Electronic Resource
Springer
Oecologia
85 (1991), S. 413-418
ISSN:
1432-1939
Keywords:
Density dependence
;
k-factor analysis
;
Bias
;
Animal populations
Source:
Springer Online Journal Archives 1860-2000
Topics:
Biology
Notes:
Summary Randomization and simulation are used to detect bias in k-factor analysis. In nine previously published data sets there is strong evidence of bias. This may result from either non-independence of observations or the arithmetic relationship used to estimate k-factors, which can generate “spurious correlations”. Randomization can be used to test for density dependence without bias. This procedure confirms the existence of densitydependent effects in 8 of the 9 populations and 11 of the 16 k-factors previously thought to have density-dependent effects.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00320618
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