Improving population size estimation at western capercaillie leks: lek counts versus genetic methods

Hunting records have proven useful for examining the historical status of wildlife populations. The number of animals harvested can provide information on past population sizes that would have been required to support harvest yields. Therefore, when statistical data on annual harvests are available, a minimum estimate of past population sizes can be calculated. A very simple method for estimating the sizes of historic wildlife populations using only annual hunting records and the maximum annual population increase rate is presented in this study. This method was applied to estimate past population sizes for Japanese sika deer (Cervus nippon yesoensis) in Hokkaido Island, Japan, using hunting records from 1873 to 1882, and assuming 15% and 35% population increase rates. The annual number of deer harvested during 1873 to 1882 ranged from 15,000 to 129,000. The minimum population size in 1873 was estimated as 349,000–473,000. This method was validated by applying it to the eastern population of Hokkaido Island in 1993 when the population size was approximately 260,000, and population sizes estimated by this method were 0.50–1.17 times the nominal population size. Thus, the population estimates from this method were approximately equal to or less than the expected population sizes, and this method can be used to obtain minimum estimates of wildlife populations. Because shorter durations of hunting records result in population size underestimates, it would be better to use hunting record of 10 years or longer in this method. In addition, the degree of underestimation may change with hunting pressure intensity on the populations, other causes of mortality, and maximum annual increase rates of the species. The method can be applied to any wildlife species for which records of annual harvest and maximum annual population increase rates of the species are available. The estimates obtained can provide benchmarks for the population size required for ecosystem conservation, and can be useful for wildlife management as they indicate the lowest limit to maintain the population.

Understanding the current population size of small, spatially aggregating populations of species is essential for their conservation. Reliable estimates of the effective population size (Ne) can be used to provide an early warning for conservation managers of the risks to genetic viability of small populations. Critically endangered, migratory swift parrots Lathamus discolor exist in a single panmictic population in Australia. In their Tasmanian breeding range, they are at severe risk of predation by introduced sugar gliders, exacerbated by deforestation. We used three genetic approaches to estimate Ne using DNA samples genotyped by microsatellite markers and existing life‐history data of swift parrots. Based on all samples, we revealed small contemporary Ne estimates across methods (range: 44–140), supporting the need to urgently address threatening processes. Using the 0.5 Ne/N ratio calculated from demographic data suggests that the minimum potential contemporary population size is below 300 individual swift parrots. This is considerably lower than the published estimates derived from expert elicitation, and accords with modeled estimates of extinction risk in this species. Our study has important implications for other threatened species with unknown population sizes and demonstrates that by utilizing available genetic data, reasonable estimates of Ne can be derived.

Evaluation of the Schnabel, geometric and nonparametric estimators of population size was performed on a model population of 100 individuals which possessed a bivariate normal home range model with ox = 1 and a, = 2, a random spatial pattern, an average initial probability of capture of p = 0.10 or p = 0.40 and either a trap happy or trap shy learning process. Data were generated from a model which simulated 10-sample mark-recapture experiments consisting of 25 traps. Evaluation of these estimators reveals the magnitude of bias and variability present in such circumstances. The results indicate that the nonparametric estimator was the most robust under these conditions. J. WILDL. MANAGE. 43(2):474-483 Estimating the size of an animal population presents complex problems to population ecologists. General reviews of some of the methods were presented by Overton (1969) and Seber (1973). Estimators which have been developed are based on various assumptions, some of which are not fully satisfied in practice. When assumptions are not satisfied, wide variation may result in the estimates. Since many different estimators are available, it is desirable to evaluate their performance under varying degrees of violation of the assumptions. This would provide the investigator a measure of the degree of robustness of the estimators and shed light into which one would be most suitable for a specific problem. Field evaluation of the estimators is possible if mark-recapture experiments are performed in areas where the true sizes of the populations are known. However, this information is rarely available. An alternate approach for determining the robustness of the estimators is to develop simulation models to mimic populations exposed to mark-recapture experiments. In such models the parameters that control the animals' response to trapping can be varied, resulting in differential magnitudes of assumption violation. Recently, simulation has been used in the study of estimators of population parameters. Gates (1969) used computer simulation to evaluate the bias of a variety of line transect density estimators. Burnham and Overton (1969) and Manly (1970) employed computer simulation in evaluating the behavior of various markrecapture estimators. In another paper, Manly (1971) used simulation to examine Jolly's (1965) variance formulas for estimators of population parameters. In the present study, computer simulation techniques were also used. The objective of this work was to evaluate the sample expectation, variance, bias, and mean square error of mark-recapture estimators of population size when the assumption of equal probability of capture of animals is violated in response to a learning process related to the previous capture history. Appreciation is extended to the Division of Forestry and Wildlife Resources, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, and to the Department of Fisheries and Wildlife, Michigan State University, East Lan' Present address: Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824. 474 J. Wildl. Manage. 43(2):1979 This content downloaded from 157.55.39.80 on Fri, 22 Apr 2016 05:57:59 UTC All use subject to http://about.jstor.org/terms SIMULATION OF LEARNED TRAP RESPONSE *Zarnoch 475 sing, Michigan, for computer time used in the development of the model and evaluation of the estimators of population size. ESTIMATORS OF POPULATION SIZE Letting N = number of individuals in the population and S = number of samples taken, the following estimators were evaluated. Schnabel.-The Schnabel estimator, modified by Chapman (1952), is defined

Estimates of population size are needed in populations targeted by recreational fisheries, to determine their conservational status and to formulate fishing regulations. Mark–recapture monitoring is a promising tool for this because of the increased popularity of catch‐and‐release angling. Northern pike, Esox lucius L., population sizes were estimated in four lakes based on experimental mark–recapture fishing using a hierarchical Bayesian model that also estimated natural mortality. The approach was compared with output from traditional Petersen’s population size estimates. Despite intensive fishing with multiple gears, the population size estimates suggested that a substantial proportion of individuals remained unobserved by fishing. Petersen’s estimates depended on the assumed rate of natural mortality, and no mortality rate scenario matched with the Bayesian estimates. Consequently, the number of individuals caught by recreational anglers is a poor proxy of population size for which estimations should be carried out using a modelling approach that also estimates natural mortality.

An effort is made to quantify some of the major sources of uncertainty associated with estimates of harp seal (Phoca groenlandica) prey consumption, with special reference to the uncertainty in estimates of seal population size. A population dynamics model is used, the inputs for which are the annual catches at age and pregnancy rates. Two parameters, instantaneous mortality rate, and the hunting selection on the pups for years prior to the availability of pregnancy data, are estimated by non-linear least squares using available survey estimates of pup production. The uncertainty in estimates of population size is estimated by Monte Carlo methods from the estimates of sampling error in the pregnancy rates and in the survey estimates of pup production, with the catches at age assumed to be known. The uncertainty in the population estimates is dominated by the uncertainty in the survey estimates.

Chapter 4 Susceptibility of Sharks, Rays and Chimaeras to Global Extinction

The Sage Grouse Centrocercus urophasianus is a species of conservation concern throughout its range in western North America. Since the 1950s, the high count of males at leks has been used as an index for monitoring populations. However, the relationship between this lek‐count index and population size is unclear, and its reliability for assessing population trends has been questioned. We used non‐invasive genetic mark‐recapture analysis of faecal and feather samples to estimate pre‐breeding population size for the Parachute‐Piceance‐Roan, a small, geographically isolated population of Sage Grouse in western Colorado, during two consecutive winters from 2012 to 2014. We estimated total pre‐breeding population size as 335 (95% confidence interval (CI): 287–382) in the first winter and 745 (95% CI: 627–864) in the second, an approximate doubling in abundance between years. Although we also observed a large increase in the spring lek‐count index between those years, high male count data poorly represented mark‐recapture estimates of male abundance in each year. Our data suggest that lek counts are useful for detecting the direction and magnitude of large changes in Sage Grouse abundance over time but they may not reliably reflect small changes in abundance that may be relevant to small populations of conservation concern.

The population of humpback whales (Megaptera novaeangliae) wintering off eastern South America was exploited by commercial whaling almost to the point of extinction in the mid-twentieth century. Since cessation of whaling in the 1970s it is recovering, but the timing and level of recovery is uncertain. We implemented a Bayesian population dynamics model describing the population’s trajectory from 1901 and projecting it to 2040 to revise a previous population status assessment that used Sampling-Importance-Resampling in a Bayesian framework. Using our alternative method for model fitting (Markov chain Monte Carlo), which is more widely accessible to ecologists, we replicate a “base case scenario” to verify the effect on model results, and introduce additional data to update the status assessment. Our approach allowed us to widen the previous informative prior on carrying capacity to better reflect scientific uncertainty around historical population levels. The updated model provided more precise estimates for population sizes over the period considered (1901–2040) and suggests that carrying capacity (K: median 22,882, mean 22,948, 95% credible interval [CI] 22,711–23,545) and minimum population size (N1958: median 305, mean 319, 95% CI 271–444) might be lower than previously estimated (K: median 24,558, mean 25,110, 95% CI 22,791–31,118; N1958: median 503, mean 850, 95% CI 159–3,943). However, posterior 95% credible intervals of parameters in the updated model overlap those of the previous study. Our approach provides an accessible framework for investigating the status of depleted animal populations for which information is available on historical mortality (e.g., catches) and intermittent estimates of population size and/or trend.

Although Bothrops insularis is critically endangered, there is no estimate of its population size based on field quantifications. Here we provide the first estimate of its population size based on plot surveys conducted at the Queimada Grande Island. We distributed 26 quadrat plots regularly in a trail that crosses the island. Plots were sampled twice in 2002. Of the 26 plots we sampled, 21 were located in forests and five in grassy areas. For the first survey, mean estimates of population size for plots located in forests and grassy areas were 2134.3 and 224.0 snakes, and the Percentage Relative Precision for these estimates were 38.6% and 277.6%, respectively. For the second survey, no snakes were found in grassy areas, whereas the mean estimate of population size for plots located in forests was 1304.3 snakes and the Percentage Relative Precision for these estimates was 93.7%. Although suffering from relatively low precision, our best estimate of population size in B. insularis is around the l...

Estimates of population size are critical for conservation and management, but accurate estimates are difficult to obtain for many species. Noninvasive genetic methods are increasingly used to estimate population size, particularly in elusive species such as large carnivores, which are difficult to count by most other methods. In most such studies, genotypes are treated simply as unique individual identifiers. Here, we develop a new estimator of population size based on pedigree reconstruction. The estimator accounts for individuals that were directly sampled, individuals that were not sampled but whose genotype could be inferred by pedigree reconstruction, and individuals that were not detected by either of these methods. Monte Carlo simulations show that the population estimate is unbiased and precise if sampling is of sufficient intensity and duration. Simulations also identified sampling conditions that can cause the method to overestimate or underestimate true population size; we present and discuss methods to correct these potential biases. The method detected 2–21% more individuals than were directly sampled across a broad range of simulated sampling schemes. Genotypes are more than unique identifiers, and the information about relationships in a set of genotypes can improve estimates of population size.

Size estimates of key populations at higher risk of HIV exposure are recognized as critical for understanding the trajectory of the HIV epidemic and planning and monitoring an effective response, especially for countries with concentrated and low epidemics such as those in Asia. To help countries estimate population sizes of key populations, global guidelines were updated in 2011 to reflect new technical developments and recent field experiences in applying these methods. In September 2013, a meeting of programme managers and experts experienced with population size estimates (PSE) for key populations was held for 13 Asian countries. This article summarizes the key results presented, shares practical lessons learnt and reviews the methodological approaches from implementing PSE in 13 countries. It is important to build capacity to collect, analyse and use PSE data; establish a technical review group; and implement a transparent, well documented process. Countries should adapt global PSE guidelines and maintain operational definitions that are more relevant and useable for country programmes. Development of methods for non-venue-based key populations requires more investment and collaborative efforts between countries and among partners.

The multispecies coalescent (MSC) model accounts for genealogical fluctuations across the genome and provides a framework for analyzing genomic data from closely related species to estimate species phylogenies and divergence times, infer interspecific gene flow, and delineate species boundaries. As the MSC model assumes correct sequences, sequencing and genotyping errors at low read depths may be a serious concern. Here, we use computer simulation to assess the impact of genotyping errors in phylogenomic data on Bayesian inference of the species tree and population parameters such as species split times, population sizes, and the rate of gene flow. The base-calling error rate is extremely influential. At the low rate of e = 0.001 (Phred score of 30), estimation of species trees and population parameters are little affected by genotyping errors even at the low depth of ∼3×. At high error rates (e = 0.005 or 0.01) and low depths (less than 10×), genotyping errors can reduce the power of species tree estimation, and introduce biases in estimates of population sizes, species divergence times, and the rate of gene flow. Treating heterozygotes in the sequences as missing data (ambiguities) may reduce the impact of genotyping errors. Our simulation suggests that it is preferable in terms of inference precision and accuracy to sequence a few samples at high depths rather than many samples at low depths.

We introduce new estimates of the size of the Eight Banner populations. Published estimates of the size of the population of the banners vary widely because of inconsistencies in the numbers reported in different original sources, and differences in the assumptions used to translated these numbers into estimates of total population size. For example, estimates of banner forces at the time of the Qing conquest vary from 60,000 to 350,000, with most clustered around 100,000-150,000. These typically multiply the numbers reported for the numbers in the banners by a scaling factor which includes an estimate of the number of household dependents. Assumptions about the scaling factor vary widely. We take a different approach used routinely by demographers to produce indirect estimates of total population size from fragmentary evidence on the numbers in specific age groups. We multiply numbers of adult males (ding) by ethnic banner reported for 1648, 1720, 1721, and 1723 in archival documents by a scaling factor that includes other age groups derived from stable population theory. In this approach, the choice of scaling factor only requires an assumption about mortality levels and the population growth rate. We produce a range of estimates of total population size based on different plausible assumptions about the life expectancy and population growth rate. We conclude that at the time of the conquest, the Eight Banner population at large was between 1.3 and 2.4 million people, and that seventy years later it had grown to between 2.6 and 4.8 million. Population in the Manchu banners in the middle seventeenth century was somewhere between 206,000 and 390,000, growing by 1720 to between 577,000 and 1.08 million.

One of the most important needs for wildlife managers is an accurate estimate of population size. Yet, for many species, including most marine species and large mammals, accurate and precise estimation of numbers is one of the most difficult of all research challenges. Open-population capture-recapture models have proven useful in many situations to estimate survival probabilities but typically have not been used to estimate population size. We show that open-population models can be used to estimate population size by developing a Horvitz-Thompson-type estimate of population size and an estimator of its variance. Our population size estimate keys on the probability of capture at each trap occasion and therefore is quite general and can be made a function of external covariates measured during the study. Here we define the estimator and investigate its bias, variance, and variance estimator via computer simulation. Computer simulations make extensive use of real data taken from a study of polar bears (Ursus maritimus) in the Beaufort Sea. The population size estimator is shown to be useful because it was negligibly biased in all situations studied. The variance estimator is shown to be useful in all situations, but caution is warranted in cases of extreme capture heterogeneity.

SummaryDispersal can push population density below strong Allee thresholds ensuring the demise of small founding populations. As a result, for isolated populations of dispersing organisms, the minimum founding population size that enables establishment can be quite different from the Allee threshold.We derive an expression for the minimum founding population size for a general integrodifference equation model of a dispersing population with a strong Allee effect. We demonstrate the utility of the general model by using it to approximate minimum founding populations for the mountain pine beetle.The minimum founding population of the general model increases linearly with the mean squared displacement of the dispersing organism. Transient dynamics of the general model suggest that population density at the point of introduction will often decrease before increasing, even when the minimum founding population size is exceeded.Calculation of minimum founding population sizes may prove useful for predicting invasion success based on propagule size and for maximizing the success of species reintroductions when strong Allee effects exist.