Species richness is a widely used surrogate for the more complex concept of biological diversity. Because species richness is often central to ecological study and the establishment of conservation priorities, the biases and merits of richness measurements demand evaluation. The jackknife and bootstrap estimators can be used to compensate for the underestimation associated with simple richness estimation (or the sum of species counted in a sample). Using data from five forest communities, we analyzed the simple measure of richness, the first-and second-order jackknife, and the bootstrap estimators with simulation and resampling methods to examine the effects of sample size on estimator performance. Performance parameters examined were systematic under-or overestimation (bias), ability to estimate consistently (precision), and ability to estimate true species richness (accuracy).For small sample sizes in all studied communities (less than ϳ25% of the total community), the least biased estimator was the second-order jackknife, followed by the firstorder jackknife, the bootstrap, and the simple richness estimator. However, with increases in sample size, the second-order jackknife, followed by the first-order jackknife and the bootstrap, became positively biased. The simple richness estimator was the most precise estimator in all studied communities, but it yielded the largest underestimate of species richness at all sample sizes. The relative precision of the four estimators did not differ across communities, but the magnitude of estimator variance is dependent on the sampled community. Differences in accuracy among the estimators were not independent of community, and accuracy patterns were associated with community species diversity. The results of this study can assist policy makers, researchers, and managers in the selection of appropriate sample sizes and estimators for richness estimation and should facilitate the ongoing assessment of local, and ultimately global, biodiversity.

Species richness is a widely used surrogate for the more complex concept of biological diversity. Because species richness is often central to ecological study and the establishment of conservation priorities, the biases and merits of richness measurements demand evaluation. The jackknife and bootstrap estimators can be used to compensate for the underestimation associated with simple richness estimation (or the sum of species counted in a sample). Using data from five forest communities, we analyzed the simple measure of richness, the first‐ and second‐order jackknife, and the bootstrap estimators with simulation and resampling methods to examine the effects of sample size on estimator performance. Performance parameters examined were systematic under‐ or overestimation (bias), ability to estimate consistently (precision), and ability to estimate true species richness (accuracy). For small sample sizes in all studied communities (less than ∼25% of the total community), the least biased estimator was the second‐order jackknife, followed by the first‐order jackknife, the bootstrap, and the simple richness estimator. However, with increases in sample size, the second‐order jackknife, followed by the first‐order jackknife and the bootstrap, became positively biased. The simple richness estimator was the most precise estimator in all studied communities, but it yielded the largest underestimate of species richness at all sample sizes. The relative precision of the four estimators did not differ across communities, but the magnitude of estimator variance is dependent on the sampled community. Differences in accuracy among the estimators were not independent of community, and accuracy patterns were associated with community species diversity. The results of this study can assist policy makers, researchers, and managers in the selection of appropriate sample sizes and estimators for richness estimation and should facilitate the ongoing assessment of local, and ultimately global, biodiversity.

This paper contains 65 references dealing with the development of sequential sampling plans in insect pest management based on Wald's Sequential Probability Ratio Test (SPRT), 25 in forest entomology and 40 in agriculture entomology. The insect(s) sampled, whether the decision procedure was based on one or two SPRTs, and the mathematical distribution and probabilities of Type I (0'.) and Type II (/3) errors used to develop the SPRTs are also given for each sequential sampling plan.

Flexible bronchoscopy (FB) and bronchoalveolar lavage (BAL) have been applied increasingly to the evaluation of pulmonary disease in children. Although several complications have been reported following FB and BAL, high fever after BAL in immunocompetent children has not previously been reported. To determine the frequency, clinical characteristics, and outcome of these complications in children who developed high fever post‐BAL, we retrospectively reviewed all bronchoscopic procedures done on an outpatient basis between August 1995 and July 1997. We identified 78 immunocompetent noncritically ill children who had undergone FB and BAL as an outpatient procedure for evaluation of underlying pulmonary disease, of whom 13 (17%) developed temperature (T) higher than or equal to 39°C (fever group). The 13 patients in the fever group had a median age of 10 (range, 4–48) months and a reported T of 39.4°C (39.1–40.6°C) occurring 7.5 (4–12) hr after BAL. To determine if there were differences in clinical or BAL fluid (BALF) characteristics, we compared each child in the fever group to two children in the nonfever group, based upon primary indications and age. There were no differences in demographic or clinical characteristics between the two groups. Lymphocyte concentrations in BALF were significantly reduced in the fever group (P = 0.03). An abnormal BALF cell differential (defined as one or more of the following: neutrophils >10%, lymphocytes >30%, or eosinophils >1%) was significantly more common in the fever group (P = 0.008, odds ratio 3.6). We conclude that high fever is a frequent adverse event following BAL in noncritically ill immunocompetent children with underlying pulmonary disease. Pre‐BAL clinical characteristics are not associated with development of high fever. However, the finding of an abnormal BALF cell differential is strongly associated with development of high fever post‐BAL. Pediatr Pulmonol. 1999; 28:139–144. © 1999 Wiley‐Liss, Inc.

Monte Carlo operating characteristic (OC) and average sample number (ASN) functions were compared with Wald's OC and ASN equations for sequential sampling plans based on Wald's sequential probability ratio test (SPRT) using the binomial, negative binomial, normal, and Poisson distributions. This comparison showed that the errors inherent in Wald's equations as a result of "overshooting" the decision boundaries of the SPRT can be large. Relative errors increased for the OC and ASN equations as the difference between the null (θ0)) and alternative (θ1) test parameter values increased. Relative errors also increased for the ASN equation as the probabilities of type I (α) and type II (β) errors increased. For discrete distributions, the relative errors also increased as θ0 increased with θ1/θ0 fixed. Wald's equations, in general, overestimate the true error probabilities and underestimate the true ASN. For the values of θ0, θ1, α, and β used in many sequential sampling plans in forestry, Wald's equations may not be adequate. For those cases where the errors in Wald's equations are important compared with the other errors associated with the sampling plan, two alternative Monte Carlo OC and ASN functions are proposed.