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hm_plethodon.Rmd
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hm_plethodon.Rmd
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---
title: "hierarchical models of Plethodontid ecology"
author: "Alex Baecher"
date: "August 15, 2017"
output:
html_document: default
pdf_document: default
word_document: default
---
```{r, Call packages unmarked and AICcmodaverage}
library(unmarked)
library(AICcmodavg)
```
```{r, Uploading Salamander count and occupancy, & covar database}
Database=read.csv("Plethodon LCW.csv", header=T)
Database_occu=read.csv("Plethodon LCW presabs.csv", header=T)
names(Database)
```
```{r, setting K for pcount models}
(K_rich <- max(Database[,14:17], na.rm = TRUE) + 20)
(K_kent <- max(Database[,10:13], na.rm = TRUE) + 20)
(K_glut <- max(Database[,6:9], na.rm = TRUE) + 20)
```
```{r, Extracting site Covariates}
CAN=Database[1:40,2]
ASP=Database[1:40,3]
ELV=Database[1:40,4]
MST=Database[1:40,5]
TPI=Database[1:40,42]
VEG=Database[1:40,43]
RAD=Database[1:40,44]
```
```{r, Extracting Detection Covariates}
CWD=array(Database[1:40,18:21])
ROC=array(Database[1:40,22:25])
DAY=array(Database[1:40,34:37])
TOD=array(Database[1:40,38:41])
LUX=array(Database[1:40,30:33])
LLD=array(Database[1:40,26:29])
```
# KEY TO NAMES
# ______________site covariates_____________________
# ASP = Beer's transformed Aspect (continuous, 0=SW -> 2=NW)
# CAN = Percent canopy closed (from photography)
# MST = Average percent soil moister from each survey
# ELV = Elevation (m)
# TPI = Topographic position index (GIS)
# RAD = Direct Solar Radiation (GIS)
# VEG = Normalized Difference Vegetation Index (GIS)
________________detection covariates________________
# ROC = # of Rocks in transect
# TOD = Time of Day
# CWD = Coarse Woody Debris (cm^3)
# DAY = Julian Date
# LLD = Leaf Litter Depth (cm)
# LUX = Luxometer reading (who the fuck knows the units)
```{r, converting covariate data to format that will match the detection probability output}
# Detection Covariates:
TOD.stack.occ <- c(TOD[,1], TOD[,2], TOD[,3], TOD[,4])
CWD.stack.occ <- c(CWD[,1], CWD[,2], CWD[,3], CWD[,4])
DAY.stack.occ <- c(DAY[,1], DAY[,2], DAY[,3], DAY[,4])
ROC.stack.occ <- c(ROC[,1], ROC[,2], ROC[,3], ROC[,4])
LUX.stack.occ <- c(LUX[,1], LUX[,2], LUX[,3], LUX[,4])
LLD.stack.occ <- c(LLD[,1], LLD[,2], LLD[,3], LLD[,4])
```
```{r, Isolating Species count data}
P_glutinosus_count=array(Database[1:40,6:9])
P_richmondi_count=array(Database[1:40,14:17])
P_kentucki_count=array(Database[1:40,10:13])
```
```{r, Create unmarked N-mixture model frames for each species}
P_richmondi_pcount=unmarkedFramePCount(
y=P_richmondi_count,
obsCovs=list(CWD=CWD, ROC=ROC, DAY=DAY, TOD=TOD, LUX=LUX, LLD=LLD),
siteCovs=data.frame(ELV=ELV, MST=MST, ASP=ASP, CAN=CAN, RAD=RAD, VEG=VEG, TPI=TPI))
P_kentucki_pcount=unmarkedFramePCount(
y=P_kentucki_count,
obsCovs=list(CWD=CWD, ROC=ROC, DAY=DAY, TOD=TOD, LUX=LUX, LLD=LLD),
siteCovs=data.frame(ELV=ELV, MST=MST, ASP=ASP, CAN=CAN, RAD=RAD, VEG=VEG, TPI=TPI))
P_glutinosus_pcount=unmarkedFramePCount(
y=P_glutinosus_count,
obsCovs=list(CWD=CWD, ROC=ROC, DAY=DAY, TOD=TOD, LUX=LUX, LLD=LLD),
siteCovs=data.frame(ELV=ELV, MST=MST, ASP=ASP, CAN=CAN, RAD=RAD, VEG=VEG, TPI=TPI))
```
```{r, Standardize all covariates (obs -> Z-scores), results="hide"}
obsCovs(P_glutinosus_pcount)<-scale(obsCovs(P_glutinosus_pcount))
siteCovs(P_glutinosus_pcount)<-scale(siteCovs(P_glutinosus_pcount))
summary(P_glutinosus_pcount)
obsCovs(P_richmondi_pcount)<-scale(obsCovs(P_richmondi_pcount))
siteCovs(P_richmondi_pcount)<-scale(siteCovs(P_richmondi_pcount))
summary(P_richmondi_pcount)
obsCovs(P_kentucki_pcount)<-scale(obsCovs(P_kentucki_pcount))
siteCovs(P_kentucki_pcount)<-scale(siteCovs(P_kentucki_pcount))
summary(P_kentucki_pcount)
```
```{r, Isolate species occurance histories }
P_glutinosus=array(Database_occu[1:40,6:9])
P_richmondi=array(Database_occu[1:40,14:17])
P_kentucki=array(Database_occu[1:40,10:13])
```
```{r, Construct unmarked occupancy model frames}
P_glutinosus_occu=unmarkedFrameOccu(y=P_glutinosus,
obsCovs=list(CWD=CWD, ROC=ROC, DAY=DAY, TOD=TOD, LUX=LUX, LLD=LLD),
siteCovs=data.frame(ELV=ELV, MST=MST, ASP=ASP, CAN=CAN, RAD=RAD, VEG=VEG, TPI=TPI))
P_richmondi_occu=unmarkedFrameOccu(y=P_richmondi,
obsCovs=list(CWD=CWD, ROC=ROC, DAY=DAY, TOD=TOD, LUX=LUX, LLD=LLD),
siteCovs=data.frame(ELV=ELV, MST=MST, ASP=ASP, CAN=CAN, RAD=RAD, VEG=VEG, TPI=TPI))
P_kentucki_occu=unmarkedFrameOccu(y=P_kentucki,
obsCovs=list(CWD=CWD, ROC=ROC, DAY=DAY, TOD=TOD, LUX=LUX, LLD=LLD),
siteCovs=data.frame(ELV=ELV, MST=MST, ASP=ASP, CAN=CAN, RAD=RAD, VEG=VEG, TPI=TPI))
```
```{r, Standardize all Covars (obs -> Z-scores) to improve estimation , results="hide"}
obsCovs(P_glutinosus_occu)<-scale(obsCovs(P_glutinosus_occu))
siteCovs(P_glutinosus_occu)<-scale(siteCovs(P_glutinosus_occu))
summary(P_glutinosus_occu)
obsCovs(P_richmondi_occu)<-scale(obsCovs(P_richmondi_occu))
siteCovs(P_richmondi_occu)<-scale(siteCovs(P_richmondi_occu))
summary(P_richmondi_occu)
obsCovs(P_kentucki_occu)<-scale(obsCovs(P_kentucki_occu))
siteCovs(P_kentucki_occu)<-scale(siteCovs(P_kentucki_occu))
summary(P_kentucki_occu)
```
```{r, examine data for correlations}
site.data<-data.frame(ELV, MST, ASP, CAN)
(pairwise.site.pearson<-cor(site.data, use="pairwise.complete.obs", method="pearson"))
(complete.site.pearson<-cor(site.data, use="complete.obs", method="pearson"))
(all.site.pearson<-cor(site.data, use="all.obs", method="pearson"))
# These all produce the same results....
LUX.stack<-c(LUX[,1],LUX[,2],LUX[,3],LUX[,4])
LLD.stack<-c(LLD[,1], LLD[,2], LLD[,3], LLD[,4])
TOD.stack<-c(TOD[,1], TOD[,2], TOD[,3], TOD[,4])
DAY.stack<-c(DAY[,1], DAY[,2], DAY[,3], DAY[,4])
CWD.stack<-c(CWD[,1], CWD[,2], CWD[,3], CWD[,4])
ROC.stack<-c(ROC[,1], ROC[,2], ROC[,3], ROC[,4])
sam.data<-data.frame(LUX.stack,LLD.stack,TOD.stack,DAY.stack,CWD.stack,ROC.stack)
cor(sam.data, use="all.obs", method="pearson")
```
## ------------ Begin: P. richmondi ------------ ##
```{r, Begin N-mixture model construction with P. richmondi detection (Pcount), results="hide"}
p_dot_lam_dot_richmondi=pcount(~1 ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_ROC_lam_dot_richmondi=pcount(~ROC ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_LUX_lam_dot_richmondi=pcount(~LUX ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_DAY_lam_dot_richmondi=pcount(~DAY ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_dot_richmondi=pcount(~TOD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_LLD_lam_dot_richmondi=pcount(~LLD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_CWD_lam_dot_richmondi=pcount(~CWD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_ROC_CWD_lam_dot_richmondi=pcount(~ROC+CWD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_DAY_lam_dot_richmondi=pcount(~TOD+DAY ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_ROC_TOD_lam_dot_richmondi=pcount(~ROC+TOD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_CWD_TOD_lam_dot_richmondi=pcount(~CWD+TOD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_CWD_DAY_lam_dot_richmondi=pcount(~CWD+DAY ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_CWD_LUX_lam_dot_richmondi=pcount(~CWD+LUX ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_ROC_LUX_DAY_CWD_TOD_LLD_lam_dot_richmondi=pcount(~CWD+LUX+ROC+TOD+LLD+DAY ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
```
# Create candidate model list for AIC selection of P. richmondi detection (Pcount)
```{r, Create candidate model list for AIC selection of P. richmondi detection (Pcount)}
richmondi.det.list=fitList(
"p(.)lam(.)" =p_dot_lam_dot_richmondi,
"p(ROC)lam(.)" =p_ROC_lam_dot_richmondi,
"p(LUX)lam(.)" =p_LUX_lam_dot_richmondi,
"p(DAY)lam(.)" =p_DAY_lam_dot_richmondi,
"p(TOD)lam(.)" =p_TOD_lam_dot_richmondi,
"p(LLD)lam(.)" =p_LLD_lam_dot_richmondi,
"p(CWD)lam(.)" =p_CWD_lam_dot_richmondi,
"p(ROC+CWD)lam(.)" =p_ROC_CWD_lam_dot_richmondi,
"p(TOD+DAY)lam(.)" =p_TOD_DAY_lam_dot_richmondi,
"p(ROC+TOD)lam(.)" =p_ROC_TOD_lam_dot_richmondi,
"p(CWD+TOD)lam(.)" =p_CWD_TOD_lam_dot_richmondi,
"p(CWD+DAY)lam(.)" =p_CWD_DAY_lam_dot_richmondi,
"p(CWD+LUX)lam(.)" =p_CWD_LUX_lam_dot_richmondi,
"p(ROC+LUX+DAY+CWD+TOD+LLD) lam(.)"=p_ROC_LUX_DAY_CWD_TOD_LLD_lam_dot_richmondi)
modSel(richmondi.det.list)
```
# beta estimates P.richmondi occu detection
```{r, beta estimates P.richmondi occu detection}
P.richmondi_det_lam_list=list(
p_dot_lam_dot_richmondi,
p_ROC_lam_dot_richmondi,
p_LUX_lam_dot_richmondi,
p_DAY_lam_dot_richmondi,
p_TOD_lam_dot_richmondi,
p_LLD_lam_dot_richmondi,
p_CWD_lam_dot_richmondi,
p_ROC_CWD_lam_dot_richmondi,
p_TOD_DAY_lam_dot_richmondi,
p_ROC_TOD_lam_dot_richmondi,
p_CWD_TOD_lam_dot_richmondi,
p_CWD_DAY_lam_dot_richmondi,
p_CWD_LUX_lam_dot_richmondi,
p_ROC_LUX_DAY_CWD_TOD_LLD_lam_dot_richmondi)
P.richmondi_det_lam_list_names=c(
"p(.)lam(.)",
"p(ROC)lam(.)",
"p(LUX)lam(.)",
"p(DAY)lam(.)",
"p(TOD)lam(.)",
"p(LLD)lam(.)",
"p(CWD)lam(.)",
"p(ROC + CWD)lam(.)",
"p(TOD + DAY)lam(.)",
"p(ROC + TOD)lam(.)",
"p(CWD + TOD)lam(.)",
"p(CWD + DAY)lam(.)",
"p(CWD + LUX)lam(.)",
"p(ROC + LUX + DAY + CWD + TOD + LLD) lam(.)")
?modavg
modavg(cand.set = P.richmondi_det_lam_list,
modnames = P.richmondi_det_lam_list_names,
parm="TOD", parm.type="detect")
modavg(cand.set = P.richmondi_det_lam_list,
modnames = P.richmondi_det_lam_list_names,
parm="CWD", parm.type="detect")
modavg(cand.set = P.richmondi_det_lam_list,
modnames = P.richmondi_det_lam_list_names,
parm="ROC", parm.type="detect")
modavg(cand.set = P.richmondi_det_lam_list,
modnames = P.richmondi_det_lam_list_names,
parm="LLD", parm.type="detect")
modavg(cand.set = P.richmondi_det_lam_list,
modnames = P.richmondi_det_lam_list_names,
parm="LUX", parm.type="detect")
modavg(cand.set = P.richmondi_det_lam_list,
modnames = P.richmondi_det_lam_list_names,
parm="DAY", parm.type="detect")
```
# Test three alternative abundance models: Poisson (default), negative binomial (NB), and zero-inflated Poisson (ZIP)
```{r, Test three alternative abundance models: Poisson (default), negative binomial (NB), and zero-inflated Poisson (ZIP)}
p_TOD_lam_ASP_richmondi_Poisson=pcount(~TOD+CWD ~ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_ASP_richmondi_NB=pcount(~TOD+CWD ~ASP, data=P_richmondi_pcount, mixture="NB", K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_ASP_richmondi_ZIP=pcount(~TOD+CWD ~ASP, data=P_richmondi_pcount, mixture="ZIP", K=K_rich, control=list(trace=T, REPORT=100))
cbind(AIC.P<-p_TOD_lam_ASP_richmondi_Poisson@AIC, AIC.NB<-p_TOD_lam_ASP_richmondi_NB@AIC, AIC.ZIP<-p_TOD_lam_ASP_richmondi_ZIP@AIC)
# Poisson takes the cake
```
# First round: Construct single site covar N-mixture models with TOD detection covars
```{r, Construct N-mixture models with TOD detection covars, results="hide"}
p_dot_lam_dot_richmondi =pcount(~1 ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_CAN_richmondi =pcount(~1 ~CAN, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_ELV_richmondi =pcount(~1 ~ELV, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_ASP_richmondi =pcount(~1 ~ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_MST_richmondi =pcount(~1 ~MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_VEG_richmondi =pcount(~1 ~VEG, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_TPI_richmondi =pcount(~1 ~TPI, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_dot_lam_RAD_richmondi =pcount(~1 ~RAD, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_dot_richmondi =pcount(~TOD ~1, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_CAN_richmondi =pcount(~TOD ~CAN, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_ASP_richmondi =pcount(~TOD ~ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_ELV_richmondi =pcount(~TOD ~ELV, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_MST_richmondi =pcount(~TOD ~MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_VEG_richmondi =pcount(~TOD ~VEG, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_TPI_richmondi =pcount(~TOD ~TPI, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_RAD_richmondi =pcount(~TOD ~RAD, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_CAN_MST_ELV_ASP_VEG_TPI_RAD_richmondi=pcount(~TOD ~CAN+MST+ELV+ASP+VEG+TPI+RAD, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
```
```{r, AIC Model Selection for P. richmondi N-mixture models}
richmondi.pcount.list<-fitList(
"p(.)lam(.)" =p_dot_lam_dot_richmondi,
"p(.)lam(CAN)" =p_dot_lam_CAN_richmondi,
"p(.)lam(ELV)" =p_dot_lam_ELV_richmondi,
"p(.)lam(ASP)" =p_dot_lam_ASP_richmondi,
"p(.)lam(MST)" =p_dot_lam_MST_richmondi,
"p(.)lam(VEG)" =p_dot_lam_VEG_richmondi,
"p(.)lam(TPI)" =p_dot_lam_TPI_richmondi,
"p(.)lam(RAD)" =p_dot_lam_RAD_richmondi,
"p(TOD)lam(dot)" =p_TOD_lam_dot_richmondi,
"p(TOD)lam(CAN)" =p_TOD_lam_CAN_richmondi,
"p(TOD)lam(ASP)" =p_TOD_lam_ASP_richmondi,
"p(TOD)lam(ELV)" =p_TOD_lam_ELV_richmondi,
"p(TOD)lam(MST)" =p_TOD_lam_MST_richmondi,
"p(TOD)lam(VEG)" =p_TOD_lam_VEG_richmondi,
"p(TOD)lam(TPI)" =p_TOD_lam_TPI_richmondi,
"p(TOD)lam(RAD)" =p_TOD_lam_RAD_richmondi,
"p(TOD)lam(CAN+MST+ELV+ASP+VEG+RAD+TPI)" =p_TOD_lam_CAN_MST_ELV_ASP_VEG_TPI_RAD_richmondi)
modSel(richmondi.pcount.list)
```
# beta estimates P.richmondi lambda
```{r, beta estimates P.richmondi lambda}
P.richmondi.lam.list=list(
p_dot_lam_dot_richmondi,
p_dot_lam_CAN_richmondi,
p_dot_lam_ELV_richmondi,
p_dot_lam_ASP_richmondi,
p_dot_lam_MST_richmondi,
p_dot_lam_VEG_richmondi,
p_dot_lam_TPI_richmondi,
p_dot_lam_RAD_richmondi,
p_TOD_lam_dot_richmondi,
p_TOD_lam_CAN_richmondi,
p_TOD_lam_ASP_richmondi,
p_TOD_lam_ELV_richmondi,
p_TOD_lam_MST_richmondi,
p_TOD_lam_VEG_richmondi,
p_TOD_lam_TPI_richmondi,
p_TOD_lam_RAD_richmondi,
p_TOD_lam_CAN_MST_ELV_ASP_VEG_TPI_RAD_richmondi)
P.richmondi.lam.list.name=c(
"p(.)lam(.)" ,
"p(.)lam(CAN)" ,
"p(.)lam(ELV)" ,
"p(.)lam(ASP)" ,
"p(.)lam(MST)" ,
"p(.)lam(VEG)" ,
"p(.)lam(TPI)" ,
"p(.)lam(RAD)" ,
"p(TOD)lam(dot)" ,
"p(TOD)lam(CAN)" ,
"p(TOD)lam(ASP)" ,
"p(TOD)lam(ELV)" ,
"p(TOD)lam(MST)" ,
"p(TOD)lam(VEG)" ,
"p(TOD)lam(TPI)" ,
"p(TOD)lam(RAD)" ,
"p(TOD)lam(CAN+MST+ELV+ASP+VEG+RAD+TPI)" )
aictab(cand.set=P.richmondi.lam.list, modnames = P.richmondi.lam.list.name, sort=T)
MST.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="MST", parm.type="lambda")
CAN.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="CAN", parm.type="lambda")
ASP.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="ASP", parm.type="lambda")
ELV.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="ELV", parm.type="lambda")
VEG.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="VEG", parm.type="lambda")
TPI.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="TPI", parm.type="lambda")
RAD.beta.ric.pcount<-modavg(cand.set = P.richmondi.lam.list,
modnames = P.richmondi.lam.list.name,
parm="RAD", parm.type="lambda")
MST.beta.ric.pcount
CAN.beta.ric.pcount
ASP.beta.ric.pcount
ELV.beta.ric.pcount
VEG.beta.ric.pcount
TPI.beta.ric.pcount
RAD.beta.ric.pcount
```
# 2nd round of P. richmondi N-mixture models: include a two parameter model with MST and ASP
```{r, Model Selection for P. richmondi N-mixture models}
p_TOD_lam_ASP_MST_richmondi =pcount(~TOD ~ASP+MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_ELV_MST_richmondi =pcount(~TOD ~ELV+MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_CAN_MST_richmondi =pcount(~TOD ~CAN+MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_VEG_MST_richmondi =pcount(~TOD ~VEG+MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_TPI_MST_richmondi =pcount(~TOD ~TPI+MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_RAD_MST_richmondi =pcount(~TOD ~RAD+MST, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_CAN_ASP_richmondi =pcount(~TOD ~CAN+ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_ELV_ASP_richmondi =pcount(~TOD ~ELV+ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_VEG_ASP_richmondi =pcount(~TOD ~VEG+ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_TPI_ASP_richmondi =pcount(~TOD ~TPI+ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
p_TOD_lam_RAD_ASP_richmondi =pcount(~TOD ~RAD+ASP, data=P_richmondi_pcount, K=K_rich, control=list(trace=T, REPORT=100))
richmondi.pcount.list.2<-fitList(
"p(.)lam(.)" =p_dot_lam_dot_richmondi,
"p(.)lam(CAN)" =p_dot_lam_CAN_richmondi,
"p(.)lam(ELV)" =p_dot_lam_ELV_richmondi,
"p(.)lam(ASP)" =p_dot_lam_ASP_richmondi,
"p(.)lam(MST)" =p_dot_lam_MST_richmondi,
"p(.)lam(VEG)" =p_dot_lam_VEG_richmondi,
"p(.)lam(TPI)" =p_dot_lam_TPI_richmondi,
"p(.)lam(RAD)" =p_dot_lam_RAD_richmondi,
"p(TOD)lam(dot)" =p_TOD_lam_dot_richmondi,
"p(TOD)lam(CAN)" =p_TOD_lam_CAN_richmondi,
"p(TOD)lam(ASP)" =p_TOD_lam_ASP_richmondi,
"p(TOD)lam(ELV)" =p_TOD_lam_ELV_richmondi,
"p(TOD)lam(MST)" =p_TOD_lam_MST_richmondi,
"p(TOD)lam(VEG)" =p_TOD_lam_VEG_richmondi,
"p(TOD)lam(TPI)" =p_TOD_lam_TPI_richmondi,
"p(TOD)lam(RAD)" =p_TOD_lam_RAD_richmondi,
"p(TOD)lam(ASP+MST)" =p_TOD_lam_ASP_MST_richmondi,
"p(TOD)lam(ELV+MST)" =p_TOD_lam_ELV_MST_richmondi,
"p(TOD)lam(CAN+MST)" =p_TOD_lam_CAN_MST_richmondi,
"p(TOD)lam(VEG+MST)" =p_TOD_lam_VEG_MST_richmondi,
"p(TOD)lam(TPI+MST)" =p_TOD_lam_TPI_MST_richmondi,
"p(TOD)lam(RAD+MST)" =p_TOD_lam_RAD_MST_richmondi,
"p(TOD)lam(CAN+ASP)" =p_TOD_lam_CAN_ASP_richmondi,
"p(TOD)lam(ELV+ASP)" =p_TOD_lam_ELV_ASP_richmondi,
"p(TOD)lam(VEG+ASP)" =p_TOD_lam_VEG_ASP_richmondi,
"p(TOD)lam(TPI+ASP)" =p_TOD_lam_TPI_ASP_richmondi,
"p(TOD)lam(RAD+ASP)" =p_TOD_lam_RAD_ASP_richmondi,
"p(TOD)lam(CAN+MST+ELV+ASP+VEG+RAD+TPI)" =p_TOD_lam_CAN_MST_ELV_ASP_VEG_TPI_RAD_richmondi)
modSel(richmondi.pcount.list.2)
```
# 2nd round of beta estimates for P. richmondi
```{r, beta estimates of P.richmondi lambda}
P.richmondi.lam.list.2=list(
p_dot_lam_dot_richmondi,
p_dot_lam_CAN_richmondi,
p_dot_lam_ELV_richmondi,
p_dot_lam_ASP_richmondi,
p_dot_lam_MST_richmondi,
p_dot_lam_VEG_richmondi,
p_dot_lam_TPI_richmondi,
p_dot_lam_RAD_richmondi,
p_TOD_lam_dot_richmondi,
p_TOD_lam_CAN_richmondi,
p_TOD_lam_ASP_richmondi,
p_TOD_lam_ELV_richmondi,
p_TOD_lam_MST_richmondi,
p_TOD_lam_VEG_richmondi,
p_TOD_lam_TPI_richmondi,
p_TOD_lam_RAD_richmondi,
p_TOD_lam_ASP_MST_richmondi,
p_TOD_lam_ELV_MST_richmondi,
p_TOD_lam_CAN_MST_richmondi,
p_TOD_lam_VEG_MST_richmondi,
p_TOD_lam_TPI_MST_richmondi,
p_TOD_lam_RAD_MST_richmondi,
p_TOD_lam_CAN_ASP_richmondi,
p_TOD_lam_ELV_ASP_richmondi,
p_TOD_lam_VEG_ASP_richmondi,
p_TOD_lam_TPI_ASP_richmondi,
p_TOD_lam_RAD_ASP_richmondi,
p_TOD_lam_CAN_MST_ELV_ASP_VEG_TPI_RAD_richmondi)
P.richmondi.lam.list.name.2=c(
"p(.)lam(.)" ,
"p(.)lam(CAN)" ,
"p(.)lam(ELV)" ,
"p(.)lam(ASP)" ,
"p(.)lam(MST)" ,
"p(.)lam(VEG)" ,
"p(.)lam(TPI)" ,
"p(.)lam(RAD)" ,
"p(TOD)lam(dot)" ,
"p(TOD)lam(CAN)" ,
"p(TOD)lam(ASP)" ,
"p(TOD)lam(ELV)" ,
"p(TOD)lam(MST)" ,
"p(TOD)lam(VEG)" ,
"p(TOD)lam(TPI)" ,
"p(TOD)lam(RAD)" ,
"p(TOD)lam(ASP+MST)" ,
"p(TOD)lam(ELV+MST)" ,
"p(TOD)lam(CAN+MST)" ,
"p(TOD)lam(VEG+MST)" ,
"p(TOD)lam(TPI+MST)" ,
"p(TOD)lam(RAD+MST)" ,
"p(TOD)lam(CAN+ASP)" ,
"p(TOD)lam(ELV+ASP)" ,
"p(TOD)lam(VEG+ASP)" ,
"p(TOD)lam(TPI+ASP)" ,
"p(TOD)lam(RAD+ASP)" ,
"p(TOD)lam(CAN+MST+ELV+ASP+VEG+RAD+TPI)" )
aictab(cand.set=P.richmondi.lam.list.2, modnames = P.richmondi.lam.list.name.2, sort=T)
MST.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="MST", parm.type="lambda")
CAN.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="CAN", parm.type="lambda")
ASP.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="ASP", parm.type="lambda")
ELV.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="ELV", parm.type="lambda")
VEG.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="VEG", parm.type="lambda")
TPI.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="TPI", parm.type="lambda")
RAD.beta.ric.pcount.2<-modavg(cand.set = P.richmondi.lam.list.2,
modnames = P.richmondi.lam.list.name.2,
parm="RAD", parm.type="lambda")
MST.beta.ric.pcount.2
CAN.beta.ric.pcount.2
ASP.beta.ric.pcount.2
ELV.beta.ric.pcount.2
VEG.beta.ric.pcount.2
TPI.beta.ric.pcount.2
RAD.beta.ric.pcount.2
```
```{r, Simultaneous model averaging and back tranformation of P. richmondi N-mixture models!!}
P.richmondi.lam.modavg=predict(richmondi.pcount.list.2, type="state")
P.richmondi.det.lam.modavg=predict(richmondi.pcount.list.2, type="det")
plogis(coef(richmondi.pcount.list.2))
summary(P.richmondi.lam.modavg)
P.richmondi.lam.modavg
boxplot(P.richmondi.lam.modavg$Predicted)
plot(CAN, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
plot(MST, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
plot(ELV, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
plot(ASP, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
plot(VEG, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
plot(TPI, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
plot(RAD, P.richmondi.lam.modavg$Predicted, ylab="ModAvg P. richmondi Abundance")
```
# Modeling P. richmondi occupancy: beginning with detection probability, results="hide"}
```{r, Modeling P. richmondi OCCUPANCY: begin with detection probability, results="hide"}
p_dot_psi_dot_richmondi_occu= occu(~1 ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_ROC_psi_dot_richmondi_occu= occu(~ROC ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_LUX_psi_dot_richmondi_occu= occu(~LUX ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_DAY_psi_dot_richmondi_occu= occu(~DAY ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_dot_richmondi_occu= occu(~TOD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_LLD_psi_dot_richmondi_occu= occu(~LLD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_CWD_psi_dot_richmondi_occu= occu(~CWD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_ROC_CWD_psi_dot_richmondi_occu= occu(~ROC+CWD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_DAY_psi_dot_richmondi_occu= occu(~TOD+DAY ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_ROC_TOD_psi_dot_richmondi_occu= occu(~ROC+TOD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_CWD_TOD_psi_dot_richmondi_occu= occu(~CWD+TOD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_CWD_DAY_psi_dot_richmondi_occu= occu(~CWD+DAY ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_CWD_LUX_psi_dot_richmondi_occu= occu(~CWD+LUX ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_ROC_LUX_DAY_CWD_TOD_LLD_psi_dot_richmondi_occu=occu(~CWD+LUX+ROC+TOD+LLD+DAY ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
```
# Create candidate model list for AIC selection of P.richmondi detection (occu)
```{r, Create candidate model list for AIC selection of P.richmondi detection (occu)}
richmondi.det.occu.list=fitList(
"p(.)psi(.)" =p_dot_psi_dot_richmondi_occu,
"p(ROC)psi(.)" =p_ROC_psi_dot_richmondi_occu,
"p(LUX)psi(.)" =p_LUX_psi_dot_richmondi_occu,
"p(DAY)psi(.)" =p_DAY_psi_dot_richmondi_occu,
"p(TOD)psi(.)" =p_TOD_psi_dot_richmondi_occu,
"p(LLD)psi(.)" =p_LLD_psi_dot_richmondi_occu,
"p(CWD)psi(.)" =p_CWD_psi_dot_richmondi_occu,
"p(ROC+CWD)psi(.)" =p_ROC_CWD_psi_dot_richmondi_occu,
"p(TOD+DAY)psi(.)" =p_TOD_DAY_psi_dot_richmondi_occu,
"p(ROC+TOD)psi(.)" =p_ROC_TOD_psi_dot_richmondi_occu,
"p(CWD+TOD)psi(.)" =p_CWD_TOD_psi_dot_richmondi_occu,
"p(CWD+DAY)psi(.)" =p_CWD_DAY_psi_dot_richmondi_occu,
"p(CWD+LUX)psi(.)" =p_CWD_LUX_psi_dot_richmondi_occu,
"p(ROC+LUX+DAY+CWD+TOD+LLD) psi(.)"=p_ROC_LUX_DAY_CWD_TOD_LLD_psi_dot_richmondi_occu)
modSel(richmondi.det.occu.list)
```
# Beta estimates of detection parameters for P. richmondi (occu)
```{r, beta estimates of detection parameters for P. richmondi (occu)}
p.richmondi_occ_det_model_list=list(
p_dot_psi_dot_richmondi_occu,
p_ROC_psi_dot_richmondi_occu,
p_LUX_psi_dot_richmondi_occu,
p_DAY_psi_dot_richmondi_occu,
p_TOD_psi_dot_richmondi_occu,
p_LLD_psi_dot_richmondi_occu,
p_CWD_psi_dot_richmondi_occu,
p_ROC_CWD_psi_dot_richmondi_occu,
p_TOD_DAY_psi_dot_richmondi_occu,
p_ROC_TOD_psi_dot_richmondi_occu,
p_CWD_TOD_psi_dot_richmondi_occu,
p_CWD_DAY_psi_dot_richmondi_occu,
p_CWD_LUX_psi_dot_richmondi_occu,
p_ROC_LUX_DAY_CWD_TOD_LLD_psi_dot_richmondi_occu)
p.richmondi_occ_det_model_names=c(
"p(.)psi(.)" ,
"p(ROC)psi(.)" ,
"p(LUX)psi(.)" ,
"p(DAY)psi(.)" ,
"p(TOD)psi(.)" ,
"p(LLD)psi(.)" ,
"p(CWD)psi(.)" ,
"p(ROC+CWD)psi(.)" ,
"p(TOD+DAY)psi(.)" ,
"p(ROC+TOD)psi(.)" ,
"p(CWD+TOD)psi(.)" ,
"p(CWD+DAY)psi(.)" ,
"p(CWD+LUX)psi(.)" ,
"p(ROC+LUX+DAY+CWD+TOD+LLD) psi(.)")
beta.cwd.ric.occu<-modavg(cand.set = p.richmondi_occ_det_model_list,
modnames = p.richmondi_occ_det_model_names,
parm="CWD", parm.type="detect")
beta.roc.ric.occu<-modavg(cand.set = p.richmondi_occ_det_model_list,
modnames = p.richmondi_occ_det_model_names,
parm="ROC", parm.type="detect")
beta.day.ric.occu<-modavg(cand.set = p.richmondi_occ_det_model_list,
modnames = p.richmondi_occ_det_model_names,
parm="DAY", parm.type="detect")
beta.tod.ric.occu<-modavg(cand.set = p.richmondi_occ_det_model_list,
modnames = p.richmondi_occ_det_model_names,
parm="TOD", parm.type="detect")
beta.lld.ric.occu<-modavg(cand.set = p.richmondi_occ_det_model_list,
modnames = p.richmondi_occ_det_model_names,
parm="LLD", parm.type="detect")
beta.lux.ric.occu<-modavg(cand.set = p.richmondi_occ_det_model_list,
modnames = p.richmondi_occ_det_model_names,
parm="LUX", parm.type="detect")
beta.cwd.ric.occu
beta.roc.ric.occu
beta.day.ric.occu
beta.tod.ric.occu
beta.lld.ric.occu
beta.lux.ric.occu
```
# Beta estimates for P. ric. detetion (occu)
```{r, Beta estimates for P. ric. detetion (occu)}
# CWD
# Model-averaged estimate: 0.17
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.24, 0.59
# ROC
# Model-averaged estimate: 0.42
# Unconditional SE: 0.34
# 95% Unconditional confidence interval: -0.23, 1.08
# DAY
# Model-averaged estimate: -0.05
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.47, 0.37
# TOD*
# Model-averaged estimate: -0.42
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.83, -0.01
# LLD
# Model-averaged estimate: -0.09
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.5, 0.33
# LUX
# Model-averaged estimate: -0.09
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.5, 0.31
# TOD was the best detection parameter
# Occupancy models of P. richmondi will only include TOD
```
# modeling P. richmondi occupancy
```{r, Constructing occupancy models for P. richmondi with TOD, results="hide"}
p_dot_psi_dot_richmondi_occu=occu(~1 ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_CAN_richmondi_occu=occu(~1 ~CAN, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_ELV_richmondi_occu=occu(~1 ~ELV, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_ASP_richmondi_occu=occu(~1 ~ASP, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_MST_richmondi_occu=occu(~1 ~MST, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_VEG_richmondi_occu=occu(~1 ~VEG, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_TPI_richmondi_occu=occu(~1 ~TPI, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_dot_psi_RAD_richmondi_occu=occu(~1 ~RAD, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_dot_richmondi_occu=occu(~TOD ~1, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_CAN_richmondi_occu=occu(~TOD ~CAN, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_ASP_richmondi_occu=occu(~TOD ~ASP, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_ELV_richmondi_occu=occu(~TOD ~ELV, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_MST_richmondi_occu=occu(~TOD ~MST, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_VEG_richmondi_occu=occu(~TOD ~VEG, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_TPI_richmondi_occu=occu(~TOD ~TPI, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_RAD_richmondi_occu=occu(~TOD ~RAD, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
p_TOD_psi_CAN_ASP_ELV_MST_VEG_TPI_RAD_richmondi_occu=occu(~TOD ~CAN+ASP+ELV+MST+VEG+TPI+RAD, data=P_richmondi_occu, control=list(trace=T, REPORT=100))
```
# Create candidate model list for AIC selection of P. richmondi occupancy
```{r, Create candidate model list for AIC selection of P. richmondi occupancy}
richmondi.occu.list=fitList(
"p(.)psi(.)" =p_dot_psi_dot_richmondi_occu,
"p(.)psi(CAN)" =p_dot_psi_CAN_richmondi_occu,
"p(.)psi(ELV)" =p_dot_psi_ELV_richmondi_occu,
"p(.)psi(ASP)" =p_dot_psi_ASP_richmondi_occu,
"p(.)psi(MST)" =p_dot_psi_MST_richmondi_occu,
"p(.)psi(VEG)" =p_dot_psi_VEG_richmondi_occu,
"p(.)psi(TPI)" =p_dot_psi_TPI_richmondi_occu,
"p(.)psi(RAD)" =p_dot_psi_RAD_richmondi_occu,
"p(TOD)psi(.)" =p_TOD_psi_dot_richmondi_occu,
"p(TOD)psi(CAN)" =p_TOD_psi_CAN_richmondi_occu,
"p(TOD)psi(ELV)" =p_TOD_psi_ELV_richmondi_occu,
"p(TOD)psi(ASP)" =p_TOD_psi_ASP_richmondi_occu,
"p(TOD)psi(MST)" =p_TOD_psi_MST_richmondi_occu,
"p(TOD)psi(VEG)" =p_TOD_psi_VEG_richmondi_occu,
"p(TOD)psi(TPI)" =p_TOD_psi_TPI_richmondi_occu,
"p(TOD)psi(RAD)" =p_TOD_psi_RAD_richmondi_occu)
modSel(richmondi.occu.list)
```
# Beta estimates of detection parameters for P. richmondi (occupancy)
```{r, beta estimates of detection parameters in occu for P. richmondi}
P.richmondi.occu.list=list(
p_dot_psi_dot_richmondi_occu,
p_dot_psi_CAN_richmondi_occu,
p_dot_psi_ELV_richmondi_occu,
p_dot_psi_ASP_richmondi_occu,
p_dot_psi_MST_richmondi_occu,
p_dot_psi_VEG_richmondi_occu,
p_dot_psi_TPI_richmondi_occu,
p_dot_psi_RAD_richmondi_occu,
p_TOD_psi_dot_richmondi_occu,
p_TOD_psi_CAN_richmondi_occu,
p_TOD_psi_ELV_richmondi_occu,
p_TOD_psi_ASP_richmondi_occu,
p_TOD_psi_MST_richmondi_occu,
p_TOD_psi_VEG_richmondi_occu,
p_TOD_psi_TPI_richmondi_occu,
p_TOD_psi_RAD_richmondi_occu
)
P.richmondi.occu.list.name=c(
"p(.)psi(.)" ,
"p(.)psi(CAN)" ,
"p(.)psi(ELV)" ,
"p(.)psi(ASP)" ,
"p(.)psi(MST)" ,
"p(.)psi(VEG)" ,
"p(.)psi(TPI)" ,
"p(.)psi(RAD)" ,
"p(TOD)psi(.)" ,
"p(TOD)psi(CAN)" ,
"p(TOD)psi(ELV)" ,
"p(TOD)psi(ASP)" ,
"p(TOD)psi(MST)" ,
"p(TOD)psi(VEG)" ,
"p(TOD)psi(TPI)" ,
"p(TOD)psi(RAD)"
)
MST.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="MST", parm.type="psi")
ELV.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="ELV", parm.type="psi")
ASP.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="ASP", parm.type="psi")
CAN.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="CAN", parm.type="psi")
VEG.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="VEG", parm.type="psi")
TPI.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="TPI", parm.type="psi")
RAD.beta.ric.occu<-modavg(cand.set = P.richmondi.occu.list,
modnames = P.richmondi.occu.list.name,
parm="RAD", parm.type="psi")
MST.beta.ric.occu
ELV.beta.ric.occu
ASP.beta.ric.occu
CAN.beta.ric.occu
VEG.beta.ric.occu
TPI.beta.ric.occu
RAD.beta.ric.occu
```
# beta estimates with shrinkage for P. richmondi lambda (occu)
```{r, beta estimates with shrinkage for P. ricmondi lambda (occu)}
# MST
# Model-averaged estimate with shrinkage: 1.22
# Unconditional SE: 1.39
# 95% Unconditional confidence interval: -1.5, 3.94
# CAN
# Model-averaged estimate with shrinkage: -0.14
# Unconditional SE: 0.44
# 95% Unconditional confidence interval: -1, 0.73
# ASP
# Model-averaged estimate with shrinkage: 0.45
# Unconditional SE: 1.09
# 95% Unconditional confidence interval: -1.69, 2.59
# ELV
# Model-averaged estimate with shrinkage: 0
# Unconditional SE: 0.06
# 95% Unconditional confidence interval: -0.11, 0.12
# VEG
# Model-averaged estimate with shrinkage: 0.06
# Unconditional SE: 0.27
# 95% Unconditional confidence interval: -0.46, 0.58
# TPI
# Model-averaged estimate with shrinkage: -0.02
# Unconditional SE: 0.33
# 95% Unconditional confidence interval: -0.68, 0.63
# RAD
# Model-averaged estimate with shrinkage: -0.06
# Unconditional SE: 0.34
# 95% Unconditional confidence interval: -0.72, 0.61
# All models feature Beta values that change sign in their 95% CI
# No parameters merit construction of 2-cov occupancy models
```
# Simultaneous model averaging and back tranformation of P. richmondi Occupancy models!!
```{r, Simultaneous model averaging and back tranformation of P. richmondi Occupancy models!!}
P.richmondi.psi.modavg=predict(richmondi.occu.list, type="state")
P.richmondi.det.occ.modavg=predict(richmondi.occu.list, type="det")
summary(P.richmondi.psi.modavg)
boxplot(P.richmondi.psi.modavg$Predicted)
plot(CAN, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
plot(MST, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
plot(ELV, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
plot(ASP, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
plot(VEG, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
plot(TPI, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
plot(RAD, P.richmondi.psi.modavg$Predicted, ylab="ModAvg P. richmondi Occupancy")
```
## ------------ END: P. richmondi --------------- ##
## ------------ Begin: P. glutinosus ------------ ##
# Begin N-mixture model construction with P. glutinosus detection
```{r, Begin N-mixture model construction with P. glutinosus detection, results="hide"}
p_dot_lam_dot_P_glutinosus_pcount=pcount(~1 ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_ROC_lam_dot_P_glutinosus_pcount=pcount(~ROC ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_LUX_lam_dot_P_glutinosus_pcount=pcount(~LUX ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_DAY_lam_dot_P_glutinosus_pcount=pcount(~DAY ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_TOD_lam_dot_P_glutinosus_pcount=pcount(~TOD ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_LLD_lam_dot_P_glutinosus_pcount=pcount(~LLD ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_CWD_lam_dot_P_glutinosus_pcount=pcount(~CWD ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_ROC_CWD_lam_dot_P_glutinosus_pcount=pcount(~ROC+CWD ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_TOD_DAY_lam_dot_P_glutinosus_pcount=pcount(~TOD+DAY ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_ROC_TOD_lam_dot_P_glutinosus_pcount=pcount(~ROC+TOD ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_CWD_TOD_lam_dot_P_glutinosus_pcount=pcount(~CWD+TOD ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_CWD_DAY_lam_dot_P_glutinosus_pcount=pcount(~CWD+DAY ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_CWD_LUX_lam_dot_P_glutinosus_pcount=pcount(~CWD+LUX ~1, data=P_glutinosus_pcount, K_kent, control=list(trace=T, REPORT=100))
p_ROC_LUX_DAY_CWD_TOD_LLD_lam_dot_P_glutinosus_pcount=pcount(~CWD+LUX+ROC+TOD+LLD+DAY ~1, K_kent, data=P_glutinosus_pcount, control=list(trace=T, REPORT=100))
```
# ranking candidate detection models for P. glutinosus N-mixture models
```{r, ranking candidate detection models for P. glutinosus N-mixture models}
P.glutinosus.det.pcount.list=fitList(
"p(.)lam(.)" =p_dot_lam_dot_P_glutinosus_pcount,
"p(ROC)lam(.)" =p_ROC_lam_dot_P_glutinosus_pcount,
"p(LUX)lam(.)" =p_LUX_lam_dot_P_glutinosus_pcount,
"p(DAY)lam(.)" =p_DAY_lam_dot_P_glutinosus_pcount,
"p(TOD)lam(.)" =p_TOD_lam_dot_P_glutinosus_pcount,
"p(LLD)lam(.)" =p_LLD_lam_dot_P_glutinosus_pcount,
"p(CWD)lam(.)" =p_CWD_lam_dot_P_glutinosus_pcount,
"p(ROC+CWD)lam(.)" =p_ROC_CWD_lam_dot_P_glutinosus_pcount,
"p(TOD+DAY)lam(.)" =p_TOD_DAY_lam_dot_P_glutinosus_pcount,
"p(ROC+TOD)lam(.)" =p_ROC_TOD_lam_dot_P_glutinosus_pcount,
"p(CWD+TOD)lam(.)" =p_CWD_TOD_lam_dot_P_glutinosus_pcount,
"p(CWD+DAY)lam(.)" =p_CWD_DAY_lam_dot_P_glutinosus_pcount,
"p(CWD+LUX)lam(.)" =p_CWD_LUX_lam_dot_P_glutinosus_pcount,
"p(ROC+LUX+DAY+CWD+TOD+LLD) lam(.)"=p_ROC_LUX_DAY_CWD_TOD_LLD_lam_dot_P_glutinosus_pcount)
modSel(P.glutinosus.det.pcount.list)
```
# beta estimation for P. glutinosus detection (Pcount)
```{r, beta estimation for P. glutinosus detection (Pcount)}
p.glutinosus_lam_det_model_list=list(
p_dot_lam_dot_P_glutinosus_pcount,
p_ROC_lam_dot_P_glutinosus_pcount,
p_LUX_lam_dot_P_glutinosus_pcount,
p_DAY_lam_dot_P_glutinosus_pcount,
p_TOD_lam_dot_P_glutinosus_pcount,
p_LLD_lam_dot_P_glutinosus_pcount,
p_CWD_lam_dot_P_glutinosus_pcount,
p_ROC_CWD_lam_dot_P_glutinosus_pcount,
p_TOD_DAY_lam_dot_P_glutinosus_pcount,
p_ROC_TOD_lam_dot_P_glutinosus_pcount,
p_CWD_TOD_lam_dot_P_glutinosus_pcount,
p_CWD_DAY_lam_dot_P_glutinosus_pcount,
p_CWD_LUX_lam_dot_P_glutinosus_pcount,
p_ROC_LUX_DAY_CWD_TOD_LLD_lam_dot_P_glutinosus_pcount)
p.glutinosus_lam_det_model_names=c(
"p(.)lam(.)",
"p(ROC)lam(.)",
"p(LUX)lam(.)",
"p(DAY)lam(.)",
"p(TOD)lam(.)",
"p(LLD)lam(.)",
"p(CWD)lam(.)",
"p(ROC+CWD)lam(.)",
"p(TOD+DAY)lam(.)",
"p(ROC+TOD)lam(.)",
"p(CWD+TOD)lam(.)",
"p(CWD+DAY)lam(.)",
"p(CWD+LUX)lam(.)",
"p(ROC+LUX+DAY+CWD+TOD+LLD)lam(.)")
modavg(cand.set = p.glutinosus_lam_det_model_list,
modnames = p.glutinosus_lam_det_model_names,
parm="CWD", parm.type="detect")
modavg(cand.set = p.glutinosus_lam_det_model_list,
modnames = p.glutinosus_lam_det_model_names,
parm="ROC", parm.type="detect")
modavg(cand.set = p.glutinosus_lam_det_model_list,
modnames = p.glutinosus_lam_det_model_names,
parm="DAY", parm.type="detect")
modavg(cand.set = p.glutinosus_lam_det_model_list,
modnames = p.glutinosus_lam_det_model_names,
parm="TOD", parm.type="detect")
modavg(cand.set = p.glutinosus_lam_det_model_list,
modnames = p.glutinosus_lam_det_model_names,
parm="LLD", parm.type="detect")
modavg(cand.set = p.glutinosus_lam_det_model_list,
modnames = p.glutinosus_lam_det_model_names,
parm="LUX", parm.type="detect")
```
# P. glutinosus detection beta estimates (Pcount)
```{r, P. glutinosus detection betas (Pcount)}
# LUX
# Model-averaged estimate: -0.09
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.5, 0.31
# LLD
# Model-averaged estimate: -0.09
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.5, 0.33
# TOD*
# Model-averaged estimate: -0.42
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.83, -0.01
# DAY
# Model-averaged estimate: -0.05
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.47, 0.37
# ROC
# Model-averaged estimate: 0.42
# Unconditional SE: 0.34
# 95% Unconditional confidence interval: -0.23, 1.08
# CWD
# Model-averaged estimate: 0.17
# Unconditional SE: 0.21
# 95% Unconditional confidence interval: -0.24, 0.59
# TOD was the only detection covariate with a 95% CI that doesn't span "0", therefore, abundance models will only feature time of day
```