Small-Mammal Density Estimation: A Field Comparison of Grid-Based vs. Web-Based Density Estimators

• Published in: Ecological monographs 2003-02, Vol.73 (1), p.1-26
• Main Authors: Parmenter, Robert R., Yates, Terry L., Anderson, David R., Burnham, Kenneth P., Dunnum, Jonathan L., Franklin, Alan B., Friggens, Michael T., Lubow, Bruce C., Miller, Michael, Olson, Gail S., Parmenter, Cheryl A., Pollard, John, Rexstad, Eric, Shenk, Tanya M., Stanley, Thomas R., White, Gary C.
• Format: Article
• Language: English
• Subjects: Analytical estimating; Animal, plant and microbial ecology; Biological and medical sciences; capture–recapture; Datasets; Density; Density estimation; density estimation: accuracy, precision, and bias; distance-sampling theory; Ecology; Estimate reliability; Estimation bias; Estimators; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Mammals; Methods and techniques (sampling, tagging, trapping, modelling...); Modeling; Population density; Population estimates; rodent density; Rodents; small mammals; trapping grids, accuracy of density estimates; trapping webs, accuracy of density estimates
• ISSN: 0012-9615; 1557-7015
• DOI: 10.1890/0012-9615(2003)073[0001:SMDEAF]2.0.CO;2

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, 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$(\hat N) and effective sampling area (\hat A) are used to calculate density (\hat D=\hat N/\hat A);$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. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets (n = 11) were developed using nine rodent species from capture-recapture live-trapping on both trapping grids and trapping webs in four replicate 4.2-ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional "saturation" trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the computation of true densities.$Density estimates (\hat D) were calculated using program CAPTURE$for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities (D) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this "blind" test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DISTANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web-based uniform and half-normal models in program DISTANCE, and the grid-based models$M_b and M_{bh} in program CAPTURE (with \hat A) adjusted by species