Northern Prairie Wildlife Research Center

Proceedings of the North Dakota Academy of Science

**Glen A. Sargeant* and Douglas H. Johnson**

Department of Wildlife Ecology, University of Wisconsin,

226 Russell Labs, 1630 Linden Drive, Madison, WI 53706 (GAS)

and United States Geological Survey, Biological Resources Division,

Northern Prairie Science Center, 8711 37th St. SE, Jamestown, ND (DHJ)

Sargeant, Glen A., and Douglas H. Johnson. 1997. Carnivore scent-station surveys: statistical considerations. Proceedings of the North Dakota Academy of Science 51:102-104.

Sargeant, Glen A., and Douglas H. Johnson. 1997. Carnivore scent-station surveys: statistical considerations. Proceedings of the North Dakota Academy of Science 51:102-104. Jamestown, ND: Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/mammals/carnivor/index.htm (Version 17DEC97).

Scent-station surveys are a popular method of monitoring temporal and geographic trends in carnivore populations. We used customary methods to analyze field data collected in Minnesota during 1986-93 and obtained unsatisfactory results. Statistical models fit poorly, individual carnivores had undue influence on summary statistics, and comparisons were confounded by factors other than abundance. We conclude that statistical properties of scent-station data are poorly understood. This fact has repercussions for carnivore research and management. In this paper, we identify especially important aspects of the design, analysis, and interpretation of scent-station surveys.

We used standard methods to analyze scent-station data collected in Minnesota during 1986-93. Although our data set was among the largest in existence, we were frustrated by inadequate sample sizes. The most popular statistical model for scent-station data fit poorly. Anomalous data had undue influence on summary statistics and affected results of statistical comparisons. To overcome these problems, we devised improved methods for using scent-station surveys to monitor temporal and geographic trends in carnivore populations.

The difficulties we encountered can be traced to a few key features of survey designs and methods of analysis. These include the spatial distribution of scent stations, the experimental unit chosen for analyses, the statistic used to summarize results, the statistical model underlying analyses, and confounding of statistical comparisons. In this paper, we discuss these aspects of the design and analysis of carnivore scent-station surveys. Our presentation will demonstrate the use of field data to resolve issues raised in this paper.

Closely spaced stations produce correlated data, but how far correlations extend is unknown. Stations placed too close to one another produce redundant data. Spacing stations more widely than necessary increases the cost of surveys and precludes intensive sampling of small areas. Subjective estimates of optimum spacing are inconsistent. Some investigators (e.g., Smith et al. [2]) have treated stations within 320 m of one another as independent samples. Others (e.g., Morrison et al. [3]) thought it necessary to separate stations by as much as 1.6 km. We have used variograms to show that correlations between stations often extend to 2000 m or more. Separating stations by this great a distance is seldom practical, so we have pursued the development of summary statistics and methods of analysis that are robust to correlations between stations.

First, visitation rates are not directly related to abundance because each station has the capacity for only one detection. When visitation rates are high, many individual carnivores encounter stations that have already been visited. These additional visits have no effect on visitation rates. The result is a nonlinear relationship between visitation rate and abundance. The form of the curve is unknown, except for the y-intercept (0) and asymptote (y=1), so visitation rates can be used only to rank abundances.

Second, visitation rates are easily influenced by factors other than abundance,
especially when sample sizes are small or visitation rates are low. These
may include weather, season, human activity, or other factors that influence
animal behavior. An ideal summary statistic would be robust to such effects.
We will use examples to demonstrate the poor performance of visitation rates
and present two alternative summary statistics: the proportion of lines that
are visited (p_{1}) and the negative natural logarithm of the proportion
of lines that are not visited (-ln[1-p_{1}]).

To our knowledge, the fit of the binomial model has never been tested. We devised a goodness-of-fit test and found the binomial distribution to be a poor model for visitation rates, but an adequate one for the proportion of lines with one or more stations visited.

- Sampling: How should stations be spatially distributed?
- Response variables: Is p
_{s}a suitable summary statistic? - Statistical models: The binomial distribution has convenient properties, but does it adequately describe field data?
- Statistical comparisons: Are comparisons confounded by unidentified factors?

- Novak, M., Baker, J.A., Obbard, M.E. and Malloch, B., eds. (1987) Wild furbearer management and conservation in North America. Ontario Trapper's Association, North Bay, 1150 pp.
- Smith, W.P., Borden, D.L. and Endres, K.M. (1994) Scent-station visits as an index to abundance of raccoons: an experimental manipulation. J Mammal 75, 637-647.
- Morrison, D.W., Edmunds, R.M., Linscombe, G. and Goertz, J.W. (1981) Evaluation of specific scent station variables in northcentral Louisiana. Proc Annu Conf of Southeast Assoc Fish and Wildl Agencies 35, 281-291.
- Roughton, R.D., and Sweeny. M.D. (1982) Refinements in scent-station methodology for assessing trends in carnivore populations. J Wildl Manage 46, 217-229.
- Johnson, K.G., and Pelton, M.R. (1981) A survey of procedures to determine relative abundance of furbearers in the southeastern United States. Proc Annu Conf Southeast Assoc Fish Wildl Agencies 35, 261-272.

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