Exercício 1
Edson Nilton de Moura Silva Júnior
12/11/2021
Creditos: diegokjkjj
Exercício 1
Foi criado uma base de dados, esta então com 2 principais parâmetros: 1) contém 20 espécies; 2) estas 20 distribuídas em 8 localidades. Com esses parâmetros, os dados foram executados, gerando a seguinte base de dados:
set.seed(999)
data.frame(
spec = paste0("sp", 1:20),
siteA = sample(c(0:20), replace=TRUE, size=20),
siteB = sample(c(0:20), replace=TRUE, size=20),
siteC = sample(c(0:20), replace=TRUE, size=20),
siteD = sample(c(0:20), replace=TRUE, size=20),
siteE = sample(c(0:20), replace=TRUE, size=20),
siteF = sample(c(0:20), replace=TRUE, size=20),
siteG = sample(c(0:20), replace=TRUE, size=20),
siteH = sample(c(0:20), replace=TRUE, size=20)
)## spec siteA siteB siteC siteD siteE siteF siteG siteH
## 1 sp1 3 4 12 1 7 18 14 3
## 2 sp2 6 3 11 5 15 8 9 5
## 3 sp3 8 14 12 14 13 20 19 5
## 4 sp4 13 15 11 20 13 17 18 17
## 5 sp5 0 10 1 0 15 19 4 3
## 6 sp6 9 1 16 12 11 15 10 9
## 7 sp7 2 8 11 3 9 2 13 19
## 8 sp8 14 0 18 13 10 17 11 17
## 9 sp9 4 17 19 17 13 1 11 16
## 10 sp10 6 7 6 14 10 0 18 17
## 11 sp11 5 3 8 15 10 7 8 5
## 12 sp12 18 3 3 2 9 20 3 3
## 13 sp13 10 8 8 0 10 5 9 16
## 14 sp14 17 6 19 20 16 4 9 14
## 15 sp15 0 1 6 12 11 18 5 3
## 16 sp16 4 12 0 14 8 5 13 9
## 17 sp17 18 1 4 20 0 8 18 7
## 18 sp18 11 12 1 12 10 15 8 9
## 19 sp19 8 6 7 2 17 3 9 13
## 20 sp20 20 1 18 3 5 11 17 1Exercício 2
Foi criado uma base de dados, esta então com 3 principais parâmetros: 1) contém 30 espécies; 2) estas 30 distribuídas em 10 localidades; 3) nas primeiras 5 localidades, a abundância variará¡ entre 0-20, e nas segundas 5 localidades, a abundância variará¡ entre 0-50. Com esses parâmetros, os dados foram executados, gerando a seguinte base de dados:
set.seed(999)
base2<-data.frame(
spec = paste0("sp", 1:30),
siteA = sample(c(0:20), replace=TRUE, size=30),
siteB = sample(c(0:20), replace=TRUE, size=30),
siteC = sample(c(0:20), replace=TRUE, size=30),
siteD = sample(c(0:20), replace=TRUE, size=30),
siteE = sample(c(0:20), replace=TRUE, size=30),
siteF = sample(c(0:50), replace=TRUE, size=30),
siteG = sample(c(0:50), replace=TRUE, size=30),
siteH = sample(c(0:5), replace=TRUE, size=30),
site1 = sample(c(0:50), replace=TRUE, size=30),
sitej = sample(c(0:50), replace=TRUE, size=30)
)
base2## spec siteA siteB siteC siteD siteE siteF siteG siteH site1 sitej
## 1 sp1 3 3 1 10 14 31 16 3 35 2
## 2 sp2 6 3 5 9 9 5 39 4 50 13
## 3 sp3 8 8 14 10 19 35 8 0 6 17
## 4 sp4 13 6 20 16 18 48 25 2 2 47
## 5 sp5 0 1 0 11 4 14 34 5 44 4
## 6 sp6 9 12 12 8 10 3 39 0 39 15
## 7 sp7 2 1 3 0 13 41 22 5 42 13
## 8 sp8 14 12 13 10 11 39 12 3 9 15
## 9 sp9 4 6 17 17 11 41 17 5 39 10
## 10 sp10 6 1 14 5 18 13 34 1 21 39
## 11 sp11 5 12 15 18 8 1 16 1 28 34
## 12 sp12 18 11 2 8 3 29 37 4 41 30
## 13 sp13 10 12 0 20 9 24 8 1 49 41
## 14 sp14 17 11 20 17 9 12 7 4 30 33
## 15 sp15 0 1 12 19 5 37 25 3 48 41
## 16 sp16 4 16 14 15 13 32 20 4 45 7
## 17 sp17 18 11 20 2 18 40 36 2 4 23
## 18 sp18 11 18 12 17 8 17 31 3 20 18
## 19 sp19 8 19 2 1 9 34 48 5 50 43
## 20 sp20 20 6 3 0 17 47 9 3 22 23
## 21 sp21 4 8 7 7 3 18 17 2 6 37
## 22 sp22 3 3 15 20 5 18 48 2 35 16
## 23 sp23 14 8 13 5 5 0 27 2 35 48
## 24 sp24 15 19 13 4 17 19 23 1 35 25
## 25 sp25 10 6 15 18 3 49 49 4 21 22
## 26 sp26 1 0 11 5 9 12 10 4 2 43
## 27 sp27 8 4 9 8 19 15 33 4 42 46
## 28 sp28 0 1 10 15 17 34 45 2 5 34
## 29 sp29 17 7 13 3 16 6 9 0 25 8
## 30 sp30 7 18 10 11 17 9 21 1 2 24Exercício 3
Foi feito o sumário sobre a base de dados construídas anteriormente.
summary(base2)## spec siteA siteB siteC
## Length:30 Min. : 0.00 Min. : 0.000 Min. : 0.0
## Class :character 1st Qu.: 4.00 1st Qu.: 3.000 1st Qu.: 5.5
## Mode :character Median : 8.00 Median : 7.500 Median :12.0
## Mean : 8.50 Mean : 8.133 Mean :10.5
## 3rd Qu.:13.75 3rd Qu.:12.000 3rd Qu.:14.0
## Max. :20.00 Max. :19.000 Max. :20.0
## siteD siteE siteF siteG siteH
## Min. : 0.00 Min. : 3.00 Min. : 0.00 Min. : 7.0 Min. :0.000
## 1st Qu.: 5.00 1st Qu.: 8.00 1st Qu.:12.25 1st Qu.:16.0 1st Qu.:1.250
## Median :10.00 Median :10.50 Median :21.50 Median :24.0 Median :3.000
## Mean :10.30 Mean :11.23 Mean :24.10 Mean :25.5 Mean :2.667
## 3rd Qu.:16.75 3rd Qu.:17.00 3rd Qu.:36.50 3rd Qu.:35.5 3rd Qu.:4.000
## Max. :20.00 Max. :19.00 Max. :49.00 Max. :49.0 Max. :5.000
## site1 sitej
## Min. : 2.00 Min. : 2.0
## 1st Qu.:11.75 1st Qu.:15.0
## Median :32.50 Median :23.5
## Mean :27.73 Mean :25.7
## 3rd Qu.:41.75 3rd Qu.:38.5
## Max. :50.00 Max. :48.0Exercício 4 e 5
Para estes estudos, foi feito o download de dados referentes à : 1) matriz de abundância de espécies de peixes (spe); 2) matriz espacial (spa) e matriz ambiental (env), como mostrado nas tabelas abaixo.
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")spe
spe## Cogo Satr Phph Babl Thth Teso Chna Pato Lele Sqce Baba Albi Gogo Eslu Pefl
## 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 5 4 3 0 0 0 0 0 0 0 0 0 0 0
## 3 0 5 5 5 0 0 0 0 0 0 0 0 0 1 0
## 4 0 4 5 5 0 0 0 0 0 1 0 0 1 2 2
## 5 0 2 3 2 0 0 0 0 5 2 0 0 2 4 4
## 6 0 3 4 5 0 0 0 0 1 2 0 0 1 1 1
## 7 0 5 4 5 0 0 0 0 1 1 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 1 3 0 0 0 0 0 5 0 0 0 0 0
## 10 0 1 4 4 0 0 0 0 2 2 0 0 1 0 0
## 11 1 3 4 1 1 0 0 0 0 1 0 0 0 0 0
## 12 2 5 4 4 2 0 0 0 0 1 0 0 0 0 0
## 13 2 5 5 2 3 2 0 0 0 0 0 0 0 0 0
## 14 3 5 5 4 4 3 0 0 0 1 1 0 1 1 0
## 15 3 4 4 5 2 4 0 0 3 3 2 0 2 0 0
## 16 2 3 3 5 0 5 0 4 5 2 2 1 2 1 1
## 17 1 2 4 4 1 2 1 4 3 2 3 4 1 1 2
## 18 1 1 3 3 1 1 1 3 2 3 3 3 2 1 3
## 19 0 0 3 5 0 1 2 3 2 1 2 2 4 1 1
## 20 0 0 1 2 0 0 2 2 2 3 4 3 4 2 2
## 21 0 0 1 1 0 0 2 2 2 2 4 2 5 3 3
## 22 0 0 0 1 0 0 3 2 3 4 5 1 5 3 4
## 23 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 24 0 0 0 0 0 0 1 0 0 2 0 0 1 0 0
## 25 0 0 0 0 0 0 0 0 1 1 0 0 2 1 0
## 26 0 0 0 1 0 0 1 0 1 2 2 1 3 2 1
## 27 0 0 0 1 0 0 1 1 2 3 4 1 4 4 1
## 28 0 0 0 1 0 0 1 1 2 4 3 1 4 3 2
## 29 0 1 1 1 1 1 2 2 3 4 5 3 5 5 4
## 30 0 0 0 0 0 0 1 2 3 3 3 5 5 4 5
## Rham Legi Scer Cyca Titi Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0 0 0 0
## 5 0 0 2 0 3 0 0 0 5 0 0 0
## 6 0 0 0 0 2 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 4 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 1 0 0 0 0 0 0 0
## 16 0 1 0 1 1 0 0 0 1 0 0 0
## 17 1 1 0 1 1 0 0 0 2 0 2 1
## 18 2 1 0 1 1 0 0 1 2 0 2 1
## 19 2 1 1 1 2 1 0 1 5 1 3 1
## 20 3 2 2 1 4 1 0 2 5 2 5 2
## 21 3 2 2 2 4 3 1 3 5 3 5 2
## 22 3 3 2 3 4 4 2 4 5 4 5 2
## 23 0 0 0 0 0 0 0 0 1 0 2 0
## 24 0 1 0 0 0 0 0 2 2 1 5 0
## 25 0 0 1 0 0 0 0 1 1 0 3 0
## 26 2 2 1 1 3 2 1 4 4 2 5 2
## 27 3 3 1 2 5 3 2 5 5 4 5 3
## 28 4 4 2 4 4 3 3 5 5 5 5 4
## 29 5 5 2 3 3 4 4 5 5 4 5 4
## 30 5 3 5 5 5 5 5 5 5 5 5 5spa
spa## X Y
## 1 85.678 20.000
## 2 84.955 20.100
## 3 92.301 23.796
## 4 91.280 26.431
## 5 92.005 29.163
## 6 95.954 36.315
## 7 98.201 38.799
## 8 99.455 46.406
## 9 109.782 55.865
## 10 130.641 66.576
## 11 142.748 81.258
## 12 147.270 85.839
## 13 156.817 89.516
## 14 159.435 92.791
## 15 150.820 91.084
## 16 132.662 87.956
## 17 128.298 93.918
## 18 130.560 102.446
## 19 128.459 105.428
## 20 114.862 103.129
## 21 97.163 90.245
## 22 88.200 86.373
## 23 79.596 83.508
## 24 74.753 78.734
## 25 67.146 74.683
## 26 53.770 71.598
## 27 43.637 68.673
## 28 30.514 61.166
## 29 20.495 43.848
## 30 0.000 41.562env
env## dfs ele slo dis pH har pho nit amm oxy bod
## 1 0.3 934 48.0 0.84 7.9 45 0.01 0.20 0.00 12.2 2.7
## 2 2.2 932 3.0 1.00 8.0 40 0.02 0.20 0.10 10.3 1.9
## 3 10.2 914 3.7 1.80 8.3 52 0.05 0.22 0.05 10.5 3.5
## 4 18.5 854 3.2 2.53 8.0 72 0.10 0.21 0.00 11.0 1.3
## 5 21.5 849 2.3 2.64 8.1 84 0.38 0.52 0.20 8.0 6.2
## 6 32.4 846 3.2 2.86 7.9 60 0.20 0.15 0.00 10.2 5.3
## 7 36.8 841 6.6 4.00 8.1 88 0.07 0.15 0.00 11.1 2.2
## 8 49.1 792 2.5 1.30 8.1 94 0.20 0.41 0.12 7.0 8.1
## 9 70.5 752 1.2 4.80 8.0 90 0.30 0.82 0.12 7.2 5.2
## 10 99.0 617 9.9 10.00 7.7 82 0.06 0.75 0.01 10.0 4.3
## 11 123.4 483 4.1 19.90 8.1 96 0.30 1.60 0.00 11.5 2.7
## 12 132.4 477 1.6 20.00 7.9 86 0.04 0.50 0.00 12.2 3.0
## 13 143.6 450 2.1 21.10 8.1 98 0.06 0.52 0.00 12.4 2.4
## 14 152.2 434 1.2 21.20 8.3 98 0.27 1.23 0.00 12.3 3.8
## 15 164.5 415 0.5 23.00 8.6 86 0.40 1.00 0.00 11.7 2.1
## 16 185.9 375 2.0 16.10 8.0 88 0.20 2.00 0.05 10.3 2.7
## 17 198.5 349 0.5 24.30 8.0 92 0.20 2.50 0.20 10.2 4.6
## 18 211.0 333 0.8 25.00 8.0 90 0.50 2.20 0.20 10.3 2.8
## 19 224.6 310 0.5 25.90 8.1 84 0.60 2.20 0.15 10.6 3.3
## 20 247.7 286 0.8 26.80 8.0 86 0.30 3.00 0.30 10.3 2.8
## 21 282.1 262 1.0 27.20 7.9 85 0.20 2.20 0.10 9.0 4.1
## 22 294.0 254 1.4 27.90 8.1 88 0.20 1.62 0.07 9.1 4.8
## 23 304.3 246 1.2 28.80 8.1 97 2.60 3.50 1.15 6.3 16.4
## 24 314.7 241 0.3 29.76 8.0 99 1.40 2.50 0.60 5.2 12.3
## 25 327.8 231 0.5 38.70 7.9 100 4.22 6.20 1.80 4.1 16.7
## 26 356.9 214 0.5 39.10 7.9 94 1.43 3.00 0.30 6.2 8.9
## 27 373.2 206 1.2 39.60 8.1 90 0.58 3.00 0.26 7.2 6.3
## 28 394.7 195 0.3 43.20 8.3 100 0.74 4.00 0.30 8.1 4.5
## 29 422.0 183 0.6 67.70 7.8 110 0.45 1.62 0.10 9.0 4.2
## 30 453.0 172 0.2 69.00 8.2 109 0.65 1.60 0.10 8.2 4.4head(spe)## Cogo Satr Phph Babl Thth Teso Chna Pato Lele Sqce Baba Albi Gogo Eslu Pefl
## 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 5 4 3 0 0 0 0 0 0 0 0 0 0 0
## 3 0 5 5 5 0 0 0 0 0 0 0 0 0 1 0
## 4 0 4 5 5 0 0 0 0 0 1 0 0 1 2 2
## 5 0 2 3 2 0 0 0 0 5 2 0 0 2 4 4
## 6 0 3 4 5 0 0 0 0 1 2 0 0 1 1 1
## Rham Legi Scer Cyca Titi Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0 0 0 0
## 5 0 0 2 0 3 0 0 0 5 0 0 0
## 6 0 0 0 0 2 0 0 0 1 0 0 0nrow(spe)## [1] 30ncol(spe)## [1] 27dim(spe)## [1] 30 27colnames(spe)## [1] "Cogo" "Satr" "Phph" "Babl" "Thth" "Teso" "Chna" "Pato" "Lele" "Sqce"
## [11] "Baba" "Albi" "Gogo" "Eslu" "Pefl" "Rham" "Legi" "Scer" "Cyca" "Titi"
## [21] "Abbr" "Icme" "Gyce" "Ruru" "Blbj" "Alal" "Anan"rownames(spe)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
## [16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"summary(spe)## Cogo Satr Phph Babl Thth
## Min. :0.00 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.00
## Median :0.00 Median :1.00 Median :3.000 Median :2.000 Median :0.00
## Mean :0.50 Mean :1.90 Mean :2.267 Mean :2.433 Mean :0.50
## 3rd Qu.:0.75 3rd Qu.:3.75 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:0.75
## Max. :3.00 Max. :5.00 Max. :5.000 Max. :5.000 Max. :4.00
## Teso Chna Pato Lele
## Min. :0.0000 Min. :0.0 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0 Median :0.0000 Median :1.000
## Mean :0.6333 Mean :0.6 Mean :0.8667 Mean :1.433
## 3rd Qu.:0.7500 3rd Qu.:1.0 3rd Qu.:2.0000 3rd Qu.:2.000
## Max. :5.0000 Max. :3.0 Max. :4.0000 Max. :5.000
## Sqce Baba Albi Gogo Eslu
## Min. :0.000 Min. :0.000 Min. :0.0 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:0.0 1st Qu.:0.000 1st Qu.:0.000
## Median :2.000 Median :0.000 Median :0.0 Median :1.000 Median :1.000
## Mean :1.867 Mean :1.433 Mean :0.9 Mean :1.833 Mean :1.333
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:1.0 3rd Qu.:3.750 3rd Qu.:2.000
## Max. :5.000 Max. :5.000 Max. :5.0 Max. :5.000 Max. :5.000
## Pefl Rham Legi Scer Cyca
## Min. :0.0 Min. :0.0 Min. :0.0000 Min. :0.0 Min. :0.0000
## 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0000 1st Qu.:0.0 1st Qu.:0.0000
## Median :0.5 Median :0.0 Median :0.0000 Median :0.0 Median :0.0000
## Mean :1.2 Mean :1.1 Mean :0.9667 Mean :0.7 Mean :0.8333
## 3rd Qu.:2.0 3rd Qu.:2.0 3rd Qu.:1.7500 3rd Qu.:1.0 3rd Qu.:1.0000
## Max. :5.0 Max. :5.0 Max. :5.0000 Max. :5.0 Max. :5.0000
## Titi Abbr Icme Gyce Ruru
## Min. :0.0 Min. :0.0000 Min. :0.0 Min. :0.000 Min. :0.0
## 1st Qu.:0.0 1st Qu.:0.0000 1st Qu.:0.0 1st Qu.:0.000 1st Qu.:0.0
## Median :1.0 Median :0.0000 Median :0.0 Median :0.000 Median :1.0
## Mean :1.5 Mean :0.8667 Mean :0.6 Mean :1.267 Mean :2.1
## 3rd Qu.:3.0 3rd Qu.:1.0000 3rd Qu.:0.0 3rd Qu.:2.000 3rd Qu.:5.0
## Max. :5.0 Max. :5.0000 Max. :5.0 Max. :5.000 Max. :5.0
## Blbj Alal Anan
## Min. :0.000 Min. :0.0 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.0 1st Qu.:0.00
## Median :0.000 Median :0.0 Median :0.00
## Mean :1.033 Mean :1.9 Mean :0.90
## 3rd Qu.:1.750 3rd Qu.:5.0 3rd Qu.:1.75
## Max. :5.000 Max. :5.0 Max. :5.00Perguntas
Com esses dados, três perguntas iniciais foram feitas: 1) dos 30 locais amostrados, qual desses apresentam a maior abundância no nº de espécies peixes? 2) E qual apresenta menos? 3) Há um gradiente de abundância com relação ao rio? 4) O pH dos locais de coleta influenciam na abundância do nº de espécies dos locais? Para isso, foram amostrados os pontos do rio em que as coletas foram feitas.
plot(spa,asp=1,type="n",main="Localização das Amostras",xlab="Coordenadas X (Km)",ylab="Coordenadas Y (Km)",ylim=c(0,120),xlim=c(0,180))
lines(spa,col="black")
text(spa,row.names(spa),cex=0.8,col="red")
text(70,10,"Nascente",cex=1.2,col="red")
text(10,35,"Foz",cex=1.2,col="red")
Dos 30 locais amostrados, qual desses apresentam a maior abundância no nº espécies de peixes? E qual apresenta a menor?
Com os dados obtidos, é possível perceber que o local 29 apresenta a maior abundância no nº espécies de peixes, com 25 ao total, enquanto que o local com a menor abundância no nº de espécies de peixes foi o local 8, com 0 ao total.
sit.pres<-apply(spe>0,1,sum)
plot(sit.pres,type="s",las=1,col="black",main="Riqueza de Espécies vs nº do Gradiente Rio Foz-Nascente",xlab="posições dos locais ao do rio",ylab="riqueza de espécies")
text(sit.pres,row.names(spe),cex=.8, col="red")
Há um gradiente de abundância com relação ao rio?
Com os resultados obtidos das análises, é possível ver que não há um gradiente no nº de espécies de peixes ao longo do gradiente do rio, em que há Áreas com descontínuas sobre a abundância do nº de espécies.
sit.pres<-apply(spe>0,1,sum)
plot(spa,asp=1,main="Mapa da Riqueza de Espécies",pch=21,col="black",bg="red",cex=5*sit.pres/max(sit.pres),xlab="Coordenadas X (Km)",ylab="Coordenadas Y (Km)")
lines(spa,col="black")
Há um gradiente de abundância com relação ao rio?
As análises mostraram que o aumento no pH não está¡ relacionado com o aumento na riqueza do nº de espécies, sendo essa variável sem influência observada.
sit.pres<-apply(spe>0,1,sum)
plot(env$pH,asp=1,main="Mapa da Riqueza de Espécies",pch=21,col="black",bg="red",cex=5*sit.pres/max(sit.pres),xlab="Locais",ylab="pH",ylim=c(1,14),xlim=c(1,30))
lines(spa,col="black")
sit.pres<-apply(spe>0,1,sum)
plot(sit.pres,type="s",las=1,col="black",main="riqueza de espécies vs nº do gradiente rio acima-rio abaixo",xlab="Posições dos Locais ao Longo do Rio",ylab="Riqueza de Espécies")
text(sit.pres,row.names(spe),cex=.8, col="red")