Monitoring abundance of the endangered Key Largo woodrat (Neotoma floridana smalli) is necessary to understand population responses to prescribed management actions. We compared efficiency of adaptive cluster sampling (ACS) and stratified-random sampling (SRS) for estimating Key Largo woodrat abundance and compared three stratification designs using poststratification. We established 40 trapping grids using a stratified random design and adaptively sampled around grids on which at least 1 individual was captured. We captured 11 individuals on 40 random grids and an additional 22 individuals on 33 adaptive grids. Despite the increased capture rate, ACS was less efficient than SRS with an estimator variance twice as large with equal sample sizes. Although poststratification effectively lowered estimator variance, our data suggest that attaining the required sample sizes to reliably estimate abundance likely will be cost-prohibitive. Monitoring of improved habitat at the patch scale along with representative controls may be more cost-effective for evaluating the success of prescribed management.