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As the density-dependent terms proved significant, we then extended our PBLR test to consider prey-dependent effects, yielding a Ricker model augmented with prey dependence (r = a - b[P.sub.t-1] + c[N.sub.t]).
The per capita rate of increase of marten was negatively related to marten density the previous year ([ILLUSTRATION FOR FIGURE 2 OMITTED]; [r.sub.t] = 0.45 - 0.00043[P.sub.t-1], [r.sup.2] = 0.29; PBLR test [Gamma] = 1.40, P [less than] 0.001).
We therefore proceeded to use the PBLR test to evaluate whether a mix of prey-dependent and density-dependent processes significantly improved on predictions of instantaneous per capita rates of increase obtained from the Ricker logistic equation.
1987; permutation test, Reddingius and Den Boer 1989; Crowley's test of attraction, Crowley 1992; PBLR test, Dennis and Taper 1994).
Dennis and Taper (1994) rejected density independence in two of the data sets using the PBLR significance test and using the exponential-growth model as the null model.
The steps for conducting the PBLR test of the joint density independence model ([H.sub.0]) vs.
The hypothesis of correlation among populations can also be studied with a PBLR test.
To conduct the PBLR test of [H.sub.0]: [[Sigma].sub.ij] = 0, i [not equal to] j, vs.
A PBLR test rejects the null hypothesis that [Sigma] is diagonal (Eq.
The multivariate stochastic logistic model fits better than the multivariate stochastic exponential growth model, according to the PBLR test of joint density dependence (Eq.
However, a PBLR test of whether or not [Sigma] is diagonal is only marginally significant (Eq.
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