The reflection method of line intercept sampling to eliminate boundary bias

When a line transect overlaps the boundary of the sampled region, it can be reflected back on top of itself into the region, thereby making it possible to include elements near the edge twice from the same transect. A practical advantage of doing so is the reduction of field time and effort compared to the customary procedure of folding the transect back into another part of the region. An estimator is presented which accounts for this procedure in a way that preserves design-unbiased estimation.     

Variance estimation in the EMAP strategy for sampling discrete ecological resources

The United States Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) is designed to describe status, trends and spatial pattern of indicators of condition of the nation's ecological resources. The proposed sampling design for EMAP is based on a triangular systematic grid and employs both variable probability and double sampling. The Horvitz-Thompson estimator provides the foundation of the design-based estimation strategy used in EMAP. However, special features of EMAP designed to accommodate the complexity of sampling environmental resources on a national scale require modifications of standard variance estimation procedures as well as development of new techniques. An overview of variance estimation methods proposed for application to EMAP's sampling strategy for discrete resources is presented.     

The use of replicated plot,line and point sampling for estimating species abundance and ecological diversity

The paper deals with the problem of estimating diversity indexes for an ecological community. First the species abundances are unbiasedly and consistently estimated using designs based on n random and independent selections of plots, points or lines over the study area. The problem of sampling elusive populations is also considered. Finally, the diversity index estimates are obtained as functions of the abundance estimates. The resulting estimators turn out to be asymptotically (n large) unbiased, even if a considerable bias may occur for a small n. Accordingly, the method of jackknifing is made use of in order to reduce bias.     

Asymptotics in adaptive cluster sampling

In this article we consider asymptotic properties of the Horvitz-Thompson and Hansen-Hurwitz types of estimators under the adaptive cluster sampling variants obtained by selecting the initial sample by simple random sampling without replacement and by unequal probability sampling with replacement. We develop an asymptotic framework, which basically assumes that the number of units in the initial sample, as well as the number of units and networks in the population tend to infinity, but that the network sizes are bounded. Using this framework we prove that under each of the two variants of adaptive sampling above mentioned, both the Horvitz-Thompson and Hansen-Hurwitz types of estimators are design-consistent and asymptotically normally distributed. In addition we show that the ordinary estimators of their variances are also design-consistent estimators.     

Species abundance estimation using point-to-plant sampling in a design-based setting

A new species abundance estimator is proposed when point-to-plant sampling is adopted in a design-based framework. The method is based on the relationship between each species abundance and the probability density function of the relative squared point-to-plant distance. Using this result, a kernel estimator for species abundance is provided and the nearest neighbor method is suggested for bandwidth selection. The proposed estimator requires no assumptions about the species point patterns nor corrections for sampling near the edges of the study region. Moreover, the estimator shows suitable statistical properties as well as good practical performance as is shown in a simulation study.     

Variance decomposition in two-stage plot sampling: theoretical and empirical results

The statistical properties of two-stage plot sampling estimators of abundance are considered. In the first stage, some spatial units are selected over the whole study area according to a suitable sampling design, while in the second stage, the selected units are surveyed with floating plot sampling to estimate the abundance within. Some insights into the accuracy of the resulting estimators are obtained by splitting the sample variance into the first and second-stage components, while performance is empirically checked by means of a simulation study. Simulation results show that, in most situations, a relevant amount of the overall variance is due to the second stage sampling.     

Design-based estimators and power analyses of trend tests for the proportion of fish that exhibit gross pathological disorders

Methods for estimating the proportion of fish that exhibit gross pathological disorders and for estimating the variance of these estimates are defined. The methods are for the situation in which a probability-based sampling design is used to collect fish for examination, but geographic locations (rather than individual fish) are assigned probabilities of being selected for sampling. To illustrate the use of the methods, they are applied to data collected during the 1992 EMAP- Estuaries sampling program in the Louisianian Province (i.e., the Gulf of Mexico). Separate estimates of the proportion of fish with gross pathological disorders are computed for demersal species, commercial species, pelagic species, and all species as one group. In addition, a test for trend in the proportion of fish that exhibit gross pathological disorders is defined, and analyses of the power of the test are presented. The power analyses are based on a general underlying model of the random distribution patterns of fish and the random process of catching fish. The power analyses also take into account the features of the sampling designs used for collecting fish. Component parameter estimates were computed using data from the 1992 EMAP-Estuaries sampling program in the Louisianian Province. Results from these analyses suggest that the EMAP-Estuaries sampling designs are capable of detecting a 0.15% change per year in the proportion of fish (all species groups combined) with gross pathological disorders in estuaries of the Louisianian Province over a 12-year period with a power of at least 80%. © Rapid Science 1998     

Predicting the variation in detection function in line transect sampling through random parameter model

A method for calibrating (localizing) detection function models in line transect sampling is proposed. The method is based on a random parameter model which supplies localized predictions of detection function parameters utilizing a few sample data points from the concerned location(s). The method has the clear advantage of being able to provide density estimates based on very few observations from a location which would be impossible through traditional methods. The method is successfully illustrated using census data on sambar (Cervus unicolor) from a set of wildlife sanctuaries in Kerala, India. The need for further research in this direction is indicated.     

Line transect sampling of Karner blue butterflies ( Lycaeides melissa samuelis)

Line transect sampling is an effective survey method for estimating butterfly densities because it provides unbiased estimates of site-density (provided key assumptions are met), and estimates are comparable among sites. For monitoring Karner blue butterflies in Wisconsin, USA, comparable estimates are required because each year a different selection of sites will be monitored. Annual state-wide indices of species abundance can be derived from the site-surveys and compared to previous year's indices to monitor trends. We advocate that line transect sampling is preferable to Pollard-Yates transects as a survey technique for monitoring Karner blue butter- flies. The Pollard-Yates surveys do not adjust for diferences in site detectability. As a consequence, estimates of among-site from Pollard-Yates surveys can be biased. © Rapid Science 1998     

Identifying the largest individual sample value from a two-way composite sampling design

Composite sampling techniques for identifying the largest individual sample value seem to be cost effective when the composite samples are internally homogeneous. However, since it is not always possible to form homogeneous composite samples, these methods can lead to higher costs than expected. In this paper we propose a two-way composite sampling design as a way to improve on the cost effectiveness of the methods available to identify the largest individual sample value.     

Plant polyphenols – implications of different sampling,storage and sample processing in biodiversity-ecosystem functioning experiments

Plant polyphenols are involved in important ecosystem processes and may affect nutrient cycling. Previous investigations have demonstrated detrimental effects of suboptimal sample treatment on the quantity of extractable plant polyphenols. We compared leaf polyphenol concentrations of 20 tree species from East China in two sample sets collected under different conditions: (a) according to established protocols and stored more than three years, (b) under conditions optimised for leaf polyphenols. We investigated the variance brought about by suboptimal sample handling as compared to the variance caused by the taxonomic range of species. Family-affiliation explained the largest proportion of variance, whereas sample handling had only minor effects. Reducing the taxonomic range increased the impact of differences in sample handling. Additionally, we showed that the concentrations of leaf polyphenols were phylogenetically more conserved than other leaf traits. Non-metric-multi-dimensional scaling revealed similar ordination patterns for leaf polyphenol concentrations in both sample sets with both ordinations being closely correlated. Finally, we computed separate ordinations including an extended set of leaf traits and found that both analyses led to similar ecological conclusions. Consequently, in studies comprising a wide taxonomic range, the adverse effects of suboptimal sample handling may be overridden by the variation brought about by phylogeny.     

Using the bootstrap with two-phase adaptive stratified samples from multiple populations at multiple locations

Recently the two-phase adaptive stratified sampling design proposed by Francis (1984) has been extended by Manly et al. (2002) for situations where several biological populations are sampled simultaneously, and where this is done at several different geographical locations in order to estimate population totals or means. The method uses the results from a first phase sample to decide how best to allocate a second phase sample to locations and strata, in order to maximise a criterion (based on estimated coefficients of variation) that measures the accuracy of estimation for population totals, for all variables at all locations. One potential problem with this method is bias in the estimators of the population totals and means. In this paper bootstrapping is considered as a means of overcoming these biases. It is shown using model populations of Pacific walrus and shellfish, based on real data, that bootstrapping is a useful tool for removing about half of the bias. This is also confirmed from some simulations using artificial data.     

A combination line transect and capture-recapture sampling model for multiple observers in aerial surveys

We present a robust sampling methodology to estimate population size using line transect and capture-recapture procedures for aerial surveys. Aerial surveys usually underestimate population density due to animals being missed. A combination of capture-recapture and line transect sampling methods with multiple observers allows violation of the assumption that all animals on the centreline are sighted from the air. We illustrate our method with an example of inanimate objects which shows evidence of failure of the assumption that all objects on the centreline have probability 1 of being detected. A simulation study is implemented to evaluate the performance of three variations of the Lincoln-Petersen estimator: the overall estimator, the stratified estimator, and the general stratified estimator based on the combined likelihood proposed in this paper. The stratified Lincoln-Petersen estimator based on the combined likelihood is found to be generally superior to the other estimators.     

Rao-Blackwell versions of the Horvitz-Thompson and Hansen-Hurwitz in adaptive cluster sampling

Thompson (1990) introduced the adaptive cluster sampling design and developed two unbiased estimators, the modified Horvitz-Thompson (HT) and Hansen-Hurwitz (HH) estimators, for this sampling design and noticed that these estimators are not a function of the minimal sufficient statistics. He applied the Rao-Blackwell theorem to improve them. Despite having smaller variances, these latter estimators have not received attention because a suitable method or algorithm for computing them was not available. In this paper we obtain closed forms of the Rao-Blackwell versions which can easily be computed. We also show that the variance reduction for the HH estimator is greater than that for the HT estimator using Rao-Blackwell versions. When the condition for extra samples is 0$$ " align="middle" border="0"> , one can expect some Rao-Blackwell improvement in the HH estimator but not in the HT estimator. Two examples are given.     

Performance of adaptive cluster sampling estimators in a multivariate setting

Adaptive cluster sampling has been proven to perform well in a univariate setting, but it may not perform well when there are several parameters of interest. The efficiency of adaptive sampling when there are several variables of interest depends on the relationship of the variables with one another.     

Bimodality of the combined removal and signs-of-activities estimator for sampling closed animal populations

The possibility of a bimodal log-likelihood function arises with certain data when the combined removal and signs-of-activities estimator is used. Bimodal log-likelihoods may, in turn, yield disjoint confidence intervals for certain confidence levels. The hypothesis that bimodality is caused by the violation of the equal catchability assumption of the removal model, leading to the combination of contradictory data/models in the combined estimator is set forth. Simulations exploring the effect of the violation of removal model assumptions on estimation and inference showed that the assumption of unequal capture probability influenced the frequency of bimodal likelihoods; similarly, extreme parameter values for probability of capture influenced the number of excessively large confidence intervals produced. A sex-specific combined estimator is developed as a remedial model tailored to the problem. The simulations suggest that both the signs-of-activities estimator and the sex-specific estimator perform equally well over the range of simulations presented, though the signs-of-activities estimator is easier to implement.     

Analysis of composite sampling data using the principle of maximum entropy

A new tool is presented based on the principle of maximum entropy for enhanced hot spot detection based on composite sampling with no (or minimal) requirement for composite breakdown. The methodology presented here is easy to implement and facilitates the use of multiple criteria for evaluating attainment of site remediation objectives. The new methodology provides very simple decision rules that can easily be used by non-statisticians and complements the use of composites for control of residual mean concentrations.     

北京市25种公路绿化植物及配置模式的绿量

计量研究公路绿地的生态服务功能有助于科学评估公路绿化的无形价值。以北京市顺义区顺平路(S305)的绿化植物群落为研究对象,运用WinScanopy For Canopy Analysis冠层分析仪采集数据,研究了公路绿化常见植物及配置模式的绿量率,并建立了25种公路绿化常见植物的绿量率模型,以期为公路绿化树种选择和群落配置提供基础分析与参考。结果表明:阔叶乔木的绿量率在3.02~4.80之间,针叶乔木的绿量率集中在4~6之间,灌木植物的绿量率较小,种植紧凑的绿篱绿量率达到3.94;不同配置模式的群落绿量率大小顺序依次为:针阔混交林>阔叶林>针叶林>灌木群落。其研究结果为公路绿化建设和绿地生态效益的计量提供了科学依据。     

Estimation of the CDF of a finite population in the presence of acalibration sample

We compare the performance of a number of estimators of the cumulative distribution function (CDF) for the following scenario: imperfect measurements are taken on an initial sample from afinite population and perfect measurements are obtained on a small calibration subset of the initial sample. The estimators we considered include two naive estimators using perfect and imperfect measurements; the ratio, difference and regression estimators for a two-phasesample; a minimum MSE estimator; Stefanski and Bay's SIMEX estimator (1996); and two proposed estimators. The proposed estimators take the form of a weighted average of perfect and imperfect measurements. They are constructed by minimizing variance among the class of weighted averages subject to an unbiasedness constraint. They differ in the manner of estimating the weight parameters. The first one uses direct sample estimates. The second one tunes the unknown parameters to an underlying normal distribution. We compare the root mean square error (RMSE) of the proposed estimator against other potential competitors through computer simulations. Our simulations show that our second estimator has the smallest RMSE among thenine compared and that the reduction in RMSE is substantial when the calibration sample is small and the error is medium or large.