Do animal size,seasons and vegetation type influence detection probability and density estimates of Serengeti ungulates?

Accurate detection of individual animals and estimation of ungulate population density might be a function of vegetation cover, animal size, observation radius or season. We assessed the effect of these factors on estimates of detection probability and density using five ungulate species in Western Serengeti National Park, Tanzania. Estimates were derived from information collected using ground surveys involving line transects targeting three resident species (impala, topi and buffalo) and two migrants (wildebeest and zebra) and analysed using DISTANCE, MANOVA, t‐test and Pearson correlation. Results showed that ground surveys that take observation radii of 100 m would appreciably estimate at least 80% of the available ungulates. Beyond 100 m radii, surveys would leave approximately 43% of individuals undetected, the reason being a substantial influence of animal size, vegetation cover and observation radius on the detection. Animal size and observation radius have interactive effects. On their own seasonal differences, they do not have any effect but in interaction with animal size have significant effects especially on the migrant species. As reliable estimates of detection and density are required for making reasonable inferences, we urge that surveys using DISTANCE approach should consider incorporating both ground and aerial survey methods and ensure adequate sample replication.     

Comparison and Assessment of Aerial and Ground Estimates of Waterbird Colonies

Abstract: Aerial surveys are often used to quantify sizes of waterbird colonies; however, these surveys would benefit from a better understanding of associated biases. We compared estimates of breeding pairs of waterbirds, in colonies across southern Louisiana, USA, made from the ground, fixed-wing aircraft, and a helicopter. We used a marked-subsample method for ground-counting colonies to obtain estimates of error and visibility bias. We made comparisons over 2 sampling periods: 1) surveys conducted on the same colonies using all 3 methods during 3–11 May 2005 and 2) an expanded fixed-wing and ground-survey comparison conducted over 4 periods (May and Jun, 2004–2005). Estimates from fixed-wing aircraft were approximately 65% higher than those from ground counts for overall estimated number of breeding pairs and for both dark and white-plumaged species. The coefficient of determination between estimates based on ground and fixed-wing aircraft was ≤0.40 for most species, and based on the assumption that estimates from the ground were closer to the true count, fixed-wing aerial surveys appeared to overestimate numbers of nesting birds of some species; this bias often increased with the size of the colony. Unlike estimates from fixed-wing aircraft, numbers of nesting pairs made from ground and helicopter surveys were very similar for all species we observed. Ground counts by one observer resulted in underestimated number of breeding pairs by 20% on average. The marked-subsample method provided an estimate of the number of missed nests as well as an estimate of precision. These estimates represent a major advantage of marked-subsample ground counts over aerial methods; however, ground counts are difficult in large or remote colonies. Helicopter surveys and ground counts provide less biased, more precise estimates of breeding pairs than do surveys made from fixed-wing aircraft. We recommend managers employ ground counts using double observers for surveying waterbird colonies when feasible. Fixed-wing aerial surveys may be suitable to determine colony activity and composition of common waterbird species. The most appropriate combination of survey approaches will be based on the need for precise and unbiased estimates, balanced with financial and logistical constraints. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):697–706; 2008)     

A Comparison of Remote Sensing and Ground-Based Methods for Monitoring Wetland Restoration Success

Abstract Efficient and accurate vegetation sampling techniques are essential for the assessment of wetland restoration success. Remotely acquired data, used extensively in many locations, have not been widely used to monitor restored wetlands. We compared three different vegetation sampling techniques to determine the accuracy associated with each method when used to determine species composition and cover in restored Pacific coast wetlands dominated by Salicornia virginica (perennial pickleweed). Two ground‐based techniques, using quadrat and line intercept sampling, and a remote sensing technique, using low altitude, high resolution, color and color infrared photographs, were applied to estimate cover in three small restoration sites. The remote technique provided an accurate and efficient means of sampling vegetation cover, but individual species could not be identified, precluding estimates of species density and distribution. Aerial photography was determined to be an effective tool for vegetation monitoring of simple (i.e., single‐species) habitat types or when species identities are not important (e.g., when vegetation is developing on a new restoration site). The efficiency associated with these vegetation sampling techniques was dependent on the scale of the assessment, with aerial photography more efficient than ground‐based sampling methods for assessing large areas. However, the inability of aerial photography to identify individual species, especially mixed‐species stands common in southern California salt marshes, limits its usefulness for monitoring restoration success. A combination of aerial photography and ground‐based methods may be the most effective means of monitoring the success of large wetland restoration projects.     

Empirically simulated study to compare and validate sampling methods used in aerial surveys of wildlife populations

This paper compares the distribution, sampling and estimation of abundance for two animal species in an African ecosystem by means of an intensive simulation of the sampling process under a geographical information system (GIS) environment. It focuses on systematic and random sampling designs, commonly used in wildlife surveys, comparing their performance to an adaptive design at three increasing sampling intensities, using the root mean square errors (RMSE). It further assesses the impact of sampling designs and intensities on estimates of population parameters. The simulation is based on data collected during a prior survey, in which geographical locations of all observed animals were recorded. This provides more detailed data than that usually available from transect surveys. The results show precision of estimates to increase with increasing sampling intensity, while no significant differences are observed between estimates obtained under random and systematic designs. An increase in precision is observed for the adaptive design, thereby validating the use of this design for sampling clustered populations. The study illustrates the benefits of combining statistical methods with GIS techniques to increase insight into wildlife population dynamics.     

Community‐based wildlife management area supports similar mammal species richness and densities compared to a national park

Community‐based conservation models have been widely implemented across Africa to improve wildlife conservation and livelihoods of rural communities. In Tanzania, communities can set aside land and formally register it as Wildlife Management Area (WMA), which allows them to generate revenue via consumptive or nonconsumptive utilization of wildlife. The key, yet often untested, assumption of this model is that economic benefits accrued from wildlife motivate sustainable management of wildlife. To test the ecological effectiveness (here defined as persistence of wildlife populations) of Burunge Wildlife Management Area (BWMA), we employed a participatory monitoring approach involving WMA personnel. At intermittent intervals between 2011 and 2018, we estimated mammal species richness and population densities of ten mammal species (African elephant, giraffe, buffalo, zebra, wildebeest, waterbuck, warthog, impala, Kirk's dik‐dik, and vervet monkey) along line transects. We compared mammal species accumulation curves and density estimates with those of time‐matched road transect surveys conducted in adjacent Tarangire National Park (TNP). Mammal species richness estimates were similar in both areas, yet observed species richness per transect was greater in TNP compared to BWMA. Species‐specific density estimates of time‐matched surveys were mostly not significantly different between BWMA and TNP, but elephants occasionally reached greater densities in TNP compared to BWMA. In BWMA, elephant, wildebeest, and impala populations showed significant increases from 2011 to 2018. These results suggest that community‐based conservation models can support mammal communities and densities that are similar to national park baselines. In light of the ecological success of this case study, we emphasize the need for continued efforts to ensure that the BWMA is effective. This will require adaptive management to counteract potential negative repercussions of wildlife populations on peoples' livelihoods. This study can be used as a model to evaluate the effectiveness of wildlife management areas across Tanzania.     

Bayesian estimation of group sizes for a coastal cetacean using aerial survey data

Many small cetacean, sirenian, and pinniped species aggregate in groups of large or variable size. Accurate estimation of group sizes is essential for estimating the abundance and distribution of these species, but is challenging as individuals are highly mobile and only partially visible. We developed a Bayesian approach for estimating group sizes using wide‐angle aerial photographic or video imagery. Our approach accounts for both availability and perception bias, including a new method (analogous to distance sampling) for estimating perception bias due to small image size in wide‐angle images. We demonstrate our approach through an application to aerial survey data for an endangered population of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska. Our results strengthen understanding of variation in group size estimates and allow for probabilistic statements about the size of detected groups. Aerial surveys are a standard tool for estimating the abundance and distribution of various marine mammal species. The role of aerial photographic and video data in wildlife assessment is expected to increase substantially with the widespread uptake of unmanned aerial vehicle technology. Key aspects of our approach are relevant to group size estimation for a broad range of marine mammal, seabird, other waterfowl, and terrestrial ungulate species.     

The problem of precision and trend detection posed by small elephant populations in West Africa

The precision of elephant estimates from aerial sample surveys and dung counts is inversely proportional to abundance. West African elephant populations are already small, and the power of a monitoring programme to detect changes in abundance diminishes as the population shrinks in size. Thus it will be difficult to evaluate the effects on elephant numbers of new management policies in West Africa. The same will be true of monitoring schemes for antelope and primate populations that are hunted for bushmeat. Elephant estimates from dung counts are more precise than those from aerial sample surveys, and changes in elephant numbers are more likely to be detected in the subregion by dung counts than by aerial sample surveys.     

Hierarchical distance sampling to estimate population sizes of common lizards across a desert ecoregion

Multispecies wildlife monitoring across large geographical regions is important for effective conservation planning in response to expected impacts from climate change and land use. Unlike many species of birds, mammals, and amphibians which can be efficiently sampled using automated sensors including cameras and sound recorders, reptiles are often much more challenging to detect, in part because of their typically cryptic behavior and generally small body sizes. Although many lizard species are more active during the day which makes them easier to detect using visual encounter surveys, they may be unavailable for sampling during certain periods of the day or year due to their sensitivity to temperature. In recognition of these sampling challenges, we demonstrate application of a recent innovation in distance sampling that adjusts for temporary emigration between repeat survey visits. We used transect surveys to survey lizards at 229 sites throughout the Mojave Desert in California, USA, 2016. We estimated a total population size of 82 million (90% CI: 65–99 million) for the three most common species of lizards across this 66,830 km² ecoregion. We mapped how density at the 1‐km² scale was predicted to vary with vegetation cover and human development. We validated these results against independent surveys from the southern portion of our study area. Our methods and results demonstrate how multispecies monitoring programs spanning arid ecoregions can better incorporate information about reptiles.     

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.     

Aerial survey of waterbirds on wetlands as a measure of river and floodplain health

1. This study highlights the use of waterbird communities as potential measures of river and floodplain health at a landscape scale. 2. The abundance and diversity of a waterbird community (54 species) was measured over 15 trips with four aerial and three ground counts per trip on a 300-ha lake in arid Australia. 3. Aerial survey estimates of individual species were significantly less precise (SE/mean) than ground counts across two (11–100 and > 1000) out of four abundance classes of waterbirds: 0–10, 11–100, 101–1000 and > 1000. Standard error/mean as a percentage decreased with increasing abundance from about 60% for the lowest abundance class to 18% for the largest abundance class. 4. Aerial survey estimates were negatively biased for species in numbers of less than 10 and greater than 5000 but unbiased compared to ground counts for other abundance classes. Aerial surveys underestimated numbers of waterbirds by 50% when there were 40 000 waterbirds. Three ground counts found about seven more waterbird species than four aerial surveys. One ground count took about 150 times longer than two aerial surveys and cost 14 times more. 5. Regression models were derived, comparing aerial survey estimates to ground counts for 31 of 36 species for which there were sufficient data. Aerial survey estimates were unbiased for most of these species (67%), negatively biased for six species and positively biased for one species. Estimates were negatively biased in species that occurred in small numbers or that dived in response to the aircraft. 6. River system health encompasses the state of floodplain wetlands. Waterbirds on an entire wetland or floodplain may be estimated by aerial survey of waterbirds; this is a coarse but effective measure of waterbird abundance. Aerial survey is considerably less costly than ground survey and potentially provides a method for measuring river and floodplain health over long periods of time at the same scale as river management.     

Bias in aerial estimates of the number of nests in White Ibis and Great Egret colonies

ABSTRACT Estimating detection error, as well as the magnitude of other potential survey biases, is essential when sampling efforts play a role in the estimation of population size and management of wildlife populations. We quantified visual biases in aerial surveys of nesting wading birds (Ciconiiformes) in colonies in the Florida Everglades using a negative binomial count regression model to compare numbers of nests in quadrats counted on the ground with numbers estimated from aerial photographs of the same quadrats. The model also allowed the determination of degree of difference between monitoring results based upon such factors as nest density, vegetative cover, and nest turnover rates. Aerial surveys of White Ibis (Eudocimus albus) colonies underestimated the true number of nests found during ground counts by 11.1%, and underestimates were significantly greater (P= 0.047) in a colony with high nest turnover. Error rates did not differ for quadrats that varied in the density of White Ibis nests did not differ, and visual bias did not increase with vegetative complexity (P= 0.73). Estimates of nest density in colonies of Great Egrets (Ardea alba) based on aerial surveys were higher than ground counts for 38% of the quadrats sampled, and mean visual bias was 23.1%. Species misidentification likely contributed to visibility bias for Great Egrets in our study, with some Snowy Egrets almost certainly mistaken for Great Egrets in aerial photos. Biases of the magnitude we observed fro Great Egrets and White Ibises can mask true population trends in long‐term monitoring and, therefore, we recommend that detection probability be explicitly evaluated when conducting aerial surveys of nesting birds.     

Statistical power of replicated helicopter surveys in southern African conservation areas

Understanding the ecology of large ungulates in southern Africa requires accurate and precise measures of population size. Recovery or exploitation of ungulates in reserves is currently instigated when population size changes exceed 15% per annum, but monitoring is usually undertaken with single counts from helicopters, for which precision and the power to detect change are untested. In essence, power being the strength of a monitoring result in showing change over time. Retrospective power analysis is a useful technique to investigate the variability of single‐count aerial surveys. Using replicated helicopter‐based counts of southern African ungulates and post hoc analysis, we investigated the power of currently used single‐count surveys across five common ungulate species and 11 wildlife reserves. We expected high variability in count data and set α = 0.1 and 0.2 (α being the type I error rate), and asked two questions: ‘How much does power vary in replicated aerial counts of southern African wildlife across reserves and species?’ and ‘Can current single‐count aerial surveys detect population changes in response to management actions or are the statistical errors intractable?’ Single counts were mostly unreliable; only one of 42 had sufficient power to detect meaningful changes in population size or their trends at α = 0.1, and only three had sufficient power at α = 0.2. Power varied widely according to species (e.g. warthog, median power at α = 0.1; 0.12–0.37: blue wildebeest, median power at α = 0.1; 0.23–0.74), and, within species, between replicates and reserves. Our retrospective calculations demonstrated insensitivity and ineffectiveness in most currently applied single counts from helicopters. Consequently, it is impossible to interpret the effects of ungulate conservation actions on many southern African reserves. Retrospective power analyses enables determination of which previous aerial surveys were useful for population assessment and adaptive management, and which should be discarded. We recommend that prospective power analyses are undertaken to determine future helicopter survey sample size and replication requirements, especially in small reserves.     

Variance of Stratified Survey Estimators With Probability of Detection Adjustments

Abstract: Estimates of wildlife population sizes are frequently constructed by combining counts of observed animals from a stratified survey of aerial sampling units with an estimated probability of detecting animals. Unlike traditional stratified survey designs, stratum-specific estimates of population size will be correlated if a common detection model is used to adjust counts for undetected animals in all strata. We illustrate this concept in the context of aerial surveys, considering 2 cases: 1) a single-detection parameter is estimated under the assumption of constant detection probabilities, and 2) a logistic-regression model is used to estimate heterogeneous detection probabilities. Naïve estimates of variance formed by summing stratum-specific estimates of variance may result in significant bias, particularly if there are a large number of strata, if detection probabilities are small, or if estimates of detection probabilities are imprecise. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):837–844; 2008)     

Censusing large mammals in Kibale National Park: evaluation of the intensity of sampling required to determine change

Monitoring programmes are essential for management of large mammal populations because they can detect population change. It is vital that we have the means to evaluate the effectiveness of protected areas. Kibale National Park is a stronghold for large mammal conservation in Uganda. Past wildlife surveys in Kibale focused on specific taxa or areas, but our large mammal survey covered the entire protected area and we evaluated the intensity of sampling required to determine population change. Using line transect sampling, we found that the distribution of large mammals was nonrandom and related to habitat‐type. However, confidence intervals of population estimates revealed that much more intensive sampling was required to detect changes in population density at a time scale reasonable for management. For many species, populations would have to decline by 40–60% for this method to detect population change. Post‐stratification decreased confidence intervals of density estimates slightly, increasing our ability to detect change. However, confidence intervals of estimates were still too large to detect a meaningful population change on a time scale that would allow management to take action. Most incidences of illegal activity were about 5 km from the park boundary; however, animal densities were not lower in this area.     

Large variation of suction sampling efficiency depending on arthropod groups,species traits,and habitat properties

Suction sampling is widely used to estimate arthropod abundance and diversity. To test the reliability of abundance data derived from suction sampling, we examined sampling efficiency across a wide range of arthropod groups and tested for effects of species traits, vegetation density, and differences between sites. Suction sampling efficiency was quantified by vacuuming an enclosed meadow area and subsequent removal of the turf, which was treated with heat extraction to collect the remaining arthropods. We obtained 250 pairs of suction and turf samples from seven grasslands with variable vegetation density. High suction sampling efficiencies between 49 and 86% were obtained for Auchenorrhyncha, Heteroptera, Araneida, Curculionoidea, Hymenoptera, and Diptera. In contrast, efficiencies were below 30% for Aphidae, Thysanoptera, Staphylinidae and other Coleoptera, and for soil arthropods such as Collembola, Isopoda, Diplopoda, and Formicidae. Efficiency varied significantly among habitats (sites) for most groups, often more than two‐fold. Surprisingly, sampling efficiency for Hymenoptera, Diplopoda, and Collembola increased with vegetation density, probably because aboveground activity of these taxa was higher in dense vegetation. Suction sampling was nearly twice as efficient for spiders living in the vegetation than for spiders living near the soil surface, and cursorial and large‐bodied spider species were more efficiently sampled than web‐builders and small species. Depending on the sampling effort, suction sampling missed between 49% (one sample) and 31% (250 samples) of the spider species present. Suction sampling efficiency varied more strongly among sites and among arthropod groups than previously recognized. Abundance data derived from suction sampling are strongly underestimated, especially for arthropods living near the soil surface. Thus, comparisons of abundance and diversity between sites should be restricted to vegetation‐dwelling species of the most efficiently sampled groups. The positive relationship of sampling efficiency with vegetation density demonstrates that variation in efficiency is mediated by arthropod behaviour.     

Conducting animal censuses amongst abrupt topography; a GIS‐based alternative to Distance

Management often bases decisions on estimates of animal density and population size. Aerial sampling is expensive whilst current ground methods, noticeably Distance sampling, assume a single detection function for each habitat and that visibility in a given habitat type declines in a smooth, increasing manner with distance from the observer. If the visibility within a habitat varies widely or is suddenly cut off, as amongst abrupt topography, these assumptions are invalidated. We present an affordable, accessible and accurate method for conducting repeated annual censuses on large herbivores in such terrain. We used a GIS habitat‐based approach. Monthly, over a two year period, we conducted repeated road transects in Ithala Game Reserve, South Africa, using GPS to record the geographic position of each sighting. These records (n = 8742) were then imported into a GIS and overlaid onto an existing habitat‐type GIS layer. With the sampled area thus defined as an irregular polygon in the GIS encompassing all records, we calculated densities of each herbivore species by habitat type and extrapolated total population size estimates. Estimates of maximum population size for wildebeest and zebra correlated (±15%) with management's estimates based on aerial surveys, walked transects and experience. White rhino are individually counted in the reserve and our estimates (year 1: 52; year 2: 57) matched these known numbers (year 1: 50; year 2: 53). Our method also yielded realistic numbers for impala, but unrealistic numbers for kudu and warthog. Our GIS‐based census technique produced realistic maximum population estimates for abundant grazing and mixed‐feeder mesoherbivores and for scarce, but highly visible, megaherbivores, but not for cryptic species. With the increasing availability of GIS data, the technique is recommended to those working in abrupt terrain frustrated by the inability of current ground census techniques, principally Distance sampling, to produce realistic population estimates.     

Comparative changes in density and demography of large herbivores in the Masai Mara Reserve and its surrounding human-dominated pastoral ranches in Kenya

Wildlife habitats in pastoral lands adjoining protected areas in east African savannas are getting progressively degraded, fragmented and compressed by expanding human populations and intensification of land use. To understand the consequences of these influences on wildlife populations, we contrasted the density and demography of 13 wild and three domestic large herbivores between the Masai Mara National Reserve and the adjoining pastoral ranches using aerial surveys conducted in the wet and dry seasons during 1977–2010. Species of different body sizes and feeding styles had different densities between landscapes and seasons. Small-sized herbivores, requiring short, nutritious grasses, and browsers were more abundant in the ranches than the reserve in both seasons. Medium-sized herbivores moved seasonally between landscapes. Larger-bodied herbivores, requiring bulk forage but less susceptible to predation, were more abundant in the reserve than the ranches. The proportions of newborn warthog (Phacochoerus africanus) and juvenile topi (Damaliscus korrigum) were higher in the ranches, with shorter grasses and lower predation risk than in the reserve. These results suggest that pastoral lands adjoining protected areas in African savannas are important as seasonal dispersal and breeding grounds for wild herbivores. However, human population growth and dramatic land use changes are progressively degrading wildlife habitats in pastoral areas, thus restricting the seasonal wildlife dispersal movements between the protected areas and adjoining pastoral lands. Conservation efforts should focus on (1) creating and maintaining functional heterogeneity in protected areas that mimic moderate pastoral grazing conditions to attract small and medium-bodied grazers and (2) securing dispersal areas, including corridors, to ensure continued seasonal large herbivore movements between protected and pastoral systems.     

Estimation and Correction of Visibility Bias in Aerial Surveys of Wintering Ducks

Abstract: Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1–100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36–42%, and associated standard errors increased 38–55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):808–813; 2008)     

Using citizen‐based survey data to determine densities of large mammals: a case study from Mana Pools National Park,Zimbabwe

Since 1993, members of the national wildlife society have undertaken annual surveys of large mammals in the Zambezi alluvial woodlands of Mana Pools National Park, Zimbabwe. Data are collected along 36 systematically‐arranged transects. We provide the first thorough assessment of the data from any survey within this long‐term project. The transect data from 2011 were analysed with DISTANCE software to assess if the data were suitable for determining the densities of large mammals using distance sampling techniques. Successful application of distance sampling depended on observers using printed, large‐scale, georeferenced satellite images onto which they mapped the location of animal groups detected. The assumptions of the distance sampling were well met and thus the 2011 survey provided reliable estimates of the densities of nine species of common large mammals on the Zambezi alluvium during the late dry season. Estimated density in this dry‐season concentration area varied from 3.6 km^?2 for kudu, to 204 km^?2 for impala. The precision of the estimates ranged from a coefficient of variation of 7.9% for elephant, to 25.5% for buffalo. For elephant, warthog and baboon, the morning and afternoon densities differed significantly.