Since the analysis of the large-scale effect is of the main interests in static and quasi-dynamic scenarios, i.e. S1 S6, for these scenarios the measurements were performed with the distance being changed with a 1 m step, Fig. 1.
Thus, the importance of the large-scale effect was examined using Cox's regression model where demographic factors identical to the ones used in logistic regression (GAM) were examined.
The degree to which ecosystem engineering produces large-scale effects is, in part, dependent on the dynamics of the patches that engineers create.
However, ERA concentrates at the small-scale level, thus, large-scale effects are difficult to assess.
Numerical results show that the small scale effects are significant for larger half wave numbers.
A special Task Group assessed the potential for a living entity to be included in return samples from various bodies as well as the potential for large scale effects if such an entity were inadvertently introduced into the Earth's biosphere.
Large scale effects (i.e., path-loss) are supposed to be slowly varying compared to the data packet length.
There could be some large scale effects from this, such as shifting wind patterns, surface ocean currents and potentially the jet stream.
We are interested in large scale effects.
While it has previously been shown that scale is important in PSRs at the local-to-landscape level, we demonstrate here that even with much larger sampling units, scaling effects are not only detectable but strong.
This is a large-scale effect but it is still doubtful whether the observed long-range correlation between gene expression levels, involving thousands of genes, can be exhaustively explained by this mechanism.
