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Adaptive sampling designs are becoming increasingly popular in environmental science, particularly for surveying rare and
aggregated populations. An adaptive sample is one in which the survey design is modified, or adapted, in some way on the basis
of information gained during the survey. There are many different adaptive survey designs that can be used to estimate animal
and plant abundance. In adaptive cluster sampling, additional sample effort is allocated during the survey to the immediate
neighborhood in which the species is found. In adaptive stratified sampling, additional sample effort is allocated during
the survey to strata of high abundance. The appealing feature of these adaptive designs is that the field biologist gets to
do what innately seems sensible when working with rare and aggregated populations—field effort is targeted around where the
species is observed in the first wave of the survey. However, there are logistical challenges of applying this principle of
targeted field effort while remaining in the framework of probability-based sampling. We propose a simplified adaptive survey
design that incorporates both targeting field effort and being logistically feasible. We show with a case study population
of rockfish that complete allocation stratified sampling is a very efficient design. 相似文献
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We consider the problem of estimating the number of distinct species S in a study area from the recorded presence or absence of species in each of a sample of quadrats. A generalized jackknife estimator of S is derived, along with an estimate of its variance. It is compared with the jackknife estimator for S proposed by Heltshe and Forrester and the empirical Bayes estimator of Mingoti and Meeden. We show that the empirical Bayes estimator has the form of a generalized jackknife estimator under a specific model for species distribution. We compare the new estimators of S to the empirical Bayes estimator via simulation. We characterize circumstances under which each is superior. 相似文献
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Empirical supremum rejection sampling 总被引:1,自引:0,他引:1
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On Chao's unequal probability sampling plan 总被引:1,自引:0,他引:1
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Bootstrap confidence intervals for adaptive cluster sampling 总被引:2,自引:0,他引:2
Consider a collection of spatially clustered objects where the clusters are geographically rare. Of interest is estimation of the total number of objects on the site from a sample of plots of equal size. Under these spatial conditions, adaptive cluster sampling of plots is generally useful in improving efficiency in estimation over simple random sampling without replacement (SRSWOR). In adaptive cluster sampling, when a sampled plot meets some predefined condition, neighboring plots are added to the sample. When populations are rare and clustered, the usual unbiased estimators based on small samples are often highly skewed and discrete in distribution. Thus, confidence intervals based on asymptotic normal theory may not be appropriate. We investigated several nonparametric bootstrap methods for constructing confidence intervals under adaptive cluster sampling. To perform bootstrapping, we transformed the initial sample in order to include the information from the adaptive portion of the sample yet maintain a fixed sample size. In general, coverages of bootstrap percentile methods were closer to nominal coverage than the normal approximation. 相似文献
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Optimizing performance of nonparametric species richness estimators under constrained sampling 总被引:1,自引:0,他引:1
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Harshana Rajakaruna D. Andrew R. Drake Farrah T. Chan Sarah A. Bailey 《Ecology and evolution》2016,6(20):7311-7322
Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species‐abundance data from a single sample into two equally sized samples, and using an appropriate incidence‐based estimator to estimate richness. To test this method, we assume a lognormal species‐abundance distribution (SAD) with varying coefficients of variation (CV), generate samples using MCMC simulations, and use the expected mean‐squared error as the performance criterion of the estimators. We test this method for Chao, Jackknife, ICE, and ACE estimators. Between abundance‐based estimators with the single sample, and incidence‐based estimators with the split‐in‐two samples, Chao2 performed the best when CV < 0.65, and incidence‐based Jackknife performed the best when CV > 0.65, given that the ratio of sample size to observed species richness is greater than a critical value given by a power function of CV with respect to abundance of the sampled population. The proposed method increases the performance of the estimators substantially and is more effective when more rare species are in an assemblage. We also show that the splitting method works qualitatively similarly well when the SADs are log series, geometric series, and negative binomial. We demonstrate an application of the proposed method by estimating richness of zooplankton communities in samples of ballast water. The proposed splitting method is an alternative to sampling a large number of individuals to increase the accuracy of richness estimations; therefore, it is appropriate for a wide range of resource‐limited sampling scenarios in ecology. 相似文献
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An unequal probability sampling scheme 总被引:2,自引:0,他引:2
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Jennifer A. Brown Mohammad Salehi M. Mohammad Moradi Gavin Bell David R. Smith 《Population Ecology》2008,50(3):239-245
How to design an efficient large-area survey continues to be an interesting question for ecologists. In sampling large areas,
as is common in environmental studies, adaptive sampling can be efficient because it ensures survey effort is targeted to
subareas of high interest. In two-stage sampling, higher density primary sample units are usually of more interest than lower
density primary units when populations are rare and clustered. Two-stage sequential sampling has been suggested as a method
for allocating second stage sample effort among primary units. Here, we suggest a modification: adaptive two-stage sequential
sampling. In this method, the adaptive part of the allocation process means the design is more flexible in how much extra
effort can be directed to higher-abundance primary units. We discuss how best to design an adaptive two-stage sequential sample. 相似文献
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We have developed four asymptotic interval estimators in closed forms for the gamma correlation under stratified random sampling, including the confidence interval based on the most commonly used weighted‐least‐squares (WLS) approach (CIWLS), the confidence interval calculated from the Mantel‐Haenszel (MH) type estimator with the Fisher‐type transformation (CIMHT), the confidence interval using the fundamental idea of Fieller's Theorem (CIFT) and the confidence interval derived from a monotonic function of the WLS estimator of Agresti's α with the logarithmic transformation (MWLSLR). To evaluate the finite‐sample performance of these four interval estimators and note the possible loss of accuracy in application of both Wald's confidence interval and MWLSLR using pooled data without accounting for stratification, we employ Monte Carlo simulation. We use the data taken from a general social survey studying the association between the income level and job satisfaction with strata formed by genders in black Americans published elsewhere to illustrate the practical use of these interval estimators. 相似文献
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