1 / 10

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

declan
Download Presentation

Occupancy Modeling: Interactions

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related