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Maritimes Ecosystem Indicator Catalogue

26 Fishing Pressure: Distribution of Fishing Pressure

Data Type: Tabular Data (within eco_indicators)

Spatial Scope: Maritimes

Duration 1970-2022

Source: Bundy et al. 2017

26.1 Introduction to Indicator

The distribution of fishing pressure is characterized by three proximal indicators.

  • Diversity of Target Species: the number of species targeted by commercial fisheries; proximal variable for fisheries effort.
  • Mean Trophic Level of Fisheries Landings: the trophic position of harvested fish.
  • Marine Trophic Index of Fisheries Landings: tropic average of landed fish, normally with a cutoff (here, 3.5) to capture change in fisheries effort in high-trophic-level species.

26.2 View Data 


library(tidyr)
library(plotly)
library(stringr)

plotly_df <- data@data %>% inner_join(global_cols3)

# function to create plot with dropdown menu ------------------------------
make_distFP_dropdown_plot <- function(df,
                                      year_col   = "year",
                                      region_col = "region",
                                      value_suffix = "_value") {

  # convert to long format
  long <- df %>%
    janitor::clean_names() %>%
    pivot_longer(
      cols = ends_with(value_suffix),
      names_to = "metric",
      values_to = "value"
    ) %>%
    # remove suffix
    mutate(
      metric = str_remove(metric, "_value")
    ) %>%
    # drop NAs (some regions don't have data for some variables or years)
    tidyr::drop_na(value)

  # find all metrics and regions
  metrics <- sort(unique(long$metric))
  regions <- unique(long[[region_col]])

  # clean names for dropdown panels, helper
  pretty_label <- function(x) str_replace(x, "diversity_target_spp_all","Diversity of Target Species") %>%
    str_replace(.,"mean_tl_landings", "Mean TL of Landings") %>% str_replace(.,"mti_landings_3_25","MTI of Landings")

  # build plot -----------------
  p <- plot_ly()

  # Add bar traces: metric1 has region1..K, metric2 has region1..K, ...
  for (metric_i in seq_along(metrics)) {
    m <- metrics[metric_i]
    
    for (region_i in regions) {
      dat <- long %>%
        filter(metric == m, .data[[region_col]] == region_i) 
      
      group_name <- unique(dat$region_group)
      color <- unique(dat$color)
      linetype <- unique(dat$linetype)
      width <- unique(dat$linewidth)
      
      # If a region truly has no data for that metric, add an empty trace
      # (keeps trace indexing stable)
      if (nrow(dat) == 0) {
        dat <- tibble::tibble(!!year_col := integer(0), value = numeric(0))
      }
      
      p <- p %>% add_lines(
        data = dat,
        x = ~.data[[year_col]],
        y = ~value,
        name = as.character(region_i),
        legendgroup = group_name,
        legendgrouptitle = list(
          text = ifelse(group_name == "ESS",
                        "Eastern Scotian Shelf Zones",
                        "Western Scotian Shelf Zones"
          )),
        showlegend = (metric_i == 1),
        visible = (metric_i == 1),
        line = list(color = color, dash = linetype),
        hovertemplate = paste0("<b>", region_i,":</b> ","%{y:.3f}<extra></extra>")      )
      
    }
  }

  n_regions <- length(regions)
  n_traces  <- length(metrics) * n_regions

  buttons <- lapply(seq_along(metrics), function(metric_i) {
    vis <- rep(FALSE, n_traces)
    shl <- rep(FALSE, n_traces)

    idx_start <- (metric_i - 1) * n_regions + 1
    idx_end   <- metric_i * n_regions
    vis[idx_start:idx_end] <- TRUE
    shl[idx_start:idx_end] <- TRUE

    list(
      method = "update",
      args = list(
        list(visible = vis, showlegend = shl),
        list(
          title = pretty_label(metrics[metric_i]),
          yaxis = list(title =pretty_label(metrics[metric_i]))
        )
      ),
      label = pretty_label(metrics[metric_i])
    )
  })

  p %>%
    layout(
      barmode = "stack",
      hovermode = "x unified",
      title = pretty_label(metrics[1]),
      xaxis = list(title = str_to_title(year_col)),  # keep one bar per year
      yaxis = list(title = pretty_label(metrics[1]), fixedrange = TRUE),
      legend = list(
        x = 1.02, xanchor = "left",
        y = 1,    yanchor = "top",
        groupclick = "toggleitem",
        itemdoubleclick = FALSE
      ),
      updatemenus = list(list(
        type = "dropdown",
        x = -.1, xanchor = "left",
        y = 1.15, yanchor = "top",
        buttons = buttons
      )),
      margin = list(r = 180, t = 80)
    )
}

# usage:
p <- make_distFP_dropdown_plot(plotly_df)
p <- p %>% config(displayModeBar= F)
  
p

Figure 26.1: Distribution of Fishing Pressure Variables in Scotian Shelf regions; 1970-2022. Use dropdown box to select a target trophic level, and click legend to toggle regions.

26.4 Relevance to Research and Stock Assessments

Increases in the diversity of target species represent expansions of fisheries effort across the marine community (Bundy, Gomez, and Cook 2017). Expansions of fishing effort across the community could be a result of decreased abundance of historically targeted species.

Decreases in mean trophic level and Marine Trophic Index oflandings represent shifts from fisheries efforts targeted towards high trophic level species towards species lower in the food chain. These shifts are consistent with the “fishing down the food web” process, where the depletion of large predatory fish has resulted in fishieries expansions towards lower trophic level groups (Pauly et al. 1998).

26.5 Variable Definitions

variable description unit
year Year of data collection
region Region over which data are summarized
DiversityTargetSpp_ALL_value Number of species targeted by commercial fishing Number of Species
MeanTL.Landings_value Mean Trophic Level of landings Trophic Level
MTI.Landings_3.25_value Marine Trophic Index of landings Trophic Level

26.6 Additional Data

Distribution of Fishing Pressure (within eco_indicators) contains data for 4VN, 4VS, 4W, 4X, ESS, and WSS. All are shown on this page, but note that NAFO divisions are nested within Scotian Shelf regions.

26.7 Get the Data 

library(marea)
data('eco_indicators')
plot(eco_indicators)

References

Bundy, Alida, Catalina Gomez, and Adam M. Cook. 2017. “Guidance Framework for the Selection and Evaluation of Ecological Indicators.” Canadian Technical Report of Fisheries and Aquatic Sciences 3232. Dartmouth, Nova Scotia, Canada: Fisheries; Oceans Canada, Bedford Institute of Oceanography. https://waves-vagues.dfo-mpo.gc.ca/Library/40648886.pdf.
Pauly, Daniel, Villy Christensen, Johanne Dalsgaard, Rainer Froese, and Francisco Torres Jr. 1998. “Fishing down Marine Food Webs.” Science 279 (5352): 860–63.