Skip to main content

Maritimes Ecosystem Indicator Catalogue

21 Resource Potential: Fished Groups

Data Type: Tabular Data (within eco_indicators)

Spatial Scope: Maritimes

Duration 1970-2022

Source: Bundy et al. 2017

21.1 Introduction to Indicator

Resource potential describes the total production capacity of the community, which can be further separated into resource potential of target groups. Harvesting removes resources from a community without replacement, and diminishes the community’s ability to replenish resources to previous levels. Thus, resource potential of fished groups can be used as an indicator of the effect of fishing on the community (Bundy, Gomez, and Cook 2017).

Resource potential of fished groups is quantified by the biomass of harvested species, quantified from fisheries-independent surveys. Here, resource potential is quantified from biomass with focus on five harvested groups:

  • Clupeids
  • Forage Fish
  • Gadoids
  • Flatfish
  • Groundfish

21.2 View Data 


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

plotly_df <- data@data %>% inner_join(global_cols2)

# function to create plot with dropdown menu ------------------------------
make_biomassFG_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("_", " ", x)) %>%  gsub("Lb","Large B",.) %>% gsub("Mb","Medium B",.)

  # 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_by(.data[[year_col]],color, region_group, region_group_label) %>%     # in case you have multiple rows per year
        summarise(value = sum(value), .groups = "drop") %>%
        arrange(.data[[year_col]])
      
        group_name <- unique(dat$region_group)
        color <- unique(dat$color)

      # 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_bars(
        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),
        marker = list(color = color),
        hovertemplate = paste0("<b>", region_i,":</b> ","%{y:,.2s}<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 = "Biomass (tonnes)")
        )
      ),
      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), type = "category"),  # keep one bar per year
      yaxis = list(title = "Biomass (tonnes)", 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)
    )
}

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

Figure 21.1: Biomass of Fished Groups in Scotian Shelf regions; 1970-2022. Use dropdown box to select a target taxonomic or functional group, and click legend to isolate regions.

21.4 Relevance to Research and Stock Assessments

Changes in resource potential for for fished groups may indicate the effects of harvesting, harvest management, and ecological factors, but can also indicate the future potential of fisheries. In Atlantic Canada, resource potential metrics are heterogenous between regions and taxa, and can be useful indicators of change at the local scale (Irvine, Pedersen, and Bundy 2025).

21.5 Variable Definitions

variable description unit
year Year of data collection
region Region over which data are summarized
biomass_{GROUP}_value Biomass of select fished groups within regions tonnes

21.6 Additional Data

Structural Change variables (within eco_indicators) contain data for 4VN, 4VS, 4W, 4X, ESS, and WSS. All are shown on this page, but note that NAFO zones are nested within Scotian Shelf regions.

21.7 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.
Irvine, Fonya, Eric J Pedersen, and Alida Bundy. 2025. “Spatial Coherence of Ecosystem Indicators Across the Scotian Shelf, Canada.” Ecological Indicators 178: 113823.