Estimating Wildlife Abundance Research Articles

Linking ecological processes and animal movements to inform timing of long‐term surveys of a migratory game bird

AbstractManagers typically estimate wildlife abundance using surveys within a timeframe that favors increased detectability; however, the ability to account for probabilities of inclusion, detection, and/or presence within a given sampling area is often limited. Cranes provide a good opportunity to research count accuracy because they are large, conspicuous, and often congregate during part of the year, typically on staging areas (i.e., fall and spring) or on wintering grounds. The objectives of this paper are twofold: (1) to evaluate how environmental factors influence crane movement in and out of crane survey areas to identify the best window of availability for annual survey counts; and (2) to evaluate environmental factors that influence overall crane survey counts from year to year. For Objective 1, a generalized linear mixed model was selected to model the probability of crane presence within survey areas using GPS transmitter data. A binary response variable representing crane movement within and outside formal survey areas was used as the dependent variable to investigate environmental covariates that influence movement into survey areas. Probability of crane presence was explained by seven covariates plus a quadratic term for Julian day number. Interactions between Palmer drought severity index (PDSI) and normalized difference vegetation index supported higher probability of crane presence in survey areas during times of drought. Probability of crane presence increased throughout the entire study period (May–October), suggesting that formal surveys in September could be augmented or replaced by surveys in October. For Objective 2, a negative binomial model with linear parameterization was selected to model crane counts using census data compiled from 1995 to 2019. Covariates were acquired at the watershed scale using Hydrologic Unit Code 6 boundaries. Of the 17 covariates investigated, we found that 18‐month precipitation (PPTgss), PDSI, and minimum temperature (Tempmin) explained most of the variability in crane census counts. High PPTgss (antecedent moisture), low PDSI (drought conditions), and low Tempmin (cold extremes) result in higher annual crane counts. The ability to link ecological processes to wildlife movement and population abundance both locally and at landscape scales has long‐ranging implications on resource projections, conservation, and the ability to deploy adaptive management.

SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision

The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, camera trapping in combination with three-dimensional (3D) measurements of habitats is highly valuable. Additionally, 3D information improves the accuracy of wildlife detection using camera trapping. This study presents a novel approach to 3D camera trapping featuring highly optimized hardware and software. This approach employs stereo vision to infer the 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES). A comprehensive evaluation of SOCRATES shows not only a 3.23% improvement in animal detection (bounding box mAP75), but also its superior applicability for estimating animal abundance using camera trap distance sampling. The software and documentation of SOCRATES is openly provided.

AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models

Appropriate field survey methods and robust modeling approaches play an important role in wildlife protection and habitat management because reliable information on wildlife distribution and abundance is important for conservation planning and actions. However, accurately estimating animal abundance is challenging in most species, as usually only a small proportion of the population can be detected during surveys. Species distribution models can predict the habitat suitability index, which differs from species abundance. We designed a method to adjust the results from species distribution models to achieve better accuracy for abundance estimation. This method comprises four steps: (1) conducting distance sampling, recording species occurrences, and surveying routes; (2) performing species distribution modeling using occurrence records and predicting animal abundance in each quadrat in the study area; (3) comparing the difference between field survey results and predicted abundance in quadrats along survey routes, adjusting model prediction, and summing up to obtain total abundance in the study area; (4) calculating uncertainty from three sources, i.e., distance sampling (using detection rate), species distribution models (using R squared), and differences between the field survey and model prediction [using the standard deviation of the ratio (observation/prediction) at different zones]. We developed an R package called abundanceR to estimate wildlife abundance and provided data for the Tibetan wild ass (Equus kiang) based on field surveys at the Three-River-Source National Park, as well as 29 layers of environmental variables covering the terrestrial areas of the planet. Our method can provide accurate estimation of abundance for animals inhabiting open areas that can be easily observed during distance sampling, and whose spatial heterogeneity of animal density within the study area can be accurately predicted using species distribution models.

Integrating video and genetic data to estimate annual age‐structured apparent survival of American black bears

AbstractCamera or genetic data are increasingly used to estimate wildlife abundance and density. We integrated video camera data with genetic data over 7 years to estimate annual age‐structured apparent survival of American black bears (Ursus americanus). We identified 70 individuals through meticulous scrutiny of 7531 video captures, cross‐referenced with 721 genetic captures from hair samples concurrently collected from stations in view of cameras. We used the Cormack–Jolly–Seber model in Program Mark to estimate annual age‐structured apparent survival for yearling males, yearling females, 2+ year‐old males, and 2+ year‐old females. We manually calculated cub survival. We compared parameter estimates based on combined video and genetic data with those based on only genetic data. Combining video and genetic data provided a means to test video‐based identification accuracy, which was highest for females (97%–100%). Annual apparent survival was highest for yearling females (φ = 0.92, SE = 0.07), followed by 2+ year‐old females (φ = 0.88, SE = 0.05), 2+ year‐old males (φ = 0.84, SE = 0.06), and yearling males (φ = 0.80, SE = 0.14). Annual cub survival (φ = 0.86, SE = 0.07) was likely biased because we could not account for mortality that occurred in‐den through early spring. Annual apparent survival and recapture probabilities derived from only genetic data were lower than those derived from combined video and genetic data. Our finding that noninvasive data can be used to estimate annual age‐structured apparent survival of a species with relatively indistinct traits is broadly relevant to wildlife research and conservation.

Errors in aerial survey count data: Identifying pitfalls and solutions.

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double‐observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

Congruence of local ecological knowledge (LEK)‐based methods and line‐transect surveys in estimating wildlife abundance in tropical forests

Abstract Effective estimation of wildlife population abundance is an important component of population monitoring, and ultimately essential for the development of conservation actions. Diurnal line‐transect surveys are one of the most applied methods for abundance estimations. Local ecological knowledge (LEK) is empirically acquired through the observation of ecological processes by local people. LEK‐based methods have only been recognized as valid scientific methods for surveying fauna abundance in the last three decades. However, the agreement between both methods has not been extensively analysed. We compared concomitant abundance data for 91 wild species (mammals, birds and tortoises) from diurnal line transects (9,221 km of trails) and a LEK‐based method (291 structured interviews) at 18 sites in Central and Western Amazonia. We used biological and socioecological factors to assess the agreements and divergences between abundance indices obtained from both methods. We found a significant agreement of population abundance indices for diurnal and game species. This relationship was also positive regardless of species sociality (solitary or social), body size and locomotion mode (terrestrial and arboreal); and of sampled forest type (upland and flooded forests). Conversely, we did not find significant abundance covariances for nocturnal and non‐game species. Despite the general agreement between methods, line transects were not effective at surveying many species occurring in the area, with 40.2% and 39.8% of all species being rarely and never detected in at least one of the survey sites. On the other hand, these species were widely reported by local informants to occur at intermediate to high abundances. Although LEK‐based methods have been long neglected by ecologists, our comparative study demonstrated their effectiveness for estimating vertebrate abundance of a wide diversity of taxa and forest environments. This can be used simultaneously with line‐transect surveys to calibrate abundance estimates and record species that are rarely sighted during surveys on foot, but that are often observed by local people during their daily extractive activities. Thus, the combination of local and scientific knowledge is a potential tool to improve our knowledge of tropical forest species and foster the development of effective strategies to meet biodiversity conservation goals.

A Review of Wildlife Abundance Estimation Models: Comparison of Models for Correct Application

In this review, the various models to estimate wildlife abundance are organized for promoting the correct application of them in animal ecology. If individuals of the target wildlife are distinguishable, the capture-recapture (CR) model and the spatially explicit capture-recapture (SECR) model can be applied to the closed population and the Jolly-Seber (JS) model can be applied to the open population. If not, the distance sampling, N-mixture model, random encounter (RE) model, random encounter and staying time (REST) model, and removal sampling can be applied to the closed population, and the harvest-based model can be applied to the open population. Recent advances in the hierarchical model and the integrated population model (IPM) make it possible to model the abundance and demographic rate of the wildlife by considering the ecological process of the target wildlife and observation process of them and to utilize the various but fragmented data. Then, the formalization of the abundance estimation model as a hierarchical model and the construction of the IPM by considering the available data and biological characteristics of the target species are useful for future research.

Automatic Acquisition and Sustainable Use of Political-Ecological Data

The sustainable management of anthropogenically-impacted ecosystems will require ongoing monitoring and advocacy by people across the globe. To this end, automatic methods are developed herein for acquiring several types of such political-ecological data. On the political side, a method is developed for gathering news articles about human actions that affect the ecosystem along with a method for identifying themes in social media that concern the consumption of an ecosystem’s products. On the ecosystem side, a method is derived for estimating wildlife abundance from purchasable high-resolution satellite images. A simple website architecture is described for holding this data and enabling its use in developing sustainable conservation policies. A rhino conservation website illustrates this architecture. A fundamental contradiction between the desire for open data on the locations of endangered flora and fauna versus the need to hide these locations from poachers is addressed through a new security protocol that enables the secure distribution of sensitive ecosystem data to trusted data consumers.

Can trackers count free-ranging wildlife as effectively and efficiently as conventional aerial survey and distance sampling? Implications for citizen science in the Kalahari, Botswana

Estimating wildlife abundance is central to conservation. We compared two widely practiced standards for counting animals - aerial strip surveys and ground line transects - with interpreted counts of animal tracks. At equal sampling intensity in semiarid savanna with good visibility all three methods produced similar population estimates and precision for six large herbivores. This comparison adds empirical support for the use of track count data to estimate population density rather than being restricted to ambiguous indices of relative abundance. Although expected to capture more species than aerial surveys, we found line transects limiting because encounter rates by direct sightings were relatively low; a minimum threshold 40 observations was achieved for only 1/3 of antelope species in 648.4 km of transect. By contrast, animal track counts returned exceedingly high encounter rates that allowed estimation of abundance for the entire large predator-prey community and mapping density-distributions more completely. Unlike aerial surveys conducted by Botswana's wildlife authority, the track survey provided opportunity to involve local people in the research process. The track survey cost 40% less than the aerial survey, and could be reduced a further 3-fold if trackers collected data autonomously without motor vehicles. Counting animals by their tracks is ultimately constrained to regions with appropriate substrates. However, in suitable environments like the Kalahari, we suggest that a citizen science driven by expert local trackers could ultimately replace conventional wildlife counts, generating knock-on benefits to conservation beyond improved data.

Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations

Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and <0.7 standard errors. Precision varied widely, from 6.1% CV to 25.0% CV. In contrast, the pair of surveys conducted around a known management removal produced an estimated change in population between the surveys that was significantly larger than the known reduction. Although the deviation between was only 9.1%, the precision estimate (CV = 1.6%) may have been artificially low. It was apparent that use of a helicopter in those surveys perturbed the horses, introducing detection error and heterogeneity in a manner that could not be corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs.

Identification and Density Estimation of American Martens (Martes americana) Using a Novel Camera-Trap Method

Camera-traps are increasingly used to estimate wildlife abundance, yet few studies exist for small-sized carnivores or comparing efficacy against traditional methods. We developed a camera-trap to identify the unique ventral patches of American martens (Martes americana). Our method was designed to: (1) determine the optimal trap configuration to photograph ventral patches; (2) evaluate the use of temporally clustered photographs to determine independence and improve identification; and (3) determine factors that influence identification probability. We tested our method by comparing camera- and live-trap density estimates using spatial capture–recapture (SCR) models. The ventral patches of radio-collared martens were most visible when traps were placed 15–20 cm above a feeding platform. Radio-collared martens (n = 14) visited camera-traps for long periods (median = 7 min) with long intervals between visits (median = 419 min), and visits by different martens at the same trap &lt;15 min apart was infrequent (n = 3) during both years. Similarly, there was complete agreement among observers that clustered photos of un-collared martens were always of the same individual. Pairwise agreement was high between observers; eight un-collared martens were identifiable by consensus on 90% (54 of 60) of recorded visits. Factors influencing identification probability were directly related to the time martens spent feeding at traps (β = 0.143, P = 0.01) and inversely proportional to the time that elapsed since traps were baited (β = −0.344, P = 0.006). Density estimates were higher and more precise for camera-trapping (0.60, 0.35–1.01 martens/km2) than live-trapping (0.45, 0.16–1.22 martens/km2), providing evidence that SCR density estimates may be biased when capture heterogeneity is present, yet cannot be accounted for due to small sample size. Our camera-trap method provides a minimally invasive and accurate tool for monitoring marten populations.

Using simulation to evaluate wildlife survey designs: polar bears and seals in the Chukchi Sea.

Logistically demanding and expensive wildlife surveys should ideally yield defensible estimates. Here, we show how simulation can be used to evaluate alternative survey designs for estimating wildlife abundance. Specifically, we evaluate the potential of instrument-based aerial surveys (combining infrared imagery with high-resolution digital photography to detect and identify species) for estimating abundance of polar bears and seals in the Chukchi Sea. We investigate the consequences of different levels of survey effort, flight track allocation and model configuration on bias and precision of abundance estimators. For bearded seals (0.07 animals km−2) and ringed seals (1.29 animals km−2), we find that eight flights traversing ≈7840 km are sufficient to achieve target precision levels (coefficient of variation (CV)<20%) for a 2.94×105 km2 study area. For polar bears (provisionally, 0.003 animals km−2), 12 flights traversing ≈11 760 km resulted in CVs ranging from 28 to 35%. Estimators were relatively unbiased with similar precision over different flight track allocation strategies and estimation models, although some combinations had superior performance. These findings suggest that instrument-based aerial surveys may provide a viable means for monitoring seal and polar bear populations on the surface of the sea ice over large Arctic regions. More broadly, our simulation-based approach to evaluating survey designs can serve as a template for biologists designing their own surveys.

Improving in-water estimates of marine turtle abundance by adjusting aerial survey counts for perception and availability biases

Aerial surveys are often used to estimate wildlife abundance. The probability of detecting an animal during a survey involves two processes: (1) availability bias when animals present in the search area are not available for detection and (2) perception bias, when some animals potentially visible to observers are missed. Estimating these two sources of bias can lead to improved abundance estimates. However, to date, no marine turtle aerial survey has quantified both biases. To improve in-water marine turtle abundance estimates from aerial counts we estimated: (1) perception bias using independent tandem observers and mark recapture models, and (2) availability bias by quantifying the effect of turtle diving behaviour and environmental conditions on the detection probability of turtles. We compared unadjusted and adjusted abundance estimates to evaluate the effects of these detection biases in aerial surveys. Adjusted data produced a substantially higher estimate of turtles than the unadjusted data. Adjusting for availability bias increased the estimates 18.7 times; adjusting for perception bias resulted in a further 5% increase. These results emphasize the need to consider availability and perception corrections to obtain robust abundance estimates. This approach has application for aerial surveys for other marine wildlife including marine mammals and large sharks.

Distance sampling with a random scale detection function

Distance sampling was developed to estimate wildlife abundance from observational surveys with uncertain detection in the search area. We present novel analysis methods for estimating detection probabilities that make use of random effects models to allow for unmodeled heterogeneity in detection. The scale parameter of the half-normal detection function is modeled by means of an intercept plus an error term varying with detections, normally distributed with zero mean and unknown variance. In contrast to conventional distance sampling methods, our approach can deal with long-tailed detection functions without truncation. Compared to a fixed effect covariate approach, we think of the random effect as a covariate with unknown values and integrate over the random effect. We expand the random scale to a mixed scale model by adding fixed effect covariates. We analyzed simulated data with large sample sizes to demonstrate that the code performs correctly for random and mixed effect models. We also generated replicate simulations with more practical sample sizes (\(^{\sim }100\)) and compared the random scale half-normal with the hazard rate detection function. As expected each estimation model was best for different simulation models. We illustrate the mixed effect modeling approach using harbor porpoise vessel survey data where the mixed effect model provided an improved model fit in comparison to a fixed effect model with the same covariates. We propose that a random or mixed effect model of the detection function scale be adopted as one of the standard approaches for fitting detection functions in distance sampling.

Use of modified snares to estimate bobcat abundance

AbstractAlthough genetic and analytical methods for estimating wildlife abundance have improved rapidly over the last decade, effective methods for collecting hair samples from terrestrial carnivores in a mark–recapture framework have lagged. Hair samples are generally collected using methods that permit sampling of multiple individuals during a single sampling period that can cause genotyping errors due to cross‐contamination. We evaluated a modified body snare as a single‐sample method to obtain bobcat hair samples suitable for individual identification using DNA analyses to estimate population size. We used a systematic grid (2.5 × 2.5 km) overlaid on a 278.5 km2 study area in Michigan's Upper Peninsula to distribute sampling effort. In each of 44 grid cells, we placed 2–6 snares at established sampling stations and collected hair samples weekly for 8 weeks during January–March 2010. We collected 230 hair samples overall, with 91% of sampling stations obtaining at least 1 hair sample. Fifty‐seven percent of samples had sufficient DNA for species identification, which included bobcat (Lynx rufus, n = 17); raccoon (Procyon lotor, n = 62); coyote, dog, or wolf (Canis spp., n = 29); fox (Vulpes vulpes or Urocyon cinereoargenteus, n = 4); and fisher (Martes pennanti, n = 1). We identified 8 individual bobcats and using Huggins closed capture population models with a one‐half mean maximum distance moved buffer, estimated 10 individuals within the trapping area (95% confidence interval = 8–28) with a density of 3.0 bobcats/100 km2. Our method provides an effective, single‐sample technique for detecting bobcats and estimating abundance. © 2012 The Wildlife Society.

Estimating sika deer abundance using camera surveys

Cameras have been used throughout the world to estimate wildlife abundance and occupancy. Abundance estimates generated by camera surveys tend to be less invasive, less costly, and more accurate than other means in certain situations. We sought to expand and test the effectiveness of camera surveys on sika deer in Maryland. In 2008, we setup surveys with a 7-day pre-bait period followed by a 7-day active camera survey with 15 cameras. In 2009, we ran the cameras for the entire 14-day survey and moved cameras after each survey to determine if biases occur when using the same camera sites. During both years and all surveys, camera density was approximately 1-camera/65-ha. The abundance estimates were similar between years and estimators. In 2009, increasing photo intervals from 1-min to 5- and 10-min intervals reduced the number of pictures by 66 and 81%, respectively, while providing similar abundance estimates. We calculated the daily detection probabilities for all identifiable deer and we used radio-collared males that occurred within 2 km of the survey grid to assist in determining the optimum survey length. Detection probability did not vary between surveys in the same year, but varied between 2008 and 2009, most likely due to unlimited bait being available during 2008 surveys. Camera surveys have proven to be an accurate and cost effective means of estimating wildlife abundance and can be used successfully to determine sika deer abundance.

A Semiparametric Model for Line Transect Sampling

This paper introduces an appealing semiparametric model for estimating wildlife abundance based on line transect data. The proposed method requires the existence of a parametric model and then improves the estimator using a kernel method. Properties of the resultant estimator are derived and an expression for the asymptotic mean square error (AMSE) of the estimator is given. Minimization of the AMSE leads to an explicit formula for an optimal choice of the smoothing parameter. Small-sample properties of the proposed estimator using the parametric half-normal model are investigated and compared with the classical kernel estimator using both simulations and real data. Numerical results show that improvements over the classical kernel estimator often can be realized even when the true density is far from the half-normal model.

Modeling Distribution and Abundance of Antarctic Baleen Whales Using Ships of Opportunity

Information on animal abundance and distribution is at the cornerstone of many wildlife and conservation strategies. However, these data can be difficult and costly to obtain for cetacean species. The expense of sufficient ship time to conduct design-unbiased line transect surveys may be simply out of reach for researchers in many countries, which nonetheless grapple with problems of conservation of endangered species, by-catch of small cetaceans in commercial fisheries, and progression toward ecosystem-based fisheries management. Recently developed spatial modeling techniques show promise for estimating wildlife abundance using non-randomized surveys, but have yet to receive much field-testing in areas where designed surveys have also been conducted. Effort and sightings data were collected along 9 650 km of transects aboard ships of opportunity in the Southern Ocean during the austral summers of 2000-2001 and 2001-2002. Generalized additive models with generalized cross-validation were used to express heterogeneity of cetacean sightings as functions of spatial covariates. Models were used to map predicted densities and to estimate abundance of humpback, minke, and fin whales in the Drake Passage and along the Antarctic Peninsula. All species' distribution maps showed strong density gradients, which were robust to jackknife resampling when each of 14 trips was removed sequentially with replacement. Looped animations of model predictions of whale density illustrate uncertainty in distribution estimates in a way that is informative to non-scientists. The best abundance estimate for humpback whales was 1 829 (95% CI: 978-3 422). Abundance of fin whales was 4 487 (95% CI: 1 326-15 179) and minke whales was 1,544 (95% CI: 1,221-1,953). These estimates agreed roughly with those reported from a designed survey conducted in the region during the previous austral summer. These estimates assumed that all animals on the trackline were detected, but preliminary results suggest that any negative bias due to violation of this assumption was likely small. Similarly, current methodological limitations prohibit inclusion of all known sources of uncertainty in the favored variance estimator. Meanwhile, our approach can be seen generally as an inexpensive pilot study to identify areas of predicted high density that could be targeted to: inform stratified designs for future line transect surveys, making them less expensive and more precise; increase efficiency of future photo-identification or biopsy studies; identify candidate time-area fisheries closures to minimize by-catch; or direct ecotourism activities. The techniques are likely to apply to areas where funding is limiting, where cetacean studies or wilderness-based tourism are just beginning, or in regions where even a very rough estimate of animal abundance is needed for conservation or management purposes.

Pooling local expert opinions for estimating mammal densities in tropical rainforests

Methods currently used for assessing wildlife density in rainforests are time and money consuming. The precision of the most commonly used methods is disputed, but accepted because more exact methods are not available. In this study a new method of wildlife density estimation is explained. The new method is less time and money consuming, but yields comparable results with classical methods. The method was tested in the field in Cameroon and compared with transect surveys in the area and with relevant literature. The Pooled Local Expert Opinion (PLEO) method is based on the knowledge of local experts. A number of hunters were asked to estimate wildlife abundance in a specified area, after which the density/km 2 was calculated for 33 wildlife species. These estimates were pooled and extrapolated for the whole study area. Elephant ( Loxodonta africana ) density outside the National Park was estimated to be 0.06 animals/km 2 , and 0.3 inside. Buffalo ( Syncerus caffer ) density for the study area was estimated at 0.2 animals/km 2 and gorilla ( Gorilla gorilla ) density at 1.05 per km 2 . Transect surveys carried out at the same time for considerably more money, taking far more time, produced too few data to calculate densities. The evaluation of the PLEO method was favourable and the method offers a substitute for conventional methods of estimating wildlife density in rainforests. The methodology is simple and it can be incorporated in many tropical biodiversity and conservation projects. It can also be used for long-term monitoring of wildlife status in a given area. In contrast with classical methods, the PLEO method is low in cost and assures local ownership of the results.

Generic Variance Expressions, Precision, and Sampling Optimization for the Sex-Age-Kill Model of Population Reconstruction

Many analytical techniques have been developed to estimate wildlife abundance from harvest data. The Sex-Age-Kill model of population reconstruction has a long history of use to monitor white-tailed deer (Odocoileus virginianus) numbers. The model is readily interpretable using data commonly collected by wildlife agencies. These features make implementation straightforward and abundance estimates understandable. However, the nonlinear nature of the estimation scheme complicates the variance structure of the model and how individual input parameters influence the precision of abundance estimates. We present generic variance expressions for adult male, adult female, juvenile, and total population abundance derived for the Sex-Age-Kill model. Using these variance expressions, we illustrate how the precision of the input parameters influences the overall precision of abundance estimates. To obtain the rough management benchmark (Robson and Regier 1964) for the precision of the abundance estimates, the individual input parameters must be estimated with coefficients of variation (CVs) < 0.15. An intermediate level of precision for accurate management would require CVs < 0.08; to achieve a high level of precision for careful research would constrain all input parameter CVs < 0.03. These results suggest that very precise field data are required for reliable population estimates. We also develop a cost function that identifies how funds should be spent to improve model estimates most economically. We suggest that researchers examine other heuristic population assessment techniques to develop rigorous methods for survey design and data analysis.