Our study is looking at annual plant Biomass (ANPP) across several sites and years.

Each site has four treatment plots (N, P, NP, C) and within each treatment plot is a blocking factor called patchtype (whether the biomass was collected under a plant or next to a plant). We only want to compare interplant patchtype with other interplant and only want to compare underplant patchtype with other underplant. EX: N plot underplant with p plot underplant.

We have 6 years of data and would like to account for precipitation (as a covariate). We want to know if sites are varying across treatment and across year within treatment.

I was advised to use a correlation structure of AR1 to account for repeated measures of year. However, I am not confident in how to structure the model, whether i need to do anything else to account for time.

  1. I only have one replicate at each site (n=15 sites) when you break down the dataset by year->site->treatment->patchtype.
  2. patchtype is nested in treatment, but i am not sure if i should treat this nested term as a fixed or random factor (i think fixed).
  3. Am i structuring the covariate of rainfall correctly by simply putting it as the first predicting variable in my model? The goal here is to ask, is biomass varying across and/or within treatment across year when you account for variation caused by rainfall (known to control lover 70% of biomass variability)?
  4. Do i also need a nested term for treatment within year to test for how each biomass under each treatment may be varying across years? year/treatment
  5. Should i be using ML or REML method?

model <- lme(Biomass ~ rainfall+year*treatment/patchtype, random = ~1 | sites, data = mydata, correlation = corAR1(), method = "REML")

About the data:

Year: 2008, 2009, 2010, 2013, 2015, and 2016

Treatments: N, P, NP, C (nutrient additions)

Patchtype: Interplant and Underplant

Biomass: ANPP in grams

Rainfall: in cm, unique for each site and each year (not different between treatment plots)

Thanks for the help!

  • $\begingroup$ You said patchtype is a blocking factor - so how can it be nested in treatment ? Does each patchtype occur only within one level of treatment ? $\endgroup$ – Robert Long Jul 9 '16 at 14:05
  • $\begingroup$ Hi @RobertLong, (i may have mis-used the term 'blocking'). Each treatment plot is split into both underplant and interplant (we call these patchtype). This occurs in every plot, regardless of treatment-every plot has both patchtypes. And the patchtype is likely to affect annual biomass. $\endgroup$ – Hannah Jul 11 '16 at 16:59
  • $\begingroup$ That's the correct usage of "blocking" - but the wrong usage of "nested". Patchtype is not nested in treatment. What do you mean by "We want to know if sites are varying across treatment and across year within treatment" ? $\endgroup$ – Robert Long Jul 11 '16 at 18:45

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