We consider estimation problems in capture-recapture models when the covariates or the auxiliary variables are measured with errors. The naive approach, which ignores measurement errors, is found to be unacceptable in the estimation of both regression parameters and population size: it yields estimators with biases increasing with the magnitude of errors, and flawed confidence intervals. To account for measurement errors, we derive a regression parameter estimator using a regression calibration method. We develop modified estimators of the population size accordingly. A simulation study shows that the resulting estimators are more satisfactory than those from either the naive approach or the simulation extrapolation (SIMEX) method. Data from a bird species Prinia flaviventris in Hong Kong are analyzed with and without the assumption of measurement errors, to demonstrate the effects of errors on estimations.

BackgroundEstimating assemblage species or class richness from samples remains a challenging, but essential, goal. Though a variety of statistical tools for estimating species or class richness have been developed, they are all singly-bounded: assuming only a lower bound of species or classes. Nevertheless there are numerous situations, particularly in the cultural realm, where the maximum number of classes is fixed. For this reason, a new method is needed to estimate richness when both upper and lower bounds are known.Methodology/Principal FindingsHere, we introduce a new method for estimating class richness: doubly-bounded confidence intervals (both lower and upper bounds are known). We specifically illustrate our new method using the Chao1 estimator, rarefaction, and extrapolation, although any estimator of asymptotic richness can be used in our method. Using a case study of Clovis stone tools from the North American Lower Great Lakes region, we demonstrate that singly-bounded richness estimators can yield confidence intervals with upper bound estimates larger than the possible maximum number of classes, while our new method provides estimates that make empirical sense.Conclusions/SignificanceApplication of the new method for constructing doubly-bound richness estimates of Clovis stone tools permitted conclusions to be drawn that were not otherwise possible with singly-bounded richness estimates, namely, that Lower Great Lakes Clovis Paleoindians utilized a settlement pattern that was probably more logistical in nature than residential. However, our new method is not limited to archaeological applications. It can be applied to any set of data for which there is a fixed maximum number of classes, whether that be site occupancy models, commercial products (e.g. athletic shoes), or census information (e.g. nationality, religion, age, race).

The Laplace method for approximating integrals is a useful technique in a number of research fields. This paper shows that it also has interesting applications in biological and ecological statistical inferences. When sample abundance or replicated incidence (i.e., presence or absence) records of each species are available, the expected low-order frequency counts in heterogeneous communities can be approximated by the Laplace method when the species discovery or detection probabilities are bounded from below by a constant. The approximation formulae as applied to one community can then be used to derive estimators of species richness and to examine their performance. The approach is also extended to obtain simple and new estimators for the number of shared species in two communities. The replicated species incidence data recorded by competing teams of the Hong Kong Big Bird Race for the years 1999 and 2000 are used to estimate the number of resident birds in Hong Kong and to illustrate the method of estimation.

Summary1. An important question in macroecology is: Can we estimate a species' abundance from its occurrence on landscape? Answers to this question are useful for estimating population size from more easily acquired distribution data and for understanding the macroecological occupancy-abundance relationship. 2. Several methods have recently been developed to address this question, but no method is general enough to provide a common solution to all species because of the wide variation in spatial distribution of species. 3. In this study, we developed a mixed Gamma-Poisson model that generalizes the negative binomial model and can characterize spatial dependence in the abundance distribution across cells. Under this framework, without any extra information, the clumping parameter and species abundance can be estimated using a map aggregation technique. This model was tested using a set of empirical census data consisting of 299 tree species from a 50-ha stem-mapped plot of Panama. 4. A comparison showed that the new method outperformed the previous methods to an appreciable degree. Particularly for abundant species in a finely gridded map (5 · 5 m), its bias is very small and the method can also reduce the root mean square error up to 30%. Like for previous methods, however, the new method's performance decreases with the increase in cell size. 5. As a by-product, the new method provides an approach to estimate spatial autocorrelation of species distribution which is otherwise difficult to estimate for presence ⁄ absence map.

In practice, when analyzing data from a capture-recapture experiment it is tempting to apply modern advanced statistical methods to the observed capture histories. However, unless the analysis takes into account that the data have only been collected from individuals who have been captured at least once, the results may be biased. Without the development of new software packages, methods such as generalized additive models, generalized linear mixed models, and simulation-extrapolation cannot be readily implemented. In contrast, the partial likelihood approach allows the analysis of a capture-recapture experiment to be conducted using commonly available software. Here we examine the efficiency of this approach and apply it to several data sets.