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Markov Chain Monte Carlo Estimation of Species Distributions: a Case Study of the Swift Fox in Western Kansas

Glen A. Sargeant, Marsha A. Sovada, Christiane C. Slivinski, Douglas H. Johnson


Abstract: Accurate maps of species distributions are essential tools for wildlife research and conservation. Unfortunately, biologists often are forced to rely on maps derived from observed occurrences recorded opportunistically during observation periods of variable length. Spurious inferences are likely to result because such maps are profoundly affected by the duration and intensity of observation and by methods used to delineate distributions, especially when detection is uncertain. We conducted a systematic survey of swift fox (Vulpes velox) distribution in western Kansas, USA, and used Markov chain Monte Carlo (MCMC) image restoration to rectify these problems. During 1997-1999, we searched 355 townships (ca. 93 km²) 1-3 times each for an average cost of $7,315 per year and achieved a detection rate (probability of detecting swift foxes, if present, during a single search) of theta-hat = 0.69 (95% Bayesian confidence interval [BCI] = [0.60, 0.77]). Our analysis produced an estimate of the underlying distribution, rather than a map of observed occurrences, that reflected the uncertainty associated with estimates of model parameters. To evaluate our results, we analyzed simulated data with similar properties. Results of our simulations suggest negligible bias and good precision when probabilities of detection on ≥1 survey occasions (cumulative probabilities of detection) exceed 0.65. Although the use of MCMC image restoration has been limited by theoretical and computational complexities, alternatives do not possess the same advantages. Image models accommodate uncertain detection, do not require spatially independent data or a census of map units, and can be used to estimate species distributions directly from observations without relying on habitat covariates or parameters that must be estimated subjectively. These features facilitate economical surveys of large regions, the detection of temporal trends in distribution, and assessments of landscape-level relations between species and habitats. Requirements for the use of MCMC image restoration include study areas that can be partitioned into regular grids of mapping units, spatially contagious species distributions, reliable methods for identifying target species, and cumulative probabilities of detection ≥0.65.

Key words: Bayesian estimation, carnivore survey, image restoration, Kansas, Markov chain Monte Carlo, species distributions, species-habitat associations, swift fox, Vulpes velox, wildlife surveys.

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This resource is based on the following resource (Northern Prairie Publication 1459):

Sargeant, Glen A., Marsha A. Sovada, Christiane C. Slivinski, Douglas H. Johnson.  2005.  Markov Chain Monte Carlo estimation of species distributions: a case study of the swift fox in western Kansas.  Journal of Wildlife Management 69:483-497.

This resource should be cited as:

Sargeant, Glen A., Marsha A. Sovada, Christiane C. Slivinski, Douglas H. Johnson.  2005.  Markov Chain Monte Carlo estimation of species distributions: a case study of the swift fox in western Kansas.  Journal of Wildlife Management 69:483-497.  Jamestown, ND: Northern Prairie Wildlife Research Center Online.  http://www.npwrc.usgs.gov/resource/methods/sfoxmcmc/index.htm  (Version 24AUG2006).


Glen A. Sargeant*, Marsha Sovada, and Douglas H. Johnson, U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th Street SE, Jamestown, ND 58401, USA.  *E-mail: glen_sargeant@usgs.gov.
Christiane C. Slivinski, Kansas Department of Wildlife and Parks, 1830 Merchant, Emporia, KS 66801, USA.  Present address: 11642 Wall Road, Caledonia, MN 55921, USA.

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