Open AccessImproving species occupancy estimation when sampling violates the closure assumption
Author(s) -
Otto Clint R. V.,
Bailey Larissa L.,
Roloff Gary J.
Publication year - 2013
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/j.1600-0587.2013.00137.x
Subject(s) - occupancy , sampling (signal processing) , environmental science , sampling bias , ecology , statistics , wildlife , replicate , biology , mathematics , sample size determination , computer science , filter (signal processing) , computer vision
Site occupancy models that account for imperfect detection of species are increasingly utilized in ecological research and wildlife monitoring. Occupancy models require replicate surveys to estimate detection probability over a time period where the occupancy status at sampled sites is assumed closed. Unlike mark–recapture models, few studies have examined how violations of closure can bias occupancy estimates. Our study design allowed us to differentiate among two processes that violate the closure assumption during a sampling season: 1) repeated destructive sampling events that result in either short‐ or long‐term site avoidance by the target species and 2) sampling occurring over a time period during which non‐random movements of the target species result in variable occupancy status. We used dynamic occupancy models to quantify the potential bias in occupancy estimation associated with these processes for a terrestrial salamander system. Our results provide strong evidence of a systematic decrease in salamander occupancy within a field season. Chronic disturbance due to repeated searches of natural cover objects accelerated natural declines in species occurrence on the forest surface as summer progressed. We also observed a strong but temporary disturbance effect on salamander detection probability associated with repeated sampling within a 24‐h. period. We generalized our findings by conducting a simulation to evaluate how violations of closure can bias occupancy estimates when local extinction occurs within a sampling season. Our simulation study revealed general sensitivity of estimates from single‐season occupancy models to violations of closure, with the strength and direction of bias varying between scenarios. Bias was minimal when extinction proba bility or the number of sample occasions was relatively low. Our research highlights the importance of addressing closure in occupancy studies and we provide multiple solutions, using both design‐ and model‐based frameworks, for minimizing bias associated with non‐random changes in occupancy and repeated sampling disturbances.