Methods of estimating ungulate populations in tropical forests

Pellet group counts, drive counts and track counts were used to estimate population abundances of one small and four large species of duiker (Cephalophus sp.) in a moist evergreen forest in northeastern Zaire. Procedures to develop estimates based on line transect counts of pellet groups are described. The Fourier Series estimator proved to be useful for estimating densities of pellet groups. Comparisons were made between population abundance in heavily and lightly hunted areas, and between small and large species. The three census methods were consistent in showing no significant difference between animal abundance in the hunted populations or between abundances of the smaller species relative to the larger species. All methods showed potential as population indices for assessing trends of rare and elusive species in forest environments. Drive counts and pellet group counts may also be useful for estimating densities.     

Animal Density and Track Counts: Understanding the Nature of Observations Based on Animal Movements

Counting animals to estimate their population sizes is often essential for their management and conservation. Since practitioners frequently rely on indirect observations of animals, it is important to better understand the relationship between such indirect indices and animal abundance. The Formozov-Malyshev-Pereleshin (FMP) formula provides a theoretical foundation for understanding the relationship between animal track counts and the true density of species. Although this analytical method potentially has universal applicability wherever animals are readily detectable by their tracks, it has long been unique to Russia and remains widely underappreciated. In this paper, we provide a test of the FMP formula by isolating the influence of animal travel path tortuosity (i.e., convolutedness) on track counts. We employed simulations using virtual and empirical data, in addition to a field test comparing FMP estimates with independent estimates from line transect distance sampling. We verify that track counts (total intersections between animals and transects) are determined entirely by density and daily movement distances. Hence, the FMP estimator is theoretically robust against potential biases from specific shapes or patterns of animal movement paths if transects are randomly situated with respect to those movements (i.e., the transects do not influence animals’ movements). However, detectability (the detection probability of individual animals) is not determined simply by daily travel distance but also by tortuosity, so ensuring that all intersections with transects are counted regardless of the number of individual animals that made them becomes critical for an accurate density estimate. Additionally, although tortuosity has no bearing on mean track encounter rates, it does affect encounter rate variance and therefore estimate precision. We discuss how these fundamental principles made explicit by the FMP formula have widespread implications for methods of assessing animal abundance that rely on indirect observations.     

Review of Some Important Techniques in Wildlife Sampling and Sampling Errors

This paper reviews some of the important methods for estimating animal numbers or densities based on (i) direct counts of population units as used in quadrat, strip, line-transect and line-intercept sampling and (ii) indirect counts and indices, such as capture-mark recapture, change-in-ratio, catch-effort methods and indices based on track counts, call, roadside and pellet-group counts, etc.     

Evaluating Methods for Counting Cryptic Carnivores

ABSTRACT Numerous techniques have been proposed to estimate carnivore abundance and density, but few have been validated against populations of known size. We used a density estimate established by intensive monitoring of a population of radiotagged leopards (Panthera pardus) with a detection probability of 1.0 to evaluate efficacy of track counts and camera-trap surveys as population estimators. We calculated densities from track counts using 2 methods and compared performance of 10 methods for calculating the effectively sampled area for camera-trapping data. Compared to our reference density (7.33 ± 0.44 leopards/100 km²), camera-trapping generally produced more accurate but less precise estimates than did track counts. The most accurate result (6.97 ± 1.88 leopards/100 km²) came from camera-trap data with a sampled area buffered by a boundary strip representing the mean maximum distance moved by leopards outside the survey area (MMDMOSA) established by telemetry. However, contrary to recent suggestions, the traditional method of using half the mean maximum distance moved from photographic recaptures did not result in gross overestimates of population density (6.56 ± 1.92 leopards/100 km²) but rather displayed the next best performance after MMDMOSA. The only track-count method comparable to reference density employed a capture-recapture framework applied to data when individuals were identified from their tracks (6.45 ± 1.43 leopards/100 km²) but the underlying assumptions of this technique limit more widespread application. Our results demonstrate that if applied correctly, camera-trap surveys represent the best balance of rigor and cost-effectiveness for estimating abundance and density of cryptic carnivore species that can be identified individually.     

Assessing the performance of index calibration survey methods to monitor populations of wide‐ranging low‐density carnivores

Apex carnivores are wide‐ranging, low‐density, hard to detect, and declining throughout most of their range, making population monitoring both critical and challenging. Rapid and inexpensive index calibration survey (ICS) methods have been developed to monitor large African carnivores. ICS methods assume constant detection probability and a predictable relationship between the index and the actual population of interest. The precision and utility of the resulting estimates from ICS methods have been questioned. We assessed the performance of one ICS method for large carnivores—track counts—with data from two long‐term studies of African lion populations. We conducted Monte Carlo simulation of intersections between transects (road segments) and lion movement paths (from GPS collar data) at varying survey intensities. Then, using the track count method we estimated population size and its confidence limits. We found that estimates either overstate precision or are too imprecise to be meaningful. Overstated precision stemmed from discarding the variance from population estimates when developing the method and from treating the conversion from tracks counts to population density as a back‐transformation, rather than applying the equation for the variance of a linear function. To effectively assess the status of species, the IUCN has set guidelines, and these should be integrated in survey designs. We propose reporting the half relative confidence interval width (HRCIW) as an easily calculable and interpretable measure of precision. We show that track counts do not adhere to IUCN criteria, and we argue that ICS methods for wide‐ranging low‐density species are unlikely to meet those criteria. Established, intensive methods lead to precise estimates, but some new approaches, like short, intensive, (spatial) capture–mark–recapture (CMR/SECR) studies, aided by camera trapping and/or genetic identification of individuals, hold promise. A handbook of best practices in monitoring populations of apex carnivores is strongly recommended.     

Estimating densities of large herbivores in tropical forests: Rigorous evaluation of a dung‐based method

When sighting‐based surveys to estimate population densities of large herbivores in tropical dense forests are not practical or affordable, surveys that rely on animal dung are sometimes used. This study tested one such dung‐based method by deriving population densities from observed dung densities of six large herbivores (chital, elephant, gaur, muntjac, sambar, and wild pig) in two habitats, dry deciduous forests (DDF) and moist deciduous forests (MDF), within Nagarahole National Park, southern India. Using the program DUNGSURV, dung pile counts, decay rates estimated from field experiments, and defecation rates derived from literature were analyzed together by a model that allows for random events affecting dung decay. Densities of chital were the highest, followed by sambar. Wild pig densities were similar in the two habitats, sambar densities were higher in DDF, and densities of the other species were higher in MDF than in DDF. We compared DUNGSURV estimates with densities estimated using distance sampling in the same season. DUNGSURV estimates were substantially higher for all species in both habitats. These differences highlight the challenges that researchers face in computing unbiased estimates of dung decay rates and in relying on defecation rates from literature. Besides the elephant, this study is the first to rigorously test the efficacy of using a dung‐based approach to estimate densities of large herbivore species in Asia, and based on this evaluation, we provide specific recommendations to address issues that require careful consideration before observed dung densities are used to derive animal densities. Our results underline the need for an experimental study of a known population in a fenced reserve to validate the true potential of using dung‐based approaches to estimate population densities.     

Assessing the accuracy of plotless density estimators using census counts to refine population estimates of the vultures of Kruger National Park

Breeding population estimates for three vulture species in Kruger National Park (KNP), South Africa, were made in 2013 using data from aerial censuses and a plotless density estimator (PDE). PDEs are distance-based methods used to assess sparse populations unsuitable for plot-based methods. A correction factor was applied to the 2013 estimates to reflect the difference between the survey counts and the PDE figures. We flew additional censuses across most of KNP and counted all visible nests to assess the 2013 estimates. Survey counts were within 95% confidence limits of corrected PDE estimates for White-backed Vulture Gyps africanus (count: 892; estimate: 904 [95% CI ±162]), at the limit of confidence for White-headed Vulture Trigonoceps occipitalis (count: 48; estimate: 60 [±13]) and outside confidence limits for Lappet-faced Vulture Torgos tracheliotos (count: 44; estimate: 78 [±18]). Uncorrected PDE estimates accurately reflected White-headed and Lappet-faced Vulture nest counts. The clustered patterns of White-backed Vulture nests and dispersed patterns of White-headed and Lappet-faced Vulture nests offer an explanation for these results and means that corrected PDE densities are inaccurate for estimating dispersed nests but accurate for estimating clustered nests. Using PDE methods, aerial surveys over ～35% of KNP are probably sufficient to assess changes in these vulture populations over time. Our results highlight these globally important breeding populations.     

Methodology matters when estimating deer abundance: a global systematic review and recommendations for improvements

Deer (Cervidae) are key components of many ecosystems and estimating deer abundance or density is important to understanding these roles. Many field methods have been used to estimate deer abundance and density, but the factors determining where, when, and why a method was used, and its usefulness, have not been investigated. We systematically reviewed journal articles published during 2004–2018 to evaluate spatio-temporal trends in study objectives, methodologies, and deer abundance and density estimates, and determine how they varied with biophysical and anthropogenic attributes. We also reviewed the precision and bias of deer abundance estimation methods. We found 3,870 deer abundance and density estimates. Most estimates (58%) were for white-tailed deer (Odocoileus virginianus), red deer (Cervus elaphus), and roe deer (Capreolus capreolus). The 6 key methods used to estimate abundance and density were pedestrian sign (track or fecal) counts, pedestrian direct counts, vehicular direct counts, aerial direct counts, motion-sensitive cameras, and harvest data. There were regional differences in the use of these methods, but a general pattern was a temporal shift from using harvest data, pedestrian direct counts, and aerial direct counts to using pedestrian sign counts and motion-sensitive cameras. Only 32% of estimates were accompanied by a measure of precision. The most precise estimates were from vehicular spotlight counts and from capture–recapture analysis of images from motion-sensitive cameras. For aerial direct counts, capture–recapture methods provided the most precise estimates. Bias was robustly assessed in only 16 studies. Most abundance estimates were negatively biased, but capture–recapture methods were the least biased. The usefulness of deer abundance and density estimates would be substantially improved by 1) reporting key methodological details, 2) robustly assessing bias, 3) reporting the precision of estimates, 4) using methods that increase and estimate detection probability, and 5) staying up to date on new methods. The automation of image analysis using machine learning should increase the accuracy and precision of abundance estimates from direct aerial counts (visible and thermal infrared, including from unmanned aerial vehicles [drones]) and motion-sensitive cameras, and substantially reduce the time and cost burdens of manual image analysis.     

Random Encounter and Staying Time Model Testing with Human Volunteers

Ecology and management programs designed to track population trends over time increasingly are using passive monitoring methods to estimate terrestrial mammal densities. Researchers use motion-sensing cameras in mammal studies because they are cost-effective and advances in statistical methods incorporate motion-sensing camera data to estimate mammal densities. Density estimation involving unmarked individuals, however, remains challenging and empirical tests of statistical models are relatively rare. We tested the random encounter and staying time model (REST), a new means of estimating the density of an unmarked population, using human volunteers and simulated camera surveys. The REST method produced unbiased estimates of density, regardless of changes in human abundance, movement rates, home range sizes, or simulated camera effort. These advances in statistical methods when applied to motion-sensing camera data provide innovative avenues of large-mammal monitoring that have the potential to be applied to a broad spectrum of conservation and management studies, provided assumptions for the REST method are rigorously tested and met. © 2020 The Wildlife Society.     

Estimating red deer abundance in a wide range of management situations in Mediterranean habitats

When human interventions interfere with the natural regulation of wildlife populations by favouring some species, overabundance can emerge. We evaluated different methods of estimating red deer abundance in a wide range of population densities from southern Spain. Distance sampling estimates were used as the reference method across 22 localities and were compared with two kilometric abundance indices (KAIs), four indices based on pellet group counts and two browsing indices (BWIs). The average red deer density estimated by distance sampling was 19.51±3.19 deer per 100 ha, showing a wide range across the study area (0.04–66.77). Distance sampling estimates correlated with the KAIs, pellet group-based index and the BWI. The agreement with distance sampling improved when groups were used instead of individuals in the KAIs, when the minimum pellet group size was fixed at 20 pellets in the dropping counts, and when only highly palatable species were used in the BWI. Thus, several direct and indirect methods can estimate red deer abundance in Mediterranean habitats from Southern Spain with appropriate modifications.     

Integrating sign surveys and telemetry data for estimating brown bear (Ursus arctos) density in the Romanian Carpathians

Accurate population size estimates are important information for sustainable wildlife management. The Romanian Carpathians harbor the largest brown bear (Ursus arctos) population in Europe, yet current management relies on estimates of density that lack statistical oversight and ignore uncertainty deriving from track surveys. In this study, we investigate an alternative approach to estimate brown bear density using sign surveys along transects within a novel integration of occupancy models and home range methods. We performed repeated surveys along 2‐km segments of forest roads during three distinct seasons: spring 2011, fall‐winter 2011, and spring 2012, within three game management units and a Natura 2000 site. We estimated bears abundances along transects using the number of unique tracks observed per survey occasion via N‐mixture hierarchical models, which account for imperfect detection. To obtain brown bear densities, we combined these abundances with the effective sampling area of the transects, that is, estimated as a function of the median (± bootstrapped SE) of the core home range (5.58 ± 1.08 km²) based on telemetry data from 17 bears tracked for 1‐month periods overlapping our surveys windows. Our analyses yielded average brown bear densities (and 95% confidence intervals) for the three seasons of: 11.5 (7.8–15.3), 11.3 (7.4–15.2), and 12.4 (8.6–16.3) individuals/100 km². Across game management units, mean densities ranged between 7.5 and 14.8 individuals/100 km². Our method incorporates multiple sources of uncertainty (e.g., effective sampling area, imperfect detection) to estimate brown bear density, but the inference fundamentally relies on unmarked individuals only. While useful as a temporary approach to monitor brown bears, we urge implementing DNA capture–recapture methods regionally to inform brown bear management and recommend increasing resources for GPS collars to improve estimates of effective sampling area.     

Estimating abundance for a savanna elephant population using mark–resight methods: a case study for the Tembe Elephant Park, South Africa

Elephants living in dense woodlands are difficult to count. Many elephant populations in Africa occur in such conditions. Estimates of these populations based on total counts, aerial counts and dung counts often lack information on precision and accuracy. We use standard mark–recapture field methods to obtain estimates of population size with associated confidence limits. We apply this approach to a closed elephant population in the Tembe Elephant Park (300 km²), South Africa. A registration count completed in 4 months gives a known population size. We evaluate mark–recapture models against the known population size. Individual identification profiles obtained for elephants during the registration count and mark–recapture events indicate that at least 167 elephants live in the park. We consider this value as an estimate of the minimum number alive. We include 189 sightings of bulls and 37 sightings of breeding herds in the mark–recapture modelling. Of the models we test (Petersen, Schnabel, Schumacher, Jolly–Seber, Bowden's, Poisson and negative binomial), Bowden's gives an estimate closest to the registration count. Assumptions of the model are not violated. For all models except one (negative binomial), our estimates improve with increased sampling intensity. Confidence intervals do not improve with increased effort except for the Schnabel model. Mark–recapture methods should be considered as reliable estimators of population size for elephants occurring in dense woodlands and forests when other methods cannot be relied on.     

Use of track counts and camera traps to estimate the abundance of roe deer in North-Eastern Italy: are they effective methods?

Population density of European roe deer Capreolus capreolus was estimated in six forest areas of North-Eastern Italy through the use of different methods. The most effective method to estimate a population density is always case-dependent and, thus, varies across study areas. Particularly, drive count and vantage point count estimates (i.e. counts by hunters) have been reported to be the most effective to assess deer densities in woodlands, but they require a high volunteer human presence, which limit their feasibility. Results of count by hunters were thus compared with estimates obtained through camera trapping and track counts. Surveys were all carried out between 2014 and 2015. The three-used method provided us with comparable density results, suggesting that they all may be applied in the study area. Track-count survey was shown to be—with equal effectiveness—the cheapest method to infer roe deer density in forest areas (i.e. near 28% cheaper than camera trapping). As to our study sites, we therefore suggest that the proposal of track-count method might provide wildlife managers with a cost-effective alternative to other count methods to estimate roe deer population density. However, it is noteworthy that track-count method may also lead to lower density estimates than the drive counts; an apparent difference in the accuracy between methods needs to be considered when choosing for a certain count method.     

Efficient,Noninvasive Genetic Sampling for Monitoring Reintroduced Wolves

ABSTRACT Traditional methods of monitoring gray wolves (Canis lupus) are expensive and invasive and require extensive efforts to capture individual animals. Noninvasive genetic sampling (NGS) is an alternative method that can provide data to answer management questions and complement already-existing methods. In a 2-year study, we tested this approach for Idaho gray wolves in areas of known high and low wolf density. To focus sampling efforts across a large study area and increase our chances of detecting reproductive packs, we visited 964 areas with landscape characteristics similar to known wolf rendezvous sites. We collected scat or hair samples from 20% of sites and identified 122 wolves, using 8–9 microsatellite loci. We used the minimum count of wolves to accurately detect known differences in wolf density. Maximum likelihood and Bayesian single-session population estimators performed similarly and accurately estimated the population size, compared with a radiotelemetry population estimate, in both years, and an average of 1.7 captures per individual were necessary for achieving accurate population estimates. Subsampling scenarios revealed that both scat and hair samples were important for achieving accurate population estimates, but visiting 75% and 50% of the sites still gave reasonable estimates and reduced costs. Our research provides managers with an efficient and accurate method for monitoring high-density and low-density wolf populations in remote areas.     

Estimating Orangutan Densities Using the Standing Crop and Marked Nest Count Methods: Lessons Learned for Conservation

Reliable estimates of great ape abundance are needed to assess distribution, monitor population status, evaluate conservation tactics, and identify priority populations for conservation. Rather than using direct counts, surveyors often count ape nests. The standing crop nest count (SCNC) method converts the standing stock of nests into animal densities using a set of parameters, including nest decay rate. Nest decay rates vary greatly over space and time, and it takes months to calculate a site-specific value. The marked nest count (MNC) method circumvents this issue and only counts new nests produced during a defined period. We compared orangutan densities calculated by the two methods using data from studies in Sumatra and Kalimantan, Indonesia. We show how animal densities calculated using nest counts should be cautiously interpreted when used to make decisions about management or budget allocation. Even with site-specific decay rates, short studies using the SCNC method may not accurately reflect the current population unless conducted at a scale sufficient to include wide-ranging orangutan movement. Density estimates from short studies using the MNC method were affected by small sample sizes and by orangutan movement. To produce reliable results, the MNC method may require a similar amount of effort as the SCNC method. We suggest a reduced reliance on the traditional line transect surveys in favor of feasible alternative methods when absolute abundance numbers are not necessary or when site-specific nest decay rates are not known. Given funding constraints, aerial surveys, reconnaissance walks, and interview techniques may be more cost-effective means of accomplishing some survey goals.     

Estimating roe deer abundance from pellet group counts in Spain: An assessment of methods suitable for Mediterranean woodlands

Despite of the generalized expansion of wild ungulates in Europe, roe deer (Capreolus capreolus) is experiencing contrasted population trends; it is expanding in some regions while declining in others likely due to the expansion of other deer species. In both extremes, reliable methods to estimate population abundance are required. We evaluated different methods of estimating deer abundance in Mediterranean woodlands based on pellet group counts. Distance Sampling applied to pellet counts and a new easier and cost-effective method based on strip-variable transect counts (FST) were assessed comparing their estimates (pellet group density) with the abundance indices obtained from traditionally used reference methods (faecal standing crop) in 61 localities (n = 183 surveys). The average roe deer density estimated from faecal standing crop was 5.56 ± 0.75 (range 0.01–20.74) deer per 100 ha. Distance Sampling and FST estimates correlated with reference methods. As a first conclusion it may be noted that all indirect methods used here can be used to estimate roe deer abundance. The selection of a given method based on pellet counts to estimate roe deer population abundance should take into account the specific objectives of the research, resources available, and the timescale in which the information is required. Among them, Distance Sampling may be used when human resources and skills are enough but FST is a rapid and efficient alternative to estimate pellet group density when they are not.     

Using the Formozov–Malyshev–Pereleshin formula to convert mammal spoor counts into density estimates for long‐term community‐level monitoring

Finding an appropriate method to monitor a wide range of mammal species simultaneously is notoriously difficult, as each method has its limitations. Here, we examine a formula, known as the Formozov–Malyshev–Pereleshin (FMP), which uses mean daily travel distances (day ranges) to convert spoor counts into density estimates. Availability of accurate estimates of day ranges is a limitation of the FMP formula. Here, we used allometry to estimate day ranges for those species that lacked empirical movement data and general additive models (GAM) to assess trends in density estimates. With this approach, we derived density estimates for 10 mammal species, regardless of whether they were abundant, or rare and elusive (e.g. carnivores). General additive models suggest that six species are stable or increasing, and four declining, although all nonsignificantly. Use of allometric estimates in lieu of empirical estimates led to falsely increased precision in density estimates, highlighting the need to fill the knowledge gap in movement ecology for certain species. Simulations were used to examine error introduced into trend estimates by this bias. We conclude that the FMP formula, when properly employed, can be an efficient method for simultaneous monitoring of multispecies in different functional groups.     

Population Estimates of Snowshoe Hares in the Southern Rocky Mountains

Abstract: In 1999 Canada lynx (Lynx canadensis) were reintroduced to the southern Rocky Mountains and in 2000 the species was listed as threatened under the Endangered Species Act in the contiguous United States (Colorado Division of Wildlife 2000, U.S. Fish and Wildlife Service 2000). To better evaluate the progress of this reintroduction, we conducted field studies to estimate population densities of snowshoe hares (Lepus americanus), the primary prey of lynx in Colorado, USA. We conducted our field studies in southwestern Colorado in winters 2002 and 2003. We estimated population densities in forested stands of mature Engelmann spruce (Picea engelmannii)-subalpine fir (Abies lasiocarpa) and mature lodgepole pine (Pinus contorta) using mark-recapture data and 3 methods for estimating effective area trapped: half trap interval, mean maximum distance moved (MMDM), and half MMDM. In Engelmann spruce-subalpine fir, we found density estimates ranged from 0.08 ± 0.03 (SE) hares/ha to 1.32 ± 0.15 hares/ha and in lodgepole pine, density estimates ranged from 0.06 ± 0.01 hares/ha to 0.34 ± 0.06 hares/ha, depending on year and method used for estimating effective area trapped. Our density estimates are similar to those reported at the low phase of the hare cycle in populations to the north (<0.1–1.1 hares/ha), where Canada lynx persist (Hodges 2000a). Although density estimates are a useful comparative tool, they depend upon methods used to estimate effective area trapped. Therefore, we urge caution in comparing our density estimates with those from other areas, which may have used dissimilar methods. We also examined effects of temperature and moon phase on capture success of snowshoe hares; extremely low temperatures affected capture success but moon phase did not. Capture success can be improved by trapping snowshoe hares at temperatures above their lower critical temperature (T_lc). If abundance estimates are derived from mark-recapture studies then effects of temperature should be included when modeling capture probabilities.     

2种调查方法对四川黑竹沟国家级自然保护区3种雉类种群密度调查的比较

种群数量是物种的重要生态学基础资料,合适的密度调查方法是数量估算的基础。2016年4-5月,采用广泛应用于鸡形目Galliformes鸟类种群密度调查的样线法和样点法,调查了四川黑竹沟国家级自然保护区3种鸡形目鸟类(白腹锦鸡Chrysolophus amherstiae、红腹角雉Tragopan temminckii和血雉Ithaginis cruentus)的种群密度。样线法和样点法估算的雄体密度分别是:白腹锦鸡1.20只/km^2和(6.31±0.98)只/km^2,红腹角雉5.41只/km^2和(0.39±0.17)只/km^2,血雉3.01只/km^2和(5.97±2.70)只/km^2。除红腹角雉外,样点法估算的白腹锦鸡、血雉种群密度均大于样线法。建议针对不同鸡形目鸟类采用不同的调查方法,并尽量扩大样本数量,从而提高调查结果的准确性。     

Density estimates of two endangered nocturnal lemur species from northern Madagascar: new results and a comparison of commonly used methods

Very little information is known of the recently described Microcebus tavaratra and Lepilemur milanoii in the Daraina region, a restricted area in far northern Madagascar. Since their forest habitat is highly fragmented and expected to undergo significant changes in the future, rapid surveys are essential to determine conservation priorities. Using both distance sampling and capture-recapture methods, we estimated population densities in two forest fragments. Our results are the first known density and population size estimates for both nocturnal species. In parallel, we compare density results from four different approaches, which are widely used to estimate lemur densities and population sizes throughout Madagascar. Four approaches (King, Kelker, Muller and Buckland) are based on transect surveys and distance sampling, and they differ from each other by the way the effective strip width is estimated. The fifth method relies on a capture-mark-recapture (CMR) approach. Overall, we found that the King method produced density estimates that were significantly higher than other methods, suggesting that it generates overestimates and hence overly optimistic estimates of population sizes in endangered species. The other three distance sampling methods provided similar estimates. These estimates were similar to those obtained with the CMR approach when enough recapture data were available. Given that Microcebus species are often trapped for genetic or behavioral studies, our results suggest that existing data can be used to provide estimates of population density for that species across Madagascar.