Introduction to Occupancy Models. Jan 8, 2016 AEC 501 Nathan J. Hostetter

Transcription

1 Introduction to Occupancy Models Jan 8, 2016 AEC 501 Nathan J. Hostetter 1

2 Occupancy Abundance often most interesting variable when analyzing a population Occupancy probability that a site is occupied Probability abundance is >0

3 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 site OR The species was not detected by the investigator

4 Occupancy studies Introduced by MacKenzie et al and Tyre et al 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

5 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

6 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

7 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

8 Model parameters Replication allows us to separate state and observation processes probability site i is occupied. p ij probability of detecting the species in site i at time j, given species is present.

9 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?

10 Blue grosbeak example N = 41 sites (spatial replication) K = 3 sample occasions (temporal replication) Example data: Site S1 S2 S

11 Model assumptions 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

12 Enough talk, Let s work through the blue grosbeak example

13 Introduction to R Basics and Occupancy modeling 13

14 Intro to R: Submitting commands Commands can be entered one at a time 2+2 [1] 4 2^4 [1] 16 14

15 The R environment R Console Where commands are executed Script file (File New script) Text file Save for later use Submit command by highlighting command at pressing Crtl R 15

16 R console: Interactive calculations #Try the following in the script file: 2+2 a < #create the object a a #returns object a A #Nope, case sensitive b< 2*3 b a+b #Use the +,, *, /, and ^ symbols # Use # to enter comments 16

17 Built in functions x1 < c(1,3,5,7) x1 mean(x1) [1] 4 sd(x1) [1] #vector #Help files?mean 17

18 Loading and storing data sets Comma 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 18

19 Saving work Save your current session in an R workspace as save.image( C:\\Documents\\whatever.RData") Load a previously saved workspace File Load workspace Save script file Click on script file File Save Check out Brian Reich s intro to R at 19

20 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? 20

21 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 21

22 Load data blgr< read.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? 22

23 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? 23

24 Occupancy analysis Unmarked Unmarked R package Fits models of animal abundance and occurrence 24

25 Install Unmarked install.packages("unmarked") #Only required first time to install library(unmarked) #loads package, required each time 25

26 Format data for occupancy analysis in unmarked ydat < blgr[,1:3] bqi < blgr[,4] #select columns 1 through 3, detection data #select column 4, bqi enrollment #use built in function to format data umf < unmarkedframeoccu(y=ydat, #Observation data must be named y sitecovs=data.frame(bqi=bqi)) #name site covariate bqi umf 26

27 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? 27

28 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 occupancy backtransform(fm2['state']) #Nope, a bit more complicated with covariates #?backtransform for options 28

29 Occupancy in unmarked Model comparison #Compare model support using AIC fitlist< fitlist(fm1, fm2) modsel(fitlist) Model npars AIC delta AICwt cumltvwt fm fm

30 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 30

31 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 31