Exercício 5
Edson Nilton de Moura Silva Júnior
09/12/2021
Creditos: diegokjkjj
Packeges exigidos
library(factoextra)## Warning: package 'factoextra' was built under R version 4.1.2## Carregando pacotes exigidos: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(permute)
library(lattice)
library(vegan)## This is vegan 2.5-7buscando o diretório e os dados.
setwd("G:/Meu Drive/UFPE/2021.1/Ecologia Numérica")
load("G:/Meu Drive/UFPE/2021.1/Ecologia Numérica/NEwR-2ed_code_data (2)/NEwR-2ed_code_data/NEwR2-Data/Doubs.RData")CCA da base spe
ENV<-env[,-1]Com isso, é possivel remover a variável dfs, que é espacial, mas não ambiental.
SPE.CCA<-cca(spe[-8,],ENV[-8,])
SPE.CCA## Call: cca(X = spe[-8, ], Y = ENV[-8, ])
##
## Inertia Proportion Rank
## Total 1.1669 1.0000
## Constrained 0.8119 0.6957 10
## Unconstrained 0.3550 0.3043 18
## Inertia is scaled Chi-square
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10
## 0.5238 0.1174 0.0647 0.0435 0.0277 0.0130 0.0103 0.0064 0.0033 0.0017
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
## 0.11112 0.06492 0.05298 0.03283 0.02666 0.01846 0.00978 0.00892
## (Showing 8 of 18 unconstrained eigenvalues)summary(SPE.CCA)##
## Call:
## cca(X = spe[-8, ], Y = ENV[-8, ])
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.1669 1.0000
## Constrained 0.8119 0.6957
## Unconstrained 0.3550 0.3043
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.5238 0.1174 0.06467 0.04353 0.02766 0.01304 0.010291
## Proportion Explained 0.4489 0.1006 0.05542 0.03730 0.02370 0.01118 0.008819
## Cumulative Proportion 0.4489 0.5495 0.60489 0.64219 0.66590 0.67708 0.685894
## CCA8 CCA9 CCA10 CA1 CA2 CA3
## Eigenvalue 0.006444 0.003301 0.001739 0.11112 0.06492 0.05298
## Proportion Explained 0.005523 0.002828 0.001491 0.09522 0.05563 0.04541
## Cumulative Proportion 0.691417 0.694245 0.695736 0.79096 0.84659 0.89200
## CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.03283 0.02666 0.01846 0.009778 0.008924 0.007580
## Proportion Explained 0.02813 0.02284 0.01582 0.008379 0.007648 0.006496
## Cumulative Proportion 0.92013 0.94297 0.95880 0.967177 0.974824 0.981321
## CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.006050 0.004378 0.003488 0.003046 0.002280 0.001718
## Proportion Explained 0.005184 0.003752 0.002989 0.002610 0.001954 0.001472
## Cumulative Proportion 0.986505 0.990257 0.993246 0.995856 0.997810 0.999282
## CA16 CA17 CA18
## Eigenvalue 0.0004838 0.0003296 2.431e-05
## Proportion Explained 0.0004146 0.0002824 2.084e-05
## Cumulative Proportion 0.9996967 0.9999792 1.000e+00
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.5238 0.1174 0.06467 0.04353 0.02766 0.01304 0.01029
## Proportion Explained 0.6452 0.1446 0.07966 0.05362 0.03407 0.01606 0.01268
## Cumulative Proportion 0.6452 0.7898 0.86943 0.92304 0.95712 0.97318 0.98585
## CCA8 CCA9 CCA10
## Eigenvalue 0.006444 0.003301 0.001739
## Proportion Explained 0.007938 0.004065 0.002143
## Cumulative Proportion 0.993792 0.997857 1.000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
##
##
## Species scores
##
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Cogo 1.22828 1.342555 -0.10906 -0.100159 0.18534 -0.074317
## Satr 1.52022 -0.421716 0.32612 -0.478415 -0.13070 -0.152732
## Phph 1.20045 -0.161518 -0.02017 0.142390 0.01173 0.175138
## Babl 0.97743 -0.255475 -0.05499 0.221357 -0.02155 0.075746
## Thth 1.29443 1.453249 -0.05728 -0.279764 0.33355 0.254947
## Teso 0.85559 1.127382 -0.06573 0.084079 0.08054 -0.245575
## Chna -0.46100 0.183183 -0.06858 0.060764 -0.35952 -0.046001
## Pato -0.14056 0.342522 0.05341 0.360135 -0.47087 -0.060211
## Lele 0.09107 -0.053870 -0.19916 0.110481 0.05680 -0.179387
## Sqce 0.01177 -0.117150 -0.36500 0.059876 0.19580 -0.149331
## Baba -0.31689 0.259956 0.14240 0.117877 -0.24005 -0.063566
## Albi -0.32755 0.229336 0.23308 0.157160 -0.22305 0.179066
## Gogo -0.26665 0.018880 -0.11623 -0.061712 -0.12414 0.002118
## Eslu -0.18252 -0.277660 -0.03434 0.037586 0.14606 0.137742
## Pefl -0.12659 -0.176025 0.03020 0.252790 0.12590 0.023888
## Rham -0.57446 0.084305 0.32200 0.053609 -0.08985 0.057710
## Legi -0.62007 0.065743 0.18508 0.005399 -0.02495 0.013164
## Scer -0.52162 -0.148838 -0.01142 -0.153528 0.19238 0.076606
## Cyca -0.58647 0.108430 0.36280 0.023499 0.08189 -0.068617
## Titi -0.29270 -0.189259 0.04018 0.161322 0.04980 -0.067383
## Abbr -0.70893 0.019004 0.36327 -0.067807 0.09745 -0.036085
## Icme -0.79079 -0.011847 0.59621 -0.187170 0.44436 0.105730
## Gyce -0.76105 -0.041287 -0.01602 -0.210332 0.04596 0.003539
## Ruru -0.35856 -0.190255 -0.33418 0.087879 0.09814 -0.064540
## Blbj -0.74241 0.004951 0.25289 -0.085053 0.07711 -0.086048
## Alal -0.64710 0.040949 -0.51840 -0.445714 -0.18001 0.136457
## Anan -0.65036 0.038946 0.37834 -0.017452 0.06610 0.067195
##
##
## Site scores (weighted averages of species scores)
##
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 1 2.90231 -3.59248 5.04291 -10.99019 -4.7248 -11.71188
## 2 2.43975 -2.49959 1.78469 -2.21766 -2.0220 1.04881
## 3 2.18453 -2.38056 1.17956 -0.76922 -1.2574 3.01218
## 4 1.43681 -2.01477 0.35299 0.70591 0.1610 2.74653
## 5 0.20167 -1.48734 -1.39453 1.51318 2.4144 -1.22925
## 6 1.16771 -1.81747 -0.50115 1.21480 0.5871 0.39246
## 7 2.07534 -2.23779 0.68700 -0.78303 -1.0432 -0.06277
## 9 0.33610 -1.49928 -3.65227 2.65603 3.5337 -3.66883
## 10 1.38696 -1.46817 -1.34646 2.06022 0.5451 1.07074
## 11 2.23445 0.39648 0.43772 -2.01398 1.1436 2.43572
## 12 2.26655 0.80356 0.54321 -2.08926 1.0862 1.92474
## 13 2.37228 2.63296 0.73116 -2.54974 1.7019 1.56827
## 14 1.92196 2.78951 0.10116 -1.48364 1.8443 1.10118
## 15 1.40666 1.98480 -0.63527 0.01651 1.1403 -3.26118
## 16 0.76550 1.33943 -0.34052 1.87765 -1.2416 -4.47936
## 17 0.32806 0.88913 -0.09699 1.67765 -2.2357 0.21434
## 18 0.07578 0.72095 -0.25536 1.48245 -1.6099 0.49089
## 19 -0.18799 0.02658 -0.62065 1.35897 -2.0108 0.52679
## 20 -0.59914 -0.07126 -0.46679 0.47383 -1.3946 0.31426
## 21 -0.71162 -0.09092 0.07444 0.12993 -0.6527 0.21050
## 22 -0.76850 -0.07165 0.21576 0.06105 -0.1062 -0.86912
## 23 -0.78322 -0.48026 -6.71095 -4.27093 -0.5972 1.13190
## 24 -0.95982 -0.12488 -3.88562 -3.72811 -1.5708 0.74052
## 25 -0.72816 -0.51767 -3.86269 -3.19230 -0.1748 1.66079
## 26 -0.83048 -0.23803 -0.30332 -0.92216 0.1731 0.51909
## 27 -0.82212 -0.18147 0.29192 -0.40338 0.3721 -0.21440
## 28 -0.85978 -0.14338 0.69694 -0.47506 0.9519 -0.23143
## 29 -0.66734 0.15806 0.81062 -0.17811 0.4473 0.40004
## 30 -0.87414 -0.08059 1.23436 -0.16784 1.0382 0.77144
##
##
## Site constraints (linear combinations of constraining variables)
##
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 1 3.58349 -4.69241 5.163034 -10.460392 -4.2174 -8.3382
## 2 1.17112 -3.52071 0.990473 0.626423 -1.9289 1.7325
## 3 1.79427 -2.22123 0.885357 -0.722446 -0.1247 1.2824
## 4 1.68327 -1.52358 0.015228 1.484626 0.1233 0.1003
## 5 0.82882 -1.73051 -1.628064 0.694959 2.0643 -0.4130
## 6 1.84781 -1.63584 0.226144 -0.274405 -0.1881 2.1801
## 7 2.05060 -0.73235 -0.279140 0.359418 1.3796 -1.2164
## 9 0.02952 -1.80313 -1.786407 2.216371 2.1408 -1.5175
## 10 1.06995 -1.04385 0.003823 -0.562023 -0.6249 -1.3263
## 11 1.55367 1.02910 0.286770 0.003986 0.5823 -0.4513
## 12 1.47097 1.04413 -0.163614 -0.053012 -0.6944 1.1969
## 13 1.56138 1.74457 -0.476732 -0.116590 0.3382 -0.1656
## 14 1.95600 2.32296 -0.102909 -0.851082 1.0895 0.5512
## 15 1.30132 1.49028 0.291083 -0.643518 0.6949 -0.9058
## 16 0.30360 0.47553 -0.284330 1.131299 -0.5654 -1.0797
## 17 0.28929 0.70902 -0.267014 0.176291 -0.4042 0.8460
## 18 0.13731 0.43204 -0.370799 0.877752 -0.9081 -0.3490
## 19 0.44286 0.73917 0.096677 0.178506 -1.0103 0.2019
## 20 -0.25350 0.16247 0.023660 0.602872 -1.7329 0.2290
## 21 -0.66014 -0.09817 -0.086573 0.817861 -1.1078 -0.4347
## 22 -0.44750 0.46080 -0.330861 -0.129368 -0.3505 -1.0452
## 23 -0.04201 1.04957 -4.149278 -4.927474 -0.1723 1.9115
## 24 -1.34040 -0.07807 -3.032487 -1.554081 0.8112 -0.4967
## 25 -1.01887 -0.85119 -4.848035 -3.778175 -1.4930 2.3199
## 26 -0.89500 -0.33984 -0.703386 -0.241858 0.3915 0.1520
## 27 -1.27686 -0.57971 0.131121 -0.310433 -0.1965 -0.4644
## 28 -0.85592 0.07735 0.636200 0.193984 0.4874 -0.4986
## 29 -0.61651 0.03421 1.078781 -0.107326 0.3596 1.4882
## 30 -0.83933 0.01874 1.139485 -0.643621 1.3816 0.1496
##
##
## Biplot scores for constraining variables
##
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## ele 0.8049 -0.53385 -0.1640 0.1433 0.09700 -0.031190
## slo 0.4534 -0.40316 0.2016 -0.4500 -0.20564 -0.472915
## dis -0.6962 0.23321 0.4344 -0.2407 0.26281 0.274195
## pH 0.1538 0.28892 0.1238 -0.1355 0.37016 -0.300204
## har -0.5533 0.49784 0.1044 -0.1189 0.46852 0.104247
## pho -0.4297 -0.03666 -0.5085 -0.4939 0.06424 0.225592
## nit -0.6912 0.18509 -0.2099 -0.1266 -0.28415 0.001309
## amm -0.4139 -0.11510 -0.6225 -0.3978 -0.17178 0.209148
## oxy 0.7861 0.38111 0.3241 0.2001 -0.24654 -0.013469
## bod -0.4582 -0.18403 -0.6361 -0.4922 0.14534 0.167807plot(SPE.CCA, main="CCA dos dados de spe e env")
Esses dados mostram que a maioria das espécies da base spe são pouco influenciadas pelas variáveis da base env. É possível observar as espécies Teso, Cogo e Thth são espécies raras, estas então influenciadas principalmente pelas variáveis pH e oxy, mostrando que a variável pH explica pouco a variação da abundância das espécies ao longo do rio. Também é possível observar que as variáveis slo e ele são as com o maior poder de explicação da variação da abundância das espécies ao longo do rio,principalmente pelas espécies Babl, Phph e Satr.
Testando a significância geral de cada eixo
anova.cca(SPE.CCA, step=1000)## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(X = spe[-8, ], Y = ENV[-8, ])
## Df ChiSquare F Pr(>F)
## Model 10 0.81186 4.1159 0.001 ***
## Residual 18 0.35505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1