Chapter 13 Tables

To demonstrate how LTA results can be summarized, tabularized, and plotted, we will use the same built-in dataset as the previous chapter: density/abundance estimates for the Hawaiian EEZ in 2010 and 2017 for striped dolphins, Fraser’s dolphins, and melon-headed whales, ran with only 100 iterations:

data(lta_result)

We created these LTA results using the following built-in processed dataset:

data(cnp_150km_1986_2020)

Summary tables

To summarize lta() results using the standard table format provided in recent NOAA stock assessment reports, use the function lta_report().

tables <- lta_report(lta_result, 
                     cruz = cnp_150km_1986_2020,
                     verbose = TRUE)

Providing the cruz object is not required, but if it is not provided, one of the five summary tables ($table1a below) will not be returned.

tables %>% names
[1] "table1a" "table1b" "table2"  "table3"  "table4"  "tableA1" "tableA2"

Table 1 in reports: Sample sizes

Table 1a

If cruz was provided, $table1a 1a will include total sighting counts for all species in the years from lta_result, broken down by region. The Ntot column holds all sightings, regardless of effort status or Beaufort sea state. Nsys holds counts of systematic-only sightings (i.e., EffType = “S” and Bft <= 6), which may still include sightings that are beyond the species-specific truncation distance and were therefore excluded from density/abundance estimation.

These counts are provided separately from the $table1b slot below, since those counts are based on the lta_result object, and will not include sightings for species that did not have a specific LTA estimate specified when it was made. We also include this separately so as to give the user full flexibility in how they summarize sighting counts by region/population/stock.

tables$table1a %>%
  DT::datatable(options=list(initComplete = htmlwidgets::JS(
    "function(settings, json) {$(this.api().table().container()).css({'font-size': '9pt'});}")
  ))

Table 1b

This table holds sighting counts used in estimates of density/abundance. The rows match the rows for Tables 3 and 4. In this table, columns are still prepared for total sightings (Ntot) and systematic sightings (Nsys), but they are left blank, since it is not clear how sightings from multiple regions in $table1a would be concatenated for this table. The user can fill in those gaps accordingly.

tables$table1b %>%
  DT::datatable(options=list(initComplete = htmlwidgets::JS(
    "function(settings, json) {$(this.api().table().container()).css({'font-size': '9pt'});}")
  ))

Table 2 in reports: Detection functions

Table 3: Parameter estimates

Table 4: Density/abundance

Appendix tables

Table A1: Study areas

Table A2: Effort totals (parsed by Beaufort sea state)

tables$tableA2
$`2010`
# A tibble: 1 × 10
  Species Stratum Effort      B0     B1     B2    B3    B4    B5     B6
  <chr>   <chr>    <dbl>   <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
1 all     HI_EEZ   15147 0.00127 0.0127 0.0383 0.116 0.478 0.306 0.0481

$`2017`
# A tibble: 1 × 10
  Species Stratum Effort       B0     B1     B2    B3    B4    B5    B6
  <chr>   <chr>    <dbl>    <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
1 all     HI_EEZ   14925 0.000845 0.0103 0.0366 0.115 0.311 0.353 0.173