Interacción de la temperatura y velocidad del viento en la abundancia de Artibeus jamaicensis, Artibeus toltecus, Thyroptera discifera y Thyroptera tricolor durante la estación seca y lloviosa en los años 2018 y 2019, en en cuatro zonas zoogeográficas de Costa Rica
Paula Mariela Vargas Jiménez, David Andrés Vega Zumbado, Nicole Sofía Sánchez Castillo, María José Torres Hidalgo
21/10/2020
Resumen
Se estudió la abundancia de poblaciones de murciélagos de las especies Artibeus jamaicencis, Artibeus toltecus, Thyroptera discifera y Thyroptera tricolor, durante los años 2017 y 2018, en dos épocas del año (estación seca y lluviosa) en 4 sitios del país, específicamente en el Área de Conservación Arenal-Tempisque, Parque Nacional Tortuguero, Area de Conservación Pacífico Central y Área de Conservación Cordillera Volcánica Central. Se capturaron 39271 organismos con ayuda de redes de niebla al nivel del suelo, además fue medida su talla total, talla del pie, la temperatura ambiental y velocidad del viento al momento de la captura. Para las variables de temperatura y velocidad el viento el análisis mostró diferencias significativas entre las poblaciones de murciélagos, sin embargo, estas diferencias fueron muy pequeñas y no contundentes. Tampoco fueron encontradas grandes diferencias entre las poblaciones por año, sitio de muestreo ni estación, ni en las tallas de los individuos. Es posible, que al ser un análisis hecho en zonas de conservación, las condiciones ambientales sean mayoritariamente constantes y las poblaciones de murciélagos no hayan sufrido efectos.
Palabras clave: Artibeus jamaicensis, Artibeus toltecus, Thyroptera discifera, Thyroptera tricolor, abundancia, temperatura, velocidad del viento.
Introducción
En los últimos años (2017 al 2018) se han observado cambios en las poblaciones de murciélagos (Chiroptera) en Costa Rica (citar). Principalmente en los géneros Artibeus (Phyllostomidae) y Thyroptera (Thyropteridae). Producto de la importancia ecológica en la depredación de insectos, el uso del néctar y frutos podría afectar el control de poblaciones de insectos, polinización y dispersión de semillas (Zamora y Bernal, 2017).
Para observar posibles cambios poblacionales del género Artibeus fueron seleccionadas dos especies principalmente frugívoras: Artribeus jamaicencis (Leach, 1821), una especie de hábitos nocturnos y coloniales, que se distribuye en los bosques húmedos y secos, en las tierras bajas y medias de todo el país, desde el nivel del mar hasta los 1.700 m. de altitud (LaVal y Rodríguez, 2002).citado por Elizondo (1999); y la especie Artibeus toltecus (Olfers, 1818), especie de hábitos nocturnos, que se distribuye en bosques húmedos y nubosos, en tierras medias de ambas vertientes del país, desde los 500-2000 m.s.n.m. (Museo Nacional de Costa Rica, 2013).
Del género Tyroptera fueron elegidas dos especies principalmente insectívoras: Thyroptera discifera de las tierras bajas y húmedas de la Zona Norte a unos 600 msnm, es de tamaño pequeño,, se hospedan en zonas de cultivo de banano ((Bocchiglieri et al., 2016)) citado por Romero (2020). La segunda es la Thyroptera tricolor(Miller, 1902), habitan en bosques húmedos en un rango de 200 - 700 msnm (Tirira, 2017) citado por INABIO (2020). Ambas presentan la característica de tener ventosas en sus extremidades para adherirse a sustratos u hojas, se han reportado enrollados en hojas de plantaciones de banano (Wilson, 1977) citado por INABIO (2020).
Para cada una de estas especies seleccionadas fueron observadas las abundancias relativa en 4 sitios: Noroeste, Suroeste, Caribe y Valle Central, durante 2 épocas del año: estación seca y estación lluviosa, además para lograr la caracterización en los cambios de las poblaciones se consideraron variables como la longitud total y longitud del pata de cada uno de los individuos, y factores como la temperatura y la velocidad del viento en cada muestreo en el que fueron capturados.
La importancia ecológica que cada uno de estos individuos aporta a los ecosistemas tropicales de Costa Rica (“agregar lo del experimento”) motivó a determinar si factores como la estación climática, la temperatura y velocidad del viento podrían influir en la abundancia relativa de las especies: Artribeus jamaicencis, Artibeus toltecus,Thyroptera discifera y Thyroptera tricolor, en las cuatro zonas de distribución geográfica (Noroeste, Suroeste, Caribe y Valle Central).
Metodología
Área de estudio: El estudio se realizó en cuatro zonas del país, al Noroeste se realizó en el Área de Conservación Arenal-Tempisque (sitio1), en el Caribe en Parque Nacional Tortuguero (sitio 2), al Suroeste en el Área de Conservación Pacífico Central (sitio 3) y en el Valle Central en el Área de Conservación Cordillera Volcánica Central (sitio 4), todos los sitios contaban con la vegetación y las temperaturas adecuadas para un posible registro de las diversas especies. El promedio de temperatura en los 4 sitios muestreados oscila entre 22°C y 27° C sin mucha diferenciación entre la época seca y la lluviosa. La altitud promedio registrada fue de 1200 m.s.n.m. La vegetación de estas zonas es variada ya que comprende bosque húmedo, seco y nuboso.
Métodos de captura: El método utilizado para la captura de las diversas especies de murciélagos se realizó mediante redes de niebla (Bracam018). Estas redes de niebla se colocaron durante la época seca en el mes de marzo y luego en la época lluviosa en octubre de los años 2017 y 2018; se realizaron un total de 12 visitas por sitio, con una estadía de 3 días en el lugar. Para la colocación de las redes de niebla, se limpio la zona de hojas y ramas que podrían dañar la red, seguidamente se colocaron estacas y parantes para dar sostén a la misma. Se usaron redes de 9 metros x 2.5 metros al nivel del suelo en un trayecto de 500 metros con una separación entre ellas de 16 metros. Durante la época seca se colocaron las redes desde las 12:00 a.m hasta las 3:00 a.m debido a que presentan su pico de actividad a estas horas. ** En la época lluviosa se colocaron desde las 10:00 p.m hasta las 5:00 a.m ya que su actividad disminuye debido a las precipitaciones. Se realizaron revisiones a las redes cada 30 minutos para evitar el escape de individuos o la muerte por estrés, hipotermia o predación.** Se movieron las redes diariamente de lugar para garantizar la captura de la mayor cantidad de individuos.
Al capturar a los organismos, se les removió de la red con mucho cuidado, con el fin de no dañar sus patas o alas. A cada individuo se le realizó la medida de tamaño total (tot_len; mm) y largo pata (hind_foot; mm) después de la realización de las medidas fueron marcados y liberados al ambiente. Además, cada vez que se trabajó con las redes de niebla se realizó la medición de la temperatura del aire (ta) con ayuda de un termohigrómetro (Termohigrómetro Mod.: 800016 Sper Scientific) y la velocidad viento (veloc, m/s)) con un anemómetro (Anemómetro GS026 Trimble). instrumento para medir murciélagos(agregar) Para la determinación de la efectividad en la dispersión de las semillas en estos individuos se recolectó 40 ejemplares de sus excreciones de cada una, 10 en cada sitio y se le aplicó un tratamiento con 3 distintas temperaturas durante 3 meses en campos concentrados, simulando las condiciones normales para el crecimiento de las plantas. Por medio de la medición continua, durante ese período de tiempo, de la biomasa y la talla máxima (cm) de cada planta se pudo estudiar la capacidad de dispersión de semillas de las especies.
Análisis de datos: Con base a los datos obtenidos en el muestreo se realizó un análisis en el entorno y lenguaje de programación R (insertar referencia) para todas las pruebas, con un nivel de significancia al 95%. Se creó un modelo de ANDEVA utilizando las variables de especie en función de la temperatura; esto con el fin de comprobar si los grupos categóricos difieren entre sí, y si el factor depende de la variable, para esto se tomó como factor la especie y la temperatura como variable. También, fueron consideradas otras variables propias del ambiente, para determinar la influencia que estas tienen sobre la abundancia de las especies de murciélagos; para el caso de los factores de sitio y estación, por estar agrupadas en categorías mutuamente excluyentes, de frecuencias absolutas y grandes, fue realizado un chi cuadrado con el fin de determinar si existen diferencias significativas entre la abundancia total por especie, por cada sitio y por cada estación climática. A partir del mismo modelo de Análisis de Varianza, utilizando los datos del éxito de germinación, se comparó el tratamiento aplicado de las temperaturas con respecto a la biomasa y talla máxima obtenida durante el experimento, para determinar alguna diferencia significativa entre ellas y su capacidad dispersora entre las especies.
Resultados
Se realizaron 1920 observaciones con un total de 39271 organismos capturados en las redes de niebla.La abundancia general para las cuatro especies de murciélagos fue de 39271 individuos, para el año 2017 fueron capturados 19351 organismos y para el año 2018 19920, por lo que fue levemente mayor la abundancia para el año 2018. En cuanto a las estaciones. en la estación seca fueron capturados 19776 organismos y en la lluviosa 19495, por lo que fue levemente mayor la abundancia de organismos en la época seca. Las abundancias por sitio y especie se evidencian en el Cuadro 1.
Cuadro 1. Numero de especies capturadas por cada sitio de muestreo
En cuanto a las temperaturas, en la estación seca fue registrada una media de temperatura del aire para el sitio 1 de 22.55°C, para el sitio 2 de 22.48°C, para el sitio 3 de 22.45°C y para el sitio 4 de 22.61°C; en la estación lluviosa la media de temperatura del aire para el sitio 1 fue de 22.44°C, para el sitio 2 de 22.53°C, para el sitio 3 de 22.43°C y para el sitio 4 de 22.38°C, por lo que, no se registraron grandes diferencias entre las temperaturas entre las diferentes estaciones para cada sitio.
En cuanto al análisis de abundancia con respecto a la temperatura, el modelo de análisis de varianza (ANDEVA) a través del modelo de Kruskal Wallis arrojó que los grupos categóricos no difieren entre sí, por tanto, la abundancia entre especies no depende de la temperatura (x2 = 3.7958, gl = 3, p-value> 0.05), y como lo muestra el siguiente gráfico las medianas son iguales o muy cercanas (Figura 1).
Figura 1.
A partir de la prueba de Wilcoxon para variables asimétricas, se obtuvo que existen diferencias entre la abundancia para cada especie en relación a la velocidad del viento (p-value<2.2e-16), donde el valor del estadístico fue W= 4911 para Artibeus jamaicensis , W= 2311 para Artibeus toltecus , W= 4851 para Thyroptera disciferay W=3163 para Thyroptera tricolor, sin embargo, como se evidencia en la Figura 2, estas diferencias fueron mínimas y las medianas fueron iguales, con diferencia de la especie 1, para la cual fue levemente menor.
Figura 2. Abundancia de las especies en función de las categorías de velocidad del viento seleccionadas.
probabilidad de tallas Frecuencia acumulada para tallas (talla dominante para cada especie ) Abundancia de especies por tamaño y talla del pie
En función de los datos registrados en el Cuadro 1, la prueba de chi cuadrado de dos vías dió como resultado que si existen diferencias significativas entre la abundancia de cada especie por sitio (x2=50.904, gl = 9, p-value < 0.05), sin embargo, como se muestra en la Figura 3, estas diferencias son muy pequeñas, por lo que no existe una dominancia de alguna especie en algún sitio en específico.
Figura 3.Promedios de la abundancia de cada especie en cada uno de los cuatro sitios seleccionados
Así mismo, en la prueba de chi cuadrado realizada respecto a la abundancia y las épocas (seca y lluviosa) se obtuvo que si existen diferencias significativas entre estas (x2=20.238, gl = 3 p-value < 0.05), no obstante, estas diferencias son muy pequeñas, por lo tanto, no se puede determinar cuál época presenta mayor abundancia, tal y como se muestra en la Figura 4.
Figura 4.Promedios de la abundancia de cada especie en época seca y época lluviosa
Resultados de abundancia con el año
En caunto al experimento realizado, los datos no presentaron una distribución normal, por lo tanto se aplicó el modelo de Kruskal Wallis donde se determinó que la biomasa de las semillas recolectadas en las 4 especies, durante los 3 diferentes tratamientos no presentaron una diferencia significativa (X2=1.6217; gl=2; p-value> 0.05).Como se muestra en la figura 6.
Figura 6.Biomasa determinada en un tratamiento de 3 diferentes temperaturas en un periodo de tres meses tres meses.
Ampliar un poquito el resultado del experimento @DavidA partir de un Análisis de Varianzas (ANDEVA) se determinó que la talla de las semillas recolectadas en las 4 especies, durante las 3 diferentes tratamiento de temperatura aplicados se obtuvó que no hay una diferencia significativa (X2=1.123; gl=2; p-value>0.05). Como se muestra en la Figura 5.
Figura 5.Talla máxima de las semillas obtenidas depués del tratamiento de tres diferentes temperaturas en un periodo de 3 meses
Discusión
Si bien las condiciones ambientales pueden afectar a las poblaciones de murciélagos, el cambio climático no está meramente relacionado a cambios de temperatura o velocidad del viento, como lo menciona el Instituto Meteorológico Nacional (2008), las variaciones estadísticas de estas no explican los impactos verdaderos del cambio climático, por tanto, esto podría explicar por qué las poblaciones de murciélagos no se encuentran estrechamente ligadas a cambios climáticos de temperatura y velocidad del viento, sino que podría estar relacionado a otros factores ambientales.
Agregar discusión de tamaños y tallas del pie*
La poca diferencia entre las poblaciones de cada una de las especies, para las variables de sitio y época es de esperarse, ya que, todas las especies fueron tomadas de áreas de conservación en zonas muy ricas en biodiversidad, las cuáles son las áreas óptimas para la supervivencia de los mismos, y como lo indica Martinez Villalobos (2018), las poblaciones de estos mamíferos han sido principalmente afectados por la deforestación, la pérdida del hábitat y la persecución humana, factores con efectos mínimos en cada una de las áreas en las que fue llevado a cabo el muestreo.
Sumando a la información anterior, los ambientes tropicales tienden a ser relativamente estables, y aunque en Costa Rica existe una marcada época seca y lluviosa, como menciona Sosa (2003), los murciélagos han sido capaces de aprovechar gran cantidad de nichos ecológicos, lo que deriva en la gran diversidad y adaptaciones de estos organismos, por ende, Costa Rica presenta un clima óptimo, sin condiciones extremas y que posibilita la disponibilidad de suministros para la supervivencia de los murciélagos. A causa de lo mencionado anteriormente, es de esperar que tampoco exista gran diferencia entre la abundancia de las especies por año, ya que esta estabilidad climática promueve una estabilidad en la abundancia relativa de especies de un mismo sitio.
Agregar discusión para metodología Como conclusión se obtiene que las poblaciones de las especies de murciélagos Artibeus jamaicensis, Artibeus toltecus, Thyroptera discifera y Thyroptera tricolor no se encuentra relacionada con la temperatura del aire ni la velocidad del viento. Por el contrario, sus poblaciones en Costa Rica se mantienen bastante homogéneas a través de los años, sin estar afectadas por factores propios de los sitios en los que habitan ni los cambios de las épocas lluviosa y seca. Agregar conclusión del experimento. Se propone ahondar en aquellos otros factores ambientales que podrían estar relacionados a los cambios poblacionales de murciélagos, tales como condiciones alimentarias, estado de los sitios de conservación. Además, sería importante estudiar otras especies, ya que, la sensibilidad ambiental podría variar.
Fuentes bibliográficas
Anexos
Códigos usados
library(readxl)
dat2 <- read_excel("C:/Users/Usuario/Documents/bdc.xlsx", sheet = "Muestreo")
dat1 <- data.frame(dat2)
# Abundancia vs temperatura (ANDEVA)
library(outliers)
library(readxl)
dat2 <- read_excel("C:/Users/Usuario/Documents/bdc.xlsx", sheet = "Muestreo", col_types = c("numeric",
"text", "text", "text", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
View(dat2)
str(dat2)## tibble [1,920 x 10] (S3: tbl_df/tbl/data.frame)
## $ year : num [1:1920] 2017 2017 2017 2017 2017 ...
## $ epoca : chr [1:1920] "seca" "seca" "seca" "seca" ...
## $ sitio : chr [1:1920] "sitio1" "sitio1" "sitio1" "sitio1" ...
## $ especie : chr [1:1920] "artibeus_sp1" "artibeus_sp1" "artibeus_sp1" "artibeus_sp1" ...
## $ muestra : num [1:1920] 1 2 3 4 5 6 7 8 9 10 ...
## $ N : num [1:1920] 33 12 34 21 25 8 11 8 17 10 ...
## $ tot_len : num [1:1920] 80.4 77.3 79.5 84.4 75 ...
## $ hind_foot: num [1:1920] 16.1 16.4 16.4 15.9 16 ...
## $ temp_aire: num [1:1920] 20 21 22 21 23 26 24 24 19 19 ...
## $ viento : num [1:1920] 1.015 0.758 1.234 1.114 0.735 ...factor1<-as.factor(dat2$especie)
is.factor(factor1)## [1] TRUEmodeloTemp<-aov(dat2$temp_aire~dat2$especie, data=dat2)
modeloTemp## Call:
## aov(formula = dat2$temp_aire ~ dat2$especie, data = dat2)
##
## Terms:
## dat2$especie Residuals
## Sum of Squares 19.956 10203.575
## Deg. of Freedom 3 1916
##
## Residual standard error: 2.307695
## Estimated effects may be unbalanced#supuestos
#1) Normalidad
shapiro.test(modeloTemp$residuals) ##
## Shapiro-Wilk normality test
##
## data: modeloTemp$residuals
## W = 0.93608, p-value < 2.2e-16#2) Balance
x<- factor1
z<- dat2$temp_aire
tapply(z, x, length)## artibeus_sp1 artibeus_sp2 thyroptera_sp1 thyroptera_sp2
## 480 480 480 480#3) Homocedasticidad
fligner.test(dat2$temp_aire,dat2$especie)##
## Fligner-Killeen test of homogeneity of variances
##
## data: dat2$temp_aire and dat2$especie
## Fligner-Killeen:med chi-squared = 1.4092, df = 3, p-value = 0.7034anova1<- kruskal.test(dat2$temp_aire~dat2$especie,data=dat2)
anova1##
## Kruskal-Wallis rank sum test
##
## data: dat2$temp_aire by dat2$especie
## Kruskal-Wallis chi-squared = 3.7958, df = 3, p-value = 0.2844#(Post-hoc)
pairwise.wilcox.test (dat2$temp_aire,dat2$especie, p.adj= "b", exact=F)##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: dat2$temp_aire and dat2$especie
##
## artibeus_sp1 artibeus_sp2 thyroptera_sp1
## artibeus_sp2 1.00 - -
## thyroptera_sp1 1.00 1.00 -
## thyroptera_sp2 0.42 1.00 0.92
##
## P value adjustment method: bonferronic<-data.frame(dat2$especie,dat2$temp_aire)
boxplot(dat2$temp_aire~dat2$especie,data = dat2, xlab = "Especies", ylab = "Temperatura (°C)", col=c("pink","yellow","red","orange"))
MediaValor2 <- tapply(dat2$temp_aire, dat2$especie, mean)
MediaValor2## artibeus_sp1 artibeus_sp2 thyroptera_sp1 thyroptera_sp2
## 22.59792 22.44792 22.55625 22.33542abajo1 <- tapply(dat2$temp_aire, dat2$especie, function(v) t.test(v)$conf.int[1])
arriba1 <- tapply(dat2$temp_aire, dat2$especie, function(v) t.test(v)$conf.int[2])
par(mfrow=c(1,1)) #filas,columnas
par(mar=c(5,5,3,2)+0.1) #margenes
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowessbarplot2(MediaValor2, plot.ci=T,
ci.l=abajo1, ci.u=arriba1,
main = "Temperatura en función de las especies" ,
xlab= "Especies" , ylab= "Temperatura (°C)" ,
ylim=c(0, 30),
col="blue")
#Abundancia vs velocidad del viento
#PRUEBAS DE HIPOTESIS PARA ABUNDANCIA Y VIENTO POR ESPECIE
#HO= Las medias de la variable de abundancia y de viento son iguales
#Hi= Las medias de la variable de abundancia y de viento no son iguales
#ESPECIE 1
vientosp1<-(dat2$viento[dat2$especie=="artibeus_sp1"])
vientosp1## [1] 1.0154334 0.7576478 1.2340887 1.1140468 0.7348013 1.8185796 1.1352666
## [8] 1.0277846 1.2645388 1.1460045 0.3101306 1.3975155 1.2316305 1.0913875
## [15] 0.6988536 1.4583457 1.2745680 0.9453532 1.9953402 1.3419634 0.5687581
## [22] 1.1188028 1.5493878 1.9846564 0.7924314 0.5231540 0.9714473 1.5722802
## [29] 0.4036426 1.4562457 0.9512174 1.6374647 0.5636267 1.1661476 1.4323867
## [36] 1.7695851 0.4741355 1.3929825 1.2696442 1.2854723 1.5410435 1.0168674
## [43] 0.8960675 1.6854945 1.2917363 1.3504716 0.8585405 0.9487780 1.1027335
## [50] 0.4972856 1.8278550 0.6825801 1.3392856 1.0141346 1.4992493 1.1764517
## [57] 1.7587273 1.4840514 0.9522247 1.4676275 1.1341287 0.6971335 1.0687047
## [64] 1.3856922 1.2789658 1.0649561 1.3770876 1.2268032 1.4151566 1.0725986
## [71] 1.2821823 0.7031441 1.5965754 1.3348701 0.5588981 1.6598895 1.1244682
## [78] 1.2773103 0.8723824 0.9507756 1.3086827 1.1877024 0.9873981 1.8440388
## [85] 0.9688049 0.3295026 0.6284942 2.2735344 1.1534190 1.1004023 0.9998832
## [92] 1.7958267 0.9550208 0.7207237 1.8863976 1.9790322 1.7344372 1.7746242
## [99] 1.6148703 1.5284068 1.0136459 1.3172611 0.9304848 1.4453378 0.7490460
## [106] 1.4713537 0.5878027 1.6521261 0.6579645 1.2539518 0.3427037 1.2434258
## [113] 0.8869161 0.8076724 0.9132968 1.5830985 0.1470707 1.5514612 1.7230008
## [120] 1.6499188 1.1508121 1.4801544 1.0790403 1.2745716 0.9937598 0.7772598
## [127] 1.1133936 1.4584057 1.1044845 1.0358942 0.7333899 1.0611128 1.0615126
## [134] 0.9612317 0.5448691 1.1960812 0.7545968 0.9143731 1.1496051 0.7614480
## [141] 0.8397806 0.2391150 1.5088033 0.9812610 1.6014859 0.9485605 0.7544753
## [148] 1.5683234 0.5553432 1.3973735 0.8611749 1.1105807 1.8699931 1.3643311
## [155] 1.8203138 1.2007550 1.5523436 1.2482594 1.2070805 1.0631882 1.3396804
## [162] 1.0282392 1.6220305 0.5513862 1.3771119 1.2334806 0.7025040 1.1262231
## [169] 1.0373381 0.6618498 1.3683398 1.0399234 1.5937302 1.2065506 1.0950400
## [176] 1.4565051 1.0242716 0.9474683 1.1303002 1.1433433 0.6616934 1.4101472
## [183] 1.4141695 1.4789505 1.1303552 1.3947845 1.2782535 2.2045204 1.5119262
## [190] 1.1089726 1.2135918 1.4907366 1.2775965 1.0869174 0.6616843 2.1678074
## [197] 1.5916033 1.5938282 1.1339442 0.6584053 1.5422305 1.2575867 1.9204341
## [204] 0.6971233 1.5265306 0.8355335 0.4959242 1.0908743 1.1199225 1.0055484
## [211] 1.0010999 1.1379071 1.2073226 2.0216869 1.4580732 1.5226393 1.3742917
## [218] 1.1980869 1.3969408 1.3688141 1.4757619 1.1335480 1.0105163 1.1990334
## [225] 1.2770117 1.0764683 2.0932659 1.1329696 0.5512634 0.8898518 2.0991268
## [232] 0.7063077 1.3394381 0.3818573 0.8583995 1.7865688 1.1909888 1.1282230
## [239] 1.8073836 0.9681560 1.5281931 0.9995899 0.9319146 1.4581536 1.5263229
## [246] 1.2511478 1.8978356 1.3656570 1.3076380 1.0405194 1.0321749 1.1172703
## [253] 1.0163239 0.9610565 1.1315770 1.7147410 0.5520423 0.2602404 0.9817907
## [260] 1.4156123 0.9090772 1.8550625 0.4852248 1.2611604 0.8271104 0.8967557
## [267] 0.9630231 1.1335344 1.2737711 0.8208740 1.0783618 0.5021618 0.9933005
## [274] 1.1357840 0.7617349 1.6928735 1.2941832 1.0607078 1.0312251 1.1025287
## [281] 1.5492112 0.9150871 0.6001849 1.7752733 0.8693785 2.0866385 1.2378446
## [288] 1.4711958 1.1444276 1.6243807 1.1521945 1.1758954 0.4848212 1.4050141
## [295] 1.3704447 1.5400538 1.9070954 1.8259727 1.7704143 1.3124710 0.3719180
## [302] 1.7816824 1.2931627 0.7616050 1.0598858 1.0925128 1.3153352 0.5168856
## [309] 1.2591095 1.1347856 0.7778527 1.6421367 0.8137810 1.2667928 1.2773407
## [316] 2.3426583 1.1856968 0.9807077 1.2363855 1.1311525 1.1992550 1.4403773
## [323] 1.8336438 1.1311874 1.3216746 1.4499292 1.2501643 1.2616230 1.0611788
## [330] 1.2632119 1.1438779 1.1885382 0.6345440 1.8192237 0.4277294 1.0156343
## [337] 1.7015179 1.6616557 1.0250526 1.3819111 0.9552772 1.1970537 1.3370161
## [344] 1.1339349 0.5925428 0.8566085 0.9918307 1.8543431 1.3690260 1.4114423
## [351] 1.0115652 1.1545635 0.9777332 1.4454864 1.1048412 1.3060610 1.3166609
## [358] 1.2913267 1.6135464 1.0977311 1.1165301 1.3430670 0.3770788 1.5698742
## [365] 1.2661443 2.0540396 0.9844351 0.5441943 0.9220371 1.1928182 0.7327224
## [372] 1.2362218 1.2208847 1.1366729 1.3406951 1.2105258 1.5990064 1.4209563
## [379] 1.6675156 0.9785853 1.0392424 1.1517794 1.5730733 1.0262895 1.2547785
## [386] 1.5571861 1.5524393 0.2083223 1.6343465 0.9163699 1.2972039 1.4614641
## [393] 1.6591924 1.1845400 0.3671192 1.2400902 1.1644149 1.7455310 1.0587606
## [400] 1.1674304 0.9019709 0.7142841 0.3444477 1.3416971 0.8895535 1.3457837
## [407] 1.5626689 1.4555326 1.2627150 1.6458645 1.1505356 0.8929358 1.3583036
## [414] 1.2360063 1.2177645 1.7936105 1.6196156 1.0346323 1.0373272 1.5055945
## [421] 1.5628588 1.3379778 1.3209373 1.2859918 1.1037719 1.1092207 1.8384949
## [428] 1.1792532 0.4440618 1.0239951 1.0342851 1.9083658 1.7957909 0.1592804
## [435] 1.3415508 1.1187268 1.3005026 1.1335094 1.3241629 0.9535530 0.7161384
## [442] 1.2882895 0.8410327 1.2491203 1.1411853 1.3891057 1.0291663 1.1330507
## [449] 0.8494931 1.0971176 0.8375978 0.1362300 1.4873671 1.0360579 1.0080001
## [456] 1.2526678 1.5164454 0.7562870 1.1169543 1.2455682 1.8785910 0.8275303
## [463] 1.5265745 0.4921981 0.4897844 1.0782280 1.0559676 0.9387843 0.5394473
## [470] 0.4681982 0.9764445 1.0319726 1.2397054 1.2538701 1.1912897 0.9972929
## [477] 1.8193403 0.3083966 1.7087114 1.9917854abundanciasp1<-(dat2$N[dat2$especie=="artibeus_sp1"])
abundanciasp1## [1] 33 12 34 21 25 8 11 8 17 10 19 33 5 23 26 36 2 28 34 9 6 27 10 14 29
## [26] 26 9 29 32 3 14 40 33 30 6 12 33 23 15 27 6 6 28 1 32 25 27 7 13 31
## [51] 35 4 7 7 8 11 25 18 27 16 5 13 12 38 20 7 32 32 12 14 35 21 23 29 9
## [76] 22 22 33 39 11 38 16 17 22 32 31 15 16 26 14 1 1 4 34 26 20 29 5 1 16
## [101] 6 25 10 29 24 19 40 16 13 13 16 34 20 24 8 20 23 4 6 13 5 28 3 12 16
## [126] 4 26 13 1 37 21 1 21 16 1 8 11 24 19 13 33 24 27 25 27 34 17 6 30 20
## [151] 9 24 38 39 5 1 35 8 18 8 14 8 19 24 6 39 24 16 15 2 22 11 16 25 5
## [176] 37 15 16 31 29 12 16 4 13 15 11 28 2 17 9 32 24 38 12 13 33 15 34 1 34
## [201] 14 32 34 16 12 10 7 35 15 37 34 13 29 13 39 20 10 23 19 4 17 35 9 5 36
## [226] 26 31 26 9 20 30 9 26 9 8 29 35 20 4 1 19 22 27 8 17 11 21 10 28 15
## [251] 11 31 2 35 29 37 3 28 24 31 21 18 39 8 36 5 20 18 9 12 18 29 2 34 20
## [276] 34 21 36 12 35 8 35 39 14 30 29 32 31 2 23 12 26 8 6 14 12 22 15 8 20
## [301] 11 3 20 34 28 33 33 40 40 6 37 23 34 22 33 6 13 15 24 40 36 3 40 13 14
## [326] 39 8 33 9 17 17 11 34 12 21 11 33 33 22 29 10 2 2 30 18 17 35 34 37 37
## [351] 24 11 20 25 24 13 34 29 19 37 31 28 6 12 21 7 39 24 1 40 3 33 39 22 27
## [376] 29 30 27 22 40 11 19 38 40 14 19 2 14 14 37 30 9 39 27 1 24 22 29 3 30
## [401] 12 38 9 17 17 10 18 23 25 31 12 35 14 3 10 4 38 8 1 27 8 31 14 9 38
## [426] 4 6 32 32 9 20 31 17 9 26 15 15 15 5 9 27 28 32 30 32 11 24 4 30 11
## [451] 3 5 32 1 13 10 19 3 39 9 11 4 19 7 28 19 7 39 4 8 5 29 31 13 34
## [476] 3 9 16 31 14shapiro.test(vientosp1) ##
## Shapiro-Wilk normality test
##
## data: vientosp1
## W = 0.99373, p-value = 0.04424shapiro.test(abundanciasp1) ##
## Shapiro-Wilk normality test
##
## data: abundanciasp1
## W = 0.95337, p-value = 3.589e-11wilcox.test(vientosp1,abundanciasp1, paired = F) ##
## Wilcoxon rank sum test with continuity correction
##
## data: vientosp1 and abundanciasp1
## W = 4911, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0#ESPECIE 2
vientosp2<-(dat2$viento[dat2$especie=="artibeus_sp2"])
vientosp2## [1] 0.709231871 1.553733263 1.156474914 1.216485439 1.533845479
## [6] 1.052914435 1.209944418 1.403108841 0.805771864 1.765470494
## [11] 0.838943886 0.509197815 1.495501272 1.531777563 0.576679135
## [16] 1.331096644 1.630552461 1.172439388 0.915408488 1.400994255
## [21] 1.439881686 1.445598946 0.972942654 1.151513552 1.327177832
## [26] 1.464520609 1.310406195 1.492001031 0.927689593 1.118298283
## [31] 1.238321625 1.355134625 1.812543267 1.029964301 1.400583659
## [36] 0.677088797 1.304043559 1.384870612 1.254971507 0.733830423
## [41] 0.953701627 0.986991456 0.582236198 1.186827725 1.429489781
## [46] 0.844673655 1.433217130 0.905425429 0.992929569 1.632992216
## [51] 1.280996761 1.068306659 1.569794540 1.476153926 0.743996621
## [56] 1.126417045 1.418300091 1.196984522 1.901572537 1.546441277
## [61] 0.868628408 1.327986830 0.921245427 0.824806050 1.152062357
## [66] 1.394312421 1.394515492 0.593373694 0.835682809 1.044952011
## [71] 1.262559910 0.802082076 1.177787266 0.858368654 0.964829612
## [76] 1.532716979 1.242160250 0.820669451 1.708673150 0.972920337
## [81] 0.629222369 1.482431351 2.001032357 1.257768246 1.250731761
## [86] 1.703163955 1.015847505 0.616854759 1.765801445 0.930260321
## [91] 1.158715126 0.660422071 0.949414313 1.721937483 1.441582530
## [96] 1.254172555 0.741827392 1.309691908 1.046803034 1.248230612
## [101] 1.324082820 0.937125302 1.327541754 0.841996950 1.735208559
## [106] 1.262126949 1.347665109 1.245473527 1.704060019 1.152991926
## [111] 0.807535667 1.367465152 1.378034456 1.270568046 1.536492138
## [116] 1.083296706 1.091235409 1.192142257 1.395050165 0.666070767
## [121] 1.657827191 1.387214196 1.627673329 1.007406918 1.608292378
## [126] 0.918395725 1.377793017 1.236452689 0.587841830 0.655103700
## [131] 0.857335530 1.180933286 1.395499817 0.933615136 0.663818758
## [136] 1.041835879 0.518437096 0.993709429 0.888771377 0.603369525
## [141] 1.480016430 1.077512012 0.958408664 1.553545582 1.245926144
## [146] 1.326605910 0.849502338 1.309741102 1.001607672 1.285124027
## [151] 1.065903901 1.558142239 0.720914893 1.256847441 0.246573993
## [156] 1.594813871 0.796510351 1.438084289 0.463794101 2.035514183
## [161] 1.690526884 1.106192110 1.042276265 0.541536504 0.771835256
## [166] 0.617787455 1.317120370 0.480782224 1.100007961 0.905561749
## [171] 1.095414790 1.679568154 0.741529225 1.800686127 0.746759326
## [176] 1.324151324 0.637274761 1.206894807 0.922794682 0.764744955
## [181] 1.274644439 0.604385588 1.443620013 1.169324234 0.990294188
## [186] 1.099682008 1.081526145 1.699566355 0.611259327 1.191428078
## [191] 0.460746209 1.523151328 1.075697018 1.522972347 0.005519259
## [196] 1.445774463 1.654099727 1.425853204 1.860326655 1.208365219
## [201] 2.133112042 0.952139868 1.175714097 1.128343537 1.614250452
## [206] 0.890025827 1.909355834 0.612196935 1.313281009 1.559501573
## [211] 1.368990304 1.488735773 1.089855205 1.042398304 1.908575537
## [216] 1.429854121 1.094423960 1.657857558 0.789400358 1.543663720
## [221] 0.625172564 0.956400566 1.113206355 1.625696341 1.043533585
## [226] 1.059366765 0.597235596 1.042873292 1.429750364 0.772381428
## [231] 0.692739190 1.141369740 0.962626041 1.384556625 0.646635212
## [236] 1.064536160 1.081118800 2.360179447 0.930696071 0.962016798
## [241] 0.808619402 1.124530928 0.808982620 1.580861413 1.496304330
## [246] 1.085301574 1.203656449 0.714934665 0.797434136 1.389873043
## [251] 1.627602740 1.058434679 1.879891604 1.085700083 1.593914134
## [256] 0.666944912 1.383946949 1.628350441 0.996392060 0.656321175
## [261] 1.189045243 1.046673528 0.589701009 0.800336373 2.135153657
## [266] 1.999150596 1.569713180 0.774966361 1.594498551 1.180277690
## [271] 1.548843873 1.132445500 0.981348468 0.727585264 1.792102206
## [276] 1.250002576 1.235455841 0.485587602 1.744302759 1.301921115
## [281] 1.327143861 1.527299321 1.273508491 1.244726901 0.969378487
## [286] 1.022262643 0.954017783 0.549517389 1.662401922 1.179533277
## [291] 1.585207917 1.923995955 0.576306908 1.746413559 0.950264415
## [296] 1.091838363 0.862501198 0.895951282 0.935232458 1.043990731
## [301] 0.948044382 0.605355611 0.939628449 1.096452195 0.782305021
## [306] 0.828318537 1.677629395 0.203162223 0.694360426 0.832495091
## [311] 1.109459092 1.510172792 1.093142277 1.533160262 0.709340137
## [316] 0.883672759 0.934940497 0.866600817 0.972094437 1.188884815
## [321] 1.792854008 1.311622113 1.739806349 0.450884427 1.449997578
## [326] 1.163247193 1.396257375 0.898437070 1.352207066 1.154884576
## [331] 0.689352223 1.086530964 1.679248001 1.812913649 0.868171428
## [336] 0.721429968 1.638864654 1.060943440 1.436327460 1.333827279
## [341] 1.031436747 1.085567586 1.486363304 1.144185873 1.319318689
## [346] 0.335508747 1.170983180 0.860822725 0.499448514 1.009339459
## [351] 1.129150001 1.626142824 1.388156355 1.090012404 1.112418505
## [356] 0.998839996 1.312798123 0.209346096 0.979083807 1.635522976
## [361] 1.908662001 1.179483017 1.171356164 1.037597479 1.598798738
## [366] 0.627768586 0.974475318 1.014650387 1.423287302 1.339274874
## [371] 0.926378553 0.924070739 1.210695778 2.200703699 0.536284565
## [376] 0.656533238 1.481110090 1.001539099 1.736486210 0.943126275
## [381] 1.615555420 2.033402952 0.816612037 1.225115958 1.849802293
## [386] 0.709033777 0.830956693 0.912394308 0.834466693 0.977778224
## [391] 0.427124942 1.312861165 0.802686992 1.410728857 0.711778573
## [396] 1.448548005 0.968600248 1.336921862 0.884627280 1.099529387
## [401] 0.929554719 2.016559689 1.660645472 0.143527412 1.204334778
## [406] 1.089873222 1.365555730 1.119847522 0.562983683 0.753152054
## [411] 1.685255283 1.666197104 1.180276125 1.285234015 1.431846555
## [416] 0.760184682 0.908714197 0.547352484 1.360054971 0.490136719
## [421] 0.904520543 1.004531861 1.244018163 1.417024704 1.806413107
## [426] 1.692101378 1.744878322 1.754547582 0.786253429 0.449931959
## [431] 1.414981197 0.810502595 1.532694030 1.076448761 1.584473168
## [436] 1.330200513 1.615896294 1.145188010 1.046033301 0.878943292
## [441] 0.646847565 1.935082379 1.316216632 1.481423315 1.096013908
## [446] -0.510970950 1.368741723 0.904297523 0.881452676 0.612627437
## [451] 0.671868861 1.064666036 0.961516265 1.373155722 0.360532245
## [456] 1.278322455 1.220061445 1.310190417 1.742483419 1.377733907
## [461] 1.538140278 1.385943588 1.025209608 1.685109098 1.316629476
## [466] 1.575490600 1.655855088 1.643106932 1.248586176 0.664040436
## [471] 1.623559535 1.132464305 1.080113754 0.979982210 1.050287622
## [476] 1.781162223 1.124953908 1.004920592 1.183358255 1.083356642abundanciasp2<-(dat2$N[dat2$especie=="artibeus_sp2"])
abundanciasp2## [1] 3 12 24 4 13 15 37 29 3 40 25 14 29 26 22 39 33 10 5 36 11 8 40 2 33
## [26] 21 32 39 29 33 10 27 37 14 38 22 4 24 25 32 34 37 12 28 2 28 37 18 32 27
## [51] 14 5 35 20 19 28 18 7 7 17 27 30 23 39 40 6 29 30 23 2 38 9 11 31 20
## [76] 36 3 2 33 34 37 12 4 28 20 12 21 31 36 8 15 5 37 11 28 30 36 7 8 34
## [101] 26 17 13 32 28 12 19 3 40 22 7 21 11 3 35 25 9 17 23 20 10 15 18 3 29
## [126] 18 35 18 29 1 36 24 19 20 12 12 16 21 6 31 39 5 18 3 34 29 39 40 23 21
## [151] 11 35 5 37 13 6 22 13 28 16 4 7 7 3 38 38 2 37 29 2 21 26 8 24 26
## [176] 10 29 40 6 12 31 1 35 22 7 6 39 18 23 4 14 9 32 17 6 25 40 40 31 11
## [201] 17 33 35 34 22 21 19 1 12 28 31 39 27 35 37 30 8 21 8 3 30 9 31 35 34
## [226] 12 6 16 34 2 16 36 6 27 22 14 20 19 8 4 12 32 38 34 15 6 34 24 29 34
## [251] 14 9 25 11 38 9 28 12 18 18 16 1 19 31 20 9 30 2 28 40 15 10 37 19 33
## [276] 6 26 12 15 23 9 4 7 2 23 39 21 5 14 3 2 33 20 14 24 1 38 14 38 27
## [301] 16 11 15 35 13 38 18 23 31 16 13 29 32 35 23 1 3 10 20 2 6 24 8 24 5
## [326] 35 3 21 33 36 27 21 9 25 5 11 35 31 10 22 32 14 38 26 14 20 26 18 8 35
## [351] 13 33 35 21 8 13 37 39 18 11 27 7 14 27 16 21 37 39 26 23 12 39 32 35 10
## [376] 21 18 13 27 17 33 31 17 30 5 40 9 17 26 1 7 21 26 26 26 28 9 24 9 15
## [401] 5 27 33 29 22 20 15 28 29 23 18 21 37 4 16 13 27 26 3 29 16 30 6 7 6
## [426] 7 2 15 40 9 8 7 29 20 16 34 27 36 35 10 33 29 36 6 2 39 21 24 11 30
## [451] 13 19 37 27 36 11 39 19 35 2 12 2 7 30 38 25 24 7 21 10 17 35 11 9 35
## [476] 37 33 19 27 37shapiro.test(vientosp2) ##
## Shapiro-Wilk normality test
##
## data: vientosp2
## W = 0.9955, p-value = 0.1822shapiro.test(abundanciasp2) ##
## Shapiro-Wilk normality test
##
## data: abundanciasp2
## W = 0.94974, p-value = 1.067e-11wilcox.test(vientosp2,abundanciasp2, paired = F) #p-value < 2.2e-16 rechazo la H0##
## Wilcoxon rank sum test with continuity correction
##
## data: vientosp2 and abundanciasp2
## W = 2311, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0#ESPECIE 3
vientosp3<-(dat2$viento[dat2$especie=="thyroptera_sp1"])
vientosp3## [1] 1.7941981 0.9323766 1.0738268 0.6215312 0.9472762 1.3229110 1.1046338
## [8] 0.7445113 1.3742615 1.8810698 0.9246752 1.2595775 0.3079428 1.4958534
## [15] 1.8718383 1.5970087 1.3635763 0.7115386 1.3932871 1.2627850 1.1380774
## [22] 0.6083556 1.2395668 1.4538952 0.9230961 1.4456428 0.9919289 0.2729743
## [29] 0.6753658 0.6460743 1.3373132 1.0762516 1.0206889 1.6506949 0.8963569
## [36] 1.4950516 1.2166485 1.0745652 1.4979481 1.6798520 1.0971820 0.6298563
## [43] 0.9800098 1.0295031 0.8278023 1.1465868 0.8193668 0.9531273 0.7591387
## [50] 1.0788045 0.9459459 0.6586689 1.1324652 0.7528847 0.9634692 1.1347651
## [57] 1.8015987 1.7304251 1.4536790 1.1991164 0.7777829 1.5339585 1.6650024
## [64] 1.2054893 0.6229413 1.2064765 1.0545618 1.3512773 0.7471162 0.8275209
## [71] 1.6433977 0.7553466 0.7081099 1.5447742 0.7940121 1.2387546 1.4861096
## [78] 1.1883288 2.0237590 0.7561243 1.4758725 1.0373957 0.9780410 1.2173285
## [85] 1.0184854 0.8086849 0.5605546 0.3482200 1.1061990 1.1545136 0.8498122
## [92] 1.8210841 1.5796719 0.7404103 1.3360496 0.8068609 2.0803947 1.1489195
## [99] 1.9983789 0.6466823 1.1611805 1.2894225 0.3170874 1.8853291 0.5792319
## [106] 1.0445435 1.4545306 1.7417277 1.2227568 0.6242025 1.4387112 0.9398059
## [113] 1.0400389 0.7478884 1.6227843 0.5272529 1.0637201 1.1856795 1.6941534
## [120] 1.3739477 1.1141523 1.3333331 0.7115489 0.8822209 1.5112224 2.1182923
## [127] 1.8886899 2.1297498 1.4975691 1.3864946 0.9377840 0.5811027 1.5623001
## [134] 0.8606698 1.4674176 2.2029418 0.7224212 1.1906237 1.9821710 0.9476169
## [141] 1.0686173 0.7941087 1.4044711 0.9072862 0.8338549 1.8231436 0.9280827
## [148] 1.2646320 1.6876186 0.9612732 1.2749436 0.5893492 1.1749776 0.9435217
## [155] 0.9048361 1.3072385 2.0566373 1.4796883 1.2750532 1.1304132 1.2228923
## [162] 1.1010928 1.4836678 0.8079415 1.6192699 1.2014051 1.2151279 1.2702156
## [169] 1.1334424 0.2547316 0.9915874 1.4141263 1.1056061 2.1531313 0.7631093
## [176] 1.5847667 1.3886253 1.5895811 1.0162725 0.9933885 1.0166160 0.2999308
## [183] 0.5677450 0.8126150 1.1316805 1.2196031 1.2565404 0.8553766 1.9784511
## [190] 1.2722087 0.7351069 1.2269077 1.7154092 1.7439483 1.1020475 0.9846832
## [197] 1.2919913 1.3012581 0.9388905 1.5150725 1.6370699 1.4525640 1.8058106
## [204] 1.0668415 1.2797548 0.8032056 1.7120471 1.2522126 1.0958308 1.2689462
## [211] 0.7544367 1.1693340 1.1190521 1.3744918 1.2910640 1.5947541 1.2999009
## [218] 1.6193188 1.0354954 1.5904020 0.9382117 0.7852322 1.2661676 0.8714146
## [225] 1.4120716 0.9637417 1.5949499 1.2844622 1.3372857 1.4097593 0.8186005
## [232] 1.6183455 1.4371658 0.7942548 1.1450654 0.8716100 1.2874127 0.6952989
## [239] 1.2373445 0.5991413 1.3096218 0.7402147 1.1131915 1.5431581 1.0930629
## [246] 1.2252580 1.3208368 1.2926216 1.0486596 1.5250315 1.5714604 1.4534589
## [253] 1.3115902 0.7530916 0.9280522 0.5564726 1.3893979 1.2551731 0.6058313
## [260] 1.1186829 1.0209188 1.3334409 0.9273696 1.0205032 1.1206074 1.4029237
## [267] 1.1353140 1.2511323 0.6796187 0.3952927 1.6899695 1.0075441 1.3511461
## [274] 1.1474311 0.4745777 0.4261356 1.3191342 1.1379708 1.7767655 1.2458690
## [281] 1.5528302 1.2314117 1.8403702 1.3573660 1.9540913 1.9337487 1.2154620
## [288] 1.4141381 0.8709521 0.9371780 1.7679820 1.1130415 1.6672133 1.2775342
## [295] 0.9777184 1.4691737 1.0928194 1.2096813 0.8443331 1.4911174 0.9775594
## [302] 1.3994550 1.7323016 1.2094293 0.9413285 2.1839579 0.6645052 0.7266160
## [309] 1.6616875 1.4538451 1.8765956 1.2780668 1.4906936 1.3521178 2.0134502
## [316] 1.5321897 1.2164942 1.7129088 1.8533133 0.8089006 1.3261666 1.5823597
## [323] 1.0142510 1.1064216 0.5669723 0.9488299 1.3672656 1.0203980 0.8158911
## [330] 0.8518479 0.7883850 1.0759734 1.1985901 1.0907744 2.0814667 1.6082795
## [337] 0.6885749 1.3416264 1.4136273 1.0210955 1.2137802 1.3632292 1.0166928
## [344] 0.9126876 0.8297900 1.6767618 1.0830760 1.4464496 0.3940049 0.7177783
## [351] 0.8690270 0.2845476 0.8683141 1.2304673 0.6248231 1.4790993 0.8647391
## [358] 1.1251967 1.6546293 0.9012178 1.7490268 0.7312867 0.8815180 1.0003289
## [365] 0.9640237 1.8718894 1.4837319 0.7406653 1.0996516 0.8180202 0.9809423
## [372] 1.0388355 0.6964762 1.6730018 1.9474513 2.2967056 0.9450865 2.0882590
## [379] 0.7812682 1.2710744 1.1733706 1.0461724 1.2157248 0.9981049 1.2326023
## [386] 1.3532348 1.8207928 0.9514714 0.8489651 1.9098506 0.9490639 1.2907046
## [393] 1.0472757 0.8207912 1.6670347 1.3562458 0.9421670 1.1270263 0.5044103
## [400] 0.5415438 1.5551631 1.8059679 0.8059196 1.2477756 0.9331306 1.5650535
## [407] 1.2919644 1.0255027 0.7449230 1.9078380 0.4156362 1.3626864 1.2187733
## [414] 0.9970890 0.4545207 0.8380106 0.9636329 1.5642066 1.2746372 1.5512036
## [421] 0.6888043 1.1018556 1.8167829 1.0202607 1.0149007 1.6005955 1.0740348
## [428] 0.7915215 1.2513560 1.3763002 0.9490730 1.4136294 1.4816704 0.8611211
## [435] 1.4392462 1.3593398 1.7363988 0.8606714 1.9349653 0.7056535 0.9461026
## [442] 1.0788825 1.9570529 0.9448097 1.0875094 1.1751168 1.1804649 0.8692626
## [449] 0.6619103 0.8964705 0.5923282 0.9920117 1.1162764 0.9006891 0.8100892
## [456] 1.2355398 0.7518877 1.4518839 1.5107437 1.6133539 1.0483024 0.9995889
## [463] 1.4440704 0.9286744 0.8291764 0.4507995 1.0854231 1.3616844 1.3649452
## [470] 0.5505516 1.8053665 1.0044340 0.2737401 1.3675074 1.2095708 0.6374584
## [477] 1.1493549 1.1736408 1.7188886 1.1488303abundanciasp3<-(dat2$N[dat2$especie=="thyroptera_sp1"])
abundanciasp3## [1] 37 10 12 8 3 10 29 22 40 29 6 4 15 37 30 36 5 20 1 18 37 20 11 15 8
## [26] 36 12 18 33 35 16 7 7 26 12 32 13 34 24 21 29 37 10 22 40 5 36 19 23 8
## [51] 14 21 15 13 23 3 11 2 3 22 35 26 1 25 4 10 32 36 8 22 10 24 35 2 22
## [76] 13 35 22 39 18 25 31 30 36 18 18 20 33 5 9 29 35 10 28 6 34 8 27 26 22
## [101] 9 35 20 40 7 35 24 15 22 28 34 9 35 19 20 5 16 6 28 10 35 12 17 5 27
## [126] 7 24 10 5 24 38 39 6 9 36 11 5 27 3 25 23 8 20 6 23 29 27 20 31 8
## [151] 21 19 14 32 15 9 16 16 40 15 11 29 19 23 31 24 30 11 21 23 27 10 30 32 22
## [176] 32 13 39 8 4 3 1 21 12 6 33 19 9 21 3 29 17 22 1 23 11 37 11 10 21
## [201] 23 4 16 32 18 17 26 21 8 27 29 17 26 4 38 25 5 15 26 36 35 25 29 8 19
## [226] 11 23 26 33 20 10 38 35 3 23 38 5 16 32 40 2 22 28 36 28 18 21 25 35 39
## [251] 4 4 30 40 25 31 40 29 28 9 31 7 13 27 31 16 13 8 9 39 15 2 31 31 2
## [276] 1 16 29 22 39 33 15 32 20 40 1 29 19 3 23 35 2 16 25 23 13 19 26 8 9
## [301] 24 24 27 16 33 8 39 1 37 2 7 31 13 11 30 35 9 1 19 28 6 16 24 25 38
## [326] 36 9 35 26 3 1 21 29 36 24 37 35 1 2 39 26 5 37 38 4 29 28 11 5 36
## [351] 21 25 1 32 14 24 38 19 16 40 25 20 33 16 1 34 17 37 2 19 1 2 18 21 17
## [376] 6 15 38 37 7 33 15 37 17 32 9 12 19 6 14 23 20 31 31 33 35 32 19 2 5
## [401] 29 12 35 17 20 36 24 24 9 9 32 19 16 5 2 28 21 37 29 29 7 36 5 38 14
## [426] 33 31 40 32 1 39 6 33 32 25 33 22 17 34 23 27 8 32 4 11 10 10 39 24 12
## [451] 35 1 33 27 7 20 33 18 10 28 26 30 14 3 8 8 14 2 5 16 38 12 24 4 39
## [476] 29 17 13 35 12shapiro.test(vientosp3) ##
## Shapiro-Wilk normality test
##
## data: vientosp3
## W = 0.99253, p-value = 0.01687shapiro.test(abundanciasp3) ##
## Shapiro-Wilk normality test
##
## data: abundanciasp3
## W = 0.95226, p-value = 2.463e-11wilcox.test(vientosp3,abundanciasp3, paired = F) #p-value < 2.2e-16 se rechaza la H0##
## Wilcoxon rank sum test with continuity correction
##
## data: vientosp3 and abundanciasp3
## W = 4851, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0#ESPECIE 4
vientosp4<-(dat2$viento[dat2$especie=="thyroptera_sp2"])
vientosp4## [1] 1.09655393 0.99761750 1.39892077 1.02526504 0.98286825 0.67225848
## [7] 1.93767492 1.20030436 2.01818678 0.53371521 1.47085221 1.58965338
## [13] 0.86011630 0.71508274 0.89863472 1.03440443 1.13896491 1.66083052
## [19] 1.02489652 0.55408722 0.76275038 1.11382255 1.09293372 0.57067516
## [25] 0.30163390 1.58105667 0.83178709 1.46244018 0.84920291 0.82944690
## [31] 1.73635512 0.77928874 2.15043335 1.09470926 1.13401638 1.10084109
## [37] 1.61889168 1.22961994 0.89218354 1.39497287 0.54615952 0.98542894
## [43] 1.62795648 1.28459434 1.39718780 1.20519940 0.48346849 1.12229106
## [49] 0.96059154 1.39096321 0.78309160 1.23796226 1.33938315 0.88582776
## [55] 1.39826082 0.93571924 0.76164230 1.17828753 1.43081206 1.33898470
## [61] 0.92931763 0.56511661 0.67703022 0.71812866 0.55398680 1.39008953
## [67] 0.75848170 1.77319464 1.10198825 0.62426277 2.06965637 0.49320212
## [73] 1.14190796 0.39133240 1.31569949 1.49940841 1.20647741 0.95856190
## [79] 1.85433313 0.68250193 0.30427048 0.97744868 1.88137251 1.62379455
## [85] 1.29173594 1.40863086 1.09978298 0.17006595 0.97969103 1.16075194
## [91] 1.20806645 1.31831452 1.60400815 2.00505174 1.52284480 1.13695513
## [97] 0.65061247 0.53527009 1.09218425 1.46782338 1.13146273 0.77573959
## [103] 1.06190484 0.92044970 1.36647608 0.65515183 2.10186272 0.59644054
## [109] 1.26914266 1.19716874 1.30981216 1.91673103 0.69272857 1.00878934
## [115] 1.35675212 1.23014046 1.38076197 0.89091413 0.80603959 1.16387709
## [121] 0.95065235 1.20145074 1.43234240 0.60162684 1.29071039 0.98487617
## [127] 0.55790547 1.04000209 1.66493065 0.63311310 1.01483911 2.06456171
## [133] 0.89977694 1.30391316 0.59602002 1.08948122 0.82878325 1.19421927
## [139] 0.94988071 0.90269747 1.60670379 1.06149374 1.36434904 0.52793218
## [145] 1.69346524 1.27746417 1.29061380 1.40530759 1.36801195 1.20456909
## [151] 0.97262564 0.75481317 1.19761997 0.67605424 0.68496377 1.28311556
## [157] 1.06484661 1.51747293 1.27893100 0.40062799 0.63436236 1.25083246
## [163] 1.13567511 1.29104186 1.47957528 1.44421246 0.83484907 -0.12484852
## [169] 1.34662366 0.42438161 1.49656191 0.74511373 0.69684783 1.77413267
## [175] 1.51499306 2.10793513 1.27194194 0.85017971 1.02657432 1.60848258
## [181] 0.95065374 0.78468941 0.80537843 1.68367549 0.76320834 0.84872120
## [187] 1.43154537 0.81170121 0.90456491 1.18593793 1.42812482 2.51428531
## [193] 1.36086588 1.11199423 0.32978289 0.95601362 1.02698648 1.44471416
## [199] 0.54187525 0.78606070 0.68416516 1.15074592 1.44572095 0.95080592
## [205] 1.69241925 0.95308049 1.08734420 0.86326396 1.46468416 0.90180167
## [211] 0.60601549 0.95032486 0.68315578 1.41412366 1.93150879 0.55264487
## [217] 1.43712504 1.64593965 1.35357045 1.19503516 0.53795073 0.52063728
## [223] 1.16030125 0.69908706 1.30667818 2.39702244 1.67712061 1.46905299
## [229] 0.50839500 0.89553515 1.85573957 1.27034202 1.00386591 0.44727042
## [235] 1.05847114 1.25785182 0.80592634 1.17388192 1.51018942 1.10034397
## [241] 1.33082211 1.09776239 1.01917276 1.34693755 1.15619226 0.82658581
## [247] 0.69730630 0.74437117 1.58462873 1.54357607 1.25586847 0.53676733
## [253] 0.85149636 1.63431736 -0.02222872 1.47056370 1.69861813 1.36727265
## [259] 1.13064753 1.81931676 1.14877582 0.50775030 1.07610208 0.68358576
## [265] 1.62120992 0.79871520 1.60616704 0.89276620 0.74391520 1.79750311
## [271] 0.64623512 0.92094422 0.88709108 1.17031223 1.31656127 0.77286317
## [277] 1.28994183 0.91773128 1.29559720 1.37547593 1.42694540 1.33221872
## [283] 1.42535719 1.25992697 1.06371286 1.16768545 1.27627256 0.79750018
## [289] 0.96920397 0.76428476 1.75716095 0.99099922 0.34867580 1.19974963
## [295] 1.04827475 1.06112050 1.90172515 1.49839113 0.84384169 1.79750179
## [301] 1.30968863 0.81526591 1.20874040 1.58008856 1.10351129 1.33858978
## [307] 1.06374701 1.38425487 1.17256642 1.38220912 1.84495349 1.26490526
## [313] 0.38629009 1.00106643 1.10421267 1.74898866 1.02828703 1.63373565
## [319] 1.18037155 1.48176221 1.36697305 1.26030391 1.49852405 1.18404886
## [325] 1.68781578 1.01488439 0.96086014 1.00853380 1.41470037 1.25335119
## [331] 0.64885682 1.09888965 0.85439843 1.25261490 1.36922587 1.30233770
## [337] 1.13873753 0.72791691 1.07441599 2.00703984 1.51703049 1.58032308
## [343] 0.82992776 1.63318938 1.18849737 1.25057467 1.18598694 1.34137072
## [349] 1.28697879 0.64571375 1.10122437 1.04889521 0.99904585 0.87507915
## [355] 1.66888477 0.82123715 1.03893352 0.84564077 0.96114010 1.99964979
## [361] 1.44830398 1.51041548 0.77414155 0.83231809 1.39141127 1.20643082
## [367] 1.40795403 1.27902622 0.90399183 1.60342821 0.83174154 1.05338775
## [373] 1.67648325 0.85835733 2.12336544 1.43624519 1.51222017 0.69514763
## [379] 1.33035099 1.47132830 0.68479462 1.21633493 0.98235421 1.76337667
## [385] 0.34415841 1.41811418 0.85472234 1.08306521 1.47024973 1.32078347
## [391] 1.23594414 1.53290014 1.27277862 1.45263947 1.71150710 1.56423690
## [397] 1.21350192 0.73728304 0.57555840 0.73307523 1.44562866 1.33018823
## [403] 1.39112677 1.47897813 2.13969213 0.89469195 1.46704746 1.07366868
## [409] 0.18524775 1.92137275 1.17957659 0.62406098 1.68426156 1.55914993
## [415] 0.98684812 0.37774646 1.88098941 1.34900363 0.73867498 0.88052584
## [421] 1.13655300 1.37414747 1.04584725 0.54138031 0.81102635 1.19435757
## [427] -0.09966830 1.11718860 0.91103945 0.70519353 1.19571258 0.30250265
## [433] 1.23656950 1.13073953 1.09184689 1.05319305 0.64246186 1.14203503
## [439] 1.01791362 1.17026485 1.52694456 0.56453559 1.32708606 0.89404102
## [445] 1.60655102 1.69072315 1.81612802 1.52044382 2.26047443 1.64163650
## [451] 0.42719794 1.38019910 1.08499763 0.69185412 1.08037096 0.88881178
## [457] 1.10478591 0.58700222 0.20579659 0.78126097 1.14096466 0.81610838
## [463] 0.81640700 0.79538518 1.07477180 0.61589841 1.32022395 1.22779396
## [469] 0.87222004 1.68547115 0.46326810 1.31762280 1.79109787 1.21053885
## [475] 0.75556383 0.61317477 1.12806999 1.36618241 0.81275041 0.92832390abundanciasp4<-(dat2$N[dat2$especie=="thyroptera_sp2"])
abundanciasp4## [1] 11 30 27 25 26 9 18 38 8 8 13 7 25 29 5 38 4 27 3 22 19 21 18 9 5
## [26] 10 27 31 30 8 5 17 14 5 37 28 24 7 25 12 40 29 23 16 33 6 25 32 10 12
## [51] 36 12 33 28 23 33 40 19 28 1 26 16 2 38 23 5 40 21 23 35 19 33 3 38 7
## [76] 15 3 18 36 10 27 25 10 29 38 21 32 13 21 21 4 10 33 11 35 38 38 17 29 8
## [101] 38 38 35 26 10 16 2 16 10 17 19 15 22 33 19 3 17 4 2 39 20 26 35 23 17
## [126] 35 2 8 11 11 36 28 22 19 25 24 40 3 36 20 35 12 24 34 15 15 38 17 35 21
## [151] 11 4 24 40 37 10 11 8 13 2 33 3 35 30 33 31 19 12 18 18 16 7 7 31 4
## [176] 30 32 30 22 25 25 17 2 19 2 40 40 1 38 39 14 36 12 17 3 3 39 1 38 7
## [201] 7 29 35 34 27 21 28 26 8 23 16 19 8 4 12 4 36 29 16 17 11 18 39 18 14
## [226] 14 38 27 24 10 15 8 15 5 27 39 36 14 23 32 30 9 39 5 17 20 2 12 19 35
## [251] 37 24 11 13 17 22 10 16 8 9 9 34 4 3 23 27 34 18 10 5 14 14 19 39 33
## [276] 6 16 28 25 26 26 19 33 38 11 24 3 5 5 19 31 8 30 37 24 17 23 10 7 14
## [301] 31 31 40 38 39 11 16 31 22 6 14 32 21 3 27 39 29 6 6 17 27 9 4 23 23
## [326] 23 24 23 40 15 19 26 39 36 12 33 37 10 2 5 36 8 17 15 1 19 28 22 5 1
## [351] 23 20 16 35 34 32 24 30 8 36 13 24 21 12 5 10 8 25 23 19 24 36 7 36 16
## [376] 2 18 27 40 5 5 3 33 20 31 16 38 12 8 3 16 1 19 4 31 25 3 15 23 20
## [401] 23 40 19 22 37 8 13 25 1 16 16 12 22 23 31 14 23 36 38 5 2 19 12 37 39
## [426] 28 35 26 27 13 22 36 30 9 35 39 1 29 6 35 24 32 30 20 24 13 21 32 40 27
## [451] 18 25 28 15 30 1 36 6 40 40 28 27 27 25 21 27 34 38 36 13 31 3 26 24 21
## [476] 35 25 35 39 1shapiro.test(vientosp4) ##
## Shapiro-Wilk normality test
##
## data: vientosp4
## W = 0.99736, p-value = 0.6452shapiro.test(abundanciasp4) ##
## Shapiro-Wilk normality test
##
## data: abundanciasp4
## W = 0.95473, p-value = 5.75e-11wilcox.test(vientosp4,abundanciasp4, paired = F) #p-value < 2.2e-16 se rechaza la H0##
## Wilcoxon rank sum test with continuity correction
##
## data: vientosp4 and abundanciasp4
## W = 3163, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0 #Probabilidad de tallas por especie
#Fdth por especie, para ver que tallas predominan
#Abundancia vs talla
#Corelación de la abundancia con el tamaño en la espcie 1
abund2<- dat1$N[dat1$especie=="artibeus_sp2"]
abund3<- dat1$N[dat1$especie=="thyroptera_sp1"]
abund4<- dat1$N[dat1$especie=="thyroptera_sp2"]
abund<- dat1$N[dat1$especie=="artibeus_sp1"]
par(mfrow=c(2,2))
library(PerformanceAnalytics)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:gplots':
##
## textplot## The following object is masked from 'package:graphics':
##
## legendabund## [1] 33 12 34 21 25 8 11 8 17 10 19 33 5 23 26 36 2 28 34 9 6 27 10 14 29
## [26] 26 9 29 32 3 14 40 33 30 6 12 33 23 15 27 6 6 28 1 32 25 27 7 13 31
## [51] 35 4 7 7 8 11 25 18 27 16 5 13 12 38 20 7 32 32 12 14 35 21 23 29 9
## [76] 22 22 33 39 11 38 16 17 22 32 31 15 16 26 14 1 1 4 34 26 20 29 5 1 16
## [101] 6 25 10 29 24 19 40 16 13 13 16 34 20 24 8 20 23 4 6 13 5 28 3 12 16
## [126] 4 26 13 1 37 21 1 21 16 1 8 11 24 19 13 33 24 27 25 27 34 17 6 30 20
## [151] 9 24 38 39 5 1 35 8 18 8 14 8 19 24 6 39 24 16 15 2 22 11 16 25 5
## [176] 37 15 16 31 29 12 16 4 13 15 11 28 2 17 9 32 24 38 12 13 33 15 34 1 34
## [201] 14 32 34 16 12 10 7 35 15 37 34 13 29 13 39 20 10 23 19 4 17 35 9 5 36
## [226] 26 31 26 9 20 30 9 26 9 8 29 35 20 4 1 19 22 27 8 17 11 21 10 28 15
## [251] 11 31 2 35 29 37 3 28 24 31 21 18 39 8 36 5 20 18 9 12 18 29 2 34 20
## [276] 34 21 36 12 35 8 35 39 14 30 29 32 31 2 23 12 26 8 6 14 12 22 15 8 20
## [301] 11 3 20 34 28 33 33 40 40 6 37 23 34 22 33 6 13 15 24 40 36 3 40 13 14
## [326] 39 8 33 9 17 17 11 34 12 21 11 33 33 22 29 10 2 2 30 18 17 35 34 37 37
## [351] 24 11 20 25 24 13 34 29 19 37 31 28 6 12 21 7 39 24 1 40 3 33 39 22 27
## [376] 29 30 27 22 40 11 19 38 40 14 19 2 14 14 37 30 9 39 27 1 24 22 29 3 30
## [401] 12 38 9 17 17 10 18 23 25 31 12 35 14 3 10 4 38 8 1 27 8 31 14 9 38
## [426] 4 6 32 32 9 20 31 17 9 26 15 15 15 5 9 27 28 32 30 32 11 24 4 30 11
## [451] 3 5 32 1 13 10 19 3 39 9 11 4 19 7 28 19 7 39 4 8 5 29 31 13 34
## [476] 3 9 16 31 14long1<- dat1$tot_len[dat1$especie=="artibeus_sp1"]
long1## [1] 80.37029 77.32682 79.53725 84.40988 74.97167 71.63561 74.08370 80.13808
## [9] 72.23255 80.08734 86.15312 80.82484 75.50269 75.10698 71.76496 82.42726
## [17] 77.38789 76.41885 78.92442 77.49108 80.23998 76.10028 82.99293 80.39411
## [25] 80.66447 82.32715 72.28163 72.07432 81.84698 81.51792 79.67537 78.57583
## [33] 72.97820 74.91495 83.16052 81.61826 76.65380 77.33656 76.62015 80.02815
## [41] 78.10369 78.42531 82.86427 85.12033 78.85939 86.04027 75.94458 82.95700
## [49] 74.43458 76.70075 75.74290 76.31072 77.00101 77.90566 83.63912 73.59398
## [57] 79.15969 82.22453 76.61253 74.28323 78.67110 73.19517 83.43230 80.31266
## [65] 79.71544 77.54737 78.99767 78.64558 78.24418 73.08995 77.72691 81.81578
## [73] 78.03633 75.21185 74.88234 74.74345 75.67229 81.48355 80.21031 74.91205
## [81] 72.15418 80.73382 69.92085 76.69056 77.00977 84.22609 79.06877 78.16334
## [89] 75.96550 79.38493 73.06797 76.88081 79.17110 72.02817 82.06586 79.71237
## [97] 78.11436 82.38426 79.72370 73.71142 79.41173 76.63721 79.86366 80.23495
## [105] 76.22692 76.72789 77.23157 80.24091 79.29783 76.63854 79.89535 78.43143
## [113] 78.09932 76.68787 77.56043 77.33436 82.17063 79.10099 77.76873 81.31952
## [121] 72.45001 80.27230 75.36853 74.46824 80.45421 74.81840 79.06073 71.69713
## [129] 79.77637 77.25783 76.28201 82.74466 81.23451 79.91796 78.37137 76.55308
## [137] 83.33967 76.44357 83.04359 81.65821 77.57920 76.60509 84.15826 75.75459
## [145] 78.67277 79.58747 76.51600 80.41707 76.62237 77.94414 81.64581 81.83870
## [153] 76.60609 73.54823 78.64958 86.97069 76.80516 79.79187 78.65482 77.48889
## [161] 81.51424 78.98435 84.50718 78.59321 77.03662 79.75078 71.78976 77.63625
## [169] 75.11671 75.46793 82.75479 75.58945 74.13027 76.73719 74.87419 78.59761
## [177] 76.84504 82.77396 75.06435 78.34567 76.56938 81.85788 79.83889 78.16454
## [185] 78.00054 78.51831 81.66230 77.01054 81.97931 81.66786 80.43140 78.45049
## [193] 79.36783 74.66834 78.96773 69.57142 77.98177 82.02562 82.14851 79.90115
## [201] 82.61351 77.49689 74.72474 82.43313 84.68393 81.61923 80.63346 78.16341
## [209] 73.43119 84.30982 73.38117 79.98534 75.80253 75.72029 77.03216 76.13464
## [217] 78.52886 78.37462 81.13448 76.24920 74.26233 77.14184 74.18040 75.29439
## [225] 76.06877 78.23602 80.06810 79.04373 73.76458 75.93580 77.13927 77.81744
## [233] 79.94885 76.23442 79.70298 70.74077 76.24923 82.07342 81.44180 76.43097
## [241] 76.39836 76.20998 75.16911 78.13277 85.01817 75.49721 78.75502 86.25387
## [249] 73.37385 78.13882 78.17121 78.60154 81.22112 77.33180 80.14689 79.31293
## [257] 71.55723 86.20401 79.47746 77.41620 79.08065 79.45347 81.40306 75.77190
## [265] 76.79820 80.01894 82.10138 73.17462 79.68945 83.13522 82.39995 78.84937
## [273] 73.46305 79.73489 81.20932 76.19514 79.69791 74.89762 76.57043 79.41129
## [281] 77.78250 78.78702 76.56082 77.89576 75.55617 77.49808 78.20974 80.30568
## [289] 76.53587 79.90372 73.07466 78.57580 79.10255 71.61290 77.98196 80.10959
## [297] 82.36802 81.06300 72.71975 84.88105 83.83855 79.57714 74.42593 80.39355
## [305] 77.03739 81.37480 78.38130 80.85144 78.38684 78.15849 76.66394 81.10278
## [313] 76.78544 75.96993 75.78368 80.02814 83.54200 80.76108 78.95314 80.34019
## [321] 76.52212 80.63509 76.84177 82.32463 77.03512 77.74088 81.30575 73.90682
## [329] 77.79799 69.17939 80.56373 78.75152 77.30252 81.70253 77.70461 84.72603
## [337] 80.08202 75.42865 78.20123 76.09909 75.48487 78.55543 79.11166 77.22215
## [345] 83.76049 74.13622 76.05490 81.20718 78.40535 78.15325 80.01789 75.28490
## [353] 80.34011 72.82822 78.95563 75.76048 73.55798 72.24217 77.92347 74.87663
## [361] 79.70556 76.91759 82.86361 80.68235 79.86020 76.69757 80.12810 80.43105
## [369] 74.81904 78.96497 77.93548 77.48485 78.65178 79.11876 81.04118 79.37092
## [377] 78.00148 74.42894 77.40295 78.33273 78.18145 79.99620 72.02081 72.19812
## [385] 79.99013 76.82390 80.26076 75.52893 76.59074 74.80327 76.93691 78.54609
## [393] 85.02114 76.12425 75.86999 71.83203 76.45940 79.60743 78.23102 76.21242
## [401] 81.32438 76.95639 82.42834 74.53864 81.09419 76.81863 78.11815 78.49469
## [409] 81.64594 79.03994 75.86540 79.82384 79.61165 76.42642 83.47016 75.91586
## [417] 79.40945 79.33026 81.79649 78.25440 78.65848 73.27145 75.81427 79.42798
## [425] 71.27064 78.10638 73.89441 77.96038 80.09222 79.81398 75.40807 76.74441
## [433] 78.62251 77.24248 76.55905 76.46156 74.44111 74.80785 78.63346 75.23766
## [441] 80.28708 78.84910 84.38000 78.63206 83.65565 78.54269 81.02319 79.15193
## [449] 75.87392 76.26703 78.96185 74.33971 80.76439 75.72920 75.17952 76.43768
## [457] 82.48066 78.38191 82.82701 79.71922 77.47763 78.05645 79.61320 81.26461
## [465] 75.45672 76.10980 82.55965 78.11946 81.19468 74.40092 78.58530 76.36417
## [473] 82.84309 75.58102 76.33518 76.94318 76.73481 78.57939 76.05557 79.59450abylong1<-data.frame(long1,abund)
shapiro.test(abund) ##
## Shapiro-Wilk normality test
##
## data: abund
## W = 0.95337, p-value = 3.589e-11shapiro.test(long1) ##
## Shapiro-Wilk normality test
##
## data: long1
## W = 0.99765, p-value = 0.7439cor.test(abund,long1, method= "s") ##
## Spearman's rank correlation rho
##
## data: abund and long1
## S = 19076755, p-value = 0.4444
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.0349847pairs(long1~abund)
chart.Correlation(abylong1, method ="s") 
#Correlación entre la abuncia con el tamaño de la especie 2
abund2## [1] 3 12 24 4 13 15 37 29 3 40 25 14 29 26 22 39 33 10 5 36 11 8 40 2 33
## [26] 21 32 39 29 33 10 27 37 14 38 22 4 24 25 32 34 37 12 28 2 28 37 18 32 27
## [51] 14 5 35 20 19 28 18 7 7 17 27 30 23 39 40 6 29 30 23 2 38 9 11 31 20
## [76] 36 3 2 33 34 37 12 4 28 20 12 21 31 36 8 15 5 37 11 28 30 36 7 8 34
## [101] 26 17 13 32 28 12 19 3 40 22 7 21 11 3 35 25 9 17 23 20 10 15 18 3 29
## [126] 18 35 18 29 1 36 24 19 20 12 12 16 21 6 31 39 5 18 3 34 29 39 40 23 21
## [151] 11 35 5 37 13 6 22 13 28 16 4 7 7 3 38 38 2 37 29 2 21 26 8 24 26
## [176] 10 29 40 6 12 31 1 35 22 7 6 39 18 23 4 14 9 32 17 6 25 40 40 31 11
## [201] 17 33 35 34 22 21 19 1 12 28 31 39 27 35 37 30 8 21 8 3 30 9 31 35 34
## [226] 12 6 16 34 2 16 36 6 27 22 14 20 19 8 4 12 32 38 34 15 6 34 24 29 34
## [251] 14 9 25 11 38 9 28 12 18 18 16 1 19 31 20 9 30 2 28 40 15 10 37 19 33
## [276] 6 26 12 15 23 9 4 7 2 23 39 21 5 14 3 2 33 20 14 24 1 38 14 38 27
## [301] 16 11 15 35 13 38 18 23 31 16 13 29 32 35 23 1 3 10 20 2 6 24 8 24 5
## [326] 35 3 21 33 36 27 21 9 25 5 11 35 31 10 22 32 14 38 26 14 20 26 18 8 35
## [351] 13 33 35 21 8 13 37 39 18 11 27 7 14 27 16 21 37 39 26 23 12 39 32 35 10
## [376] 21 18 13 27 17 33 31 17 30 5 40 9 17 26 1 7 21 26 26 26 28 9 24 9 15
## [401] 5 27 33 29 22 20 15 28 29 23 18 21 37 4 16 13 27 26 3 29 16 30 6 7 6
## [426] 7 2 15 40 9 8 7 29 20 16 34 27 36 35 10 33 29 36 6 2 39 21 24 11 30
## [451] 13 19 37 27 36 11 39 19 35 2 12 2 7 30 38 25 24 7 21 10 17 35 11 9 35
## [476] 37 33 19 27 37long2<- dat1$tot_len[dat1$especie=="artibeus_sp2"]
long2## [1] 62.34097 64.74636 61.62496 63.79078 64.04642 60.67838 63.12682 64.72736
## [9] 62.85891 62.87625 61.88567 61.86262 63.23609 62.94133 62.69071 66.25063
## [17] 64.15587 61.35121 64.16879 62.58588 62.81757 64.53772 61.69462 61.24101
## [25] 64.05016 62.43984 63.18668 62.39046 61.99426 64.37366 61.84492 62.72033
## [33] 63.23432 62.39731 64.63402 61.45729 63.94134 64.08491 64.69417 65.18578
## [41] 61.29334 65.03393 64.51316 61.76160 64.65928 62.80280 64.35179 64.05305
## [49] 61.24093 61.68568 61.71910 64.26367 62.08284 63.78464 63.42450 63.04816
## [57] 61.40387 62.84554 61.38630 62.75414 64.87352 62.99266 65.26419 62.04771
## [65] 60.73845 61.74415 62.17427 62.44678 63.62857 61.10906 65.88934 62.78406
## [73] 62.02073 62.89723 63.54630 61.62090 64.65517 60.69971 61.78649 60.51382
## [81] 65.43527 62.09366 60.61784 59.30169 65.49389 65.89656 60.65405 64.46331
## [89] 62.71346 61.92491 62.39997 62.99640 63.03987 64.04957 61.29561 63.63284
## [97] 62.86235 61.31900 59.81645 61.56324 61.63305 63.27713 63.05952 61.61959
## [105] 64.04575 61.62657 63.19479 60.81904 64.59291 63.14052 59.95582 63.89067
## [113] 61.82818 62.92460 61.54821 64.55936 61.12612 61.93084 62.59481 65.34953
## [121] 62.00451 64.59935 62.54646 64.50197 61.04658 62.34787 61.08488 61.26419
## [129] 64.72368 64.67943 63.43598 65.07891 62.65839 63.39882 64.85567 62.51711
## [137] 65.62039 61.88878 63.71863 62.50060 65.55653 62.13787 61.03099 63.28362
## [145] 64.97984 65.84464 62.58134 61.47105 64.49767 61.51683 61.42635 64.21053
## [153] 64.70592 64.59214 65.63116 62.75362 62.19967 63.05524 62.80569 62.17571
## [161] 61.99862 64.27282 67.28045 60.86941 64.53260 63.46124 62.26759 61.80106
## [169] 64.26190 65.76741 64.10407 61.51011 63.33812 64.61711 62.83391 62.21861
## [177] 63.02958 64.07036 62.51110 63.01826 60.68481 59.59462 61.36782 58.60048
## [185] 63.22709 63.05884 62.85656 60.92745 62.91367 61.04898 63.75728 63.01376
## [193] 61.73959 61.29733 64.98473 64.35520 64.50373 62.24698 63.55500 62.06937
## [201] 63.90983 61.59742 61.51213 63.09328 64.76953 61.20739 63.10941 64.38213
## [209] 62.87743 62.50000 60.54793 61.84475 64.93073 62.40609 62.62337 64.62894
## [217] 61.55605 62.32960 61.22198 63.52019 61.78867 63.24179 62.89399 62.80078
## [225] 65.61312 59.89734 63.55371 61.96871 61.80033 64.12785 63.48939 63.54560
## [233] 64.24450 61.40505 63.35700 64.40638 61.75911 65.82934 62.24329 63.05744
## [241] 62.43054 64.17676 63.22013 61.49176 60.89349 64.56978 64.38989 64.80614
## [249] 65.33856 63.28243 64.43841 63.93582 64.78478 63.25280 66.47691 64.48196
## [257] 62.60483 62.95328 61.72093 63.07679 60.98025 61.94870 64.56312 62.51722
## [265] 64.01546 62.10915 62.11149 64.41371 63.97040 64.48254 62.16489 63.49023
## [273] 64.82390 64.42086 65.09710 60.88935 63.09862 63.06658 62.41364 65.20832
## [281] 65.31439 63.17896 63.49017 62.42554 61.47060 62.14095 65.21096 63.61593
## [289] 61.76243 62.25846 64.33591 65.26004 64.64487 60.88113 60.80930 63.96009
## [297] 64.54209 62.79299 64.52985 64.56250 63.07169 61.30381 63.42726 63.89807
## [305] 63.62377 61.89565 65.57671 65.18971 63.44227 64.14635 63.32423 65.33945
## [313] 65.47047 62.98932 61.73973 61.25980 63.28705 65.60209 61.24930 64.69548
## [321] 62.00213 64.05847 61.57257 59.68462 63.88721 64.11304 64.33695 64.05074
## [329] 62.08918 60.69345 63.71723 61.60735 63.87640 62.61200 63.07949 60.58036
## [337] 64.59497 62.34952 64.08588 65.30787 62.04634 62.74759 62.07902 62.28243
## [345] 63.23669 62.49588 63.12009 66.65498 61.48757 61.46427 63.75090 62.46817
## [353] 60.34364 62.50586 62.80002 62.54450 64.02412 61.64522 62.57059 63.49518
## [361] 61.49880 64.09011 63.71481 62.10480 63.84603 63.26401 64.80116 63.92821
## [369] 65.61324 62.34917 63.68776 63.48075 62.83976 62.29008 66.34369 61.86998
## [377] 60.64088 60.34530 63.46146 63.35074 60.81213 61.45798 64.24489 61.96226
## [385] 63.52508 61.87919 62.33843 63.06513 62.43578 64.88368 65.38078 60.62639
## [393] 62.61814 65.90677 63.79157 62.34511 64.66589 64.25082 63.51838 62.94423
## [401] 66.47430 61.56634 60.97708 63.45959 62.65502 63.87211 62.93119 62.73600
## [409] 64.65352 63.19949 64.08231 64.97659 66.86351 62.21830 63.83727 61.98569
## [417] 62.13622 62.24094 66.71955 63.48799 64.93783 61.20205 62.00716 63.41218
## [425] 60.98744 63.79418 62.60380 60.56498 61.02107 62.82072 62.80913 63.15258
## [433] 63.06462 63.81034 64.26118 62.06595 61.23426 63.56311 60.31988 62.49174
## [441] 64.94990 65.28220 60.36616 60.97317 60.84903 64.65913 67.02956 62.64873
## [449] 60.60070 62.72060 63.08203 62.59810 62.14028 62.45724 61.20792 63.92776
## [457] 61.22496 63.30663 63.39080 59.95853 62.41982 64.58062 60.96235 62.92006
## [465] 63.67013 61.08216 62.68452 66.44158 64.76383 63.59335 63.59574 61.81575
## [473] 61.06975 63.10139 62.55272 62.99085 63.20428 64.63729 60.33485 63.06954abylong2<-data.frame(long2,abund2)
shapiro.test(abund2) ##
## Shapiro-Wilk normality test
##
## data: abund2
## W = 0.94974, p-value = 1.067e-11shapiro.test(long2) ##
## Shapiro-Wilk normality test
##
## data: long2
## W = 0.99533, p-value = 0.1603cor.test(abund2,long2, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund2 and long2
## S = 18405411, p-value = 0.9749
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.001438187pairs(long2~abund2)
chart.Correlation(abylong2, method ="s") 
#Correlación entre la abuncia con el tamaño de la especie 3
abund3## [1] 37 10 12 8 3 10 29 22 40 29 6 4 15 37 30 36 5 20 1 18 37 20 11 15 8
## [26] 36 12 18 33 35 16 7 7 26 12 32 13 34 24 21 29 37 10 22 40 5 36 19 23 8
## [51] 14 21 15 13 23 3 11 2 3 22 35 26 1 25 4 10 32 36 8 22 10 24 35 2 22
## [76] 13 35 22 39 18 25 31 30 36 18 18 20 33 5 9 29 35 10 28 6 34 8 27 26 22
## [101] 9 35 20 40 7 35 24 15 22 28 34 9 35 19 20 5 16 6 28 10 35 12 17 5 27
## [126] 7 24 10 5 24 38 39 6 9 36 11 5 27 3 25 23 8 20 6 23 29 27 20 31 8
## [151] 21 19 14 32 15 9 16 16 40 15 11 29 19 23 31 24 30 11 21 23 27 10 30 32 22
## [176] 32 13 39 8 4 3 1 21 12 6 33 19 9 21 3 29 17 22 1 23 11 37 11 10 21
## [201] 23 4 16 32 18 17 26 21 8 27 29 17 26 4 38 25 5 15 26 36 35 25 29 8 19
## [226] 11 23 26 33 20 10 38 35 3 23 38 5 16 32 40 2 22 28 36 28 18 21 25 35 39
## [251] 4 4 30 40 25 31 40 29 28 9 31 7 13 27 31 16 13 8 9 39 15 2 31 31 2
## [276] 1 16 29 22 39 33 15 32 20 40 1 29 19 3 23 35 2 16 25 23 13 19 26 8 9
## [301] 24 24 27 16 33 8 39 1 37 2 7 31 13 11 30 35 9 1 19 28 6 16 24 25 38
## [326] 36 9 35 26 3 1 21 29 36 24 37 35 1 2 39 26 5 37 38 4 29 28 11 5 36
## [351] 21 25 1 32 14 24 38 19 16 40 25 20 33 16 1 34 17 37 2 19 1 2 18 21 17
## [376] 6 15 38 37 7 33 15 37 17 32 9 12 19 6 14 23 20 31 31 33 35 32 19 2 5
## [401] 29 12 35 17 20 36 24 24 9 9 32 19 16 5 2 28 21 37 29 29 7 36 5 38 14
## [426] 33 31 40 32 1 39 6 33 32 25 33 22 17 34 23 27 8 32 4 11 10 10 39 24 12
## [451] 35 1 33 27 7 20 33 18 10 28 26 30 14 3 8 8 14 2 5 16 38 12 24 4 39
## [476] 29 17 13 35 12long3<- dat1$tot_len[dat1$especie=="thyroptera_sp1"]
long3## [1] 45.23085 45.59515 46.83900 36.78146 38.57950 46.92646 41.96756 39.93173
## [9] 52.02596 40.78179 35.28143 44.53654 41.73810 46.50171 39.47996 40.62723
## [17] 38.37577 38.44652 44.77994 41.19155 41.42881 42.90333 44.42677 44.94772
## [25] 42.69720 43.24476 40.14044 44.01372 43.20871 40.77420 49.33714 44.36962
## [33] 45.39321 44.13397 42.08245 39.16664 47.20540 45.22394 36.16013 42.14497
## [41] 43.32892 37.85206 42.02413 40.99010 45.93308 38.92681 43.16217 45.97011
## [49] 43.47590 47.23610 42.37313 48.42829 39.01530 39.38709 47.28561 39.78633
## [57] 43.38138 41.03081 43.22520 38.20523 38.13238 40.16224 47.91822 45.61205
## [65] 37.08090 45.66497 39.37099 43.72582 43.24172 41.86691 41.60224 40.55435
## [73] 44.21902 40.87168 39.41668 40.85004 41.02472 41.83988 34.83987 38.64678
## [81] 38.98396 46.74127 42.96552 39.06093 37.49055 40.80783 41.29891 43.67256
## [89] 36.67835 42.18864 48.16784 42.21239 39.03694 42.89144 39.56530 44.76163
## [97] 38.51980 42.06445 46.18439 37.25228 42.39569 48.66893 37.92526 43.36375
## [105] 42.50082 39.21406 40.18939 39.56860 44.82786 42.38466 43.43121 41.39655
## [113] 36.33266 49.43735 46.33380 34.11148 41.66467 45.78056 39.92297 42.83360
## [121] 40.43287 38.29360 42.09143 34.85372 40.17902 41.33215 48.42437 43.02633
## [129] 48.64881 47.01364 41.98658 37.35719 47.12273 42.50462 38.70422 45.63477
## [137] 48.46406 43.40839 38.65217 33.73753 45.83150 43.67612 43.18857 49.06648
## [145] 45.20396 47.30507 42.44599 41.79824 48.53501 38.42447 41.51735 41.46417
## [153] 42.02513 46.26796 48.29025 45.24742 39.40251 32.58157 41.35802 43.06092
## [161] 38.91473 44.29286 40.80162 44.62243 45.23236 39.79843 37.10938 40.65958
## [169] 38.30229 46.98066 42.04333 36.25474 43.67721 45.62677 42.91786 44.98780
## [177] 45.08443 42.07712 32.90166 41.78398 42.98029 41.83610 47.98884 46.39129
## [185] 37.80105 43.69618 42.36762 48.82789 37.16923 44.02490 34.15180 46.77083
## [193] 45.86029 45.47674 42.15086 41.81816 46.74112 45.21282 45.07830 37.30197
## [201] 36.94039 42.19146 40.70636 45.73790 47.84329 39.76838 42.88789 41.12503
## [209] 42.94719 49.04934 44.95452 42.48557 40.07673 35.27605 40.60447 39.12187
## [217] 50.59435 40.74519 46.20297 38.49260 41.01054 44.49657 36.87442 41.78559
## [225] 41.55381 50.35941 39.87272 44.60485 48.55847 40.04705 34.66877 38.76587
## [233] 39.39554 42.57866 36.94928 46.02967 38.05392 39.42472 42.96800 42.00276
## [241] 45.04589 44.28759 44.24811 41.98117 41.31118 43.43845 42.59190 50.03103
## [249] 40.56092 41.07146 35.96791 42.39260 39.15746 38.60347 41.93253 37.80909
## [257] 44.16730 41.70063 32.65912 45.63812 40.08405 49.84033 42.88954 44.31559
## [265] 44.49868 39.64332 47.06409 42.21587 35.39035 41.16054 39.32099 37.87806
## [273] 40.28773 33.86921 39.75194 46.68951 37.88779 41.06746 42.97911 42.42959
## [281] 43.02111 44.73403 37.95605 44.55570 40.08521 37.16795 44.60290 45.75861
## [289] 47.18181 46.30544 33.73840 33.43040 43.96020 45.53773 41.76390 41.51647
## [297] 42.07709 40.14541 37.56639 47.76968 42.94912 38.62113 40.33533 39.15488
## [305] 40.59470 41.96937 50.60319 39.43775 36.65700 41.85212 46.73875 37.24119
## [313] 44.61158 44.85475 39.73227 44.58844 41.24097 48.20982 42.80259 42.25849
## [321] 40.41926 41.27485 37.59833 34.82292 38.77962 39.59521 39.05568 46.87792
## [329] 46.41414 36.27675 39.40068 43.29398 34.26009 41.22363 42.83001 48.53698
## [337] 46.35132 42.76018 46.43989 38.62159 40.17940 45.22700 36.79414 37.49106
## [345] 38.28828 46.41939 43.54746 43.01226 43.51579 43.75570 41.38890 33.41946
## [353] 40.63713 50.80005 48.19783 38.56004 44.66136 40.15634 39.72718 44.76843
## [361] 38.34624 50.45116 40.07224 36.19381 45.03011 44.09248 36.89385 40.59831
## [369] 39.94469 42.49209 41.92400 40.96006 38.92869 40.18897 46.29087 46.86455
## [377] 42.28959 37.42614 45.06450 35.30617 38.19361 41.36342 41.10674 34.93234
## [385] 38.18833 37.27294 40.51119 38.39171 37.48353 42.07133 41.43465 43.38501
## [393] 46.83899 40.99286 49.94427 37.91419 39.61328 44.69131 42.42915 37.82924
## [401] 37.33091 35.66638 40.28254 37.29634 48.19204 33.10675 39.97098 41.45624
## [409] 47.65267 42.50570 40.21302 40.80257 46.08802 39.72267 42.83769 42.34509
## [417] 39.08595 46.89251 42.08756 43.51565 37.28865 38.03547 41.64749 40.85248
## [425] 36.08500 47.04257 40.02464 45.81605 47.48213 39.94785 44.60833 31.32648
## [433] 49.48905 41.63367 38.51461 43.66257 41.96606 42.98439 42.15567 40.97070
## [441] 36.12190 40.77631 43.85436 43.20537 41.10605 42.07869 46.00452 45.73184
## [449] 42.52284 38.46541 38.18655 42.76499 41.14575 44.68399 37.56322 38.72466
## [457] 43.23938 40.96309 46.66936 47.75106 39.34610 46.98490 45.16214 40.49442
## [465] 48.61740 44.13764 50.12470 42.48618 41.14918 48.87940 39.97239 44.20597
## [473] 41.82290 49.99904 37.94704 34.40780 43.93825 43.37900 42.64211 44.59076abylong3<-data.frame(long3,abund3)
shapiro.test(abund3) ##
## Shapiro-Wilk normality test
##
## data: abund3
## W = 0.95226, p-value = 2.463e-11shapiro.test(long3) ##
## Shapiro-Wilk normality test
##
## data: long3
## W = 0.9966, p-value = 0.4063cor.test(abund3,long3, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund3 and long3
## S = 18417856, p-value = 0.9867
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.0007630342pairs(long3~abund3)
chart.Correlation(abylong3, method ="s") 
#Correlación entre la abuncia con el tamaño de la especie 4
abund4## [1] 11 30 27 25 26 9 18 38 8 8 13 7 25 29 5 38 4 27 3 22 19 21 18 9 5
## [26] 10 27 31 30 8 5 17 14 5 37 28 24 7 25 12 40 29 23 16 33 6 25 32 10 12
## [51] 36 12 33 28 23 33 40 19 28 1 26 16 2 38 23 5 40 21 23 35 19 33 3 38 7
## [76] 15 3 18 36 10 27 25 10 29 38 21 32 13 21 21 4 10 33 11 35 38 38 17 29 8
## [101] 38 38 35 26 10 16 2 16 10 17 19 15 22 33 19 3 17 4 2 39 20 26 35 23 17
## [126] 35 2 8 11 11 36 28 22 19 25 24 40 3 36 20 35 12 24 34 15 15 38 17 35 21
## [151] 11 4 24 40 37 10 11 8 13 2 33 3 35 30 33 31 19 12 18 18 16 7 7 31 4
## [176] 30 32 30 22 25 25 17 2 19 2 40 40 1 38 39 14 36 12 17 3 3 39 1 38 7
## [201] 7 29 35 34 27 21 28 26 8 23 16 19 8 4 12 4 36 29 16 17 11 18 39 18 14
## [226] 14 38 27 24 10 15 8 15 5 27 39 36 14 23 32 30 9 39 5 17 20 2 12 19 35
## [251] 37 24 11 13 17 22 10 16 8 9 9 34 4 3 23 27 34 18 10 5 14 14 19 39 33
## [276] 6 16 28 25 26 26 19 33 38 11 24 3 5 5 19 31 8 30 37 24 17 23 10 7 14
## [301] 31 31 40 38 39 11 16 31 22 6 14 32 21 3 27 39 29 6 6 17 27 9 4 23 23
## [326] 23 24 23 40 15 19 26 39 36 12 33 37 10 2 5 36 8 17 15 1 19 28 22 5 1
## [351] 23 20 16 35 34 32 24 30 8 36 13 24 21 12 5 10 8 25 23 19 24 36 7 36 16
## [376] 2 18 27 40 5 5 3 33 20 31 16 38 12 8 3 16 1 19 4 31 25 3 15 23 20
## [401] 23 40 19 22 37 8 13 25 1 16 16 12 22 23 31 14 23 36 38 5 2 19 12 37 39
## [426] 28 35 26 27 13 22 36 30 9 35 39 1 29 6 35 24 32 30 20 24 13 21 32 40 27
## [451] 18 25 28 15 30 1 36 6 40 40 28 27 27 25 21 27 34 38 36 13 31 3 26 24 21
## [476] 35 25 35 39 1long4<- dat1$tot_len[dat1$especie=="thyroptera_sp2"]
long4## [1] 26.44218 26.78533 26.62318 26.64542 26.03013 26.09634 26.00922 25.97445
## [9] 27.02817 26.11817 26.19628 26.22844 26.67062 26.05149 26.04023 26.70942
## [17] 26.51103 26.66172 27.16536 27.08982 25.93832 27.21403 26.30706 25.73063
## [25] 26.51526 27.09262 26.70157 25.95124 26.50828 26.60462 27.75278 26.50705
## [33] 27.39046 25.95993 26.28900 26.65298 26.54034 27.09993 26.68432 25.69175
## [41] 26.37989 26.35061 26.16842 26.60843 26.95845 26.45636 27.00461 26.03202
## [49] 26.07778 26.02386 26.59829 26.46438 26.37178 26.54466 26.06400 25.67681
## [57] 27.03258 26.93989 26.33955 27.01568 27.00764 26.35972 26.97136 25.81123
## [65] 27.05647 25.87149 25.59282 26.59405 25.97330 26.37734 26.00443 26.53899
## [73] 25.68686 26.32798 25.86401 26.22128 27.50628 27.22504 26.87728 26.90995
## [81] 26.48027 27.36494 26.55022 25.98305 26.39131 26.52228 26.66213 26.00211
## [89] 27.40223 26.38029 27.14303 26.07979 26.29998 26.34472 26.42846 26.16815
## [97] 26.64878 26.49110 26.64216 27.30056 26.49331 26.01736 25.34077 27.06415
## [105] 26.39675 25.78183 26.29501 26.13348 26.49056 27.04080 26.30072 26.99775
## [113] 26.10582 26.18996 26.64567 26.91868 26.80844 26.21832 26.46191 26.18590
## [121] 26.46798 27.13075 26.67080 26.22001 26.86299 26.31113 26.52155 25.86018
## [129] 26.62845 26.62299 25.14589 26.02975 26.63086 25.50616 26.73513 26.14357
## [137] 26.13887 26.15818 25.79236 26.55468 27.10093 26.24351 26.30620 26.67611
## [145] 26.76397 25.62799 26.77991 26.32447 26.32984 26.45401 25.77946 25.51311
## [153] 25.59285 26.18177 26.36180 26.66260 26.10914 25.43275 25.85872 26.26778
## [161] 25.84064 25.96886 25.65415 26.80604 27.13849 26.80098 26.20450 26.49991
## [169] 26.01620 27.11807 26.59857 26.45577 26.52192 26.58120 26.73153 26.66174
## [177] 26.59931 26.42282 25.94288 27.00731 26.82613 26.04908 26.55007 27.01915
## [185] 26.50858 26.21380 25.99095 26.44531 25.76045 25.95041 25.32049 26.52432
## [193] 26.48694 25.95133 25.92623 26.74236 26.78647 26.43905 26.39935 26.06738
## [201] 26.68050 25.82704 26.63658 26.24667 26.64500 26.76076 26.50269 26.58175
## [209] 26.40876 25.52171 26.19947 26.88474 26.70706 25.51591 27.11140 25.97514
## [217] 27.09571 26.17448 27.36413 26.81076 25.68841 25.32206 26.97718 26.58096
## [225] 25.45903 26.53542 26.95948 25.44519 25.48582 26.54046 26.99066 27.07402
## [233] 26.86113 26.45213 26.40248 27.91897 26.19458 27.20433 25.96253 26.25982
## [241] 26.56930 25.83085 25.62248 26.56672 25.94448 26.02163 25.49407 26.59073
## [249] 26.73296 25.91705 26.53500 26.65322 26.23824 26.91399 26.64900 26.00154
## [257] 25.86467 26.44173 26.49658 25.85204 26.33319 26.43196 26.56716 27.13182
## [265] 26.91345 26.15793 26.36636 26.75173 26.24924 25.64438 26.14490 26.03000
## [273] 26.78378 25.67084 26.72610 25.98533 26.59741 25.77638 26.42019 26.41158
## [281] 26.24558 25.67369 25.18632 26.93180 26.15482 27.53267 26.81564 26.09624
## [289] 26.10209 26.21615 26.31309 27.56009 26.37049 25.96819 24.98748 26.24449
## [297] 26.61101 26.61624 26.41691 25.65583 26.49610 25.65518 25.74520 26.61269
## [305] 25.80260 26.19933 26.30590 27.27865 26.22267 26.42625 25.92877 26.57658
## [313] 26.18357 26.70842 26.19934 26.73629 26.66809 26.36114 26.22628 26.45047
## [321] 26.78379 26.27633 26.75452 26.60830 26.83926 25.44644 25.67514 26.81240
## [329] 25.64358 25.73400 25.64932 26.17096 26.51734 26.41838 26.11115 27.05676
## [337] 26.06680 26.34848 27.02640 26.39734 25.98493 25.72279 25.75230 26.39807
## [345] 26.47026 26.69737 26.69202 25.78150 26.35243 26.75390 26.56465 26.39910
## [353] 26.95587 26.29361 25.99627 26.93335 25.55159 26.76579 25.96250 26.96141
## [361] 26.27261 27.23995 26.69499 26.69790 26.77003 26.52244 26.43149 25.70619
## [369] 25.73017 26.72483 25.72498 25.86668 26.50692 26.81667 26.62284 26.16629
## [377] 26.36178 26.50411 26.96235 26.19120 26.80073 26.55840 26.28364 27.43488
## [385] 26.30906 26.67074 26.59958 25.61036 26.55709 25.65524 26.76745 26.73638
## [393] 26.43359 25.96351 26.27253 26.84245 26.31849 26.95317 26.83717 26.87458
## [401] 27.09784 26.13156 26.06441 26.14393 26.02868 26.78388 25.85704 26.19135
## [409] 26.49589 26.21167 26.78441 25.86329 27.00641 25.48433 26.56477 26.43673
## [417] 27.47092 26.98342 25.40688 26.86563 26.37226 26.60739 27.61760 25.74756
## [425] 26.94182 26.47983 26.37198 26.30053 26.14172 27.60121 27.02462 25.52331
## [433] 26.70942 27.24607 27.27643 26.63665 25.96823 26.27366 26.98851 26.31407
## [441] 26.16584 26.63468 26.67040 26.68981 26.43985 27.09047 26.56933 26.14897
## [449] 25.28119 25.92332 26.80185 26.10675 27.00105 26.73044 26.53144 27.22623
## [457] 26.36290 26.01688 26.65795 26.63836 26.53224 27.21089 25.73028 26.27589
## [465] 26.33170 26.38320 26.21567 26.16758 26.20028 26.54756 27.23121 27.07097
## [473] 26.37482 26.47713 26.45713 25.83679 26.42483 25.92199 26.10956 26.41766abylong4<-data.frame(long4,abund4)
shapiro.test(abund4) ##
## Shapiro-Wilk normality test
##
## data: abund4
## W = 0.95473, p-value = 5.75e-11shapiro.test(long4) ##
## Shapiro-Wilk normality test
##
## data: long4
## W = 0.99814, p-value = 0.8874cor.test(abund4,long4, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund4 and long4
## S = 19416871, p-value = 0.2426
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.05343725pairs(long4~abund4)
chart.Correlation(abylong4, method ="s") 
#Correlación entre la abuncia con el tamaño del pie en la especie 1
abund## [1] 33 12 34 21 25 8 11 8 17 10 19 33 5 23 26 36 2 28 34 9 6 27 10 14 29
## [26] 26 9 29 32 3 14 40 33 30 6 12 33 23 15 27 6 6 28 1 32 25 27 7 13 31
## [51] 35 4 7 7 8 11 25 18 27 16 5 13 12 38 20 7 32 32 12 14 35 21 23 29 9
## [76] 22 22 33 39 11 38 16 17 22 32 31 15 16 26 14 1 1 4 34 26 20 29 5 1 16
## [101] 6 25 10 29 24 19 40 16 13 13 16 34 20 24 8 20 23 4 6 13 5 28 3 12 16
## [126] 4 26 13 1 37 21 1 21 16 1 8 11 24 19 13 33 24 27 25 27 34 17 6 30 20
## [151] 9 24 38 39 5 1 35 8 18 8 14 8 19 24 6 39 24 16 15 2 22 11 16 25 5
## [176] 37 15 16 31 29 12 16 4 13 15 11 28 2 17 9 32 24 38 12 13 33 15 34 1 34
## [201] 14 32 34 16 12 10 7 35 15 37 34 13 29 13 39 20 10 23 19 4 17 35 9 5 36
## [226] 26 31 26 9 20 30 9 26 9 8 29 35 20 4 1 19 22 27 8 17 11 21 10 28 15
## [251] 11 31 2 35 29 37 3 28 24 31 21 18 39 8 36 5 20 18 9 12 18 29 2 34 20
## [276] 34 21 36 12 35 8 35 39 14 30 29 32 31 2 23 12 26 8 6 14 12 22 15 8 20
## [301] 11 3 20 34 28 33 33 40 40 6 37 23 34 22 33 6 13 15 24 40 36 3 40 13 14
## [326] 39 8 33 9 17 17 11 34 12 21 11 33 33 22 29 10 2 2 30 18 17 35 34 37 37
## [351] 24 11 20 25 24 13 34 29 19 37 31 28 6 12 21 7 39 24 1 40 3 33 39 22 27
## [376] 29 30 27 22 40 11 19 38 40 14 19 2 14 14 37 30 9 39 27 1 24 22 29 3 30
## [401] 12 38 9 17 17 10 18 23 25 31 12 35 14 3 10 4 38 8 1 27 8 31 14 9 38
## [426] 4 6 32 32 9 20 31 17 9 26 15 15 15 5 9 27 28 32 30 32 11 24 4 30 11
## [451] 3 5 32 1 13 10 19 3 39 9 11 4 19 7 28 19 7 39 4 8 5 29 31 13 34
## [476] 3 9 16 31 14pie1<- dat1$hind_foot[dat1$especie=="artibeus_sp1"]
pie1## [1] 16.09827 16.36419 16.36146 15.91064 16.04647 16.33400 15.96951 19.53166
## [9] 19.66114 17.56782 17.12033 17.47825 17.41240 17.45466 17.58668 18.07524
## [17] 17.64754 16.68719 16.79518 15.88187 16.84705 17.90343 16.75628 16.16126
## [25] 19.43783 14.28082 16.74702 15.93574 16.34926 17.24108 17.28116 19.04180
## [33] 17.25148 17.10714 16.51059 17.17372 17.21105 16.80119 17.58674 17.32109
## [41] 17.31257 16.93260 16.83873 16.91388 17.29035 16.05903 16.54579 17.79943
## [49] 15.78593 15.76022 18.22338 19.38887 19.08650 16.49338 16.87403 16.68623
## [57] 16.85284 17.24065 15.64571 15.82851 17.59371 16.26516 16.18401 15.76850
## [65] 18.30514 19.26145 14.75094 18.38495 18.47660 17.08536 16.50402 17.46168
## [73] 16.85633 17.26477 18.39676 18.05341 18.62467 16.30496 17.72593 17.00174
## [81] 17.43675 16.24920 15.57598 17.73342 15.75185 17.70988 17.17003 16.47754
## [89] 17.02209 15.53812 16.50988 17.27121 17.48315 17.01354 17.92156 15.81432
## [97] 17.72000 15.98875 17.40578 16.21510 18.23453 16.98616 18.43475 17.58323
## [105] 19.03547 17.18038 17.35139 16.73404 17.55986 18.50308 16.44178 15.85609
## [113] 16.41958 16.28644 18.49309 18.24055 19.19706 17.24100 18.10386 16.62209
## [121] 17.78174 16.42143 18.21960 16.36978 16.12552 16.15248 15.54992 14.87791
## [129] 18.50317 15.64738 18.36002 16.48994 17.10019 17.34431 17.47251 15.99325
## [137] 16.89038 18.93413 17.84956 16.12136 15.90335 17.50810 17.30289 16.10299
## [145] 15.05215 18.34779 17.63999 18.43008 16.17974 16.06016 16.06324 18.98694
## [153] 16.37937 16.96729 17.16480 15.91142 17.05334 16.71225 15.18220 17.57444
## [161] 14.04986 16.42466 17.25525 16.68770 17.36233 16.84502 17.98754 15.56565
## [169] 17.09025 17.26608 15.48778 17.13711 16.30710 16.78318 17.32764 15.55090
## [177] 16.60610 19.79207 17.98369 15.57155 18.10396 16.51713 17.78298 18.31753
## [185] 16.32771 17.53935 17.25763 17.95208 16.53176 17.34036 17.46069 16.12789
## [193] 16.45084 17.45441 17.71149 18.31209 16.89373 18.03480 18.22568 17.96358
## [201] 16.45639 16.50950 16.38875 17.79738 17.58307 17.11895 16.83845 17.89741
## [209] 17.53633 16.93611 16.81773 15.87046 16.47809 18.56161 16.01286 17.70319
## [217] 17.25932 15.66752 15.77560 18.65623 16.46387 16.39021 14.48977 17.33654
## [225] 16.51054 15.08548 16.56608 17.09748 15.32431 16.36273 16.67548 18.49942
## [233] 16.96649 17.34762 17.57804 16.98409 17.66720 17.48467 17.22939 17.06487
## [241] 15.15292 14.89975 18.20534 17.06552 15.08651 17.47072 16.88008 18.52761
## [249] 17.83694 16.42522 17.07213 17.35804 18.20126 15.64313 15.26335 17.89188
## [257] 18.43393 16.06406 16.95461 17.75165 19.21141 16.13935 16.94782 16.78618
## [265] 17.78358 18.42242 17.90274 17.54367 18.03366 17.22096 18.04427 16.19702
## [273] 15.09667 17.18785 17.34606 17.93311 16.29600 18.24494 17.69995 15.47984
## [281] 17.38724 17.23713 17.33179 15.40786 16.10818 17.64138 16.92766 16.71041
## [289] 17.20884 16.61333 16.73363 16.02215 19.13415 17.98757 17.11681 18.01274
## [297] 17.34489 17.99428 16.59274 19.86857 17.34838 16.88755 14.87869 14.68203
## [305] 17.47470 16.15276 15.28024 16.42020 15.04929 16.41148 16.02540 16.96969
## [313] 16.33901 16.52191 17.79610 17.65187 17.17812 15.53490 17.74157 18.30924
## [321] 17.73285 15.38643 16.55820 17.35119 16.16168 16.05387 17.13999 18.68791
## [329] 16.45934 17.60919 17.11385 18.56270 17.15117 17.07950 16.49368 15.88420
## [337] 16.88908 15.91807 19.30849 17.15841 18.28745 16.27931 16.49002 14.77912
## [345] 17.30621 14.83594 16.80477 15.55557 18.32200 17.78689 16.09918 18.15442
## [353] 16.64257 17.38562 17.31310 16.87399 17.05920 17.04912 16.46767 18.19535
## [361] 17.39306 17.71985 17.83268 17.15562 15.72097 16.52915 18.03262 15.66201
## [369] 14.88601 16.07442 16.85836 17.65178 17.70384 16.95016 16.98999 17.42807
## [377] 16.83051 16.83711 17.85947 16.74780 17.10674 18.15916 17.84408 17.91640
## [385] 18.47177 18.30627 16.37212 15.80144 18.72720 15.57320 19.85735 17.10328
## [393] 16.23196 19.03895 16.61675 16.05121 17.42592 18.59712 16.44324 17.60188
## [401] 16.79750 15.73757 16.56969 17.24270 15.78039 16.30678 16.58583 16.85750
## [409] 17.17211 17.51545 19.88043 17.05248 18.28936 17.04103 14.28380 14.39564
## [417] 16.89724 15.98398 17.24855 17.84052 15.11482 17.80388 16.07809 14.71289
## [425] 18.53600 17.45312 19.13487 17.35468 16.34636 15.69483 18.36968 17.29664
## [433] 17.60109 17.26114 15.67973 18.71447 18.22204 16.40498 15.97585 17.76151
## [441] 16.18288 17.30415 17.12009 18.79215 14.61061 16.02288 15.36505 17.77643
## [449] 16.66479 17.04420 16.60975 18.55268 15.89425 16.91677 16.73329 19.37850
## [457] 15.44313 16.35646 17.17143 17.85397 15.15338 18.56867 17.65910 17.18057
## [465] 16.94845 15.88672 18.44914 18.76631 16.14019 17.15984 17.46484 16.55791
## [473] 16.44493 18.72597 17.17217 16.45337 16.94507 17.24490 17.48533 17.53963abypie1<-data.frame(pie1,abund)
shapiro.test(abund) ##
## Shapiro-Wilk normality test
##
## data: abund
## W = 0.95337, p-value = 3.589e-11shapiro.test(pie1) ##
## Shapiro-Wilk normality test
##
## data: pie1
## W = 0.99724, p-value = 0.6051cor.test(abund, pie1, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund and pie1
## S = 19642024, p-value = 0.151
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.06565265pairs(pie1~abund)
chart.Correlation(abypie1, method ="s") 
#Correlación entre la abundancia con el tamaño del pie en la especie 2
abund2## [1] 3 12 24 4 13 15 37 29 3 40 25 14 29 26 22 39 33 10 5 36 11 8 40 2 33
## [26] 21 32 39 29 33 10 27 37 14 38 22 4 24 25 32 34 37 12 28 2 28 37 18 32 27
## [51] 14 5 35 20 19 28 18 7 7 17 27 30 23 39 40 6 29 30 23 2 38 9 11 31 20
## [76] 36 3 2 33 34 37 12 4 28 20 12 21 31 36 8 15 5 37 11 28 30 36 7 8 34
## [101] 26 17 13 32 28 12 19 3 40 22 7 21 11 3 35 25 9 17 23 20 10 15 18 3 29
## [126] 18 35 18 29 1 36 24 19 20 12 12 16 21 6 31 39 5 18 3 34 29 39 40 23 21
## [151] 11 35 5 37 13 6 22 13 28 16 4 7 7 3 38 38 2 37 29 2 21 26 8 24 26
## [176] 10 29 40 6 12 31 1 35 22 7 6 39 18 23 4 14 9 32 17 6 25 40 40 31 11
## [201] 17 33 35 34 22 21 19 1 12 28 31 39 27 35 37 30 8 21 8 3 30 9 31 35 34
## [226] 12 6 16 34 2 16 36 6 27 22 14 20 19 8 4 12 32 38 34 15 6 34 24 29 34
## [251] 14 9 25 11 38 9 28 12 18 18 16 1 19 31 20 9 30 2 28 40 15 10 37 19 33
## [276] 6 26 12 15 23 9 4 7 2 23 39 21 5 14 3 2 33 20 14 24 1 38 14 38 27
## [301] 16 11 15 35 13 38 18 23 31 16 13 29 32 35 23 1 3 10 20 2 6 24 8 24 5
## [326] 35 3 21 33 36 27 21 9 25 5 11 35 31 10 22 32 14 38 26 14 20 26 18 8 35
## [351] 13 33 35 21 8 13 37 39 18 11 27 7 14 27 16 21 37 39 26 23 12 39 32 35 10
## [376] 21 18 13 27 17 33 31 17 30 5 40 9 17 26 1 7 21 26 26 26 28 9 24 9 15
## [401] 5 27 33 29 22 20 15 28 29 23 18 21 37 4 16 13 27 26 3 29 16 30 6 7 6
## [426] 7 2 15 40 9 8 7 29 20 16 34 27 36 35 10 33 29 36 6 2 39 21 24 11 30
## [451] 13 19 37 27 36 11 39 19 35 2 12 2 7 30 38 25 24 7 21 10 17 35 11 9 35
## [476] 37 33 19 27 37pie2<- dat1$hind_foot[dat1$especie=="artibeus_sp2"]
pie2## [1] 12.16641 12.88435 12.33136 11.58459 11.95099 11.68351 15.24793 12.79314
## [9] 12.82723 13.40912 14.73512 13.78532 13.19551 10.76477 13.56869 13.81510
## [17] 13.81094 14.01569 12.19013 13.72563 13.98176 11.80462 13.21105 11.93627
## [25] 13.67712 13.38035 12.88591 12.49152 12.58896 13.75572 13.62871 11.49560
## [33] 12.84444 13.42867 14.24128 14.02033 13.46716 13.90277 12.99512 13.43927
## [41] 13.49058 14.36578 13.14928 13.87103 13.56870 13.05383 12.38578 13.68441
## [49] 11.36473 12.25267 12.75665 12.95243 13.49280 14.07370 12.09477 16.47418
## [57] 13.54138 12.75055 15.42487 14.45141 11.93392 14.65577 12.34569 13.27327
## [65] 12.27003 13.01920 12.90997 13.57579 14.80785 15.00330 15.33745 13.44880
## [73] 13.55811 14.55956 13.80126 14.44688 13.90666 13.07016 12.70007 12.26976
## [81] 14.20358 11.42320 14.59212 13.81039 13.34754 14.48716 12.31804 12.13799
## [89] 11.70870 14.38109 13.74352 12.39298 12.49605 12.93336 13.50360 13.67756
## [97] 13.36775 11.82444 13.20480 13.51352 15.35826 13.14180 14.38353 10.47314
## [105] 13.07438 13.36662 12.65895 13.15905 14.08612 13.57456 14.00941 15.05439
## [113] 13.24505 13.50855 13.46338 14.24623 11.40302 11.57991 15.18991 10.82122
## [121] 12.19087 13.91958 13.52404 13.00733 13.08658 12.09135 12.17230 11.94443
## [129] 12.57971 12.42637 14.51394 13.32166 12.93972 13.00503 12.33545 15.16003
## [137] 13.36307 14.60040 12.80626 14.35179 13.22582 12.36206 10.92523 12.39862
## [145] 13.21306 12.83655 14.01494 16.02244 15.27292 13.51611 12.86016 12.02088
## [153] 11.93963 11.92457 14.66677 11.94410 14.40055 13.72653 12.13756 12.08076
## [161] 13.96701 12.91318 13.42312 13.06650 13.83214 15.37216 12.27111 12.97984
## [169] 13.40480 12.17460 16.05047 14.47903 13.66657 13.42168 13.14866 14.82845
## [177] 14.57824 12.92292 13.18580 15.62582 16.04232 12.71987 13.96047 15.72385
## [185] 12.88828 13.38478 11.28783 11.61616 12.70683 11.84196 14.85273 12.83300
## [193] 12.77725 14.67352 11.41779 13.10023 14.48183 14.24219 12.95436 13.81356
## [201] 10.86257 12.51406 12.25708 13.40698 13.13964 13.30480 12.38664 12.78646
## [209] 14.23736 12.54930 13.69515 11.55381 12.94065 14.74074 15.17435 12.98161
## [217] 14.76874 10.53271 13.35291 13.59214 13.26047 14.34241 13.61230 14.66115
## [225] 11.34454 15.01309 12.33913 16.32463 15.38803 13.93509 13.42666 13.05884
## [233] 13.86340 14.23125 13.70420 12.19729 14.69699 12.07244 14.59715 11.63041
## [241] 12.40440 10.94088 14.94992 13.66209 12.73866 14.48313 14.10610 13.33042
## [249] 12.42856 12.85240 14.03145 14.90491 13.78872 12.95807 14.22890 13.55616
## [257] 13.76129 14.03812 13.74806 13.19557 13.20855 13.56558 13.15792 14.61420
## [265] 14.50225 15.09990 16.49952 15.47574 14.30995 12.42486 12.83520 10.81013
## [273] 12.08250 12.36547 12.28643 14.10564 13.56840 11.91708 12.60835 13.10158
## [281] 14.58176 14.64803 13.84455 12.03721 12.62277 14.61459 11.58802 15.08416
## [289] 14.58549 13.34966 13.50168 10.78047 14.41538 13.81990 15.64730 11.99164
## [297] 14.51754 13.63271 13.58266 13.82767 14.06787 13.26634 13.76111 13.90917
## [305] 11.69727 13.76135 12.15913 13.42097 14.12849 12.01830 11.58807 11.91085
## [313] 13.40107 12.63451 12.53530 11.82347 13.08217 12.75048 14.76338 11.37527
## [321] 16.00477 13.04061 14.78787 13.24754 14.88372 13.00106 13.16799 13.29668
## [329] 10.19811 13.31037 12.77636 13.08844 14.00147 14.94429 13.02806 14.43024
## [337] 13.89126 12.47697 14.24968 15.32030 13.48205 14.02943 13.96192 11.89374
## [345] 12.64268 12.62370 12.14596 12.03314 13.45406 13.02517 13.72696 10.66970
## [353] 13.82904 14.14579 12.64518 15.71310 12.13058 12.85227 13.79153 12.91810
## [361] 13.13268 13.86832 12.75295 13.58113 12.88984 12.54171 12.39068 12.60409
## [369] 13.05965 12.02279 12.59355 11.74166 10.34407 12.50147 13.29460 15.32224
## [377] 13.41223 13.35742 11.22425 12.53801 14.59188 11.09843 13.20430 12.44842
## [385] 13.80862 12.85851 10.71672 14.20615 14.74388 11.46553 13.04631 11.73167
## [393] 14.08770 12.64686 13.11139 13.00620 15.38558 13.67607 12.76046 12.72337
## [401] 13.79608 15.01965 13.18286 13.87928 13.51447 12.15996 12.96233 14.04960
## [409] 13.63356 13.89553 12.18134 14.54128 13.23304 13.13540 14.19734 12.80219
## [417] 12.35441 14.29389 13.40050 13.84716 12.56889 11.75324 12.65747 13.97468
## [425] 14.10814 12.35825 12.77059 12.85717 13.23755 14.64649 13.59457 13.37328
## [433] 11.75607 14.19441 12.83014 12.43714 11.11236 13.22910 11.84006 13.62859
## [441] 15.02129 12.77504 14.12992 12.90857 14.15673 12.41690 11.40362 12.14236
## [449] 12.13967 14.46896 12.97131 13.66735 14.25253 14.08259 12.74739 13.38586
## [457] 12.39482 14.17008 12.66525 13.83140 15.39650 13.72288 12.55277 13.42092
## [465] 15.72695 14.39960 11.40063 12.21839 14.11793 12.21473 14.31798 12.39525
## [473] 14.77677 12.52145 11.73620 13.58548 12.34415 14.00053 14.00214 13.21677abypie2<-data.frame(pie2,abund2)
shapiro.test(abund2) ##
## Shapiro-Wilk normality test
##
## data: abund2
## W = 0.94974, p-value = 1.067e-11shapiro.test(pie2) ##
## Shapiro-Wilk normality test
##
## data: pie2
## W = 0.99815, p-value = 0.8907cor.test(abund2, pie2, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund2 and pie2
## S = 18079256, p-value = 0.6758
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.01913333pairs(pie2~abund2)
chart.Correlation(abypie2, method ="s") 
#Correlación entre la abuncia con el tamaño del pie en la especie 3
abund3## [1] 37 10 12 8 3 10 29 22 40 29 6 4 15 37 30 36 5 20 1 18 37 20 11 15 8
## [26] 36 12 18 33 35 16 7 7 26 12 32 13 34 24 21 29 37 10 22 40 5 36 19 23 8
## [51] 14 21 15 13 23 3 11 2 3 22 35 26 1 25 4 10 32 36 8 22 10 24 35 2 22
## [76] 13 35 22 39 18 25 31 30 36 18 18 20 33 5 9 29 35 10 28 6 34 8 27 26 22
## [101] 9 35 20 40 7 35 24 15 22 28 34 9 35 19 20 5 16 6 28 10 35 12 17 5 27
## [126] 7 24 10 5 24 38 39 6 9 36 11 5 27 3 25 23 8 20 6 23 29 27 20 31 8
## [151] 21 19 14 32 15 9 16 16 40 15 11 29 19 23 31 24 30 11 21 23 27 10 30 32 22
## [176] 32 13 39 8 4 3 1 21 12 6 33 19 9 21 3 29 17 22 1 23 11 37 11 10 21
## [201] 23 4 16 32 18 17 26 21 8 27 29 17 26 4 38 25 5 15 26 36 35 25 29 8 19
## [226] 11 23 26 33 20 10 38 35 3 23 38 5 16 32 40 2 22 28 36 28 18 21 25 35 39
## [251] 4 4 30 40 25 31 40 29 28 9 31 7 13 27 31 16 13 8 9 39 15 2 31 31 2
## [276] 1 16 29 22 39 33 15 32 20 40 1 29 19 3 23 35 2 16 25 23 13 19 26 8 9
## [301] 24 24 27 16 33 8 39 1 37 2 7 31 13 11 30 35 9 1 19 28 6 16 24 25 38
## [326] 36 9 35 26 3 1 21 29 36 24 37 35 1 2 39 26 5 37 38 4 29 28 11 5 36
## [351] 21 25 1 32 14 24 38 19 16 40 25 20 33 16 1 34 17 37 2 19 1 2 18 21 17
## [376] 6 15 38 37 7 33 15 37 17 32 9 12 19 6 14 23 20 31 31 33 35 32 19 2 5
## [401] 29 12 35 17 20 36 24 24 9 9 32 19 16 5 2 28 21 37 29 29 7 36 5 38 14
## [426] 33 31 40 32 1 39 6 33 32 25 33 22 17 34 23 27 8 32 4 11 10 10 39 24 12
## [451] 35 1 33 27 7 20 33 18 10 28 26 30 14 3 8 8 14 2 5 16 38 12 24 4 39
## [476] 29 17 13 35 12pie3<- dat1$hind_foot[dat1$especie=="thyroptera_sp1"]
pie3## [1] 9.895572 10.932894 10.883044 11.572597 10.089462 8.052213 9.275959
## [8] 9.133476 8.686325 9.540028 9.258306 10.377630 10.751893 10.284078
## [15] 9.708340 11.756870 10.622364 9.687638 10.164238 10.733478 9.368517
## [22] 9.409300 10.058256 11.037100 9.588049 9.136952 10.465692 10.744696
## [29] 10.810248 10.459390 8.405887 11.637815 10.206407 11.625193 9.372196
## [36] 10.607306 10.022101 11.326949 8.851483 10.025639 7.939201 8.404268
## [43] 9.336440 10.993725 9.265043 11.241737 13.119820 9.326320 8.837618
## [50] 9.472072 11.029391 10.732761 11.549222 11.183467 8.360888 12.455226
## [57] 10.261454 11.738200 10.934461 9.170081 9.271263 12.321471 9.560153
## [64] 12.064195 8.207919 9.067029 8.752582 12.498069 9.510892 10.566367
## [71] 11.322532 11.560417 9.054848 9.561527 8.399105 10.618038 12.115572
## [78] 10.641401 11.604944 11.361096 9.462974 10.693206 11.006495 9.439718
## [85] 8.316046 11.429836 10.931550 11.471633 10.703939 11.742762 10.495508
## [92] 11.457151 12.698776 10.016448 10.661912 10.405567 10.359676 8.918744
## [99] 10.989691 11.087597 11.504193 9.866113 12.597922 11.673403 13.154755
## [106] 10.413053 8.939514 11.787990 9.614919 11.836740 10.152802 10.846171
## [113] 10.719710 10.018162 10.510412 10.618509 9.216754 8.287011 9.580864
## [120] 11.345138 9.331333 9.928077 9.275714 12.940034 11.203680 8.715274
## [127] 10.345425 11.653954 9.491091 9.683448 11.461939 11.639843 7.809084
## [134] 12.342222 11.080449 9.053126 10.688751 12.325222 8.602800 9.367790
## [141] 10.635603 9.761875 9.593977 9.951910 8.976405 10.391094 10.245639
## [148] 9.782877 9.051610 9.189251 10.229457 10.932373 11.459061 9.575443
## [155] 10.858848 8.908860 12.473894 10.268381 9.711374 11.756780 10.969594
## [162] 11.724591 12.577580 9.834271 10.010796 9.694550 13.184348 9.350451
## [169] 10.186330 10.211752 10.582256 9.675600 11.535628 10.962759 9.993870
## [176] 10.035752 9.604696 8.757730 11.997057 7.955036 9.921849 10.874021
## [183] 8.425709 12.800732 8.867459 9.119810 10.772601 8.815547 11.324593
## [190] 11.587346 12.201764 11.339263 10.804526 7.632787 11.937390 10.495298
## [197] 10.569639 9.522467 9.440980 8.505229 10.759471 9.124862 11.939745
## [204] 9.675458 8.284932 10.919598 10.483656 10.193944 10.199515 11.095113
## [211] 8.940956 12.135828 9.744433 10.766807 10.452540 8.254773 11.597644
## [218] 8.910234 10.127932 11.050982 12.035624 9.385528 12.324421 9.741588
## [225] 11.399234 11.148446 9.325717 10.039718 9.402895 11.715909 9.285801
## [232] 11.647054 12.058922 10.288689 10.892380 10.527794 9.100010 7.248567
## [239] 11.029937 10.888745 9.817388 10.131964 11.044238 10.677138 10.307155
## [246] 9.131715 8.708704 10.255192 8.814664 9.519952 10.522127 9.991817
## [253] 11.736695 9.816468 9.617600 7.747699 9.141967 10.077845 9.044444
## [260] 10.703954 10.913112 11.527770 11.234664 12.092309 9.829247 10.044279
## [267] 9.177206 12.560677 10.643450 9.241380 10.020780 9.061547 10.661682
## [274] 9.512895 11.505059 9.388270 9.163807 10.279164 10.175375 8.940026
## [281] 9.937713 9.995478 11.620306 8.494389 10.147003 11.452977 11.328510
## [288] 11.244472 9.845290 11.629542 10.055688 8.966649 10.979373 8.732635
## [295] 9.780944 11.055097 9.724002 8.508652 10.235985 8.552065 10.743376
## [302] 10.707500 10.121548 11.905096 11.047433 9.799643 11.803439 11.449446
## [309] 10.133383 10.112638 10.232665 11.077649 10.057359 10.481447 11.297310
## [316] 9.867051 10.338065 8.725085 8.886563 10.060978 11.323743 8.400025
## [323] 11.184660 9.813318 9.547817 8.604053 10.352779 9.884549 8.641372
## [330] 9.446490 10.488788 10.063268 8.584241 9.960893 10.066207 9.379328
## [337] 11.180976 9.796278 12.385399 10.045031 10.726359 10.487457 9.977546
## [344] 10.409155 9.695018 11.085680 10.532595 11.104561 11.449657 9.032485
## [351] 11.171688 7.996937 9.867220 9.631581 10.070112 9.510625 10.991171
## [358] 10.132656 12.130677 11.586535 11.088816 11.231445 9.632056 8.541021
## [365] 10.588244 9.875149 9.359650 8.564827 9.974074 9.652156 10.249284
## [372] 9.895400 9.675833 9.648497 11.434908 9.248008 9.436830 9.874340
## [379] 9.570924 10.173310 9.819291 10.360693 11.065070 8.907264 10.120958
## [386] 7.252199 10.581140 10.861981 11.909067 9.948757 10.782765 10.005896
## [393] 9.958800 10.283635 9.144996 10.901560 11.664222 11.646330 8.738056
## [400] 9.540650 9.780813 9.001646 10.196197 10.212203 11.426238 9.423633
## [407] 9.649452 9.859523 11.176584 9.545309 10.446090 9.884792 10.868134
## [414] 10.478556 11.644775 12.287794 10.841752 9.214662 11.715941 11.004545
## [421] 11.342546 10.518789 10.681427 10.994508 10.919033 9.490279 9.453794
## [428] 10.121226 9.893500 11.219189 10.186457 9.162522 11.840943 9.992222
## [435] 12.012689 9.326159 10.909107 9.260818 11.087819 8.393494 8.613526
## [442] 10.475298 8.450102 9.776612 11.691284 11.982046 11.140726 8.887852
## [449] 9.353100 10.425393 7.834133 10.398815 8.724521 10.018469 11.009670
## [456] 10.312461 12.678891 9.975322 9.750975 11.742339 12.662389 11.060192
## [463] 10.891878 10.349180 10.310773 9.967372 11.014091 9.273153 11.492997
## [470] 11.473783 9.326939 10.513736 8.558046 12.059494 11.949221 10.185671
## [477] 11.044489 9.215117 10.592929 9.088670abypie3<-data.frame(pie3,abund3)
shapiro.test(abund3) ##
## Shapiro-Wilk normality test
##
## data: abund3
## W = 0.95226, p-value = 2.463e-11shapiro.test(pie3) ##
## Shapiro-Wilk normality test
##
## data: pie3
## W = 0.99655, p-value = 0.3941cor.test(abund3, pie3, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund3 and pie3
## S = 18774730, p-value = 0.6844
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.01859873pairs(pie3~abund3)
chart.Correlation(abypie3, method ="s") 
#Correlación entre la abuncia con el tamaño del pie en la especie 4
abund4## [1] 11 30 27 25 26 9 18 38 8 8 13 7 25 29 5 38 4 27 3 22 19 21 18 9 5
## [26] 10 27 31 30 8 5 17 14 5 37 28 24 7 25 12 40 29 23 16 33 6 25 32 10 12
## [51] 36 12 33 28 23 33 40 19 28 1 26 16 2 38 23 5 40 21 23 35 19 33 3 38 7
## [76] 15 3 18 36 10 27 25 10 29 38 21 32 13 21 21 4 10 33 11 35 38 38 17 29 8
## [101] 38 38 35 26 10 16 2 16 10 17 19 15 22 33 19 3 17 4 2 39 20 26 35 23 17
## [126] 35 2 8 11 11 36 28 22 19 25 24 40 3 36 20 35 12 24 34 15 15 38 17 35 21
## [151] 11 4 24 40 37 10 11 8 13 2 33 3 35 30 33 31 19 12 18 18 16 7 7 31 4
## [176] 30 32 30 22 25 25 17 2 19 2 40 40 1 38 39 14 36 12 17 3 3 39 1 38 7
## [201] 7 29 35 34 27 21 28 26 8 23 16 19 8 4 12 4 36 29 16 17 11 18 39 18 14
## [226] 14 38 27 24 10 15 8 15 5 27 39 36 14 23 32 30 9 39 5 17 20 2 12 19 35
## [251] 37 24 11 13 17 22 10 16 8 9 9 34 4 3 23 27 34 18 10 5 14 14 19 39 33
## [276] 6 16 28 25 26 26 19 33 38 11 24 3 5 5 19 31 8 30 37 24 17 23 10 7 14
## [301] 31 31 40 38 39 11 16 31 22 6 14 32 21 3 27 39 29 6 6 17 27 9 4 23 23
## [326] 23 24 23 40 15 19 26 39 36 12 33 37 10 2 5 36 8 17 15 1 19 28 22 5 1
## [351] 23 20 16 35 34 32 24 30 8 36 13 24 21 12 5 10 8 25 23 19 24 36 7 36 16
## [376] 2 18 27 40 5 5 3 33 20 31 16 38 12 8 3 16 1 19 4 31 25 3 15 23 20
## [401] 23 40 19 22 37 8 13 25 1 16 16 12 22 23 31 14 23 36 38 5 2 19 12 37 39
## [426] 28 35 26 27 13 22 36 30 9 35 39 1 29 6 35 24 32 30 20 24 13 21 32 40 27
## [451] 18 25 28 15 30 1 36 6 40 40 28 27 27 25 21 27 34 38 36 13 31 3 26 24 21
## [476] 35 25 35 39 1pie4<- dat1$hind_foot[dat1$especie=="artibeus_sp2"]
pie4## [1] 12.16641 12.88435 12.33136 11.58459 11.95099 11.68351 15.24793 12.79314
## [9] 12.82723 13.40912 14.73512 13.78532 13.19551 10.76477 13.56869 13.81510
## [17] 13.81094 14.01569 12.19013 13.72563 13.98176 11.80462 13.21105 11.93627
## [25] 13.67712 13.38035 12.88591 12.49152 12.58896 13.75572 13.62871 11.49560
## [33] 12.84444 13.42867 14.24128 14.02033 13.46716 13.90277 12.99512 13.43927
## [41] 13.49058 14.36578 13.14928 13.87103 13.56870 13.05383 12.38578 13.68441
## [49] 11.36473 12.25267 12.75665 12.95243 13.49280 14.07370 12.09477 16.47418
## [57] 13.54138 12.75055 15.42487 14.45141 11.93392 14.65577 12.34569 13.27327
## [65] 12.27003 13.01920 12.90997 13.57579 14.80785 15.00330 15.33745 13.44880
## [73] 13.55811 14.55956 13.80126 14.44688 13.90666 13.07016 12.70007 12.26976
## [81] 14.20358 11.42320 14.59212 13.81039 13.34754 14.48716 12.31804 12.13799
## [89] 11.70870 14.38109 13.74352 12.39298 12.49605 12.93336 13.50360 13.67756
## [97] 13.36775 11.82444 13.20480 13.51352 15.35826 13.14180 14.38353 10.47314
## [105] 13.07438 13.36662 12.65895 13.15905 14.08612 13.57456 14.00941 15.05439
## [113] 13.24505 13.50855 13.46338 14.24623 11.40302 11.57991 15.18991 10.82122
## [121] 12.19087 13.91958 13.52404 13.00733 13.08658 12.09135 12.17230 11.94443
## [129] 12.57971 12.42637 14.51394 13.32166 12.93972 13.00503 12.33545 15.16003
## [137] 13.36307 14.60040 12.80626 14.35179 13.22582 12.36206 10.92523 12.39862
## [145] 13.21306 12.83655 14.01494 16.02244 15.27292 13.51611 12.86016 12.02088
## [153] 11.93963 11.92457 14.66677 11.94410 14.40055 13.72653 12.13756 12.08076
## [161] 13.96701 12.91318 13.42312 13.06650 13.83214 15.37216 12.27111 12.97984
## [169] 13.40480 12.17460 16.05047 14.47903 13.66657 13.42168 13.14866 14.82845
## [177] 14.57824 12.92292 13.18580 15.62582 16.04232 12.71987 13.96047 15.72385
## [185] 12.88828 13.38478 11.28783 11.61616 12.70683 11.84196 14.85273 12.83300
## [193] 12.77725 14.67352 11.41779 13.10023 14.48183 14.24219 12.95436 13.81356
## [201] 10.86257 12.51406 12.25708 13.40698 13.13964 13.30480 12.38664 12.78646
## [209] 14.23736 12.54930 13.69515 11.55381 12.94065 14.74074 15.17435 12.98161
## [217] 14.76874 10.53271 13.35291 13.59214 13.26047 14.34241 13.61230 14.66115
## [225] 11.34454 15.01309 12.33913 16.32463 15.38803 13.93509 13.42666 13.05884
## [233] 13.86340 14.23125 13.70420 12.19729 14.69699 12.07244 14.59715 11.63041
## [241] 12.40440 10.94088 14.94992 13.66209 12.73866 14.48313 14.10610 13.33042
## [249] 12.42856 12.85240 14.03145 14.90491 13.78872 12.95807 14.22890 13.55616
## [257] 13.76129 14.03812 13.74806 13.19557 13.20855 13.56558 13.15792 14.61420
## [265] 14.50225 15.09990 16.49952 15.47574 14.30995 12.42486 12.83520 10.81013
## [273] 12.08250 12.36547 12.28643 14.10564 13.56840 11.91708 12.60835 13.10158
## [281] 14.58176 14.64803 13.84455 12.03721 12.62277 14.61459 11.58802 15.08416
## [289] 14.58549 13.34966 13.50168 10.78047 14.41538 13.81990 15.64730 11.99164
## [297] 14.51754 13.63271 13.58266 13.82767 14.06787 13.26634 13.76111 13.90917
## [305] 11.69727 13.76135 12.15913 13.42097 14.12849 12.01830 11.58807 11.91085
## [313] 13.40107 12.63451 12.53530 11.82347 13.08217 12.75048 14.76338 11.37527
## [321] 16.00477 13.04061 14.78787 13.24754 14.88372 13.00106 13.16799 13.29668
## [329] 10.19811 13.31037 12.77636 13.08844 14.00147 14.94429 13.02806 14.43024
## [337] 13.89126 12.47697 14.24968 15.32030 13.48205 14.02943 13.96192 11.89374
## [345] 12.64268 12.62370 12.14596 12.03314 13.45406 13.02517 13.72696 10.66970
## [353] 13.82904 14.14579 12.64518 15.71310 12.13058 12.85227 13.79153 12.91810
## [361] 13.13268 13.86832 12.75295 13.58113 12.88984 12.54171 12.39068 12.60409
## [369] 13.05965 12.02279 12.59355 11.74166 10.34407 12.50147 13.29460 15.32224
## [377] 13.41223 13.35742 11.22425 12.53801 14.59188 11.09843 13.20430 12.44842
## [385] 13.80862 12.85851 10.71672 14.20615 14.74388 11.46553 13.04631 11.73167
## [393] 14.08770 12.64686 13.11139 13.00620 15.38558 13.67607 12.76046 12.72337
## [401] 13.79608 15.01965 13.18286 13.87928 13.51447 12.15996 12.96233 14.04960
## [409] 13.63356 13.89553 12.18134 14.54128 13.23304 13.13540 14.19734 12.80219
## [417] 12.35441 14.29389 13.40050 13.84716 12.56889 11.75324 12.65747 13.97468
## [425] 14.10814 12.35825 12.77059 12.85717 13.23755 14.64649 13.59457 13.37328
## [433] 11.75607 14.19441 12.83014 12.43714 11.11236 13.22910 11.84006 13.62859
## [441] 15.02129 12.77504 14.12992 12.90857 14.15673 12.41690 11.40362 12.14236
## [449] 12.13967 14.46896 12.97131 13.66735 14.25253 14.08259 12.74739 13.38586
## [457] 12.39482 14.17008 12.66525 13.83140 15.39650 13.72288 12.55277 13.42092
## [465] 15.72695 14.39960 11.40063 12.21839 14.11793 12.21473 14.31798 12.39525
## [473] 14.77677 12.52145 11.73620 13.58548 12.34415 14.00053 14.00214 13.21677abypie4<-data.frame(pie4,abund4)
shapiro.test(abund4) ##
## Shapiro-Wilk normality test
##
## data: abund4
## W = 0.95473, p-value = 5.75e-11shapiro.test(pie4) ##
## Shapiro-Wilk normality test
##
## data: pie4
## W = 0.99815, p-value = 0.8907cor.test(abund4, pie4, method= "spearman") ##
## Spearman's rank correlation rho
##
## data: abund4 and pie4
## S = 17908560, p-value = 0.5349
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.0283942pairs(pie4~abund4)
chart.Correlation(abypie4, method ="s") 
#Abundancia vs sitio
e1s1 <- dat1$N[dat1$sitio=="sitio1" & dat1$especie=="artibeus_sp1"]
e1s1## [1] 33 12 34 21 25 8 11 8 17 10 19 33 5 23 26 36 2 28 34 9 6 27 10 14 29
## [26] 26 9 29 32 3 5 28 3 12 16 4 26 13 1 37 21 1 21 16 1 8 11 24 19 13
## [51] 33 24 27 25 27 34 17 6 30 20 19 22 27 8 17 11 21 10 28 15 11 31 2 35 29
## [76] 37 3 28 24 31 21 18 39 8 36 5 20 18 9 12 31 28 6 12 21 7 39 24 1 40
## [101] 3 33 39 22 27 29 30 27 22 40 11 19 38 40 14 19 2 14 14 37sum(e1s1)## [1] 2386e1s2 <- dat1$N[dat1$sitio=="sitio2" & dat1$especie=="artibeus_sp1"]
e1s2## [1] 14 40 33 30 6 12 33 23 15 27 6 6 28 1 32 25 27 7 13 31 35 4 7 7 8
## [26] 11 25 18 27 16 9 24 38 39 5 1 35 8 18 8 14 8 19 24 6 39 24 16 15 2
## [51] 22 11 16 25 5 37 15 16 31 29 18 29 2 34 20 34 21 36 12 35 8 35 39 14 30
## [76] 29 32 31 2 23 12 26 8 6 14 12 22 15 8 20 30 9 39 27 1 24 22 29 3 30
## [101] 12 38 9 17 17 10 18 23 25 31 12 35 14 3 10 4 38 8 1 27sum(e1s2)## [1] 2319e1s3 <- dat1$N[dat1$sitio=="sitio3" & dat1$especie=="artibeus_sp1"]
e1s3## [1] 5 13 12 38 20 7 32 32 12 14 35 21 23 29 9 22 22 33 39 11 38 16 17 22 32
## [26] 31 15 16 26 14 12 16 4 13 15 11 28 2 17 9 32 24 38 12 13 33 15 34 1 34
## [51] 14 32 34 16 12 10 7 35 15 37 11 3 20 34 28 33 33 40 40 6 37 23 34 22 33
## [76] 6 13 15 24 40 36 3 40 13 14 39 8 33 9 17 8 31 14 9 38 4 6 32 32 9
## [101] 20 31 17 9 26 15 15 15 5 9 27 28 32 30 32 11 24 4 30 11sum(e1s3)## [1] 2512e1s4 <- dat1$N[dat1$sitio=="sitio4" & dat1$especie=="artibeus_sp1"]
e1s4## [1] 1 1 4 34 26 20 29 5 1 16 6 25 10 29 24 19 40 16 13 13 16 34 20 24 8
## [26] 20 23 4 6 13 34 13 29 13 39 20 10 23 19 4 17 35 9 5 36 26 31 26 9 20
## [51] 30 9 26 9 8 29 35 20 4 1 17 11 34 12 21 11 33 33 22 29 10 2 2 30 18
## [76] 17 35 34 37 37 24 11 20 25 24 13 34 29 19 37 3 5 32 1 13 10 19 3 39 9
## [101] 11 4 19 7 28 19 7 39 4 8 5 29 31 13 34 3 9 16 31 14sum(e1s4)## [1] 2235e2s1 <- dat1$N[dat1$sitio=="sitio1" & dat1$especie=="artibeus_sp2"]
e2s1## [1] 3 12 24 4 13 15 37 29 3 40 25 14 29 26 22 39 33 10 5 36 11 8 40 2 33
## [26] 21 32 39 29 33 10 15 18 3 29 18 35 18 29 1 36 24 19 20 12 12 16 21 6 31
## [51] 39 5 18 3 34 29 39 40 23 21 12 32 38 34 15 6 34 24 29 34 14 9 25 11 38
## [76] 9 28 12 18 18 16 1 19 31 20 9 30 2 28 40 27 7 14 27 16 21 37 39 26 23
## [101] 12 39 32 35 10 21 18 13 27 17 33 31 17 30 5 40 9 17 26 1sum(e2s1)## [1] 2597e2s2 <- dat1$N[dat1$sitio=="sitio2" & dat1$especie=="artibeus_sp2"]
e2s2## [1] 10 27 37 14 38 22 4 24 25 32 34 37 12 28 2 28 37 18 32 27 14 5 35 20 19
## [26] 28 18 7 7 17 11 35 5 37 13 6 22 13 28 16 4 7 7 3 38 38 2 37 29 2
## [51] 21 26 8 24 26 10 29 40 6 12 15 10 37 19 33 6 26 12 15 23 9 4 7 2 23
## [76] 39 21 5 14 3 2 33 20 14 24 1 38 14 38 27 7 21 26 26 26 28 9 24 9 15
## [101] 5 27 33 29 22 20 15 28 29 23 18 21 37 4 16 13 27 26 3 29sum(e2s2)## [1] 2363e2s3 <- dat1$N[dat1$sitio=="sitio3" & dat1$especie=="artibeus_sp2"]
e2s3## [1] 27 30 23 39 40 6 29 30 23 2 38 9 11 31 20 36 3 2 33 34 37 12 4 28 20
## [26] 12 21 31 36 8 31 1 35 22 7 6 39 18 23 4 14 9 32 17 6 25 40 40 31 11
## [51] 17 33 35 34 22 21 19 1 12 28 16 11 15 35 13 38 18 23 31 16 13 29 32 35 23
## [76] 1 3 10 20 2 6 24 8 24 5 35 3 21 33 36 16 30 6 7 6 7 2 15 40 9
## [101] 8 7 29 20 16 34 27 36 35 10 33 29 36 6 2 39 21 24 11 30sum(e2s3)## [1] 2478e2s4 <- dat1$N[dat1$sitio=="sitio4" & dat1$especie=="artibeus_sp2"]
e2s4## [1] 15 5 37 11 28 30 36 7 8 34 26 17 13 32 28 12 19 3 40 22 7 21 11 3 35
## [26] 25 9 17 23 20 31 39 27 35 37 30 8 21 8 3 30 9 31 35 34 12 6 16 34 2
## [51] 16 36 6 27 22 14 20 19 8 4 27 21 9 25 5 11 35 31 10 22 32 14 38 26 14
## [76] 20 26 18 8 35 13 33 35 21 8 13 37 39 18 11 13 19 37 27 36 11 39 19 35 2
## [101] 12 2 7 30 38 25 24 7 21 10 17 35 11 9 35 37 33 19 27 37sum(e2s4)## [1] 2543e3s1 <- dat1$N[dat1$sitio=="sitio1" & dat1$especie=="thyroptera_sp1"]
e3s1## [1] 37 10 12 8 3 10 29 22 40 29 6 4 15 37 30 36 5 20 1 18 37 20 11 15 8
## [26] 36 12 18 33 35 35 12 17 5 27 7 24 10 5 24 38 39 6 9 36 11 5 27 3 25
## [51] 23 8 20 6 23 29 27 20 31 8 2 22 28 36 28 18 21 25 35 39 4 4 30 40 25
## [76] 31 40 29 28 9 31 7 13 27 31 16 13 8 9 39 25 20 33 16 1 34 17 37 2 19
## [101] 1 2 18 21 17 6 15 38 37 7 33 15 37 17 32 9 12 19 6 14sum(e3s1)## [1] 2405e3s2 <- dat1$N[dat1$sitio=="sitio2" & dat1$especie=="thyroptera_sp1"]
e3s2## [1] 16 7 7 26 12 32 13 34 24 21 29 37 10 22 40 5 36 19 23 8 14 21 15 13 23
## [26] 3 11 2 3 22 21 19 14 32 15 9 16 16 40 15 11 29 19 23 31 24 30 11 21 23
## [51] 27 10 30 32 22 32 13 39 8 4 15 2 31 31 2 1 16 29 22 39 33 15 32 20 40
## [76] 1 29 19 3 23 35 2 16 25 23 13 19 26 8 9 23 20 31 31 33 35 32 19 2 5
## [101] 29 12 35 17 20 36 24 24 9 9 32 19 16 5 2 28 21 37 29 29sum(e3s2)## [1] 2427e3s3 <- dat1$N[dat1$sitio=="sitio3" & dat1$especie=="thyroptera_sp2"]
e3s3## [1] 26 16 2 38 23 5 40 21 23 35 19 33 3 38 7 15 3 18 36 10 27 25 10 29 38
## [26] 21 32 13 21 21 25 17 2 19 2 40 40 1 38 39 14 36 12 17 3 3 39 1 38 7
## [51] 7 29 35 34 27 21 28 26 8 23 31 31 40 38 39 11 16 31 22 6 14 32 21 3 27
## [76] 39 29 6 6 17 27 9 4 23 23 23 24 23 40 15 2 19 12 37 39 28 35 26 27 13
## [101] 22 36 30 9 35 39 1 29 6 35 24 32 30 20 24 13 21 32 40 27sum(e3s3)## [1] 2692e3s4 <- dat1$N[dat1$sitio=="sitio4" & dat1$especie=="thyroptera_sp2"]
e3s4## [1] 4 10 33 11 35 38 38 17 29 8 38 38 35 26 10 16 2 16 10 17 19 15 22 33 19
## [26] 3 17 4 2 39 16 19 8 4 12 4 36 29 16 17 11 18 39 18 14 14 38 27 24 10
## [51] 15 8 15 5 27 39 36 14 23 32 19 26 39 36 12 33 37 10 2 5 36 8 17 15 1
## [76] 19 28 22 5 1 23 20 16 35 34 32 24 30 8 36 18 25 28 15 30 1 36 6 40 40
## [101] 28 27 27 25 21 27 34 38 36 13 31 3 26 24 21 35 25 35 39 1sum(e3s4)## [1] 2576e4s1 <- dat1$N[dat1$sitio=="sitio1" & dat1$especie=="thyroptera_sp2"]
e4s1## [1] 11 30 27 25 26 9 18 38 8 8 13 7 25 29 5 38 4 27 3 22 19 21 18 9 5
## [26] 10 27 31 30 8 20 26 35 23 17 35 2 8 11 11 36 28 22 19 25 24 40 3 36 20
## [51] 35 12 24 34 15 15 38 17 35 21 30 9 39 5 17 20 2 12 19 35 37 24 11 13 17
## [76] 22 10 16 8 9 9 34 4 3 23 27 34 18 10 5 13 24 21 12 5 10 8 25 23 19
## [101] 24 36 7 36 16 2 18 27 40 5 5 3 33 20 31 16 38 12 8 3sum(e4s1)## [1] 2300e4s2 <- dat1$N[dat1$sitio=="sitio2" & dat1$especie=="thyroptera_sp2"]
e4s2## [1] 5 17 14 5 37 28 24 7 25 12 40 29 23 16 33 6 25 32 10 12 36 12 33 28 23
## [26] 33 40 19 28 1 11 4 24 40 37 10 11 8 13 2 33 3 35 30 33 31 19 12 18 18
## [51] 16 7 7 31 4 30 32 30 22 25 14 14 19 39 33 6 16 28 25 26 26 19 33 38 11
## [76] 24 3 5 5 19 31 8 30 37 24 17 23 10 7 14 16 1 19 4 31 25 3 15 23 20
## [101] 23 40 19 22 37 8 13 25 1 16 16 12 22 23 31 14 23 36 38 5sum(e4s2)## [1] 2434e4s3 <- dat1$N[dat1$sitio=="sitio3" & dat1$especie=="thyroptera_sp2"]
e4s3## [1] 26 16 2 38 23 5 40 21 23 35 19 33 3 38 7 15 3 18 36 10 27 25 10 29 38
## [26] 21 32 13 21 21 25 17 2 19 2 40 40 1 38 39 14 36 12 17 3 3 39 1 38 7
## [51] 7 29 35 34 27 21 28 26 8 23 31 31 40 38 39 11 16 31 22 6 14 32 21 3 27
## [76] 39 29 6 6 17 27 9 4 23 23 23 24 23 40 15 2 19 12 37 39 28 35 26 27 13
## [101] 22 36 30 9 35 39 1 29 6 35 24 32 30 20 24 13 21 32 40 27sum(e4s3)## [1] 2692e4s4 <- dat1$N[dat1$sitio=="sitio4" & dat1$especie=="thyroptera_sp2"]
e4s4## [1] 4 10 33 11 35 38 38 17 29 8 38 38 35 26 10 16 2 16 10 17 19 15 22 33 19
## [26] 3 17 4 2 39 16 19 8 4 12 4 36 29 16 17 11 18 39 18 14 14 38 27 24 10
## [51] 15 8 15 5 27 39 36 14 23 32 19 26 39 36 12 33 37 10 2 5 36 8 17 15 1
## [76] 19 28 22 5 1 23 20 16 35 34 32 24 30 8 36 18 25 28 15 30 1 36 6 40 40
## [101] 28 27 27 25 21 27 34 38 36 13 31 3 26 24 21 35 25 35 39 1sum(e4s4)## [1] 2576Sp1 <- c(2386, 2319, 2512, 2235)
Sp2 <- c(2597, 2363, 2478, 2543)
Sp3 <- c(2405, 2427, 2437, 2567)
Sp4 <- c(2300, 2434, 2692, 2576)
sitio_especie <- data.frame(Sp1, Sp2, Sp3, Sp4 )
sitio_especie## Sp1 Sp2 Sp3 Sp4
## 1 2386 2597 2405 2300
## 2 2319 2363 2427 2434
## 3 2512 2478 2437 2692
## 4 2235 2543 2567 2576row.names(sitio_especie) <- c("Noroeste", "Caribe", "suroeste", "ValleCentral")
sitio_especie ## Sp1 Sp2 Sp3 Sp4
## Noroeste 2386 2597 2405 2300
## Caribe 2319 2363 2427 2434
## suroeste 2512 2478 2437 2692
## ValleCentral 2235 2543 2567 2576chisq.test(sitio_especie)$expected## Sp1 Sp2 Sp3 Sp4
## Noroeste 2331.771 2462.273 2426.502 2467.454
## Caribe 2296.871 2425.420 2390.185 2430.523
## suroeste 2435.507 2571.815 2534.452 2577.226
## ValleCentral 2387.851 2521.492 2484.860 2526.797chisq.test(sitio_especie)##
## Pearson's Chi-squared test
##
## data: sitio_especie
## X-squared = 50.904, df = 9, p-value = 7.281e-08qchisq(0.95,9)## [1] 16.91898library(Rmisc)## Loading required package: lattice## Loading required package: plyrsum = summarySE(dat1, measurevar= "N", groupvars=c("especie", "sitio"), na.rm=TRUE)
sum## especie sitio N N sd se ci
## 1 artibeus_sp1 sitio1 120 19.88333 11.23110 1.0252542 2.030106
## 2 artibeus_sp1 sitio2 120 19.32500 11.35313 1.0363939 2.052164
## 3 artibeus_sp1 sitio3 120 20.93333 11.30905 1.0323706 2.044197
## 4 artibeus_sp1 sitio4 120 18.62500 11.35527 1.0365899 2.052551
## 5 artibeus_sp2 sitio1 120 21.64167 11.52257 1.0518621 2.082792
## 6 artibeus_sp2 sitio2 120 19.69167 11.29720 1.0312889 2.042055
## 7 artibeus_sp2 sitio3 120 20.65000 12.15353 1.1094608 2.196843
## 8 artibeus_sp2 sitio4 120 21.19167 11.33989 1.0351860 2.049772
## 9 thyroptera_sp1 sitio1 120 20.04167 11.88064 1.0845487 2.147515
## 10 thyroptera_sp1 sitio2 120 20.22500 10.74804 0.9811574 1.942790
## 11 thyroptera_sp1 sitio3 120 20.30833 11.73789 1.0715179 2.121713
## 12 thyroptera_sp1 sitio4 120 21.39167 11.91729 1.0878947 2.154140
## 13 thyroptera_sp2 sitio1 120 19.16667 11.05018 1.0087387 1.997403
## 14 thyroptera_sp2 sitio2 120 20.28333 11.08727 1.0121242 2.004107
## 15 thyroptera_sp2 sitio3 120 22.43333 12.03687 1.0988110 2.175756
## 16 thyroptera_sp2 sitio4 120 21.46667 11.84134 1.0809616 2.140412#Plot3
library(ggplot2)
df2 <- data.frame(Especie=rep(c("Artibeus jamaicencis", "Artibeus toltecus","Thyroptera discifera", "Thyroptera tricolor" ), each=4),
Sitio=rep(c("S1", "S2", "S3", "S4"),4),
Abundancia=c(sum))
df2## Especie Sitio Abundancia.especie Abundancia.sitio Abundancia.N
## 1 Artibeus jamaicencis S1 artibeus_sp1 sitio1 120
## 2 Artibeus jamaicencis S2 artibeus_sp1 sitio2 120
## 3 Artibeus jamaicencis S3 artibeus_sp1 sitio3 120
## 4 Artibeus jamaicencis S4 artibeus_sp1 sitio4 120
## 5 Artibeus toltecus S1 artibeus_sp2 sitio1 120
## 6 Artibeus toltecus S2 artibeus_sp2 sitio2 120
## 7 Artibeus toltecus S3 artibeus_sp2 sitio3 120
## 8 Artibeus toltecus S4 artibeus_sp2 sitio4 120
## 9 Thyroptera discifera S1 thyroptera_sp1 sitio1 120
## 10 Thyroptera discifera S2 thyroptera_sp1 sitio2 120
## 11 Thyroptera discifera S3 thyroptera_sp1 sitio3 120
## 12 Thyroptera discifera S4 thyroptera_sp1 sitio4 120
## 13 Thyroptera tricolor S1 thyroptera_sp2 sitio1 120
## 14 Thyroptera tricolor S2 thyroptera_sp2 sitio2 120
## 15 Thyroptera tricolor S3 thyroptera_sp2 sitio3 120
## 16 Thyroptera tricolor S4 thyroptera_sp2 sitio4 120
## Abundancia.N.1 Abundancia.sd Abundancia.se Abundancia.ci
## 1 19.88333 11.23110 1.0252542 2.030106
## 2 19.32500 11.35313 1.0363939 2.052164
## 3 20.93333 11.30905 1.0323706 2.044197
## 4 18.62500 11.35527 1.0365899 2.052551
## 5 21.64167 11.52257 1.0518621 2.082792
## 6 19.69167 11.29720 1.0312889 2.042055
## 7 20.65000 12.15353 1.1094608 2.196843
## 8 21.19167 11.33989 1.0351860 2.049772
## 9 20.04167 11.88064 1.0845487 2.147515
## 10 20.22500 10.74804 0.9811574 1.942790
## 11 20.30833 11.73789 1.0715179 2.121713
## 12 21.39167 11.91729 1.0878947 2.154140
## 13 19.16667 11.05018 1.0087387 1.997403
## 14 20.28333 11.08727 1.0121242 2.004107
## 15 22.43333 12.03687 1.0988110 2.175756
## 16 21.46667 11.84134 1.0809616 2.140412sum <- data.frame(sum)
sum$N.1 <- round(sum$N.1, 1)
sum## especie sitio N N.1 sd se ci
## 1 artibeus_sp1 sitio1 120 19.9 11.23110 1.0252542 2.030106
## 2 artibeus_sp1 sitio2 120 19.3 11.35313 1.0363939 2.052164
## 3 artibeus_sp1 sitio3 120 20.9 11.30905 1.0323706 2.044197
## 4 artibeus_sp1 sitio4 120 18.6 11.35527 1.0365899 2.052551
## 5 artibeus_sp2 sitio1 120 21.6 11.52257 1.0518621 2.082792
## 6 artibeus_sp2 sitio2 120 19.7 11.29720 1.0312889 2.042055
## 7 artibeus_sp2 sitio3 120 20.6 12.15353 1.1094608 2.196843
## 8 artibeus_sp2 sitio4 120 21.2 11.33989 1.0351860 2.049772
## 9 thyroptera_sp1 sitio1 120 20.0 11.88064 1.0845487 2.147515
## 10 thyroptera_sp1 sitio2 120 20.2 10.74804 0.9811574 1.942790
## 11 thyroptera_sp1 sitio3 120 20.3 11.73789 1.0715179 2.121713
## 12 thyroptera_sp1 sitio4 120 21.4 11.91729 1.0878947 2.154140
## 13 thyroptera_sp2 sitio1 120 19.2 11.05018 1.0087387 1.997403
## 14 thyroptera_sp2 sitio2 120 20.3 11.08727 1.0121242 2.004107
## 15 thyroptera_sp2 sitio3 120 22.4 12.03687 1.0988110 2.175756
## 16 thyroptera_sp2 sitio4 120 21.5 11.84134 1.0809616 2.140412p <- ggplot(data= sum, aes(x=sitio, y=N.1, fill=especie)) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=N.1), vjust=1.6, color="white",
position = position_dodge(0.9), size=3.5)+
scale_fill_brewer(palette="Paired")+
theme_minimal() +
geom_errorbar(aes(ymin=N.1-ci, ymax=N.1+ci), width=.2,
position=position_dodge(.9))+labs(title="", x="Sitio", y = "Abundancia")+
theme_classic()
p
#Abundancia vs época
library(Rmisc)
sum = summarySE(dat1, measurevar= "N", groupvars=c("epoca", "especie"), na.rm=TRUE)
sum## epoca especie N N sd se ci
## 1 lluviosa artibeus_sp1 240 18.91667 11.50120 0.7423991 1.462481
## 2 lluviosa artibeus_sp2 240 20.76250 11.56887 0.7467674 1.471086
## 3 lluviosa thyroptera_sp1 240 20.21250 11.24288 0.7257248 1.429634
## 4 lluviosa thyroptera_sp2 240 21.33750 11.65754 0.7524908 1.482361
## 5 seca artibeus_sp1 240 20.46667 11.08199 0.7153397 1.409176
## 6 seca artibeus_sp2 240 20.82500 11.59510 0.7484602 1.474421
## 7 seca thyroptera_sp1 240 20.77083 11.87917 0.7667970 1.510544
## 8 seca thyroptera_sp2 240 20.33750 11.42772 0.7376560 1.453138sum <- data.frame(tapply(dat1$N,paste(dat1$epoca,dat1$especie, sep = "+" ), sum))
esp.abund <- data.frame(lluviosa=sum[c(1:4),], seca=sum[c(5:8),])
row.names(esp.abund)<- c("artibeus_sp1", "artibeus_sp2", "thyroptera_sp1", "thyroptera_sp2")
esp.abund## lluviosa seca
## artibeus_sp1 4540 4912
## artibeus_sp2 4983 4998
## thyroptera_sp1 4851 4985
## thyroptera_sp2 5121 4881chisq.test(esp.abund)##
## Pearson's Chi-squared test
##
## data: esp.abund
## X-squared = 20.238, df = 3, p-value = 0.0001515qchisq(0.95, 3)## [1] 7.814728library(Rmisc)
sum = summarySE(dat1, measurevar= "N", groupvars=c("especie", "epoca"), na.rm=TRUE)
sum## especie epoca N N sd se ci
## 1 artibeus_sp1 lluviosa 240 18.91667 11.50120 0.7423991 1.462481
## 2 artibeus_sp1 seca 240 20.46667 11.08199 0.7153397 1.409176
## 3 artibeus_sp2 lluviosa 240 20.76250 11.56887 0.7467674 1.471086
## 4 artibeus_sp2 seca 240 20.82500 11.59510 0.7484602 1.474421
## 5 thyroptera_sp1 lluviosa 240 20.21250 11.24288 0.7257248 1.429634
## 6 thyroptera_sp1 seca 240 20.77083 11.87917 0.7667970 1.510544
## 7 thyroptera_sp2 lluviosa 240 21.33750 11.65754 0.7524908 1.482361
## 8 thyroptera_sp2 seca 240 20.33750 11.42772 0.7376560 1.453138library(ggplot2)
grafico <- data.frame(Especie=rep(c("Artibeus jamaicencis", "Artibeus toltecus","Thyroptera discifera", "Thyroptera tricolor" ), each=4),
epoca=rep(c("seca","lluviosa"),2),
Abundancia=c(sum))
grafico## Especie epoca Abundancia.especie Abundancia.epoca
## 1 Artibeus jamaicencis seca artibeus_sp1 lluviosa
## 2 Artibeus jamaicencis lluviosa artibeus_sp1 seca
## 3 Artibeus jamaicencis seca artibeus_sp2 lluviosa
## 4 Artibeus jamaicencis lluviosa artibeus_sp2 seca
## 5 Artibeus toltecus seca thyroptera_sp1 lluviosa
## 6 Artibeus toltecus lluviosa thyroptera_sp1 seca
## 7 Artibeus toltecus seca thyroptera_sp2 lluviosa
## 8 Artibeus toltecus lluviosa thyroptera_sp2 seca
## 9 Thyroptera discifera seca artibeus_sp1 lluviosa
## 10 Thyroptera discifera lluviosa artibeus_sp1 seca
## 11 Thyroptera discifera seca artibeus_sp2 lluviosa
## 12 Thyroptera discifera lluviosa artibeus_sp2 seca
## 13 Thyroptera tricolor seca thyroptera_sp1 lluviosa
## 14 Thyroptera tricolor lluviosa thyroptera_sp1 seca
## 15 Thyroptera tricolor seca thyroptera_sp2 lluviosa
## 16 Thyroptera tricolor lluviosa thyroptera_sp2 seca
## Abundancia.N Abundancia.N.1 Abundancia.sd Abundancia.se Abundancia.ci
## 1 240 18.91667 11.50120 0.7423991 1.462481
## 2 240 20.46667 11.08199 0.7153397 1.409176
## 3 240 20.76250 11.56887 0.7467674 1.471086
## 4 240 20.82500 11.59510 0.7484602 1.474421
## 5 240 20.21250 11.24288 0.7257248 1.429634
## 6 240 20.77083 11.87917 0.7667970 1.510544
## 7 240 21.33750 11.65754 0.7524908 1.482361
## 8 240 20.33750 11.42772 0.7376560 1.453138
## 9 240 18.91667 11.50120 0.7423991 1.462481
## 10 240 20.46667 11.08199 0.7153397 1.409176
## 11 240 20.76250 11.56887 0.7467674 1.471086
## 12 240 20.82500 11.59510 0.7484602 1.474421
## 13 240 20.21250 11.24288 0.7257248 1.429634
## 14 240 20.77083 11.87917 0.7667970 1.510544
## 15 240 21.33750 11.65754 0.7524908 1.482361
## 16 240 20.33750 11.42772 0.7376560 1.453138grafico2<- data.frame(sum)
grafico2$N.1 <- round(grafico2$N.1, 1)
grafico2## especie epoca N N.1 sd se ci
## 1 artibeus_sp1 lluviosa 240 18.9 11.50120 0.7423991 1.462481
## 2 artibeus_sp1 seca 240 20.5 11.08199 0.7153397 1.409176
## 3 artibeus_sp2 lluviosa 240 20.8 11.56887 0.7467674 1.471086
## 4 artibeus_sp2 seca 240 20.8 11.59510 0.7484602 1.474421
## 5 thyroptera_sp1 lluviosa 240 20.2 11.24288 0.7257248 1.429634
## 6 thyroptera_sp1 seca 240 20.8 11.87917 0.7667970 1.510544
## 7 thyroptera_sp2 lluviosa 240 21.3 11.65754 0.7524908 1.482361
## 8 thyroptera_sp2 seca 240 20.3 11.42772 0.7376560 1.453138f<- ggplot(data= grafico2, aes(x=epoca, y=N.1, fill=especie)) +
geom_bar(stat="identity", position=position_dodge())+
geom_text(aes(label=N.1), vjust=1.6, color="white",
position = position_dodge(0.9), size=3.5)+
scale_fill_brewer(palette="Paired")+
theme_minimal() +
geom_errorbar(aes(ymin=N.1-ci, ymax=N.1+ci), width=.2,
position=position_dodge(.9))+labs(title="", x="epoca", y = "Abundancia")+
theme_classic()
f
#Abundancia vs año
#ESPECIE 1
año17esp1 <- sum(na.omit(dat2$N[dat2$year=="2017" & dat2$especie=="artibeus_sp1"]))
año17esp1## [1] 4548año18esp1 <- sum(na.omit(dat2$N[dat2$year=="2018" & dat2$especie=="artibeus_sp1"]))
año18esp1## [1] 4904#ESPECIE 2
año17esp2 <- sum(na.omit(dat2$N[dat2$year=="2017" & dat2$especie=="artibeus_sp2"]))
año17esp2## [1] 5026año18esp2 <- sum(na.omit(dat2$N[dat2$year=="2018" & dat2$especie=="artibeus_sp2"]))
año18esp2## [1] 4955#ESPECIE 3
año17esp3 <- sum(na.omit(dat2$N[dat2$year=="2017" & dat2$especie=="thyroptera_sp1"]))
año17esp3## [1] 4819año18esp3 <- sum(na.omit(dat2$N[dat2$year=="2018" & dat2$especie=="thyroptera_sp1"]))
año18esp3## [1] 5017#ESPECIE 4
año17esp4 <- sum(na.omit(dat2$N[dat2$year=="2017" & dat2$especie=="thyroptera_sp2"]))
año17esp4## [1] 4958año18esp4 <- sum(na.omit(dat2$N[dat2$year=="2018" & dat2$especie=="thyroptera_sp2"]))
año18esp4## [1] 5044AB_años <- data.frame(año17esp1, año17esp2, año17esp3, año17esp4, año18esp1, año18esp2, año18esp3, año18esp4)
AB_años## año17esp1 año17esp2 año17esp3 año17esp4 año18esp1 año18esp2 año18esp3
## 1 4548 5026 4819 4958 4904 4955 5017
## año18esp4
## 1 5044chisq.test(AB_años)##
## Chi-squared test for given probabilities
##
## data: AB_años
## X-squared = 38.001, df = 7, p-value = 3.029e-06qchisq(0.95, 7)## [1] 14.06714 #Experimento (ANDEVA)
#biomasa y temperatura como factor
library(readxl)
ExperimentoBD <- read_excel("C:/Users/Usuario/Documents/bdc.xlsx", sheet = "Experimento")
head(ExperimentoBD)## # A tibble: 6 x 4
## especie temperatura biomasa talla_max
## <chr> <dbl> <dbl> <dbl>
## 1 artibeus1 25 16 14
## 2 thyroptera4 25 17 8
## 3 artibeus2 25 20 13
## 4 thyroptera3 25 26 21
## 5 artibeus1 25 38 14
## 6 thyroptera4 25 56 20str(ExperimentoBD)## tibble [480 x 4] (S3: tbl_df/tbl/data.frame)
## $ especie : chr [1:480] "artibeus1" "thyroptera4" "artibeus2" "thyroptera3" ...
## $ temperatura: num [1:480] 25 25 25 25 25 25 25 25 25 25 ...
## $ biomasa : num [1:480] 16 17 20 26 38 56 25 19 58 26 ...
## $ talla_max : num [1:480] 14 8 13 21 14 20 15 19 22 7 ...ExperimentoBD$temperatura<- as.factor(ExperimentoBD$temperatura)
str(ExperimentoBD)## tibble [480 x 4] (S3: tbl_df/tbl/data.frame)
## $ especie : chr [1:480] "artibeus1" "thyroptera4" "artibeus2" "thyroptera3" ...
## $ temperatura: Factor w/ 3 levels "15","20","25": 3 3 3 3 3 3 3 3 3 3 ...
## $ biomasa : num [1:480] 16 17 20 26 38 56 25 19 58 26 ...
## $ talla_max : num [1:480] 14 8 13 21 14 20 15 19 22 7 ...ANOVA<-aov(ExperimentoBD$biomasa~ExperimentoBD$temperatura)
shapiro.test(ANOVA$residuals) #Normalidad de residuos, no paramétricos##
## Shapiro-Wilk normality test
##
## data: ANOVA$residuals
## W = 0.95677, p-value = 1.185e-10tapply(ExperimentoBD$biomasa, ExperimentoBD$temperatura, length) #Balanceado## 15 20 25
## 160 160 160fligner.test(ExperimentoBD$biomasa~ExperimentoBD$temperatura) #Homogeneidad de varianzas##
## Fligner-Killeen test of homogeneity of variances
##
## data: ExperimentoBD$biomasa by ExperimentoBD$temperatura
## Fligner-Killeen:med chi-squared = 0.50036, df = 2, p-value = 0.7787ANOVA<- kruskal.test(ExperimentoBD$biomasa~ExperimentoBD$temperatura)
ANOVA ##
## Kruskal-Wallis rank sum test
##
## data: ExperimentoBD$biomasa by ExperimentoBD$temperatura
## Kruskal-Wallis chi-squared = 1.6217, df = 2, p-value = 0.4445library(ggplot2)
Exp1<- data.frame(ExperimentoBD$temperatura, ExperimentoBD$biomasa)
o<-ggplot(Exp1, aes(ExperimentoBD$temperatura, ExperimentoBD$biomasa))
o+scale_alpha_continuous(name="Biomasa")+ scale_x_discrete(name="Temperatura")+ geom_point() + geom_boxplot()
#Otro gráfico
MediaValor1 <- tapply(ExperimentoBD$biomasa, ExperimentoBD$temperatura, mean)
MediaValor1## 15 20 25
## 38.72500 38.83750 37.05625abajo2 <- tapply(ExperimentoBD$biomasa, ExperimentoBD$temperatura, function(v) t.test(v)$conf.int[1])
abajo2## 15 20 25
## 36.58230 36.61011 34.92421arriba2 <- tapply(ExperimentoBD$biomasa, ExperimentoBD$temperatura, function(v) t.test(v)$conf.int[2])
arriba2## 15 20 25
## 40.86770 41.06489 39.18829par(mfrow=c(1,1)) #filas,columnas
par(mar=c(5,5,3,2)+0.1) #margenes
library(gplots)
barplot2(MediaValor1, plot.ci=T,
ci.l=abajo2, ci.u=arriba2,
main = "Biomasa en función de la Temperatura" ,
xlab= "Temperatura(C)" , ylab= "Biomasa" ,
ylim=c(0, 50),
col="blue")
##Análisis de varianza con la varibles de talla máxima y temperatura como factor
str(ExperimentoBD)## tibble [480 x 4] (S3: tbl_df/tbl/data.frame)
## $ especie : chr [1:480] "artibeus1" "thyroptera4" "artibeus2" "thyroptera3" ...
## $ temperatura: Factor w/ 3 levels "15","20","25": 3 3 3 3 3 3 3 3 3 3 ...
## $ biomasa : num [1:480] 16 17 20 26 38 56 25 19 58 26 ...
## $ talla_max : num [1:480] 14 8 13 21 14 20 15 19 22 7 ...ANOVA2<-aov(ExperimentoBD$talla_max~ ExperimentoBD$temperatura)
shapiro.test(ANOVA2$residuals) #No paramétricos##
## Shapiro-Wilk normality test
##
## data: ANOVA2$residuals
## W = 0.94976, p-value = 1.074e-11tapply(ExperimentoBD$talla_max, ExperimentoBD$temperatura, length) #Balanceados## 15 20 25
## 160 160 160fligner.test(ExperimentoBD$talla_max~ ExperimentoBD$temperatura) #Cumple la homoceasticidad##
## Fligner-Killeen test of homogeneity of variances
##
## data: ExperimentoBD$talla_max by ExperimentoBD$temperatura
## Fligner-Killeen:med chi-squared = 1.6478, df = 2, p-value = 0.4387ANOVA2<-kruskal.test(ExperimentoBD$talla_max~ ExperimentoBD$temperatura)
ANOVA2 ##
## Kruskal-Wallis rank sum test
##
## data: ExperimentoBD$talla_max by ExperimentoBD$temperatura
## Kruskal-Wallis chi-squared = 1.123, df = 2, p-value = 0.5704Exp2<- data.frame(ExperimentoBD$temperatura, ExperimentoBD$talla_max)
ggplot(Exp2, aes(ExperimentoBD$temperatura, ExperimentoBD$talla_max, colours=ExperimentoBD$temperatura))+ scale_alpha_continuous(name="Talla")+ scale_x_discrete(name="Temperatura")+ geom_point() +geom_col()
par(mfrow=c(3,2))
MediaValor3 <- tapply(ExperimentoBD$talla_max, ExperimentoBD$temperatura, mean)
MediaValor3## 15 20 25
## 13.9375 14.3375 14.5000abajo3 <- tapply(ExperimentoBD$talla_max, ExperimentoBD$temperatura, function(v) t.test(v)$conf.int[1])
arriba3 <- tapply(ExperimentoBD$talla_max, ExperimentoBD$temperatura, function(v) t.test(v)$conf.int[2])
par(mfrow=c(1,1)) #filas,columnas
par(mar=c(5,5,3,2)+0.1) #margenes
library(gplots)
barplot2(MediaValor3, plot.ci=T,
ci.l=abajo3, ci.u=arriba3,
main = "Talla máxima en función de la temperatura" ,
xlab= "Temperatura (ºC)" , ylab= "Talla (cm)" ,
ylim=c(0, 20),
col="blue")