Sampling Design Trade-Offs in Occupancy Studies with Imperfect Detection: Examples and Software Author(s): Larissa L. Bailey, James E. Hines, James D. Nichols, Darryl I. MacKenzie Source: Ecological Applications, Vol. 17, No. 1 (Jan., 2007), pp. 281-290 Published by: Ecological Society of America Stable URL: http://www.jstor.org/stable/40061993 . Accessed: 28/01/2011 15:07 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at . http://www.jstor.org/action/showPublisher?publisherCode=esa. 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Mackenzie2 1 U.S. Geological Survey, Patuxent WildlifeResearch Center, 12100 Beech Forest Road, Laurel, Maryland 20708-4017 USA 2Proteus Wildlife Research Consultants, PO Box 5193, Dunedin, New Zealand Abstract. Researchers have used occupancy, or probability of occupancy, as a response or state variable in a variety of studies (e.g., habitat modeling), and occupancy is increasingly favored by numerous state, federal, and international agencies engaged in monitoring programs. Recent advances in estimation methods have emphasized that reliable inferences can be made from these types of studies if detection and occupancy probabilities are simultaneously estimated. The need for temporal replication at sampled sites to estimate detection probability creates a trade-off between spatial replication (number of sample sites distributed within the area of interest/inference) and temporal replication (number of repeated surveys at each site). Here, we discuss a suite of questions commonly encountered during the design phase of occupancy studies, and we describe software (program GENPRES) developed to allow investigators to easily explore design trade-offs focused on particularities of their study system and sampling limitations. We illustrate the utility of program GENPRES using an amphibian example from Greater Yellowstone National Park, USA. Key words: Ambystoma tigrinum; detection probability; monitoring; program GENPRES; site occupancy; survey design; tiger salamanders; YellowstoneNational Park. Introduction occupancy studies (also see Field et al. 2005). MacKenzie and three for Various fields of ecology use occupancy, or probability Royle (2005) investigated possible designs studies: "standard design," in which each that a patch is occupied by a species, as a state variable to single-season site is the same number of times; "double- address hypotheses about habitat associations (e.g., surveyed in which a subset of sites is Scott et al. 2002), species distribution (e.g., Fisher and sampling design," surveyed times and the sites are Shaffer 1996, Van Buskirk 2005), and metapopulation multiple remaining sampled only once; and "removal in which sites are dynamics (e.g., Hames et al. 2001, Barbraud et al. 2003, design," surveyed times until the is detected. The Martinez-Solano et al. 2003). Often, occupancy is the multiple target species authors found that there was an number of state variable of interest to wildlife managers assessing optimal for each of whether the impacts of management actions (Mazerolle et al. repeated surveys design, regardless the was to: achieve- a desired level of 2005), and it is commonly the focal variable in long-term objective (1) for minimal total or minimize monitoring programs (Manley et al. 2004). Recent precision survey effort; (2) the variance of the estimator for a papers have emphasized that reliable inferences from occupancy (var(\(/)) total number of and these types of studies require estimating occupancy from given surveys (MacKenzie Royle of on the detection-nondetection (presence-absence) data in a 2005). This optimal number surveys depended manner that deals with detection probabilities <1 study design and the true occupancy and detection for the but was of (Moilanen 2002, MacKenzie et al. 2002, 2003, 2005, probability target species, independent Gu and Swihart 2004). A key feature of these new the number of study sites. In general, the standard design estimation methods is that they generally require both outperformed the double-sampling design, and the spatial and temporal replication. The need for temporal removal design was also promising if detection proba- replication at sampled sites to estimate detection bility was believed to be constant across surveys. probability creates a trade-off between spatial replication MacKenzie and Royle (2005) considered their results (number of sample sites distributed within the area of to be a useful starting point for practitioners planning interest/inference) and temporal replication (number of occupancy studies, but cautioned that their results were repeated surveys at each site). based on a simple model, namely one that assumed that MacKenzie and Royle (2005) presented the first occupancy probability was similar across all sites and investigation of these trade-offs, and their findings that detection probability was constant over both time provide some needed guidance for efficient design of and space (sites). Furthermore, their results were based on asymptotic (large sample) methods and the results may not hold for studies with small sample sizes. These received 31 December 2005; revised 1 May 2006; Manuscript and recommend that be accepted 5 May 2006. Corresponding Editor: J. Van Buskirk. authors, others, study designs 3 E-mail: [email protected] evaluated on a case-by-case basis, tailoring the design to 281 282 LARISSA L. BAILEY ET AL. Ecologica|AppUcations specific scientific goals, the biology of the target species, sites and time, except when differences can be modeled and logistical or economical constraints. with covariates (e.g., habitat features); and (3) the target In this paper, we describe software (program GEN- species is identified correctly. PRES) developed to allow investigators to explore design model and parameter definitions trade-offs focused on the specifics of their study systems Single-season and attendant sampling limitations. The program MacKenzie et al. (2002, 2005, 2006) defined a permits investigation of both single-season and multi- probability-based model that consisted of two kinds of ple-season situations, with one or more groups of sites parameters: v|/ represents the probability that a site is that differ in their sampling intensity, occupancy, or occupied by the target species, and pj is the probability of detection probabilities. Users supply information on the detecting the species at an occupied site during the y'th model type (single- or multiple-season), construct a independent survey of a site. Assuming that detection relevant sampling design using a flexible general frame- histories from all s sites are independent, maximum work, and provide realistic parameter values, based on likelihood methods are used to estimate occupancy and either guesses or, preferably, on analysis of pilot data. detection probability. Additionally, it is possible to model Evaluations can be performed via analytic-numeric either occupancy or detection probability as a function of approximations (based on expected values of sample measured covariates using the logistic equation data) or simulations with either predefined or user- exp(X,-p) defined occupancy models. Detection histories are ' generated and can be saved as input files for multi- l+exp(X,-p) model evaluation other software such as using programs where 9, representsthe parameterof interest for site /, X, is MARK and Burnham or PRES- program (White 1999) the row vector of covariate information for site /, and p is ENCE (MacKenzie et al. 2006). We illustrate the use of the column vector of coefficients to be estimated. GENPRES an from program using amphibian example Occupancy probability may be modeled as a function of the Greater Yellowstone National Park Network. site-specific covariates that do not change during the Background: Occupancy Models season (e.g., habitat type), whereas detection probability and Parameter Estimation may be modeled as a function of either site-specific or survey-specific covariates (e.g., weather conditions or while for Estimating occupancy probability adjusting observer). The same modeling procedure also can be used detection has been the focus of several recent imperfect with a Bayesian