I have a community matrix (samples x species of animals). I sampled the animals weekly over many years (in this example, three years). I want to figure out how sampling timing (start week and duration a.k.a. number of weeks) affects species richness. Here is an example data set:
Data <- data.frame(
Year = rep(c('1996', '1997', '1998'), each = 5),
Week = rep(c('1', '2', '3', '4', '5'), 3),
Species1 =sample(0:5, 15, replace=T),
Species2 =sample(0:5, 15, replace=T),
Species3 =sample(0:5, 15, replace=T)
)
The outcome that I want is something along the lines of:
Year StartWeek Duration(weeks) SpeciesRichness
1996 1 1 2
1996 1 2 3
1996 1 3 1
...
1998 5 1 1
I had tried doing this via a combination of rollapply and vegan's specnumber, but got a sample x species matrix instead of a vector of Species Richness. Weird.
For example, I thought that this should give me species richness for sampling windows of two weeks:
test<-rollapply(Data[3:5],width=2,specnumber,align="right")
Thank you for your help!
I figured it out by breaking up the task into two parts: 1. Summing up species abundances using rollapplyr, as implemented in a ddplyr mutate_each thingamabob 2. Calculating species richness using vegan.
I did this for each sampling duration window separately.
Here is the bare bones version (I just did this successively for each sampling duration that I wanted by changing the width argument):
weeksum2 <- function(x) {rollapply(x, width = 2, align = 'left', sum, fill=NA)}
sum2weeks<-Data%>%
arrange(Year, Week)%>%
group_by(Year)%>%
mutate_each(funs(weeksum2), -Year, -Week)
weeklyspecnumber2<-specnumber(sum2weeks[,3:ncol(sum2weeks)],
groups = interaction(sum2weeks$Week, sum2weeks$Year))
weeklyspecnumber2<-unlist(weeklyspecnumber2)
weeklyspecnumber2<-as.data.frame(weeklyspecnumber2)
weeklyspecnumber2$WeekYear<-as.factor(rownames(weeklyspecnumber2))
weeklyspecnumber2<-tidyr::separate(weeklyspecnumber2, WeekYear, into = c('Week', 'Year'), sep = '[.]')