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Pollinator Occupancy Modeling

Pollinator Occupancy Modeling. Fabian Ochoa. Occupancy modeling. Detection => occupancy Non-detection != absence Still had two tasks: Pollinators & Interactions. Delegation. Provided code was for interactions not Pollinators. Found a way to fit for Pollinator Occupancy Modeling .

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Pollinator Occupancy Modeling

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  1. Pollinator Occupancy Modeling Fabian Ochoa

  2. Occupancy modeling • Detection => occupancy • Non-detection != absence • Still had two tasks: Pollinators & Interactions.

  3. Delegation • Provided code was for interactions not Pollinators. • Found a way to fit for Pollinator Occupancy Modeling. • Tailor provided code to pollinators • Debug code step by step • Brainstorm covariates

  4. Calculating prevalence • Pollinator prevalence per plot per meadow. • Average of detection history vector

  5. Narrowing to 20 • Previous calculation ordered • Change nPolls to be worked with throughout code

  6. None, light, breezy clouded, shaded, sunny

  7. Numbers represent average of the calculations. Numbers are calculated using the median between covariates: Shaded light, and light wind. Reconsider. Akaike information criterion

  8. Akaike information criterion • Measure of the relative quality of a statistical model. • Helps with model selection • Goodness of fit –vs- complexity (trade off)

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