Population estimation with sparse data: the role of estimators versus indices revisited

Abstract

The use of indices to evaluate small-mammal populations has been heavily criticized, yet a review of small-mammal studies published from 1996 through 2000 indicated that indices are still the primary methods employed for measuring populations. The literature review also found that 98% of the samples collected in these studies were too small for reliable selection among population-estimation models. Researchers therefore generally have a choice between using a default estimator or an index, a choice for which the consequences have not been critically evaluated. We examined the use of a closed-population enumeration index, the number of unique individuals captured (Mt+1), and 3 population estimators for estimating simulated small populations (N = 50) under variable effects of time, trap-induced behavior, individual heterogeneity in trapping probabilities, and detection probabilities. Simulation results indicated that the estimators produced population estimates with low bias and high precision when the estimator reflected the underlying sources of variation in capture probability. However, when the underlying sources of variation deviated from model assumptions, bias was often high and results were inconsistent. In our simulations, Mt+1 generally exhibited lower variance and less sensitivity to the sources of variation in capture probabilities than the estimators.