LobsterWatch Santa Barbara

Bringing you this not-very-scientifically-sound report on the state of everyone’s favorite crustacean

Authors

Affiliations

Sam Csik

National Center for Ecological Analysis and Synthesis

An Bui

Department of Ecology, Evolution & Marine Biology, University of California, Santa Barbara

Published

May 14, 2023

Background

Lobsters are ecologically important marine predators and also very tasty treats. Because people love them for lots of different reasons, we’re reporting on the status of these large ocean bugs at both MPA and non-MPA sites in the Santa Barbara Channel.

Methods

The following data collection methods were copied verbatim from associated metadata records (citation below).

The number and sizes of spiny lobster are recorded by divers in four 60 x 5 m (300 m2 total) lobster transects that are associated with each permanent SBC LTER kelp forest community survey transect.

Data on the abundance and size of the California spiny lobster, Panulirus interruptus, are collected annually in late summer at five sites (Arroyo Quemado, Carpinteria, Isla Vista, Mohawk and Naples) along the mainland coast of the Santa Barbara Channel. Two of these sites (Naples and Isla Vista) were designated as Marine Protected Areas (MPA) in January 2012. The abundances of algae, invertebrates and fish have been monitored at these sites since 2001 in replicate 40 m x 2 m permanent SBC LTER transects. Although lobsters are counted in these transects, their density is not high enough to obtain reliable estimates of abundance. Therefore, the additional surveys were initiated in 2012, which encompass a much larger sampling area and focus exclusively on lobster.

Data Citation

Santa Barbara Coastal LTER, D. Reed, and R. Miller. 2022. SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012 ver 8. Environmental Data Initiative. https://doi.org/10.6073/pasta/25aa371650a671bafad64dd25a39ee18.

Sites of Interest

We explore lobster abundances at two MPA (Marine Protected Area) sites – Naples Reef (NAPL) and Isla Vista (IVEE) – and three non-MPA sites – Arroyo Quemado (AQUE), Mohawk Reef (MOHK), and Carpinteria Reef (CARP):

Code

#..................get unique site/location data.................
sites <- lobs |> 
  select(site, protection_status, lat, lon) |> 
  distinct()

#......................split MPA vs non-MPA......................
mpa <- sites |> 
  filter(protection_status == "MPA")

non_mpa <- sites |> 
  filter(protection_status == "non-MPA")
  
#.......................create custom icon.......................
lobster_icon <- makeIcon(
  iconUrl = here::here("media", "lobster.png"),
  iconWidth = 30, iconHeight = 30
)

#...........................create map...........................

# initialize map ----
site_map <- leaflet() |> 
  
  # add base map tiles (use `addTiles()` for Google maps tiles, OR `addProviderTiles()` for 3rd party base maps: https://leaflet-extras.github.io/leaflet-providers/preview/) ----
  addProviderTiles(providers$Esri.WorldImagery, group = "ESRI World Imagery") |> # modify base map appearance with options: `providerTileOptions(opacity = 0.5, noWrap = FALSE)`
  addProviderTiles(providers$Esri.OceanBasemap, group = "ESRI Oceans") |> 
  
  # add mini map ----
  addMiniMap(toggleDisplay = TRUE, minimized = TRUE) |> 
  
  # set view over Santa Barbara Channel ----
  setView(lng =  -119.83, lat = 34.44, zoom = 9) |> 
  
  # add MPA markers ----
  addMarkers(data = mpa, group = "MPA Sites",
             icon = lobster_icon,
             lng = ~lon, lat = ~lat,
             popup = paste("Site Name:", mpa$site, "<br>",
                           "Coorinates (lat/long):", mpa$lat, ",", mpa$lon)) |>
  
  # add non-MPA markers ----
  addMarkers(data = non_mpa, group = "Non-MPA Sites",
             icon = lobster_icon,
             lng = ~lon, lat = ~lat,
             popup = paste("Site Name:", non_mpa$site, "<br>",
                           "Coorinates (lat/long):", non_mpa$lat, ",", non_mpa$lon)) |>

  # add layers control (toggle base map tiles & markers based on group IDs) ----
  addLayersControl(
    baseGroups = c("ESRI World Imagery", "ESRI Oceans"),
    overlayGroups = c("MPA Sites", "Non-MPA Sites")) |> 
  
  # add reset map button ----
  leaflet.extras::addResetMapButton()
                   

# print map ----
site_map

Results

There are more lobsters in MPAs than non-MPAs!

Code

# adding a column to lobs_summary 
lobs_summary_marker <- lobs_summary %>% 
  
  # create a new column called "marker" ----
  mutate(marker = paste("Site:", site, "<br>",
                      "Year:", year, "<br>",
                      "Status:", protection_status, "<br>",
                      "Lobster count:", n))

# creating a new static plot
static_with_marker <- lobs_summary_marker %>% 
  
  # create boxplot of mpa vs non-mpa lobster counts ----
  ggplot(aes(x = protection_status, y = n, text = marker, group = protection_status)) +
  
  # geoms: boxplot and jitter
  geom_boxplot(width = 0.5, outlier.shape = NA) +
  geom_point(aes(color = site, shape = site), size = 4, alpha = 0.8, 
             position = position_jitter(width = 0.25, height = 0, seed = 1)) +
  
  # update colors ----
  scale_color_manual(values = c("#91B38A", "#9565CC", "#CCC065", "#658ACC", "#CC6565")) +
  scale_shape_manual(values = c(15, 25, 17, 18, 19)) +
  
  # update labels ----
  labs(x = "Protection Status",
       y = "Lobster Counts",
       color = "Site", 
       shape = "Site") + 
  
  # theme ----
  theme_linedraw() +
  theme(axis.text = element_text(size = 10),
        axis.title = element_text(size = 13),
        legend.text = element_text(size = 10),
        legend.title = element_text(size = 11))

# tooltip = "text" corresponds to the aes() text call from above!
lobs_interactive <- ggplotly(static_with_marker, tooltip = "text") %>% 
  
  # layout: most formatting goes here! ----
  layout(
    font = list(family = "Times"),
  
    # editing the marker/tooltip/hoverlabel
    hoverlabel = list(
      # editing the font: all goes in a list()
      font = list(
        family = "Times",
        size = 13,
        color = "#FFFFFF",
        align = "left"
      )
    )
  )

# print plot ----
lobs_interactive

Appendix

Explore and download the raw data used throughout this report:

Code

#................customize your interactive table................
datatable(data = lobs, 
          
          # make the column names nice ----
          colnames = c("Year", "Date", "Site", "Protection status", "Transect", "Replicate", 
                       "Size (mm)", "Count", "Latitude", "Longitude"),
          
          # column filters: sliders and drop down menus ----
          filter = "top", 
          
          # extensions: lots of these! ----
          extensions = c("Buttons", "ColReorder"),
          
          # options ----
          options = list(
            # list 10 entries at once
            pageLength = 10, 
            # automatically size columns
            autoWidth = TRUE,
            # highlight entries that match search term
            searchHighlight = TRUE,
            # allow regular expressions and case insensitive searches
            search = list(regex = TRUE, caseInsensitive = TRUE),
            dom = "Bfrtip",
            # buttons options
            buttons = c("copy", "csv", "excel", "pdf", "print", "colvis"),
            # links to extension call
            colReorder = TRUE)
          ) %>% 
  
  # styling cells: coloring site background
  formatStyle(
    "site",
    # styleEqual allows matches to column contents
    backgroundColor = styleEqual(
      levels = list("NAPL", "IVEE", "AQUE", "MOHK", "CARP"),
      values = c("NAPL" = "#91B38A", 
                 "IVEE" = "#9565CC", 
                 "AQUE" = "#CCC065", 
                 "MOHK" = "#658ACC", 
                 "CARP" = "#CC6565")
    )
  )

--- title: "LobsterWatch Santa Barbara" subtitle: "Bringing you this not-very-scientifically-sound report on the state of everyone's favorite crustacean" authors: - name: Sam Csik affiliation: National Center for Ecological Analysis and Synthesis orcid: 0000-0002-5300-3075 - name: An Bui affiliation: Department of Ecology, Evolution & Marine Biology, University of California, Santa Barbara orcid: 0000-0002-9548-7776 date: May 14, 2023 execute: message: false warning: false format: html: toc: true toc-location: left code-fold: true code-tools: true theme: sandstone --- ```{r setup} #| eval: true #| echo: false #.........................load libraries......................... library(here) library(tidyverse) library(plotly) library(DT) library(leaflet) library(leaflet.extras) #..........................read in data.......................... lobs <- readRDS(file = here::here("data", "lobsters.rds")) # creating new data frame ---- lobs_summary <- lobs %>% # calculate total lobster counts by protection status, site, & year (each point will represent lobster counts at a single site for each year from 2012-2018) ---- group_by(protection_status, site, year) %>% # count the total number of lobsters summarize(n = sum(total_count)) ``` ## Background Lobsters are ecologically important marine predators and also very tasty treats. Because people love them for lots of different reasons, we're reporting on the status of these large ocean bugs at both MPA and non-MPA sites in the Santa Barbara Channel. ## Methods *The following data collection methods were copied verbatim from associated metadata records (citation below).* The number and sizes of spiny lobster are recorded by divers in four 60 x 5 m (300 m2 total) lobster transects that are associated with each permanent SBC LTER kelp forest community survey transect. Data on the abundance and size of the California spiny lobster, *Panulirus interruptus*, are collected annually in late summer at five sites (Arroyo Quemado, Carpinteria, Isla Vista, Mohawk and Naples) along the mainland coast of the Santa Barbara Channel. Two of these sites (Naples and Isla Vista) were designated as Marine Protected Areas (MPA) in January 2012. The abundances of algae, invertebrates and fish have been monitored at these sites since 2001 in replicate 40 m x 2 m permanent SBC LTER transects. Although lobsters are counted in these transects, their density is not high enough to obtain reliable estimates of abundance. Therefore, the additional surveys were initiated in 2012, which encompass a much larger sampling area and focus exclusively on lobster. ::: {.callout-note} ## Data Citation Santa Barbara Coastal LTER, D. Reed, and R. Miller. 2022. **SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012** ver 8. Environmental Data Initiative. <https://doi.org/10.6073/pasta/25aa371650a671bafad64dd25a39ee18>. ::: ## Sites of Interest We explore lobster abundances at **two MPA (Marine Protected Area) sites** -- Naples Reef (NAPL) and Isla Vista (IVEE) -- and **three non-MPA sites** -- Arroyo Quemado (AQUE), Mohawk Reef (MOHK), and Carpinteria Reef (CARP): ```{r leaflet map} #| eval: true #| echo: true #..................get unique site/location data................. sites <- lobs |> select(site, protection_status, lat, lon) |> distinct() #......................split MPA vs non-MPA...................... mpa <- sites |> filter(protection_status == "MPA") non_mpa <- sites |> filter(protection_status == "non-MPA") #.......................create custom icon....................... lobster_icon <- makeIcon( iconUrl = here::here("media", "lobster.png"), iconWidth = 30, iconHeight = 30 ) #...........................create map........................... # initialize map ---- site_map <- leaflet() |> # add base map tiles (use `addTiles()` for Google maps tiles, OR `addProviderTiles()` for 3rd party base maps: https://leaflet-extras.github.io/leaflet-providers/preview/) ---- addProviderTiles(providers$Esri.WorldImagery, group = "ESRI World Imagery") |> # modify base map appearance with options: `providerTileOptions(opacity = 0.5, noWrap = FALSE)` addProviderTiles(providers$Esri.OceanBasemap, group = "ESRI Oceans") |> # add mini map ---- addMiniMap(toggleDisplay = TRUE, minimized = TRUE) |> # set view over Santa Barbara Channel ---- setView(lng = -119.83, lat = 34.44, zoom = 9) |> # add MPA markers ---- addMarkers(data = mpa, group = "MPA Sites", icon = lobster_icon, lng = ~lon, lat = ~lat, popup = paste("Site Name:", mpa$site, " ", "Coorinates (lat/long):", mpa$lat, ",", mpa$lon)) |> # add non-MPA markers ---- addMarkers(data = non_mpa, group = "Non-MPA Sites", icon = lobster_icon, lng = ~lon, lat = ~lat, popup = paste("Site Name:", non_mpa$site, " ", "Coorinates (lat/long):", non_mpa$lat, ",", non_mpa$lon)) |> # add layers control (toggle base map tiles & markers based on group IDs) ---- addLayersControl( baseGroups = c("ESRI World Imagery", "ESRI Oceans"), overlayGroups = c("MPA Sites", "Non-MPA Sites")) |> # add reset map button ---- leaflet.extras::addResetMapButton() # print map ---- site_map ``` ## Results There are more lobsters in MPAs than non-MPAs! ```{r summary-marker-df} #| eval: true #| echo: true # adding a column to lobs_summary lobs_summary_marker <- lobs_summary %>% # create a new column called "marker" ---- mutate(marker = paste("Site:", site, " ", "Year:", year, " ", "Status:", protection_status, " ", "Lobster count:", n)) # creating a new static plot static_with_marker <- lobs_summary_marker %>% # create boxplot of mpa vs non-mpa lobster counts ---- ggplot(aes(x = protection_status, y = n, text = marker, group = protection_status)) + # geoms: boxplot and jitter geom_boxplot(width = 0.5, outlier.shape = NA) + geom_point(aes(color = site, shape = site), size = 4, alpha = 0.8, position = position_jitter(width = 0.25, height = 0, seed = 1)) + # update colors ---- scale_color_manual(values = c("#91B38A", "#9565CC", "#CCC065", "#658ACC", "#CC6565")) + scale_shape_manual(values = c(15, 25, 17, 18, 19)) + # update labels ---- labs(x = "Protection Status", y = "Lobster Counts", color = "Site", shape = "Site") + # theme ---- theme_linedraw() + theme(axis.text = element_text(size = 10), axis.title = element_text(size = 13), legend.text = element_text(size = 10), legend.title = element_text(size = 11)) # tooltip = "text" corresponds to the aes() text call from above! lobs_interactive <- ggplotly(static_with_marker, tooltip = "text") %>% # layout: most formatting goes here! ---- layout( font = list(family = "Times"), # editing the marker/tooltip/hoverlabel hoverlabel = list( # editing the font: all goes in a list() font = list( family = "Times", size = 13, color = "#FFFFFF", align = "left" ) ) ) # print plot ---- lobs_interactive ``` ## Appendix Explore and download the raw data used throughout this report: ```{r DT} #| eval: true #| echo: true #................customize your interactive table................ datatable(data = lobs, # make the column names nice ---- colnames = c("Year", "Date", "Site", "Protection status", "Transect", "Replicate", "Size (mm)", "Count", "Latitude", "Longitude"), # column filters: sliders and drop down menus ---- filter = "top", # extensions: lots of these! ---- extensions = c("Buttons", "ColReorder"), # options ---- options = list( # list 10 entries at once pageLength = 10, # automatically size columns autoWidth = TRUE, # highlight entries that match search term searchHighlight = TRUE, # allow regular expressions and case insensitive searches search = list(regex = TRUE, caseInsensitive = TRUE), dom = "Bfrtip", # buttons options buttons = c("copy", "csv", "excel", "pdf", "print", "colvis"), # links to extension call colReorder = TRUE) ) %>% # styling cells: coloring site background formatStyle( "site", # styleEqual allows matches to column contents backgroundColor = styleEqual( levels = list("NAPL", "IVEE", "AQUE", "MOHK", "CARP"), values = c("NAPL" = "#91B38A", "IVEE" = "#9565CC", "AQUE" = "#CCC065", "MOHK" = "#658ACC", "CARP" = "#CC6565") ) ) ```