abstract bibtex

Plotless density estimators (PDEs) can be efficient alternatives to quadrat sampling for estimating the density of stationary objects. Variable area transect (VAT) sampling had been identified, and optimized relative to effort, in previous Monte Carlo simulated population studies as a low-labor field method that demonstrated superior estimation properties among many PDEs considered. However, natural populations tend to be much more complex and less predictable in spatial distribution than computer generated populations. Therefore, we carried out a Monte Carlo simulation study that used 17 fully enumerated field populations rather than simulated populations. These natural populations represented a variety of population densities and spatial patterns. We focused on assessing the effect of the number of population members searched for along each transect (r), sample size, and transect width on estimation quality. Using relative root mean-squared error and relative bias as criteria, the optimal range for r was 5 to 7 population members encountered from each start point. Sample size was best if n > 20, but returns in estimation quality diminished by n = 40. Transect width was previously uninvestigated and found to be the most important design factor affecting estimation quality. Field studies should strive for transects as wide as logistically reasonable.

@ARTICLE{Engeman2005, author = {Engeman, R.M. and Nielson, R.M. and Sugihara, R.T.}, title = {Evaluation of optimized variable area transect sampling using totally enumerated field data sets}, journal = {Environmetrics}, year = {2005}, volume = {16}, pages = {767--772}, number = {7}, abstract = {Plotless density estimators (PDEs) can be efficient alternatives to quadrat sampling for estimating the density of stationary objects. Variable area transect (VAT) sampling had been identified, and optimized relative to effort, in previous Monte Carlo simulated population studies as a low-labor field method that demonstrated superior estimation properties among many PDEs considered. However, natural populations tend to be much more complex and less predictable in spatial distribution than computer generated populations. Therefore, we carried out a Monte Carlo simulation study that used 17 fully enumerated field populations rather than simulated populations. These natural populations represented a variety of population densities and spatial patterns. We focused on assessing the effect of the number of population members searched for along each transect (r), sample size, and transect width on estimation quality. Using relative root mean-squared error and relative bias as criteria, the optimal range for r was 5 to 7 population members encountered from each start point. Sample size was best if n > 20, but returns in estimation quality diminished by n = 40. Transect width was previously uninvestigated and found to be the most important design factor affecting estimation quality. Field studies should strive for transects as wide as logistically reasonable.}, address = {National Wildlife Research Center, 4101 LaPorte Ave, Fort Collins, CO 80521-2154, United States}, keywords = {Density estimation, Distance sampling, Spatial pattern}, owner = {eric}, subdatabase = {distance}, timestamp = {2006.11.05} }

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