二化螟种群密度的克力格估值及其模拟抽样

为设计可靠合理的二化螟幼虫种群密度抽样方案,从二化螟幼虫空间分布原始总体出发,另构建了一个随机总体和一个顺序总体,采用无放回随机抽样、间隔变程以上无放回随机抽样和基于克力格估值且初始点随机的顺序抽样对3总体进行了模拟抽样比较．结果表明,间隔变程以上随机抽样对原始总体平均数的估计优于随机抽样,且随总体聚集程度增加,间隔变程以上随机抽样愈优;正确识别种群空间格局极为重要,对聚集分布总体采用随机抽样和对随机分布总体采用间隔变程以上随机抽样均将降低抽样估计精度．针对随机抽样在应用上的局限性,提出了一种基于地统计学克力格估值、初始点随机的顺序抽样方案：它以初始点随机保证随机性,以顺序抽样保证可操作性,以二化螟种群空间分布的区域变量属性保证克力格样本较调查样本对局域样本和总体的平均数估计为优;且聚集范围一定时,总体聚集强度愈大,克力格样本局域估计和全局估计愈优于调查样本;取样间隔(以变程为标准)极为重要,样方的空间布局要平衡考虑相互独立的样方对数和变程范围内的样方对数。     

Using time‐of‐detection to evaluate detectability assumptions in temporally replicated aural count indices: an example with Ring‐necked Pheasants

ABSTRACT The validity of treating counts as indices to abundance is based on the assumption that the expected detection probability, E(p), is constant over time or comparison groups or, more realistically, that variation in p is small relative to variation in population size that investigators seek to detect. Unfortunately, reliable estimates of E(p) and var(p) are lacking for most index methods. As a case study, we applied the time‐of‐detection method to temporally replicated (within season) aural counts of crowing male Ring‐necked Pheasants (Phasianus colchicus) at 18 sites in southern Minnesota in 2007 to evaluate the detectability assumptions. More specifically, we used the time‐of‐detection method to estimate E(p) and var(p), and then used these estimates in a Monte Carlo simulation to evaluate bias‐variance tradeoffs associated with adjusting count indices for imperfect detection. The estimated mean detection probability in our case study was 0.533 (SE = 0.030) and estimated spatial variation in E(p) was 0.081 (95% CI: 0.057–0.126). On average, both adjusted (for) and unadjusted counts of crowing males qualitatively described the simulated relationship between pheasant abundance and grassland abundance, but the bias‐variance tradeoff was smaller for adjusted counts (MSE = 0.003 vs. 0.045, respectively). Our case study supports the general recommendation to use, whenever feasible, formal population‐estimation procedures (e.g., mark‐recapture, distance sampling, double sampling) to account for imperfect detection. However, we caution that interpreting estimates of absolute abundance can be complicated, even if formal estimation methods are used. For example, the time‐of‐detection method was useful for evaluating detectability assumptions in our case study and the method could be used to adjust aural count indices for imperfect detection. Conversely, using the time‐of‐detection method to estimate absolute abundances in our case study was problematic because the biological populations and sampling coverage could not be clearly delineated. These estimation and inference challenges may also be important in other avian surveys that involve mobile species (whose home ranges may overlap several sampling sites), temporally replicated counts, and inexact sampling coverage.     

Applications of species accumulation curves in large-scale biological data analysis

The species accumulation curve, or collector’s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non-parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45–63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and k-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.     

Associated LAH Numbers,Factorial Series Distributions and Unbiased Estimation of a Size Parameter

A finite population consists of kN individuals of N different categories with k individuals each. It is required to estimate the unknown parameter N, the number of different classes in the population. A sequential sampling scheme is considered in which individuals are sampled until a preassigned number of repetitions of already observed categories occur in the sample. Corresponding fixed sample size schemes were considered by Charalambides (1981). The sequential sampling scheme has the advantage of always allowing unbiased estimation of the size parameter N. It is shown that relative to Charalambides' fixed sample size scheme only minor adjustments are required to account for the sequential scheme. In particular, MVU estimators of parametric functions are expressible in terms of the C-numbers introduced by Charalambides.     

Lincoln estimates of mallard (Anas platyrhynchos) abundance in North America

Estimates of range‐wide abundance, harvest, and harvest rate are fundamental for sound inferences about the role of exploitation in the dynamics of free‐ranging wildlife populations, but reliability of existing survey methods for abundance estimation is rarely assessed using alternative approaches. North American mallard populations have been surveyed each spring since 1955 using internationally coordinated aerial surveys, but population size can also be estimated with Lincoln's method using banding and harvest data. We estimated late summer population size of adult and juvenile male and female mallards in western, midcontinent, and eastern North America using Lincoln's method of dividing (i) total estimated harvest, , by estimated harvest rate, , calculated as (ii) direct band recovery rate, , divided by the (iii) band reporting rate, . Our goal was to compare estimates based on Lincoln's method with traditional estimates based on aerial surveys. Lincoln estimates of adult males and females alive in the period June–September were 4.0 (range: 2.5–5.9), 1.8 (range: 0.6–3.0), and 1.8 (range: 1.3–2.7) times larger than respective aerial survey estimates for the western, midcontinent, and eastern mallard populations, and the two population estimates were only modestly correlated with each other (western: r = 0.70, 1993–2011; midcontinent: r = 0.54, 1961–2011; eastern: r = 0.50, 1993–2011). Higher Lincoln estimates are predictable given that the geographic scope of inference from Lincoln estimates is the entire population range, whereas sampling frames for aerial surveys are incomplete. Although each estimation method has a number of important potential biases, our review suggests that underestimation of total population size by aerial surveys is the most likely explanation. In addition to providing measures of total abundance, Lincoln's method provides estimates of fecundity and population sex ratio and could be used in integrated population models to provide greater insights about population dynamics and management of North American mallards and most other harvested species.     

大中型兽类种群数量估算的研究进展

大中型兽类种群数量的估算是动物生态学中重要的基本问题, 受到研究者、管理者和公众的共同关注。国际上从20世纪中期开始研究该问题, 已出现了多种研究方法和相应案例, 且还在快速发展, 但世界各地仍有很多物种的种群数量尚未知晓。在我国, 从20世纪80年代开始调查大中型兽类种群数量, 取得了重要进展, 也还有很多物种的种群数量尚不清楚。因此, 有必要归纳国际上种群数量估算的研究进展, 同时, 总结国内研究的现状、优势和趋势, 供研究者参考。本文首先选择估算大中型兽类种群数量的原理、数据来源和模型这3个要素归纳出简明的研究框架, 将现有的多种方法置于其中予以阐述。在该框架下, 根据估算原理分为4大类方法, 为距离取样法、标志重捕法、基于遇见率法和遥感影像直接计数法。针对每一大类方法, 论述其基本原理模型和模型假设, 说明能实现该原理的相应数据来源(视觉观测、红外相机拍摄、DNA微卫星识别、卫星定位跟踪、声音监测或遥感影像)的特点及如何实现该原理, 评价其适用性及优缺点, 并选择其中具有可比性的方法予以比较评价。其次, 参照该研究框架, 总结我国的研究现状, 分析未来发展的优势和趋势: 我国的红外相机数据积累充分, 可以发展以此为数据源的距离取样法、标志重捕法和基于遇见率法; 发展以粪便样品为数据来源的距离取样法和粪便DNA标志重捕法; 相比地面调查数据, 获取高分辨率遥感影像数据更容易, 尽量以此估算符合适用条件的大中型兽类的种群数量。最后, 本文提出了适用于我国大中型兽类种群数量的估算方法的选择流程, 供研究者参考。     

Further Results in Estimating the Classification Error in Discriminance Analysis

Basing on the approach by McLachlan (1977) a procedure for the conditional and common error estimation of the classification error in discriminance analysis is described for k ≧ 2 classes. As a rapid procedure for large sample sizes and feature numbers, a modification of the resubstitution method is proposed being favourable with respect to computing time. Both methods provide useful estimations for the probability of misclassification. In calculating the weighting function w, deviations from preconditions known from the MANOVA such as the skewness, the truncation or the inequality of the covariance matrices, hardly play any role; it appears that only a variation of the sample sizes of the classes substantially influences the weighting functions. The error rates of the tested error estimation methods likewise in effect depend on the sample sizes of the classes. Violations of the mentioned preconditions in the form described above result in different variations of the error estimates, depending on these sample sizes. A comparison between error estimation and allocation relative to a simulated population demonstrates the goodness of the used error estimation procedures.     

Estimating age from adult occlusal wear: A modification of the miles method

The Miles method of age estimation relies on molar wear to estimate age and is widely used in bioarcheological contexts. However, because the method requires physical seriation and a sample of subadults to estimate wear rates it cannot be applied to many samples. Here, we modify the Miles method by scoring occlusal wear and estimating molar wear rates from adult wear gradients in 311 hunter‐gatherers and provide formulae to estimate the error associated with each age estimate. A check of the modified method in a subsample (n = 22) shows that interval estimates overlap in all but one case with age categories estimated from traditional methods; this suggests that the modifications have not hampered the ability of the Miles method to estimate age even in heterogeneous samples. As expected, the error increases with age and in populations with smaller sample sizes. These modifications allow the Miles method to be applied to skeletal samples of adult crania that were previously only amenable to cranial suture age estimation, and importantly, provide a measure of uncertainty for each age estimate. Am J Phys Anthropol 149:181–192, 2012. © Wiley Periodicals, Inc.     

Using helicopter counts to estimate the abundance of Himalayan tahr in New Zealand's Southern Alps

Estimating the abundance and density of mountain ungulates is difficult because of rugged and remote terrain, high elevations, and rapidly changing weather. Helicopter surveys could overcome these problems, but researchers have seldom applied helicopter-based survey methods at large spatial scales in mountain terrain. We used helicopters to count introduced Himalayan tahr (Hemitragus jemlahicus) at 117 plots, each of 4 km², in New Zealand's Southern Alps during 2016–2019. The sampling frame was 7,844 km² and we located the plots at the vertices of an 8-km grid superimposed over the sampling frame (i.e., a systematic random sampling design). We conducted 3 repeat counts at each plot during summer–autumn. We used the repeat counts to estimate tahr abundance and density, corrected for imperfect detection, using a dynamic N-mixture model for open populations. We estimated the population of tahr in the sampling frame using design-based, finite sampling methods and model-based inference procedures. The mean estimated density of tahr on each plot varied from zero to 31.7 tahr/km². The mean densities of tahr varied among management units, ranging from 0.3 to 10.7 tahr/km², and exceeded specified intervention densities in 6 of the 7 management units. The total design-based estimate of tahr abundance in the sampling frame was 34,500 (95% CI = 27,750–42,900), with a coefficient of variation (CV) of 0.11. The corresponding model-based estimate of total abundance was similar (34,550, 95% CI = 30,250–38,700) but was substantially more precise (CV = 0.06) than the design-based estimate. The precision of the estimates for the individual management units was also better than that of the design-based estimates, with CVs of <0.20 for all but 1 management unit. Our study provides a repeatable method for sampling mountain ungulates. More generally, robust estimation of abundance and density of mountain ungulates is possible by combining aerial surveys and open population models with an objective, probabilistic sampling design.     

Transience in the humpback whale population of New Caledonia and implications for abundance estimation

A phenomenon of transience in the humpback whale population breeding in New Caledonia has been highlighted in recent analyses. We used these data to illustrate the risk of flawed inference when transience is not properly accounted for in abundance estimation of resident populations. Transients are commonly defined as individuals that pass through the sampling area once, i.e., have a null probability of being caught again, and therefore induce heterogeneity in the detection process. The presence of transients can lead to severe bias in the estimation of abundance and we demonstrate how to correct for this feature when estimating abundance of resident populations. In New Caledonia, very different conclusions about the number of resident whales in the southern lagoon between 1999 and 2005 are obtained when the abundance estimate accounts for the transient whales. Without correction, the estimates of the abundance were up to twice as high across all years compared to the estimates of the resident population when a correction for transients had been incorporated. Having reliable population estimates when assessing the status of endangered species is essential in documenting recovery and monitoring of population trends. Therefore, we encourage researchers to account for transients when reporting abundances of resident populations.     

Estimating abundance and population trends when detection is low and highly variable: A comparison of three methods for the Hermann's tortoise

Assessing population trends is a basic prerequisite to carrying out adequate conservation strategies. Selecting an appropriate method to monitor animal populations can be challenging, particularly for low-detection species such as reptiles. This study compares 3 detection-corrected abundance methods (capture–recapture, distance sampling, and N-mixture) used to assess population size of the threatened Hermann's tortoise. We used a single dataset of 432 adult tortoise observations collected at 118 sampling sites in the Plaine des Maures, southeastern France. We also used a dataset of 520 tortoise observations based on radiotelemetry data collected from 10 adult females to estimate and model the availability (g0) needed for distance sampling. We evaluated bias for N-mixture and capture–recapture, by using simulations based on different values of detection probabilities. Finally, we conducted a power analysis to estimate the ability of the 3 methods to detect changes in Hermann's tortoise abundances. The abundance estimations we obtained using distance sampling and N-mixture models were respectively 1.75 and 2.19 times less than those obtained using the capture–recapture method. Our results indicated that g0 was influenced by temperature variations and can differ for the same temperature on different days. Simulations showed that the N-mixture models provide unstable estimations for species with detection probabilities <0.5, whereas capture–recapture estimations were unbiased. Power analysis showed that none of the 3 methods were precise enough to detect slow population changes. We recommend that great care should be taken when implementing monitoring designs for species with large variation in activity rates and low detection probabilities. Although N-mixture models are easy to implement, we would not recommend using them in situations where the detection probability is very low at the risk of providing biased estimates. Among the 3 methods allowing estimation of tortoise abundances, capture–recapture should be preferred to assess population trends. © 2013 The Wildlife Society.     

REML estimation of variance parameters in nonlinear mixed effects models using the SAEM algorithm

Nonlinear mixed effects models are now widely used in biometrical studies, especially in pharmacokinetic research or for the analysis of growth traits for agricultural and laboratory species. Most of these studies, however, are often based on ML estimation procedures, which are known to be biased downwards. A few REML extensions have been proposed, but only for approximated methods. The aim of this paper is to present a REML implementation for nonlinear mixed effects models within an exact estimation scheme, based on an integration of the fixed effects and a stochastic estimation procedure. This method was implemented via a stochastic EM, namely the SAEM algorithm. The simulation study showed that the proposed REML estimation procedure considerably reduced the bias observed with the ML estimation, as well as the residual mean squared error of the variance parameter estimations, especially in the unbalanced cases. ML and REML based estimators of fixed effects were also compared via simulation. Although the two kinds of estimates were very close in terms of bias and mean square error, predictions of individual profiles were clearly improved when using REML vs. ML. An application of this estimation procedure is presented for the modelling of growth in lines of chicken.     

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture–recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture–recapture to illustrate volatility across estimation methods.     

The influence of sample size on two approaches to estimate Black Grouse Lyrurus tetrix population size using non-invasive sampling methods

Population size is an important parameter to monitor for species conservation and management. This is especially important for rare and endangered species, as declines can give information about anthropogenic impacts and the need for new conservation measures. To estimate population size, various methods of analysis can be used, for which sample size is an important factor. Sample size is particularly important to consider when applying non-invasive sampling strategies such as sampling faeces or feathers/hairs as a source of DNA, as a means to limit disturbance and stress for the species of concern. We investigated a Black Grouse Lyrurus tetrix population in the eastern part of the Alps, in East Tyrol (Austria), and estimated population size using two approaches: capture–recapture and rarefaction. With a set of 12 polymorphic microsatellite markers, we identified genotypes from faeces and feathers (backed up with 23 tissue samples) and checked for population substructure and gene flow among sampling sites. We estimated population size using four different models from the two approaches (molecular capture–recapture: TIRM, TIRMpart; rarefaction: hyperbolic function – Kohn, exponential function – Eggert). To evaluate the impact of sample size on the estimations, we used the full dataset of 500 samples (‘complete’ dataset) and half the dataset of 250 samples (‘half’ dataset). We also estimated the population size for each sex separately using complete and half datasets to check for sex-specific differences in population size. We found similar results in three of four models (capture–recapture: capwire TIRM, capwire TIRMpart; rarefaction: rarefaction Kohn). Using just half of the data increased the uncertainties in the estimation of population size in all models used and deviations were particularly large in females, which indicated a sex bias. Only the complete dataset of males had an observation rate of more than two observations/individual, and this observation rate meets the recommendation for using the capwire models. This indicates that, for species with different sex-specific detectability, larger sample sizes do not generally imply higher observation rates. We conclude that calculating the observation rates and population-size estimations for each sex separately can improve overall population-size estimation, especially in species with biased sex ratios and those that exhibit sex-specific behaviour.     

Capwire: a R package for estimating population census size from non‐invasive genetic sampling

Non‐invasive genetic sampling is an increasingly popular approach for investigating the demographics of natural populations. This has also become a useful tool for managers and conservation biologists, especially for those species for which traditional mark–recapture studies are not practical. However, the consequence of collecting DNA indirectly is that an individual may be sampled multiple times per sampling session. This requires alternative statistical approaches to those used in traditional mark–recapture studies. Here we present the R package capwire , an implementation of the population size estimators of Miller et al. (Molecular Ecology 2005; 14 : 1991), which were designed to deal specifically with this type of sampling. The aim of this project is to enable users across platforms to easily manipulate their data and interact with existing R packages. We have also provided functions to simulate data under a variety of scenarios to allow for rigorous testing of the robustness of the method and to facilitate further development of this approach.     

Bayesian Estimation of Hunting Success Rate and Harvest for Spatially Correlated Post‐stratified Data

We consider the estimation of success rate and harvest under post survey stratification at the sub‐domain (county) level. Often in this situation, the population size for the sub‐domain is unknown and the random mechanism that dictates the sample size for sub‐domains is ignored. Finding good estimators of success rate and harvest is very important for wildlife abundance. A Bayesian hierarchical model is developed to estimate both success rate and harvest simultaneously. The model includes a random sub‐domain sample size correlated with the number of successes in the sub‐domain, fixed week effects, random geographic effects, and spatial correlations between neighboring sub‐domains. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method developed is illustrated using data from the Missouri Turkey Hunting Survey. The estimation of success rate is improved by treating the the sub‐domain sample size as a random variable instead of a fixed constant. The Bayesian model yields a reasonable harvest estimation. The spatial pattern of the estimated harvest matches the pattern of the check station data.     

A field test of the distance sampling method using Golden‐cheeked Warblers

ABSTRACT Criteria for delisting Golden‐cheeked Warblers (Dendroica chrysoparia) include protection of sufficient breeding habitat to ensure the continued existence of 1000 to 3000 singing males in each of eight recovery regions for ≥10 consecutive years. Hence, accurate abundance estimation is an integral component in the recovery of this species. I conducted a field test to determine if the distance sampling method provided unbiased abundance estimates for Golden‐cheeked Warblers and develop recommendations to improve the accuracy of estimates by minimizing the effects of violating this method's assumptions. To determine if observers could satisfy the assumptions that birds are detected at the point with certainty and at their initial locations, I compared point‐transect sampling estimates from 2‐, 3‐, 4‐, and 5‐min time intervals to actual abundance determined by intensive territory monitoring. Point‐transect abundance estimates were 15%, 29%, 43%, and 59% greater than actual abundance (N= 156) for the 2‐, 3‐, 4‐, and 5‐min intervals, respectively. Point‐transect sampling produced unbiased estimates of Golden‐cheeked Warbler abundance when counts were limited to 2 min (N= 154–207). Abundance estimates derived from point‐transect sampling were likely greater than actual abundance because observers did not satisfy the assumption that birds were detected at their initial locations due to the frequent movements and large territory sizes of male Golden‐cheeked Warblers. To minimize the effect of movement on abundance estimates, I recommend limiting counts of singing males to 2‐min per point. Counts for other species in similar habitats with similar behavior and movement patterns also should be limited to 2 min when unbiased estimates are important and conducting field tests of the point‐transect distance sampling method is not possible.     

Evaluation of three field monitoring-density estimation protocols and their relevance to Komodo dragon conservation

Finding practical ways to robustly estimate abundance or density trends in threatened species is a key facet for effective conservation management. Further identifying less expensive monitoring methods that provide adequate data for robust population density estimates can facilitate increased investment into other conservation initiatives needed for species recovery. Here we evaluated and compared inference-and cost-effectiveness criteria for three field monitoring-density estimation protocols to improve conservation activities for the threatened Komodo dragon (Varanus komodoensis). We undertook line-transect counts, cage trapping and camera monitoring surveys for Komodo dragons at 11 sites within protected areas in Eastern Indonesia to collect data to estimate density using distance sampling methods or the Royle–Nichols abundance induced heterogeneity model. Distance sampling estimates were considered poor due to large confidence intervals, a high coefficient of variation and that false absences were obtained in 45 % of sites where other monitoring methods detected lizards present. The Royle–Nichols model using presence/absence data obtained from cage trapping and camera monitoring produced highly correlated density estimates, obtained similar measures of precision and recorded no false absences in data collation. However because costs associated with camera monitoring were considerably less than cage trapping methods, albeit marginally more expensive than distance sampling, better inference from this method is advocated for ongoing population monitoring of Komodo dragons. Further the cost-savings achieved by adopting this field monitoring method could facilitate increased expenditure on alternative management strategies that could help address current declines in two Komodo dragon populations.     

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.     

The CountEm software: simple,efficient and unbiased population size estimation

Population size estimation is essential in ecology and conservation studies. Aerial photography can facilitate this laborious task with high resolution images. However, in images with thousands of individuals exhaustive manual counting is tedious, slow and difficult to verify. Computer vision software may work under some particular conditions but they are generally biased and known to fail in several situations. The CountEm software is a simple alternative based on geometric sampling. It provides a fast and unbiased size estimation for all sorts of populations. The only requirement is that the discrete objects (e.g. animals) in the target population are unambiguously distinguishable for counting in a still image. Typical relative standard errors in the 5–10% range are obtained after counting ~200 properly sampled animals in about 5 min irrespective of population size. The CountEm ver. 1.4.1 is presented here, which includes a guided mode with a simple software interface.