I have some ecological data collected from n independent sites spanning a relatively large spatial scale (>10 degrees of latitude), and from each site, n replicate samples were collected but no associated spatial information (e.g. coordinates) recorded. So, assuming spatial dependence (if any) is only operating at a within-site scale:
Question: Is there any way to test for spatial autocorrelation among samples (i.e. at the replicate level) without their associated coordinates?
A user-friendly push in the right direction wrt this question would be very much appreciated.
After reading up on this question, perhaps a
Moran's I test based on (1) a binary weights matrix indicating neighbouring (1's) and non-neighbouring (0's) site (A or B) replicates (1, 2, 3), for example:
A1 A2 A3 B1 B2 B3 A1 A2 1 A3 1 1 B1 0 0 1 B2 0 0 0 1 B3 0 0 0 1 1
and (2) a distance- or dissimilarity-based matrix of the measured variable, for example:
A1 A2 A3 B1 B2 B3 A1 A2 0.4 A3 0.5 0.4 B1 1.2 1.3 1.4 B2 1.4 1.3 1.2 0.4 B3 1.4 1.3 1.2 0.5 0.3
Does this seem right? If so, a worked example in R would be good, as I'm presently having coding issues (using package