Detection of density-dependent effects on caribou numbers from a series of census data
The main objective of this paper is to review and discuss the applicability of statistical procedures for the detection of density dependence based on a series of annual or multi-annual censuses. Regression models for which the statistic value under the null hypothesis of density independence is set...
| Published in: | Rangifer |
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| Main Author: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Septentrio Academic Publishing
1991
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| Subjects: | |
| Online Access: | https://septentrio.uit.no/index.php/rangifer/article/view/992 https://doi.org/10.7557/2.11.4.992 |
| Summary: | The main objective of this paper is to review and discuss the applicability of statistical procedures for the detection of density dependence based on a series of annual or multi-annual censuses. Regression models for which the statistic value under the null hypothesis of density independence is set a priori (slope = 0 or 1), generate spurious indications of density dependence. These tests are inappropriate because low sample sizes, high variance, and sampling error consistently bias the slope when applied to a finite number of population estimates. Two distribution-free tests are reviewed for which the rejection region for the hypothesis of density independence is derived intrinsically from the data through a computer-assisted permutation process. The "randomization test" gives the best results as the presence of a pronounced trend in the sequence of population estimates does not affect test results. The other non-parametric test, the "permutation test", gives reliable results only if the population fluctuates around a long-term equilibrium density. Both procedures are applied to three sets of data (Pukaskwa herd, Avalon herd, and a hypothetical example) that represent quite divergent population trajectories over time. |
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