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RESEARCH ARTICLE
Contents Vol 32(3)

Indexing principles and a widely applicable paradigm for indexing animal populations

Richard M. Engeman
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National Wildlife Research Center, 4101 LaPorte Avenue, Fort Collins, CO 80521-2154, USA. Email: richard.m.engeman@aphis.usda.gov

Wildlife Research 32(3) 203-210 https://doi.org/10.1071/WR03120
Submitted: 22 December 2003  Accepted: 3 March 2005   Published: 22 June 2005

Abstract

Monitoring animal populations is an essential component of wildlife research and management. Population indices can be efficient methods for monitoring populations when more labour-intensive density-estimation procedures are impractical or invalid to apply, and many monitoring objectives can be couched in an indexing framework. Indexing procedures obtain maximal utility if they exhibit key characteristics, including being practical to apply, being sensitive to changes or differences in the target species’ population, having an inherent variance formula and allowing for precision in index values, and relying on as few assumptions as possible. Additional useful characteristics include being able simultaneously to monitor multiple animal species and to describe spatial characteristics of the species monitored. Here, a paradigm is presented that promotes the characteristics that make indices most useful. Observations are made at stations located throughout the area of interest. Stations can take many forms, depending on the observations, and range from points for visual counts to tracking plots to chew cards, and many others. A wide variety of observation methods for many animal species can fit into this format. Observations are made at each station on multiple occasions for each indexing session. Geographic location data for each station are encouraged to be collected. No assumptions of independence are made among stations, nor among observation occasions. Measurements made at each station are required to be continuous or unboundedly discrete. The formula for a general index to describe population levels is presented and its variance formula is derived. Issues relevant to the application of this methodology, and indices in general, are discussed.


Acknowledgments

J. Bourassa and M. Pierce provided valuable help with identifying software for measuring areas. K. Fagerstone, B. Kimball, T. Mathies, R. Sterner and K. VerCauteren provided helpful reviews of the manuscript.


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Appendix 1. Example for calculating the General Index

The data in Table 2 were collected for assessing a dingo population on a cattle station in south-west Queensland, Australia. A sample of s = 50 tracking plots was placed on dirt roads throughout the study area and observed for d = 4 consecutive days. The number of track intrusions into each plot by dingoes was observed each day. The average number of sets of intrusions per plot per day were 0.94, 0.82, 1.30, 0.82 for Days 1, 2, 3, 4, respectively (Table 2). The GI index value was calculated as:

(0.94 + 0.82 + 1.30 + 0.82)/4 = 0.97.

Application of VARCOMP in SAS produced variance component estimates of σ s 2 = 0.1075, σ d 2 = 0.0199, and σe 2 = 1.5767. We can use the equal-sample-size formula because all plots were measurable on each of the four days, i.e. p1 = p2 = p3 = p4 = 50 for Days 1–4. Insertion of the above information into the equal-sample-size equation for var(GI) yields:

var(GI) = 0.1075/50 + 0.0199/4 + 1.5767/200 = 0.0150

standard error (s.e.) = 0.122

coefficient of variation (c.v.) = 0.126.