Locate start and end of consecutive values below t

I have a named vector vec containing observations for various dates:

vec <- c("20160101"=10,
         "20160215"=35,
         "20160315"=50,
         "20160330"=75,
         "20160410"=10,
         "20160515"=60,
         "20160605"=35,
         "20160630"=30,
         "20160725"=55,
         "20160815"=28,
         "20160905"=60,
         "20161005"=80,
         "20161115"=35,
         "20161225"=15)

In a first step I want to know how many runs are below a specified threshold of 45 and have a minimum length of 2:

#threshold
thrs <- 45

#reclass and calculate runs
reclass            <- vec
reclass[vec>thrs]  <- 1
reclass[vec<=thrs] <- 0

runs <- rle(reclass)
below_thrs <- sum(runs$values[runs$length>=2] == 0)

> below_thrs
[1] 3

Now I want to find the start and end dates of these three runs. Expected output:

1, 20160101, 20160215
2, 20160605, 20160630
3, 20161115, 20161225

Any help is greatly appreciated.

vec<-c(vec, "Dummy"=-1) #add a dummy that takes a value that doesnt exist in the threshold, because runs$length has a blank col name for the last column

reclass            <- c(vec)
reclass[vec>thrs]  <- 1
reclass[vec<=thrs & vec>=0] <- 0 #be careful not to assign these categories to the dummy
runs <- rle(reclass)

then purely by looking at the pattern....

> runs$lengths
20160315 20160410 20160515 20160605 20160725 20160815 20160905 20161115    Dummy          
       2        2        1        1        2        1        1        2        2        1 
> runs$values
20160215 20160330 20160410 20160515 20160630 20160725 20160815 20161005 20161225    Dummy 
       0        1        0        1        0        1        0        1        0       -1 
> (endingDates<-names(runs$values[runs$values==0 & runs$lengths >=2]))
[1] "20160215" "20160630" "20161225"
> (offset<-runs$lengths[which(names(runs$values) %in% endingDates)]-1)
20160315 20160725    Dummy 
       1        1        1 
> (startingDates <- names(reclass)[which(names(reclass) %in% endingDates) - offset])
[1] "20160101" "20160605" "20161115"