In the exemplary work flow of the iNEXT vignette, the ciliates dataset is analyzed to show the diversity estimation based on raw species incidence data. Ciliates is a List of 3 object, made up of 3 habitats with multiple samples each. The dataset I am planning to analyze with iNEXT consists of species abundance data of 4 habitats with a varying number of samples.
This is what the ciliates dataset structure looks like:
List of 3
$ EtoshaPan : int [1:365, 1:19] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(, "dimnames")=List of 2
.. ..$ : chr [1:365] "Acaryophrya.collaris" ...
.. ..$ : chr [1:19] "x53" "x54" "x55" "x56" ...
$ CentralNamibDesert : int [1:365, 1:17] 0 0 0 0 0 1 0 0 0 0 ...
..- attr(, "dimnames")=List of 2
.. ..$ : chr [1:365] "Acaryophrya.collaris" ...
.. ..$ : chr [1:17] "x31" "x32" "x34" "x35" ...
$ SouthernNamibDesert: int [1:365, 1:15] 0 0 0 0 0 0 0 0 0 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:365] "Acaryophrya.collaris" ...
.. ..$ : chr [1:15] "x9" "x17" "x19" "x20" ...
When converting my abundance data to raw incidence data and creating a List of 4 object, (4 habitats with several samples each), just like the exemplary ciliates dataset, the iNEXT algorithm and ggiNEXT visualization work smoothly. I am able to choose the number of samples of the most extensively sampled habitat as "endpoint" and the diversity curves of all habitats are extrapolated to this point. This is what our dataset structure looks like (after converting to raw incidence data):
List of 4
$ Leafs : num [1:314, 1:29] 0 0 0 0 0 0 1 1 0 0 ...
..- attr(, "dimnames")=List of 2
.. ..$ : chr [1:314] "Foram1" "Foram2" "Foram3" "Foram4" ...
.. ..$ : chr [1:29] "Blatt_3B_1" "Blatt_3B_2" "Blatt_3B_3" "Blatt_3B_4" ...
$ Sprouts : num [1:314, 1:20] 0 0 1 1 1 0 1 0 0 0 ...
..- attr(, "dimnames")=List of 2
.. ..$ : chr [1:314] "Foram1" "Foram2" "Foram3" "Foram4" ...
.. ..$ : chr [1:20] "Wurz_3B_1" "Wurz_3B_2" "Wurz_3B_3" "Wurz_3B_4" ...
$ Redalgae : num [1:314, 1:16] 0 0 1 0 1 0 1 0 0 0 ...
..- attr(, "dimnames")=List of 2
.. ..$ : chr [1:314] "Foram1" "Foram2" "Foram3" "Foram4" ...
.. ..$ : chr [1:16] "ReAl_Co_1" "ReAl_Co_2" "ReAl_Co_3" "ReAl_Co_4" ...
$ Posidonia: num [1:314, 1:49] 0 0 0 0 0 0 1 1 0 0 ...
..- attr(, "dimnames")=List of 2
.. ..$ : chr [1:314] "Foram1" "Foram2" "Foram3" "Foram4" ...
.. ..$ : chr [1:49] "Blatt_3B_1" "Blatt_3B_2" "Blatt_3B_3" "Blatt_3B_4" ...
However, we also would like to estimate diversity based on our original abundance data. When passing our List of 4 object with abundance data as input to the iNEXT function with datatype = "abundance" , the following error output appears:
Error in FUN(X[[i]], ...) : invalid data structure
So it seems as for abundance data, iNEXT does not work with complex list objects consisting of several habitats with multiple samples each, but only with list objects looking like the spider one:
List of 2
$ Girdled: num [1:26] 46 22 17 15 15 9 8 6 6 4 ...
$ Logged : num [1:37] 88 22 16 15 13 10 8 8 7 7 ...
Why is this the case? Let's imagine for inter- and extrapolation, I am not interested in the diversity as a function of individuals (as shown in the vignette exemplary workflow for the spider dataset), but as a function of sample units. Without being able to pass a list object such as ciliates (with abundance data in this case, of course) to the iNEXT algorithm, I would have to pool all my samples within each habitat to one single list to make it work with iNEXT and to be able to compare the habitats. This way, however, we would lose the information stored in each single sample list.
Has this issue occurred before? Is there a simple solution for this problem I am currently not aware of? What is the reason behind not being able to analyze abundance data stored in a more complex list object such as ciliates with the iNEXT algorithm?
Thanks for the help.
