic capture–recapture sampling of goral populations. This approach may be more effective than traditional visual surveys of montane ungulates that tend to underestimate the population abundance. The use of camera traps will undoubtedly enhance goral monitoring efforts, aiding in the conservation of this rare species.

Comparing capture-recapture, mark-resight, and spatial mark-resight models for estimating puma densities via camera traps

Abstract Camera-trapping surveys, in combination with traditional capture–recapture or spatially explicit capture–recapture techniques, have become popular for estimating the density of individually identifiable carnivores. When only a portion of the population is uniquely identifiable, traditional and spatial mark–resight models provide a viable alternative. We reanalyzed a data set that used photographic capture–recapture methods to estimate the densities of pumas (Puma concolor) across 3 study sites in Belize, Argentina, and Bolivia using newer, more-advanced modeling including spatial and nonspatial mark–resight techniques. Additionally, we assessed how photo identification influenced density estimates by comparing estimates based on capture histories constructed by 3 independent investigators. We estimated the abundances of pumas using mark–resight models in program MARK and then estimated densities ad hoc. We also estimated densities directly using spatial mark–resight models implemented in a Bayesi...

Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture–recapture

We studied the influence of surveyed area size on density estimates by means of camera-trapping in a low-density felid population (1-2 individuals/100 km(2) ). We applied non-spatial capture-recapture (CR) and spatial CR (SCR) models for Eurasian lynx during winter 2005/2006 in the northwestern Swiss Alps by sampling an area divided into 5 nested plots ranging from 65 to 760 km(2) . CR model density estimates (95% CI) for models M0 and Mh decreased from 2.61 (1.55-3.68) and 3.6 (1.62-5.57) independent lynx/100 km(2) , respectively, in the smallest to 1.20 (1.04-1.35) and 1.26 (0.89-1.63) independent lynx/100 km(2) , respectively, in the largest area surveyed. SCR model density estimates also decreased with increasing sampling area but not significantly. High individual range overlaps in relatively small areas (the edge effect) is the most plausible reason for this positive bias in the CR models. Our results confirm that SCR models are much more robust to changes in trap array size than CR models, thus avoiding overestimation of density in smaller areas. However, when a study is concerned with monitoring population changes, large spatial efforts (area surveyed ≥760 km(2) ) are required to obtain reliable and precise density estimates with these population densities and recapture rates.

Monitoring an Endangered savannah ungulate, Grevy's zebraEquus grevyi: choosing a method for estimating population densities

AbstractMethods that accurately estimate animal abundance or density are crucial for wildlife management. Although numerous techniques are available, there have been few comparisons of the precision and cost-effectiveness of different approaches. We assess the precision and cost of three methods for estimating densities of the Endangered Grevy's zebraEquus grevyi. We compare distance sampling and photographic capture–recapture, and a new technique, the random encounter model (REM) that uses camera-trap encounter rates to estimate density. All three methods provide comparable density estimates for Grevy's zebra and are preferable to the common practice of raw counts. Photographic capture–recapture is the most precise and line-transect distance sampling the least precise. Line transects and photographic capture–recapture surveys are cost-effective in the first year and REM is most cost-effective in the long-term. The methods used here for Grevy's zebra may be applied to other rangeland ungulates. We suggest that for single species monitoring programmes in which individuals can be identified, photographic capture–recapture surveys may be the preferred method for estimating wildlife abundances. When encounter rates are low, distance sampling lacks the precision of the other methods but its cost advantage may make it appropriate for long-term or multi-species monitoring programmes. The REM is an efficient and precise method of estimating densities but has high initial equipment costs. We believe REM has the potential to work well for many species but it requires independent estimates of animal movements and group size.

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

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

Photographic Capture-Recapture Sampling for Assessing Populations of the Indian Gliding Lizard Draco dussumieri

The usage of invasive tagging methods to assess lizard populations has often been criticised, due to the potential negative effects of marking, which possibly cause increased mortality or altered behaviour. The development of safe, less invasive techniques is essential for improved ecological study and conservation of lizard populations. In this study, we describe a photographic capture-recapture (CR) technique for estimating Draco dussumieri (Agamidae) populations. We used photographs of the ventral surface of the patagium to identify individuals. To establish that the naturally occurring blotches remained constant through time, we compared capture and recapture photographs of 45 pen-marked individuals after a 30 day interval. No changes in blotches were observed and individual lizards could be identified with 100% accuracy. The population density of D. dussumieri in a two hectare areca-nut plantation was estimated using the CR technique with ten sampling occasions over a ten day period. The resulting recapture histories for 24 individuals were analysed using population models in the program CAPTURE. All models indicated that nearly all individuals were captured. The estimated probability for capturing D. dussumieri on at least one occasion was 0.92 and the estimated population density was 13±1.65 lizards/ha. Our results demonstrate the potential for applying CR to population studies in gliding lizards (Draco spp.) and other species with distinctive markings.

Density estimations of the Eurasian lynx (Lynx lynx) in the Swiss Alps

Use of photographic capture—recapture analyses to estimate abundance of species with distinctive natural marks has become an important tool for monitoring rare or cryptic species, or both. Two different methods are available to estimate density: nonspatial capture—recapture models where the trap polygon is buffered with the half or full mean maximum distance moved by animals captured at more than 1 trap (1/2 MMDM or MMDM, respectively); or spatial capture—recapture (SCR) models that explicitly incorporate movement into the model. We used data from radiotracked Eurasian lynx (Lynx lynx) in the northwestern Swiss Alps (NWSA) during a low (1.0 lynx/100 km2) and a high (1.9–2.1 lynx/100 km2) lynx population density to test if lynx space use was density dependent. Second, we compared lynx density estimates resulting from these 2 different methods using camera-trapping data collected during winters 2007–2008 and 2009–2010 in the NWSA. Our results indicated lynx space use was negatively correlated with density. Lynx density estimates in all habitats using MMDM (0.86 and 0.97 lynx/100 km2 in winters 2007–2008 and 2009–2010, respectively) were significantly lower than SCR model estimates, whereas there was no significant difference between SCR model (1.47 and 1.38) and 1/2 MMDM (1.37 and 1.51) density estimates. In the NWSA, which currently harbors the most abundant lynx population in Switzerland, 1/2 MMDM and SCR models provided more realistic lynx density estimates compared to the MMDM, which lies in the lower range of densities. Overall, the SCR model is preferable because it considers animal movements explicitly and is not biased by an informal estimation of the effective sampling area.

Density estimation of Asian bears using photographic capture–recapture sampling based on chest marks

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 km 2 , and Khlong Samor-Pun, 40 km 2 ) 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 5 3.04) and 5.8 (SE 5 2.31) black bears per 100 km 2 and 5.9 (SE 5 3.07) and 4.3 (SE 5 2.32) sun bears per 100 km 2 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.

Bayesian estimation of humpback whale (Megaptera novaeangliae) population abundance and movement patterns in southeastern Alaska

We used a mechanistic movement model within a Bayesian framework to estimate survival, abundance, and rate of increase for a population of humpback whales ( Megaptera novaeangliae ) subject to a long-term photographic capture–recapture effort in southeastern Alaska, USA (SEAK). Multiple competing models were fitted that differed in movement, recapture rates, and observation error using deviance information criterion. The median annual survival probability in the selected model was 0.996 (95% central probability interval (CrI): 0.984, 0.999), which is among the highest reported for this species. Movement among areas was temporally dynamic, although whales exhibited high area fidelity (probability of returning to same area of ≥0.75) throughout the study. Median abundance was 1585 whales in 2008 (95% CrI: 1455, 1644). Incorporating an abundance estimate of 393 (95% confidence interval: 331, 455) whales from 1986, the median rate of increase was 5.1% (95% CrI: 4.4%, 5.9%). Although applied here to cetaceans in SEAK, the framework provides a flexible approach for estimating mortality and movement in populations that move among sampling areas.

Optimizing individual identification and survey effort for photographic capture–recapture sampling of species with temporally variable morphological traits

AbstractEndangered, wide‐ranging megafauna have many threats to contend with during their struggle for survival in an ever‐increasing human dominance of the environment. Reliable monitoring of endangered large mammal populations is therefore a critical conservation requirement. Photographic capture–recapture (CR) techniques have opened up avenues for population monitoring of individually recognizable large mammal species. The efficient application of these techniques, however, can be constrained by challenges in reliably identifying individuals arising from the use of multiple, and potentially variable traits, as well as issues of temporal sampling of populations in the field. We address these key problems by describing an automated process of rapidly identifying individualAsian elephants (Elephas maximus) from photographs, and comparing resultantCR‐based population parameter estimates with those obtained using supervised visual identification of individuals. In addition, we assess the temporal effort necessary for robust estimation of demographic parameters in our study population. Morphological traits that maintain constancy over time, including variations in tusk characteristics, and ear fold and lobe shape, proved the most reliable for individual identification and subsequent estimation of population parameters. The use of temporally variable traits contributed to high probabilities of misidentification and biased estimates of population size. We found a minimum of seven sampling occasions necessary for reliable population estimation. Our study contributes to design issues forCRstudies by providing insights into optimality of sampling effort such that precision of parameter estimates are not compromised while minimizing survey costs. We demonstrate the importance of accurate individual identification in the context of such studies and recommend the use of fixed morphological traits as the optimal individual identification strategy for species where animals are distinguished on the basis of multiple attributes, including some that may be variable over time.

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

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