Statistical Efficiency in Distance Sampling.

Citation data:

PloS one, ISSN: 1932-6203, Vol: 11, Issue: 3, Page: e0149298

Publication Year:
2016
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Repository URL:
http://ro.uow.edu.au/eispapers/5316
PMID:
26950934
DOI:
10.1371/journal.pone.0149298; 10.1371/journal.pone.0149298.g003; 10.1371/journal.pone.0149298.t003; 10.1371/journal.pone.0149298.g002; 10.1371/journal.pone.0149298.g007; 10.1371/journal.pone.0149298.g004; 10.1371/journal.pone.0149298.t002; 10.1371/journal.pone.0149298.g006; 10.1371/journal.pone.0149298.t001; 10.1371/journal.pone.0149298.g005; 10.1371/journal.pone.0149298.g001
PMCID:
PMC4780711; 4780711
Author(s):
Robert Graham Clark; Shyamal D Peddada
Publisher(s):
Public Library of Science (PLoS); Figshare
Tags:
Medicine; Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Genetics; Ecology; Cancer; 69999 Biological Sciences not elsewhere classified; 19999 Mathematical Sciences not elsewhere classified; hazard rate model; strip transect estimation; detection functions; factor; strip width; distance sampling distance sampling; simulation; strip transect estimators; assumption; half-normal distance function; distance sampling estimator; object; Engineering; Science and Technology Studies
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Distance sampling is a technique for estimating the abundance of animals or other objects in a region, allowing for imperfect detection. This paper evaluates the statistical efficiency of the method when its assumptions are met, both theoretically and by simulation. The theoretical component of the paper is a derivation of the asymptotic variance penalty for the distance sampling estimator arising from uncertainty about the unknown detection parameters. This asymptotic penalty factor is tabulated for several detection functions. It is typically at least 2 but can be much higher, particularly for steeply declining detection rates. The asymptotic result relies on a model which makes the strong assumption that objects are uniformly distributed across the region. The simulation study relaxes this assumption by incorporating over-dispersion when generating object locations. Distance sampling and strip transect estimators are calculated for simulated data, for a variety of overdispersion factors, detection functions, sample sizes and strip widths. The simulation results confirm the theoretical asymptotic penalty in the non-overdispersed case. For a more realistic overdispersion factor of 2, distance sampling estimation outperforms strip transect estimation when a half-normal distance function is correctly assumed, confirming previous literature. When the hazard rate model is correctly assumed, strip transect estimators have lower mean squared error than the usual distance sampling estimator when the strip width is close enough to its optimal value (± 75% when there are 100 detections; ± 50% when there are 200 detections). Whether the ecologist can set the strip width sufficiently accurately will depend on the circumstances of each particular study.