1. Occupancy Modeling: Interactions Kyra Stillman

2. Importance • Determine the actual occupancy • Monitor population fluctuations • Deduce what affects occupancy rates

3. Variables • Attempt to find pand ψ, detection and occupancy probabilities • Covariates influence occupancy and detection • R can calculate the most likely values

4. Questions • Is occupancy modeling a viable option in considering the 2011 interactions? • If so, which covariates produce the best model?

5. Determining influential covariates • There are six detection and two occupancy covariates, making for 256 possible combinations • Narrowed down to three detection and two occupancy

6. Akaike Information Criterion • AIC measures trade-off between fit and info loss • Good criterion for comparing occupancy models • Lowest comparative AIC means best fitting model

7. Results • Top three models were WindLightPlantPoll/Round, PlantPoll/RoundPoll, and PlantPoll/Round • AIC increased drastically after PlantPoll was removed • Round as occupancy was in top five models and top three models w/out PlantPoll

8. p-Values • p-value is a measure of statistical significance • None of the covariates had p < 0.05 • p < 0.05 on Const models only because lack of extra but confounding data • Cannot use models to state actual occupancy probability • Can compare models to each other to examine covariates

9. Naïve Model and Graph • Naïve model where detection probability is assumed 1 • Const/Const moves occupancy up and detection down as expected • WindLightPlantPoll/Round very high values Red: Naïve Blue: Const/Const Green: WindLightPlantPoll/Round

10. Conclusions • PlantPres significant covariate due to how data was collected • Round significant in more traditional sense • Cannot use models to determine actual occupancy/detection rate • Too little data, especially for specialist interactions