Multiscale sources of variation in ecological variables: modeling spatial dispersion, elaborating sampling designs

Detection of structured spatial variation and identification of spatial scales are important aspects of ecological studies. Spatial structures can correspond to physical features of the environment or to intrinsic characteristics of ecological processes and phenomena. Spatial variability has been approached through several techniques such as classical analysis of variance, or the calculation of fractal dimensions, correlograms or variograms. Under certain assumptions, these techniques are all closely related to one another and represent equivalent tools to characterize spatial structures. Our perception of ecological variables and processes depends on the scale at which variables are measured. We propose simple nested sampling designs enabling the detection of a wide range of spatial structures that show the relationships among nested spatial scales. When it is known that the phenomenon under study is structured as a nested series of spatial scales, this provides useful information to estimate suitable sampling intervals, which are essential to establish the relationships between spatial patterns and ecological phenomena. The use of nested sampling designs helps in choosing the most suitable solutions to reduce the amount of random variation resulting from a survey. These designs are obtained by increasing the sampling intensity to detect a wider spectrum of frequencies, or by revisiting the sampling technique to select more representative sampling units.