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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
importance
Importance
  • Determine the actual occupancy
  • Monitor population fluctuations
  • Deduce what affects occupancy rates
variables
Variables
  • Attempt to find pand ψ, detection and occupancy probabilities
  • Covariates influence occupancy and detection
  • R can calculate the most likely values
questions
Questions
  • Is occupancy modeling a viable option in considering the 2011 interactions?
  • If so, which covariates produce the best model?
determining influential covariates
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
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
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-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
na ve model and graph
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
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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