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 Hi list members,
 I'm doing some analysis about differences in behaviours between rural and urban birds and, after reading and searching in different sources, I have a lot of doubts about how I'm performing them. I would greatly appreciate any feedback from you. Here are my questions, models and results:

 

Background
 I performed some behavioural test on individuals belonging to the same territory (breeding birds), some of them located in rural areas and others in urban ones. I have 

7 variables describing different behaviours in 178 breeding birds, most of them sharing territories as they are mates. Some of these  variables should be considered as censored data.
 My main questions are: 1) which is the relationship between these behaviours and 2) whether urban birds differ in these behaviours (means) and/or in the strength of their relationships compare with rural ones. 

 

 ######################################################################

MODELS AND RESULTS

QUESTION 1) which is the relationship between the behaviours measured:

 prior = list(R = list(V = diag(7), nu = 8), G = list(G1 = list(V = diag(7), nu = 8)))

 m1 <- MCMCglmm(fixed = cbind (var1, var2min, var2max, var3min, var3max, var4min, var4max, var5, var6min, var6max, var7) ~ trait - 1, random = ~ us(trait):nest, rcov =  ~ us(trait):units, prior = prior, family =c("gaussian", "cengaussian", "cengaussian", "cengaussian", "poisson", "cengaussian", "poisson"), nitt = 60000, burnin = 1000, thin = 25, data = datos)



I obtained the correlation between behaviours with the general formula:

 model$VCV[,"var1:var2.nest"]/sqrt(model$VCV[,"var1:var1.nest"]*model$VCV[,"var2:var2.nest"]))



so that:

cor(var2:var1): 0.379; 95%CI = 0.376 - 0.383
 cor(var3:var1): 0.246; 95%CI = 0.242 - 0.249
 cor(var4:var1): 0.150; 95%CI = 0.146 - 0.155
 cor(var5:var1): -0.022; 95%CI = -0.027 - -0.017
 cor(var6:var1): 0.171; 95%CI = 0.167 - 0.176
 cor(var7:var1): 0.001; 95%CI = -0.004 - 0.006
 cor(var3:var2): 0.364; 95%CI = 0.360 - 0.369
 cor(var4:var2): 0.121; 95%CI = 0.115 - 0.127
 cor(var5:var2): -0.037; 95%CI = -0.044 - -0.030
 cor(var6:var2): 0.209; 95%CI = 0.203 - 0.215
 cor(var7:var2): -0.031; 95%CI = -0.038 - -0.024
 cor(var4:var3): 0.062; 95%CI = 0.056 - 0.068
 cor(var5:var3): 0.037; 95%CI = 0.030 - 0.045
 cor(var6:var3): 0.210; 95%CI = 0.204 - 0.216
 cor(var7:var3): 0.028; 95%CI = 0.021 - 0.035
 cor(var5:var4): -0.436; 95%CI = -0.442 - -0.429
 cor(var6:var4): 0.133; 95%CI = 0.126 - 0.140
 cor(var7:var4): -0.120; 95%CI = -0.128 - -0.111
 cor(var6:var5): -0.160; 95%CI = -0.169 - -0.151
 cor(var7:var5): 0.346; 95%CI = 0.336 - 0.357
 cor(var7:var6): -0.371; 95%CI = -0.379 - -0.364



So my first response would be that yes, all my behavioural measures are correlated (with different strength or sign). Just to be sure: even if birds are nested within territories (nest), these correlations are at the individual level (within the individual), and nest is a random term because we replicate individuals within the same territory, but nothing about resemblance between mates. OK? 



######################################################################

QUESTION 2) urban birds differ in mean or the strength of the relationship between these behaviours compare with rural ones. I include in models the term "habitat" which is a factor with 2 levels (urban or rural).



Here I have some doubts, as I'm not sure how to do the model:



m2a <- MCMCglmm(fixed = cbind(var1, var2min, var2max, var3min, var3max, var4min, var4max, var5, var6min, var6max, var7) ~ trait - 1 + habitat, random = ~ us(trait):nest, rcov = ~ us(trait):units, prior = prior,family = c("gaussian", "cengaussian", "cengaussian", "cengaussian", "poisson", "cengaussian", "poisson"), nitt =  60000, burnin = 1000, thin = 25, data = datos)



m2b <- MCMCglmm(fixed = cbind(var1, var2min, var2max, var3min, var3max, var4min, var4max, var5, var6min, var6max, var7) ~ trait - 1 + trait:habitat, random = ~ us(trait):nest, rcov = ~ us(trait):units, prior = prior,family = c("gaussian", "cengaussian", "cengaussian", "cengaussian", "poisson", "cengaussian", "poisson"), nitt =  60000, burnin = 1000, thin = 25, data = datos)



m2a, and m2b are different models, but I'm not sure which is their meanings: after reading, what I understood is that m2a test the hypothesis that the relationship 

between variables changes but in the same way between habitats, while in m2b the idea is that habitat type affect the relationship between variables differently. 



 DIC(m2a): 1514.612 
 DIC(m2b): 1517.572
  
 m2a is the best model, but m2b is close (∆DIC= 2.96), so should I conclude that the relationship between variables is similar in both habitat types?



Then, I don't know how to obtain the correlations between the different behaviours using this model (m2b). I find a recomendation in the Rlist, something like this: 



m2c <- MCMCglmm(fixed = cbind(var1, var2min, var2max, var3min, var3max, var4min, var4max, var5, var6min, var6max, var7) ~  trait - 1 + trait:habitat,random = ~ us(trait:at.level(habitat, 1)):nest + us(trait:at.level(habitat, 2)):nest, rcov = ~ us(trait):units, prior = list(R = list(V = diag(7), nu = 8), G = list(G1 = list(V =  diag(7), nu = 8), G2 = list(V = diag(7), nu = 8))), family = c("gaussian", "cengaussian", "cengaussian", "cengaussian", "poisson", "cengaussian", "poisson"), nitt = 60000, burnin = 1000, thin = 25, data = datos)



summary(m2c)

  Iterations = 1001:59976
  Thinning interval  = 25
  Sample size  = 2360 



 DIC(m2c): 1566.367 

  G-structure:  ~us(trait:at.level(habitat, 1)):nest
                                                                        post.mean l-95% CI u-95% CI eff.samp
 var1:at.level(habitat, 1):var1:at.level(habitat, 1).nest              0.136304  0.09799  0.17641  2360.00
 var2:at.level(habitat, 1):var1:at.level(habitat, 1).nest          0.015494 -0.03910  0.06556  1774.69
 var3:at.level(habitat, 1):var1:at.level(habitat, 1).nest          0.002703 -0.06701  0.07044  1596.13
 var4:at.level(habitat, 1):var1:at.level(habitat, 1).nest         0.024925 -0.05294  0.11054  1532.04
 var5:at.level(habitat, 1):var1:at.level(habitat, 1).nest         -0.031660 -0.19513  0.12782  1915.77
 var6:at.level(habitat, 1):var1:at.level(habitat, 1).nest        0.008893 -0.09836  0.11516  1174.13
 var7:at.level(habitat, 1):var1:at.level(habitat, 1).nest        -0.020421 -0.18110  0.14512  1425.96
 var1:at.level(habitat, 1):var2:at.level(habitat, 1).nest          0.015494 -0.03910  0.06556  1774.69
 var2:at.level(habitat, 1):var2:at.level(habitat, 1).nest      0.410275  0.26237  0.59275   934.78
 var3:at.level(habitat, 1):var2:at.level(habitat, 1).nest      0.057976 -0.12494  0.23465   330.66
 var4:at.level(habitat, 1):var2:at.level(habitat, 1).nest    -0.003364 -0.22167  0.19782   772.17
 var5:at.level(habitat, 1):var2:at.level(habitat, 1).nest     -0.056700 -0.45976  0.39309   612.34
 var6:at.level(habitat, 1):var2:at.level(habitat, 1).nest   -0.021934 -0.30452  0.25433   371.81
 var7:at.level(habitat, 1):var2:at.level(habitat, 1).nest    -0.017623 -0.49615  0.39792   548.59
 var1:at.level(habitat, 1):var3:at.level(habitat, 1).nest          0.002703 -0.06701  0.07044  1596.13
 var2:at.level(habitat, 1):var3:at.level(habitat, 1).nest      0.057976 -0.12494  0.23465   330.66
 var3:at.level(habitat, 1):var3:at.level(habitat, 1).nest      0.642849  0.34019  0.98322   305.33
 var4:at.level(habitat, 1):var3:at.level(habitat, 1).nest    -0.054583 -0.33766  0.21244   623.97
 var5:at.level(habitat, 1):var3:at.level(habitat, 1).nest      0.108017 -0.45137  0.68393   513.78
 var6:at.level(habitat, 1):var3:at.level(habitat, 1).nest   -0.005969 -0.40265  0.35762   368.33
 var7:at.level(habitat, 1):var3:at.level(habitat, 1).nest     0.047296 -0.54854  0.64387   425.52
 var1:at.level(habitat, 1):var4:at.level(habitat, 1).nest         0.024925 -0.05294  0.11054  1532.04
 var2:at.level(habitat, 1):var4:at.level(habitat, 1).nest    -0.003364 -0.22167  0.19782   772.17
 var3:at.level(habitat, 1):var4:at.level(habitat, 1).nest    -0.054583 -0.33766  0.21244   623.97
 var4:at.level(habitat, 1):var4:at.level(habitat, 1).nest    0.838939  0.33055  1.51471   356.01
 var5:at.level(habitat, 1):var4:at.level(habitat, 1).nest    -0.993110 -2.24249 -0.01864   263.03
 var6:at.level(habitat, 1):var4:at.level(habitat, 1).nest   0.336994 -0.23357  1.00589   181.84
 var7:at.level(habitat, 1):var4:at.level(habitat, 1).nest   -0.735804 -2.00597  0.11626   165.63
 var1:at.level(habitat, 1):var5:at.level(habitat, 1).nest         -0.031660 -0.19513  0.12782  1915.77
 var2:at.level(habitat, 1):var5:at.level(habitat, 1).nest     -0.056700 -0.45976  0.39309   612.34
 var3:at.level(habitat, 1):var5:at.level(habitat, 1).nest      0.108017 -0.45137  0.68393   513.78
 var4:at.level(habitat, 1):var5:at.level(habitat, 1).nest    -0.993110 -2.24249 -0.01864   263.03
 var5:at.level(habitat, 1):var5:at.level(habitat, 1).nest      2.948040  0.50599  6.20484   213.50
 var6:at.level(habitat, 1):var5:at.level(habitat, 1).nest   -0.878025 -2.40912  0.36680   157.95
 var7:at.level(habitat, 1):var5:at.level(habitat, 1).nest     1.928700 -0.45261  4.73825   105.74
 var1:at.level(habitat, 1):var6:at.level(habitat, 1).nest        0.008893 -0.09836  0.11516  1174.13
 var2:at.level(habitat, 1):var6:at.level(habitat, 1).nest   -0.021934 -0.30452  0.25433   371.81
 var3:at.level(habitat, 1):var6:at.level(habitat, 1).nest   -0.005969 -0.40265  0.35762   368.33
 var4:at.level(habitat, 1):var6:at.level(habitat, 1).nest   0.336994 -0.23357  1.00589   181.84
 var5:at.level(habitat, 1):var6:at.level(habitat, 1).nest   -0.878025 -2.40912  0.36680   157.95
 var6:at.level(habitat, 1):var6:at.level(habitat, 1).nest  1.283158  0.40113  2.59803   124.27
 var7:at.level(habitat, 1):var6:at.level(habitat, 1).nest  -1.125402 -2.76629  0.06360   155.35
 var1:at.level(habitat, 1):var7:at.level(habitat, 1).nest        -0.020421 -0.18110  0.14512  1425.96
 var2:at.level(habitat, 1):var7:at.level(habitat, 1).nest    -0.017623 -0.49615  0.39792   548.59
 var3:at.level(habitat, 1):var7:at.level(habitat, 1).nest     0.047296 -0.54854  0.64387   425.52
 var4:at.level(habitat, 1):var7:at.level(habitat, 1).nest   -0.735804 -2.00597  0.11626   165.63
 var5:at.level(habitat, 1):var7:at.level(habitat, 1).nest     1.928700 -0.45261  4.73825   105.74
 var6:at.level(habitat, 1):var7:at.level(habitat, 1).nest  -1.125402 -2.76629  0.06360   155.35
 var7:at.level(habitat, 1):var7:at.level(habitat, 1).nest    2.999515  0.57516  6.63337    80.53

                ~us(trait:at.level(habitat, 2)):nest
                                                                         post.mean l-95% CI u-95% CI eff.samp
 var1:at.level(habitat, 2):var1:at.level(habitat, 2).nest              0.1656123  0.11786  0.22106   2360.0
 var2:at.level(habitat, 2):var1:at.level(habitat, 2).nest          0.0334815 -0.02249  0.09568   1884.9
 var3:at.level(habitat, 2):var1:at.level(habitat, 2).nest          0.0276997 -0.03648  0.10608   1703.1
 var4:at.level(habitat, 2):var1:at.level(habitat, 2).nest         0.0094879 -0.06105  0.08723   1740.3
 var5:at.level(habitat, 2):var1:at.level(habitat, 2).nest          0.0073708 -0.10499  0.12369   2054.9
 var6:at.level(habitat, 2):var1:at.level(habitat, 2).nest        0.0210704 -0.06467  0.10751   1239.8
 var7:at.level(habitat, 2):var1:at.level(habitat, 2).nest        -0.0225721 -0.17452  0.12613   1311.9
 var1:at.level(habitat, 2):var2:at.level(habitat, 2).nest          0.0334815 -0.02249  0.09568   1884.9
 var2:at.level(habitat, 2):var2:at.level(habitat, 2).nest      0.3486283  0.22524  0.50847   1254.3
 var3:at.level(habitat, 2):var2:at.level(habitat, 2).nest      0.0893576 -0.02938  0.22749   1103.3
 var4:at.level(habitat, 2):var2:at.level(habitat, 2).nest     0.0170261 -0.12427  0.15590   1276.6
 var5:at.level(habitat, 2):var2:at.level(habitat, 2).nest      0.0204992 -0.19159  0.26654   1316.1
 var6:at.level(habitat, 2):var2:at.level(habitat, 2).nest    0.0539765 -0.09843  0.21485   1039.3
 var7:at.level(habitat, 2):var2:at.level(habitat, 2).nest    -0.0663682 -0.33668  0.22229    842.1
 var1:at.level(habitat, 2):var3:at.level(habitat, 2).nest          0.0276997 -0.03648  0.10608   1703.1
 var2:at.level(habitat, 2):var3:at.level(habitat, 2).nest      0.0893576 -0.02938  0.22749   1103.3
 var3:at.level(habitat, 2):var3:at.level(habitat, 2).nest      0.4424986  0.24772  0.65768    958.6
 var4:at.level(habitat, 2):var3:at.level(habitat, 2).nest     0.0209836 -0.15302  0.18846   1117.1
 var5:at.level(habitat, 2):var3:at.level(habitat, 2).nest      0.0226774 -0.26750  0.30541   1367.4
 var6:at.level(habitat, 2):var3:at.level(habitat, 2).nest    0.0731457 -0.12227  0.28489    883.4
 var7:at.level(habitat, 2):var3:at.level(habitat, 2).nest    -0.0251657 -0.36957  0.31977    755.0
 var1:at.level(habitat, 2):var4:at.level(habitat, 2).nest         0.0094879 -0.06105  0.08723   1740.3
 var2:at.level(habitat, 2):var4:at.level(habitat, 2).nest     0.0170261 -0.12427  0.15590   1276.6
 var3:at.level(habitat, 2):var4:at.level(habitat, 2).nest     0.0209836 -0.15302  0.18846   1117.1
 var4:at.level(habitat, 2):var4:at.level(habitat, 2).nest    0.4854582  0.23937  0.76309   1100.7
 var5:at.level(habitat, 2):var4:at.level(habitat, 2).nest    -0.1886537 -0.56073  0.10975    930.3
 var6:at.level(habitat, 2):var4:at.level(habitat, 2).nest  -0.0009642 -0.22502  0.21617    968.9
 var7:at.level(habitat, 2):var4:at.level(habitat, 2).nest    0.0949310 -0.36272  0.50778    760.9
 var1:at.level(habitat, 2):var5:at.level(habitat, 2).nest          0.0073708 -0.10499  0.12369   2054.9
 var2:at.level(habitat, 2):var5:at.level(habitat, 2).nest      0.0204992 -0.19159  0.26654   1316.1
 var3:at.level(habitat, 2):var5:at.level(habitat, 2).nest      0.0226774 -0.26750  0.30541   1367.4
 var4:at.level(habitat, 2):var5:at.level(habitat, 2).nest    -0.1886537 -0.56073  0.10975    930.3
 var5:at.level(habitat, 2):var5:at.level(habitat, 2).nest      1.0214578  0.38161  1.84704    756.4
 var6:at.level(habitat, 2):var5:at.level(habitat, 2).nest    0.0261338 -0.36384  0.41970   1040.3
 var7:at.level(habitat, 2):var5:at.level(habitat, 2).nest     0.0375229 -0.70157  0.93888    704.7
 var1:at.level(habitat, 2):var6:at.level(habitat, 2).nest        0.0210704 -0.06467  0.10751   1239.8
 var2:at.level(habitat, 2):var6:at.level(habitat, 2).nest    0.0539765 -0.09843  0.21485   1039.3
 var3:at.level(habitat, 2):var6:at.level(habitat, 2).nest    0.0731457 -0.12227  0.28489    883.4
 var4:at.level(habitat, 2):var6:at.level(habitat, 2).nest  -0.0009642 -0.22502  0.21617    968.9
 var5:at.level(habitat, 2):var6:at.level(habitat, 2).nest    0.0261338 -0.36384  0.41970   1040.3
 var6:at.level(habitat, 2):var6:at.level(habitat, 2).nest  0.6139191  0.28345  1.00710    784.2
 var7:at.level(habitat, 2):var6:at.level(habitat, 2).nest  -0.2443531 -0.82849  0.18576    816.9
 var1:at.level(habitat, 2):var7:at.level(habitat, 2).nest        -0.0225721 -0.17452  0.12613   1311.9
 var2:at.level(habitat, 2):var7:at.level(habitat, 2).nest    -0.0663682 -0.33668  0.22229    842.1
 var3:at.level(habitat, 2):var7:at.level(habitat, 2).nest    -0.0251657 -0.36957  0.31977    755.0
 var4:at.level(habitat, 2):var7:at.level(habitat, 2).nest    0.0949310 -0.36272  0.50778    760.9
 var5:at.level(habitat, 2):var7:at.level(habitat, 2).nest     0.0375229 -0.70157  0.93888    704.7
 var6:at.level(habitat, 2):var7:at.level(habitat, 2).nest  -0.2443531 -0.82849  0.18576    816.9
 var7:at.level(habitat, 2):var7:at.level(habitat, 2).nest    1.6442687  0.44836  3.38122    288.9

 R-structure:  ~us(trait):units
                                 post.mean  l-95% CI u-95% CI eff.samp
 var1:var1.units         0.062164  0.051459  0.07416   2360.0
 var2:var1.units         0.006561 -0.008157  0.02402   1853.0
 var3:var1.units         0.004655 -0.015026  0.02618   1677.4
 var4:var1.units         0.009616 -0.024670  0.04814   1686.9
 var5:var1.units         -0.020257 -0.099360  0.06894   1409.0
 var6:var1.units         0.009687 -0.024687  0.04640   2162.1
 var7:var1.units         -0.018389 -0.088376  0.04871   1997.5
 var1:var2.units         0.006561 -0.008157  0.02402   1853.0
 var2:var2.units         0.171884  0.133601  0.22208   1575.2
 var3:var2.units        0.011132 -0.041338  0.06197    629.9
 var4:var2.units    -0.032961 -0.106789  0.03922   1463.1
 var5:var2.units      0.029247 -0.141493  0.20121   1366.6
 var6:var2.units   -0.019018 -0.102393  0.06462   1130.2
 var7:var2.units     0.025949 -0.131932  0.19386   1098.0
 var1:var3.units          0.004655 -0.015026  0.02618   1677.4
 var2:var3.units      0.011132 -0.041338  0.06197    629.9
 var3:var3.units      0.297220  0.194598  0.41320    656.3
 var4:var3.units    -0.003164 -0.171143  0.15681    384.9
 var5:var3.units      0.024245 -0.378239  0.40849    361.4
 var6:var3.units    0.011216 -0.140303  0.19338    376.0
 var7:var3.units     0.002151 -0.367842  0.33249    362.4
 var1:var4.units         0.009616 -0.024670  0.04814   1686.9
 var2:var4.units    -0.032961 -0.106789  0.03922   1463.1
 var3:var4.units    -0.003164 -0.171143  0.15681    384.9
 var4:var4.units    0.865977  0.555265  1.23769    691.4
 var5:var4.units    -1.616190 -2.369540 -0.97822    540.4
 var6:var4.units   0.275559  0.043621  0.52288    780.3
 var7:var4.units   -0.620830 -1.223925 -0.11597    420.1
 var1:var5.units         -0.020257 -0.099360  0.06894   1409.0
 var2:var5.units      0.029247 -0.141493  0.20121   1366.6
 var3:var5.units      0.024245 -0.378239  0.40849    361.4
 var4:var5.units    -1.616190 -2.369540 -0.97822    540.4
 var5:var5.units      4.603627  2.611698  6.65959    245.5
 var6:var5.units   -0.795076 -1.442050 -0.24446    545.7
 var7:var5.units     1.954516  0.586883  3.49957    301.4
 var1:var6.units        0.009687 -0.024687  0.04640   2162.1
 var2:var6.units   -0.019018 -0.102393  0.06462   1130.2
 var3:var6.units    0.011216 -0.140303  0.19338    376.0
 var4:var6.units   0.275559  0.043621  0.52288    780.3
 var5:var6.units   -0.795076 -1.442050 -0.24446    545.7
 var6:var6.units  0.857967  0.522307  1.27700    458.0
 var7:var6.units  -1.067046 -1.813576 -0.52592    356.9
 var1:var7.units        -0.018389 -0.088376  0.04871   1997.5
 var2:var7.units     0.025949 -0.131932  0.19386   1098.0
 var3:var7.units     0.002151 -0.367842  0.33249    362.4
 var4:var7.units   -0.620830 -1.223925 -0.11597    420.1
 var5:var7.units     1.954516  0.586883  3.49957    301.4
 var6:var7.units  -1.067046 -1.813576 -0.52592    356.9
 var7:var7.units    3.018702  1.379693  5.09078    205.3



Location effects: cbind(var1, var2, logcaprmax, var3, logtcaemax, var4, logzorromax, var5, var6, loghalconmax, var7) ~ trait - 1 + trait:habitat 
                           post.mean l-95% CI u-95% CI eff.samp   pMCMC    
 traitvar1                 1.68859  1.59946  1.76748   2360.0 < 4e-04 ***
 traitvar2             1.11222  0.94128  1.27189   1142.8 < 4e-04 ***
 traitvar3             1.30170  1.06826  1.59399    290.6 < 4e-04 ***
 traitvar4            1.26373  0.87937  1.62822    294.9 < 4e-04 ***
 traitvar5            -0.18299 -0.97919  0.56381    353.9 0.66441   
 traitvar6           1.48153  0.89085  2.01253    120.3 < 4e-04 ***
 traitvar7           -1.08708 -2.03436 -0.19270    127.1 0.01949 *  
 traitvar1:habitat1        -0.48558 -0.60980 -0.36445   1922.9 < 4e-04 ***
 traitvar2:habitat1    -0.66787 -0.89365 -0.43531   1141.6 < 4e-04 ***
 traitvar3:habitat1    -0.48361 -0.82642 -0.15277    437.0 0.00847 ** 
 traitvar4:habitat1   -0.29592 -0.79364  0.16455    401.0 0.22797   
 traitvar5:habitat1     0.06428 -0.91838  1.02105    500.2 0.90254   
 traitvar6:habitat1  -0.55078 -1.21994  0.10046    175.9 0.10000 .  
 traitvar7:habitat1   -0.44890 -1.67698  0.75958    189.2 0.45932   
 ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1



and correlations for the variables for the two levels of habitat are:

habitat = RURAL
 var2:var1: 0.06581789; 95% CI = 0.06133871 - 0.07029706
 var3:var1: 0.008656465; 95% CI = 0.00392139 - 0.01339154
 var4:var1: 0.07288664; 95% CI = 0.06811906 - 0.07765422
 var5:var1: -0.04830374; 95% CI = -0.05330634 - -0.04330114
 var6:var1: 0.02026321; 95% CI = 0.01521869 - 0.02530773
 var7:var1: -0.03021775; 95% CI = -0.03528822 - -0.02514727
 var3:var2: 0.1111226; 95% CI = 0.1044198 - 0.1178254
 var4:var2: -0.004587257; 95% CI = -0.01146466 - 0.002290146
 var5:var2: -0.05334263; 95% CI = -0.06069082 - -0.04599444
 var6:var2: -0.02931499; 95% CI = -0.03688508 - -0.0217449
 var7:var2: -0.01759684; 95% CI = -0.02531301 - -0.00988067
 var4:var3: -0.07251074; 95% CI = -0.07990936 - -0.06511211
 var5:var3: 0.07728087; 95% CI = 0.06903799 - 0.08552375
 var6:var3: -0.006868517; 95% CI = -0.01497649 - 0.001239459
 var7:var3: 0.03703707; 95% CI = 0.02839657 - 0.04567758
 var5:var4: -0.5945241; 95% CI = -0.601466 - -0.5875823
 var6:var4: 0.2954133; 95% CI = 0.2859855 - 0.3048411
 var7:var4: -0.43075; 95% CI = -0.4396149 - -0.421885
 var4:var5: -0.5945241; 95% CI = -0.601466 - -0.5875823
 var6:var5: -0.4162762; 95% CI = -0.4261684-0.406384
 var7:var5: 0.6000609; 95% CI = 0.590975 - 0.6091469
 var7:var6: -0.5416963; 95% CI = -0.5498123 - -0.5335803

habitat = URBAN
 var2:var1: 0.1381003; 95% CI = 0.1332987 - 0.1429019
 var3:var1: 0.1009115; 95% CI = 0.09578722 - 0.1060357
 var4:var1: 0.03348612; 95% CI = 0.02817647 - 0.03879578
 var5:var1: 0.0186588; 95% CI = 0.01314655 - 0.02417105
 var6:var1: 0.06562708; 95% CI = 0.06023977 - 0.07101439
 var7:var1: -0.04184128; 95% CI = -0.04743616 - -0.0362464
 var3:var2: 0.2239385; 95% CI = 0.2178998 - 0.2299772
 var4:var2: 0.04107495; 95% CI = 0.03427635 - 0.04787355
 var5:var2: 0.03300118; 95% CI = 0.02560607 - 0.04039628
 var6:var2: 0.1144631; 95% CI = 0.1079228 - 0.1210033
 var7:var2: -0.08634689; 95% CI = -0.09352826 - -0.07916552
 var4:var3: 0.04332442; 95% CI = 0.03626806 - 0.05038079
 var5:var3: 0.03267068; 95% CI = 0.02472427 - 0.04061708
 var6:var3: 0.1357245; 95% CI = 0.1285366 - 0.1429123
 var7:var3: -0.03101644; 95% CI = -0.03889608 - -0.0231368
 var5:var4: -0.2505957; 95% CI = -0.2585397 - -0.2426517
 var6:var4: -0.002795438; 95% CI = -0.01065961 - 0.005068738
 var7:var4: 0.1007233; 95% CI = 0.09204078 - 0.1094059
 var4:var5: -0.2505957; 95% CI = -0.2585397 - -0.2426517
 var6:var5: 0.03341757; 95% CI = 0.02415877 - 0.04267638
 var7:var5: 0.02453784; 95% CI = 0.01333783 - 0.03573786
 var7:var6: -0.2279325; 95% CI = -0.2364717 - -0.2193934



However, the DIC of this model is the worst!
 My main question regarding this part of the analysis is how can I know that habitat significantly affect the correlation between behaviours and which are the mean values observed for each behaviour in each habitat type. For the first part of the question, DIC of the m2c model (1566.367) is higher than the DIC of m1 model (without habitat effect) and m2 (with "trait -1 + habitat" as fixed effect), but I know that DIC is not equivalent to AIC, so I'm not sure how much I should trust on it...



I know it's too much, but I would greatly appreciate some feedback from you. As you can see, my statistical baggage is low and I'm trying to fix that with manuals and 

forum posts which sometimes can be very confusing.



Cheers,


Martina




Dra. Martina Carrete 
Dpt Physical, Chemical and Natural Systems 
Universidad Pablo de Olavide, 
Ctra. Utrera km 1 
41013, Sevilla

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