Wildlife Research Wildlife Research Society
Ecology, management and conservation in natural and modified habitats

Indexing principles and a widely applicable paradigm for indexing animal populations

Richard M. Engeman

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


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.


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.


Allen, L. , Engeman, R. M. , and Krupa, H. (1996). Evaluation of three relative abundance indices for assessing dingo populations. Wildlife Research 23, 197–206.
CrossRef |

Andelt, W. F. , and Andelt, S. H. (1984). Diet bias in scat deposition rate surveys of coyote density. Wildlife Society Bulletin 12, 74–77.

Anthony R. M. , and Barnes V. G. Jr (1983). Plot occupancy for indicating pocket gopher abundance and conifer damage. In ‘Vertebrate Pest Control and Management Materials: Fourth Symposium, ASTM STP 817’. (Ed. D. E. Kaukeinen.) pp. 247–255. (American Society for Testing and Materials: Philadelphia, PA.)

Barret, R. H. (1983). Smoked aluminum track plots for determining furbearer distribution and relative abundance. California Department of Fish and Game 69, 188–190.

Burnham, K. P. , Anderson, D. R. , and Laake, J. L. (1980). Estimation of density from line transect sampling of biological populations. Wildlife Monographs 72, 1–202.

Byers, R. E. (1975). A rapid method for assessing pine vole control in orchards. Horticulture Science 10, 391–392.

Caughley G. (1977). ‘Analysis of Vertebrate Populations.’ (Wiley & Sons: New York.)

Caughley G. , and Sinclair A. (1994). ‘Wildlife Ecology and Management.’ (Blackwell Science: Cambridge, MA.)

Caughley J. , Donkin C. , and Strong K. (1998). Managing mouse plagues in rural Australia. In ‘Proceedings of the Eighteenth Vertebrate Pest Conference’. pp. 160–165.

Chitty D. (1954). ‘Control of Rats and Mice.’ (Clarendon Press: Oxford, UK.)

Choquenot D. , McIlroy J. , and Korn T. (1996). ‘Managing Vertebrate Pests: Feral Pigs.’ (Bureau of Resource Sciences, Australian Government Publishing Service: Canberra.)

Davison R. P. (1980). The effects of exploitation on some parameters of coyote populations. Ph.D. Thesis, Utah State University, Logan, UT.

Engeman, R. M. (2003). More on the need to get the basics right: population indices. Wildlife Society Bulletin 31, 286–287.

Engeman, R. M. , and Sugihara, R. T. (1998). Optimization of variable area transect sampling using Monte Carlo simulation. Ecology 79, 1425–1434.

Engeman, R. M. , and Whisson, D. A. (2003). A visual method for indexing muskrat populations. International Biodeterioration & Biodegradation 52, 101–106.
CrossRef |

Engeman R. M. , and Witmer G. W. (2000). IPM strategies: indexing difficult to monitor populations of pest species. ‘Proceedings of the Nineteenth Vertebrate Pest Conference’. pp. 183–189.

Engeman, R. M. , Campbell, D. L. , and Evans, J. (1993). A comparison of 2 activity measures for northern pocket gophers. Wildlife Society Bulletin 21, 70–73.

Engeman, R. , Sugihara, R. , Pank, L. , and Dusenberry, W. (1994). A comparison of plotless density estimators using Monte Carlo simulation. Ecology 75, 1769–1779.

Engeman R. M. , Otis D. L. , Bromaghin J. F. , and Dusenberry W. E. (1989). On the use of the R50. In ‘Vertebrate Pest Control and Management Materials. Vol. 6, STP1055’. (Eds K. Fagerstone and R. Curnow.) pp. 13–18. (American Society for Testing and Materials: Philadelphia, PA.)

Engeman, R. M. , Pipas, M. J. , Gruver, K. S. , and Allen, L. (2000). Monitoring coyote populations with a passive activity index. Wildlife Research 27, 553–557.

Engeman, R. M. , Constantin, B. , Nelson, M. , Woolard, J. , and Bourassa, J. (2001). Monitoring changes in feral swine population and spatial distribution of activity. Environmental Conservation 28, 235–240.

Engeman, R. M. , Pipas, M. J. , Gruver, K. S. , Bourassa, J. , and Allen, L. (2002). Plot placement when using a passive tracking index to simultaneously monitor multiple species of animals. Wildlife Research 29, 85–90.
CrossRef |

Engeman, R. M. , Christensen, K. L. , Pipas, M. J. , and Bergman, D. L. (2003a). Population monitoring in support of a rabies vaccination program for skunks in Arizona. Journal of Wildlife Diseases 39, 746–750.
PubMed |

Engeman, R. M. , Martin, R. E. , Constantin, B. , Noel, R. , and Woolard, J. (2003b). Monitoring predators to optimize turtle nest protection through control. Biological Conservation 113, 171–178.
CrossRef |

Fagerstone, K. A. , and Biggins, D. E. (1986). Comparison of capture–recapture and visual count indices of prairie dog densities in black-footed ferret habitat. Great Basin Naturalist Memoirs 8, 94–98.

Fiedler L. A. (1994). ‘Rodent Pest Management in Eastern Africa.’ (Food and Agriculture Organization of the United Nations: Rome.)

Hopkins, B. (1954). A new method for determining the type of distribution of plant individuals. Annals of Botany 18, 213–227.

Krebs C. J. (1998). ‘Ecological Methodology.’ (Benjamin/Cummings: Menlo Park, CA.)

Leidloff A. C. (2000). Habitat utilisation by the grassland melomys (Melomys burtoni) and the swamp rat (Rattus lutrelus) in a coastal heathland of Bribie Island, south-east Queensland. Ph.D. Thesis, Queensland University of Technology, Brisbane.

Mahon, P. S. , Banks, P. B. , and Dickman, C. R. (1998). Population indices for wild carnivores: a critical study in sand dune habitat, south-western Queensland. Wildlife Research 25, 11–22.
CrossRef |

McKelvey, K. S. , and Pearson, D. E. (2001). Population estimation with sparse data: the role of estimators versus indices revisited. Canadian Journal of Zoology 79, 1754–1765.
CrossRef |

Menkens, G. E. , Biggins, D. E. , and Anderson, S. H. (1990). Visual counts as an index of white-tailed prairie dog density. Wildlife Society Bulletin 18, 290–296.

Otis, D. L. , Burnham, K. P. , White, G. C. , and Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs 62,

Powell, K. L. , Robel, R. J. , Kemp, K. E. , and Nellis, M. D. (1994). Above ground counts of black-tailed prairie dogs: temporal nature and relationship to burrow entrance density. Journal of Wildlife Management 58, 361–366.

Proulx, G. , and Gilbert, F. F. (1984). Estimating muskrat population trends by house counts. Journal of Wildlife Management 48, 917–922.

Reid, V. H. , Hansen, R. M. , and Ward, R. L. (1966). Counting mounds and earth plugs to census mountain pocket gophers. Journal of Wildlife Management 30, 327–334.

Robbins C. S. , Bystrack D. , and Geissler P. H. (1986). The breeding bird survey: its first fifteen years, 1965–1979. Fish and Wildlife Service Research Publication No. 157. (USDI: Washington, DC.)

Ryan, D. A. , and Heywood, A. (2003). Improving the precision of longitudinal ecological surveys using precisely defined observational units. Environmetrics 14, 283–293.
CrossRef |

SAS Institute (1996). ‘SAS/STAT User’s Guide.’ (SAS Institute: Carey, NC.)

Searle S. R. , Casella G. , and McCulloch C. E. (1992). ‘Variance Components.’ (Wiley & Sons: New York.)

Servoss, W. , Engeman, R. M. , Fairaizl, S. , Cummings, J. L. , and Groninger, N. P. (2000). Wildlife hazard assessment at Phoenix Sky Harbor International Airport. International Biodeterioration & Biodegradation 45, 111–127.
CrossRef |

Severson, K. E. , and Plumb, G. E. (1998). Comparisons of methods to estimate population densities of black-tailed prairie dogs. Wildlife Society Bulletin 26, 859–866.

Stancyk S. E. (1982). Non-human predators of sea turtles and their control. In ‘Biology and Conservation of Sea Turtles’. (Ed. K. A. Bjorndal.) pp. 139–152. (Smithsonian Institution Press: Washington, DC.)

Tobin, M. E. , Richmond, M. E. , and Engeman, R. M. (1992). Comparison of methods for detecting voles under apple trees. Proceedings of the Eastern Wildlife Damage Control Conference 5, 201–204.

White, G. C. (2001). Why take calculus? Rigor in wildlife management. Wildlife Society Bulletin 29, 380–386.

Zielinski W. J. , and Kucera T. E. (1995). American marten, fisher, lynx, and wolverine: survey methods for their detection. General Technical Report PSW-GTR-157. (USDA/Forest Service: Albany, CA.)

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.

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