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occupancy.R
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occupancy.R
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############################################################
## occupancy analyses for bird abundance/range
## Requires tidyverse, reshape2, data.table and unmarked####
## type = trivial, null, nosp, nosptime, nb, nosptimenb
## cutoff removes vagrants
occufreq = function(data, species, mig, resolution, type = "null", nb = 4, cutoff = 0)
{
require(tidyverse)
require(reshape2)
require(data.table)
require(unmarked)
load("neighbours.RData")
spec = species[mig == "LM"]
species = c(species,spec)
mig[mig == "LM"] = "LMS"
mig = c(mig,rep("LMW",length(spec)))
data = data %>%
filter(EFFORT.DISTANCE.KM <= 50, year > 2013)
## remove unlikely observations/passage
if (cutoff != 0)
{
d = data %>%
filter(ALL.SPECIES.REPORTED == 1) %>%
group_by(gridg5,month) %>% mutate(lists = n_distinct(group.id)) %>% ungroup %>%
filter(COMMON.NAME %in% species) %>%
group_by(gridg5,month,COMMON.NAME) %>% summarize(freq = n_distinct(group.id)/max(lists)) %>% ungroup %>%
filter(freq < cutoff) %>% select(gridg5,month,COMMON.NAME)
d = left_join(d,data)
data = anti_join(data,d)
}
# create dataframe to store occupancy and detection proabability estimates across species and spatial resolutions
res = c("g2","g3","g4")
est = array(data=NA,dim=c(length(species),3,length(res)),
dimnames=list(species,c("detprob","occ","occ.se"),c("50km","80km","100km")))
data1 = nocturnallistcheck(data)
for(s in 1:length(species))
{
if (mig[s] == "S" | mig[s] == "W")
{
temp1 = data %>%
filter(COMMON.NAME == species[s]) %>%
distinct(month)
temp2 = data1 %>%
filter(COMMON.NAME == species[s]) %>%
distinct(month)
datai = temp1 %>% left_join(data)
datac = temp2 %>% left_join(data1)
}
if (mig[s] == "R")
{
datai = data
datac = data1
}
if (mig[s] == "LMS")
{
datai = data %>%
filter(month %in% c(5:8))
datac = data1 %>%
filter(month %in% c(5:8))
}
if (mig[s] == "LMW")
{
datai = data %>%
filter(month %in% c(11:12,1:2))
datac = data1 %>%
filter(month %in% c(11:12,1:2))
}
selexp = expandbyspecies(datac,species[s])
selexp = selexp[sample(1:nrow(selexp)),]
selexp$month[selexp$month %in% c(11,12,1,2)] = "Win"
selexp$month[selexp$month %in% c(3,4,5,6)] = "Sum"
selexp$month[selexp$month %in% c(7,8,9,10)] = "Mon"
for(r in resolution)
{
if(r == "g2")
{ nb4g = nb4g2
nb8g = nb8g2
lpg = selexp %>%
group_by(gridg2) %>% summarize(lpg = n())
listcutoff = quantile(lpg$lpg, 0.95, na.rm=TRUE)
inc = datai %>%
mutate(gridg = gridg2)
selexp = selexp %>%
arrange(gridg2) %>%
mutate(gridg = gridg2) %>%
group_by(gridg) %>% mutate(group.id = 1:n())}
if(r == "g3")
{ nb4g = nb4g3
nb8g = nb8g3
lpg = selexp %>%
group_by(gridg3) %>% summarize(lpg = n())
listcutoff = quantile(lpg$lpg, 0.95, na.rm=TRUE)
inc = datai %>%
mutate(gridg = gridg3)
selexp = selexp %>%
arrange(gridg3) %>%
mutate(gridg = gridg3) %>%
group_by(gridg) %>% mutate(group.id = 1:n())}
if(r == "g4")
{ nb4g = nb4g4
nb8g = nb8g4
lpg = selexp %>%
group_by(gridg4) %>% summarize(lpg = n())
listcutoff = quantile(lpg$lpg, 0.95, na.rm=TRUE)
inc = datai %>%
mutate(gridg = gridg4)
selexp = selexp %>%
arrange(gridg4) %>%
mutate(gridg = gridg4) %>%
group_by(gridg) %>% mutate(group.id = 1:n())}
nbt = selexp %>%
group_by(gridg) %>% summarize(fl = sum(OBSERVATION.COUNT)) %>%
mutate(fl=replace(fl, fl > 1, 1))
fil = sum(nbt$fl)
len = length(nbt$fl)
nbti = inc %>%
group_by(gridg) %>% summarize(fl = sum(OBSERVATION.COUNT)) %>%
mutate(fl=replace(fl, fl > 1, 1))
fil1 = sum(nbti$fl)
len = len-fil+fil1
fil = fil1
setDT(selexp)
det = dcast(selexp, gridg ~ group.id, value.var = "OBSERVATION.COUNT")
cov.month = dcast(selexp, gridg ~ group.id, value.var = "month")
cov.nosp = dcast(selexp, gridg ~ group.id, value.var = "no.sp")
det = setDF(det)
cov.month = setDF(cov.month)
cov.nosp = setDF(cov.nosp)
det = det[,1:listcutoff]
cov.month = cov.month[,1:listcutoff]
cov.nosp = cov.nosp[,1:listcutoff]
if(type == "nb" | type == "nosptimenb")
{
nbt$gridg = as.character(nbt$gridg)
nbt$gridg = as.numeric(nbt$gridg)
nbti$gridg = as.character(nbti$gridg)
nbti$gridg = as.numeric(nbti$gridg)
for (i in 1:length(nbt$gridg))
{
temp = as.numeric(nb4g[[nbt$gridg[i]]])
sm = sum(nbti[nbti$gridg %in% temp,]$fl)/length(temp)
nbt$nb4[i] = sm
temp = as.numeric(nb8g[[nbt$gridg[i]]])
sm = sum(nbti[nbti$gridg %in% temp,]$fl)/length(temp)
nbt$nb8[i] = sm
}
nbt$gridg = as.character(nbt$gridg)
tp1 = nbt %>% select(-fl)
tp = left_join(nbti,tp1)
nbt = nbt[,-2]
nbtx = tp[tp$fl != 1,]
detn = data.frame(gridg = det[,1])
detn= left_join(detn,nbt)
}
if (type == "trivial")
{
est[s,"detprob", match(r,res)] = NA
est[s,"occ", match(r,res)] = fil/len
est[s,"occ.se", match(r,res)] = 0
}
if (type == "null")
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs =NULL, obsCovs = NULL)
occ_det = tryCatch({occu(~1 ~1, data=umf, starts = c(0,0))},error = function(cond){"skip"})
if (!is.character(occ_det))
{
f1 = predict(occ_det, type = "det", newdata = data.frame(0))
f1 = mean(f1$Predicted)
f2 = predict(occ_det, type = "state", newdata = data.frame(0))
f2 = f2 %>% filter(!is.na(Predicted))
f2a = f2$Predicted
f2b = f2$SE
est[s,"detprob", match(r,res)] = f1
est[s,"occ", match(r,res)] = f2a
est[s,"occ.se", match(r,res)] = f2b
}
}
if (type == "nosp")
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs =NULL, obsCovs = list(cov1 = cov.nosp[,-1]))
occ_det = tryCatch({occu(~cov1 ~1, data=umf, starts = c(0,0,0))},error = function(cond){"skip"})
if (!is.character(occ_det))
{
f1 = predict(occ_det, type = "det", newdata = data.frame(cov1=20))
f1 = mean(f1$Predicted)
f2 = predict(occ_det, type = "state", newdata = data.frame(0))
f2 = f2 %>% filter(!is.na(Predicted))
f2a = f2$Predicted
f2b = f2$SE
est[s,"detprob", match(r,res)] = f1
est[s,"occ", match(r,res)] = f2a
est[s,"occ.se", match(r,res)] = f2b
}
}
if (type == "nosptime")
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs =NULL, obsCovs = list(cov1 = cov.nosp[,-1],
cov2 = cov.month[,-1]))
occ_det = tryCatch({occu(~cov1*cov2 ~1, data=umf, starts = c(0,0,0,0,0,0,0))},
error = function(cond){"skip"})
if (!is.character(occ_det))
{
f1 = predict(occ_det, type = "det", newdata = data.frame(cov1=20, cov2=factor(c("Mon","Win","Sum"))))
f1 = mean(f1$Predicted)
f2 = predict(occ_det, type = "state", newdata = data.frame(0))
f2 = f2 %>% filter(!is.na(Predicted))
f2a = f2$Predicted
f2b = f2$SE
est[s,"detprob", match(r,res)] = f1
est[s,"occ", match(r,res)] = f2a
est[s,"occ.se", match(r,res)] = f2b
}
}
if (type == "nb")
{
if (nb == 4)
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs = data.frame(nb4g = detn$nb4), obsCovs = NULL)
occ_det = tryCatch({occu(~1 ~nb4g, data=umf, starts = c(0,0,0))},
error = function(cond){"skip"})
newdat1 = data.frame(0)
newdat2 = data.frame(nb4g=nbtx$nb4)
}
if (nb == 8)
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs = data.frame(nb8g = detn$nb8), obsCovs = NULL)
occ_det = tryCatch({occu(~1 ~nb8g, data=umf, starts = c(0,0,0))},
error = function(cond){"skip"})
newdat1 = data.frame(0)
newdat2 = data.frame(nb8g=nbtx$nb8)
}
if (!is.character(occ_det))
{
f1 = predict(occ_det, newdata = newdat1, type = "det")
f1 = mean(f1$Predicted)
f2 = predict(occ_det, newdata = newdat2, type = "state")
f2 = f2 %>% filter(!is.na(Predicted))
f2a = (sum(f2$Predicted) + fil)/len
f2b = erroradd(f2$SE)/len
est[s,"detprob", match(r,res)] = f1
est[s,"occ", match(r,res)] = f2a
est[s,"occ.se", match(r,res)] = f2b
}
}
if (type == "nosptimenb")
{
if (nb == 4)
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs = data.frame(nb4g = detn$nb4), obsCovs = list(cov1 = cov.nosp[,-1],
cov2 = cov.month[,-1]))
occ_det = tryCatch({occu(~cov1*cov2 ~nb4g, data=umf, starts = c(0,0,0,0,0,0,0,0))},
error = function(cond){"skip"})
newdat1 = data.frame(cov1=20, cov2=factor(c("Mon","Win","Sum")))
newdat2 = data.frame(nb4g=nbtx$nb4)
}
if (nb == 8)
{
umf = unmarkedFrameOccu(y=det[,-1], siteCovs = data.frame(nb8g = detn$nb8), obsCovs = list(cov1 = cov.nosp[,-1],
cov2 = cov.month[,-1]))
occ_det = tryCatch({occu(~cov1*cov2 ~nb8g, data=umf, starts = c(0,0,0,0,0,0,0,0))},
error = function(cond){"skip"})
newdat1 = data.frame(cov1=20, cov2=factor(c("Mon","Win","Sum")))
newdat2 = data.frame(nb8g=nbtx$nb8)
}
if (!is.character(occ_det))
{
f1 = predict(occ_det, newdata = newdat1, type = "det")
f1 = mean(f1$Predicted)
f2 = predict(occ_det, newdata = newdat2, type = "state")
f2 = f2 %>% filter(!is.na(Predicted))
f2a = (sum(f2$Predicted) + fil)/len
f2b = erroradd(f2$SE)/len
est[s,"detprob", match(r,res)] = f1
est[s,"occ", match(r,res)] = f2a
est[s,"occ.se", match(r,res)] = f2b
}
}
}
}
estdf = data.frame(rep(res, each = length(species)))
names(estdf) = "resolution"
estdf$species = rep(rownames(est), length(res))
for (i in 1:length(res))
{
if (i == 1){temp = est[,,i]}
if (i != 1){temp = rbind(temp,est[,,i])}
}
estdf$detprob = temp[,1]
estdf$occ = temp[,2]
estdf$occ.se = temp[,3]
estdf$type = type
estdf$nb = nb
return(estdf)
}