Laboratorio de Ecología de Comunidades: Biodiversidad
18 Feb 2019
Análisis de Biodiversidad con vegan y BiodiversityR
Preparación del Sistema
Antes de empezar con los procedimientos R para el análisis de la biodiversidad, vamos a tratar de tener el sistema preparado para acceder a los archivos y tener los paquetes necesarios instalados y activados.
Creación del directorio para R
Mediante Finder (Mac) o Explorer (Windows), debes crear una carpeta (“folder”) para uso exclusivo de R, y en el que debes poner los archivos de datos y otros que te indique. Luego de crearlo no puedes moverlo ni cambiarle el nombre. Sugiero un nombre como Rlabecocom.Designación del directorio (instrucciones para Mac, en Windows pueden ser algo diferentes)
En el menú RStudio, abrir el menú Preferences. Debe aparecer una ventana con varias alternativas. Seleccionar General, y buscar (“Browse”) el directorio (“Working directory”) con la carpeta que vas a usar. En la parte de abajo pulsar Apply y OK. Usualmente este paso solo hay que hacerlo una vez.Selección del directorio de trabajo
Seleccionar el menú de Session, luego el de Set Working Directory, y finalmente el de Choose Directory. Aparecerá la ventana de directorios; verificar si está seleccionado el correcto, si no, hacer el cambio. Oprimir Open al final. Ya debes estar lista/o para trabajar con tus archivos.
Objetivos
Este módulo tiene como objetivo principal enseñar el uso de paquetes y funciones de R para el análisis básico de la biodiversidad.
Los datos provienen de los árboles de la parcela de estudio de ‘Barro Colorado Island’, Panamá.
Paquetes de R
Deben instalar los siguientes paquetes:
- vegan (requiere además: permute, lattice)
- kableExtra (para tablas)
- ggplot2 (para gráficas)
- BiodiversityR (IMPORTANTE: instalarlo desde la consola de R, usando instal.packages)
library(vegan)
library(kableExtra)
data(BCI) #datos de ejemplos para seguir el artículo de Oksanen (2018)
library(BiodiversityR)1. Índices de Diversidad
Con la función diversity podemos calcular los índices más comunes de diversidad:
- Shannon–Weaver
\[H = -\sum_{i=1}^{S}p_{i}log_{b}p_i\] * Simpson
\[D = 1 - \sum_{i=1}^{S}p_{i}^2\] donde \(p_i\) es la proporción de la especies \(i\), S es el número de especies, y b la base del logaritmo (usualmente se usa ln, y en este caso se usa \(H'\)).
No tenemos una función para igualdad (J, “evenness”), pero podemos calcularla como:
\[J = \frac{H'}{logS}\]
#usando vegan
H <- diversity(BCI, index = "shannon")
D <- diversity(BCI, index = "simpson")
S <- specnumber(BCI)
J <- H/log(specnumber(BCI))
indices <- data.frame(S,H,D,J)
kable(indices, format = "markdown", col.names = c("S, Riqueza", "H, Shannon", "D, Simpson", "J, Igualdad"))| S, Riqueza | H, Shannon | D, Simpson | J, Igualdad |
|---|---|---|---|
| 93 | 4.018412 | 0.9746293 | 0.8865579 |
| 84 | 3.848471 | 0.9683393 | 0.8685692 |
| 90 | 3.814059 | 0.9646078 | 0.8476046 |
| 94 | 3.976563 | 0.9716117 | 0.8752597 |
| 101 | 3.969940 | 0.9678267 | 0.8602030 |
| 85 | 3.776575 | 0.9627557 | 0.8500724 |
| 82 | 3.836811 | 0.9672014 | 0.8706729 |
| 88 | 3.908381 | 0.9671998 | 0.8729254 |
| 90 | 3.761331 | 0.9534257 | 0.8358867 |
| 94 | 3.889803 | 0.9663808 | 0.8561634 |
| 87 | 3.859814 | 0.9658398 | 0.8642843 |
| 84 | 3.698414 | 0.9550599 | 0.8347024 |
| 93 | 3.982373 | 0.9692075 | 0.8786069 |
| 98 | 4.017494 | 0.9718626 | 0.8762317 |
| 93 | 3.956635 | 0.9709057 | 0.8729284 |
| 93 | 3.916821 | 0.9686598 | 0.8641446 |
| 93 | 3.736897 | 0.9545126 | 0.8244489 |
| 89 | 3.944985 | 0.9676685 | 0.8788828 |
| 109 | 4.013094 | 0.9655820 | 0.8554245 |
| 100 | 4.077327 | 0.9748589 | 0.8853802 |
| 99 | 3.969925 | 0.9686058 | 0.8639438 |
| 91 | 3.755413 | 0.9548316 | 0.8325271 |
| 99 | 4.062575 | 0.9723529 | 0.8841064 |
| 95 | 3.979427 | 0.9694268 | 0.8738548 |
| 105 | 4.074718 | 0.9726152 | 0.8755378 |
| 91 | 3.947749 | 0.9709567 | 0.8751655 |
| 99 | 3.980281 | 0.9669962 | 0.8661975 |
| 85 | 3.693896 | 0.9499296 | 0.8314621 |
| 86 | 3.688721 | 0.9481041 | 0.8281171 |
| 97 | 3.851598 | 0.9602659 | 0.8419326 |
| 77 | 3.724967 | 0.9635807 | 0.8575355 |
| 88 | 3.784873 | 0.9565267 | 0.8453403 |
| 86 | 3.740392 | 0.9586946 | 0.8397172 |
| 92 | 3.821670 | 0.9607876 | 0.8451677 |
| 83 | 2.641859 | 0.7983976 | 0.5978626 |
| 92 | 3.846109 | 0.9648567 | 0.8505725 |
| 88 | 3.791703 | 0.9565015 | 0.8468656 |
| 82 | 3.516082 | 0.9365144 | 0.7978912 |
| 84 | 3.530494 | 0.9360204 | 0.7968043 |
| 80 | 3.234849 | 0.9137131 | 0.7382083 |
| 102 | 4.052495 | 0.9731442 | 0.8762204 |
| 87 | 3.966614 | 0.9731849 | 0.8881987 |
| 86 | 3.736254 | 0.9569632 | 0.8387881 |
| 81 | 3.705016 | 0.9578733 | 0.8431126 |
| 81 | 3.609518 | 0.9528853 | 0.8213811 |
| 86 | 3.810489 | 0.9646728 | 0.8554539 |
| 102 | 3.920917 | 0.9672083 | 0.8477709 |
| 91 | 3.913725 | 0.9676412 | 0.8676229 |
| 91 | 3.778851 | 0.9609552 | 0.8377231 |
| 93 | 3.906616 | 0.9679784 | 0.8618931 |
Podemos calcular las estadísticas básicas de estos índices:
#índice de Shannon, H'
med <- mean(H)
var <- var(H)
max <- max(H)
min <- min(H)
indxstat <- data.frame(med,var,max,min)
kable(indxstat, format = "markdown", col.names = c("Media H", "Varianza H", "Máximo H", "Mínimo H"))| Media H | Varianza H | Máximo H | Mínimo H |
|---|---|---|---|
| 3.82084 | 0.0548212 | 4.077327 | 2.641859 |
O calcular los índices aplicados a todas las muestras en conjunto (“pooled”):
#índices de diversidad en conjunto con BiodiversityR
Hp <- diversityresult(BCI, index=("Shannon"), method=("pooled"))
Dp <- diversityresult(BCI, index=("Simpson"), method=("pooled"))
Jp <- diversityresult(BCI, index=("Jevenness"), method=("pooled"))
indxpool <- data.frame(Hp[1,1],Dp[1,1],Jp[1,1])
kable(indxpool, format = "markdown", col.names = c("H conjunto","D conjunto","J conjunto"))| H conjunto | D conjunto | J conjunto |
|---|---|---|
| 4.2704088 | 0.97367554 | 0.78846558 |
2. Curvas de Acumulación de Especies
Podemos estudiar la acumulación de especies, a medida que los sitios de muestreo aumenta. Usamos el paquete vegan, con la función specaccum:
#por sitios
sac <- specaccum(BCI)## Warning in cor(x > 0): the standard deviation is zeroplot(sac, ci.type="polygon", ci.col="yellow") #ver vegan para opciones
#por individuos
sac <- specaccum(BCI, method = "rarefaction")
plot(sac, xvar = "individual", ci.type="polygon", ci.col="yellow") #ver vegan para opciones
3. Rarefacción
Podemos calcular la riqueza de especies, usando la muestra con el menor número de individuos como referencia (o cualquier otro número de individuos)
#para el menor número de individuos en un sitio
Srar <- rarefy(BCI, min(rowSums(BCI)))
Srar## 1 2 3 4 5 6 7 8
## 84.339919 76.531650 79.115036 82.465714 86.909013 78.509526 76.347682 81.881359
## 9 10 11 12 13 14 15 16
## 83.268796 81.971485 81.500755 81.484116 87.186725 88.805622 83.528898 84.721466
## 17 18 19 20 21 22 23 24
## 88.434147 88.425662 97.839306 91.173340 91.203463 83.074280 99.000000 89.659706
## 25 26 27 28 29 30 31 32
## 94.545767 84.636381 91.217288 80.957589 83.495200 84.882386 71.453575 79.733155
## 33 34 35 36 37 38 39 40
## 77.770611 82.814084 61.137583 83.726338 80.999589 73.479288 77.077939 69.083284
## 41 42 43 44 45 46 47 48
## 94.574482 81.330351 79.705394 74.922631 72.177707 79.291538 91.464518 84.569537
## 49 50
## 82.227172 84.193101
## attr(,"Subsample")
## [1] 340#para un número establecido
Srar <- rarefy(BCI, 1000)## Warning in rarefy(BCI, 1000): requested 'sample' was larger than smallest site
## maximum (340)Srar## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 93 84 90 94 101 85 82 88 90 94 87 84 93 98 93 93 93 89 109 100
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 99 91 99 95 105 91 99 85 86 97 77 88 86 92 83 92 88 82 84 80
## 41 42 43 44 45 46 47 48 49 50
## 102 87 86 81 81 86 102 91 91 93
## attr(,"Subsample")
## [1] 10004. Abundancia versus Rango
Los índices de diversidad son indicadores de la distribución de la abundancia (o proporción) de las especies. Podemos visualizar esta diversidad mediante las gráficas de abundancia vs rango.
#cuando los datos no están en un data frame "homogéneo" podemos usar 'as.data.frame'
BCIdf <- as.data.frame(BCI)
#usando BiodiversityR
RkAb <- rankabundance(BCI)
RkAb #especies ordenadas según su abundancia (también: proportion, logabun)## rank abundance proportion plower pupper
## Faramea.occidentalis 1 1717 8.0 6.9 9.1
## Trichilia.tuberculata 2 1681 7.8 6.8 8.9
## Alseis.blackiana 3 983 4.6 3.7 5.5
## Oenocarpus.mapora 4 788 3.7 3.1 4.2
## Poulsenia.armata 5 755 3.5 2.5 4.6
## Quararibea.asterolepis 6 724 3.4 2.9 3.9
## Hirtella.triandra 7 681 3.2 2.5 3.9
## Gustavia.superba 8 644 3.0 0.7 5.3
## Virola.sebifera 9 617 2.9 2.5 3.2
## Protium.tenuifolium 10 381 1.8 1.4 2.2
## Tetragastris.panamensis 11 379 1.8 1.4 2.1
## Guarea.guidonia 12 376 1.8 1.4 2.2
## Cordia.lasiocalyx 13 364 1.7 1.4 2.0
## Socratea.exorrhiza 14 346 1.6 0.9 2.4
## Prioria.copaifera 15 345 1.6 1.1 2.1
## Cordia.bicolor 16 325 1.5 1.1 1.9
## Tabernaemontana.arborea 17 322 1.5 1.2 1.8
## Beilschmiedia.pendula 18 294 1.4 0.9 1.8
## Simarouba.amara 19 289 1.3 1.1 1.6
## Heisteria.concinna 20 288 1.3 1.1 1.6
## Drypetes.standleyi 21 285 1.3 0.8 1.9
## Cecropia.insignis 22 264 1.2 0.9 1.6
## Randia.armata 23 248 1.2 0.9 1.4
## Guatteria.dumetorum 24 244 1.1 0.9 1.4
## Apeiba.glabra 25 236 1.1 0.9 1.3
## Jacaranda.copaia 26 236 1.1 0.8 1.4
## Hasseltia.floribunda 27 229 1.1 0.8 1.3
## Swartzia.simplex.var.grandiflora 28 218 1.0 0.8 1.3
## Pouteria.reticulata 29 203 0.9 0.8 1.1
## Astrocaryum.standleyanum 30 201 0.9 0.7 1.2
## Brosimum.alicastrum 31 188 0.9 0.8 1.0
## Ocotea.whitei 32 184 0.9 0.5 1.2
## Eugenia.oerstediana 33 177 0.8 0.7 1.0
## Maquira.guianensis.costaricana 34 167 0.8 0.6 0.9
## Virola.surinamensis 35 164 0.8 0.5 1.0
## Unonopsis.pittieri 36 163 0.8 0.6 1.0
## Alchornea.costaricensis 37 156 0.7 0.5 0.9
## Zanthoxylum.ekmanii 38 149 0.7 0.5 0.9
## Triplaris.cumingiana 39 147 0.7 0.4 0.9
## Xylopia.macrantha 40 143 0.7 0.3 1.0
## Lonchocarpus.heptaphyllus 41 121 0.6 0.4 0.7
## Swartzia.simplex.continentalis 42 118 0.5 0.4 0.7
## Protium.costaricense 43 111 0.5 0.4 0.6
## Hura.crepitans 44 101 0.5 0.3 0.6
## Casearia.arborea 45 100 0.5 0.3 0.6
## Guapira.myrtiflora 46 99 0.5 0.4 0.6
## Croton.billbergianus 47 98 0.5 0.3 0.6
## Inga.semialata 48 98 0.5 0.3 0.6
## Tachigali.versicolor 49 98 0.5 0.3 0.6
## Luehea.seemannii 50 93 0.4 0.3 0.5
## Adelia.triloba 51 92 0.4 0.2 0.6
## Garcinia.intermedia 52 92 0.4 0.3 0.5
## Dendropanax.arboreus 53 88 0.4 0.3 0.5
## Cassipourea.guianensis 54 87 0.4 0.3 0.5
## Guettarda.foliacea 55 85 0.4 0.3 0.5
## Chrysophyllum.argenteum 56 85 0.4 0.3 0.5
## Trichilia.pallida 57 82 0.4 0.2 0.5
## Eugenia.florida 58 81 0.4 0.3 0.5
## Pterocarpus.rohrii 59 80 0.4 0.3 0.5
## Sloanea.terniflora 60 78 0.4 0.3 0.5
## Inga.sapindoides 61 76 0.4 0.3 0.4
## Miconia.argentea 62 70 0.3 0.2 0.5
## Guarea.fuzzy 63 68 0.3 0.2 0.4
## Tabebuia.rosea 64 68 0.3 0.2 0.4
## Inga.nobilis 65 67 0.3 0.2 0.4
## Zanthoxylum.panamense 66 67 0.3 0.2 0.4
## Lindackeria.laurina 67 64 0.3 0.2 0.4
## Cordia.alliodora 68 63 0.3 0.2 0.4
## Spondias.radlkoferi 69 63 0.3 0.2 0.4
## Platymiscium.pinnatum 70 61 0.3 0.2 0.4
## Turpinia.occidentalis 71 58 0.3 0.2 0.4
## Calophyllum.longifolium 72 55 0.3 0.2 0.3
## Coussarea.curvigemma 73 55 0.3 0.1 0.4
## Eugenia.nesiotica 74 55 0.3 0.2 0.3
## Casearia.sylvestris 75 54 0.3 0.2 0.3
## Aspidosperma.desmanthum 76 52 0.2 0.2 0.3
## Inga.cocleensis 77 52 0.2 0.1 0.4
## Lacmellea.panamensis 78 51 0.2 0.2 0.3
## Protium.panamense 79 50 0.2 0.2 0.3
## Inga.goldmanii 80 49 0.2 0.2 0.3
## Cupania.seemannii 81 47 0.2 0.2 0.3
## Picramnia.latifolia 82 45 0.2 0.1 0.3
## Zanthoxylum.juniperinum 83 45 0.2 0.1 0.3
## Platypodium.elegans 84 43 0.2 0.1 0.3
## Terminalia.oblonga 85 43 0.2 0.0 0.4
## Hieronyma.alchorneoides 86 41 0.2 0.1 0.3
## Trattinnickia.aspera 87 40 0.2 0.1 0.3
## Astronium.graveolens 88 39 0.2 0.1 0.3
## Ceiba.pentandra 89 39 0.2 0.1 0.2
## Celtis.schippii 90 38 0.2 0.1 0.2
## Guazuma.ulmifolia 91 38 0.2 0.1 0.3
## Ocotea.oblonga 92 36 0.2 0.1 0.2
## Attalea.butyracea 93 33 0.2 0.1 0.2
## Dipteryx.oleifera 94 33 0.2 0.1 0.2
## Lacistema.aggregatum 95 33 0.2 0.1 0.2
## Nectandra.cissiflora 96 33 0.2 0.1 0.2
## Trophis.caucana 97 33 0.2 0.1 0.3
## Trophis.racemosa 98 32 0.1 0.1 0.2
## Pouteria.stipitata 99 31 0.1 0.1 0.2
## Handroanthus.guayacan 100 30 0.1 0.1 0.2
## Ocotea.cernua 101 29 0.1 0.1 0.2
## Spondias.mombin 102 29 0.1 0.0 0.2
## Andira.inermis 103 28 0.1 0.1 0.2
## Sorocea.affinis 104 28 0.1 0.1 0.2
## Terminalia.amazonia 105 28 0.1 0.1 0.2
## Allophylus.psilospermus 106 27 0.1 0.1 0.2
## Annona.spraguei 107 27 0.1 0.1 0.2
## Laetia.thamnia 108 27 0.1 0.1 0.2
## Erythrina.costaricensis 109 26 0.1 0.1 0.2
## Inga.acuminata 110 26 0.1 0.0 0.2
## Sterculia.apetala 111 26 0.1 0.1 0.2
## Symphonia.globulifera 112 26 0.1 0.1 0.2
## Cecropia.obtusifolia 113 25 0.1 0.1 0.2
## Macrocnemum.roseum 114 25 0.1 0.0 0.2
## Chrysophyllum.cainito 115 25 0.1 0.1 0.2
## Virola.multiflora 116 25 0.1 0.1 0.2
## Aegiphila.panamensis 117 23 0.1 0.1 0.2
## Casearia.aculeata 118 23 0.1 0.0 0.2
## Ficus.tonduzii 119 23 0.1 0.1 0.2
## Genipa.americana 120 23 0.1 0.1 0.2
## Anacardium.excelsum 121 22 0.1 0.0 0.2
## Coccoloba.coronata 122 22 0.1 0.1 0.1
## Ocotea.puberula 123 22 0.1 0.0 0.2
## Apeiba.tibourbou 124 21 0.1 0.1 0.1
## Elaeis.oleifera 125 21 0.1 0.0 0.2
## Maytenus.schippii 126 21 0.1 0.1 0.1
## Perebea.xanthochyma 127 21 0.1 0.0 0.2
## Inga.pezizifera 128 20 0.1 0.0 0.2
## Cavanillesia.platanifolia 129 19 0.1 0.0 0.1
## Erythroxylum.macrophyllum 130 18 0.1 0.0 0.1
## Sapium.glandulosum 131 17 0.1 0.0 0.1
## Diospyros.artanthifolia 132 16 0.1 0.0 0.1
## Cinnamomum.triplinerve 133 16 0.1 0.0 0.1
## Siparuna.pauciflora 134 16 0.1 0.0 0.1
## Mosannona.garwoodii 135 15 0.1 0.0 0.1
## Posoqueria.latifolia 136 15 0.1 0.0 0.1
## Trema.micrantha 137 15 0.1 0.0 0.1
## Inga.spectabilis 138 14 0.1 0.0 0.1
## Inga.umbellifera 139 14 0.1 0.0 0.1
## Licania.hypoleuca 140 14 0.1 0.0 0.1
## Coccoloba.manzinellensis 141 13 0.1 0.0 0.1
## Desmopsis.panamensis 142 13 0.1 0.0 0.1
## Pourouma.bicolor 143 13 0.1 0.0 0.1
## Siparuna.guianensis 144 13 0.1 0.0 0.1
## Hampea.appendiculata 145 13 0.1 0.0 0.1
## Cupania.latifolia 146 12 0.1 0.0 0.1
## Eugenia.galalonensis 147 12 0.1 0.0 0.1
## Garcinia.madruno 148 12 0.1 0.0 0.1
## Laetia.procera 149 12 0.1 0.0 0.1
## Solanum.hayesii 150 12 0.1 0.0 0.1
## Theobroma.cacao 151 12 0.1 0.0 0.1
## Vochysia.ferruginea 152 12 0.1 0.0 0.1
## Guarea.grandifolia 153 10 0.0 0.0 0.1
## Inga.laurina 154 10 0.0 0.0 0.1
## Inga.punctata 155 10 0.0 0.0 0.1
## Licania.platypus 156 10 0.0 0.0 0.1
## Marila.laxiflora 157 10 0.0 0.0 0.1
## Nectandra.lineata 158 10 0.0 0.0 0.1
## Zuelania.guidonia 159 10 0.0 0.0 0.1
## Pachira.sessilis 160 9 0.0 0.0 0.1
## Piper.reticulatum 161 9 0.0 0.0 0.1
## Miconia.affinis 162 8 0.0 0.0 0.1
## Pseudobombax.septenatum 163 8 0.0 0.0 0.1
## Spachea.membranacea 164 8 0.0 0.0 0.1
## Ficus.costaricana 165 7 0.0 0.0 0.1
## Ficus.obtusifolia 166 7 0.0 0.0 0.1
## Heisteria.acuminata 167 7 0.0 0.0 0.1
## Miconia.hondurensis 168 7 0.0 0.0 0.1
## Myrospermum.frutescens 169 7 0.0 0.0 0.1
## Tetrathylacium.johansenii 170 7 0.0 0.0 0.1
## Ficus.yoponensis 171 6 0.0 0.0 0.0
## Ficus.trigonata 172 5 0.0 0.0 0.0
## Hirtella.americana 173 5 0.0 0.0 0.1
## Inga.ruiziana 174 5 0.0 0.0 0.1
## Lafoensia.punicifolia 175 5 0.0 0.0 0.1
## Myrcia.gatunensis 176 5 0.0 0.0 0.0
## Ochroma.pyramidale 177 5 0.0 0.0 0.1
## Ormosia.coccinea 178 5 0.0 0.0 0.0
## Tocoyena.pittieri 179 5 0.0 0.0 0.0
## Cupania.rufescens 180 4 0.0 0.0 0.0
## Ficus.maxima 181 4 0.0 0.0 0.0
## Nectandra.purpurea 182 4 0.0 0.0 0.0
## Psidium.friedrichsthalianum 183 4 0.0 0.0 0.0
## Quassia.amara 184 4 0.0 0.0 0.0
## Vachellia.melanoceras 185 3 0.0 0.0 0.0
## Amaioua.corymbosa 186 3 0.0 0.0 0.0
## Casearia.commersoniana 187 3 0.0 0.0 0.0
## Chamguava.schippii 188 3 0.0 0.0 0.0
## Ficus.insipida 189 3 0.0 0.0 0.0
## Ficus.popenoei 190 3 0.0 0.0 0.0
## Ormosia.macrocalyx 191 3 0.0 0.0 0.0
## Sapium.broadleaf 192 3 0.0 0.0 0.0
## Talisia.princeps 193 3 0.0 0.0 0.0
## Acalypha.diversifolia 194 2 0.0 0.0 0.0
## Casearia.guianensis 195 2 0.0 0.0 0.0
## Cedrela.odorata 196 2 0.0 0.0 0.0
## Cespedesia.spathulata 197 2 0.0 0.0 0.0
## Chrysochlamys.eclipes 198 2 0.0 0.0 0.0
## Enterolobium.schomburgkii 199 2 0.0 0.0 0.0
## Inga.oerstediana 200 2 0.0 0.0 0.0
## Margaritaria.nobilis 201 2 0.0 0.0 0.0
## Pouteria.fossicola 202 2 0.0 0.0 0.0
## Psychotria.grandis 203 2 0.0 0.0 0.0
## Schizolobium.parahyba 204 2 0.0 0.0 0.0
## Thevetia.ahouai 205 2 0.0 0.0 0.0
## Trichanthera.gigantea 206 2 0.0 0.0 0.0
## Abarema.macradenia 207 1 0.0 0.0 0.0
## Acalypha.macrostachya 208 1 0.0 0.0 0.0
## Alchornea.latifolia 209 1 0.0 0.0 0.0
## Alibertia.edulis 210 1 0.0 0.0 0.0
## Banara.guianensis 211 1 0.0 0.0 0.0
## Brosimum.guianense 212 1 0.0 0.0 0.0
## Chimarrhis.parviflora 213 1 0.0 0.0 0.0
## Maclura.tinctoria 214 1 0.0 0.0 0.0
## Colubrina.glandulosa 215 1 0.0 0.0 0.0
## Cupania.cinerea 216 1 0.0 0.0 0.0
## Ficus.colubrinae 217 1 0.0 0.0 0.0
## Miconia.elata 218 1 0.0 0.0 0.0
## Ormosia.amazonica 219 1 0.0 0.0 0.0
## Pachira.quinata 220 1 0.0 0.0 0.0
## Senna.dariensis 221 1 0.0 0.0 0.0
## Talisia.nervosa 222 1 0.0 0.0 0.0
## Trichospermum.galeottii 223 1 0.0 0.0 0.0
## Vismia.baccifera 224 1 0.0 0.0 0.0
## Zanthoxylum.setulosum 225 1 0.0 0.0 0.0
## accumfreq logabun rankfreq
## Faramea.occidentalis 8.0 3.2 0.4
## Trichilia.tuberculata 15.8 3.2 0.9
## Alseis.blackiana 20.4 3.0 1.3
## Oenocarpus.mapora 24.1 2.9 1.8
## Poulsenia.armata 27.6 2.9 2.2
## Quararibea.asterolepis 31.0 2.9 2.7
## Hirtella.triandra 34.2 2.8 3.1
## Gustavia.superba 37.2 2.8 3.6
## Virola.sebifera 40.0 2.8 4.0
## Protium.tenuifolium 41.8 2.6 4.4
## Tetragastris.panamensis 43.6 2.6 4.9
## Guarea.guidonia 45.3 2.6 5.3
## Cordia.lasiocalyx 47.0 2.6 5.8
## Socratea.exorrhiza 48.6 2.5 6.2
## Prioria.copaifera 50.2 2.5 6.7
## Cordia.bicolor 51.8 2.5 7.1
## Tabernaemontana.arborea 53.3 2.5 7.6
## Beilschmiedia.pendula 54.6 2.5 8.0
## Simarouba.amara 56.0 2.5 8.4
## Heisteria.concinna 57.3 2.5 8.9
## Drypetes.standleyi 58.6 2.5 9.3
## Cecropia.insignis 59.9 2.4 9.8
## Randia.armata 61.0 2.4 10.2
## Guatteria.dumetorum 62.2 2.4 10.7
## Apeiba.glabra 63.3 2.4 11.1
## Jacaranda.copaia 64.4 2.4 11.6
## Hasseltia.floribunda 65.4 2.4 12.0
## Swartzia.simplex.var.grandiflora 66.5 2.3 12.4
## Pouteria.reticulata 67.4 2.3 12.9
## Astrocaryum.standleyanum 68.3 2.3 13.3
## Brosimum.alicastrum 69.2 2.3 13.8
## Ocotea.whitei 70.1 2.3 14.2
## Eugenia.oerstediana 70.9 2.2 14.7
## Maquira.guianensis.costaricana 71.7 2.2 15.1
## Virola.surinamensis 72.4 2.2 15.6
## Unonopsis.pittieri 73.2 2.2 16.0
## Alchornea.costaricensis 73.9 2.2 16.4
## Zanthoxylum.ekmanii 74.6 2.2 16.9
## Triplaris.cumingiana 75.3 2.2 17.3
## Xylopia.macrantha 76.0 2.2 17.8
## Lonchocarpus.heptaphyllus 76.5 2.1 18.2
## Swartzia.simplex.continentalis 77.1 2.1 18.7
## Protium.costaricense 77.6 2.0 19.1
## Hura.crepitans 78.1 2.0 19.6
## Casearia.arborea 78.5 2.0 20.0
## Guapira.myrtiflora 79.0 2.0 20.4
## Croton.billbergianus 79.5 2.0 20.9
## Inga.semialata 79.9 2.0 21.3
## Tachigali.versicolor 80.4 2.0 21.8
## Luehea.seemannii 80.8 2.0 22.2
## Adelia.triloba 81.2 2.0 22.7
## Garcinia.intermedia 81.7 2.0 23.1
## Dendropanax.arboreus 82.1 1.9 23.6
## Cassipourea.guianensis 82.5 1.9 24.0
## Guettarda.foliacea 82.9 1.9 24.4
## Chrysophyllum.argenteum 83.3 1.9 24.9
## Trichilia.pallida 83.7 1.9 25.3
## Eugenia.florida 84.0 1.9 25.8
## Pterocarpus.rohrii 84.4 1.9 26.2
## Sloanea.terniflora 84.8 1.9 26.7
## Inga.sapindoides 85.1 1.9 27.1
## Miconia.argentea 85.4 1.8 27.6
## Guarea.fuzzy 85.8 1.8 28.0
## Tabebuia.rosea 86.1 1.8 28.4
## Inga.nobilis 86.4 1.8 28.9
## Zanthoxylum.panamense 86.7 1.8 29.3
## Lindackeria.laurina 87.0 1.8 29.8
## Cordia.alliodora 87.3 1.8 30.2
## Spondias.radlkoferi 87.6 1.8 30.7
## Platymiscium.pinnatum 87.9 1.8 31.1
## Turpinia.occidentalis 88.1 1.8 31.6
## Calophyllum.longifolium 88.4 1.7 32.0
## Coussarea.curvigemma 88.7 1.7 32.4
## Eugenia.nesiotica 88.9 1.7 32.9
## Casearia.sylvestris 89.2 1.7 33.3
## Aspidosperma.desmanthum 89.4 1.7 33.8
## Inga.cocleensis 89.6 1.7 34.2
## Lacmellea.panamensis 89.9 1.7 34.7
## Protium.panamense 90.1 1.7 35.1
## Inga.goldmanii 90.3 1.7 35.6
## Cupania.seemannii 90.6 1.7 36.0
## Picramnia.latifolia 90.8 1.7 36.4
## Zanthoxylum.juniperinum 91.0 1.7 36.9
## Platypodium.elegans 91.2 1.6 37.3
## Terminalia.oblonga 91.4 1.6 37.8
## Hieronyma.alchorneoides 91.6 1.6 38.2
## Trattinnickia.aspera 91.8 1.6 38.7
## Astronium.graveolens 91.9 1.6 39.1
## Ceiba.pentandra 92.1 1.6 39.6
## Celtis.schippii 92.3 1.6 40.0
## Guazuma.ulmifolia 92.5 1.6 40.4
## Ocotea.oblonga 92.7 1.6 40.9
## Attalea.butyracea 92.8 1.5 41.3
## Dipteryx.oleifera 93.0 1.5 41.8
## Lacistema.aggregatum 93.1 1.5 42.2
## Nectandra.cissiflora 93.3 1.5 42.7
## Trophis.caucana 93.4 1.5 43.1
## Trophis.racemosa 93.6 1.5 43.6
## Pouteria.stipitata 93.7 1.5 44.0
## Handroanthus.guayacan 93.9 1.5 44.4
## Ocotea.cernua 94.0 1.5 44.9
## Spondias.mombin 94.1 1.5 45.3
## Andira.inermis 94.3 1.4 45.8
## Sorocea.affinis 94.4 1.4 46.2
## Terminalia.amazonia 94.5 1.4 46.7
## Allophylus.psilospermus 94.6 1.4 47.1
## Annona.spraguei 94.8 1.4 47.6
## Laetia.thamnia 94.9 1.4 48.0
## Erythrina.costaricensis 95.0 1.4 48.4
## Inga.acuminata 95.1 1.4 48.9
## Sterculia.apetala 95.3 1.4 49.3
## Symphonia.globulifera 95.4 1.4 49.8
## Cecropia.obtusifolia 95.5 1.4 50.2
## Macrocnemum.roseum 95.6 1.4 50.7
## Chrysophyllum.cainito 95.7 1.4 51.1
## Virola.multiflora 95.8 1.4 51.6
## Aegiphila.panamensis 96.0 1.4 52.0
## Casearia.aculeata 96.1 1.4 52.4
## Ficus.tonduzii 96.2 1.4 52.9
## Genipa.americana 96.3 1.4 53.3
## Anacardium.excelsum 96.4 1.3 53.8
## Coccoloba.coronata 96.5 1.3 54.2
## Ocotea.puberula 96.6 1.3 54.7
## Apeiba.tibourbou 96.7 1.3 55.1
## Elaeis.oleifera 96.8 1.3 55.6
## Maytenus.schippii 96.9 1.3 56.0
## Perebea.xanthochyma 97.0 1.3 56.4
## Inga.pezizifera 97.1 1.3 56.9
## Cavanillesia.platanifolia 97.2 1.3 57.3
## Erythroxylum.macrophyllum 97.2 1.3 57.8
## Sapium.glandulosum 97.3 1.2 58.2
## Diospyros.artanthifolia 97.4 1.2 58.7
## Cinnamomum.triplinerve 97.5 1.2 59.1
## Siparuna.pauciflora 97.5 1.2 59.6
## Mosannona.garwoodii 97.6 1.2 60.0
## Posoqueria.latifolia 97.7 1.2 60.4
## Trema.micrantha 97.7 1.2 60.9
## Inga.spectabilis 97.8 1.1 61.3
## Inga.umbellifera 97.9 1.1 61.8
## Licania.hypoleuca 97.9 1.1 62.2
## Coccoloba.manzinellensis 98.0 1.1 62.7
## Desmopsis.panamensis 98.1 1.1 63.1
## Pourouma.bicolor 98.1 1.1 63.6
## Siparuna.guianensis 98.2 1.1 64.0
## Hampea.appendiculata 98.2 1.1 64.4
## Cupania.latifolia 98.3 1.1 64.9
## Eugenia.galalonensis 98.4 1.1 65.3
## Garcinia.madruno 98.4 1.1 65.8
## Laetia.procera 98.5 1.1 66.2
## Solanum.hayesii 98.5 1.1 66.7
## Theobroma.cacao 98.6 1.1 67.1
## Vochysia.ferruginea 98.6 1.1 67.6
## Guarea.grandifolia 98.7 1.0 68.0
## Inga.laurina 98.7 1.0 68.4
## Inga.punctata 98.8 1.0 68.9
## Licania.platypus 98.8 1.0 69.3
## Marila.laxiflora 98.9 1.0 69.8
## Nectandra.lineata 98.9 1.0 70.2
## Zuelania.guidonia 99.0 1.0 70.7
## Pachira.sessilis 99.0 1.0 71.1
## Piper.reticulatum 99.0 1.0 71.6
## Miconia.affinis 99.1 0.9 72.0
## Pseudobombax.septenatum 99.1 0.9 72.4
## Spachea.membranacea 99.2 0.9 72.9
## Ficus.costaricana 99.2 0.8 73.3
## Ficus.obtusifolia 99.2 0.8 73.8
## Heisteria.acuminata 99.3 0.8 74.2
## Miconia.hondurensis 99.3 0.8 74.7
## Myrospermum.frutescens 99.3 0.8 75.1
## Tetrathylacium.johansenii 99.4 0.8 75.6
## Ficus.yoponensis 99.4 0.8 76.0
## Ficus.trigonata 99.4 0.7 76.4
## Hirtella.americana 99.4 0.7 76.9
## Inga.ruiziana 99.5 0.7 77.3
## Lafoensia.punicifolia 99.5 0.7 77.8
## Myrcia.gatunensis 99.5 0.7 78.2
## Ochroma.pyramidale 99.5 0.7 78.7
## Ormosia.coccinea 99.5 0.7 79.1
## Tocoyena.pittieri 99.6 0.7 79.6
## Cupania.rufescens 99.6 0.6 80.0
## Ficus.maxima 99.6 0.6 80.4
## Nectandra.purpurea 99.6 0.6 80.9
## Psidium.friedrichsthalianum 99.6 0.6 81.3
## Quassia.amara 99.7 0.6 81.8
## Vachellia.melanoceras 99.7 0.5 82.2
## Amaioua.corymbosa 99.7 0.5 82.7
## Casearia.commersoniana 99.7 0.5 83.1
## Chamguava.schippii 99.7 0.5 83.6
## Ficus.insipida 99.7 0.5 84.0
## Ficus.popenoei 99.7 0.5 84.4
## Ormosia.macrocalyx 99.8 0.5 84.9
## Sapium.broadleaf 99.8 0.5 85.3
## Talisia.princeps 99.8 0.5 85.8
## Acalypha.diversifolia 99.8 0.3 86.2
## Casearia.guianensis 99.8 0.3 86.7
## Cedrela.odorata 99.8 0.3 87.1
## Cespedesia.spathulata 99.8 0.3 87.6
## Chrysochlamys.eclipes 99.8 0.3 88.0
## Enterolobium.schomburgkii 99.8 0.3 88.4
## Inga.oerstediana 99.9 0.3 88.9
## Margaritaria.nobilis 99.9 0.3 89.3
## Pouteria.fossicola 99.9 0.3 89.8
## Psychotria.grandis 99.9 0.3 90.2
## Schizolobium.parahyba 99.9 0.3 90.7
## Thevetia.ahouai 99.9 0.3 91.1
## Trichanthera.gigantea 99.9 0.3 91.6
## Abarema.macradenia 99.9 0.0 92.0
## Acalypha.macrostachya 99.9 0.0 92.4
## Alchornea.latifolia 99.9 0.0 92.9
## Alibertia.edulis 99.9 0.0 93.3
## Banara.guianensis 99.9 0.0 93.8
## Brosimum.guianense 99.9 0.0 94.2
## Chimarrhis.parviflora 99.9 0.0 94.7
## Maclura.tinctoria 99.9 0.0 95.1
## Colubrina.glandulosa 100.0 0.0 95.6
## Cupania.cinerea 100.0 0.0 96.0
## Ficus.colubrinae 100.0 0.0 96.4
## Miconia.elata 100.0 0.0 96.9
## Ormosia.amazonica 100.0 0.0 97.3
## Pachira.quinata 100.0 0.0 97.8
## Senna.dariensis 100.0 0.0 98.2
## Talisia.nervosa 100.0 0.0 98.7
## Trichospermum.galeottii 100.0 0.0 99.1
## Vismia.baccifera 100.0 0.0 99.6
## Zanthoxylum.setulosum 100.0 0.0 100.0#gráfica de rango-abundancia
rankabunplot(RkAb, scale='abundance', addit=FALSE, specnames=c(1))
#gráfica usando la función básica 'plot'
plot(RkAb[,1], RkAb[,2], type = "h", xlab = "Rango", ylab = "Abundancia")
Ajuste de la curva rango-abundancia a modelos (curvas)
La función radfit calcula los parámetros y compara los modelos o curvas de distribución de la abundancia. El modelo con menor desviación y menor AIC (Akaike Information Criterion, una medida de la calidad de un modelo estadístico) es el que mejor describe la distribución de la abundancia en función de su rango.
#para 1 sitio de muestreo
mod <- radfit(BCI[25,])
mod##
## RAD models, family poisson
## No. of species 105, total abundance 442
##
## par1 par2 par3 Deviance AIC BIC
## Null 63.78814 361.75032 361.75032
## Preemption 0.0390856 60.67696 360.63914 363.29310
## Lognormal 0.859211 1.09131 20.16488 322.12705 327.43498
## Zipf 0.120918 -0.799371 28.79720 330.75938 336.06730
## Mandelbrot 0.908445 -1.30737 5.59741 5.06656 309.02874 316.99062plot(mod)
#para varios sitios de muestreo
mods <- radfit(BCI[c(1,10,20,30,40,50),])
mods##
## Deviance for RAD models:
##
## 1 10 20 30 40 50
## Null 39.52608 77.27375 45.06735 109.91252 334.17193 66.62941
## Preemption 21.89394 62.72098 33.72793 108.69134 286.80522 51.51428
## Lognormal 25.15277 20.47701 19.01778 21.58282 91.77575 21.25403
## Zipf 61.04653 39.70656 42.50316 17.40046 45.38713 38.87217
## Mandelbrot 4.22707 9.83528 7.28598 7.73133 38.38318 9.38148plot(mods)
Referencias
Kindt, R. 2019. Package for Community Ecology and Suitability Analysis. Package ‘BiodiversityR’, CRAN Repository. http://www.worldagroforestry.org/output/tree-diversity-analysis
Oksanen, J. 2018. Vegan: ecological diversity. Processed with vegan 2.4-6 in R version 3.4.3 (2017-11-30). https://cran.r-project.org/web/packages/vegan/vignettes/diversity-vegan.pdf
Oksanen, J. 2019. Community Ecology Package. Package ‘vegan’, CRAN Repository. <https://cran.r-project.org, https://github.com/vegandevs/vegan>