Bayesian meta-analysis of demographic parameters in three small, temperate passerines

Accurate estimates of population parameters are vital for estimating extinction risk. Such parameters, however, are typically not available for threatened populations. We used a recently developed software tool based on Markov Chain Monte Carlo methods for carrying out Bayesian inference (the BUGS package) to estimate four demographic parameters; the intrinsic growth rate, the strength of density dependence, and the demographic and environmental variance, in three species of small temperate passerines from two sets of time series data taken from a dipper and a song sparrow population, and from previously obtained frequentist estimates of the same parameters in the great tit. By simultaneously modeling variation in these demographic parameters across species and using the resulting distributions as priors in the estimation for individual species, we improve the estimates for each individual species. This framework also allows us to make probabilistic statements about plausible parameter values for small passerines temperate birds in general which is often critically needed in management of species for which little or no data are available. We also discuss how our work relates to recently developed theory on dynamic stochastic population models, and finally note some important differences between frequentist and Bayesian methods.     

Measuring Under-Five Mortality: Validation of New Low-Cost Methods

Background

There has been increasing interest in measuring under-five mortality as a health indicator and as a critical measure of human development. In countries with complete vital registration systems that capture all births and deaths, under-five mortality can be directly calculated. In the absence of a complete vital registration system, however, child mortality must be estimated using surveys that ask women to report the births and deaths of their children. Two survey methods exist for capturing this information: summary birth histories and complete birth histories. A summary birth history requires a minimum of only two questions: how many live births has each mother had and how many of them have survived. Indirect methods are then applied using the information from these two questions and the age of the mother to estimate under-five mortality going back in time prior to the survey. Estimates generated from complete birth histories are viewed as the most accurate when surveys are required to estimate under-five mortality, especially for the most recent time periods. However, it is much more costly and labor intensive to collect these detailed data, especially for the purpose of generating small area estimates. As a result, there is a demand for improvement of the methods employing summary birth history data to produce more accurate as well as subnational estimates of child mortality.

Methods and Findings

We used data from 166 Demographic and Health Surveys (DHS) to develop new empirically based methods of estimating under-five mortality using children ever born and children dead data. We then validated them using both in- and out-of-sample analyses. We developed a range of methods on the basis of three dimensions of the problem: (1) approximating the average length of exposure to mortality from a mother''s set of children using either maternal age or time since first birth; (2) using cohort and period measures of the fraction of children ever born that are dead; and (3) capturing country and regional variation in the age pattern of fertility and mortality. We focused on improving estimates in the most recent time periods prior to a survey where the traditional indirect methods fail. In addition, all of our methods incorporated uncertainty. Validated against under-five estimates generated from complete birth histories, our methods outperformed the standard indirect method by an average of 43.7% (95% confidence interval [CI] 41.2–45.2). In the 5 y prior to the survey, the new methods resulted in a 53.3% (95% CI 51.3–55.2) improvement. To illustrate the value of this method for local area estimation, we applied our new methods to an analysis of summary birth histories in the 1990, 2000, and 2005 Mexican censuses, generating subnational estimates of under-five mortality for each of 233 jurisdictions.

Conclusions

The new methods significantly improve the estimation of under-five mortality using summary birth history data. In areas without vital registration data, summary birth histories can provide accurate estimates of child mortality. Because only two questions are required of a female respondent to generate these data, they can easily be included in existing survey programs as well as routine censuses of the population. With the wider application of these methods to census data, countries now have the means to generate estimates for subnational areas and population subgroups, important for measuring and addressing health inequalities and developing local policy to improve child survival. Please see later in the article for the Editors'' Summary     

Predicting Grizzly Bear Density in Western North America

Conservation of grizzly bears (Ursus arctos) is often controversial and the disagreement often is focused on the estimates of density used to calculate allowable kill. Many recent estimates of grizzly bear density are now available but field-based estimates will never be available for more than a small portion of hunted populations. Current methods of predicting density in areas of management interest are subjective and untested. Objective methods have been proposed, but these statistical models are so dependent on results from individual study areas that the models do not generalize well. We built regression models to relate grizzly bear density to ultimate measures of ecosystem productivity and mortality for interior and coastal ecosystems in North America. We used 90 measures of grizzly bear density in interior ecosystems, of which 14 were currently known to be unoccupied by grizzly bears. In coastal areas, we used 17 measures of density including 2 unoccupied areas. Our best model for coastal areas included a negative relationship with tree cover and positive relationships with the proportion of salmon in the diet and topographic ruggedness, which was correlated with precipitation. Our best interior model included 3 variables that indexed terrestrial productivity, 1 describing vegetation cover, 2 indices of human use of the landscape and, an index of topographic ruggedness. We used our models to predict current population sizes across Canada and present these as alternatives to current population estimates. Our models predict fewer grizzly bears in British Columbia but more bears in Canada than in the latest status review. These predictions can be used to assess population status, set limits for total human-caused mortality, and for conservation planning, but because our predictions are static, they cannot be used to assess population trend.     

Performance of ground-based and aerial survey methods for monitoring wildlife assemblages in a conservation area of northern Tanzania

Validating and improving field-sampling techniques for estimating wildlife community composition and population size is essential for wildlife management and conservation. We conducted ground distance sampling surveys along line transects and block counts from a small aircraft in Manyara Ranch in Northern Tanzania and contrasted estimates of species richness and species-specific densities from both sampling techniques. We used regression analyses (logistic regression and generalized linear mixed models) and model selection to investigate whether a species’ body size, group size, body color, as well as vegetation cover explained the variation in species presence/absence and relative density differences in aerial vs. ground-based sampling. Ground surveys detected significantly more species than aerial surveys. However, aerial surveys detected three species that were missed by ground surveys (African lions, African buffalo, and spotted hyena). Model selection suggested that species with smaller body mass and small group sizes were more likely to be missed in aerial surveys. Densities estimated from the aerial surveys were generally but non-significantly lower than the densities estimated from the ground surveys, with the exception of density estimates for African elephants which were slightly higher from aerial surveys. Density differences between the two methods were greater for species with small group size, light body color, and in areas with denser vegetation cover; these variables explained 75% of the variation in density differences between the two survey methods. Albeit being similar in operational costs in our relatively small study area, ground surveys yielded (1) more complete information with respect to wildlife community composition and (2) density estimates were mostly higher and (3) more precise and (4) appear more feasible to be implemented in community-based conservation schemes.     

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard Panthera pardus

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km² (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.     

Sampling bias in snow leopard population estimation studies

Accurate assessments of the status of threatened species and their conservation planning require reliable estimation of their global populations and robust monitoring of local population trends. We assessed the adequacy and suitability of studies in reliably estimating the global snow leopard (Panthera uncia) population. We compiled a dataset of all the peer-reviewed published literature on snow leopard population estimation. Metadata analysis showed estimates of snow leopard density to be a negative exponential function of area, suggesting that study areas have generally been too small for accurate density estimation, and sampling has often been biased towards the best habitats. Published studies are restricted to six of the 12 range countries, covering only 0.3–0.9% of the presumed global range of the species. Re-sampling of camera trap data from a relatively large study site (c.1684 km²) showed that small-sized study areas together with a bias towards good quality habitats in existing studies may have overestimated densities by up to five times. We conclude that current information is biased and inadequate for generating a reliable global population estimate of snow leopards. To develop a rigorous and useful baseline and to avoid pitfalls, there is an urgent need for (a) refinement of sampling and analytical protocols for population estimation of snow leopards (b) agreement and coordinated use of standardized sampling protocols amongst researchers and governments across the range, and (c) sampling larger and under-represented areas of the snow leopard's global range.     

基于红外相机数据评估华南地区豹猫的种群密度和活动节律

可靠的种群密度数据对野生动物的保护和管理十分重要。豹猫(Prionailurus bengalensis)是中国分布最广且常见的猫科动物, 但野生种群密度估算的研究并不多。本研究于2020年6月至2021年5月在香港新界嘉道理农场暨植物园开展红外相机调查, 利用空间标记-重捕法估算当地豹猫的种群密度并用核密度估计方法分析其活动节律。本次调查以网格方式布置红外相机, 在约1.5 km²的研究范围之内设置了19个相机位点, 每个位点安装2台相机以获取豹猫身体两侧花纹来进行个体识别。连续12个月调查共捕获113次有效的豹猫拍摄事件, 当中仅61次事件的照片足够清晰以进行个体识别。基于种群封闭的要求, 我们以2个月为单位将12个月的数据分为6个采样期去分析豹猫种群密度, 结果显示仅两个采样期的估算值最为准确, 分别为0.64 ± 0.31 (0.26-1.55)只/km²和0.87 ± 0.48 (0.31-2.40)只/km², 是已知全球豹猫密度最高的地点之一。结果还发现, 雨季研究地点的豹猫并无明显的日活动节律, 在旱季则偏夜行-晨昏行性多一些, 但也有一定的日间活动; 雨季和旱季的日活动节律无显著差异。本研究是首次以个体识别配以空间标记-重捕模型对中国大陆地区豹猫种群密度调查的研究; 我们也提出一些关于红外相机架设方法的建议, 以提高照片个体识别的准确度并增加重捕次数, 最后提高密度估算的准确度。本研究也进一步证明豹猫适应性极强, 在活动节律上表现出极高的可塑性, 在严格保护下可以恢复健康的种群。     

Efficient,Noninvasive Genetic Sampling for Monitoring Reintroduced Wolves

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

Estimating the age structure of a buried adult population: A new statistical approach applied to archaeological digs in France

Paleodemographers have developed several methods for estimating the age structure of historical populations in absence of civil registration data. Starting from biological indicators alone, they use a reference population of known sex and age to assess the conditional distribution of the biological indicator given age. However, the small amount of data available and the unstable nature of the related statistical problem mean that most methods are disappointing. Using the most reliable reference data possible, we propose a simple statistical method, integrating the maximum amount of information included in the actual data, which quite significantly improves age estimates for a buried population. Here the method is applied to a French cemetery used from Late Antiquity to the end of the Early Middle Ages. Am J Phys Anthropol, 2013. © 2012 Wiley Periodicals, Inc.     

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture–recapture data

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles Myodes rufocanus from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.     

Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study

1. Automated recording units are commonly used by consultants to assess environmental impacts and to monitor animal populations. Although estimating population density of bats using stationary acoustic detectors is key for evaluating environmental impacts, estimating densities from call activity data is only possible through recently developed numerical methods, as the recognition of calling individuals is impossible.
2. We tested the applicability of generalized random encounter models (gREMs) for determining population densities of three bat species (Common pipistrelle Pipistrellus pipistrellus, Northern bat Eptesicus nilssonii, and Natterer's bat Myotis nattereri) based on passively collected acoustical data. To validate the results, we compared them to (a) density estimates from the literature and to (b) Royle–Nichols (RN) models of detection/nondetection data.
3. Our estimates for M. nattereri matched both the published data and RN‐model results. For E. nilssonii, the gREM yielded similar estimates to the RN‐models, but the published estimates were more than twice as high. This discrepancy might be because the high‐altitude flight of E. nilssonii is not accounted for in gREMs. Results of gREMs for P. pipistrellus were supported by published data but were ~10 times higher than those of RN‐models. RN‐models use detection/nondetection data, and this loss of information probably affected population estimates of very active species like P. pipistrellus.
4. gREM models provided realistic estimates of bat population densities based on automatically recorded call activity data. However, the average flight altitude of species should be accounted for in future analyses. We suggest including flight altitude in the calculation of the detection range to assess the detection sphere more accurately and to obtain more precise density estimates.

     

On numerical uncertainty in evaluation of pest population size

Obtaining reliable estimates of pest insect species abundance is an essential part of ecological monitoring programs. It is often the case that data available for obtaining such estimates are sparse which in turn makes achieving an accurate evaluation difficult. This is especially true for strongly heterogeneous pest population density distributions. In our paper we discuss the accuracy of a mean density estimate when a certain class of high aggregation density distributions is considered and a standard statistical method is employed to handle sparse sampled data. It will be shown in the paper that conventional conclusions about the accuracy of the pest population size evaluation do not work when the data are sparse and a new approach is required. Namely, if the number of traps is small, an estimate of the mean density becomes a random variable with an error of high magnitude and we have to compute the probability of an accurate estimate rather than computing the estimate itself. We have obtained a probability of an accurate estimate based on the assumption that only one trap falls within a sub-domain where the pest population density is different from zero. The probability has been calculated for the one-dimensional and the two-dimensional problem.     

Estimating the abundance of rare and elusive carnivores from photographic-sampling data when the population size is very small

Conservation and management agencies require accurate and precise estimates of abundance when considering the status of a species and the need for directed actions. Due to the proliferation of remote sampling cameras, there has been an increase in capture–recapture studies that estimate the abundance of rare and/or elusive species using closed capture–recapture estimators (C–R). However, data from these studies often do not meet necessary statistical assumptions. Common attributes of these data are (1) infrequent detections, (2) a small number of individuals detected, (3) long survey durations, and (4) variability in detection among individuals. We believe there is a need for guidance when analyzing this type of sparse data. We highlight statistical limitations of closed C–R estimators when data are sparse and suggest an alternative approach over the conventional use of the Jackknife estimator. Our approach aims to maximize the probability individuals are detected at least once over the entire sampling period, thus making the modeling of variability in the detection process irrelevant, estimating abundance accurately and precisely. We use simulations to demonstrate when using the unconditional-likelihood M ₀ (constant detection probability) closed C–R estimator with profile-likelihood confidence intervals provides reliable results even when detection varies by individual. If each individual in the population is detected on average of at least 2.5 times, abundance estimates are accurate and precise. When studies sample the same species at multiple areas or at the same area over time, we suggest sharing detection information across datasets to increase precision when estimating abundance. The approach suggested here should be useful for monitoring small populations of species that are difficult to detect.     

Demographic window to aging in the wild: constructing life tables and estimating survival functions from marked individuals of unknown age

Summary We address the problem of establishing a survival schedule for wild populations. A demographic key identity is established, leading to a method whereby age-specific survival and mortality can be deduced from a marked cohort life table established for individuals that are randomly sampled at unknown age and marked, with subsequent recording of time-to-death. This identity permits the construction of life tables from data where the birth date of subjects is unknown. An analogous key identity is established for the continuous case in which the survival schedule of the wild population is related to the density of the survival distribution in the marked cohort. These identities are explored for both life tables and continuous lifetime data. For the continuous case, they are implemented with statistical methods using non-parametric density estimation methods to obtain flexible estimates for the unknown survival distribution of the wild population. The analytical model provided here serves as a starting point to develop more complex models for residual demography, i.e. models for estimating survival of wild populations in which age-at-entry is unknown and using remaining information in randomly encountered individuals. This is a first step towards a broad new concept of 'expressed demographic information content of marked or captured individuals'.     

Estimating the size of a transient population

Migratory populations often stop over for short periods of time at predictable sites along their migration routes. These staging areas can be heavily used and are potentially critical to the survival of the migrants. This paper presents a method for estimating the number of individuals using such an area and their average residence time. The estimator is based on daily population estimates and records of repeat sightings of identifiable individuals. Its application is illustrated with observations on a population of migrating birds, some of which could be identified from bands that were readable from a distance.     

Assessment of census techniques for interspecific comparisons of tropical rainforest bird densities: a field evaluation in the Western Ghats, India

Studies comparing different bird censusing methods are useful for assessing relative biases, synthesizing data across studies, and designing bird population monitoring programmes. A field study was carried out in mid-elevation tropical rainforest in the Western Ghats to compare bird density estimates from line transect, point count and territory spot-mapping methods. Interspecific comparisons were made using data for 13 common resident bird species, including two endemics. Variable-width line transect density estimates were highly correlated with, but slightly (17%) higher than, those produced by territory spot-mapping. Although densities from variable-width point counts and spot-mapping were highly positively correlated, the estimates were 95% higher on average in the former. Higher density estimates relative to spot-mapping were produced mainly for the most abundant species, probably due to their mobility and the inclusion of additional individuals that enter the count area during the count period. Fixed-width strip transects and point counts produced density estimates that were highly correlated with, but significantly lower than, variable-width estimates. Wherever possible, territory spot-mapping and line transects are recommended for density estimates; the former may yield additional information on spatial distribution of birds. Fixed-width transects or point counts, being easier to apply, may be used for large-scale monitoring programmes. Interspecific variation in flocking systems and the poor visibility in dense rainforest vegetation indicate the need for care in collection of data on flock size and its variation, which is necessary for estimating the density of individuals. The variation across methods suggests the need for further research using multiple methods across years and marked individuals to verify territoriality and accuracy.     

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.     

Estimating population density from indirect sign: track counts and the Formozov–Malyshev–Pereleshin formula

For many purposes it is often desirable to estimate animal population densities over large areas. Where total counts are not possible and sightings are relatively rare, a range of methods exists to estimate densities from indirect sign. Such methods are frequently unreliable and usually require independent calibration or confirmation. We present an analytical method for estimating population density from track counts. The method, widely known in the Russian Federation but not in the English language scientific literature, requires counts of tracks of known age, together with estimates of animal daily travel distances. We use simulations to verify the theoretical basis of the approach and to indicate potential precision that may be achieved. We illustrate application of the approach using a large data set on ungulate track counts in the Russian Far East. We suggest that under most circumstances, nonparametric bootstrapping will be the most appropriate method for deriving estimates of confidence intervals about density estimates. As with other approaches to estimating density from indirect sign, the method that we discuss is vulnerable to violations of an array of underlying assumptions. However, it is easily applied and could represent an important method by which the relationship between indices of abundance and absolute density can be understood.     

Comparison of point transect distance and traditional acoustic point-count sampling of hoolock gibbons in Htamanthi Wildlife Sanctuary,Myanmar

Effective conservation demands more accurate and reliable methods of survey and monitoring of populations. Surveys of gibbon populations have relied mostly on mapping of groups in “listening areas” using acoustical point-count data. Traditional methods of estimating density in have usually used counts of gibbon groups within fixed-radius areas or areas bounded by terrain barriers to sound transmission, and have not accounted for possible decline in detectability with distance. In this study we sampled the eastern hoolock gibbon (Hoolock leucogenys) population in Htamanthi Wildlife Sanctuary (WS), Myanmar, using two methods: the traditional point-count method with fixed-radius listening areas, and a newer method using point-transect Distance analysis from a sample point established in the center of each listening point array. The basic data were obtained by triangulating on singing groups from four LPs for 4 days, in 10 randomly selected sample areas within the sanctuary. The point transect method gave an average density of 3.13 groups km⁻², higher than the estimates of group density within fixed-radius areas without correction for detectability. A new method of analysis of singing probability per day (p[1]) gave an estimate of 0.547. Htamanthi WS is an important conservation area containing an estimated 7000 (95% confidence interval: 5000–10,000) hoolock groups. Surveys at Htamanthi WS and locations in the Hukaung Valley suggest that the extensive evergreen forests in northern Myanmar have the capacity to support 2–4 (average about 3) groups of hoolock gibbons per km², but most forests in its range have yet to be surveyed.