Dear list,
i need to evaluate the influence of temporal and spatial distance
between two communities on their abundance weighted Raup Crick distance.
The abundance weighted Raup Crick distance (see Stegen et al, 2012, 13,
ISMEJ)? is formulated as a difference between observed and the mean of
simulated bray curtis distances,
is scaled to the interval -1 and +1, and knows by definition only three
outcomes:
A value of <-0.95 represents homogenizing dispersal between two communities
A value of > 0.95 indicates a dispersal limitation between two communities
A value of < |0.95| is undefined ("undominated")
Thus, i have actually no interest in the actual value, but only in the
outcome said value is associated with.
Since i have many observation pairs (70,000) i would like to avoid
linear regression as in distance-decay regression.
What i want to know is:
i) if low spatial and/or high temporal distances faciliate homogeneity
by dispersal and
ii) if high spatial distances and/or low temporal distances faciliate
dispersal limitation.
Is it valid to turn my outcome variable into a factor with the
aforementioned three levels, and then formulate two logistic regression
models (for case >0.95 and for case <-0.95) or a multinomial regression
model for all three levels,
e.g. multinom(Factor~Space*Time)?
If so, what would a model look like in R?
Thank you for your advice.
Dr. Tim Richter-Heitmann University of Bremen Microbial Ecophysiology Group (AG Friedrich) FB02 - Biologie/Chemie Leobener Stra?e (NW2 A2130) D-28359 Bremen Tel.: 0049(0)421 218-63062 Fax: 0049(0)421 218-63069