A unit of Lasting Forests Sustained forests; sustained profits evolving since March 30, 1999

Such estimates are:

• variable
• dynamic
• rarely used
• trends often suffice
• richness often suffices
• can lead to inflated expectations
• populations recover quickly and are lost (migration flyways change) used for political purposes
• few experts
• few equations meet all assumptions
• analyses of data are often limited
• benefits, not numbers, are the real interest
• rare populations can rarely be estimated
• large populations rarely need precise estimates
• there's density dependence
• there is sex-related behavior influencing productive numbers (monogamy, polyandry, polygamy, promiscuity)

Procedures:

1. Resist
2. Resist, then use richness
3. Use relative values as density estimates (probably more than on Area X)
4. Use trends in harvests, counts, etc.
5. Use trends and correlations with trap nights, track counts, coo-call counts, harvest trends, trapped animals, fur reports, mail-carrier counts, bird census.
6. Adjust counts where feasible with phenologic observations
7. Use log of trends log(x+1)
8. Compare areas. Use similarity index(s)
9. Pielou's s = 2j / 2ab - (a+b)j
   j is the species on both area A and B
10. Assume animals are correlated (e.g., with deer harvests)
11. Make adjustments in records for illegal harvests
12. Use representative habitats (stratified work with GIS)
13. Do total counts (time-area counts), cumulative counts
14. Track and sign counts (pellets, etc.)
15. Drives with track counts
16. Roadside counts
17. Transects (with width and without width)
18. Trap nights
19. Use animal weight to estimate home range, than divide area into home-ranges for animals.

    A powerful estimator is available for population abundance using estimates of animal home range. One or two animals per home-range area should approximate the total animals within an area and contribute to convergence on a useful population estimate.(These equations are based on data from many studies in many areas. The high R-square values are impressive.)

    Where H = home range in hectares and W is weight in gram: H = 0.002 W1.02 herbivores H = 0.059W0.92 omnivores H = 0.11W1.36 carnivores And taken together, H = 0.008W1.08 with r2 = 0.6 H = f (1/precipitation, latitude) H = f(sex, age) related to H = f(weight) Food consumption/day in grams Carnivores Where FCc is food consumption by carnivores then: FCc = 1.7W0.68 ± 0.02 approximately FCc = 1.7 W0.66 grams Birds FCb = 0.51W0.63 ± 0.11 approximately FCb = 0.5 W0.66

    From: Harestad, A. S. and F. L. Bunnell. 1979. Home range and body weight - a reevaluation. Ecology 60(2):389-402.

20. Use the bounded count procedure:

    Concept: If you see more on some days, how long will it take to see them all?

    Observations: 100, 98, 118, 112, 113, 62, 105 Select largest number A = 118 Select next largest B = 113 N = 2A-B = (2 x 118) - 113 = 236 - 113 = 123 NL = 118 (at least these were all seen) NU = A + ((1.0 - a / a) (A - B)) NU = 118 + )(1 - 1.05)/0.05)(118 - 113) NU = 118 + (19)(5) = 213

21. When an estimate is needed and funds are available, then use Lincoln-Petersen based model.

    Premise: total marked animals (M) are to the unknown number (N) of animals, as the marked animals (m) are to the total animals in a sample. Thus: N = Mn/m

    The standard error is ([M2n(n-m)] / m3)0.5 Where M = 27, n = 23, m = 17; then SE = 4.5 Since N = 36.5, the upper limit of the estimate is (36.5 + 2(4.5)) = 45.4 and the lower limit (2 standard errors) is (36.5 - 9.0) = 27.5 The population, 95 times out of 100, will lie between 27 and 46 animals.

22. Use Bailey's unbiased estimator:

    N = (M(n+1) / (m +1)) - 1 SE = ((M2 (n+1)(n-m)) / ((M + 1)2 (m + 2)))0.5

23. Schnabel
24. Schumacher-Eschmeyer
25. Jolly-Seber
26. Minta and Mangel Ecology 70(6): 1738, December, 1989.
27. Use Removal rate or the Removal Index:

    Example: Pheasants

    100 cock "crows" per hour before the hunt or in an early survey 3 removed Therefore: 70 crows heard later (30 "lost") X = 10 There were 10 pheasant cocks originally. Similar analyses can be made with track counts after deer hunts.

28. Use Indicators:
    1. feces
    2. burrows
    3. cavities
    4. lodges
    5. grouse drumming
    6. turkeys gobbling

29. Use Moran plot of density indicators over time to gain insights about stability of the population.

Resisting the desire or pressure to estimate populations is counterintuitive for the person who gets into the field of faunal resource management because of their love of animals or desire to "work with" animals. Estimating abundance needs to be resisted. Failing that, converge on an estimate.

This Web site is maintained by R. H. Giles, Jr.
Last revision January 17, 2000.