R help - Mar 2011 - vegan CCA I am Completely new to ordination analyses

Dear list,

I am trying to predict species volume from bioclimatic data, I have various
sites and I have a data frame with species volume and
the corresponding bioclimatic data for each site.

I read on a discussion forum that you can use ordination to predict species
abundance (in my case volume) from 'new' climate data for sites where
you do
not know the abundance.

Unfortunately I cannot work out how to do this in the r package vegan, in
fact I cannot even get a simple CCA to work; I have attached a sample of my
data (volume and bioclimate data for around 100 of my sites) and the code I
used is below.

Does the output look right? why am I getting a 0 output? Is
there something wrong with my data, data format, r code etc?

All help is greatly appreciated,
Thank you in advance,
Kitty


########################################################
#R code
data<-read.table("y:directory\\sample.data",header=T)
names(data)
attach(data)
library(vegan)
# I multiplied up the volume because I thought that the issue may have been
the fact that I had x.x numbers but I still got the same problem
sumvol<-sum_vol*10000
cca2<-cca(sumvol~bioclim6+bioclim9+bioclim11)
cca2

######   output:

Call: cca(formula = sumvol ~ bioclim6 + bioclim9 + bioclim11)

      Inertia Rank
Total       0
Inertia is mean squared contingency coefficient

########################################################
-------------- next part --------------
sum_vol	bioclim6	bioclim9	bioclim11	bioclim12	bioclim13	bioclim15
0.59	207	262	250	3270	395	32
3.19	204	257	247	3333	365	21
1.39	215	270	262	3134	352	19
1.31	213	266	255	2879	339	19
0.85	192	252	244	2192	308	34
6.85	197	257	249	1947	337	55
7.52	189	250	242	2275	351	41
7.15	198	257	250	1997	332	50
10.57	204	263	255	1816	296	53
0.96	201	260	252	1823	297	53
6.44	208	268	258	1801	314	65
1.77	211	271	261	1782	311	64
5.01	201	260	252	1833	294	50
4.17	201	260	251	1851	314	57
1.75	203	262	254	1860	320	58
0.87	202	261	253	1854	305	52
2.42	214	275	263	1632	322	85
1.41	212	276	262	1646	306	80
0.74	210	268	260	1876	295	52
4.58	210	270	260	1788	296	57
3.48	211	270	261	1796	292	53
0.81	214	276	264	1720	323	73
5.58	201	260	244	1974	412	65
0.74	209	264	257	1866	339	65
5.49	214	274	261	1948	326	58
0.32	215	270	263	1839	320	62
2.38	207	268	254	2096	367	59
5.65	209	271	257	2042	378	59
11.41	217	273	264	1863	285	48
7.77	203	263	250	2177	398	59
1.01	217	276	264	2013	323	47
3.87	216	276	263	2164	409	59
8.81	208	269	254	2276	389	53
1.99	208	269	254	2276	389	53
3.12	208	269	254	2276	389	53
2.57	214	275	261	2229	375	54
1.41	212	273	259	2118	370	57
4.81	217	277	263	2242	389	54
3.09	211	274	257	2040	412	63
1.57	207	270	254	2146	401	57
3.96	208	271	255	2126	403	58
1.25	207	269	252	2012	390	60
3.02	203	268	250	1996	412	63
2.14	207	270	254	2146	401	57
0.68	206	264	248	2122	372	56
2.96	204	264	248	2011	377	60
3.82	214	275	260	2231	385	53
18.43	213	275	259	2196	388	54
19.4645	201	263	250	2919	451	56
3.9238	203	265	251	2908	443	56
4.0787	202	262	249	2951	446	57
1.5395	208	266	252	3124	476	60
0.8704	208	267	253	3314	512	61
1.2534	212	266	251	2456	328	45
33.7226	215	267	252	2434	324	44
2.8521	215	267	252	2434	324	44
2.7095	215	267	252	2434	324	44
26.8579	215	267	252	2434	324	44
31.3444	215	267	252	2434	324	44
20.4718	215	267	252	2434	324	44
19.9616	215	267	252	2434	324	44
12.5338	215	267	252	2434	324	44
6.7382	210	262	247	2430	327	47
1.22	210	264	254	2906	338	19
1.71	215	267	256	2897	326	18
11.49	204	256	249	3471	365	15
4.66	205	258	250	3454	346	13
1.25	207	259	251	3459	353	13
1.33	206	258	250	3455	362	14
6.88	206	258	250	3439	370	16
9.69	206	260	250	3432	370	16
1.93	204	257	249	3465	353	13
0.39	207	262	252	3184	349	17
1.2	207	262	252	3036	347	18
1.38	208	262	252	2928	341	19
3.75	209	265	254	2855	335	20
10.71	204	258	249	3154	342	17
1.51	205	255	251	3067	318	18
1.87	205	257	250	3474	374	20
1.51	205	254	252	3010	311	19
1.48	204	256	251	2686	287	23
1.31	204	256	249	3579	361	15
22.5	205	258	250	3499	345	12
0.64	204	257	249	3403	350	14
0.85	202	255	250	2758	321	24
1.56	203	256	251	2800	322	24
0.56	204	254	251	3337	341	17
0.94	203	255	251	2722	306	24
1.88	209	261	257	2920	348	27
0.88	208	260	256	2908	365	30
1.2	215	270	261	2600	325	22
3.4	217	271	262	2610	316	21
1.01	215	270	261	2675	326	21
7.91	215	269	261	2562	320	23
1.55	214	269	259	2637	317	21
2.09	214	267	259	2569	308	21
0.78	213	268	258	2617	314	21
0.61	216	271	264	2592	349	29
2.14	210	264	256	2806	327	21
1.55	213	267	258	2721	324	20
0.5	214	267	260	2640	321	23
0.81	209	263	255	2818	330	21
1.57	215	270	262	2500	323	30
2.41	218	274	266	2144	303	35

kitty <kitty.a1000 <at> gmail.com> writes:
> 
> Dear list,
> 
> I am trying to predict species volume from bioclimatic data, I have various
> sites and I have a data frame with species volume and
> the corresponding bioclimatic data for each site.
> 
> I read on a discussion forum that you can use ordination to predict species
> abundance (in my case volume) from 'new' climate data for sites
where you do
> not know the abundance.
> 
> Unfortunately I cannot work out how to do this in the r package vegan, in
> fact I cannot even get a simple CCA to work; I have attached a sample of my
> data (volume and bioclimate data for around 100 of my sites) and the code I
> used is below.
> 
> Does the output look right? why am I getting a 0 output? Is
> there something wrong with my data, data format, r code etc?
> 
Ordination, including cca(), is multivariate analysis. If you only have one 
dependent variate, you should use univariate methods (regression etc.). 
Some multivariate methods fall back to univariate methods if you only 
have one dependent variable, but cca() does not do so in a meaningful way. 
In fact cca() does do this, but not meaningfully: your residual variation is 
zero and rank is zero. Switch to regression.

Cheers, Jari Oksanen