next up previous index
Next: biometric.soc.12 Up: Biometric Society (ENAR & Previous: biometric.soc.10


Sponsoring Section/Society: WNAR

Session Slot: 10:30-12:20 Thursday

Estimated Audience Size: xx-xxx

AudioVisual Request: xxx

Session Title: Application of Adaptive Cluster Sampling to Biological Populations

Theme Session: No

Applied Session: Yes

Session Content: Public policy is linked to management of biological populations because population changes can have differential consequences to society. For example, society faces tradeoffs when conservation of rare or endangered species interferes with other public and private uses of land, water, or mineral resources. Alternatively, consumers of commercially valuable species, such as ocean fisheries, can have their interest curtailed when population size drops to a point where managers restrict harvest. In either case, accurate estimates of population parameters are critical to successful conservation and informed public debate. However, due to spatial clustering and rarity of many biological populations, population parameters are often difficult to estimate accurately, and yield of individual plants or animals is often low. Application of conventional finite-population sampling schemes to rare and clustered populations may not provide sufficient information to make effective policy given the sampling effort usually afforded to biological surveys.

Adaptive cluster sampling is a class of unconventional sampling techniques specifically designed for sampling rare and clustered populations. It increases sampling effort where high abundance is observed. Its application to study of highly aggregated biological populations promises more information - more precise estimates, higher yield of individuals - compared to non-adaptive sampling techniques (e.g., waterfowl wintering in central Florida, Pacific hake larvae off California coast, freshwater mussels in eastern North American rivers). However, questions still remain about its application: what degree of rarity and clustering is required for adaptive cluster sampling to be efficient, how can design factors be set optimally, how can final sample size be limited? Answers to these questions likely will precede widespread application of adaptive sampling techniques to the study of biological populations.

This session will bring together researchers active in the theoretical development and application of adaptive cluster sampling techniques to present and discuss recent developments, effective application, and important areas of research.

Session Organizer: Lo, Nancy C.H. National Marine Fisheries Service

Address: Southwest Fisheries Science Center P.O. Box 271 La Jolla, CA 92038

Phone: 619-546-7123

Fax: 619-546-5656


Co-organizer of the session is David R. Smith, U.S. Geological Survey, Biological Resources Division. He is also the third speaker and contact information for him is provided below.

Session Timing: 110 minutes total (Sorry about format):

Opening Remarks by Chair - 5 minutes First Speaker - 25 minutes Second Speaker - 25 minutes Third Speaker - 25 minutes Discussion lead by Discussant - 20 minutes Floor Discussion lead by Chair - 10 minutes

Session Chair: Lo, Nancy C.H. National Marine Fisheries Service

Address: Southwest Fisheries Science Center P.O. Box 271 La Jolla, CA 92038

Phone: 619-546-7123

Fax: 619-546-5656


1. Restricted Adaptive Cluster Sampling

Brown, Jennifer,   Department of Mathematics, University of Cantebury

Address: Biomathematics Resarch Centre Department of Mathematics and Statistics University of Canterbury Private Bag 4800, Christchurch New Zealand

Phone: 03-364-2987 ext 7684

Fax: 03-364-2587


Abstract: Adaptive cluster sampling can be an efficient design for sampling rare and patchy populations. With this design the initial sample size is fixed but the size of the final sample (and total sampling effort) can not be predicted prior to sampling. For some populations the final sample size can be quite variable depending on the level of patchiness. Restricted adaptive cluster sampling is a proposed modification where a limit is placed on the sample size prior to sampling and quadrats are selected sequentially for the initial sample. As a result there is less variation in the final sample size and the total sampling effort can be predicted with some certainty, which is important for many ecological studies. Estimates of density are biased with the restricted design but under some circumstances the bias can be estimated by bootstrapping.

2. Sequential Sampling Designs to Estimate Abundance of Rare Populations

Christman, Mary C.,   American Univeristy

Address: Department of Mathematics and Statistics American University 4400 Massachusetts Ave NW Washington, DC 20016-8050

Phone: 202-885-3123

Fax: 202-885-3155


Abstract: Of interest is designing sampling schemes for rare populations where we utilize information about the degree of clustering of the elements being sampled. Some existing designs, such as adaptive cluster sampling, have been developed that exploit that information but which can have some problems associated with them. These include a random sample size and a design which behaves poorly when the population is neither rare nor highly clustered. We consider a sequential sampling design that combines the positive aspects of adaptive sampling and of sequential analysis. Several sampling designs that are based on taking samples sequentially until some predefined stopping condition is met and which incorporates an adaptive sampling component are developed. For the designs considered, estimators and measures of their accuracy are given and their behavior analyzed.

3. Observations of a Practicing Adaptive Sampler: Lessons Learned, Conclusions Drawn, and Questions Remaining

Smith, David R.,   Biological Resources Division, U.S. Geological Survey

Address: U.S. Geological Survey Biological Resources Division 1700 Leetown Rd. Kearneysville, WV 25430

Phone: 304-725-8461x383

Fax: 304-728-6509


Abstract: Adaptive cluster sampling methods are intuitively appealing because they permit biologists to do what comes naturally - increase sampling effort where rare organisms are discovered. However, it is my impression that while biologists are intrigued by the technique, widespread application is lagging. Through a review of the design of a biological survey that makes use of adaptive cluster sampling, I will highlight the decisions that give biologists pause. Much of the problem in design of such a survey is avoiding over and under sampling. I will illustrate potential problems and make some recommendations based on how I have practiced adaptive cluster sampling. Finally, I will question whether increased efficiency is sufficient justification for application of adaptive cluster sampling, especially for rare populations; rather I will suggest that increased yield of individuals and species is its main benefit.

Discussant: Thompson, Steve K.   Pennsylvania State University

Address: Department of Statistics Pennsylvania State University 326 Classroom Bldg. University Park, PA 16802-2111

Phone: 814-865-3235

Fax: 814-863-7114


List of speakers who are nonmembers: None ????

next up previous index
Next: biometric.soc.12 Up: Biometric Society (ENAR & Previous: biometric.soc.10
David Scott