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

home <- "D:/BoxFiles/"
tnc <- "C:/Users/diego.lizcano/"

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

##
datos.raw <- read_excel(paste(home, "Box Sync/CodigoR/Biodiv_Caqueta/data/Datos_cameratrap_Caqueta_fixed.xlsx", sep=""))

predios <- st_read(paste(home, "Box Sync/CodigoR/Biodiv_Caqueta/shp/prediosAgroforesteriaPiedemonte.shp", sep=""))

Reading layer prediosAgroforesteriaPiedemonte' from data sourceD:Sync_Caqueta.shp’ using driver `ESRI Shapefile’ Simple feature collection with 1792 features and 26 fields geometry type: MULTIPOLYGON dimension: XY bbox: xmin: 1099776 ymin: 617840.4 xmax: 1141460 ymax: 670269.7 epsg (SRID): NA proj4string: +proj=tmerc +lat_0=4.596200416666666 +lon_0=-77.07750791666666 +k=1 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +units=m +no_defs

san_miguel <- st_read(paste(home, "Box Sync/CodigoR/Biodiv_Caqueta/shp/san_miguel.shp", sep=""))

Reading layer san_miguel' from data sourceD:Sync_Caqueta_miguel.shp’ using driver `ESRI Shapefile’ Simple feature collection with 292 features and 29 fields geometry type: MULTIPOLYGON dimension: XY bbox: xmin: 1089542 ymin: 614395 xmax: 1094099 ymax: 623402.5 epsg (SRID): NA proj4string: +proj=tmerc +lat_0=4.596200416666666 +lon_0=-77.07750791666666 +k=1 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +units=m +no_defs

predios_sanMiguel <- st_read(paste(home, "Box Sync/CodigoR/Biodiv_Caqueta/shp/prediosAgroforesteriaPiedemonte_sanMiguel_2.shp", sep=""))

Reading layer prediosAgroforesteriaPiedemonte_sanMiguel_2' from data sourceD:Sync_Caqueta_sanMiguel_2.shp’ using driver `ESRI Shapefile’ Simple feature collection with 2084 features and 26 fields geometry type: MULTIPOLYGON dimension: XY bbox: xmin: 1089542 ymin: 614395 xmax: 1141460 ymax: 670269.7 epsg (SRID): NA proj4string: +proj=tmerc +lat_0=4.596200416666666 +lon_0=-77.07750791666666 +k=1 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +units=m +no_defs

def_2019 <- raster(paste(home, "Box Sync/CodigoR/Biodiv_Caqueta/defore_raster/def_2019.tif", sep=""))



# casas.sp1 <- as(casas, "Spatial") # Create Spatial* object
# casas.sp <- as(casas.sp1, "SpatialPoints")
# casas.ppp <- as(casas.sp, "ppp")   # make ppp
# casas.dist <- distmap(casas.ppp) # make distance map
# 
#

Lugar del muestreo

# datos.raw

# get the centroid
centercoord<- c(mean(as.numeric( datos.raw$Longitude)), 
                mean(as.numeric( datos.raw$Latitude)))


elevation<-raster::getData("SRTM",lon=centercoord[1], lat=centercoord[2])

e<-extent (-76.4, -75.7, 1.05, 1.65) # make the extent
elevation.crop<-crop(elevation, e) # cut elevation to small window

## save shp
# p <- as(e, 'SpatialPolygons')
# crs(p) <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
# shapefile(p, paste(tnc, "Box Sync/CodigoR/Biodiv_Caqueta/shp/extent.shp", sep=""))

# plot(elevation.crop)
# points(datos.raw$Longitude, datos.raw$Latitude, add=T)

datos.raw$Lon <- as.numeric(datos.raw$Longitude)
datos.raw$Lat <- as.numeric(datos.raw$Latitude)

# make a large sf object
datos.raw_sf = st_as_sf(datos.raw, coords = c("Lon", "Lat"), 
                        crs = "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")

camaras <- datos.raw_sf

# mapview (list(predios, camaras) ,
#         zcol = list("COB_ACTUAL", "Sampling_Event"), 
#         cex= list(NA, 0.8),
#         legend = list(TRUE, TRUE),
#         col.regions = list(topo.colors, heat.colors), # terrain.colors
#         alpha = list(0.5, 0.1),
#         alpha.regions=list(0.5, 0.1)
#                 )


# tmap_mode("plot") ## tmap mode set to plotting 
# fincas <- tm_shape(predios) + tm_polygons("COB_ACTUAL") + 
#    # tm_legend(show = FALSE) +
#    tm_shape(camaras) + 
#    tm_symbols(col="red", size = 0.1)
# 
# resgu <- tm_shape(san_miguel) + tm_polygons("COB_ACTUAL") +
#   tm_shape(camaras) + 
#    tm_symbols(col="red", size = 0.1) 
# 
# tmap_arrange(resgu, fincas)

predios_sanMiguel.geo <- st_transform(predios_sanMiguel, "+proj=longlat +datum=WGS84 +ellps=WGS84
+towgs84=0,0,0")

tmap_mode("plot") ## tmap mode set to plotting 

# # get fondo de osm
# bb <- c(-76.31, 1.05111 , -75.7, 1.65)
# caqueta_osm1 <- read_osm(bb, type="osm")
# qtm(CBS_osm1)
# 
# tm_shape(predios_sanMiguel.geo) + tm_polygons("COB_ACTUAL", colorNA =NULL, border.col = "grey") +
#   tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
#     tm_dots(col = "Sampling_Event", palette = "Dark2", size = 0.25, 
#             shape = 16, title = "Camara", legend.show = TRUE,
#   legend.is.portrait = TRUE,
#   legend.z = NA) +
#     tm_layout(scale = .5,
#             # legend.position = c(.78,.72), 
#             legend.outside.size = 0.1,
#             legend.title.size = 1.6,
#             legend.height = 0.9,
#             legend.width = 1.5,
#             legend.text.size = 1.2, 
#             legend.hist.size = 0.5) + 
#   tm_layout(frame=F) + tm_scale_bar()


# use osm data by bounding box
bb <- c(-76.31, 1.05111 , -75.7, 1.65)
# 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
caqueta_osm1 <- read_osm(bb, type="stamen-terrain") # type puede ser tambien bing, osm

qtm(caqueta_osm1) + tm_shape(predios_sanMiguel.geo) + 
  tm_polygons("COB_ACTUAL", colorNA =NULL, border.col = NULL, palette = "Dark2",) +  
  tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Sampling_Event", palette = "Set1", size = 0.25, 
            shape = 16, title = "Camara", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +
    tm_layout(scale = .5,
            # legend.position = c(.78,.72), 
            legend.outside.size = 0.1,
            legend.title.size = 1.6,
            legend.height = 0.9,
            legend.width = 1.5,
            legend.text.size = 1.2, 
            legend.hist.size = 0.5) + 
  tm_layout(frame=F) + tm_scale_bar()

##### con facets 
# tm_shape(predios_sanMiguel.geo) + 
# tm_polygons("COB_ACTUAL", palette = "Dark2", colorNA =NULL, border.col = "grey") +
#   tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
#     tm_dots(col = "red", size = 0.25, 
#             shape = 16, title = "Camara", legend.show = TRUE,
#   legend.is.portrait = TRUE,
#   legend.z = NA) + tm_facets(by = "Sampling_Event", free.coords = TRUE)
#     tm_layout(scale = .5,
#             # legend.position = c(.78,.72), 
#             legend.outside.size = 0.1,
#             legend.title.size = 1.6,
#             legend.height = 0.9,
#             legend.width = 1.5,
#             legend.text.size = 1.2, 
#             legend.hist.size = 0.5) + 
#   tm_layout(frame=F) + tm_scale_bar()

Duración del muestreo

Las trampas cámara permanecieron activas desde mediados de septiembre 2019 hasta comienzos de diciembre 2019.

Calendario fotografias, inicio y finalizacion de cada cámara

Especies registradas

Las especies registradas en todo em muestreo fueron 87 entre aves y mamíferos.

Mamíferos detectados

Se detectaron 34 especies de mamíferos

	species	events	phothos	RAI	naiveoccu
Dasyprocta fuliginosa	Dasyprocta fuliginosa	207	207	3.96551724137931	0.741379310344828
Cuniculus paca	Cuniculus paca	231	231	4.42528735632184	0.620689655172414
Cabassous unicinctus	Cabassous unicinctus	28	28	0.53639846743295	0.293103448275862
Tamandua tetradactyla	Tamandua tetradactyla	62	62	1.18773946360153	0.482758620689655
Dasypus novemcinctus	Dasypus novemcinctus	143	143	2.73946360153257	0.568965517241379
Eira barbara	Eira barbara	66	66	1.26436781609195	0.551724137931034
Canis lupus familiaris	Canis lupus familiaris	18	18	0.344827586206897	0.275862068965517
Procyon cancrivorus	Procyon cancrivorus	37	37	0.708812260536398	0.258620689655172
Leopardus wiedii	Leopardus wiedii	6	6	0.114942528735632	0.0862068965517241
Myoprocta pratti	Myoprocta pratti	16	16	0.306513409961686	0.0517241379310345
Dasypus unknown	Dasypus unknown	40	40	0.766283524904214	0.241379310344828
Dasypus kappleri	Dasypus kappleri	35	35	0.670498084291188	0.258620689655172
Marmosa unknown	Marmosa unknown	10	10	0.191570881226054	0.0517241379310345
Nasua nasua	Nasua nasua	46	46	0.881226053639847	0.413793103448276
Galictis vittata	Galictis vittata	3	3	0.0574712643678161	0.0517241379310345
Didelphis marsupialis	Didelphis marsupialis	35	35	0.670498084291188	0.155172413793103
Proechimys unknown	Proechimys unknown	76	76	1.45593869731801	0.120689655172414
Coendou prehensilis	Coendou prehensilis	1	1	0.0191570881226054	0.0172413793103448
Leopardus pardalis	Leopardus pardalis	12	12	0.229885057471264	0.155172413793103
Saimiri sciureus	Saimiri sciureus	3	3	0.0574712643678161	0.0344827586206897
Homo sapiens	Homo sapiens	1	1	0.0191570881226054	0.0172413793103448
Pecari tajacu	Pecari tajacu	43	43	0.823754789272031	0.258620689655172
Microsciurus flaviventer	Microsciurus flaviventer	4	4	0.0766283524904215	0.0517241379310345
Leopardus unknown	Leopardus unknown	1	1	0.0191570881226054	0.0172413793103448
Sciurus igniventris	Sciurus igniventris	38	38	0.727969348659004	0.120689655172414
Puma yagouaroundi	Puma yagouaroundi	13	13	0.24904214559387	0.137931034482759
Sciurus unknown	Sciurus unknown	8	8	0.153256704980843	0.103448275862069
Philander andersoni	Philander andersoni	5	5	0.0957854406130268	0.0689655172413793
Philander opossum	Philander opossum	16	16	0.306513409961686	0.0517241379310345
Caluromys lanatus	Caluromys lanatus	1	1	0.0191570881226054	0.0172413793103448
Puma concolor	Puma concolor	6	6	0.114942528735632	0.0862068965517241
Panthera onca	Panthera onca	4	4	0.0766283524904215	0.0689655172413793
Tremarctos ornatus	Tremarctos ornatus	3	3	0.0574712643678161	0.0517241379310345
Microsciurus unknown	Microsciurus unknown	10	10	0.191570881226054	0.0344827586206897

RAI es: Relative Abundance Index

Distribución posterior de la riqueza de especies de mamíferos

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 = 90 #dias 
especies = MultiSpeciesSiteOcc(nrepls=nrepls, x=X1) #### run stan

## Posterior computed in 13.8757859349251 minutes

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

## Inference for Stan model: 9ba1cd7743adcbf9a81658d6760a5218.
## 4 chains, each with iter=5000; warmup=2500; thin=1; 
## post-warmup draws per chain=2500, total post-warmup draws=10000.
## 
##                 mean se_mean   sd     2.5%      25%      50%      75%    97.5%
## alpha          -1.30    0.01 0.35    -1.97    -1.53    -1.31    -1.08    -0.59
## beta           -4.38    0.01 0.32    -5.08    -4.57    -4.35    -4.16    -3.81
## Omega           0.88    0.00 0.06     0.74     0.84     0.89     0.93     0.98
## sigma_uv[1]     1.46    0.01 0.28     1.02     1.26     1.42     1.62     2.08
## sigma_uv[2]     1.40    0.01 0.29     0.93     1.19     1.36     1.57     2.05
## rho_uv         -0.04    0.00 0.24    -0.51    -0.20    -0.03     0.13     0.41
## E_N            35.19    0.03 2.44    29.78    33.62    35.46    37.03    39.03
## E_N_2          36.71    0.03 1.63    34.00    35.00    37.00    38.00    40.00
## lp__        -1736.70    0.19 8.29 -1754.16 -1742.10 -1736.18 -1730.85 -1721.80
##             n_eff Rhat
## alpha        1101    1
## beta          823    1
## Omega        5383    1
## sigma_uv[1]  3005    1
## sigma_uv[2]  1555    1
## rho_uv       2306    1
## E_N          5383    1
## E_N_2        2715    1
## lp__         1897    1
## 
## Samples were drawn using NUTS(diag_e) at Sat Feb 22 22:30:31 2020.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at 
## convergence, Rhat=1).

# 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 = 58 
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)

## [1] 35.16579

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

## [1] 35.50177

# ##Plot in ggplot
# 
# sims <- extract(especies$fit);
# 
# freq <- rep(0,S);
# N <- sims$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(0,34,35,36,37,38,39,40,41)) +
#   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 = 34. Especies esperadas en rojo = 35.1-35.4. El número esperado esta corrregido por la detectabilidad.

Mamíferos en las cámaras

Por géneros

## Select Mammals

datos.raw.mam <- filter(datos.raw, Class=="MAMMALIA")

###
## Google key
 register_google(key = "AIzaSyDJmcl2kNxd_-qbmajoHobxa6G-RFR_f8s", write = TRUE)

overlay <- stat_density2d(
  aes(x = Lon, y = Lat, fill = ..level.., alpha = ..level..),
  bins = 10, geom = "polygon",
  data = datos.raw.mam) 

### add camera traps
map <- get_map(c(-76.058971, 1.335207), zoom = 10, 
               source = 'google', maptype = "terrain", color ="bw")
# ggmap(map) #, fullpage = TRUE)


g1<-  ggmap(map, extent = 'device') + geom_point(data = datos.raw.mam, 
                                                 aes(x=Lon, y=Lat), 
                                                 colour = "red", size = I(2),  
                                                 alpha = 1/10, na.rm = TRUE) +
                                            overlay + 
                                scale_fill_gradient(low = "blue", high = "red")

# con google
g3<- g1 + overlay + scale_fill_gradient(low = "blue", high = "red") + 
        facet_wrap(~ Genus, ncol = 7)

g3

Aves en las cámaras

Se registraron 32 especies de aves.

Por géneros:

## Select Mammals

datos.raw.ave <- filter(datos.raw, Class=="AVES")

##
## Google key
## register_google(key = "AIzaSyDJmcl2kNxd_-qbmajoHobxa6G-RFR_f8s", write = TRUE)

overlay <- stat_density2d(
  aes(x = Lon, y = Lat, fill = ..level.., alpha = ..level..),
  bins = 10, geom = "polygon",
  data = datos.raw.ave) 

### add camera traps
map <- get_map(c(-76.058971, 1.335207), zoom = 10, 
               source = 'google', maptype = "terrain", color ="bw")
# ggmap(map) #, fullpage = TRUE)


g1<-  ggmap(map, extent = 'device') + geom_point(data = datos.raw.ave, 
                                                 aes(x=Lon, y=Lat), 
                                                 colour = "red", size = I(2),  
                                                 alpha = 1/10, na.rm = TRUE) +
                                            overlay + 
                                scale_fill_gradient(low = "blue", high = "red")

# con google
g3<- g1 + overlay + scale_fill_gradient(low = "blue", high = "red") + 
        facet_wrap(~ Genus, ncol = 8)

g3

Analisis de Ocupación para Mamíferos

Covariables espaciales

Usamos cuatro variables geográficas (altitud, pendiente, aspecto y rugosidad) y tres relacionadas a aspectos humanos (distancia a la deforestación, cobertura actual, y tipo de sitio (parque, resguardo, finca)).

# again
datos.raw.mam <- filter(datos.raw, Class=="MAMMALIA")

# elevation.crop
slope<-terrain(elevation.crop, opt=c("aspect", "slope"), unit='degrees', neighbors=4)
rough <- terrain(elevation.crop, opt = c("roughness"))

cam.cords <- as.data.frame(unique(cbind(datos.raw.mam$Lon, datos.raw.mam$Lat)))
coordinates(cam.cords) <- ~V1+V2 #make sppatial data fram
geo <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") #def cord
proj4string(cam.cords)<-geo # set cords


# convert deforestation to image, owin and put distances
def_2019_m <- projectRaster(def_2019, crs=crs(predios))
dist_def_owin <- distmap(as.owin(as.im(def_2019_m)))
dist_def_m <- raster(dist_def_owin, crs=crs(predios))# back to raster
dist_def <- projectRaster(from=dist_def_m, to=elevation.crop) # back to geo

## Camaras en parque
index_parque <- which(datos.raw.mam$Sampling_Event == "PNN Alto Fragua Indi Wasi")
datos.raw.mam.parq <- datos.raw.mam[index_parque,]
cams.in.park <- unique(datos.raw.mam.parq$Camera_Trap_Name)
# put names from unique.... not sure if correct!
cam.cords$Camera_Trap_Name <- unique(datos.raw.mam$Camera_Trap_Name)

## make sf
cam.cords.sf <- st_as_sf(cam.cords)

# Covs non scaled
elev.ovr <- raster::extract(elevation.crop, cam.cords, method='bilinear')
slope.ovr <- raster::extract(slope, cam.cords, method='bilinear')
rough.ovr <- raster::extract(rough, cam.cords, method='bilinear')
dist_def.ovr <- raster::extract(dist_def, cam.cords, method='bilinear')

# Covs scaled
elev.ovr.s <- raster::extract(scale(elevation.crop), cam.cords, method='bilinear')
slope.ovr.s <- raster::extract(scale(slope), cam.cords, method='bilinear')
rough.ovr.s <- raster::extract(scale(rough), cam.cords, method='bilinear')
dist_def.ovr.s <- raster::extract(scale(dist_def), cam.cords, method='bilinear')



# add to table
cam.cords$elev<-elev.ovr
cam.cords$slope<-slope.ovr[,1]
cam.cords$aspect<-slope.ovr[,2]
cam.cords$roughness<-rough.ovr
cam.cords$dist_def <- dist_def.ovr

cam.covs.s <- cam.cords  %>% as.data.frame() 
cam.covs.s2 <- cam.covs.s[,-1]
medias <- apply(cam.covs.s2, 2, mean)
desvi <- apply(cam.covs.s2, 2, sd)


#Standarize covs

cam.covs.s$elev  <- (cam.covs.s2[,1]-medias[1])/desvi[1]
cam.covs.s$slope <- (cam.covs.s2[,2]-medias[1])/desvi[1]
cam.covs.s$aspect  <- (cam.covs.s2[,3]-medias[3])/desvi[3]
cam.covs.s$roughness  <- (cam.covs.s2[,4]-medias[4])/desvi[4]
cam.covs.s$dist_def  <- (cam.covs.s2[,5]-medias[5])/desvi[5]

##
## Stack rasters
##

cov.stack<-stack(elevation.crop, slope, rough, dist_def)
names(cov.stack) <- c("elev", "slope", "aspect", "roughness", "dist_def" )

cov.stack.scale <- scale(cov.stack)

###

predios.geo <- st_transform(predios, geo)

### overlay cameras in predios old fashion
pts <- as_Spatial(cam.cords.sf) # make sp
p <- as_Spatial(predios.geo) # make sp
cams_in_predio<-over(pts, p) # 

index_cams_in_predio <- which(!is.na(cams_in_predio$MUNICIPIO)) # 

### overlay cameras in San Miguel
sanmiguel.geo <- st_transform(san_miguel, geo)
pts <- as_Spatial(cam.cords.sf) # make sp
p <- as_Spatial(sanmiguel.geo) # make sp
cams_in_resguar<-over(pts, p) # 

index_cams_in_resguar <- which(!is.na(cams_in_resguar$MUNICIPIO))

### camaras parque
pts$Camera_Trap_Name <- as.character(pts$Camera_Trap_Name)


cam.cords.sf$COB_ACTUAL <- factor(cams_in_predio$COB_ACTUAL)

cam.cords.sf[index_cams_in_predio,]$COB_ACTUAL <- cams_in_predio[index_cams_in_predio,]$COB_ACTUAL

cam.cords.sf[index_cams_in_resguar,]$COB_ACTUAL <- cams_in_resguar[index_cams_in_resguar,]$COB_ACTUAL

cam.cords.sf[index_cams_in_resguar,]$COB_ACTUAL <- cams_in_resguar[index_cams_in_resguar,]$COB_ACTUAL

# put parque as bosques
cam.cords.sf[c(47,48,49,50,51,58),]$COB_ACTUAL <- cams_in_resguar[index_cams_in_resguar,]$COB_ACTUAL[1]

# put restante NAs como transicion
index_na <- which(is.na(cam.cords.sf$COB_ACTUAL))

cam.cords.sf[index_na,]$COB_ACTUAL <- factor(c(rep("vegetacion en transicion", 19)),levels=levels(cam.cords.sf$COB_ACTUAL))

### parque, resguardo, predio
### camaras parque
cam.cords.sf$Sitio <- NA

cam.cords.sf[index_cams_in_predio,]$Sitio <- "Predio"
cam.cords.sf[index_cams_in_resguar,]$Sitio <- "Resguardo" 
# put parque 
cam.cords.sf[c(47,48,49,50,51,58),]$Sitio <- "Parque" 
# put restante NAs como fuera
index_na <- which(is.na(cam.cords.sf$Sitio))
cam.cords.sf[index_na,]$Sitio <- "Fuera"


#### cameras in park
# which(pts$Camera_Trap_Name == c( "CT-AFIW-1-1", "CT-AFIW-1-2"))
# which(pts$Camera_Trap_Name == c( "CT-AFIW-1-5", "CT-AFIW-1-6"))
# which(pts$Camera_Trap_Name == c( "CT-AFIW-1-4", "CT-AFIW-1-3"))

cam.covs.s$COB_ACTUAL <- cam.cords.sf$COB_ACTUAL
cam.covs.s$Sitio <- cam.cords.sf$Sitio

## COVARIABLES
cam.and.covs1<-as.data.frame(cam.covs.s)


# ### map covs
# mapview(cov.stack) 
# ## map cams predios
# mapview(sanmiguel.geo, zcol ="COB_ACTUAL") +  
#   mapview(predios.geo, zcol="COB_ACTUAL") + 
#   mapview(cam.cords.sf, color="red", legend = FALSE, cex = 0.5) 


pal8 <- c("#33A02C", "#B2DF8A", "#FDBF6F", "#1F78B4", "#999999", "#E31A1C", "#E6E6E6", "#A6CEE3")
palcam <- c("#B01FB5", "#08F3FF", "#E31A1C")

pal20 <- c("#003200", "#3C9600", "#006E00", "#556E19", "#00C800", "#8CBE8C",
           "#467864", "#B4E664", "#9BC832", "#EBFF64", "#F06432", "#9132E6",
           "#E664E6", "#9B82E6", "#B4FEF0", "#646464", "#C8C8C8", "#FF0000",
           "#FFFFFF", "#5ADCDC")



ele <- tm_shape(cov.stack) +
    tm_raster("elev", palette = "-viridis", n = 15, title = "Elevation") + 
    tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Sampling_Event", palette = palcam, size = 0.20, 
            shape = 16, title = "Camara", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +  tm_layout(
            legend.title.size = 1.7) + 
  tm_layout(frame=F) + tm_scale_bar()

slo <- tm_shape(cov.stack) +
    tm_raster("slope", palette = "viridis", n = 15, title = "Slope") + 
    tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Sampling_Event", palette = palcam, size = 0.20, 
            shape = 16, title = "Camara", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +  tm_layout(
            legend.title.size = 1.7) + 
  tm_layout(frame=F) + tm_scale_bar()

asp <- tm_shape(cov.stack) +
    tm_raster("aspect", palette = "YlGn", n = 15, title = "Aspect") + 
    tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Sampling_Event", palette = palcam, size = 0.20, 
            shape = 16, title = "Camara", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +  tm_layout(
            legend.title.size = 1.7) + 
  tm_layout(frame=F) + tm_scale_bar()


rou <- tm_shape(cov.stack) +
    tm_raster("roughness", palette = "-YlGn", n = 15, title = "Roughness") + 
    tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Sampling_Event", palette = palcam, size = 0.20, 
            shape = 16, title = "Camara", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +  tm_layout(
            legend.title.size = 1.7) + 
  tm_layout(frame=F) + tm_scale_bar()


dis <- tm_shape(cov.stack) +
    tm_raster("dist_def", palette = "-plasma", n = 15, title = "Deforestation distance") + 
    tm_shape(camaras) + # tm_symbols (col="red", size = 0.25) + 
    tm_dots(col = "Sampling_Event", palette = palcam, size = 0.20, 
            shape = 16, title = "Camara", legend.show = TRUE,
  legend.is.portrait = TRUE,
  legend.z = NA) +  tm_layout(
            legend.title.size = 1.7) + 
  tm_layout(frame=F) + tm_scale_bar()

# tmap_arrange(ele, slo, asp, rou)
ele

slo

asp

rou

dis

#xtable (as.data.frame(cam.and.covs1))
kable(as.data.frame(cam.and.covs1)) %>% 
      kable_styling(bootstrap_options = 
                      c("striped", "hover", "condensed", font_size = 9))

Camera_Trap_Name	elev	slope	aspect	roughness	dist_def	V1	V2	COB_ACTUAL	Sitio
CT-AFIW-1-8	-0.6693649	-2.322020	-0.4251662	-1.3012782	0.6104946	-75.84083	1.535806	bosques	Predio
CT-AFIW-1-7	-0.5386041	-2.309863	1.8462762	-1.0614645	-0.9869170	-75.85028	1.522833	vegetacion en transicion	Fuera
CT-AFIW-1-9	-0.7564668	-2.321654	-0.0906407	-1.5311548	-0.1203147	-75.84408	1.529000	bosques	Predio
CT-AFIW-1-10	-0.1767510	-2.269216	-0.5059435	1.0180896	0.0784244	-75.82803	1.549139	vegetacion en transicion	Fuera
CT-AFIW-1-11	-0.4186949	-2.259077	-0.3675547	1.2902977	0.6054175	-75.83436	1.545139	bosques	Predio
CT-AFIW-1-12	-0.4625065	-2.314275	1.5049021	-0.8734253	-1.2088627	-75.82222	1.542028	bosques	Predio
CT-AFIW-1-14	-0.4311586	-2.303041	1.3258099	-0.8650163	0.2799432	-75.81439	1.548500	bosques	Predio
CT-AFIW-1-15	-0.1009872	-2.304512	-0.4266258	-0.8599201	0.6685820	-75.81439	1.560083	vegetacion en transicion	Fuera
CT-AFIW-1-16	-0.6574776	-2.297094	-0.5635037	-0.6375428	0.1701772	-75.82061	1.550556	bosques	Predio
CT-AFIW-1-17	-0.3676774	-2.267899	1.2806004	0.6945254	-0.5295888	-75.82644	1.535639	bosques	Predio
CT-AFIW-1-18	0.3309294	-2.280616	-0.3319731	0.3844637	1.5780233	-75.80931	1.568444	bosques	Predio
CT-AFIW-1-19	0.3129630	-2.258011	-1.6612320	1.4920072	0.9817314	-75.80200	1.564861	vegetacion en transicion	Fuera
CT-AFIW-1-20	-0.3180127	-2.288361	1.1913468	0.3335194	-0.1701827	-75.81403	1.539361	vegetacion en transicion	Fuera
CT-AFIW-1-21	0.1903066	-2.262126	1.6356136	1.1602398	0.2683295	-75.80667	1.542694	vegetacion en transicion	Fuera
CT-AFIW-1-22	0.5813199	-2.309726	0.8627371	-0.5116304	-0.4497380	-75.80939	1.531167	bosques	Predio
CT-AFIW-1-23	-0.6942196	-2.264736	0.4300611	0.6639633	0.0515561	-75.83983	1.542611	vegetacion en transicion	Fuera
CT-AFIW-1-24	-0.2416896	-2.293100	-1.1938267	0.1323107	-0.8950877	-75.82006	1.535306	vegetacion en transicion	Fuera
CT-AFIW-1-25	-0.6881688	-2.328005	0.8966592	-1.5711683	-0.5923906	-75.87367	1.532194	pastos	Predio
CT-AFIW-1-26	-0.0912342	-2.272599	0.2996924	0.3212615	1.0124212	-75.88575	1.535056	vegetacion en transicion	Fuera
CT-AFIW-1-27	-0.3774312	-2.309526	-1.0262264	-0.8780617	0.0263207	-75.87864	1.534194	vegetacion en transicion	Predio
CT-AFIW-1-28	0.8495252	-2.257972	-1.3946882	0.5368834	-0.4432335	-75.87706	1.553694	vegetacion en transicion	Fuera
CT-AFIW-1-29	0.1984568	-2.261883	-1.9210438	0.9914195	1.2314749	-75.88797	1.533139	vegetacion en transicion	Fuera
CT-AFIW-1-30	0.5103741	-2.240299	-0.0158780	1.5404672	0.5478651	-75.90219	1.529750	vegetacion en transicion	Fuera
CT-AFIW-1-31	-0.0221117	-2.308882	0.4771265	-0.9254035	-0.0440753	-75.88772	1.520833	vegetacion en transicion	Fuera
CT-AFIW-1-32	-0.0744297	-2.294974	-0.4256760	-0.6435920	-0.1050594	-75.88717	1.518500	vegetacion en transicion	Fuera
CT-AFIW-1-34	-0.1691323	-2.309295	0.2874015	0.3070988	-0.3183033	-75.92203	1.509222	vegetacion en transicion	Fuera
CT-AFIW-1-35	-0.2703956	-2.312427	0.4476524	-0.8247567	-1.1739184	-75.85139	1.516944	vegetacion en transicion	Fuera
CT-AFIW-1-36	-0.4937606	-2.305720	0.4567440	-1.1616162	-0.7061692	-75.85822	1.518222	bosques	Predio
CT-AFIW-1-37	-0.2357195	-2.301665	-0.3604720	-0.5236833	-0.1121556	-75.86117	1.530611	bosques	Predio
CT-AFIW-1-38	0.2481229	-2.319956	1.4086757	-1.4128627	-1.3410184	-75.87475	1.475444	bosques	Predio
CT-AFIW-1-39	-0.4255390	-2.294659	1.3404286	0.1582374	-0.9552180	-75.87475	1.481750	bosques	Predio
CT-AFIW-1-40	-0.0415865	-2.284170	-0.8588986	0.1053042	-0.0406261	-75.88736	1.483444	bosques	Predio
CT-AFIW-1-41	0.3632995	-2.314655	0.5345403	0.1139631	0.6659447	-75.89378	1.482111	bosques	Predio
CT-AFIW-1-42	0.6202420	-2.292063	-0.3858112	-0.4426666	0.4235581	-75.89144	1.476472	bosques	Predio
CT-AFIW-1-43	0.5511602	-2.289567	-1.4498003	-0.3308345	-0.5251492	-75.88611	1.472972	vegetacion en transicion	Fuera
CT-AFIW-1-44	-0.7742559	-2.264142	1.0291184	0.3215135	-0.8016758	-75.85000	1.483333	vegetacion en transicion	Fuera
CT-AFIW-1-45	-0.5242043	-2.299789	0.1903305	-0.4878956	-0.1685666	-75.86231	1.495083	bosques	Predio
CT-AFIW-1-46	-0.7914000	-2.285993	0.9245992	-0.3405309	-0.4807521	-75.86342	1.490500	bosques	Predio
CT-AFIW-1-47	-0.8548798	-2.296439	-1.9496149	-0.5230203	-1.2869491	-75.86947	1.485278	bosques	Predio
CT-AFIW-1-48	-0.2429061	-2.238501	-1.8262325	2.2971894	-1.2965552	-75.87000	1.480528	bosques	Predio
CT-AFIW-1-49	-0.7362853	-2.247935	0.9931458	1.0566121	-1.2518536	-75.83600	1.509417	bosques	Predio
CT-AFIW-1-50	-0.7178417	-2.285756	-1.4979455	-0.3917217	-0.6103341	-75.84339	1.509611	bosques	Predio
CT-AFIW-1-51	-0.8776636	-2.282890	-1.0289258	-0.2805530	-0.4808293	-75.84589	1.503972	vegetacion en transicion	Predio
CT-AFIW-1-52	-0.4244310	-2.296001	0.2702602	-0.4347180	-1.1730784	-75.85269	1.515667	vegetacion en transicion	Fuera
CT-AFIW-1-53	-0.2834987	-2.302718	0.6081296	-0.5134921	-0.1851771	-75.85917	1.511444	bosques	Predio
CT-AFIW-1-54	-0.1566151	-2.305462	0.7101630	-1.0182201	-1.1811712	-75.91242	1.473083	bosques	Predio
CT-AFIW-1-1	2.3443358	-2.275088	-0.8071963	1.1580119	0.7003707	-75.98244	1.462944	bosques	Parque
CT-AFIW-1-2	3.4198179	-2.251851	-0.2752464	1.3901170	1.9192611	-75.99000	1.461889	bosques	Parque
CT-AFIW-1-5	3.7598909	-2.261446	-0.4766583	1.7417629	3.8350744	-75.99850	1.484250	bosques	Parque
CT-AFIW-1-6	1.7092723	-2.271856	-0.1262901	1.2120523	1.1911516	-75.98210	1.480761	bosques	Parque
CT-AFIW-1-4	2.3921858	-2.260119	0.2267244	0.9944309	2.5720698	-75.99278	1.478000	bosques	Parque
CT-AFIW-1-55	-1.0767768	-2.308525	-0.4125192	-0.8228893	-0.3565134	-76.24292	1.134000	bosques	Resguardo
CT-AFIW-1-56	-0.6381828	-2.323538	1.2787608	-1.1562312	0.2747134	-76.24994	1.139889	bosques	Resguardo
CT-AFIW-1-57	-0.5709431	-2.307542	-0.2577885	-0.5246787	0.1866435	-76.24947	1.147750	bosques	Resguardo
CT-AFIW-1-58	-0.6819952	-2.282662	1.3117846	0.3015049	0.0872361	-76.24950	1.155778	bosques	Resguardo
CT-AFIW-1-59	-1.0218654	-2.332652	0.4657523	-1.8622968	-0.7643528	-76.25644	1.156389	bosques	Resguardo
CT-AFIW-1-60	-0.6751600	-2.273788	-1.5041163	1.9936310	-0.6378988	-76.25950	1.147111	bosques	Resguardo
CT-AFIW-1-3	1.3898227	-2.277139	-0.6675421	1.0014478	1.4269314	-75.98692	1.474000	bosques	Parque

# ## nearest neibour operations
# library(nngeo)
# 
# # Spatial join in sf
# p1 = st_join(cam.cords.sf, predios.geo, join = st_nn)
# p1
# 
# # Visuzlize join
# l = st_connect(cam.cords.sf, predios.geo, dist = 1)
# plot(st_geometry(predios.geo))
# plot(st_geometry(cam.cords.sf), add = TRUE)
# plot(st_geometry(l), col = "red", lwd = 2, add = TRUE)

Las covariables numéricas se estandarizaron, centradas en cero.

Función para automatizar modelos

Construimos 21 modelos generales para probar de forma automatizada cada especie con más de 10 registros.

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

      ########################
      ### make unmarked object 
      ########################
      library(unmarked)
      sp15<-f.shrink.matrix.to15(matrix = mat.per.sp[[sp_number]])
      sp_UMF <- unmarkedFrameOccu(sp15)
      
      # plot(sp_UMF, panels=1)
      # title(main=as.character(sp.names[sp_number]))
      
      # add some  covariates
      siteCovs(sp_UMF) <- cam.and.covs1
      
      #######################
      ## occu models 
      #######################
      
      #  covariates of detection and occupancy in that order.
      fm0 <- occu(~ 1 ~ 1, sp_UMF) 
      fm1 <- occu(~ 1 ~ elev, sp_UMF, starts = c(0.01,0.01,0.01))
      # fm1_1 <- occu(~ 1 ~ I(elev)^2, sp_UMF)
      fm2 <- occu(~ 1 ~ slope, sp_UMF, starts = c(0.01,0.01,0.01))
      fm3 <- occu(~ 1 ~ aspect, sp_UMF, starts = c(0.01,0.01,0.01))
      fm4 <- occu(~ 1 ~ roughness, sp_UMF, starts = c(0.01,0.01,0.01))
      fm5 <- occu(~ 1 ~ dist_def, sp_UMF, starts = c(0.01,0.01,0.01))
      # fm5_5 <- occu(~ 1 ~ I(dist_def)^2, sp_UMF)
      fm6 <- occu(~ 1 ~ COB_ACTUAL, sp_UMF, starts = c(0.0001,0.0001,0.01,0.001))
      fm7 <- occu(~ 1 ~ Sitio, sp_UMF, starts = c(0.0001,0.0001,0.01,0.001,0.01))
      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(slope)' = fm2,
        'p(.)psi(aspect)' = fm3,
        'p(.)psi(roughness)' = fm4,
        'p(.)psi(dist_def)' = fm5,
        'p(.)psi(COB_ACTUAL)' = fm6,
        'p(.)psi(Sitio)' = fm7,
        'p(elev)psi(elev)' = fm8,
        'p(elev)psi(slope)' = fm9,
        'p(elev)psi(aspect)' = fm10,
        'p(elev)psi(roughness)' = fm11,
        'p(elev)psi(dist_def)' = fm12,
        'p(elev)psi(COB_ACTUAL)' = fm13,
        'p(elev)psi(Sitio)' = fm14,
        'p(roughness)psi(elev)' = fm15,
        'p(roughness)psi(slope)' = fm16,
        'p(roughness)psi(aspect)' = fm17,
        'p(roughness)psi(roughness)' = fm18,
        'p(roughness)psi(dist_def)' = fm19,
        'p(roughness)psi(COB_ACTUAL)' = fm20,
        'p(roughness)psi(Sitio)' = fm21
              )
      
      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
      
      ##Print los 7 primeros modelos
      return(mat_models)
      # 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)

Modelos de ocupacion por especie

Ñeque (Dasyprocta fuliginosa)

Selección de Modelos

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

[1] “Dasyprocta fuliginosa”

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

	Dasyprocta fuliginosa models	nPars	AIC	delta	AICwt	cumltvWt
7	p(.)psi(COB_ACTUAL)	4	715.4022	0.000000	0.2698736	0.2698736
21	p(roughness)psi(COB_ACTUAL)	5	716.9944	1.592212	0.1217351	0.3916087
1	p(.)psi(.)	2	717.3236	1.921410	0.1032599	0.4948686
14	p(elev)psi(COB_ACTUAL)	5	717.4309	2.028642	0.0978693	0.5927378
6	p(.)psi(dist_def)	3	718.7541	3.351917	0.0505011	0.6432389
5	p(.)psi(roughness)	3	718.9570	3.554761	0.0456303	0.6888693
2	p(.)psi(elev)	3	719.2728	3.870575	0.0389651	0.7278344
4	p(.)psi(aspect)	3	719.3098	3.907567	0.0382510	0.7660854
3	p(.)psi(slope)	3	719.3141	3.911884	0.0381685	0.8042539
20	p(roughness)psi(dist_def)	4	720.3880	4.985738	0.0223111	0.8265650
19	p(roughness)psi(roughness)	4	720.5957	5.193465	0.0201101	0.8466751
13	p(elev)psi(dist_def)	4	720.7481	5.345859	0.0186347	0.8653098
16	p(roughness)psi(elev)	4	720.8800	5.477741	0.0174455	0.8827553
18	p(roughness)psi(aspect)	4	720.9046	5.502414	0.0172316	0.8999869
17	p(roughness)psi(slope)	4	720.9168	5.514562	0.0171273	0.9171142
12	p(elev)psi(roughness)	4	720.9533	5.551084	0.0168174	0.9339316
9	p(elev)psi(elev)	4	721.2697	5.867498	0.0143565	0.9482881
11	p(elev)psi(aspect)	4	721.3082	5.906005	0.0140828	0.9623709
10	p(elev)psi(slope)	4	721.3123	5.910052	0.0140543	0.9764252
8	p(.)psi(Sitio)	5	721.4707	6.068511	0.0129837	0.9894089
22	p(roughness)psi(Sitio)	6	723.0774	7.675221	0.0058144	0.9952233
15	p(elev)psi(Sitio)	6	723.4706	8.068421	0.0047767	1.0000000

Boruga (Cuniculus paca)

Cuniculus paca

Selección de Modelos

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

[1] “Cuniculus paca”

    sp2 <- f.sp.occu.models(sp_number = 2)
    # 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
1	p(.)psi(.)	2	655.1402	0.0000000	0.0893170	0.0893170
7	p(.)psi(COB_ACTUAL)	4	655.1674	0.0272338	0.0881090	0.1774260
14	p(elev)psi(COB_ACTUAL)	5	655.2356	0.0953721	0.0851578	0.2625837
21	p(roughness)psi(COB_ACTUAL)	5	655.6700	0.5297740	0.0685323	0.3311160
5	p(.)psi(roughness)	3	655.9334	0.7932182	0.0600743	0.3911903
12	p(elev)psi(roughness)	4	655.9393	0.7991316	0.0598970	0.4510873
3	p(.)psi(slope)	3	656.1977	1.0574579	0.0526393	0.5037265
10	p(elev)psi(slope)	4	656.2100	1.0697782	0.0523160	0.5560425
19	p(roughness)psi(roughness)	4	656.3057	1.1655463	0.0498699	0.6059125
17	p(roughness)psi(slope)	4	656.5753	1.4351133	0.0435816	0.6494941
2	p(.)psi(elev)	3	656.7711	1.6309523	0.0395164	0.6890105
9	p(elev)psi(elev)	4	656.7736	1.6333574	0.0394689	0.7284794
4	p(.)psi(aspect)	3	656.8727	1.7325360	0.0375594	0.7660388
11	p(elev)psi(aspect)	4	656.9367	1.7964753	0.0363776	0.8024164
6	p(.)psi(dist_def)	3	657.1375	1.9973472	0.0329015	0.8353179
16	p(roughness)psi(elev)	4	657.1891	2.0488817	0.0320645	0.8673824
13	p(elev)psi(dist_def)	4	657.1976	2.0574438	0.0319276	0.8993100
18	p(roughness)psi(aspect)	4	657.3220	2.1818305	0.0300024	0.9293123
20	p(roughness)psi(dist_def)	4	657.5821	2.4419325	0.0263436	0.9556559
15	p(elev)psi(Sitio)	6	658.5512	3.4110036	0.0162272	0.9718831
8	p(.)psi(Sitio)	5	658.6407	3.5005535	0.0155167	0.9873998
22	p(roughness)psi(Sitio)	6	659.0572	3.9169596	0.0126002	1.0000000

Armadillo cola trapo (Cabassous unicinctus)

Selección de Modelos

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

[1] “Cabassous unicinctus”

    sp3 <- f.sp.occu.models(sp_number = 3)
    # xtable::xtable(sp3) #, format = "rst")
    # kable(Table1, "latex", booktabs = T) # para pdf
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp3) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Cabassous unicinctus models	nPars	AIC	delta	AICwt	cumltvWt
11	p(elev)psi(aspect)	4	227.7096	0.000000	0.5233951	0.5233951
4	p(.)psi(aspect)	3	230.7510	3.041428	0.1143910	0.6377861
18	p(roughness)psi(aspect)	4	232.8497	5.140128	0.0400558	0.6778419
2	p(.)psi(elev)	3	232.9590	5.249404	0.0379259	0.7157678
1	p(.)psi(.)	2	233.0813	5.371735	0.0356757	0.7514435
16	p(roughness)psi(elev)	4	233.6281	5.918460	0.0271427	0.7785861
6	p(.)psi(dist_def)	3	233.8047	6.095077	0.0248485	0.8034346
8	p(.)psi(Sitio)	5	234.0441	6.334481	0.0220452	0.8254799
12	p(elev)psi(roughness)	4	234.3199	6.610329	0.0192050	0.8446849
22	p(roughness)psi(Sitio)	6	234.6820	6.972417	0.0160246	0.8607095
3	p(.)psi(slope)	3	234.8771	7.167465	0.0145356	0.8752452
20	p(roughness)psi(dist_def)	4	234.8873	7.177675	0.0144616	0.8897068
9	p(elev)psi(elev)	4	234.9287	7.219129	0.0141650	0.9038718
13	p(elev)psi(dist_def)	4	234.9659	7.256294	0.0139042	0.9177760
10	p(elev)psi(slope)	4	235.0063	7.296748	0.0136258	0.9314017
7	p(.)psi(COB_ACTUAL)	4	235.0438	7.334191	0.0133730	0.9447748
5	p(.)psi(roughness)	3	235.0551	7.345546	0.0132973	0.9580721
14	p(elev)psi(COB_ACTUAL)	5	235.2457	7.536150	0.0120886	0.9701607
17	p(roughness)psi(slope)	4	235.8335	8.123892	0.0090105	0.9791712
15	p(elev)psi(Sitio)	6	236.0392	8.329627	0.0081297	0.9873009
21	p(roughness)psi(COB_ACTUAL)	5	236.5188	8.809242	0.0063963	0.9936972
19	p(roughness)psi(roughness)	4	236.5483	8.838676	0.0063028	1.0000000

Oso hormiguero-mielero (Tamandua tetradactyla)

Selección de Modelos

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

[1] “Tamandua tetradactyla”

    sp4 <- f.sp.occu.models(sp_number = 4)
    # xtable::xtable(sp4) #, format = "rst")
    # kable(Table1, "latex", booktabs = T) # para pdf
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp4) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Tamandua tetradactyla models	nPars	AIC	delta	AICwt	cumltvWt
6	p(.)psi(dist_def)	3	398.0831	0.0000000	0.2143303	0.2143303
20	p(roughness)psi(dist_def)	4	398.1668	0.0836602	0.2055498	0.4198802
1	p(.)psi(.)	2	399.6696	1.5865329	0.0969555	0.5168357
13	p(elev)psi(dist_def)	4	399.7430	1.6598635	0.0934650	0.6103006
3	p(.)psi(slope)	3	401.4940	3.4108601	0.0389426	0.6492432
5	p(.)psi(roughness)	3	401.6182	3.5351271	0.0365966	0.6858398
4	p(.)psi(aspect)	3	401.6536	3.5705276	0.0359545	0.7217943
2	p(.)psi(elev)	3	401.6660	3.5829102	0.0357326	0.7575269
7	p(.)psi(COB_ACTUAL)	4	402.1130	4.0299001	0.0285760	0.7861029
16	p(roughness)psi(elev)	4	402.2143	4.1311538	0.0271653	0.8132683
19	p(roughness)psi(roughness)	4	402.2824	4.1992851	0.0262555	0.8395238
18	p(roughness)psi(aspect)	4	402.2919	4.2088280	0.0261305	0.8656543
17	p(roughness)psi(slope)	4	402.3058	4.2227347	0.0259495	0.8916038
21	p(roughness)psi(COB_ACTUAL)	5	402.9021	4.8189974	0.0192598	0.9108636
10	p(elev)psi(slope)	4	403.4898	5.4067040	0.0143560	0.9252196
12	p(elev)psi(roughness)	4	403.6078	5.5246849	0.0135336	0.9387532
9	p(elev)psi(elev)	4	403.6267	5.5435606	0.0134065	0.9521597
11	p(elev)psi(aspect)	4	403.6269	5.5438032	0.0134049	0.9655645
8	p(.)psi(Sitio)	5	403.8944	5.8112673	0.0117269	0.9772914
14	p(elev)psi(COB_ACTUAL)	5	404.0992	6.0160527	0.0105856	0.9878770
22	p(roughness)psi(Sitio)	6	404.7096	6.6264505	0.0078013	0.9956783
15	p(elev)psi(Sitio)	6	405.8908	7.8077395	0.0043217	1.0000000

Armadillo 9 bandas(Dasypus novemcinctus)

Dasypus novemcinctus

Selección de Modelos

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

[1] “Dasypus novemcinctus”

    sp5 <- f.sp.occu.models(sp_number = 5)
    # xtable::xtable(sp5) #, format = "rst")
    # kable(Table1, "latex", booktabs = T) # para pdf
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp5) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Dasypus novemcinctus models	nPars	AIC	delta	AICwt	cumltvWt
4	p(.)psi(aspect)	3	569.6456	0.0000000	0.1616436	0.1616436
18	p(roughness)psi(aspect)	4	570.1599	0.5142894	0.1249919	0.2866355
1	p(.)psi(.)	2	570.1713	0.5256178	0.1242860	0.4109215
11	p(elev)psi(aspect)	4	571.6391	1.9934384	0.0596608	0.4705823
6	p(.)psi(dist_def)	3	571.7062	2.0605884	0.0576909	0.5282732
3	p(.)psi(slope)	3	572.0754	2.4297541	0.0479672	0.5762404
2	p(.)psi(elev)	3	572.1602	2.5146121	0.0459745	0.6222149
5	p(.)psi(roughness)	3	572.1707	2.5250920	0.0457343	0.6679492
20	p(roughness)psi(dist_def)	4	572.2123	2.5666220	0.0447944	0.7127436
17	p(roughness)psi(slope)	4	572.4401	2.7944422	0.0399717	0.7527153
19	p(roughness)psi(roughness)	4	572.6128	2.9671195	0.0366654	0.7893808
16	p(roughness)psi(elev)	4	572.6190	2.9733243	0.0365519	0.8259326
7	p(.)psi(COB_ACTUAL)	4	572.7270	3.0813303	0.0346303	0.8605629
21	p(roughness)psi(COB_ACTUAL)	5	573.1707	3.5250204	0.0277402	0.8883031
13	p(elev)psi(dist_def)	4	573.6961	4.0504713	0.0213309	0.9096341
10	p(elev)psi(slope)	4	574.0501	4.4045149	0.0178702	0.9275043
9	p(elev)psi(elev)	4	574.1433	4.4976180	0.0170574	0.9445617
12	p(elev)psi(roughness)	4	574.1510	4.5053504	0.0169916	0.9615533
14	p(elev)psi(COB_ACTUAL)	5	574.7068	5.0611532	0.0128689	0.9744223
8	p(.)psi(Sitio)	5	574.8393	5.1936947	0.0120438	0.9864660
22	p(roughness)psi(Sitio)	6	575.4021	5.7564415	0.0090900	0.9955560
15	p(elev)psi(Sitio)	6	576.8333	7.1876779	0.0044440	1.0000000

Tayra (Eira barbara)

Eira barbara

Selección de Modelos

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

[1] “Eira barbara”

    sp6 <- f.sp.occu.models(sp_number = 6)
    #xtable::xtable(sp6) #, format = "rst")
    # kable(Table1, "latex", booktabs = T) # para pdf
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp6) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Eira barbara models	nPars	AIC	delta	AICwt	cumltvWt
1	p(.)psi(.)	2	422.1768	0.000000	0.1516514	0.1516514
5	p(.)psi(roughness)	3	423.2656	1.088844	0.0879847	0.2396361
3	p(.)psi(slope)	3	423.4021	1.225311	0.0821814	0.3218175
4	p(.)psi(aspect)	3	423.4120	1.235189	0.0817765	0.4035940
2	p(.)psi(elev)	3	423.8818	1.705042	0.0646549	0.4682489
7	p(.)psi(COB_ACTUAL)	4	423.9825	1.805696	0.0614815	0.5297304
6	p(.)psi(dist_def)	3	424.1201	1.943334	0.0573927	0.5871231
8	p(.)psi(Sitio)	5	424.8190	2.642192	0.0404671	0.6275901
12	p(elev)psi(roughness)	4	425.1340	2.957167	0.0345705	0.6621607
11	p(elev)psi(aspect)	4	425.1734	2.996586	0.0338958	0.6960565
19	p(roughness)psi(roughness)	4	425.2528	3.075978	0.0325766	0.7286331
10	p(elev)psi(slope)	4	425.2701	3.093322	0.0322954	0.7609285
18	p(roughness)psi(aspect)	4	425.2854	3.108579	0.0320499	0.7929784
17	p(roughness)psi(slope)	4	425.3894	3.212560	0.0304262	0.8234046
16	p(roughness)psi(elev)	4	425.7718	3.595042	0.0251300	0.8485347
14	p(elev)psi(COB_ACTUAL)	5	425.7791	3.602345	0.0250384	0.8735731
9	p(elev)psi(elev)	4	425.8095	3.632716	0.0246611	0.8982342
21	p(roughness)psi(COB_ACTUAL)	5	425.8647	3.687879	0.0239902	0.9222244
13	p(elev)psi(dist_def)	4	425.8678	3.690985	0.0239530	0.9461773
20	p(roughness)psi(dist_def)	4	425.9447	3.767860	0.0230497	0.9692271
22	p(roughness)psi(Sitio)	6	426.7519	4.575060	0.0153952	0.9846222
15	p(elev)psi(Sitio)	6	426.7541	4.577324	0.0153778	1.0000000

Perro domestico (Canis lupus familiaris)

Hessian error

Selección de Modelos

    # print(as.character(sp.names[sp_number=7]))
    # f.sp.occu.models(sp_number = 7)

Mapache (Procyon cancrivorus)

Procyon cancrivorus

Selección de Modelos

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

[1] “Procyon cancrivorus”

    sp8 <- f.sp.occu.models(sp_number = 8)
    # xtable::xtable(sp8) #, format = "rst")
    # kable(Table1, "latex", booktabs = T) # para pdf
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp8) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Procyon cancrivorus models	nPars	AIC	delta	AICwt	cumltvWt
5	p(.)psi(roughness)	3	238.7272	0.0000000	0.1325675	0.1325675
3	p(.)psi(slope)	3	238.9068	0.1796588	0.1211782	0.2537456
19	p(roughness)psi(roughness)	4	239.1255	0.3983482	0.1086267	0.3623724
1	p(.)psi(.)	2	239.3428	0.6156725	0.0974418	0.4598142
17	p(roughness)psi(slope)	4	239.5004	0.7732636	0.0900585	0.5498727
12	p(elev)psi(roughness)	4	240.0899	1.3627129	0.0670699	0.6169426
2	p(.)psi(elev)	3	240.2201	1.4929359	0.0628420	0.6797846
10	p(elev)psi(slope)	4	240.3100	1.5828762	0.0600786	0.7398632
6	p(.)psi(dist_def)	3	241.2055	2.4782871	0.0383958	0.7782590
4	p(.)psi(aspect)	3	241.2240	2.4967975	0.0380421	0.8163011
7	p(.)psi(COB_ACTUAL)	4	242.0501	3.3229812	0.0251687	0.8414697
9	p(elev)psi(elev)	4	242.1212	3.3940650	0.0242899	0.8657596
16	p(roughness)psi(elev)	4	242.1904	3.4631953	0.0234646	0.8892242
13	p(elev)psi(dist_def)	4	242.7732	4.0460582	0.0175326	0.9067568
11	p(elev)psi(aspect)	4	242.8906	4.1634049	0.0165335	0.9232904
20	p(roughness)psi(dist_def)	4	242.9405	4.2133530	0.0161257	0.9394161
18	p(roughness)psi(aspect)	4	243.0587	4.3315062	0.0152007	0.9546167
8	p(.)psi(Sitio)	5	243.2335	4.5063598	0.0139281	0.9685449
14	p(elev)psi(COB_ACTUAL)	5	243.7157	4.9885567	0.0109442	0.9794891
21	p(roughness)psi(COB_ACTUAL)	5	243.9604	5.2331894	0.0096842	0.9891733
15	p(elev)psi(Sitio)	6	245.0292	6.3020248	0.0056750	0.9948484
22	p(roughness)psi(Sitio)	6	245.2227	6.4955543	0.0051516	1.0000000

Guatincito (Myoprocta pratti)

Selección de Modelos

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

[1] “Myoprocta pratti”

    sp10 <- f.sp.occu.models(sp_number = 10)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp10) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Myoprocta pratti models	nPars	AIC	delta	AICwt	cumltvWt
2	p(.)psi(elev)	3	72.40022	0.0000000	0.1557516	0.1557516
9	p(elev)psi(elev)	4	72.69409	0.2938689	0.1344682	0.2902198
16	p(roughness)psi(elev)	4	73.42207	1.0218571	0.0934413	0.3836612
6	p(.)psi(dist_def)	3	73.44185	1.0416345	0.0925219	0.4761830
8	p(.)psi(Sitio)	5	73.56036	1.1601386	0.0871990	0.5633821
13	p(elev)psi(dist_def)	4	73.66015	1.2599348	0.0829547	0.6463368
15	p(elev)psi(Sitio)	6	73.89557	1.4953564	0.0737429	0.7200796
20	p(roughness)psi(dist_def)	4	74.43497	2.0347503	0.0563109	0.7763905
22	p(roughness)psi(Sitio)	6	74.61786	2.2176434	0.0513898	0.8277803
1	p(.)psi(.)	2	75.61356	3.2133431	0.0312366	0.8590170
5	p(.)psi(roughness)	3	76.41240	4.0121787	0.0209507	0.8799677
12	p(elev)psi(roughness)	4	76.52661	4.1263940	0.0197878	0.8997555
4	p(.)psi(aspect)	3	77.00719	4.6069738	0.0155611	0.9153166
11	p(elev)psi(aspect)	4	77.01220	4.6119863	0.0155222	0.9308388
3	p(.)psi(slope)	3	77.29554	4.8953193	0.0134719	0.9443106
10	p(elev)psi(slope)	4	77.37690	4.9766854	0.0129348	0.9572454
19	p(roughness)psi(roughness)	4	77.41850	5.0182818	0.0126685	0.9699139
18	p(roughness)psi(aspect)	4	77.89755	5.4973282	0.0099702	0.9798841
17	p(roughness)psi(slope)	4	78.26147	5.8612558	0.0083115	0.9881956
7	p(.)psi(COB_ACTUAL)	4	79.48273	7.0825126	0.0045132	0.9927088
14	p(elev)psi(COB_ACTUAL)	5	79.51813	7.1179084	0.0044340	0.9971428
21	p(roughness)psi(COB_ACTUAL)	5	80.39705	7.9968315	0.0028572	1.0000000

Armadillo espuelón (Dasypus kappleri)

Selección de Modelos

    print(as.character(sp.names[sp_number=12]))

[1] “Dasypus kappleri”

    sp12 <- f.sp.occu.models(sp_number = 12)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp12) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

	Dasypus kappleri models	nPars	AIC	delta	AICwt	cumltvWt
9	p(elev)psi(elev)	4	221.6454	0.000000	0.3311277	0.3311277
13	p(elev)psi(dist_def)	4	223.2863	1.640960	0.1457691	0.4768967
11	p(elev)psi(aspect)	4	224.0513	2.405954	0.0994372	0.5763340
14	p(elev)psi(COB_ACTUAL)	5	224.3522	2.706813	0.0855497	0.6618837
10	p(elev)psi(slope)	4	224.5253	2.879904	0.0784571	0.7403408
12	p(elev)psi(roughness)	4	224.5261	2.880721	0.0784251	0.8187658
1	p(.)psi(.)	2	226.3961	4.750685	0.0307891	0.8495550
15	p(elev)psi(Sitio)	6	227.0319	5.386540	0.0224039	0.8719588
4	p(.)psi(aspect)	3	227.6255	5.980143	0.0166504	0.8886092
2	p(.)psi(elev)	3	227.6977	6.052287	0.0160605	0.9046697
6	p(.)psi(dist_def)	3	228.1268	6.481425	0.0129590	0.9176287
7	p(.)psi(COB_ACTUAL)	4	228.1795	6.534164	0.0126218	0.9302505
5	p(.)psi(roughness)	3	228.2262	6.580797	0.0123309	0.9425813
3	p(.)psi(slope)	3	228.2611	6.615754	0.0121172	0.9546985
16	p(roughness)psi(elev)	4	228.7666	7.121232	0.0094111	0.9641096
18	p(roughness)psi(aspect)	4	229.1253	7.479978	0.0078657	0.9719753
20	p(roughness)psi(dist_def)	4	229.3008	7.655405	0.0072052	0.9791805
21	p(roughness)psi(COB_ACTUAL)	5	229.6002	7.954807	0.0062034	0.9853839
19	p(roughness)psi(roughness)	4	229.7592	8.113811	0.0057293	0.9911133
17	p(roughness)psi(slope)	4	229.7610	8.115654	0.0057241	0.9968373
8	p(.)psi(Sitio)	5	231.8410	10.195628	0.0020232	0.9988606
22	p(roughness)psi(Sitio)	6	232.9893	11.343917	0.0011394	1.0000000

Chucha (Didelphis marsupialis)

Didelphis marsupialis

Selección de Modelos

    print(as.character(sp.names[sp_number=16]))

[1] “Didelphis marsupialis”

    sp16 <- f.sp.occu.models(sp_number = 16)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp16) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Didelphis marsupialis models	nPars	AIC	delta	AICwt	cumltvWt
1	p(.)psi(.)	2	174.1524	0.0000000	0.1077135	0.1077135
4	p(.)psi(aspect)	3	174.4711	0.3187225	0.0918460	0.1995595
18	p(roughness)psi(aspect)	4	174.4951	0.3427083	0.0907511	0.2903106
8	p(.)psi(Sitio)	5	174.9942	0.8418011	0.0707091	0.3610197
22	p(roughness)psi(Sitio)	6	175.2019	1.0495442	0.0637330	0.4247527
11	p(elev)psi(aspect)	4	175.7868	1.6343999	0.0475734	0.4723261
2	p(.)psi(elev)	3	176.0364	1.8839931	0.0419920	0.5143181
16	p(roughness)psi(elev)	4	176.0449	1.8924857	0.0418140	0.5561321
6	p(.)psi(dist_def)	3	176.1399	1.9875354	0.0398733	0.5960054
5	p(.)psi(roughness)	3	176.1418	1.9894599	0.0398350	0.6358404
3	p(.)psi(slope)	3	176.1439	1.9914994	0.0397944	0.6756347
19	p(roughness)psi(roughness)	4	176.2047	2.0523437	0.0386020	0.7142367
15	p(elev)psi(Sitio)	6	176.2535	2.1011282	0.0376718	0.7519084
17	p(roughness)psi(slope)	4	176.2890	2.1365995	0.0370095	0.7889179
20	p(roughness)psi(dist_def)	4	176.2954	2.1429925	0.0368914	0.8258093
7	p(.)psi(COB_ACTUAL)	4	176.3988	2.2463725	0.0350329	0.8608423
21	p(roughness)psi(COB_ACTUAL)	5	176.4898	2.3374436	0.0334735	0.8943157
13	p(elev)psi(dist_def)	4	177.2879	3.1355109	0.0224596	0.9167753
10	p(elev)psi(slope)	4	177.3248	3.1723767	0.0220494	0.9388248
9	p(elev)psi(elev)	4	177.3742	3.2217995	0.0215112	0.9603360
12	p(elev)psi(roughness)	4	177.3756	3.2232181	0.0214960	0.9818320
14	p(elev)psi(COB_ACTUAL)	5	177.7120	3.5596213	0.0181680	1.0000000

Ocelote (Leopardus pardalis)

Leopardus pardalis

Selección de Modelos

    print(as.character(sp.names[sp_number=19]))

[1] “Leopardus pardalis”

    sp19 <- f.sp.occu.models(sp_number = 19)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp19) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Leopardus pardalis models	nPars	AIC	delta	AICwt	cumltvWt
1	p(.)psi(.)	2	123.4711	0.0000000	0.1157897	0.1157897
5	p(.)psi(roughness)	3	124.0431	0.5720713	0.0869854	0.2027751
2	p(.)psi(elev)	3	124.0807	0.6095930	0.0853687	0.2881438
3	p(.)psi(slope)	3	124.7971	1.3260064	0.0596666	0.3478104
8	p(.)psi(Sitio)	5	125.0476	1.5765783	0.0526405	0.4004509
4	p(.)psi(aspect)	3	125.1687	1.6976471	0.0495485	0.4499995
18	p(roughness)psi(aspect)	4	125.1791	1.7080815	0.0492907	0.4992901
13	p(elev)psi(dist_def)	4	125.2013	1.7302737	0.0487468	0.5480369
11	p(elev)psi(aspect)	4	125.4350	1.9639853	0.0433707	0.5914076
16	p(roughness)psi(elev)	4	125.4389	1.9678871	0.0432861	0.6346937
6	p(.)psi(dist_def)	3	125.4710	1.9999267	0.0425982	0.6772919
12	p(elev)psi(roughness)	4	125.4758	2.0047063	0.0424965	0.7197885
20	p(roughness)psi(dist_def)	4	125.6229	2.1518110	0.0394830	0.7592715
17	p(roughness)psi(slope)	4	125.7972	2.3261094	0.0361878	0.7954592
10	p(elev)psi(slope)	4	125.8265	2.3554781	0.0356603	0.8311195
9	p(elev)psi(elev)	4	125.9102	2.4391741	0.0341987	0.8653182
19	p(roughness)psi(roughness)	4	125.9349	2.4638522	0.0337794	0.8990976
22	p(roughness)psi(Sitio)	6	126.4524	2.9813272	0.0260785	0.9251761
15	p(elev)psi(Sitio)	6	126.8117	3.3406294	0.0217902	0.9469663
7	p(.)psi(COB_ACTUAL)	4	127.0252	3.5541087	0.0195842	0.9665505
21	p(roughness)psi(COB_ACTUAL)	5	127.2856	3.8145824	0.0171927	0.9837432
14	p(elev)psi(COB_ACTUAL)	5	127.3976	3.9265249	0.0162568	1.0000000

Cerrillo - zaino (Pecari tajacu)

Pecari tajacu

Selección de Modelos

    print(as.character(sp.names[sp_number=22]))

[1] “Pecari tajacu”

    sp22 <- f.sp.occu.models(sp_number = 22)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp22) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Pecari tajacu models	nPars	AIC	delta	AICwt	cumltvWt
17	p(roughness)psi(slope)	4	235.2073	0.000000	0.6122422	0.6122422
19	p(roughness)psi(roughness)	4	238.8253	3.617946	0.1002989	0.7125411
16	p(roughness)psi(elev)	4	240.2313	5.024015	0.0496561	0.7621972
10	p(elev)psi(slope)	4	240.4732	5.265925	0.0439989	0.8061961
20	p(roughness)psi(dist_def)	4	240.8836	5.676286	0.0358371	0.8420333
9	p(elev)psi(elev)	4	241.0875	5.880163	0.0323640	0.8743973
18	p(roughness)psi(aspect)	4	241.8286	6.621263	0.0223426	0.8967399
13	p(elev)psi(dist_def)	4	242.1169	6.909580	0.0193431	0.9160830
12	p(elev)psi(roughness)	4	242.8539	7.646553	0.0133812	0.9294642
3	p(.)psi(slope)	3	243.1037	7.896364	0.0118100	0.9412742
11	p(elev)psi(aspect)	4	243.3928	8.185484	0.0102204	0.9514946
21	p(roughness)psi(COB_ACTUAL)	5	243.6492	8.441923	0.0089905	0.9604851
1	p(.)psi(.)	2	243.7734	8.566060	0.0084494	0.9689345
2	p(.)psi(elev)	3	244.3572	9.149879	0.0063103	0.9752449
14	p(elev)psi(COB_ACTUAL)	5	244.7787	9.571344	0.0051113	0.9803562
6	p(.)psi(dist_def)	3	244.8122	9.604875	0.0050263	0.9853825
5	p(.)psi(roughness)	3	245.2554	10.048031	0.0040274	0.9894099
4	p(.)psi(aspect)	3	245.5102	10.302867	0.0035456	0.9929554
22	p(roughness)psi(Sitio)	6	245.8475	10.640192	0.0029953	0.9959507
15	p(elev)psi(Sitio)	6	246.7894	11.582055	0.0018703	0.9978210
7	p(.)psi(COB_ACTUAL)	4	246.9719	11.764607	0.0017071	0.9995281
8	p(.)psi(Sitio)	5	249.5438	14.336433	0.0004719	1.0000000

Ardilla (Sciurus igniventris)

Sciurus igniventris

Selección de Modelos

    print(as.character(sp.names[sp_number=25]))

[1] “Sciurus igniventris”

    sp25 <- f.sp.occu.models(sp_number = 25)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
     kable(sp25) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Sciurus igniventris models	nPars	AIC	delta	AICwt	cumltvWt
10	p(elev)psi(slope)	4	120.9315	0.0000000	0.2626535	0.2626535
11	p(elev)psi(aspect)	4	121.6801	0.7485681	0.1806482	0.4433017
12	p(elev)psi(roughness)	4	121.9148	0.9832995	0.1606432	0.6039449
13	p(elev)psi(dist_def)	4	121.9434	1.0118615	0.1583654	0.7623102
9	p(elev)psi(elev)	4	122.0963	1.1648083	0.1467061	0.9090164
14	p(elev)psi(COB_ACTUAL)	5	123.8451	2.9135845	0.0611936	0.9702100
15	p(elev)psi(Sitio)	6	125.3288	4.3972782	0.0291425	0.9993525
3	p(.)psi(slope)	3	136.3598	15.4282723	0.0001173	0.9994697
6	p(.)psi(dist_def)	3	137.1316	16.2001072	0.0000797	0.9995495
1	p(.)psi(.)	2	137.1604	16.2288574	0.0000786	0.9996280
2	p(.)psi(elev)	3	137.7990	16.8675003	0.0000571	0.9996851
5	p(.)psi(roughness)	3	137.8121	16.8805575	0.0000567	0.9997419
17	p(roughness)psi(slope)	4	138.0753	17.1438325	0.0000497	0.9997916
4	p(.)psi(aspect)	3	138.2663	17.3347854	0.0000452	0.9998368
20	p(roughness)psi(dist_def)	4	138.6517	17.7201550	0.0000373	0.9998741
16	p(roughness)psi(elev)	4	139.2664	18.3349453	0.0000274	0.9999015
19	p(roughness)psi(roughness)	4	139.4304	18.4989160	0.0000253	0.9999268
18	p(roughness)psi(aspect)	4	139.6136	18.6820604	0.0000230	0.9999498
8	p(.)psi(Sitio)	5	139.9613	19.0297810	0.0000194	0.9999692
7	p(.)psi(COB_ACTUAL)	4	140.5965	19.6649565	0.0000141	0.9999833
22	p(roughness)psi(Sitio)	6	141.4885	20.5570286	0.0000090	0.9999923
21	p(roughness)psi(COB_ACTUAL)	5	141.8098	20.8782692	0.0000077	1.0000000

Yagouaroundi (Puma yagouaroundi)

Sciurus igniventris

Selección de Modelos

    print(as.character(sp.names[sp_number=26]))

[1] “Puma yagouaroundi”

    sp26 <- f.sp.occu.models(sp_number = 26)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
    kable(sp26) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Puma yagouaroundi models	nPars	AIC	delta	AICwt	cumltvWt
4	p(.)psi(aspect)	3	123.7794	0.0000000	0.1991217	0.1991217
18	p(roughness)psi(aspect)	4	124.5855	0.8060798	0.1330702	0.3321919
1	p(.)psi(.)	2	125.1225	1.3431325	0.1017328	0.4339247
11	p(elev)psi(aspect)	4	125.4529	1.6734863	0.0862434	0.5201681
6	p(.)psi(dist_def)	3	125.8869	2.1074837	0.0694200	0.5895880
20	p(roughness)psi(dist_def)	4	126.7782	2.9988176	0.0444563	0.6340444
5	p(.)psi(roughness)	3	126.8357	3.0562761	0.0431973	0.6772417
2	p(.)psi(elev)	3	126.8947	3.1153500	0.0419401	0.7191818
3	p(.)psi(slope)	3	127.0675	3.2881171	0.0384692	0.7576509
13	p(elev)psi(dist_def)	4	127.2646	3.4852237	0.0348588	0.7925097
19	p(roughness)psi(roughness)	4	127.4139	3.6345238	0.0323513	0.8248610
16	p(roughness)psi(elev)	4	128.0326	4.2531792	0.0237439	0.8486049
17	p(roughness)psi(slope)	4	128.0732	4.2938124	0.0232664	0.8718713
7	p(.)psi(COB_ACTUAL)	4	128.0993	4.3199024	0.0229649	0.8948362
9	p(elev)psi(elev)	4	128.3501	4.5707178	0.0202582	0.9150944
12	p(elev)psi(roughness)	4	128.4948	4.7154605	0.0188438	0.9339382
10	p(elev)psi(slope)	4	128.7925	5.0131014	0.0162382	0.9501764
8	p(.)psi(Sitio)	5	128.9100	5.1306527	0.0153113	0.9654877
21	p(roughness)psi(COB_ACTUAL)	5	129.4781	5.6987479	0.0115253	0.9770129
14	p(elev)psi(COB_ACTUAL)	5	129.8330	6.0535728	0.0096517	0.9866646
22	p(roughness)psi(Sitio)	6	130.4683	6.6889424	0.0070248	0.9936894
15	p(elev)psi(Sitio)	6	130.6827	6.9033656	0.0063106	1.0000000

Chuchita 4 ojos - zariguella (Philander opossum)

Selección de Modelos

    print(as.character(sp.names[sp_number=29]))

[1] “Philander opossum”

    sp29 <- f.sp.occu.models(sp_number = 29)
    # xtable::xtable(sp1, "html") #, format = "rst")
    # kable(Table1, "latex", booktabs = T, format = "rst") # para pdf
  kable(sp29) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 8))

	Philander opossum models	nPars	AIC	delta	AICwt	cumltvWt
5	p(.)psi(roughness)	3	68.62094	0.0000000	0.2160118	0.2160118
12	p(elev)psi(roughness)	4	69.42803	0.8070833	0.1442851	0.3602970
19	p(roughness)psi(roughness)	4	69.55897	0.9380251	0.1351413	0.4954382
3	p(.)psi(slope)	3	70.00266	1.3817147	0.1082535	0.6036917
17	p(roughness)psi(slope)	4	70.75891	2.1379642	0.0741694	0.6778611
10	p(elev)psi(slope)	4	70.89312	2.2721740	0.0693555	0.7472166
16	p(roughness)psi(elev)	4	72.64519	4.0242448	0.0288818	0.7760984
21	p(roughness)psi(COB_ACTUAL)	5	72.69670	4.0757583	0.0281474	0.8042458
22	p(roughness)psi(Sitio)	6	72.95550	4.3345531	0.0247310	0.8289767
2	p(.)psi(elev)	3	73.10123	4.4802908	0.0229930	0.8519697
8	p(.)psi(Sitio)	5	73.12861	4.5076649	0.0226804	0.8746501
9	p(elev)psi(elev)	4	73.18356	4.5626170	0.0220657	0.8967158
18	p(roughness)psi(aspect)	4	73.64148	5.0205382	0.0175502	0.9142660
20	p(roughness)psi(dist_def)	4	73.65209	5.0311445	0.0174574	0.9317233
15	p(elev)psi(Sitio)	6	73.69048	5.0695365	0.0171254	0.9488488
1	p(.)psi(.)	2	74.46489	5.8439448	0.0116274	0.9604761
6	p(.)psi(dist_def)	3	74.77331	6.1523682	0.0099657	0.9704418
7	p(.)psi(COB_ACTUAL)	4	75.43463	6.8136863	0.0071599	0.9776017
4	p(.)psi(aspect)	3	75.44331	6.8223623	0.0071289	0.9847305
13	p(elev)psi(dist_def)	4	75.59156	6.9706178	0.0066195	0.9913501
11	p(elev)psi(aspect)	4	76.43278	7.8118403	0.0043467	0.9956967
14	p(elev)psi(COB_ACTUAL)	5	76.45286	7.8319212	0.0043033	1.0000000

References

Información de la sesión en R.

print(sessionInfo(), locale = FALSE)

## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 14393)
## 
## Matrix products: default
## 
## attached base packages:
## [1] parallel  grid      stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] unmarked_0.13-1      Rcpp_1.0.3           rstan_2.19.3        
##  [4] StanHeaders_2.21.0-1 OpenStreetMap_0.3.4  osmdata_0.1.3       
##  [7] tmaptools_2.0-2      tmap_2.3-2           stargazer_5.2.2     
## [10] kableExtra_1.1.0     knitr_1.28           forcats_0.4.0       
## [13] dplyr_0.8.4          purrr_0.3.3          readr_1.3.1         
## [16] tidyr_1.0.2          tibble_2.1.3         tidyverse_1.3.0     
## [19] readxl_1.3.1         raster_3.0-12        spatstat_1.63-0     
## [22] rpart_4.1-15         nlme_3.1-144         spatstat.data_1.4-3 
## [25] mapview_2.7.0        sf_0.8-1             maptools_0.9-9      
## [28] rgdal_1.4-8          sp_1.4-0             stringr_1.4.0       
## [31] lubridate_1.7.4      scales_1.1.0         plyr_1.8.5          
## [34] reshape2_1.4.3       Hmisc_4.3-1          Formula_1.2-3       
## [37] survival_3.1-8       lattice_0.20-38      xtable_1.8-4        
## [40] ggmap_3.0.0          chron_2.3-55         ggplot2_3.2.1       
## [43] zoo_1.8-7            reshape_0.8.8       
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.5       lwgeom_0.2-1          lazyeval_0.2.2       
##   [4] splines_3.6.1         crosstalk_1.0.0       leaflet_2.0.3        
##   [7] inline_0.3.15         digest_0.6.24         htmltools_0.4.0      
##  [10] fansi_0.4.1           magrittr_1.5          checkmate_2.0.0      
##  [13] tensor_1.5            cluster_2.1.0         modelr_0.1.5         
##  [16] matrixStats_0.55.0    prettyunits_1.1.1     jpeg_0.1-8.1         
##  [19] colorspace_1.4-1      rvest_0.3.5           haven_2.2.0          
##  [22] xfun_0.12             callr_3.4.2           crayon_1.3.4         
##  [25] jsonlite_1.6.1        glue_1.3.1            polyclip_1.10-0      
##  [28] gtable_0.3.0          webshot_0.5.2         pkgbuild_1.0.6       
##  [31] abind_1.4-5           DBI_1.1.0             viridisLite_0.3.0    
##  [34] htmlTable_1.13.3      units_0.6-5           foreign_0.8-75       
##  [37] stats4_3.6.1          htmlwidgets_1.5.1     httr_1.4.1           
##  [40] RColorBrewer_1.1-2    acepack_1.4.1         loo_2.2.0            
##  [43] pkgconfig_2.0.3       XML_3.99-0.3          rJava_0.9-11         
##  [46] farver_2.0.3          nnet_7.3-12           dbplyr_1.4.2         
##  [49] deldir_0.1-25         tidyselect_1.0.0      labeling_0.3         
##  [52] rlang_0.4.4           later_1.0.0           munsell_0.5.0        
##  [55] cellranger_1.1.0      tools_3.6.1           cli_2.0.1            
##  [58] generics_0.0.2        broom_0.5.4           evaluate_0.14        
##  [61] fastmap_1.0.1         yaml_2.2.1            goftest_1.2-2        
##  [64] processx_3.4.2        leafsync_0.1.0        fs_1.3.1             
##  [67] satellite_1.0.2       RgoogleMaps_1.4.5.3   mime_0.9             
##  [70] xml2_1.2.2            compiler_3.6.1        rstudioapi_0.11      
##  [73] curl_4.3              png_0.1-7             e1071_1.7-3          
##  [76] spatstat.utils_1.17-0 reprex_0.3.0          stringi_1.4.6        
##  [79] ps_1.3.2              rgeos_0.5-2           Matrix_1.2-18        
##  [82] classInt_0.4-2        vctrs_0.2.2           pillar_1.4.3         
##  [85] lifecycle_0.1.0       data.table_1.12.8     bitops_1.0-6         
##  [88] httpuv_1.5.2          R6_2.4.1              latticeExtra_0.6-29  
##  [91] promises_1.1.0        KernSmooth_2.23-16    gridExtra_2.3        
##  [94] codetools_0.2-16      dichromat_2.0-0       MASS_7.3-51.5        
##  [97] assertthat_0.2.1      rjson_0.2.20          withr_2.1.2          
## [100] mgcv_1.8-31           hms_0.5.3             class_7.3-15         
## [103] rmarkdown_2.1         shiny_1.4.0           base64enc_0.1-3

Analisis de Ocupación, Agroforestería para la Conservación

Diego J. Lizcano, Angelica Diaz-Pulido

20/2/2020

Lugar del muestreo

Duración del muestreo

Especies registradas

Mamíferos detectados

Distribución posterior de la riqueza de especies de mamíferos

Mamíferos en las cámaras

Aves en las cámaras

Analisis de Ocupación para Mamíferos

Covariables espaciales

Función para automatizar modelos

Modelos de ocupacion por especie

Ñeque (Dasyprocta fuliginosa)

Selección de Modelos

Boruga (Cuniculus paca)

Selección de Modelos

Armadillo cola trapo (Cabassous unicinctus)

Selección de Modelos

Oso hormiguero-mielero (Tamandua tetradactyla)

Selección de Modelos

Armadillo 9 bandas(Dasypus novemcinctus)

Selección de Modelos

Tayra (Eira barbara)

Selección de Modelos

Perro domestico (Canis lupus familiaris)

Hessian error

Selección de Modelos

Mapache (Procyon cancrivorus)

Selección de Modelos

Guatincito (Myoprocta pratti)

Selección de Modelos

Armadillo espuelón (Dasypus kappleri)

Selección de Modelos

Chucha (Didelphis marsupialis)

Selección de Modelos

Ocelote (Leopardus pardalis)

Selección de Modelos

Cerrillo - zaino (Pecari tajacu)

Selección de Modelos

Ardilla (Sciurus igniventris)

Selección de Modelos

Yagouaroundi (Puma yagouaroundi)

Selección de Modelos

Chuchita 4 ojos - zariguella (Philander opossum)

Selección de Modelos

References

Información de la sesión en R.