Chapter 3 — Supplementary information

In [1]:

.ch3_out <- if (exists("ch3_out_dir")) ch3_out_dir else file.path(normalizePath("."), "outputs")
EnvBio_summary_table <- read.csv(file.path(.ch3_out, "tbl-env-fish-summary.csv"), check.names = FALSE)

Length–weight relationship used to estimate missing kōura weights. Observed (measured) and model-predicted body weights are shown in relation to orbital carapace length (OCL). The log₁₀–log₁₀ regression was fitted to individuals with both length and weight measurements (n = 240) and used to estimate missing weights (n = 81). Predicted values were back-transformed using a lognormal bias correction factor. The fitted regression equation is shown on the plot.

Raw relationships between all environmental predictors examined and kōura occurrence, CPUE, and BPUE across all 60 surveyed littoral sites. Each panel shows observed values at individual sites (n = 60 for occurrence; n = 33 for CPUE and BPUE). Points are jittered slightly for occurrence data to reduce overplotting. Predictors highlighted in bold were retained in final GAM models.

In [2]:

col_nms_env <- c("Lake","Variable","Unit","n","Mean","Median","Min","Max","CI low","CI high")
if (knitr::is_html_output()) {
  DT::datatable(EnvBio_summary_table, colnames = col_nms_env, filter = "top", rownames = FALSE,
                options = list(pageLength = 20, dom = 'Bfrtip', buttons = c('csv', 'excel')),
                extensions = 'Buttons') |>
    DT::formatRound(columns = c("Mean","Median","Min","Max","CI_low","CI_high"), digits = 2)
} else {
  knitr::kable(EnvBio_summary_table, digits = 2,
               align = c("l","l","l","r","r","r","r","r","r","r"),
               col.names = col_nms_env)
}

In [3]:

Distribution of environmental and biotic variables measured at littoral sampling sites across five Te Arawa lakes in the Rotorua region of Aotearoa New Zealand.