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G A R Y C . W H I T E
to remove the sampling variance from a series of estimates to obtain an estimate of the underlying process variation (which might be temporal or spatial
variation). The procedure is explained in Burnham et al. (1987).
Consider the example situation of estimating overwinter survival rates each
year for 10 years from a deer population. Each year, the survival rate is different from the overall mean because of snow depth, cold weather, and other factors. Let the true but unknown overall mean be S. Then the survival rate for
each year can be considered to be S plus some deviation attributable to temporal variation, with the expected value of the e i equal to zero:
Environmental Variation
Mean
Year I
Year I
1
S
S+ e 1
S 1
2
S
S+ e 2
S 2
3
S
S+ e 3
S 3
4
S
S+ e 4
S 4
5
S
S+ e 5
S 5
6
S
S+ e 6
S 6
7
S
S+ e 7
S 7
8
S
S+ e 8
S 8
9
S
S+ e 9
S 9
10
S
S+ e 10
S 10
Mean
S
S ෆ
S ෆ
The true population mean S is computed as S ෆ:
S ෆ = ᎏ
10
ᎏ
with the variance of the S i is computed as
σ 2 = ᎏ
10
ᎏ
⌺
10
i=1
S i
⌺
10
i=1
(S i – S ෆ) 2