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Maritimes Ecosystem Indicator Catalogue

25 Fishing Pressure: Fished Groups

Data Type: Tabular Data (within eco_indicators)

Spatial Scope: Maritimes

Duration 1970-2022

Source: Bundy et al. 2017

25.1 Introduction to Indicator

Fishing pressure is a relative metric of intensity of exploitation on fished groups. Here, fishing pressure is presented per guild and calculated as a ratio: fishing pressure = total biomass / total landings (Bundy, Gomez, and Cook 2017).

Since fishing pressure is calculated as a ratio, changes in fishing pressure can either result from changes to total landings, changes to total biomass, or both. Conversely, stability in fishing pressure does not guarantee that total landings and total biomass are also stable; if each value changes in the same direction, fishing pressure might remain stable.

This indicator focuses on the fishing pressure on fished groups: Clupeids, Flatfish, Forage Fish, Gadoids, Groundfish, and Skates.

25.2 View Data 


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

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

# function to create plot with dropdown menu ------------------------------
make_biomass_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 <- unique(long$metric)
  regions <- unique(long[[region_col]])

  # clean names for dropdown panels, helper
  pretty_label <- function(x) str_to_title(gsub("fp_", "", x)) 

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

  # add line traces
  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 = "Fishing Pressure")
        )
      ),
      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 = "Fishing Pressure", fixedrange = TRUE),
      legend = list(
        x = 1.02, xanchor = "left",
        y = 1,    yanchor = "top",
        groupclick = "toggleitem",
        itemdoubleclick = FALSE
      ),
      updatemenus = list(list(
        type = "dropdown",
        x = 0, xanchor = "left",
        y = 1.15, yanchor = "top",
        buttons = buttons
      )),
      margin = list(r = 180, t = 80),
            # add horizontal line at 1
      shapes = list(
        type = "line",
        xref = "paper", 
        yref = "y",     
        x0 = 0,        
        x1 = 1,        
        y0 = 1,         
        y1 = 1,         
        line = list(color = "black", width = 2, dash = "dash") 
      )
    )
}

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

Figure 25.1: Fishing pressure in all NAFO regions and Scotian Shelf regions over time; 1970-2022.

25.4 Relevance to Research and Stock Assessments

Fishing pressure affects populations both by direct removal of individuals from the population, and by alteration of the size-structure and demographic properties of populations. Fishing pressure generally targets larger, older fish, thus truncating the age of the remaining population by removing mature and fecund individuals (Tu, Chen, and Hsieh 2018). High fishing pressure can contribute to the decline or collapse of exploited fisheries (Pinsky and Byler 2015; Essington et al. 2015).

25.5 Variable Definitions

variable description unit
year Year of data collection
region Area over which observations are summarized
FP_{TAXON}_value Fishing Pressure, calculated as Biomass/Landings Proportion

25.6 Additional Data

Fishing Pressure on Fished Groups variables (within eco_indicators) contain 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.

25.7 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.
Essington, Timothy E, Pamela E Moriarty, Halley E Froehlich, Emma E Hodgson, Laura E Koehn, Kiva L Oken, Margaret C Siple, and Christine C Stawitz. 2015. “Fishing Amplifies Forage Fish Population Collapses.” Proceedings of the National Academy of Sciences 112 (21): 6648–52.
Pinsky, Malin L, and David Byler. 2015. “Fishing, Fast Growth and Climate Variability Increase the Risk of Collapse.” Proceedings of the Royal Society B: Biological Sciences 282 (1813).
Tu, Chen-Yi, Kuan-Ting Chen, and Chih-hao Hsieh. 2018. “Fishing and Temperature Effects on the Size Structure of Exploited Fish Stocks.” Scientific Reports 8 (1): 7132.