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Maritimes Ecosystem Indicator Catalogue

18 Stability & Resistance: Stability of Trophic Community

Data Type: Tabular Data (within eco_indicators)

Spatial Scope: Maritimes

Duration 1970-2022

Source: Bundy et al. 2017

18.1 Introduction to Indicator

Stability of the trophic community is indicative of an ecosystem’s stability and resistance (Bundy, Gomez, and Cook 2017). Stability of the trophic community is quantified by biomass of fish captured in RV surveys, classified into trophic levels 2, 3 and 4.

18.2 View Data 


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

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

# function to create plot with dropdown menu ------------------------------
make_biomassTL_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) %>%
    arrange(region,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_to_title(gsub("biomass_", "", x)) %>% gsub("Tl","Trophic Level ",.)

  # 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_biomassTL_dropdown_plot(plotly_df)
p <- p %>% config(displayModeBar= F)
  
p

Figure 18.1: Biomass of Trophic Levels in Scotian Shelf regions; 1970-2022. Use dropdown box to select a target trophic level, and click legend to isolate regions.

18.4 Relevance to Research and Stock Assessments

Assessment of patterns of trophic structure through time reveal patterns of stability in marine food webs. In this region, trophic structure has shifted in both the Eastern and Western Scotian Shelf regions since the 1970s, indicating structural change in the community. Because fishing activities generally target species at higher trophic levels, shifts towards communities dominated by lower trophic level species could be the result of harvest pressure at the higher trophic levels, and shifts in the opposite direction could indicate fisheries recovery or effective management.

18.5 Variable Definitions

variable description unit
year Year of data collection
region Region over which data are summarized
biomass_TL{X}_value Biomass of select Trophic Levels within regions tonnes

18.6 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 divisions are nested within Scotian Shelf regions.

18.7 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.