Introduction to Occupancy Models Key to in-class exercise are in blue
Jan 8, 2016 AEC 501 Nathan J. Hostetter Occupancy Abundance often most interesting variable whenanalyzing 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 site OR The species was not detected by the investigator Occupancy studies Introduced by MacKenzie et al and Tyre et al Allows for collection of data that is less intensive thanthose based on abundance estimation Use a designed survey method like we discussedbefore simple random, stratified random, systematic,or double Multiple site visits are required to estimate detectionand probability of occurrence Why occupancy? Data to estimate abundance can be difficult tocollect, require more time and effort, might be morelimited 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 ofinterest Model parameters Replication allows us to separate state andobservation 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 successionalhabitat 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 S3 1 2 3 41 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 orexplained by covariates Probability of occupancy is constant across sites or explained by covariates Enough talk, Lets work through the blue grosbeak example Introduction to R Basics and Occupancy modeling Intro to R: Submitting commands
Commands can be entered one at a time 2+2 [1] 4 2^4 [1] 16 The R environment Script file (File|New script) R Console 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+2 a < #create the object a a #returns object a A #Nope, case sensitive b