PROYECTO VIDA SILVESTRE – PVS

El Proyecto Vida Silvestre (PVS) es una iniciativa liderada por Ecopetrol S.A., Wildlife Conservation Society (WCS) Colombia y Fundación Mario Santo Domingo, cuya primera fase fue ejecutada junto con diez organizaciones nacionales entre el 2014 y el 2017. El objetivo principal del proyecto es implementar programas de conservación para especies paisaje, como estrategia para mantener los niveles de biodiversidad. Para la primera fase se seleccionaron 10 especies y dos regiones de Colombia, de gran importancia biológica y cultural, pero que enfrentan crecientes presiones que amenazan los recursos naturales. Estas regiones corresponden a la cuenca media del Rio Magdalena y a la región de la Orinoquía. Se desarrolló un modelo de intervención novedoso y participativo, para contrarrestar las presiones o amenazas y mantener la biodiversidad en estos dos paisajes de Colombia.

PROYECTO VIDA SILVESTRE – PVS EN LOS LLANOS ORIENTALES

En la región de los Llanos Orientales, la primera fase de implementación del PVS tuvo un alcance superior a 88.000 hectáreas, y logró establecer áreas de protección por medio de Acuerdos Comunitarios de Conservación y declarando Reservas Naturales de la Sociedad Civil (RNSC). En Arauca, específicamente en Cravo Norte, cuatro reservas de ese tipo hoy abarcan 3.322 hectáreas, y la cuenca medio del río Bita se consolidó como área de interés para conservar un corredor natural que proteja a la Danta.

De igual modo, las implementaciones en los Llanos Orientales corresponden a procesos de restauración, establecimiento de viveros, monitoreo y seguimiento de especies, y actividades con las comunidades locales. Actualmente cerca de 500 hectáreas se encuentran en procesos de restauración, mediante restauración activa (siembras) y aislamiento para regeneración natural. Bajo restauración activa se encuentran 44 hectáreas de siembra de palmas de Moriche, Congrio y otras especies nativas, y 28 hectáreas de bosques con enriquecimiento. También, se construyeron cinco viveros comunitarios para propagar semillas de Congrio, Moriche y otras especies que contribuyen a la restauración del hábitat de la Danta. Gracias a ello fue posible sembrar 6.000 y 15.000 plántulas de Moriche y Congrio, respectivamente. La presencia y participación constante del PVS en el territorio, permitió incrementar la confianza y fortalecer el tejido social, promoviendo y generando espacios de encuentro para establecer vínculos comunitarios más fuertes donde primen la solidaridad, cooperatividad y confianza. Estad implementaciones se desarrollaron de la mano de cinco organizaciones: Fundación Orinoquia Biodiversa, F. Yoluka ONG, Corporación Ambiental La Pedregoza, F. Omacha y f. Palmarito.

DISEÑO DE MONITOREO CON CÁMARAS TRAMPA

Características importantes para el diseño.

Temporalidad: el monitoreo se realizó cada año entre 2015-2017, durante la época seca para facilitar el trabajo en campo. Cada cámara se dejó, al menos, 45 días en el paisaje de estudio. Número y ubicación: fueron seleccionadas entre 45 y 68 cuadrículas de 1Km2, área correspondiente al ámbito de hogar de la mayoría de las especies de interés. En cada cuadricula se instaló una cámara trampa. La ubicación de las cuadrículas se definió a partir de los siguientes criterios:

Fuentes de agua, saladeros y letrinas. Presencia de al menos 20% de bosque. Diferentes distancias de centros poblados y vías. Permisos concertados con los propietarios.

carga paquetes y datos

library(rgdal) # basic maps and coords
library(maptools) # tools to convert
# library(unmarked)
library (sf) # spatial maps
library (mapview) # nice html maps
library (spatstat) # distance maps
library (raster) # raster 
library (readxl) # read excel data
library (tidyverse) # Data handling
library (dplyr) # Data frames handling
library (ggmap) # maps in ggplot
library (knitr) # make documents
library (xtable) # tables
library (kableExtra) #Table html
library (stargazer) # tables
library (tmap) #nice plain maps
library (tmaptools) # mas mapas 
library (osmdata) # read osm
library (OpenStreetMap) # osm maps 
library (grid) # mix maps
library (GADMTools) # subset GADM

library (stars) # for data cubes and rasterize sf
library (fasterize) # raster sf
library (plainview)
library (leaflet)
library (leafpop)
library (camtrapR)

#####################
## extra functions ##
#####################

home <- "E:/R/"

##### extra functions
source(paste(home, "Biodiv_Caqueta/R/TEAM_code.R", sep=""))
source(paste(home, "Biodiv_Caqueta/R/calendar.R", sep=""))
source(paste(home, "Biodiv_Caqueta/R/MultiSpeciesSiteOcc_Stan.R", sep=""))


#####################
## read data sets  ##
#####################

##### rad SHP ####
rio.magna <- st_read("E:/R/WCS/shp/rio_epsg3116_magna_bog.shp")

## Reading layer `rio_epsg3116_magna_bog' from data source `E:\R\WCS\shp\rio_epsg3116_magna_bog.shp' using driver `ESRI Shapefile'
## Simple feature collection with 22 features and 17 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 1618438 ymin: 1127838 xmax: 1718072 ymax: 1182112
## projected CRS:  MAGNA_SIRGAS_Colombia_Bogota_zone

celdas.magna <- st_read("E:/R/WCS/shp/CeldasLlanos.shp")

## Reading layer `CeldasLlanos' from data source `E:\R\WCS\shp\CeldasLlanos.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1183 features and 4 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: 951520.1 ymin: 1112190 xmax: 1045190 ymax: 1177459
## projected CRS:  MAGNA-SIRGAS / Colombia East zone

rio <- st_transform(rio.magna, "+proj=longlat +ellps=GRS80 +no_defs")
celdas <- st_transform(celdas.magna, "+proj=longlat +ellps=GRS80 +no_defs")

##### Read datos.raw
datos.raw1 <- CT_Llanos_AH <- read_excel("E:/R/WCS/data/CT_Llanos-AH_2015fix.xlsx", 
    sheet = "Record_Llanos_CT-PVS")


#### elimina dos camaras sin coordenadas
ind <- which(is.na(datos.raw1$Lat))
datos.raw <- datos.raw1[-ind,]

#### make sf object
datos.raw_sf <- st_as_sf(datos.raw, coords = c("Long", "Lat"), 
                        crs = "+proj=longlat +ellps=GRS80 +no_defs")

Problemas corregidos

Datos del 2015 tienen mal la coordenada x (Lon), pareciera ser por 6 grados de error a la “izquierda”. Se resto 6 grados a la longitud asumiendo que este es el error.

En los datos del 2015 hay dos camaras (station 209, 137) que no tienen coordenadas. Estas se eliminaron desde el excel.

Analisis preliminar camaras 2017

# datos.raw 

camaras <-  st_transform (datos.raw_sf, "+proj=longlat +ellps=GRS80 +no_defs") 
camaras_17 <- camaras %>% filter (Year =="2017" ) # & Year <="2017")
# camaras_16 <- camaras %>% filter (Year =="2016" ) # & Year <="2017")
# camaras_17 <- camaras %>% filter (Year =="2017" ) # & Year <="2017")

# Add column to get activity graph using activityDensity from camtrapR
# Add column to get activity graph using activityDensity from camtrapR
camaras$DateTimeOriginal <- as.character(camaras$DateRecord)
camaras_17$DateTimeOriginal <- as.character(camaras_17$DateRecord)


# mapview (celdas, aplha = 0.1 ) + mapview (camaras_17, 
#                  zcol = c("Predio" ),
#                  map.types = c("OpenStreetMap",  "Esri.WorldImagery" ),
#                  leafletHeight = 8, 
#                  # burst = TRUE, 
#                  hide = TRUE
#                  ) + mapview (rio, col.regions = "blue")

# Centroides EPSG:3116 Colombia Magna
centroide <- st_transform(camaras_17, 3116) %>% 
  sf::st_geometry()  %>%
  #sf::st_polygonize() %>% 
  sf::st_centroid() %>% 
  # this is the crs from d, which has no EPSG code:
  sf::st_transform(., '+proj=longlat +ellps=GRS80 +no_defs')   #%>%
  # since you want the centroids in a second geometry col:
  # st_geometry() 


# get raster elevation as template
elevation<-raster::getData("SRTM",lon=centroide[[1]][1], lat=centroide[[1]][2])
e<-extent (celdas) # make the extent
elevation.crop<-crop(elevation, e) # cut elevation to small window

elevation.crop.utm <- projectRaster(elevation.crop,
                  crs = " +proj=utm +zone=18 +ellps=intl +towgs84=307,304,-318,0,0,0,0 +units=m +no_defs ")
rio.utm <- st_transform(rio, crs(elevation.crop.utm))
########################### 
# convert rio to distance #
########################### 
rio.raster <- fasterize (rio.utm, elevation.crop.utm)
dist_rio_owin <- distmap(as.owin(as.im(rio.raster)))
dist_rio_m <- raster(dist_rio_owin, crs=crs(elevation.crop.utm))# back to raster
dist_rio <- projectRaster(from=dist_rio_m, to=elevation.crop) # back to geo

# fix rio shp
rio$Rio <- "Bita"

########################### 
# drop duplicates in camera points #
###########################


# put in the same CRS
camaras_loc <- st_transform (camaras_17, "+proj=longlat +datum=WGS84 +no_defs ")

# drop duplicated
# camaras_unique <- st_difference(camaras_loc, Station2)


########################### 
# Filter by 2017  #
###########################
camaras_unique <- camaras_loc %>% distinct () 
cams_17 <- camaras_17 %>% distinct () 



# camaras_unique %>% filter(station %in% cams) 

########################### 
# extract from river and strm #
###########################

# Covs non scaled
dist_rio.ovr <- raster::extract(dist_rio, as(camaras_unique, "Spatial"), method='bilinear')

# Covs scaled
dist_rio.ovr.s <- raster::extract(scale(dist_rio), as(camaras_unique, "Spatial"), method='bilinear')

# Covs non scaled
elev_ovr <- raster::extract(elevation.crop, as(camaras_unique, "Spatial"), method='bilinear')

# Covs scaled
elev_ovr.s <- raster::extract(scale(elevation.crop), as(camaras_unique, "Spatial"), method='bilinear')

# put covs in table
camaras_unique$dist_rio <- dist_rio.ovr
camaras_unique$dist_rio.s <- dist_rio.ovr.s
camaras_unique$elev <- elev_ovr
camaras_unique$elev.s <- elev_ovr.s

# camera names
camaras_unique$station <- unique(camaras_loc$Station2)



cov_stack <- stack(elevation.crop, dist_rio)
names(cov_stack) <- c("elev.s", "dist_rio.s")
plot(cov_stack)

#### agregate to make raster smaller
elevation.crop2 <- aggregate(cov_stack[[1]], fact=3)
dist_rio2 <- aggregate(cov_stack[[2]], fact=3)
cov_stack2 <- stack(scale(elevation.crop2), scale(dist_rio2))

# use osm data by bounding box
bb <- c(-68.5752, 5.55629 , -67.61 , 6.230916)
# predios_map <- get_map(bb, maptype = "toner-background", zoom = 10)
# ggmap(predios_map) + 
#   geom_sf(data = camaras,
#           inherit.aes = FALSE,
#           colour = "red",
#           fill = "#004529",
#           alpha = .5,
#           size = 4,
#           shape = 21) +
#   labs(x = "", y = "")

# get fondo de osm


vichada_osm1 <- read_osm(bb, type="stamen-terrain",  mergeTiles = TRUE) 

depto_window <- qtm(vichada_osm1) + tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(camaras_17) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Predio", palette = "Set1", size = 0.25, 
            shape = 16, title = "Cámara en predio", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)



# print map
depto_window

Duración del muestreo

Las trampas cámara permanecieron activas desde mediados de xx 2015 hasta xx 2015.

datos.raw$Photo_Date <- datos.raw$Date
datos.raw$Photo_Time <- datos.raw$Time

datos.raw$StationFact<-as.factor(datos.raw$Station2) %>% as.numeric
library(lubridate)
datos.raw$Date2<-ymd(datos.raw$Date)
 
datos.raw.17<- datos.raw %>% 
  filter(Year=="2017")%>%      
  group_by(StationFact, Year) %>% 
dplyr::summarise(StationFact=first(StationFact), Year=first(Year), Camera_Trap_Start_Date = min(Date, na.rm=T), Camera_Trap_End_Date = max(Date2, na.rm = TRUE))%>%
merge(., datos.raw, all.x=T, all.y=F)

datos.raw.17$Photo_Time <- substr(as.character(datos.raw.17$Time), 12, 19)
datos.raw.17$Camera_Trap_Name <- datos.raw.17$Station2
  
f.calendar.yr(dataset = datos.raw.17, yr_toplot = 1)

Calendario de fotografias, con fecha de inicio y fin de cada cámara.

Especies registradas (Mamíferos)

Las especies registradas en todo en muestreo fueron 12 mamíferos.

Mamíferos

Se detectaron 12 especies.

# library(xtable)




datos.raw.mam <- filter(datos.raw.17, Clase=="Mamíferos") 


mat.per.sp<-f.matrix.creator2(data = datos.raw.mam, year = 2017)
# mat.per.sp <- mat.per.sp[-1] # elimina NA 
sp.names<-names(mat.per.sp) # species names


# counting how many (total) records per species by all days
cont.per.sp<-data.frame(row.names = sp.names)
row.per.sp<-as.data.frame(matrix(nrow = length(sp.names), ncol=c(69)))# num cameras 59 con ave
col.per.sp<-as.data.frame(matrix(nrow = length(sp.names), ncol=c(74)))# num days
rownames(row.per.sp)<-sp.names
rownames(col.per.sp)<-sp.names

for (i in 1:length(mat.per.sp)){
  cont.per.sp[i,1]<-sum(apply(as.data.frame(mat.per.sp [[i]]),FUN=sum,na.rm=T, MARGIN = 1))
  
  row.per.sp[i,]<-apply(mat.per.sp[[i]],1, function(x) sum(x, na.rm=T))
  # row.per.sp[i,which(row.per.sp[i,]>0)]<-1 # convert to presence absence  1 and 0
  
  col.per.sp[i,]<-apply(mat.per.sp[[i]],2, function(x) sum(x, na.rm=T))
  # col.per.sp[i,which(col.per.sp[i,]>0)]<-1 # convert to presence absence  1 and 0
 }

cont.per.sp$especie<-rownames(cont.per.sp)
colnames(cont.per.sp)<-c("Numero_de_fotos","especie")
# print(as.data.frame(arrange(df = cont.per.sp, desc(Numero_de_fotos))))

# xtable(as.data.frame(arrange(df = cont.per.sp, desc(Numero_de_fotos))))

pict_sp <- as.data.frame(cont.per.sp)

############################################################
## first Table species, naive occu 
############################################################
presence<-row.per.sp
presence[presence > 0]<-1 # replace to 1
naiveoccu<-apply(X = presence,FUN = sum, MARGIN = 1 ) / 69 #divided number of sites
events<-apply(X = row.per.sp ,FUN = sum, MARGIN = 1 )
RAI<-(events/(74 * 69)) * 100 # 78 is average sampling effort, in days per camera
Table1<-as.data.frame(cbind(species= names(events), events=as.numeric(events), 
                            phothos=as.numeric(pict_sp$Numero_de_fotos), RAI, naiveoccu))
# Table1<-  Table1[with(Table1, order(-events)), ]# arrange(Table1, desc(events))
# xtable::xtable(Table1)#, format = "rst")

Table1 <- Table1[ order( -events), ] #ordena por eventos descendente

kable(Table1[2:5]) %>% kable_styling (bootstrap_options = c("striped", "hover", "condensed", font_size = 8)) # %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

	events	phothos	RAI	naiveoccu
Dasyprocta_fuliginosa	156	156	3.05522914218566	0.246376811594203
Tayassu_pecari	149	149	2.91813552683118	0.565217391304348
Cuniculus_paca	140	140	2.7418723070897	0.420289855072464
Tapirus_terrestris	96	96	1.88014101057579	0.492753623188406
Leopardus_pardalis	49	49	0.959655307481394	0.347826086956522
Odocoileus_cariacou	47	47	0.920485703094399	0.289855072463768
Myrmecophaga_tridactyla	20	20	0.391696043869957	0.188405797101449
Puma_concolor	12	12	0.235017626321974	0.173913043478261
Eira_barbara	9	9	0.176263219741481	0.0869565217391304
Didelphis_marsupialis	8	8	0.156678417547983	0.0869565217391304
Sciurus_igniventris	6	6	0.117508813160987	0.072463768115942
Marmosa_sp	6	6	0.117508813160987	0.0144927536231884
Metachirus_nudicaudatus	5	5	0.0979240109674892	0.0144927536231884
Hydrochoerus_hydrochaeris	4	4	0.0783392087739914	0.0434782608695652
Sus_scrofa	4	4	0.0783392087739914	0.0289855072463768
Tamandua_tetradactyla	4	4	0.0783392087739914	0.0579710144927536
Dasypus_novemcinctus	4	4	0.0783392087739914	0.0434782608695652
Pteronura_brasiliensis	3	3	0.0587544065804935	0.0434782608695652
Puma_yagouaroundi	2	2	0.0391696043869957	0.0289855072463768
Cebus_olivaceus	2	2	0.0391696043869957	0.0289855072463768
Panthera_onca	2	2	0.0391696043869957	0.0144927536231884
Priodontes_maximus	1	1	0.0195848021934978	0.0144927536231884
Cerdocyon_thous	1	1	0.0195848021934978	0.0144927536231884

# kable(Table1, "latex", booktabs = T) # para pdf

RAI es: Relative Abundance Index

Distribución posterior de la riqueza de especies de mamíferos

Por camaras

Riqueza de especies y acumulación, modelando la ocurrencia y la detectabilidad. Este análisis sigue el método de Dorazio et al. (2006).

############################################################
## Distribucion posterior de la riqueza de especies
############################################################

# Riqueza de especies y acumulación, modelando la ocurrencia y la detectabilidad. 
# Este análisis sigue el método de Dorazio et al. (2006).
# 40 minutos en PC de casa

x <- as.matrix(row.per.sp)



X1 = as.matrix(row.per.sp) # col.per.sp por dias y row.per.sp por sitios (camaras)
nrepls = 74 #dias 
especies = MultiSpeciesSiteOcc(nrepls=nrepls, x=X1) #### run stan

print(especies$fit, c("alpha", "beta", "Omega", "sigma_uv", "rho_uv", "E_N", "E_N_2", "lp__"));

# summary(especies$fit$sims.matrix)

# alpha.post = especies$fit$sims.matrix[,"alpha"]
# sigmaU.post = especies$fit$sims.matrix[,"sigma.u"]
# N.post = especies$fit$sims.matrix[,"N"]


# Stan Equivalent
alpha.post =  especies$fit@sim$samples[[1]]$alpha
sigmaU.post = especies$fit@sim$samples[[1]]$`sigma_uv[1]`
N.post =      especies$fit@sim$samples[[1]]$E_N

nsites = 69 
cum_sp<-CumNumSpeciesPresent (nsites=nsites, alpha=alpha.post, sigmaU=sigmaU.post, N=N.post)


# #histogram of posteriors openwinbugs
# hist(especies$fit$sims.matrix[,"N"],breaks = c(16:30), xlab="Number of mammal species", ylab="Relative frecuency", main="")
# abline(v=length(row.per.sp[,1]),col="blue", lty = 2) # -> lines.histogram(*) observadas
# abline(v=median(especies$fit$sims.matrix[,"N"]),col="red", lty = 2) # -> esperadas por detectabilidad
# 
# mean(especies$fit$sims.matrix[,"N"])
# median(especies$fit$sims.matrix[,"N"])


hist(especies$fit@sim$samples[[1]]$E_N , xlab="Number of mammal species", ylab="Relative frecuency", main="")
abline(v=length(row.per.sp[,1]),col="blue", lty = 2) # -> lines.histogram(*) observadas
abline(v=median(especies$fit@sim$samples[[1]]$E_N),col="red", lty = 2) # -> esperadas por detectabilidad


mean(especies$fit@sim$samples[[1]]$E_N)
median(especies$fit@sim$samples[[1]]$E_N)






##Plot in ggplot

S <- 15 + 5  #species
sims <- extract(especies$fit)

freq <- rep(0,S) # sp
N <- sims$E_N_2
for (i in 1:length(N)){
  freq[N[i]] <- freq[N[i]] + 1
}

freq <- freq / length(N)

dat <- data.frame(freq)
dat <- cbind(1:S, dat)
colnames(dat)[1] <- "N"
# dat <- dat[80:90, ];

N_bar_plot <-
  ggplot(data=dat, aes(x=N, y=freq)) +
  geom_bar(stat="identity") +
  scale_x_continuous(name="Mammal species (N)",
                     breaks=c(10,11,12,13,14,15,16,17,18,19, 20)) +
  scale_y_continuous(name="relative frequency",
                     breaks=(0.1 * (0:6))) +
  ggtitle("Posterior: Number of Species (N)");

plot(N_bar_plot)

# Posterior Probabilities of Occurrence and Detection

ilogit <- function(u) { return(1 / (1 + exp(-u))); }

df_psi_theta <- data.frame(cbind(ilogit(sims$logit_psi_sim),
                                 ilogit(sims$logit_theta_sim)));
colnames(df_psi_theta)[1] <- "psi";
colnames(df_psi_theta)[2] <- "theta";

psi_density_plot <-
  ggplot(df_psi_theta, aes(x=psi)) +
  geom_line(stat = "density", adjust=1) +
  scale_x_continuous(name="probability of occurrence",
                     limits=c(0,1), breaks=(0.2 * (0:5))) +
  scale_y_continuous(name="probability density",
                     limits=c(0,5), breaks=(0:4)) +
  ggtitle("Posterior: Occurrence (psi)");

plot(psi_density_plot);

# #####################
# #### compare to vegan
# #####################
# library(vegan)
# sac <- specaccum(t(row.per.sp))
# plot(sac)
# 
# sp1 <- specaccum(t(row.per.sp), method = "random")
# sp2 <- specaccum(t(row.per.sp),  method = "rarefaction")
# sp3 <- specaccum(t(row.per.sp),  method = "collector")
# 
# # # summary(sp1)
# plot(sp1, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue")
# boxplot(sp1, col="yellow", add=TRUE, pch="+")
# # 
# plot(sp2, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue") # rarefaction
# #plot(sp2)
# # boxplot(sp1, col="yellow", add=TRUE, pch="+")
# 
# plot(sp3, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue")
# #plot(sp2)
# 
# H <- diversity(t(row.per.sp))
# simp <- diversity(t(row.per.sp), "simpson")
# invsimp <- diversity(t(row.per.sp), "inv")
# r.2 <- rarefy(t(row.per.sp), 2)
# alpha <- fisher.alpha(t(row.per.sp))
# pairs(cbind(H, simp, invsimp, r.2, alpha), pch="+", col="blue")
# 
# ## Species richness (S) and Pielou's evenness (J):
# S_1 <- specnumber(t(row.per.sp)) ## rowSums(BCI > 0) does the same...
# J <- H/log(S_1)
# 
# rarecurve(row.per.sp)#,step = 20, sample = raremax)
# ## Rarefaction
# (raremax <- max(rowSums(row.per.sp)))
# Srare <- rarefy(t(row.per.sp), raremax)
# plot(S_1, Srare, xlab = "Observed No. of Species", ylab = "Rarefied No. of Species")
# abline(0, 1)
# rarecurve(row.per.sp)#, step = 2, sample = raremax, col = "blue", cex = 0.6)
#

Especies observadas en azul = 12. Especies esperadas en rojo = 14.3. El número esperado esta corrregido por la detectabilidad. Hay por lo menos dos especies que no se detectaron.

Función para automatizar modelos

Construimos 17 modelos generales para probar de forma automatizada cada especie con más de 10 registros.

Datos colapsados a 15 eventos

    ##########################################
    #########  Models 
    ##########################################
    f.sp.occu.models <- function(sp_number){

      ########################
      ### make unmarked object 
      ########################
      library(unmarked)
      sp15<-f.shrink.matrix.to15(matrix = mat.per.sp[[sp_number]])
      y <- mat.per.sp[[sp_number]]
      sp_UMF <- unmarkedFrameOccu(y)
      
      
      ## start colapsed
      sp_UMF_15 <- unmarkedFrameOccu(sp15)
      fm0_15 <- occu(~ 1 ~ 1, sp_UMF_15, starts = c(0.01,0.01), se=T) 
      # get the linear estimates
      psi <- backTransform(fm0_15, type="state")
      p <- backTransform(fm0_15, type="det")
      ## end colapsed
      
      # Filter by 2015 cams #
      cams <- row.names(mat.per.sp[[sp_number]])
      camaras_2017 <- camaras_unique %>% filter(station %in% cams) 
      
      # plot(sp_UMF, panels=1)
      # title(main=as.character(sp.names[sp_number]))
      
      # add some  covariates
      cam.and.covs1 <- as.data.frame(camaras_2017)[,2:6]
      siteCovs(sp_UMF) <- cam.and.covs1
      
      #######################
      ## occu models 
      #######################
      
      #  covariates of detection and occupancy in that order.
      fm0 <- occu(~ 1 ~ 1, sp_UMF, starts = c(0.01,0.01), se=T) 
      fm1 <- occu(~ 1 ~ elev.s, sp_UMF, starts = c(0.01,0.01,0.01))
      fm2 <- occu(~ 1 ~ I(elev.s)^2, sp_UMF)
      fm3 <- occu(~ 1 ~ dist_rio.s, sp_UMF, starts = c(0.01,0.01,0.01))
      fm4 <- occu(~ 1 ~ elev.s + dist_rio.s, sp_UMF, starts = c(0.01,0.01,0.01,0.01))
      fm5 <- occu(~ 1 ~ I(dist_rio.s)^2, sp_UMF)
      fm6 <- occu(~ elev.s ~ 1, sp_UMF) 
      fm7 <- occu(~ elev.s ~ elev.s, sp_UMF, starts = c(0.01,0.01,0.01,0.01))
      fm8 <- occu(~ elev.s ~ I(elev.s)^2, sp_UMF)
      fm9 <- occu(~ elev.s ~ dist_rio.s, sp_UMF, starts = c(0.01,0.01,0.01, 0.01))
      fm10 <- occu(~ elev.s ~ elev.s + dist_rio.s, sp_UMF, starts = c(0.01,0.01,0.01,0.01,0.01))
      fm11 <- occu(~ elev.s ~ I(dist_rio.s)^2, sp_UMF)
      fm12 <- occu(~ dist_rio.s ~ 1, sp_UMF) 
      fm13 <- occu(~ dist_rio.s ~ elev.s, sp_UMF, starts = c(0.01,0.01,0.01,0.01))
      fm14 <- occu(~ dist_rio.s ~ I(elev.s)^2, sp_UMF)
      fm15 <- occu(~ dist_rio.s ~ dist_rio.s, sp_UMF, starts = c(0.01,0.01,0.01,0.01))
      fm16 <- occu(~ dist_rio.s ~ elev.s + dist_rio.s, sp_UMF, starts = c(0.01,0.01,0.01,0.01,0.01))
      fm17 <- occu(~ dist_rio.s ~ I(dist_rio.s)^2, sp_UMF)
      # 
      # 
      # fm8 <- occu(~ elev ~ elev, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm9 <- occu(~ elev ~ slope, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm10 <- occu(~ elev ~ aspect, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm11 <- occu(~ elev ~ roughness, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm12 <- occu(~ elev ~ dist_def, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm13 <- occu(~ elev ~ COB_ACTUAL, sp_UMF)
      # fm14 <- occu(~ elev ~ Sitio, sp_UMF)
      # fm15 <- occu(~ roughness ~ elev, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm16 <- occu(~ roughness ~ slope, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm17 <- occu(~ roughness ~ aspect, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm18 <- occu(~ roughness ~ roughness, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm19 <- occu(~ roughness ~ dist_def, sp_UMF, starts = c(0.01,0.01,0.01,0.1))
      # fm20 <- occu(~ roughness ~ COB_ACTUAL, sp_UMF)
      # fm21 <- occu(~ roughness ~ Sitio, sp_UMF)

      
      # put the names of each model
      models <- fitList(
        'p(.)psi(.)' = fm0,
        'p(.)psi(elev)' = fm1,
        'p(.)psi(elev2)' = fm2,
        'p(.)psi(dist_rio)' = fm3,
        'p(.)psi(elev + dist_rio)' = fm4,
        'p(.)psi(dist_rio2)' = fm5,
        'p(elev)psi(.)' = fm6,
        'p(elev)psi(elev)' = fm7,
        'p(elev)psi(elev2)' = fm8,
        'p(elev)psi(dist_rio)' = fm9,
        'p(elev)psi(elev + dist_rio)' = fm10,
        'p(elev)psi(dist_rio2)' = fm11,
        'p(dist_rio)psi(.)' = fm12,
        'p(dist_rio)psi(elev)' = fm13,
        'p(dist_rio)psi(elev2)' = fm14,
        'p(dist_rio)psi(dist_rio)' = fm15,
        'p(dist_rio)psi(elev + dist_rio)' = fm16,
        'p(dist_rio)psi(dist_rio2)' = fm17
        
              )
      
      ms <- modSel(models)
      # (ms)
      
      #This part store some models coeficients in a table (mat_models) to compare on screen
      ms_AIC_models<-as.data.frame(ms@ Full[1], row.names = NULL) #store model name
      modelo<-paste("_", as.character(as.character(sp.names[sp_number])), 
                    "_", " models", sep="") # fix model name addin species
      ma_nPars<-as.data.frame(ms@Full$nPars) #store parameter number
      ms_AIC_values<- as.data.frame(ms@Full$AIC) #store AIC values
      ms_AIC_delta<- as.data.frame(ms@Full$delta) #store AIC delta values
      ms_AIC_AICwt<- as.data.frame(ms@Full$AICwt) #store AIC wt values
      ms_AIC_cumultw<-as.data.frame(ms@Full$cumltvWt) #store model name
      ms_m<-as.data.frame(row.names(ms_AIC_models)) #store m number
      ms_formula<- as.data.frame(ms@Full$formula) #store model formula
      mat_models <- cbind(ms_AIC_models, ma_nPars, ms_AIC_values, ms_AIC_delta, ms_AIC_AICwt, ms_AIC_cumultw) #paste in matrix
      colnames(mat_models)<-c(modelo, "nPars",'AIC', "delta", "AICwt", "cumltvWt") # change row names
      
      mat_models$psi_p_fm0 <- NA
      mat_models[1,7] <- psi@estimate
      mat_models[2,7] <- p@estimate
      
      # put all in a list 
      result <- (mat_models)
      
      ##Print los 7 primeros modelos
      return (result)
      # xtable::xtable(mat_models, type = "html")
      
      # kable(mat_models) # para pdf
      # print(spname)
      # print (mat_models[c(1:7),])
      # as.character(sp.names[sp_number])
      
    }

Los datos se colapsaron a 15 dias (al estilo TEAM para evitar el Hessian error).

Venado (Odocoileus_cariacou)

Selección de Modelos

    print(as.character(sp.names[sp_number=1]))

## [1] "Odocoileus_cariacou"

    sp2 <- f.sp.occu.models(sp_number = 1)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Odocoileus_cariacou models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
5	p(.)psi(elev + dist_rio)	4	430.7070	0.0000000	0.2251011	0.2251011	0.3366860
17	p(dist_rio)psi(elev + dist_rio)	5	431.2465	0.5395411	0.1718770	0.3969782	0.2003161
15	p(dist_rio)psi(elev2)	4	432.2253	1.5183472	0.1053593	0.5023374	NA
14	p(dist_rio)psi(elev)	4	432.2253	1.5183472	0.1053593	0.6076967	NA
2	p(.)psi(elev)	3	432.4004	1.6934620	0.0965266	0.7042233	NA
3	p(.)psi(elev2)	3	432.4004	1.6934620	0.0965266	0.8007499	NA
1	p(.)psi(.)	2	433.0260	2.3190685	0.0705990	0.8713489	NA
13	p(dist_rio)psi(.)	3	433.3893	2.6823584	0.0588723	0.9302212	NA
4	p(.)psi(dist_rio)	3	434.4357	3.7287337	0.0348894	0.9651106	NA
6	p(.)psi(dist_rio2)	3	434.4357	3.7287337	0.0348894	1.0000000	NA
7	p(elev)psi(.)	3	478.0045	47.2975389	0.0000000	1.0000000	NA
8	p(elev)psi(elev)	4	480.0108	49.3038726	0.0000000	1.0000000	NA
9	p(elev)psi(elev2)	4	480.0109	49.3039016	0.0000000	1.0000000	NA
12	p(elev)psi(dist_rio2)	4	480.0129	49.3059016	0.0000000	1.0000000	NA
10	p(elev)psi(dist_rio)	4	480.0129	49.3059049	0.0000000	1.0000000	NA
16	p(dist_rio)psi(dist_rio)	4	481.1003	50.3933649	0.0000000	1.0000000	NA
18	p(dist_rio)psi(dist_rio2)	4	481.1004	50.3933846	0.0000000	1.0000000	NA
11	p(elev)psi(elev + dist_rio)	5	482.0123	51.3053107	0.0000000	1.0000000	NA

Actividad del Venado

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=1]))

Mapa del Venado

venado <- filter(camaras_17, Species=="Odocoileus_cariacou")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- venado %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos Venado", "geometry")


venado_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos Venado", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
venado_window

analisis Venado p(.)psi(elev) sp=1

doing row 1000 of 80004 doing row 2000 of 80004 doing row 3000 of 80004 doing row 4000 of 80004 doing row 5000 of 80004 doing row 6000 of 80004 doing row 7000 of 80004 doing row 8000 of 80004 doing row 9000 of 80004 doing row 10000 of 80004 doing row 11000 of 80004 doing row 12000 of 80004 doing row 13000 of 80004 doing row 14000 of 80004 doing row 15000 of 80004 doing row 16000 of 80004 doing row 17000 of 80004 doing row 18000 of 80004 doing row 19000 of 80004 doing row 20000 of 80004 doing row 21000 of 80004 doing row 22000 of 80004 doing row 23000 of 80004 doing row 24000 of 80004 doing row 25000 of 80004 doing row 26000 of 80004 doing row 27000 of 80004 doing row 28000 of 80004 doing row 29000 of 80004 doing row 30000 of 80004 doing row 31000 of 80004 doing row 32000 of 80004 doing row 33000 of 80004 doing row 34000 of 80004 doing row 35000 of 80004 doing row 36000 of 80004 doing row 37000 of 80004 doing row 38000 of 80004 doing row 39000 of 80004 doing row 40000 of 80004 doing row 41000 of 80004 doing row 42000 of 80004 doing row 43000 of 80004 doing row 44000 of 80004 doing row 45000 of 80004 doing row 46000 of 80004 doing row 47000 of 80004 doing row 48000 of 80004 doing row 49000 of 80004 doing row 50000 of 80004 doing row 51000 of 80004 doing row 52000 of 80004 doing row 53000 of 80004 doing row 54000 of 80004 doing row 55000 of 80004 doing row 56000 of 80004 doing row 57000 of 80004 doing row 58000 of 80004 doing row 59000 of 80004 doing row 60000 of 80004 doing row 61000 of 80004 doing row 62000 of 80004 doing row 63000 of 80004 doing row 64000 of 80004 doing row 65000 of 80004 doing row 66000 of 80004 doing row 67000 of 80004 doing row 68000 of 80004 doing row 69000 of 80004 doing row 70000 of 80004 doing row 71000 of 80004 doing row 72000 of 80004 doing row 73000 of 80004 doing row 74000 of 80004 doing row 75000 of 80004 doing row 76000 of 80004 doing row 77000 of 80004 doing row 78000 of 80004 doing row 79000 of 80004 doing row 80000 of 80004

Ardilla (Sciurus_igniventris)

Selección de Modelos

    print(as.character(sp.names[sp_number=2]))

## [1] "Sciurus_igniventris"

    sp2 <- f.sp.occu.models(sp_number = 2)

## Hessian is singular.
## Hessian is singular.
## Hessian is singular.
## Hessian is singular.
## Hessian is singular.
## Hessian is singular.

    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Sciurus_igniventris models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
7	p(elev)psi(.)	3	86.80613	0.000000	0.1529875	0.1529875	0.2460585
11	p(elev)psi(elev + dist_rio)	5	87.81140	1.005269	0.0925475	0.2455350	0.0364041
15	p(dist_rio)psi(elev2)	4	87.99824	1.192111	0.0842932	0.3298281	NA
14	p(dist_rio)psi(elev)	4	87.99825	1.192112	0.0842931	0.4141212	NA
13	p(dist_rio)psi(.)	3	88.04857	1.242439	0.0821985	0.4963197	NA
12	p(elev)psi(dist_rio2)	4	88.61167	1.805538	0.0620281	0.5583477	NA
10	p(elev)psi(dist_rio)	4	88.61168	1.805543	0.0620279	0.6203757	NA
17	p(dist_rio)psi(elev + dist_rio)	5	88.62410	1.817965	0.0616439	0.6820195	NA
8	p(elev)psi(elev)	4	88.80613	2.000000	0.0562809	0.7383005	NA
9	p(elev)psi(elev2)	4	88.80613	2.000000	0.0562809	0.7945814	NA
1	p(.)psi(.)	2	89.19036	2.384231	0.0464437	0.8410251	NA
16	p(dist_rio)psi(dist_rio)	4	89.38327	2.577135	0.0421734	0.8831985	NA
18	p(dist_rio)psi(dist_rio2)	4	89.38327	2.577135	0.0421734	0.9253718	NA
3	p(.)psi(elev2)	3	91.19036	4.384231	0.0170857	0.9424575	NA
2	p(.)psi(elev)	3	91.19036	4.384231	0.0170857	0.9595432	NA
6	p(.)psi(dist_rio2)	3	91.19036	4.384231	0.0170857	0.9766288	NA
4	p(.)psi(dist_rio)	3	91.19036	4.384231	0.0170857	0.9937145	NA
5	p(.)psi(elev + dist_rio)	4	93.19036	6.384231	0.0062855	1.0000000	NA

Actividad de la ardilla

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=2]))

Mapa de la Ardilla

ardilla <- filter(camaras_17, Species=="Sciurus_igniventris")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- ardilla  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos ardilla", "geometry")


ardilla_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos ardilla", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
ardilla_window

Ocelote (Leopardus_pardalis)

Selección de Modelos

    print(as.character(sp.names[sp_number=3]))

## [1] "Leopardus_pardalis"

    sp2 <- f.sp.occu.models(sp_number = 3)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Leopardus_pardalis models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
11	p(elev)psi(elev + dist_rio)	5	468.3480	0.000000	0.7049357	0.7049357	0.4916105
8	p(elev)psi(elev)	4	471.9804	3.632305	0.1146581	0.8195938	0.1303306
9	p(elev)psi(elev2)	4	471.9804	3.632305	0.1146581	0.9342518	NA
5	p(.)psi(elev + dist_rio)	4	474.5562	6.208121	0.0316281	0.9658799	NA
1	p(.)psi(.)	2	476.4397	8.091663	0.0123330	0.9782129	NA
4	p(.)psi(dist_rio)	3	477.7869	9.438897	0.0062881	0.9845010	NA
6	p(.)psi(dist_rio2)	3	477.7869	9.438897	0.0062881	0.9907891	NA
3	p(.)psi(elev2)	3	478.4135	10.065498	0.0045968	0.9953859	NA
2	p(.)psi(elev)	3	478.4135	10.065498	0.0045968	0.9999827	NA
7	p(elev)psi(.)	3	491.9380	23.589910	0.0000053	0.9999880	NA
15	p(dist_rio)psi(elev2)	4	493.6958	25.347796	0.0000022	0.9999902	NA
14	p(dist_rio)psi(elev)	4	493.7131	25.365093	0.0000022	0.9999924	NA
12	p(elev)psi(dist_rio2)	4	493.9255	25.577453	0.0000020	0.9999943	NA
10	p(elev)psi(dist_rio)	4	493.9255	25.577491	0.0000020	0.9999963	NA
17	p(dist_rio)psi(elev + dist_rio)	5	494.1022	25.754138	0.0000018	0.9999981	NA
13	p(dist_rio)psi(.)	3	495.1158	26.767725	0.0000011	0.9999992	NA
18	p(dist_rio)psi(dist_rio2)	4	497.1154	28.767393	0.0000004	0.9999996	NA
16	p(dist_rio)psi(dist_rio)	4	497.1154	28.767395	0.0000004	1.0000000	NA

Actividad del Ocelote

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=3]))

Mapa del Ocelote

ocelote <- filter(camaras_17, Species=="Leopardus_pardalis")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- ocelote  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos ocelote", "geometry")


ocelote_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos ocelote", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
ocelote_window

analisis Ocelote p(elev)psi(elev + dist) sp=3

Danta (Tapirus_terrestris)

Selección de Modelos

    print(as.character(sp.names[sp_number=4]))

## [1] "Tapirus_terrestris"

    sp2 <- f.sp.occu.models(sp_number = 4)

## Hessian is singular.

    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Tapirus_terrestris models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
11	p(elev)psi(elev + dist_rio)	5	811.4492	0.000000	0.4888444	0.4888444	0.5572193
8	p(elev)psi(elev)	4	813.0870	1.637815	0.2155379	0.7043823	0.2147943
9	p(elev)psi(elev2)	4	813.0885	1.639344	0.2153732	0.9197555	NA
17	p(dist_rio)psi(elev + dist_rio)	5	816.3331	4.883952	0.0425240	0.9622795	NA
14	p(dist_rio)psi(elev)	4	818.0123	6.563136	0.0183655	0.9806451	NA
15	p(dist_rio)psi(elev2)	4	818.0124	6.563186	0.0183651	0.9990101	NA
7	p(elev)psi(.)	3	826.4408	14.991620	0.0002715	0.9992816	NA
5	p(.)psi(elev + dist_rio)	4	827.6560	16.206833	0.0001479	0.9994295	NA
3	p(.)psi(elev2)	3	827.7490	16.299763	0.0001412	0.9995707	NA
2	p(.)psi(elev)	3	827.8320	16.382816	0.0001354	0.9997061	NA
10	p(elev)psi(dist_rio)	4	828.4399	16.990680	0.0000999	0.9998060	NA
12	p(elev)psi(dist_rio2)	4	828.4399	16.990690	0.0000999	0.9999060	NA
13	p(dist_rio)psi(.)	3	830.7262	19.277042	0.0000319	0.9999378	NA
1	p(.)psi(.)	2	831.4376	19.988375	0.0000223	0.9999601	NA
16	p(dist_rio)psi(dist_rio)	4	832.7262	21.277042	0.0000117	0.9999719	NA
18	p(dist_rio)psi(dist_rio2)	4	832.7262	21.277044	0.0000117	0.9999836	NA
6	p(.)psi(dist_rio2)	3	833.4383	21.989080	0.0000082	0.9999918	NA
4	p(.)psi(dist_rio)	3	833.4383	21.989083	0.0000082	1.0000000	NA

Actividad de la Danta

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=4]))

Mapa de la Danta

danta <- filter(camaras_17, Species=="Tapirus_terrestris")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- danta  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos danta", "geometry")


danta_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos danta", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
danta_window

analisis Danta p(elev)psi(elev+dist_rio) sp=1

Hessian is singular. doing row 1000 of 80004 doing row 2000 of 80004 doing row 3000 of 80004 doing row 4000 of 80004 doing row 5000 of 80004 doing row 6000 of 80004 doing row 7000 of 80004 doing row 8000 of 80004 doing row 9000 of 80004 doing row 10000 of 80004 doing row 11000 of 80004 doing row 12000 of 80004 doing row 13000 of 80004 doing row 14000 of 80004 doing row 15000 of 80004 doing row 16000 of 80004 doing row 17000 of 80004 doing row 18000 of 80004 doing row 19000 of 80004 doing row 20000 of 80004 doing row 21000 of 80004 doing row 22000 of 80004 doing row 23000 of 80004 doing row 24000 of 80004 doing row 25000 of 80004 doing row 26000 of 80004 doing row 27000 of 80004 doing row 28000 of 80004 doing row 29000 of 80004 doing row 30000 of 80004 doing row 31000 of 80004 doing row 32000 of 80004 doing row 33000 of 80004 doing row 34000 of 80004 doing row 35000 of 80004 doing row 36000 of 80004 doing row 37000 of 80004 doing row 38000 of 80004 doing row 39000 of 80004 doing row 40000 of 80004 doing row 41000 of 80004 doing row 42000 of 80004 doing row 43000 of 80004 doing row 44000 of 80004 doing row 45000 of 80004 doing row 46000 of 80004 doing row 47000 of 80004 doing row 48000 of 80004 doing row 49000 of 80004 doing row 50000 of 80004 doing row 51000 of 80004 doing row 52000 of 80004 doing row 53000 of 80004 doing row 54000 of 80004 doing row 55000 of 80004 doing row 56000 of 80004 doing row 57000 of 80004 doing row 58000 of 80004 doing row 59000 of 80004 doing row 60000 of 80004 doing row 61000 of 80004 doing row 62000 of 80004 doing row 63000 of 80004 doing row 64000 of 80004 doing row 65000 of 80004 doing row 66000 of 80004 doing row 67000 of 80004 doing row 68000 of 80004 doing row 69000 of 80004 doing row 70000 of 80004 doing row 71000 of 80004 doing row 72000 of 80004 doing row 73000 of 80004 doing row 74000 of 80004 doing row 75000 of 80004 doing row 76000 of 80004 doing row 77000 of 80004 doing row 78000 of 80004 doing row 79000 of 80004 doing row 80000 of 80004

Tayra (Eira_barbara)

Selección de Modelos

muy pocos registros… no confiable!

    print(as.character(sp.names[sp_number=5]))

## [1] "Eira_barbara"

    sp2 <- f.sp.occu.models(sp_number = 5)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Eira_barbara models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
7	p(elev)psi(.)	3	116.9166	0.0000000	0.1231880	0.1231880	0.2792935
13	p(dist_rio)psi(.)	3	117.3462	0.4296355	0.0993744	0.2225624	0.0385355
1	p(.)psi(.)	2	117.5329	0.6163022	0.0905191	0.3130815	NA
6	p(.)psi(dist_rio2)	3	117.9720	1.0553602	0.0726775	0.3857590	NA
4	p(.)psi(dist_rio)	3	117.9720	1.0553607	0.0726775	0.4584364	NA
8	p(elev)psi(elev)	4	118.3155	1.3989052	0.0612069	0.5196433	NA
9	p(elev)psi(elev2)	4	118.3155	1.3989056	0.0612068	0.5808501	NA
2	p(.)psi(elev)	3	118.7342	1.8176014	0.0496457	0.6304958	NA
3	p(.)psi(elev2)	3	118.7342	1.8176014	0.0496457	0.6801415	NA
10	p(elev)psi(dist_rio)	4	118.8814	1.9648313	0.0461223	0.7262637	NA
12	p(elev)psi(dist_rio2)	4	118.8815	1.9648423	0.0461220	0.7723858	NA
15	p(dist_rio)psi(elev2)	4	119.2165	2.2998391	0.0390090	0.8113948	NA
14	p(dist_rio)psi(elev)	4	119.2165	2.2998548	0.0390087	0.8504035	NA
18	p(dist_rio)psi(dist_rio2)	4	119.3434	2.4267636	0.0366103	0.8870138	NA
16	p(dist_rio)psi(dist_rio)	4	119.3434	2.4267770	0.0366101	0.9236238	NA
11	p(elev)psi(elev + dist_rio)	5	119.6920	2.7753850	0.0307540	0.9543778	NA
5	p(.)psi(elev + dist_rio)	4	119.7215	2.8048409	0.0303044	0.9846822	NA
17	p(dist_rio)psi(elev + dist_rio)	5	121.0860	4.1693882	0.0153178	1.0000000	NA

Actividad de la Tayra

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=5]))

Mapa de la Tayra

tayra <- filter(camaras_17, Species=="Eira_barbara")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- tayra  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos tayra", "geometry")


tayra_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos tayra", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
tayra_window

Chucha (Didelphis_marsupialis)

Selección de Modelos

muy pocos registros… no confiable!

    print(as.character(sp.names[sp_number=6]))

## [1] "Didelphis_marsupialis"

    sp2 <- f.sp.occu.models(sp_number = 6)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Didelphis_marsupialis models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
8	p(elev)psi(elev)	4	105.2930	0.0000000	0.1608537	0.1608537	0.1998604
9	p(elev)psi(elev2)	4	105.2930	0.0000000	0.1608537	0.3217074	0.0595698
12	p(elev)psi(dist_rio2)	4	105.5724	0.2794433	0.1398784	0.4615859	NA
10	p(elev)psi(dist_rio)	4	105.5725	0.2794509	0.1398779	0.6014638	NA
17	p(dist_rio)psi(elev + dist_rio)	5	106.8226	1.5296057	0.0748655	0.6763292	NA
11	p(elev)psi(elev + dist_rio)	5	107.2397	1.9467280	0.0607721	0.7371014	NA
3	p(.)psi(elev2)	3	107.7898	2.4967846	0.0461595	0.7832609	NA
2	p(.)psi(elev)	3	107.7959	2.5029474	0.0460175	0.8292784	NA
14	p(dist_rio)psi(elev)	4	107.8597	2.5667071	0.0445736	0.8738520	NA
15	p(dist_rio)psi(elev2)	4	107.8597	2.5667251	0.0445732	0.9184252	NA
1	p(.)psi(.)	2	109.6877	4.3946812	0.0178706	0.9362958	NA
16	p(dist_rio)psi(dist_rio)	4	110.1332	4.8401734	0.0143021	0.9505979	NA
18	p(dist_rio)psi(dist_rio2)	4	110.1332	4.8401814	0.0143021	0.9649000	NA
7	p(elev)psi(.)	3	110.2007	4.9076715	0.0138275	0.9787275	NA
13	p(dist_rio)psi(.)	3	111.6513	6.3582760	0.0066950	0.9854224	NA
6	p(.)psi(dist_rio2)	3	111.6735	6.3805255	0.0066209	0.9920434	NA
4	p(.)psi(dist_rio)	3	111.6735	6.3805256	0.0066209	0.9986643	NA
5	p(.)psi(elev + dist_rio)	4	114.8750	9.5820201	0.0013357	1.0000000	NA

Actividad de la Chucha

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=6]))

Mapa de la Chucha

chucha <- filter(camaras_17, Species=="Didelphis_marsupialis")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- chucha  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos chucha", "geometry")


chucha_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos chucha", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
chucha_window

Pecari (Tayassu_pecari)

Selección de Modelos

    print(as.character(sp.names[sp_number=7]))

## [1] "Tayassu_pecari"

    sp2 <- f.sp.occu.models(sp_number = 7)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Tayassu_pecari models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
17	p(dist_rio)psi(elev + dist_rio)	5	1083.817	0.0000000	0.2714290	0.2714290	0.6272833
13	p(dist_rio)psi(.)	3	1083.839	0.0228188	0.2683498	0.5397788	0.2584680
14	p(dist_rio)psi(elev)	4	1085.273	1.4559634	0.1310683	0.6708471	NA
15	p(dist_rio)psi(elev2)	4	1085.273	1.4559635	0.1310683	0.8019155	NA
18	p(dist_rio)psi(dist_rio2)	4	1085.833	2.0163076	0.0990423	0.9009577	NA
16	p(dist_rio)psi(dist_rio)	4	1085.833	2.0163078	0.0990423	1.0000000	NA
3	p(.)psi(elev2)	3	1147.270	63.4535723	0.0000000	1.0000000	NA
2	p(.)psi(elev)	3	1147.296	63.4796549	0.0000000	1.0000000	NA
5	p(.)psi(elev + dist_rio)	4	1149.274	65.4577503	0.0000000	1.0000000	NA
1	p(.)psi(.)	2	1154.209	70.3920522	0.0000000	1.0000000	NA
7	p(elev)psi(.)	3	1156.127	72.3104903	0.0000000	1.0000000	NA
6	p(.)psi(dist_rio2)	3	1156.209	72.3920531	0.0000000	1.0000000	NA
4	p(.)psi(dist_rio)	3	1156.209	72.3920531	0.0000000	1.0000000	NA
10	p(elev)psi(dist_rio)	4	1158.127	74.3104489	0.0000000	1.0000000	NA
12	p(elev)psi(dist_rio2)	4	1158.127	74.3104531	0.0000000	1.0000000	NA
8	p(elev)psi(elev)	4	1158.127	74.3104868	0.0000000	1.0000000	NA
9	p(elev)psi(elev2)	4	1158.129	74.3126029	0.0000000	1.0000000	NA
11	p(elev)psi(elev + dist_rio)	5	1160.127	76.3104851	0.0000000	1.0000000	NA

Actividad del Pecari

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=7]))

Mapa del Pecari

pecari <- filter(camaras_17, Species=="Tayassu_pecari")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- pecari  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos pecari", "geometry")


pecari_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos pecari", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
pecari_window

analisis Pecari p(dist_rio)psi(elev+dost_rio) sp=1

Boruga (Cuniculus_paca)

Selección de Modelos

    print(as.character(sp.names[sp_number=8]))

## [1] "Cuniculus_paca"

    sp2 <- f.sp.occu.models(sp_number = 8)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Cuniculus_paca models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
11	p(elev)psi(elev + dist_rio)	5	958.6460	0.000000	0.3177152	0.3177152	0.4551684
9	p(elev)psi(elev2)	4	960.1173	1.471294	0.1522476	0.4699628	0.2862908
8	p(elev)psi(elev)	4	960.1173	1.471294	0.1522476	0.6222104	NA
7	p(elev)psi(.)	3	960.1899	1.543901	0.1468196	0.7690300	NA
17	p(dist_rio)psi(elev + dist_rio)	5	962.0306	3.384591	0.0584902	0.8275203	NA
5	p(.)psi(elev + dist_rio)	4	963.4849	4.838855	0.0282679	0.8557882	NA
14	p(dist_rio)psi(elev)	4	963.6595	5.013459	0.0259047	0.8816929	NA
15	p(dist_rio)psi(elev2)	4	963.6595	5.013459	0.0259047	0.9075977	NA
13	p(dist_rio)psi(.)	3	963.8839	5.237834	0.0231556	0.9307533	NA
3	p(.)psi(elev2)	3	964.9017	6.255662	0.0139200	0.9446733	NA
2	p(.)psi(elev)	3	964.9017	6.255662	0.0139200	0.9585932	NA
1	p(.)psi(.)	2	965.3980	6.751920	0.0108612	0.9694544	NA
16	p(dist_rio)psi(dist_rio)	4	965.5276	6.881587	0.0101794	0.9796338	NA
18	p(dist_rio)psi(dist_rio2)	4	965.5276	6.881587	0.0101794	0.9898131	NA
4	p(.)psi(dist_rio)	3	966.9124	8.266401	0.0050934	0.9949066	NA
6	p(.)psi(dist_rio2)	3	966.9125	8.266417	0.0050934	1.0000000	NA
10	p(elev)psi(dist_rio)	4	1077.1658	118.519783	0.0000000	1.0000000	NA
12	p(elev)psi(dist_rio2)	4	1077.1661	118.520074	0.0000000	1.0000000	NA

Actividad de la Boruga

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=8]))

Mapa de la Boruga

boruga <- filter(camaras_17, Species=="Cuniculus_paca")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- boruga  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos boruga", "geometry")


boruga_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos boruga", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
boruga_window

analisis Boruga p(elev)psi(elev+dost_rio) sp=1

Ñeque-Guara (Dasyprocta_fuliginosa)

Selección de Modelos

    print(as.character(sp.names[sp_number=9]))

## [1] "Dasyprocta_fuliginosa"

    sp2 <- f.sp.occu.models(sp_number = 9)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Dasyprocta_fuliginosa models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
7	p(elev)psi(.)	3	843.9997	0.000000	0.2845833	0.2845833	0.2546188
12	p(elev)psi(dist_rio2)	4	845.3883	1.388563	0.1421303	0.4267136	0.4362057
10	p(elev)psi(dist_rio)	4	845.3883	1.388563	0.1421303	0.5688439	NA
8	p(elev)psi(elev)	4	845.8052	1.805501	0.1153851	0.6842291	NA
9	p(elev)psi(elev2)	4	845.8052	1.805501	0.1153851	0.7996142	NA
11	p(elev)psi(elev + dist_rio)	5	846.9823	2.982564	0.0640551	0.8636693	NA
13	p(dist_rio)psi(.)	3	847.9459	3.946188	0.0395645	0.9032338	NA
16	p(dist_rio)psi(dist_rio)	4	849.3442	5.344465	0.0196641	0.9228979	NA
18	p(dist_rio)psi(dist_rio2)	4	849.3442	5.344465	0.0196641	0.9425620	NA
15	p(dist_rio)psi(elev2)	4	849.7618	5.762055	0.0159586	0.9585206	NA
14	p(dist_rio)psi(elev)	4	849.7618	5.762055	0.0159586	0.9744792	NA
17	p(dist_rio)psi(elev + dist_rio)	5	850.9210	6.921298	0.0089386	0.9834178	NA
1	p(.)psi(.)	2	851.8865	7.886800	0.0055159	0.9889336	NA
6	p(.)psi(dist_rio2)	3	853.3107	9.310964	0.0027062	0.9916398	NA
4	p(.)psi(dist_rio)	3	853.3107	9.310967	0.0027062	0.9943460	NA
3	p(.)psi(elev2)	3	853.7173	9.717546	0.0022084	0.9965544	NA
2	p(.)psi(elev)	3	853.7173	9.717546	0.0022084	0.9987627	NA
5	p(.)psi(elev + dist_rio)	4	854.8760	10.876237	0.0012373	1.0000000	NA

Actividad del Ñeque

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=9]))

Mapa del Ñeque

neque <- filter(camaras_17, Species=="Dasyprocta_fuliginosa")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- neque  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos ñeque", "geometry")


neque_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos ñeque", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
neque_window

Chiguiro (Hydrochoerus_hydrochaeris)

Selección de Modelos

    print(as.character(sp.names[sp_number=10]))

## [1] "Hydrochoerus_hydrochaeris"

    sp2 <- f.sp.occu.models(sp_number = 10)

## Hessian is singular.
## Hessian is singular.

    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Hydrochoerus_hydrochaeris models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
13	p(dist_rio)psi(.)	3	54.69605	0.000000	0.2502802	0.2502802	0.0764020
15	p(dist_rio)psi(elev2)	4	55.82256	1.126512	0.1424976	0.3927778	0.0868302
14	p(dist_rio)psi(elev)	4	55.88285	1.186801	0.1382662	0.5310439	NA
16	p(dist_rio)psi(dist_rio)	4	56.71485	2.018807	0.0912112	0.6222551	NA
18	p(dist_rio)psi(dist_rio2)	4	56.75664	2.060588	0.0893255	0.7115806	NA
12	p(elev)psi(dist_rio2)	4	57.73199	3.035942	0.0548504	0.7664310	NA
17	p(dist_rio)psi(elev + dist_rio)	5	57.73447	3.038425	0.0547824	0.8212134	NA
10	p(elev)psi(dist_rio)	4	58.24532	3.549269	0.0424338	0.8636473	NA
9	p(elev)psi(elev2)	4	58.88761	4.191562	0.0307780	0.8944253	NA
11	p(elev)psi(elev + dist_rio)	5	59.19803	4.501988	0.0263531	0.9207784	NA
8	p(elev)psi(elev)	4	59.29315	4.597098	0.0251292	0.9459076	NA
7	p(elev)psi(.)	3	59.46201	4.765965	0.0230946	0.9690022	NA
5	p(.)psi(elev + dist_rio)	4	59.83793	5.141878	0.0191374	0.9881396	NA
3	p(.)psi(elev2)	3	62.98005	8.284001	0.0039772	0.9921168	NA
2	p(.)psi(elev)	3	63.06060	8.364555	0.0038202	0.9959370	NA
1	p(.)psi(.)	2	64.04020	9.344155	0.0023408	0.9982778	NA
6	p(.)psi(dist_rio2)	3	66.04029	11.344243	0.0008611	0.9991389	NA
4	p(.)psi(dist_rio)	3	66.04029	11.344243	0.0008611	1.0000000	NA

Actividad del Chiguiro

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=10]))

Mapa del Chiguiro

chiguiro <- filter(camaras_17, Species=="Hydrochoerus_hydrochaeris")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- chiguiro  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos chiguiro", "geometry")


chiguiro_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos chiguiro", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
chiguiro_window

Puma (Puma_concolor)

Selección de Modelos

    print(as.character(sp.names[sp_number=11]))

## [1] "Puma_concolor"

    sp2 <- f.sp.occu.models(sp_number = 11)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Puma_concolor models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
1	p(.)psi(.)	2	157.7201	0.000000	0.2198848	0.2198848	0.9993792
7	p(elev)psi(.)	3	159.6794	1.959300	0.0825541	0.3024389	0.0181376
13	p(dist_rio)psi(.)	3	159.7170	1.996914	0.0810160	0.3834549	NA
2	p(.)psi(elev)	3	159.7184	1.998219	0.0809632	0.4644181	NA
4	p(.)psi(dist_rio)	3	159.7184	1.998279	0.0809607	0.5453788	NA
6	p(.)psi(dist_rio2)	3	159.7185	1.998320	0.0809591	0.6263379	NA
3	p(.)psi(elev2)	3	159.7191	1.998945	0.0809338	0.7072717	NA
8	p(elev)psi(elev)	4	161.6783	3.958187	0.0303869	0.7376585	NA
10	p(elev)psi(dist_rio)	4	161.6785	3.958388	0.0303838	0.7680423	NA
12	p(elev)psi(dist_rio2)	4	161.6786	3.958499	0.0303821	0.7984245	NA
9	p(elev)psi(elev2)	4	161.6795	3.959415	0.0303682	0.8287927	NA
15	p(dist_rio)psi(elev2)	4	161.7159	3.995771	0.0298212	0.8586138	NA
14	p(dist_rio)psi(elev)	4	161.7159	3.995776	0.0298211	0.8884349	NA
18	p(dist_rio)psi(dist_rio2)	4	161.7159	3.995782	0.0298210	0.9182559	NA
16	p(dist_rio)psi(dist_rio)	4	161.7159	3.995807	0.0298206	0.9480765	NA
5	p(.)psi(elev + dist_rio)	4	161.7188	3.998718	0.0297773	0.9778538	NA
11	p(elev)psi(elev + dist_rio)	5	163.6786	5.958437	0.0111773	0.9890311	NA
17	p(dist_rio)psi(elev + dist_rio)	5	163.7162	5.996081	0.0109689	1.0000000	NA

Actividad del Puma

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=11]))

Mapa del Puma

puma <- filter(camaras_17, Species=="Puma_concolor")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- puma  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos puma", "geometry")


puma_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos puma", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
puma_window

Oso palmero (Myrmecophaga_tridactyla)

Selección de Modelos

    print(as.character(sp.names[sp_number=13]))

## [1] "Myrmecophaga_tridactyla"

    sp2 <- f.sp.occu.models(sp_number = 13)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp2) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Myrmecophaga_tridactyla models	nPars	AIC	delta	AICwt	cumltvWt	psi_p_fm0
5	p(.)psi(elev + dist_rio)	4	231.3263	0.0000000	0.1794647	0.1794647	0.3415188
2	p(.)psi(elev)	3	231.8466	0.5202811	0.1383571	0.3178218	0.0850134
3	p(.)psi(elev2)	3	231.8466	0.5202815	0.1383571	0.4561789	NA
1	p(.)psi(.)	2	232.8276	1.5013023	0.0847179	0.5408968	NA
11	p(elev)psi(elev + dist_rio)	5	233.1927	1.8663691	0.0705833	0.6114802	NA
17	p(dist_rio)psi(elev + dist_rio)	5	233.2997	1.9733883	0.0669057	0.6783859	NA
8	p(elev)psi(elev)	4	233.7737	2.4473236	0.0527897	0.7311756	NA
9	p(elev)psi(elev2)	4	233.7737	2.4473236	0.0527897	0.7839654	NA
6	p(.)psi(dist_rio2)	3	233.9940	2.6676705	0.0472826	0.8312480	NA
4	p(.)psi(dist_rio)	3	233.9940	2.6676710	0.0472826	0.8785306	NA
12	p(elev)psi(dist_rio2)	4	234.5574	3.2311119	0.0356740	0.9142047	NA
10	p(elev)psi(dist_rio)	4	234.5574	3.2311120	0.0356740	0.9498787	NA
16	p(dist_rio)psi(dist_rio)	4	235.8735	4.5472108	0.0184742	0.9683528	NA
18	p(dist_rio)psi(dist_rio2)	4	235.8735	4.5472108	0.0184742	0.9868270	NA
7	p(elev)psi(.)	3	237.2757	5.9493541	0.0091642	0.9959912	NA
13	p(dist_rio)psi(.)	3	240.0322	8.7059141	0.0023095	0.9983007	NA
15	p(dist_rio)psi(elev2)	4	242.0321	10.7057564	0.0008497	0.9991503	NA
14	p(dist_rio)psi(elev)	4	242.0321	10.7057670	0.0008497	1.0000000	NA

Actividad del Oso palmero

activityDensity (recordTable = camaras,
                 species     = as.character(sp.names[sp_number=13]))

Mapa del Oso palmero

oso <- filter(camaras_17, Species=="Myrmecophaga_tridactyla")
by_sp <- camaras_17 %>%  group_by(Species) %>% tally()
by_sp_predio <- oso  %>%  group_by(Predio) %>% tally()
names(by_sp_predio) <-  c("Predio", "Fotos oso palmero", "geometry")


oso_window <- qtm(vichada_osm1) + 
  tm_shape(celdas) + tm_borders(lwd=1, alpha = .5) + 
  tm_shape(rio) +
  tm_polygons("Rio", colorNA =NULL, border.col = "blue", palette = "blue") +  #tm_symbols (size = 0.5) +
  tm_shape(by_sp_predio) + # tm_symbols (col="red", size = 0.25) + 
    tm_bubbles(size = "Fotos oso palmero", col = "red", border.col= "red", alpha= 0.5, 
               legend.size.is.portrait=TRUE) +
    # tm_dots(col = "Species", size = 0.25, 
    #        shape = 16, title = "Especie", legend.show = TRUE,
    #       legend.is.portrait = TRUE, legend.z = NA) +
    tm_layout(scale = 0.9, #font symbol sizes,are controlled by this value 
            outer.margins = c(0,.1,0,.2), #bottom, left, top, right margin
            legend.position = c(1.01,.1), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=T) + tm_scale_bar() + 
  tm_compass(type="arrow", position=c("right", "center"), show.labels = 1)

#plot
oso_window

analisis Oso Palmero p(.)psi(elev+dost_rio) sp=1

Información de la sesión en R.

print(sessionInfo(), locale = FALSE)

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 14393)
## 
## Matrix products: default
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] unmarked_1.0.1      scales_1.1.1        Hmisc_4.4-1        
##  [4] Formula_1.2-4       survival_3.2-7      lattice_0.20-41    
##  [7] chron_2.3-56        zoo_1.8-8           reshape_0.8.8      
## [10] lubridate_1.7.9.2   plyr_1.8.6          reshape2_1.4.4     
## [13] camtrapR_2.0.3      leafpop_0.0.6       leaflet_2.0.3      
## [16] plainview_0.1.1     fasterize_1.0.3     stars_0.4-3        
## [19] abind_1.4-5         GADMTools_3.8-1     classInt_0.4-3     
## [22] OpenStreetMap_0.3.4 osmdata_0.1.4       tmaptools_3.1      
## [25] tmap_3.2            stargazer_5.2.2     kableExtra_1.3.1   
## [28] xtable_1.8-4        knitr_1.30          ggmap_3.0.0        
## [31] forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2        
## [34] purrr_0.3.4         readr_1.4.0         tidyr_1.1.2        
## [37] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0    
## [40] readxl_1.3.1        raster_3.4-5        spatstat_1.64-1    
## [43] rpart_4.1-15        nlme_3.1-150        spatstat.data_1.5-2
## [46] mapview_2.9.0       sf_0.9-6            maptools_1.0-2     
## [49] rgdal_1.5-18        sp_1.4-4           
## 
## loaded via a namespace (and not attached):
##   [1] uuid_0.1-4              backports_1.2.0         secr_4.3.1             
##   [4] systemfonts_0.3.2       lwgeom_0.2-5            splines_4.0.3          
##   [7] crosstalk_1.1.0.1       digest_0.6.27           foreach_1.5.1          
##  [10] htmltools_0.5.0         leaflet.providers_1.9.0 fansi_0.4.1            
##  [13] checkmate_2.0.0         magrittr_2.0.1          cluster_2.1.0          
##  [16] tensor_1.5              modelr_0.1.8            RcppParallel_5.0.2     
##  [19] R.utils_2.10.1          svglite_1.2.3.2         jpeg_0.1-8.1           
##  [22] colorspace_2.0-0        rvest_0.3.6             haven_2.3.1            
##  [25] xfun_0.19               leafem_0.1.3            crayon_1.3.4           
##  [28] jsonlite_1.7.1          brew_1.0-6              iterators_1.0.13       
##  [31] glue_1.4.2              polyclip_1.10-0         gtable_0.3.0           
##  [34] webshot_0.5.2           overlap_0.3.3           DBI_1.1.0              
##  [37] Rcpp_1.0.5              htmlTable_2.1.0         viridisLite_0.3.0      
##  [40] units_0.6-7             foreign_0.8-80          stats4_4.0.3           
##  [43] htmlwidgets_1.5.2       httr_1.4.2              RColorBrewer_1.1-2     
##  [46] ellipsis_0.3.1          farver_2.0.3            pkgconfig_2.0.3        
##  [49] XML_3.99-0.5            rJava_0.9-13            R.methodsS3_1.8.1      
##  [52] nnet_7.3-14             dbplyr_2.0.0            deldir_0.2-3           
##  [55] labeling_0.4.2          tidyselect_1.1.0        rlang_0.4.8            
##  [58] ggspatial_1.1.4         munsell_0.5.0           cellranger_1.1.0       
##  [61] tools_4.0.3             cli_2.1.0               generics_0.1.0         
##  [64] broom_0.7.2             evaluate_0.14           yaml_2.2.1             
##  [67] goftest_1.2-2           leafsync_0.1.0          fs_1.5.0               
##  [70] satellite_1.0.2         RgoogleMaps_1.4.5.3     RcppNumerical_0.4-0    
##  [73] R.oo_1.24.0             xml2_1.3.2              compiler_4.0.3         
##  [76] rstudioapi_0.13         curl_4.3                png_0.1-7              
##  [79] e1071_1.7-4             spatstat.utils_1.17-0   reprex_0.3.0           
##  [82] stringi_1.5.3           highr_0.8               gdtools_0.2.2          
##  [85] rgeos_0.5-5             Matrix_1.2-18           vctrs_0.3.5            
##  [88] rosm_0.2.5              pillar_1.4.6            lifecycle_0.2.0        
##  [91] data.table_1.13.2       bitops_1.0-6            latticeExtra_0.6-29    
##  [94] R6_2.5.0                KernSmooth_2.23-18      gridExtra_2.3          
##  [97] codetools_0.2-18        gdalUtils_2.0.3.2       dichromat_2.0-0        
## [100] MASS_7.3-53             assertthat_0.2.1        rjson_0.2.20           
## [103] withr_2.3.0             mgcv_1.8-33             parallel_4.0.3         
## [106] hms_0.5.3               class_7.3-17            rmarkdown_2.5          
## [109] base64enc_0.1-3

Datos FotoTrampeo WCS Vichada

Diego J. Lizcano, German Forero, Carlos, Leonor, Ana

PROYECTO VIDA SILVESTRE – PVS