Introduction to Occupancy Models
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Transcript of Introduction to Occupancy Models
Occupancy
• Abundance often most interesting variable when analyzing a population
• Occupancy – probability that a site is occupied• Probability abundance is >0
Detection/non‐detection data
• Presence data rise from a two part process • The species occurs in the region of interest AND• The species is discovered by an investigator
• What do absence data tell us? • The species does not occur at that particular siteOR• The species was not detected by the investigator
Occupancy studies
• Introduced by MacKenzie et al. 2002 and Tyre et al. 2003
• Allows for collection of data that is less intensive than those based on abundance estimation
• Use a designed survey method like we discussed before – simple random, stratified random, systematic, or double
• Multiple site visits are required to estimate detection and probability of occurrence
Why occupancy?• Data to estimate abundance can be difficult to collect, require more time and effort, might be more limited in spatial/temporal scope
• Obtaining presence/absence data is • Usually less intensive • Cheaper • Can cover a larger area or time frame • Might be more practical for certain objectives
Why occupancy?
• Some common reasons and objectives• Extensive monitoring programs• Distribution (e.g., ranges shifts, invasive species, etc.) • Habitat selection• Meta‐population dynamics• Species interactions • Species richness
Occupancy studies
• Key design issues: Replication
• Temporal replication: • repeat visits to sample units
• Spatial replication: • randomly selected ‘sites’ or sample units within area of interest
Model parameters
• Replication allows us to separate state and observation processes
‐probability site i is occupied.
pij ‐probability of detecting the species in site i at time j, given species is present.
Blue grosbeak example
• Associated with shrub and field habitats, medium sized trees, and edges
• Voluntary program to restore high‐quality early successional habitat in Southern Georgia (BQI – bobwhite quail initiative)
• Are grosbeaks more likely to use fields enrolled in BQI program?
Blue grosbeak example
• N = 41 sites (spatial replication)
• K = 3 sample occasions (temporal replication)
• Example data:Site S1 S2 S31 1 1 12 1 1 03 0 0 0… … … …41 0 1 0
• Sites are closed to changes in occupancy state between sampling occasions• Duration between surveys
• The detection process is independent at each site• Distance between sites
• Probability of detection is constant across sites and visits or explained by covariates
• Probability of occupancy is constant across sites or explained by covariates
Model assumptions
Enough talk,
Let’s work through the blue grosbeak example
Introduction to R
Basics andOccupancy modeling
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Intro to R:Submitting commands
Commands can be entered one at a time2+2[1] 4
2^4[1] 16
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The R environment
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• Script file (File|New script)• Text file• Save for later use• Submit command by highlighting command at pressing “Crtl R”
• R Console•Where commands are executed
R console: Interactive calculations
#Try the following in the script file:2+2a <‐ 2 + 2 #create the object aa #returns object aA #Nope, case sensitive
b<‐2*3ba+b#Use the +, ‐, *, /, and ^ symbols# Use “#” to enter comments
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Built in functions
x1 <‐ c(1,3,5,7) #vectorx1mean(x1)[1] 4sd(x1)[1] 2.581989
#Help files?mean
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Loading and storing data setsComma separated variable (CSV)
• Create a CSV file in excel by clicking “save as” and scrolling to “.csv”. CSV files can be opened in excel, but also in any other text editor.
• Say “C:\Documents\data.csv” is an .csv file. To load a csv file:dat <‐ read.csv(“C:\\Documents\\data.csv",header=TRUE)dat
• ?read.csv #for further help
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Saving work
• Save your current session in an R workspace assave.image(“C:\\Documents\\whatever.RData")
• Load a previously saved workspaceFile|Load workspace
• Save script file• Click on script file• File|Save
Check out Brian Reich’s intro to R athttp://www4.stat.ncsu.edu/~reich/ST590/code/Data
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Intro to Occupancy analysis in R
Blue grosbeak example• Associated with shrub and field habitats, medium sized trees, and edges
• Voluntary program to restore high‐quality early successional habitat in Southern Georgia (BQI – bobwhite quail initiative)
• Are grosbeaks more likely to use fields enrolled in BQI program?
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Intro to Occupancy analysis in R
Blue grosbeak example• 41 fields were surveyed • Each field visited on 3 occasions during the 2001 breeding season
• A 500 m transect was surveyed on each field• Data on detection/non‐detection
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Load data
blgr<‐ read.csv("www.cals.ncsu.edu/course/zo501/blgr.csv")head(blgr) #first 5 rows
#y.1, y.2, y.3 are detection/non‐detection surveys
summary(blgr)dim(blgr) #dimensions of the data (how many sites?)colSums(blgr) #sums the columns
#how many fields were enrolled in bqi?#how many fields had blgr detections in during first survey?#what is the naïve occupancy if only the first survey was conducted?
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Covariates
• Site level covariates• Data that is site specific but does not change with repeated visits• e.g., forest cover, percent urban, tree height, on/off road, etc.
• Observation level covariates• Data that is collected specific to the sample occasion and site• e.g., time of day, day of year, wind, etc.
What type of covariate is bqi?
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Occupancy analysis – Unmarked
• Unmarked • R package• Fits models of animal abundance and occurrence• https://cran.r‐project.org/web/packages/unmarked/unmarked.pdf
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Install Unmarked
install.packages("unmarked") #Only required first time to installlibrary(unmarked) #loads package, required each time
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Format data for occupancy analysis in unmarked
ydat <‐ blgr[,1:3] #select columns 1 through 3, detection databqi <‐ blgr[,4] #select column 4, bqi enrollment
#use built in function to format dataumf <‐ unmarkedFrameOccu(y=ydat, #Observation data must be named ‘y’
siteCovs=data.frame(bqi=bqi)) #name site covariate bqi
umf
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Occupancy in unmarked #run occupancy model with no covariates# occu(~detection ~occupancy)fm1 <‐ occu(~ 1 ~ 1, umf )
fm1 #look at the output
#Get the estimates for detection backTransform(fm1['det'])
#Get the estimates for occupancy#remember, occupancy is our ‘state variable’backTransform(fm1['state'])#higher or lower than naïve occupancy? Why?
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Occupancy in unmarked ‐ Covariates #effect of bqi# occu(~detection ~occupancy)fm2 <‐ occu(~ 1 ~ bqi, umf )
fm2 #look at the output#interpret bqi parameter
#Get the estimates for detection backTransform(fm2['det'])
#Get the estimates for occupancybackTransform(fm2['state'])#Nope, a bit more complicated with covariates#?backTransform for options
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Occupancy in unmarked – Model comparison
#Compare model support using AICfitlist<‐fitList(fm1, fm2)modSel(fitlist)
Model nPars AIC delta AICwt cumltvWtfm1 2 172.19 0.00 0.61 0.61fm2 3 173.12 0.93 0.39 1.00
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Summary• Occupancy (presence/absence)
• Usually less intensive to collect• Often less expensive• Can cover a larger area or time frame• Several important fields in ecology focus on occupancy• Might be more practical for monitoring
• True census is often (always) impossible• Must account for detection probability
• Requires clear objectives• Quantity to be estimated• Temporal and spatial scope• Precision• Practical constraints
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EXTRA – Format observation covariates in unmarked
# observation covariates are recorded at each site during each survey
# (e.g., wind, temperature, time of day , date, etc..)
# observation covariate data will be a matrix with the same dimensions as the observation data (e.g., rows = number of sites, columns = number of visits)
#use built in function to format data
#assume date was recorded for each survey and entered as date.1, date.2, date.3
#these data are not available, but this shows how to write the script
umf <‐ unmarkedFrameOccu(y=ydat, #Observation data must be named ‘y’
siteCovs=data.frame(bqi=bqi), #name site covariate bqi
obsCovs=list(date=blgr[,c("date.1", "date.2", "date.3")])) #name date covariate date
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