1 / 10

# 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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about 'Occupancy Modeling: Interactions' - declan

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Occupancy Modeling: Interactions

Kyra Stillman

• Determine the actual occupancy

• Monitor population fluctuations

• Deduce what affects occupancy rates

• Attempt to find pand ψ, detection and occupancy probabilities

• Covariates influence occupancy and detection

• R can calculate the most likely values

• Is occupancy modeling a viable option in considering the 2011 interactions?

• If so, which covariates produce the best model?

• 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

• 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 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

• 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