[R-sig-ME] How to include multiple temporal processes in one model?

[Thierry Onkelinx]

Dear Adriaan,

You could get some inspiration from our analysis on the breeding bird
survey data. The report is in Dutch. Your name increases my posterior
belief that you understand Dutch ;-)

a/b/e) I expect a strong correlation between time of day and direction. I'd
only keep the direction. Time of day probably won't provide that much more
information and requires a more complex model.
c) We tried to avoid structural zeros as much as possible by defining a
relevant subset of location and within the season. E.g. if we hardly
ever find a species in forests, then exclude the forested sites for that
species. Or ignore the first period of the season when the species is known
to arrive late in the season.
d) This shouldn't be a big issue. It will affect the uncertainty on the
year estimates.

We modeled the year effect as a first order random walk per stratum. In
your case I'd consider a first order random walk along the year and a
second order random walk along the day of year.

Onkelinx, T. *et al.* (2021). Trends op basis van de Algemene
Broedvogelmonitoring Vlaanderen (ABV). Rapporten van het Instituut voor
Natuur- en Bosonderzoek 2021 (14). Instituut voor Natuur- en Bosonderzoek,
Brussel. DOI: <https://inbo.github.io/abv-rapport/2020/#>
https://doi.org/10.21436/inbor.34162521

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op wo 12 jan. 2022 om 11:31 schreef Adriaan de Jong <Adriaan.de.Jong using slu.se
>:

> Hi,
>
> Present:
> 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).
>
> Objectives:
> 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).
>
> Problems/limitations:
> 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.
> Cheers,
> Adjan
>
> Adriaan "Adjan" de Jong
> Associate professor
> Dept of Wildlife, Fish, and Environmental Studies
> Swedish University of Agricultural Sciences
>
> Data structure (fake numbers)
> YearMonthDayHourMinuteDirectionCount
> 199742591505
> 1997514153510
> 1997515745016
> .
> .
> 20218281000
>
> 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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