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I have a dataset of marine debris items (number of items standardized per effort: Items/(number of volunteersHoursLenght * Hours * Length)) taken from 2 main locations (WA and Queensland) in Australia (8 Sub Sites in total: 4 in WA and 4 in Queensland) at irregular sampling intervals over a period 15 years.

I have a dataset of marine debris items (number of items standardized per effort: Items/(number of volunteersHoursLenght)) taken from 2 main locations (WA and Queensland) in Australia (8 Sub Sites in total: 4 in WA and 4 in Queensland) at irregular sampling intervals over a period 15 years.

I have a dataset of marine debris items (number of items standardized per effort: Items/(number of volunteers * Hours * Length)) taken from 2 main locations (WA and Queensland) in Australia (8 Sub Sites in total: 4 in WA and 4 in Queensland) at irregular sampling intervals over a period 15 years.

4 clean-up the code block
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m4<m4 <- gamm(Ieffort ~ s(DaysIa)+MARPOL++ MARPOL + s(nMonth, bs = "ps", k = 12), random=list
           random = list(Site=~1Site = ~1,Location=~1 Location = ~1), data = d)
m4<- gamm(Ieffort ~ s(DaysIa)+MARPOL+ s(nMonth, bs = "ps", k = 12), random=list(Site=~1,Location=~1),data = d)
m4 <- gamm(Ieffort ~ s(DaysIa)+ MARPOL + s(nMonth, bs = "ps", k = 12), 
           random = list(Site = ~1, Location = ~1), data = d)
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How can I handle the fact that the measurements were not taken at a regular interval, the seasonality and the possible spatial correlation? Am I on the right track?

How can I handle the fact that the measurements were not taken at a regular interval, the seasonality and the possible spatial correlation? Am I on the right track?

2 improved formatting
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