[R] Time series (trend over time) for irregular sampling dates and multiple sites

Catarina Serra Gonçalves c@t@r|n@@g @end|ng |rom gm@||@com
Tue Apr 30 16:57:43 CEST 2019

I have a dataset of marine debris items (number of items standardized per
effort: Items/(number of volunteers*Hours*Lenght)) 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 want to test if there is a change over the years on the amount of debris
in these locations and more specifically a change after the implementation
of a mitigation strategy (in 2013).
Here’s the head of the data:[image: enter image description here]
<https://i.stack.imgur.com/VNIpb.png>Description of each one of the
varables in the dataframe:

*eventid *= each sampling (clean-up) event Location = Queensland and New
South Wales Sites = all the 9 sampling beaches

*Date *= specific dates for the clean-up events (day-month-year)

*Date1 *= specific dates for the clean-up events (day-month-year) on the
POSICXT format Year= Year of sampling event (2004 to 2018)

*Month*= Month of the sampling event (jan to dec)

*nMonth*= a number was determined to the respective month of the sampling
event (1 to 12)

*Day*= Day of sampling (1 to 31) Days = Days since the first date of clean
up = just another way of using the dates

*MARPOL *= before and after implementation (factor with 2 levels)

*DaysC *= days between sampling events for the same sites = number of days
since the previous clean-up event

*DaysI *= Days since intervention, all the dates before implementation are
zero, and after we count the number of days since the implementation date
(1 jan 2013)

*DaysIa*= same as DayI but instead of zero for before the intervention we
have negative values (days)

*Items *= number of fishing and shipping items counted in each clean-up

*Hours *= hours spent by all volunteers together at each clean up event

*Lenght *= Lenght of beach sampled by all volunteers together at each clean
up event volunteers = all volunteers at each clean up event

*HoursVolunteer *= hours spent bt each volunteer at each clean up event

*Ieffort *= the items standarized by the effort (hours, volunteers and

*GrossWeight & **GrossTotal are not relevant *

My data has a few problems: (1) I think I will need to fix the effects of
seasonal variation (Monthly) and (2) of possible spatial correlation
(probability of finding an item is higher after finding one since they can
come from the same ship). (3) How do I handle the fact that the
measurements were not taken at a regular interval?

I was trying to use GAMs to analyse the data and see the trends over time.
The model I came across is the following:

m4<- gamm(Ieffort ~ s(DaysIa)+MARPOL+ s(nMonth, bs = "ps", k = 12),
random=list(Site=~1,Location=~1),data = d)

*thank you in advance.*
*Catarina Serra Gonçalves *
PhD candidate

Adrift Lab  <https://adriftlab.org>
University of Tasmania <http://www.utas.edu.au/> | Institute for Marine and
Antarctic Studies  <http://www.imas.utas.edu.au/>
Launceston, TAS | Australia

Personal website <https://catarinasg.wixsite.com/acserra>
<https://catarinasg.wixsite.com/acserra>| E-mail  <acserra using utas.edu.au> |
Twitter <https://twitter.com/CatarinaSerraG>
Research Gate
<https://www.researchgate.net/profile/Catarina_Serra_Goncalves> | Google
Scholar <https://scholar.google.pt/citations?user=8nBrRFwAAAAJ&hl=en>

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