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Occupancy Modeling: Interactions

Occupancy Modeling: Interactions. Kyra Stillman. Importance. Determine the actual occupancy Monitor population fluctuations Deduce what affects occupancy rates. Variables. Attempt to find p and ψ , detection and occupancy probabilities Covariates influence occupancy and detection

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Occupancy Modeling: Interactions

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  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

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