Interpreting results from iNEXT (100% sample completeness--is this possible?)

[elegiacally]

Good day everyone!

I've been using iNEXT for estimating asymptotic mammal species richness and sample completeness in a forest and need some help interpreting the data. I've used camera trapping to survey a species-poor forest and after running the dataset through iNEXT, it's giving a species richness estimate of 2 (same as observed) as well as a sample coverage 1 (=100%).

Is that possible? Here's my code:

#data here
ct <-structure(list(Station = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("CT01", 
"CT02", "CT03"), class = "factor"), Date = structure(c(10L, 11L, 
12L, 12L, 13L, 14L, 16L, 16L, 17L, 17L, 1L, 1L, 15L, 15L, 15L, 
15L, 15L, 18L, 19L, 2L), .Label = c("1/11/19", "13/11/19", "14/11/19", 
"15/11/19", "17/11/19", "18/11/19", "22/11/19", "23/11/19", "25/10/19", 
"26/10/19", "27/10/10", "27/10/19", "28/10/19", "29/10/19", "3/11/19", 
"30/10/19", "31/10/19", "6/11/19", "7/11/19"), class = "factor"), 
    Time = structure(c(23L, 22L, 25L, 28L, 24L, 15L, 31L, 20L, 
    18L, 19L, 2L, 8L, 3L, 7L, 6L, 16L, 17L, 21L, 9L, 14L), .Label = c("0:34:24", 
    "0:43:46", "13:07:10", "16:49:34", "17:26:28", "19:06:56", 
    "19:11:56", "19:13:28", "19:34:58", "19:53:00", "19:56:42", 
    "2:33:36", "2:34:40", "20:21:42", "20:27:00", "20:31:42", 
    "20:32:08", "22:25:22", "23:19:00", "23:50:24", "23:50:44", 
    "3:19:00", "4:26:00", "4:28:00", "4:46:00", "4:56:04", "5:13:32", 
    "5:18:00", "5:19:00", "5:56:00", "6:31:00"), class = "factor"), 
    DateTimeOriginal = structure(c(18L, 18L, 18L, 18L, 19L, 20L, 
    20L, 17L, 22L, 23L, 1L, 2L, 11L, 13L, 12L, 14L, 15L, 24L, 
    25L, 3L), .Label = c("1/11/19 0:43", "1/11/19 19:13", "13/11/19 20:21", 
    "14/11/19 17:26", "15/11/19 19:56", "17/11/19 0:34", "18/11/19 4:56", 
    "22/11/19 5:13", "23/11/19 2:33", "23/11/19 2:34", "3/11/19 13:07", 
    "3/11/19 19:06", "3/11/19 19:11", "3/11/19 20:31", "3/11/19 20:32", 
    "30/10/19 23:22", "30/10/19 23:50", "30/10/19 23:58", "30/10/19 23:59", 
    "31/10/19 0:00", "31/10/19 16:49", "31/10/19 22:25", "31/10/19 23:19", 
    "6/11/19 23:50", "7/11/19 19:34"), class = "factor"), Scientific_name_1 = structure(c(2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L), .Label = c("Callosciurus_notatus", "Rattus_tiomanicus"
    ), class = "factor"), Abundance_1 = c(2L, 1L, 4L, 3L, 4L, 
    3L, 1L, 2L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 3L, 3L, 1L, 1L, 4L
    )), row.names = c(NA, 20L), class = "data.frame")


ct$trapnights <- paste(ct$Station, ct$Date, sep="_") 
ct.matrix <- xtabs(Abundance_1~Scientific_name_1+trapnights, data=ct) #create contingency table
ct.df1 <- ifelse(ct.matrix>0,1,0)  #create species incidence matrix
ct.df2<- list(ct.df1) #convert matrix > list
(ct.inext <- iNEXT(ct.df2, datatype="incidence_raw")) #run iNEXT

estimateD(ct.df2, datatype="incidence_raw") #Obtains sampling coverage
   t   method order SC    qD qD.LCL qD.UCL
1 14 observed     0  1 2.000  1.066  2.934
2 14 observed     1  1 1.278  0.898  1.657
3 14 observed     2  1 1.142  0.861  1.424

> ChaoRichness(ct.df2, datatype="incidence_raw")  #Obtains Chao estimate - 
  Observed Estimator Est_s.e. 95% Lower 95% Upper
1        2         2    0.472         2     3.585