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I've recently conducted a camera trapping study with the aim of assessing the utility of camera traps for detecting a target species and understanding the factors that influence their detection. However, I'm having trouble trying to develop a model formula. The 11 cameras remained at the same locations over successive deployments (11-16 days in length) within my site (temporal pseudoreplication) and were not spaced far enough apart to truly be considered independent (spatial pseudoreplciation). Therefore I thought a mixed effects model with binomial errors would be the best approach.

My data:

Response variable: Binary. Presence or absence of the species at each camera per time block

Explanatory variables:

  • Camera ID
  • Deployment number- 5 levels. Cameras were baited at the start of each new deployment
  • Days since deployment- days numbered from 1-16
  • Date- Total number of trapping days, 1-66.
  • Time Block- Each day divided into 4 time blocks
  • Moon phase- Prevailing moon phase on each day of the study, categorical, 4 levels
  • Rainfall- average rainfall (mm) measured per day

My thought was to treat camera ID as a random effect, which would account for pseudoreplication in space. But how should I account for time? Especially since it is measured at several different levels (deployments, days, days within deployments, and Time blocks within days). Also how to account for the fact that Moon Phase and Rainfall were measured on a daily basis.

Any and all suggestions would be greatly appreciated

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