Site occupancy models are commonly used by ecologists to estimate the probabilities of species site occupancy and of species detection. This study addresses the influence on site occupancy and detection estimates of variation in species availability among surveys within sites. Such variation in availability may result from temporary emigration, nonavailability of the species for detection, and sampling sites spatially when species presence is not uniform within sites. We demonstrate, using Monte Carlo simulations and aquatic vegetation data, that variation in availability and heterogeneity in the probability of availability may yield biases in the expected values of the site occupancy and detection estimates that have traditionally been associated with low鈥恉etection probabilities and heterogeneity in those probabilities. These findings confirm that the effects of availability may be important for ecologists and managers, and that where such effects are expected, modification of sampling designs and/or analytical methods should be considered. Failure to limit the effects of availability may preclude reliable estimation of the probability of site occupancy.
In Vitro diagnostic (IVD) reagent stability is typically evaluated using regression analysis of measurand drift across time following CLSI guideline EP25-A. The corresponding stability duration establishment has several limitations. The stability duration conclusion is based on a two-stage acceptance criteria using the p-value of the regression slope followed by the 95% confidence interval (CI) on the fitted regression line if the p-value < 0.05. This analysis technique is based on traditional statistical hypothesis testing; however, the statistical equivalence testing framework better represents the goals of IVD reagent stability evaluation. The resulting stability duration CI does not achieve 95% coverage probability and the statistical properties of the estimated stability duration are substantially impacted by presence of common variance components not accounted for during power analysis and by not accounting for variability in the baseline estimate. The current proposal based on the equivalence testing framework uses a one-stage acceptance criteria based on the 95% CI for proportional measurand drift derived from the regression fit. The proposed methodology was applied to automated immunoassay data (Akbas et al., 2016). Monte Carlo simulation studies are presented to illustrate the improved statistical properties of the current proposal along with an example power analysis for study design.
Royle and Link (Ecology 86(9):2505-2512, 2005 proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework. We use Markov chain Monte Carlo to compute Bayes estimates from a simulated sample from the posterior distribution. Based on simulations, this elaborated Royle-Link model yields nearly unbiased estimates of multinomial and correct classification probability estimates when classification probabilities are allowed to vary according to the normal distribution on the logit scale or according to the Beta distribution. The method is illustrated using categorical submersed aquatic vegetation data.
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