[R-sig-ME] How to include multiple temporal processes in one model?
Adriaan de Jong
Adr|@@n@de@Jong @end|ng |rom @|u@@e
Wed Jan 12 11:30:36 CET 2022
a 25 year series of count data of individuals of one migratory bird species observed from my driver's seat (2815 counts from the same c. 20 km road transect). The dataset includes the variables: Year, Month, Day, Hour, Minute (5 min precision), (driving)Direction and Count(result) (sample below).
1. Has there been a trend in the numbers over the years?
2. How do the numbers generally vary over the breeding season? (I live in northern Sweden and the breeding/observation season is April-August)
I have no intentions to make predictions for neither future developments (temporal extrapolation) nor other transects (spatial extrapolation).
a. The sampling has been opportunistic (which was a main point because no extra effort was needed) and thus, unevenly spread over the hours of the day with more counts in the morning and late afternoon (most are from commuting to work).
b. The distribution of the timing over the day has varied over the years.
c. The dataset contains a significant proportion (43%) of zero counts, especially during the early and late parts of the breeding season.
d. The number of transect counts has varied over the years (range 66-167, but no clear trend over the years)
e. The direction of driving has an impact on what can be seen (non-flat landscape) and thus, needs to be included as a covariate (random effect?)
(I can provide graphs of frequency distributions if needed)
My question is:
How should I include the three temporal factors (year, time of season and time of day) and driving direction in the logistic models for the two different objectives?
Thanks in advance for your suggestions and comments.
Adriaan "Adjan" de Jong
Dept of Wildlife, Fish, and Environmental Studies
Swedish University of Agricultural Sciences
Data structure (fake numbers)
PS. I understand I have to combine the Mont and Day, and the Hour and Minute variables into two new variables for Time of season and Time of day..
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