Sara Franceschi

Repeatability and agreement of 2 Scheimpflug analyzers in measuring the central corneal thickness and anterior chamber angle, volume, and depth.

Purpose To evaluate the repeatability and agreement of 2 Scheimpflug tomographies (Pentacam HR, Oculus GmbH; Sirius, CSO Inc.) in measuring the central corneal thickness (CCT) and depth (ACD), volume (ACV), and angle (ACA) of the anterior chamber. Methods Three consecutive measurements were performed in one eye of each subject, with both instruments. The following were evaluated: CCT, ACD, ACV, and ACA. Repeatability was assessed by the Friedman test and agreement was assessed by 4 different Wilcoxon tests for paired data. Results The study enrolled 30 patients. With Sirius tomography the Friedman test p values for testing homogeneity across measurements was with respect to CCT 0.320, ACD 0.315, ACV 0.171, and ACA 0.020; and with Pentacam CCT 0.191, ACD 0.010, ACV 0.704, and ACA 0.150. The Wilcoxon test p values for testing the measurement homogeneity between the corneal tomography Sirius and Pentacam showed the following: ACD 0.000, ACD 0.000, ACV 0.853, and ACA 0.000. Conclusions The measurements may be considered substantially stable for each corneal tomography and each variable, but the 2 corneal tomographies do not produce statistically equivalent measurements.

Design-based treatment of unit nonresponse in environmental surveys using calibration weighting.

Unit nonresponse is often a problem in sample surveys. It arises when the values of the survey variable cannot be recorded for some sampled units. In this paper, the use of nonresponse calibration weighting to treat nonresponse is considered in a complete design-based framework. Nonresponse is viewed as a fixed characteristic of the units. The approach is suitable in environmental and forest surveys when sampled sites cannot be reached by field crews. Approximate expressions of design-based bias and variance of the calibration estimator are derived and design-based consistency is investigated. Choice of auxiliary variables to perform calibration is discussed. Sen-Yates-Grundy, Horvitz-Thompson, and jackknife estimators of the sampling variance are proposed. Analytical and Monte Carlo results demonstrate the validity of the procedure when the relationship between survey and auxiliary variables is similar in respondent and nonrespondent strata. An application to a forest survey performed in Northeastern Italy is considered.

Inference on diversity from forest inventories: a review

A number of international agreements and commitments emphasize the importance of appropriate monitoring protocols and assessments as prerequisites for sound conservation and management of the world’s forest ecosystems. Mandated periodic surveys, like forest inventories, provide a unique opportunity to identify and properly satisfy natural resource management information needs. Distinctively, there is an increasing need for detecting diversity by means of unambiguous diversity measures. Because all diversity measures are functions of tree species abundances, estimation of tree diversity indices and profiles is inevitably performed by estimating tree species abundances and then estimating indices and profiles as functions of the abundance estimates. This strategy can be readily implemented in the framework of current forest inventory approaches, where tree species abundances are routinely estimated by means of plots placed onto the surveyed area in accordance with probabilistic schemes. The purpose of this paper is to assess the effectiveness of this strategy by reviewing theoretical results from published case studies. Under uniform random sampling (URS), that is when plots are uniformly and independently located on the study region, consistency and asymptotic normality of diversity index estimators follow from standard limit theorems as the sampling effort increases. In addition, variance estimation and bias reduction are achieved using the jackknife method. Despite its theoretical simplicity, URS may lead to uneven coverage of the study region. In order to avoid unbalanced sampling, the use of tessellation stratified sampling (TSS) is suggested. TSS involves covering the study region by a polygonal grid and randomly selecting a plot in each polygon. Under TSS, the diversity index estimators are consistent, asymptotically normal and more precise than those achieved using URS. Variance estimation is possible and there is no need to reduce bias.

Tree Community Ordering by Diversity Profiles: an Application to Chestnut Coppices

The ecological and economical relevance of sweet chestnut (Castanea sativa Mill.) has long been related to its widespread geographical distribution and multipurpose product potential. In Italy, chestnut management represents a paradigmatic example of the potential conflict between landowner targets and tree species diversity conservation. Distinctively, the relationships between silvicultural treatment and tree species diversity of chestnut coppices are here investigated by means of diversity profiles to assess tree diversity of six stands in Central Italy. The stands were purposively selected in such a way to be characterized by the same site conditions but with different silvicultural features (age, number of thinning). Plot sampling was performed across the stands and their tree diversity was compared and ordered by means of intrinsic diversity profiles estimated from the sample data. The achieved results suggest alternative suitable options for managing chestnut coppice stands in order to enhance tree biodiversity while maintaining timber production.

Design-based inference on Bernstein type estimators for continuous populations

The estimation of the values of a variable at any point of a study area is performed using Bernstein polynomials when the sampling scheme is implemented by selecting a point in each polygon of a regular grid overimposed onto the area. The evaluation of the precision of the resulting estimates is investigated under a completely design-based framework. Moreover, as the main contribution to the mean squared error of the Bernstein-type estimator is due to the bias, also a pseudo-jackknife estimator is proposed. The performance of both estimators is investigated theoretically and by means of a simulation study. An application to a soil survey performed in Berkshire Downs in Oxfordshire (UK) is considered.