Dear Andrew,
if i understand your code correctly, you first find a signifant
rda(abund~dist), confirmed by NMDS ordination, but
then you cannot find significant correlation between the residuals of
a linear model (abund~dist).
You may want to have a look at Dray et al 2012 "Community ecology in
the age of multivariate multiscale spatial analysis", especially their
Figure 1.
They distuingish between "spatial structure", "spatial dependence",
and "spatial autocorrelation", the latter sometimes referred to as
"residual Spatial autocorrelation".
As far as i have understood the literature, datasets may exhibit
spatial trends, but may not feature autocorrelation, and vice versa,
and sometimes the range of the residual autocorrelation
is larger than the study site and may be mixed up with spatial trends.
I hope this helps.
Tim
Zitat von Andrew Halford <andrew.half...@gmail.com>:
Hi Listers,
I am trying to better understand how spatial autocorrelation is determined.
I have attached 2 txt files one is a fish species x sites abundance table
and the other is a distance file with the lat and longs for each site
(converted to cartesian coords). I have run the tests for spatial
correlation using the methodology from the Borcard et al. Use R! book.
The result was no significant spatial correlation. However an nmds
ordination of the fish data shows 3 very strongly clustered groups of sites
which corresponds to 3 oceanic islands. A permutation analysis of the fish
resemblance matrix with a distance matrix was also highly significant with
a rho=0.646 spearmans rank correlation. To my mind I would have thought
that with such strong groupings in the data that spatial correlation would
be present at the scale of islands? Can someone help me explain this result?
My analysis below
fish_hellinger <- decostand (fish,"hellinger")
fish.xy # spatial dataset space_ed.txt
#test for overall trend
lineartest <- anova(rda(fish_hellinger,fish.xy))
Permutation test for rda under reduced model
Model: rda(X = fish_hellinger, Y = fish.xy)
Df Var F N.Perm Pr(>F)
Model 2 0.17148 8.6098 199 0.005 ** ##significant linear trend
Residual 14 0.13942
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# fit linear trend to the fish data and then extract residuals for
subsequent use in spatial analysis
fish_hellinger_detrended <- resid(lm(as.matrix(fish_hellinger)~
.,data=fish.xy))
# make a distance matrix from the residuals data for input to the
correlogram call
fish_hellinger_D1 <- dist(fish_hellinger_detrended)
# perform the mantel-based test for spatial correlation
(fish_correlogram <-
mantel.correlog(fish_hellinger_D1,XY=fish.xy,nperm=99))
Mantel Correlogram Analysis
Call:
mantel.correlog(D.eco = fish_hellinger_D1, XY = fish.xy, nperm = 99)
class.index n.dist Mantel.cor Pr(Mantel) Pr(corrected)
D.cl.1 118.013676 462.000000 -0.042424 0.11 0.11
D.cl.2 352.037024 0.000000 NA NA NA
D.cl.3 586.060371 0.000000 NA NA NA
D.cl.4 820.083719 0.000000 NA NA NA
D.cl.5 1054.107067 384.000000 0.033639 0.30 NA
D.cl.6 1288.130414 0.000000 NA NA NA
D.cl.7 1522.153762 0.000000 NA NA NA
D.cl.8 1756.177109 320.000000 NA NA NA
D.cl.9 1990.200457 0.000000 NA NA NA
D.cl.10 2224.223804 0.000000 NA NA NA
D.cl.11 2458.247152 240.000000 NA NA NA
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