The Robustness of Lognormal-Based Estimators of Abundance

Abstract

We test the robustness of a method for estimating abundance that assumes that the underlying distribution of the nonzero observations is lognormal (Pennington, 1983, Biometrics 39, 281-286). Violations in model assumptions that cannot reliably be detected with moderate sample sizes (-40) lead to biases and large reductions in efficiency. Unless it can be clearly demonstrated from repeated sampling that nonzero values follow a lognormal distribution, the sample mean and variance are more robust than lognormal-based estimators of mean and variance of population abundance.