Occupancy Modeling: Interactions

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# Occupancy Modeling: Interactions - PowerPoint PPT Presentation

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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Presentation Transcript

### Occupancy Modeling: Interactions

Kyra Stillman

Importance
• Determine the actual occupancy
• Monitor population fluctuations
• Deduce what affects occupancy rates
Variables
• Attempt to find pand ψ, detection and occupancy probabilities
• Covariates influence occupancy and detection
• R can calculate the most likely values
Questions
• Is occupancy modeling a viable option in considering the 2011 interactions?
• If so, which covariates produce the best model?
Determining influential covariates
• There are six detection and two occupancy covariates, making for 256 possible combinations
• Narrowed down to three detection and two occupancy
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
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
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
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

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