Estimating population size for sparse data in capture-recapture experiments

TL;DR: A likelihood- based approach to dealing with temporary emigration is presented that permits estimation under different models of temporary em migration and yields tests for completely random and Markovian emigration.

TL;DR: This work develops the first statistically rigorous nonparametric method for estimating the minimum number of additional individuals, samples, or sampling area required to detect any arbitrary proportion of the estimated asymptotic species richness.

TL;DR: This book aims to bridge the gap between field-based biologists and statisticians as new methods are developed to deal with more complex data by helping biologists understand state-of-the-art statistical methods for capture–recapture analysis.

TL;DR: In this paper, two general classes of density estimation models have been developed: models that use data sets from capture-recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size (N) and effective sampling area (Â) are used to calculate density (D = N/Â), and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data.

TL;DR: A point estimator and its associated confidence interval for the size of a closed population are proposed under models that incorporate heterogeneity of capture probability andumerical results show that the proposed confidence interval performs satisfactorily in maintaining the nominal levels.

TL;DR: The problem of finding robust estimators of population size in closed K-sample capture-recapture experiments is considered and a general estimation procedure is given which does not depend on any assumptions about the form of the distribution of capture probabilities.

TL;DR: In this paper, the authors cast sequential mark-recapture experiments into a Bayesian framework using a "noninformative" discrete uniform improper prior (a priori the-obical) distribution.