The nature and strength of relationships between two or more parameters (for example breeding success of birds and environmental parameters of macro and micro-habitat) can be examined using Linear regression models or Generalized Linear Models (McCullagh & Nelder 1989), where the dependent variable must be modeled, specifying their distribution type (Poisson for counts, Logistic binomial, for 1 or 0; ecc.). The independent predictive variables (regressors) are the possible explanation of first variable. A stepwise backward procedure can be carried out using all data pooled in order to select the best predictors using AIC criterion to select the lowest AIC values (Akaike 1974, Anon 1999). Each model use different regressors and have different predictive power, that can be confronted by means of AIC criterion (but not only with AIC!).

Then, the Akaike information criterion (AIC) is a measure of the relative goodness of fit of a statistical model. For more details, and for learn how this measure operate, read:

Akaike, H. 1974: A new look at the statistical model identification. -- IEEE Transactions on Automatic Control 19: 716-722.

Anon 1999: S-PLUS 2000 Guide to Statistics, Vol. 1. MathSoft, Seattle, WA.

Thanx sir

A very good explanation of using AIC approaches to assess the strength of biological hypotheses is described by Mazerolle:

Mazerolle, Marc J. 2006. Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27 (March): 169-180. doi:10.1163/156853806777239922

Well worth a read.