Estudio de caso 01
Hipótesis 1. La descarga de aguas servidas genera cambios en la composición y diversidad de diatomeas, temporal y espacialmente
Si fuese posible determinar diferencias en la composición de diatomeas, indique cuales son las especies que aportan en un 60% a la diferencias detectadas. Apoye sus resultados con un análisis de ordenación, dando prioridad a la representación gráfica de sus resultados.
library(readxl)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(ade4)
library(PerformanceAnalytics)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## ################################### WARNING ###################################
## # We noticed you have dplyr installed. The dplyr lag() function breaks how #
## # base R's lag() function is supposed to work, which breaks lag(my_xts). #
## # #
## # If you call library(dplyr) later in this session, then calls to lag(my_xts) #
## # that you enter or source() into this session won't work correctly. #
## # #
## # All package code is unaffected because it is protected by the R namespace #
## # mechanism. #
## # #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
## # #
## # You can use stats::lag() to make sure you're not using dplyr::lag(), or you #
## # can add conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
## # dplyr from breaking base R's lag() function. #
## ################################### WARNING #####################################
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
##
## legenddiatom.env <- read_xlsx("./bases_datos/diatomeas.xlsx",
sheet = 1,
col_names = TRUE)## New names:
## • `` -> `...3`diatom.bio <- read_xlsx("./bases_datos/diatomeas.xlsx",
sheet = 2)## New names:
## • `` -> `...3`#ADONIS
set.seed(1979)
#SEPARAR FACTORES (CARACTERES) Y PARAMETROS (NUMEROS)
diatom.sp<- diatom.bio[ ,-c(1,2,3)] #NUMEROS
diatom.factor<- diatom.bio[ ,c(1,2,3)] #CARACTERES
adonis2(diatom.sp~diatom.factor$sitio, diatom.factor)adonis2(diatom.sp~diatom.factor$mes,diatom.factor)R/ Existen diferencias entre la diversidad de diatomeas y el mes (F= 5,84, gl=2;9, p= 0.002)
#SIMPER
diatom.simp<- simper(diatom.sp, diatom.bio$mes)
summary(diatom.simp)##
## Contrast: Agosto_Febrero
##
## average sd ratio ava avb cumsum p
## Sp81 0.33940 0.13307 2.55000 0.00062 0.67940 0.413 0.006 **
## Sp17 0.08610 0.06857 1.25600 0.23750 0.11870 0.518 0.236
## Sp60 0.05220 0.05199 1.00400 0.10500 0.00060 0.582 0.003 **
## Sp6 0.05190 0.02634 1.97000 0.08125 0.12620 0.645 0.301
## Sp13 0.03410 0.03322 1.02500 0.07437 0.01250 0.686 0.096 .
## Sp36 0.03120 0.00884 3.53600 0.06312 0.00060 0.725 0.001 ***
## Sp15 0.02950 0.02124 1.39100 0.06000 0.00190 0.761 0.030 *
## Sp52 0.02470 0.02209 1.11800 0.04938 0.00000 0.791 0.019 *
## Sp39 0.01940 0.02821 0.68700 0.03563 0.00810 0.814 0.665
## Sp45 0.01910 0.02479 0.76900 0.03812 0.00000 0.838 0.918
## Sp32 0.01660 0.02653 0.62400 0.03313 0.00060 0.858 0.125
## Sp30 0.01410 0.01231 1.14200 0.03188 0.00630 0.875 0.969
## Sp78 0.01160 0.00615 1.88000 0.02750 0.00440 0.889 0.013 *
## Sp29 0.00780 0.00697 1.12000 0.01562 0.00000 0.898 0.019 *
## Sp51 0.00780 0.00334 2.34000 0.01813 0.00250 0.908 0.067 .
## Sp38 0.00720 0.00670 1.07300 0.01500 0.00620 0.917 0.776
## Sp5 0.00530 0.00638 0.83200 0.00125 0.01060 0.923 0.445
## Sp31 0.00530 0.00744 0.71400 0.01063 0.00000 0.930 0.087 .
## Sp46 0.00500 0.00496 1.00800 0.01000 0.00060 0.936 0.140
## Sp35 0.00440 0.00461 0.94900 0.00875 0.00000 0.941 0.087 .
## Sp2 0.00410 0.00727 0.55900 0.00812 0.00000 0.946 0.611
## Sp12 0.00340 0.00437 0.78700 0.00625 0.00190 0.950 0.330
## Sp59 0.00310 0.00281 1.11100 0.00563 0.00370 0.954 0.973
## Sp28 0.00280 0.00432 0.65100 0.00562 0.00000 0.957 0.087 .
## Sp50 0.00280 0.00294 0.95700 0.00562 0.00000 0.961 0.095 .
## Sp34 0.00220 0.00264 0.82800 0.00438 0.00000 0.964 0.431
## Sp65 0.00220 0.00391 0.55900 0.00438 0.00000 0.966 0.230
## Sp7 0.00190 0.00266 0.70500 0.00375 0.00000 0.968 0.068 .
## Sp62 0.00190 0.00194 0.96800 0.00375 0.00060 0.971 0.803
## Sp24 0.00170 0.00218 0.78700 0.00188 0.00250 0.973 0.935
## Sp23 0.00160 0.00280 0.55900 0.00313 0.00000 0.975 0.243
## Sp41 0.00130 0.00224 0.55900 0.00250 0.00000 0.976 0.255
## Sp48 0.00130 0.00129 0.96800 0.00250 0.00000 0.978 0.068 .
## Sp1 0.00090 0.00168 0.55900 0.00000 0.00190 0.979 0.256
## Sp8 0.00090 0.00168 0.55900 0.00000 0.00190 0.980 0.536
## Sp10 0.00090 0.00168 0.55900 0.00188 0.00000 0.981 0.255
## Sp21 0.00090 0.00107 0.87600 0.00188 0.00000 0.982 0.065 .
## Sp42 0.00090 0.00168 0.55900 0.00188 0.00000 0.983 0.230
## Sp61 0.00090 0.00168 0.55900 0.00188 0.00000 0.985 0.255
## Sp70 0.00090 0.00056 1.67700 0.00188 0.00000 0.986 0.019 *
## Sp27 0.00080 0.00101 0.77500 0.00125 0.00060 0.987 0.310
## Sp4 0.00060 0.00112 0.55900 0.00000 0.00130 0.987 0.998
## Sp9 0.00060 0.00112 0.55900 0.00125 0.00000 0.988 0.243
## Sp18 0.00060 0.00112 0.55900 0.00125 0.00000 0.989 0.255
## Sp19 0.00060 0.00065 0.96800 0.00125 0.00000 0.990 0.065 .
## Sp22 0.00060 0.00112 0.55900 0.00000 0.00130 0.991 0.240
## Sp25 0.00060 0.00112 0.55900 0.00125 0.00000 0.991 0.243
## Sp40 0.00060 0.00112 0.55900 0.00125 0.00000 0.992 0.255
## Sp44 0.00060 0.00112 0.55900 0.00125 0.00000 0.993 0.243
## Sp66 0.00060 0.00112 0.55900 0.00125 0.00000 0.994 0.243
## Sp75 0.00060 0.00065 0.96800 0.00125 0.00000 0.994 0.087 .
## Sp77 0.00060 0.00065 0.96800 0.00062 0.00130 0.995 0.733
## Sp3 0.00030 0.00056 0.55900 0.00062 0.00000 0.995 0.255
## Sp11 0.00030 0.00056 0.55900 0.00000 0.00060 0.996 0.240
## Sp16 0.00030 0.00056 0.55900 0.00062 0.00000 0.996 0.660
## Sp33 0.00030 0.00056 0.55900 0.00000 0.00060 0.997 0.240
## Sp54 0.00030 0.00056 0.55900 0.00000 0.00060 0.997 0.970
## Sp55 0.00030 0.00056 0.55900 0.00062 0.00000 0.997 0.230
## Sp56 0.00030 0.00056 0.55900 0.00000 0.00060 0.998 0.998
## Sp58 0.00030 0.00056 0.55900 0.00000 0.00060 0.998 0.240
## Sp64 0.00030 0.00056 0.55900 0.00062 0.00000 0.999 0.255
## Sp67 0.00030 0.00056 0.55900 0.00062 0.00000 0.999 0.909
## Sp72 0.00030 0.00056 0.55900 0.00062 0.00000 0.999 0.827
## Sp74 0.00030 0.00056 0.55900 0.00062 0.00000 1.000 0.243
## Sp76 0.00030 0.00056 0.55900 0.00000 0.00060 1.000 0.232
## Sp14 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.662
## Sp20 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.662
## Sp26 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.998
## Sp37 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.660
## Sp43 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.666
## Sp47 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.681
## Sp49 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.681
## Sp53 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.681
## Sp57 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.978
## Sp63 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.681
## Sp68 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.982
## Sp69 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.681
## Sp71 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.001 ***
## Sp73 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.660
## Sp79 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.660
## Sp80 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Contrast: Agosto_Junio
##
## average sd ratio ava avb cumsum p
## Sp17 0.08819 0.07151 1.23330 0.23750 0.08438 0.127 0.156
## Sp30 0.07878 0.06766 1.16440 0.03188 0.18062 0.241 0.150
## Sp60 0.05241 0.05403 0.97000 0.10500 0.00375 0.317 0.002 **
## Sp45 0.04193 0.03394 1.23540 0.03812 0.08875 0.378 0.183
## Sp26 0.03911 0.04586 0.85270 0.00000 0.07750 0.434 0.001 ***
## Sp56 0.03862 0.01779 2.17070 0.00000 0.07625 0.490 0.001 ***
## Sp13 0.03269 0.03095 1.05610 0.07437 0.02625 0.537 0.169
## Sp6 0.03185 0.03517 0.90580 0.08125 0.10938 0.583 0.938
## Sp15 0.03107 0.02275 1.36560 0.06000 0.00000 0.628 0.005 **
## Sp39 0.02807 0.02488 1.12800 0.03563 0.04000 0.668 0.195
## Sp52 0.02557 0.02297 1.11310 0.04938 0.00000 0.706 0.001 ***
## Sp4 0.02161 0.01130 1.91230 0.00000 0.04250 0.737 0.001 ***
## Sp36 0.01925 0.01161 1.65780 0.06312 0.02563 0.764 0.227
## Sp32 0.01715 0.02775 0.61810 0.03313 0.00000 0.789 0.015 *
## Sp59 0.01704 0.01851 0.92070 0.00563 0.03750 0.814 0.057 .
## Sp38 0.00962 0.00788 1.22110 0.01500 0.01500 0.828 0.372
## Sp29 0.00809 0.00725 1.11550 0.01562 0.00000 0.840 0.001 ***
## Sp78 0.00741 0.00537 1.37870 0.02750 0.01875 0.850 0.710
## Sp24 0.00738 0.00890 0.82860 0.00188 0.01437 0.861 0.151
## Sp81 0.00709 0.01176 0.60250 0.00062 0.01250 0.871 0.995
## Sp51 0.00557 0.00324 1.71990 0.01813 0.01437 0.879 0.811
## Sp31 0.00550 0.00772 0.71240 0.01063 0.00000 0.887 0.001 ***
## Sp46 0.00518 0.00509 1.01760 0.01000 0.00062 0.895 0.082 .
## Sp2 0.00472 0.00670 0.70500 0.00812 0.00188 0.901 0.151
## Sp35 0.00453 0.00479 0.94580 0.00875 0.00000 0.908 0.001 ***
## Sp57 0.00437 0.00452 0.96820 0.00000 0.00875 0.914 0.017 *
## Sp67 0.00360 0.00363 0.99360 0.00062 0.00688 0.919 0.115
## Sp12 0.00324 0.00489 0.66150 0.00625 0.00062 0.924 0.461
## Sp5 0.00315 0.00456 0.69000 0.00125 0.00500 0.929 0.878
## Sp69 0.00313 0.00559 0.55900 0.00000 0.00625 0.933 0.240
## Sp28 0.00291 0.00448 0.64960 0.00562 0.00000 0.937 0.001 ***
## Sp50 0.00291 0.00269 1.08140 0.00562 0.00125 0.942 0.039 *
## Sp72 0.00244 0.00350 0.69850 0.00062 0.00437 0.945 0.194
## Sp62 0.00243 0.00187 1.30240 0.00375 0.00562 0.949 0.355
## Sp34 0.00227 0.00160 1.41410 0.00438 0.00375 0.952 0.266
## Sp65 0.00227 0.00406 0.55770 0.00438 0.00000 0.955 0.001 ***
## Sp7 0.00194 0.00276 0.70260 0.00375 0.00000 0.958 0.001 ***
## Sp43 0.00188 0.00335 0.55900 0.00000 0.00375 0.961 0.229
## Sp68 0.00165 0.00116 1.42170 0.00000 0.00313 0.963 0.001 ***
## Sp23 0.00162 0.00290 0.55770 0.00313 0.00000 0.965 0.001 ***
## Sp49 0.00156 0.00280 0.55900 0.00000 0.00313 0.968 0.240
## Sp41 0.00129 0.00232 0.55770 0.00250 0.00000 0.970 0.001 ***
## Sp48 0.00129 0.00134 0.96480 0.00250 0.00000 0.971 0.001 ***
## Sp54 0.00129 0.00092 1.41280 0.00000 0.00250 0.973 0.004 **
## Sp21 0.00097 0.00111 0.87300 0.00188 0.00000 0.975 0.001 ***
## Sp70 0.00097 0.00058 1.66520 0.00188 0.00000 0.976 0.001 ***
## Sp10 0.00097 0.00174 0.55770 0.00188 0.00000 0.978 0.001 ***
## Sp42 0.00097 0.00174 0.55770 0.00188 0.00000 0.979 0.001 ***
## Sp61 0.00097 0.00174 0.55770 0.00188 0.00000 0.980 0.001 ***
## Sp77 0.00081 0.00066 1.24350 0.00062 0.00188 0.982 0.095 .
## Sp79 0.00071 0.00128 0.55900 0.00000 0.00125 0.983 0.001 ***
## Sp9 0.00065 0.00116 0.55770 0.00125 0.00000 0.984 0.001 ***
## Sp18 0.00065 0.00116 0.55770 0.00125 0.00000 0.984 0.001 ***
## Sp19 0.00065 0.00067 0.96480 0.00125 0.00000 0.985 0.001 ***
## Sp25 0.00065 0.00116 0.55770 0.00125 0.00000 0.986 0.001 ***
## Sp27 0.00065 0.00116 0.55770 0.00125 0.00000 0.987 0.415
## Sp40 0.00065 0.00116 0.55770 0.00125 0.00000 0.988 0.001 ***
## Sp44 0.00065 0.00116 0.55770 0.00125 0.00000 0.989 0.001 ***
## Sp66 0.00065 0.00116 0.55770 0.00125 0.00000 0.990 0.001 ***
## Sp75 0.00065 0.00067 0.96480 0.00125 0.00000 0.991 0.001 ***
## Sp8 0.00062 0.00112 0.55900 0.00000 0.00125 0.992 0.757
## Sp14 0.00062 0.00112 0.55900 0.00000 0.00125 0.993 0.244
## Sp47 0.00062 0.00112 0.55900 0.00000 0.00125 0.994 0.240
## Sp53 0.00062 0.00112 0.55900 0.00000 0.00125 0.995 0.240
## Sp63 0.00062 0.00112 0.55900 0.00000 0.00125 0.996 0.240
## Sp16 0.00050 0.00067 0.74730 0.00062 0.00062 0.996 0.192
## Sp37 0.00036 0.00064 0.55900 0.00000 0.00062 0.997 0.001 ***
## Sp73 0.00036 0.00064 0.55900 0.00000 0.00062 0.997 0.001 ***
## Sp3 0.00032 0.00058 0.55770 0.00062 0.00000 0.998 0.001 ***
## Sp55 0.00032 0.00058 0.55770 0.00062 0.00000 0.998 0.001 ***
## Sp64 0.00032 0.00058 0.55770 0.00062 0.00000 0.999 0.001 ***
## Sp74 0.00032 0.00058 0.55770 0.00062 0.00000 0.999 0.001 ***
## Sp20 0.00031 0.00056 0.55900 0.00000 0.00062 1.000 0.244
## Sp80 0.00031 0.00056 0.55900 0.00000 0.00062 1.000 0.240
## Sp1 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.669
## Sp11 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.666
## Sp22 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.666
## Sp33 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.666
## Sp58 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.666
## Sp71 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.001 ***
## Sp76 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Contrast: Febrero_Junio
##
## average sd ratio ava avb cumsum p
## Sp81 0.34470 0.13841 2.49050 0.67940 0.01250 0.421 0.001 ***
## Sp30 0.08820 0.07055 1.25070 0.00630 0.18062 0.529 0.003 **
## Sp6 0.05650 0.04182 1.35090 0.12620 0.10938 0.598 0.144
## Sp17 0.04980 0.04568 1.08970 0.11870 0.08438 0.659 0.908
## Sp45 0.04470 0.04062 1.10000 0.00000 0.08875 0.714 0.063 .
## Sp26 0.03910 0.04586 0.85270 0.00000 0.07750 0.762 0.001 ***
## Sp56 0.03830 0.01781 2.14950 0.00060 0.07625 0.808 0.005 **
## Sp4 0.02100 0.01138 1.84180 0.00130 0.04250 0.834 0.005 **
## Sp39 0.01890 0.01782 1.06260 0.00810 0.04000 0.857 0.712
## Sp59 0.01700 0.01913 0.89050 0.00370 0.03750 0.878 0.046 *
## Sp36 0.01310 0.00794 1.65230 0.00060 0.02563 0.894 0.882
## Sp13 0.00930 0.00641 1.44420 0.01250 0.02625 0.905 0.911
## Sp38 0.00810 0.00861 0.93850 0.00620 0.01500 0.915 0.644
## Sp78 0.00770 0.00503 1.52750 0.00440 0.01875 0.925 0.654
## Sp24 0.00740 0.00880 0.83690 0.00250 0.01437 0.934 0.174
## Sp51 0.00710 0.00553 1.28010 0.00250 0.01437 0.942 0.126
## Sp5 0.00620 0.00629 0.98810 0.01060 0.00500 0.950 0.287
## Sp57 0.00440 0.00452 0.96820 0.00000 0.00875 0.955 0.022 *
## Sp67 0.00360 0.00390 0.92820 0.00000 0.00688 0.960 0.042 *
## Sp69 0.00310 0.00559 0.55900 0.00000 0.00625 0.964 0.252
## Sp62 0.00280 0.00184 1.51250 0.00060 0.00562 0.967 0.102
## Sp72 0.00250 0.00368 0.66630 0.00000 0.00437 0.970 0.045 *
## Sp34 0.00200 0.00075 2.62200 0.00000 0.00375 0.972 0.651
## Sp43 0.00190 0.00335 0.55900 0.00000 0.00375 0.975 0.252
## Sp60 0.00180 0.00140 1.28000 0.00060 0.00375 0.977 0.998
## Sp68 0.00170 0.00116 1.42170 0.00000 0.00313 0.979 0.001 ***
## Sp49 0.00160 0.00280 0.55900 0.00000 0.00313 0.981 0.252
## Sp8 0.00130 0.00164 0.78160 0.00190 0.00125 0.982 0.193
## Sp12 0.00110 0.00157 0.71680 0.00190 0.00062 0.984 0.832
## Sp54 0.00110 0.00091 1.24210 0.00060 0.00250 0.985 0.071 .
## Sp2 0.00100 0.00121 0.84720 0.00000 0.00188 0.986 0.658
## Sp1 0.00100 0.00174 0.55770 0.00190 0.00000 0.988 0.001 ***
## Sp15 0.00100 0.00174 0.55770 0.00190 0.00000 0.989 0.978
## Sp79 0.00070 0.00128 0.55900 0.00000 0.00125 0.990 0.001 ***
## Sp22 0.00060 0.00116 0.55770 0.00130 0.00000 0.990 0.001 ***
## Sp77 0.00060 0.00067 0.96480 0.00130 0.00188 0.991 0.367
## Sp14 0.00060 0.00112 0.55900 0.00000 0.00125 0.992 0.240
## Sp47 0.00060 0.00112 0.55900 0.00000 0.00125 0.993 0.252
## Sp50 0.00060 0.00112 0.55900 0.00000 0.00125 0.993 0.940
## Sp53 0.00060 0.00112 0.55900 0.00000 0.00125 0.994 0.252
## Sp63 0.00060 0.00112 0.55900 0.00000 0.00125 0.995 0.252
## Sp46 0.00050 0.00067 0.74730 0.00060 0.00062 0.996 0.926
## Sp16 0.00040 0.00064 0.55900 0.00000 0.00062 0.996 0.413
## Sp37 0.00040 0.00064 0.55900 0.00000 0.00062 0.996 0.001 ***
## Sp73 0.00040 0.00064 0.55900 0.00000 0.00062 0.997 0.001 ***
## Sp11 0.00030 0.00058 0.55770 0.00060 0.00000 0.997 0.001 ***
## Sp27 0.00030 0.00058 0.55770 0.00060 0.00000 0.998 0.663
## Sp32 0.00030 0.00058 0.55770 0.00060 0.00000 0.998 0.907
## Sp33 0.00030 0.00058 0.55770 0.00060 0.00000 0.998 0.001 ***
## Sp58 0.00030 0.00058 0.55770 0.00060 0.00000 0.999 0.001 ***
## Sp76 0.00030 0.00058 0.55770 0.00060 0.00000 0.999 0.001 ***
## Sp20 0.00030 0.00056 0.55900 0.00000 0.00062 1.000 0.240
## Sp80 0.00030 0.00056 0.55900 0.00000 0.00062 1.000 0.252
## Sp3 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp7 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.904
## Sp9 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.663
## Sp10 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp18 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp19 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.902
## Sp21 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.902
## Sp23 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.663
## Sp25 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.663
## Sp28 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.903
## Sp29 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.978
## Sp31 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.903
## Sp35 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.903
## Sp40 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp41 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp42 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.658
## Sp44 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.663
## Sp48 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.904
## Sp52 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.978
## Sp55 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.658
## Sp61 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp64 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.667
## Sp65 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.658
## Sp66 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.663
## Sp70 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.978
## Sp71 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.001 ***
## Sp74 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.663
## Sp75 0.00000 0.00000 NaN 0.00000 0.00000 1.000 0.903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999R/ Las especies que aportan cerca de un 60% a las diferencias detectadas fueron:
Entre Agosto_Febrero
Sp_36 en un 72% (p<0.001)
Sp_52 en un 76% (p<0.05)
Sp_39 en un 79% (p<0.05)
Entre Agosto_Junio
Sp_15 en un 63% (p<0.01)
Sp_52 en un 71% (p<0.001)
Sp_4 en un 74% (p<0.001)
Entre Febrero_Junio
Sp_26 en un 76% (p<0.001)
#NMDS
diatom.sp1<- diatom.sp[ ,-8]
diatom.hell<- decostand(diatom.sp1, method = 'hellinger')
diatom.nmds<-metaMDS(diatom.hell,distance = "bray", autotransform = FALSE) ## Run 0 stress 0.09863467
## Run 1 stress 0.1677085
## Run 2 stress 0.156303
## Run 3 stress 0.11227
## Run 4 stress 0.156303
## Run 5 stress 0.09863468
## ... Procrustes: rmse 3.745568e-05 max resid 8.46298e-05
## ... Similar to previous best
## Run 6 stress 0.1021942
## Run 7 stress 0.09863469
## ... Procrustes: rmse 7.216164e-05 max resid 0.0001900769
## ... Similar to previous best
## Run 8 stress 0.1006393
## Run 9 stress 0.1021942
## Run 10 stress 0.1142011
## Run 11 stress 0.1568508
## Run 12 stress 0.1021942
## Run 13 stress 0.09863469
## ... Procrustes: rmse 8.397074e-05 max resid 0.000215009
## ... Similar to previous best
## Run 14 stress 0.1021942
## Run 15 stress 0.09863476
## ... Procrustes: rmse 0.00017273 max resid 0.000450319
## ... Similar to previous best
## Run 16 stress 0.1021943
## Run 17 stress 0.11227
## Run 18 stress 0.1021942
## Run 19 stress 0.1623123
## Run 20 stress 0.1021943
## *** Best solution repeated 4 timeswindows()
diatom.nmds$stress## [1] 0.09863467plot(diatom.nmds, type="t", main="NMDS DIATOMEAS")
Hipótesis 2. La descarga de aguas servidas genera cambios en los parámetros físico químicos del agua.
Utilice la técnica de cluster para verificar si existen patrones de similitud temporales o espaciales en la composición de parámetros físico químicos. Muestre sus resultados utilizando gráficas y complemente con los análisis estadísticos necesarios para justificar la conformación del cluster. Registre el proceso estadístico que utilizó para sus análisis.
library(vegan)
library(labdsv)## Loading required package: mgcv## Loading required package: nlme## This is mgcv 1.8-42. For overview type 'help("mgcv-package")'.## Registered S3 method overwritten by 'labdsv':
## method from
## summary.dist ade4## This is labdsv 2.0-1
## convert existing ordinations with as.dsvord()##
## Attaching package: 'labdsv'## The following object is masked from 'package:stats':
##
## densitylibrary(clustsig)
library(PerformanceAnalytics)
diatom.env1<- diatom.env[ ,-c(1,2,3)] #quitar caracteres
#TRANSFORMACION
diatom.st<- decostand(diatom.env1, method = 'standardize')
#MATRIZ
diatom.euc<- vegdist(diatom.st, method = 'euclidean')
#METODO SINGLE
env.single<- hclust(diatom.euc, method = 'single')
#METODO COMPLETE
env.complete<- hclust(diatom.euc, method = 'complete')
#METODO AVERAGE
env.ave<- hclust(diatom.euc, method = 'average')
#SELECCION DE CLUSTER
coph01<- cophenetic(env.single)
cor(coph01, diatom.euc)## [1] 0.8757353coph02<- cophenetic(env.complete)
cor(coph02, diatom.euc)## [1] 0.8900852coph03<- cophenetic(env.ave)
cor(coph03, diatom.euc)## [1] 0.8931573#SE ESCOGE 0.8931 (METODO AVERAGE)
#SIMPROF
analisis.simprof <- simprof(diatom.euc,
method.cluster = "average",
method.distance = "euclidean")
simprof.plot(analisis.simprof)
## 'dendrogram' with 2 branches and 16 members total, at height 10.66414R/ El mejor método es el average; 0.8931.
Hipótesis 3. Los parámetros físico químicos varían entre los sitios y fechas de muestreo en el área de estudio
Considere sus análisis al 95% y resuelva anotando la nomenclatura estadística formal.
diatom.factor<- diatom.env[ ,c(1,2,3)] #NUMEROS
diatom.parametros<- diatom.env[ ,-c(1,2,3)] #CARACTERES
#TEMPERATURA
anova1<-aov(diatom.env1$temp~diatom.factor$Sitio)
summary(anova1)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 5.13 1.71 0.13 0.94
## Residuals 12 157.54 13.13#NO HAY DIFERENCIA DE LA TEMP ENTRE SITIOS
anova2<-aov(diatom.env1$temp~diatom.factor$Mes)
summary(anova2)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 153.04 51.01 63.53 1.23e-07 ***
## Residuals 12 9.63 0.80
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DE LA TEMP ENTRE MESES
#pH
anova3<-aov(diatom.env1$ph~diatom.factor$Sitio)
summary(anova3)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 0.283 0.09435 0.464 0.713
## Residuals 12 2.442 0.20348#NO HAY DIFERENCIA DEL PH ENTRE SITIOS
anova4<-aov(diatom.env1$ph~diatom.factor$Mes)
summary(anova4)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 2.0857 0.6952 13.05 0.000436 ***
## Residuals 12 0.6391 0.0533
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DEL PH ENTRE MESES
#TURB
anova5<-aov(diatom.env1$turb~diatom.factor$Sitio)
summary(anova5)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 636 212.1 0.086 0.966
## Residuals 12 29518 2459.8#NO HAY DIFERENCIA DE LA TURBIDEZ ENTRE SITIOS
anova6<-aov(diatom.env1$turb~diatom.factor$Mes)
summary(anova6)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 28855 9618 88.83 1.84e-08 ***
## Residuals 12 1299 108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DE LA TURBIDEZ ENTRE MESES
#COND
anova7<-aov(diatom.env1$cond~diatom.factor$Sitio)
summary(anova7)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 660 220.2 0.295 0.829
## Residuals 12 8964 747.0#NO HAY DIFERENCIA DE COND ENTRE SITIOS
anova8<-aov(diatom.env1$cond~diatom.factor$Mes)
summary(anova8)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 8385 2795.0 27.05 1.26e-05 ***
## Residuals 12 1240 103.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DE COND ENTRE MESES
#OD
anova9<-aov(diatom.env1$od~diatom.factor$Sitio)
summary(anova9)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 0.1269 0.04229 0.431 0.735
## Residuals 12 1.1775 0.09813#NO HAY DIFERENCIA DEL OD ENTRE SITIOS
anova10<-aov(diatom.env1$od~diatom.factor$Mes)
summary(anova10)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 1.1319 0.3773 26.25 1.48e-05 ***
## Residuals 12 0.1725 0.0144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DEL OD ENTRE MESES
#SST
anova11<-aov(diatom.env1$sst~diatom.factor$Sitio)
summary(anova11)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 318.4 106.14 1.502 0.264
## Residuals 12 847.9 70.66#NO HAY DIFERENCIA DE LA SST ENTRE SITIOS
anova12<-aov(diatom.env1$sst~diatom.factor$Mes)
summary(anova12)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 666.3 222.09 5.33 0.0145 *
## Residuals 12 500.0 41.67
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DE LA SST ENTRE MESES
#DBO
anova13<-aov(diatom.env1$dbo~diatom.factor$Sitio)
summary(anova13)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 6.71 2.237 0.438 0.73
## Residuals 12 61.28 5.107#NO HAY DIFERENCIA DEL DBO ENTRE SITIOS
anova14<-aov(diatom.env1$dbo~diatom.factor$Mes)
summary(anova14)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 55.15 18.38 17.19 0.000122 ***
## Residuals 12 12.84 1.07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DEL DBO ENTRE MESES
#DQO
anova15<-aov(diatom.env1$dqo~diatom.factor$Sitio)
summary(anova15)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Sitio 3 52.2 17.42 0.298 0.826
## Residuals 12 701.5 58.46#NO HAY DIFERENCIA DEL DQO ENTRE SITIOS
anova16<-aov(diatom.env1$dqo~diatom.factor$Mes)
summary(anova16)## Df Sum Sq Mean Sq F value Pr(>F)
## diatom.factor$Mes 3 680.8 226.92 37.3 2.32e-06 ***
## Residuals 12 73.0 6.08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#SI HAY DIFERENCIA DEL DQO ENTRE MESESR/ Hubieron diferencias estadísticamente significativas entre la temperatura, el pH, la turbiedad, la conductividad, el oxígeno disuelto, los sólidos sedimentables totales, la demanda biológica de oxígeno, la demanda química de oxígeno y el tiempo o meses (p<0.05) pero no hubieron diferencias entre las mismas variables y los sitios de muestreo (p>0.05)