Center of Biomass Conditions

Environmental Conditions at Encounter Locations for Marine Species

Author

Affiliation

Gulf of Maine Research Institute

Published

August 2, 2022

Encounter Temperatures of Size-at-Age Species

Species encountering temperature changes may respond to a changing environment in a number of ways. One approach is that species may choose to change location to a more suitable environment. However; this approach may not be an option for all species. Certain species, and individuals of other species, may lack the mobility/awareness needed to relocate and must survive/persist in the new environment.

This document will look at the different temperatures that species have been caught at. Temperatures will be taken from the CTD casts deployed as part of the groundfish survey to take advantage of bottom temperatures.

The general hypothesis follows that species that are better able to track favorable temperatures should experience a lesser physiological toll than species that are unable to track temperature change. Changes in size-at-age should consequently be smaller in highly mobile species than less adaptable ones.

Species Used:

The species used here will match the 16 species included as part of the size-at-age analysis. Those species are:

Code

# Size at age species
saa_species <- c("acadian redfish", "american plaice", 
                 "atlantic cod", "atlantic herring", 
                 "atlantic mackerel", "black sea bass", 
                 "butterfish", "haddock", "pollock", "red hake", 
                 "scup", "silver hake", "summer flounder", 
                 "white hake", "winter flounder", 
                 "witch flounder", "yellowtail flounder")

# table for printing
spec_df <- data.frame("Species" = saa_species)
spec_df %>% knitr::kable()

Species
acadian redfish
american plaice
atlantic cod
atlantic herring
atlantic mackerel
black sea bass
butterfish
haddock
pollock
red hake
scup
silver hake
summer flounder
white hake
winter flounder
witch flounder
yellowtail flounder

Data for these 17 species comes from fisheries independent trawl survey data collected by NOAA and the NEFSC.

Code

# Load the regional temperatures from {targets}
withr::with_dir(rprojroot::find_root('_targets.R'), 
                tar_load(nefsc_stratified))

# Filter to just them, and the two time regimes
trawldat <- nefsc_stratified %>% 
  filter(comname %in% saa_species)

# Pull the main columns to speed things up
trawldat <- trawldat %>% 
  select(cruise6, station, stratum, tow, survey_area,
         svvessel, source, est_year, est_towdate,
         season, decdeg_beglat, decdeg_beglon, 
         avgdepth, bottemp, comname, scientific_name,
         abundance, biomass_kg, 
         catchsex, sum_weight_kg)


# Set up the regimes
trawldat <- trawldat %>% 
  mutate(temp_regime = ifelse(between(est_year, 2000, 2009), "Early Regime", NA),
         temp_regime = ifelse(between(est_year, 2010, 2019), "Warm Regime", temp_regime),
         temp_regime = ifelse(est_year < 2010, "Historical Context", temp_regime))

Code

# Summarize to total biomass per tow:

####  WORKING HERE  ####

# 1. station_totals = biomass and abundance from each tow for all species
# Totals up the abundance and biomass across sex*
station_totals <- trawldat %>% 
  group_by(est_year, survey_area, stratum, tow, est_towdate, avgdepth, bottemp, decdeg_beglat, decdeg_beglon, temp_regime, comname) %>% 
  summarise(
    biomass_kg = sum(biomass_kg, na.rm = T),
    .groups = "drop")



# 2. yrly_avgs = average and sd of biomass 
year_avgs <- station_totals %>% 
  group_by(temp_regime, comname, est_year) %>% 
  summarise(
    total_biomass   = sum(biomass_kg),
    avg_biomass     = mean(biomass_kg),
    biomass_sd      = sd(biomass_kg),
    avg_depth       = weightedMean(avgdepth, w = biomass_kg, na.rm = T),
    avg_temp        = weightedMean(bottemp, w = biomass_kg, na.rm = T),
    avg_lat         = weightedMean(decdeg_beglat, w = biomass_kg, na.rm = T),
    avg_lon         = weightedMean(decdeg_beglon, w = biomass_kg, na.rm = T),
    depth_sd        = weightedSd(avgdepth, w = biomass_kg, na.rm = T),
    temp_sd         = weightedSd(bottemp, w = biomass_kg, na.rm = T),
    lat_sd          = weightedSd(decdeg_beglat, w = biomass_kg, na.rm = T),
    lon_sd          = weightedSd(decdeg_beglon, w = biomass_kg, na.rm = T),
    .groups = "drop"
  )


# Variance across all years within each species
global_benchmarks <- station_totals %>% 
  group_by(comname) %>% 
  summarise(
    avg_biomass     = mean(biomass_kg),
    biomass_sd      = sd(biomass_kg),
    avg_depth       = weightedMean(avgdepth, w = biomass_kg, na.rm = T),
    avg_temp        = weightedMean(bottemp, w = biomass_kg, na.rm = T),
    avg_lat         = weightedMean(decdeg_beglat, w = biomass_kg, na.rm = T),
    avg_lon         = weightedMean(decdeg_beglon, w = biomass_kg, na.rm = T),
    depth_sd        = weightedSd(avgdepth, w = biomass_kg, na.rm = T),
    temp_sd         = weightedSd(bottemp, w = biomass_kg, na.rm = T),
    lat_sd          = weightedSd(decdeg_beglat, w = biomass_kg, na.rm = T),
    lon_sd          = weightedSd(decdeg_beglon, w = biomass_kg, na.rm = T),
    .groups = "drop"
  )

Regional surface Temperatures

Using OISST data for the region, and a 30-year climatology, we know that the region has experienced the following trend in sea surface temperature anomalies:

Code

# What were temps doing over the whole region:
oisst_all <- oisst_access_timeseries("nmfs_trawl_regions", poly_name = "inuse strata", mac_os = "mojave")

# Make Yearly averages
oisst_yrs <- oisst_all %>% 
  mutate(time = as.Date(time)) %>% 
  group_by(year = lubridate::year(time)) %>% 
  summarise(
    temp = mean(area_wtd_sst),
    clim = mean(area_wtd_clim),
    anom = mean(area_wtd_anom))# %>% 
  #filter(between(year, 2000, 2019))


ggplot(oisst_yrs, aes(year, anom)) +
  geom_line(linetype = 2, size = 0.75) +
  geom_point(size = 2, color = gmri_cols("gmri blue")) +
  labs(title = "Survey Area Temperature Anomalies",
       x = "Year",
       y = "Temperature Anomaly °C")

Yearly-Averaged Preferences

The following interactive plots display the biomass weighted averages for what temperatures, depths, and latitudes Each species was caught at as part of the groundfish survey.

Code

# Standardize all the actual measurements
z_data <- map_dfr(saa_species, function(filter_spec){
  
  glob_metrics <- filter(global_benchmarks, comname == filter_spec)
  year_avgs %>% 
    filter(comname == filter_spec) %>% 
    mutate(
      depth_z = (avg_depth - glob_metrics$"avg_depth") / glob_metrics$"depth_sd",
      temp_z  = (avg_temp - glob_metrics$"avg_temp") / glob_metrics$"temp_sd",
      lat_z   = (avg_lat - glob_metrics$"avg_lat") / glob_metrics$"lat_sd",
      lon_z   = (avg_lon - glob_metrics$"avg_lon") / glob_metrics$"lon_sd"
    )
  
})

For anything that is standardized, it is done so against the average value for that species using data from all years.

Code

# Reformat years as a date - messes with slider
# year_avgs <- mutate(year_avgs, est_year = as.Date(str_c(est_year, "-06-01")))

# Define data to use for js
ojs_define(pref_data = z_data)

Now we write the filtering function that will transform the data passed to observable using the values of est_year and comname.

Code

// Filtering Function
filtered = transpose(pref_data).filter(function(trawldat) {
  return year_min < trawldat.est_year &&
         comname.includes(trawldat.comname);
})

To use our filter we’ll need some inputs, and we’ll want to be able to use the values of these inputs in our filtering function. To do this, we use the viewof keyword and with some standard Inputs:

Plot Controls

The following controls will update the data for all plots simultaneously allowing direct comparison across multiple variables and species simultaneously.

Code

viewof year_min = Inputs.range(
  [1970, 2019], 
  {value: 1970, step: 1, label: "Starting Year:"}
)

viewof comname = Inputs.checkbox(
  ["acadian redfish", "american plaice", 
   "atlantic cod", "atlantic herring", 
   "atlantic mackerel", "black sea bass", 
   "butterfish", "haddock", "pollock", "red hake", 
   "scup", "silver hake", "summer flounder", 
   "white hake", "winter flounder", 
   "witch flounder", "yellowtail flounder"], 
  { value: ["white hake", "black sea bass", "yellowtail flounder", "silver hake"], 
    label: "Species:"
  }
)

Finally, we’ll plot the filtered data using Observable Plot (an open-source JavaScript library for quick visualization of tabular data):

Code

Plot.plot({
  style: "overflow: visible;",
  y:{grid : true, 
     label: "↑ Latitude (N)"},
  x:{grid : false, 
     label: "Year →", 
     tickFormat: d3.format(".1d")},
  marks: [
      Plot.line(filtered, {
        x: "est_year", 
        y: "avg_lat",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(filtered, Plot.selectLast({
        x: "est_year",
        y: "avg_lat",
        z: "comname",
        text: "comname",
        textAnchor: "start",
        dx: 3
    }))
    ]
  }
)

Code

Plot.plot({
  style: "overflow: visible;",
  y:{grid : true, 
     reverse: true, 
     label: "↓ Depth (m)"},
  x:{grid : false, 
     label: "Year →", 
     tickFormat: d3.format(".1d")},
  marks: [
      Plot.line(filtered, {
        x: "est_year", 
        y: "avg_depth",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(filtered, Plot.selectLast({
        x: "est_year",
        y: "avg_depth",
        z: "comname",
        text: "comname",
        textAnchor: "start",
        dx: 3
    }))
    ]
  }
)

Code

Plot.plot({
  style: "overflow: visible;",
  y:{grid : true, 
     label: "↑ Temperature (C)"},
  x:{grid : false, 
     label: "Year →", 
     tickFormat: d3.format(".1d")},
  marks: [
      Plot.line(filtered, {
        x: "est_year", 
        y: "avg_temp",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(filtered, Plot.selectLast({
        x: "est_year",
        y: "avg_temp",
        z: "comname",
        text: "comname",
        textAnchor: "start",
        dx: 3
    }))
    ]
  }
)

Standardized Shifts:

The following figures all display how a given variable has shifted relative to the overall variance seen for that variable x species. This allows for easier comparison across species.

Code

Plot.plot({
  style: "overflow: visible;",
  y:{grid : true, 
     label: "↑ Latitude (z)"},
  x:{grid : false, 
     label: "Year →", 
     tickFormat: d3.format(".1d")},
  marks: [
      Plot.line(filtered, {
        x: "est_year", 
        y: "lat_z",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(filtered, Plot.selectLast({
        x: "est_year",
        y: "lat_z",
        z: "comname",
        text: "comname",
        textAnchor: "start",
        dx: 3
    }))
    ]
  }
)

Code

Plot.plot({
  style: "overflow: visible;",
  y:{grid : true, 
     reverse: true, 
     label: "↓ Depth (z)"},
  x:{grid : false, 
     label: "Year →", 
     tickFormat: d3.format(".1d")},
  marks: [
      Plot.line(filtered, {
        x: "est_year", 
        y: "depth_z",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(filtered, Plot.selectLast({
        x: "est_year",
        y: "depth_z",
        z: "comname",
        text: "comname",
        textAnchor: "start",
        dx: 3
    }))
    ]
  }
)

Code

Plot.plot({
  style: "overflow: visible;",
  y:{grid : true, 
     label: "↑ Temperature (z)"},
  x:{grid : false, 
     label: "Year →", 
     tickFormat: d3.format(".1d")},
  marks: [
      Plot.line(filtered, {
        x: "est_year", 
        y: "temp_z",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(filtered, Plot.selectLast({
        x: "est_year",
        y: "temp_z",
        z: "comname",
        text: "comname",
        textAnchor: "start",
        dx: 3
    }))
    ]
  }
)

Change Pathways

Over the course of several years the mechanism by which a species may seek relief may change. This may be due to some sort of geographical restrictions that may limit the accessibility to deeper water, or some physical barriers that may prevent northward movements.

By displaying how both these axes have shifted through time it may help disclose how a species is moving and during which periods.

Code

path_filtered = transpose(pref_data).filter(function(trawldat) {
  return year_start < trawldat.est_year && species.includes(trawldat.comname);
})

// Species view controls
viewof year_start = Inputs.range(
  [1970, 2019], 
  {value: 1990, step: 5, label: "Starting Year:"}
)



viewof species = Inputs.checkbox(
  ["acadian redfish", "american plaice", 
   "atlantic cod", "atlantic herring", 
   "atlantic mackerel", "black sea bass", 
   "butterfish", "haddock", "pollock", "red hake", 
   "scup", "silver hake", "summer flounder", 
   "white hake", "winter flounder", 
   "witch flounder", "yellowtail flounder"], 
  { value: ["black sea bass"], 
    label: "Species:"
  }
)

Code

Plot.plot({
  style: "overflow: visible;",
  x:{grid : false, 
     label: "↑ Latitude (z)"},
  y:{grid : true, 
     reverse: true,
     label: "↓ Depth (z)"},
  marks: [
      Plot.line(path_filtered, {
        x: "lat_z", 
        y: "depth_z",
        curve: "catmull-rom",
        stroke: "comname", 
        marker: "circle"}),
      Plot.text(path_filtered, {
        filter: d => d.est_year % 5 == 0,
        x: "lat_z",
        y: "depth_z",
        z: "est_year",
        text: d => `${d.est_year}`,
        dy: -8
    })
    ], 
  
  // Include a legend for the color channel 
  color: {
    legend: true
  }
})

--- title: "Center of Biomass Conditions" author: name: "Adam Kemberling" url: https://github.com/adamkemberling affiliation: Gulf of Maine Research Institute description: | Environmental Conditions at Encounter Locations for Marine Species date: "Updated on: `r Sys.Date()`" format: html: code-fold: true code-tools: true df-print: kable toc: true editor: source execute: echo: true warning: false message: false fig.height: 10 fig.width: 6 fig.align: "center" comment: "" --- ### Encounter Temperatures of Size-at-Age Species Species encountering temperature changes may respond to a changing environment in a number of ways. One approach is that species may choose to change location to a more suitable environment. However; this approach may not be an option for all species. Certain species, and individuals of other species, may lack the mobility/awareness needed to relocate and must survive/persist in the new environment. This document will look at the different temperatures that species have been caught at. Temperatures will be taken from the CTD casts deployed as part of the groundfish survey to take advantage of bottom temperatures. The general hypothesis follows that species that are better able to track favorable temperatures should experience a lesser physiological toll than species that are unable to track temperature change. Changes in size-at-age should consequently be smaller in highly mobile species than less adaptable ones. ```{r survdat data} #| echo = FALSE #### Packages #### library(tidyverse) library(gmRi) library(targets) library(matrixStats) # Set theme theme_set(theme_gmri()) #### Functions #### ``` `r gmRi::use_gmri_style_rmd(css_file = "gmri_rmarkdown.css")` ### Species Used: The species used here will match the 16 species included as part of the size-at-age analysis. Those species are: ```{r} # Size at age species saa_species <- c("acadian redfish", "american plaice", "atlantic cod", "atlantic herring", "atlantic mackerel", "black sea bass", "butterfish", "haddock", "pollock", "red hake", "scup", "silver hake", "summer flounder", "white hake", "winter flounder", "witch flounder", "yellowtail flounder") # table for printing spec_df <- data.frame("Species" = saa_species) spec_df %>% knitr::kable() ``` Data for these `r nrow(spec_df)` species comes from fisheries independent trawl survey data collected by NOAA and the NEFSC. ```{r data prep} # Load the regional temperatures from {targets} withr::with_dir(rprojroot::find_root('_targets.R'), tar_load(nefsc_stratified)) # Filter to just them, and the two time regimes trawldat <- nefsc_stratified %>% filter(comname %in% saa_species) # Pull the main columns to speed things up trawldat <- trawldat %>% select(cruise6, station, stratum, tow, survey_area, svvessel, source, est_year, est_towdate, season, decdeg_beglat, decdeg_beglon, avgdepth, bottemp, comname, scientific_name, abundance, biomass_kg, catchsex, sum_weight_kg) # Set up the regimes trawldat <- trawldat %>% mutate(temp_regime = ifelse(between(est_year, 2000, 2009), "Early Regime", NA), temp_regime = ifelse(between(est_year, 2010, 2019), "Warm Regime", temp_regime), temp_regime = ifelse(est_year < 2010, "Historical Context", temp_regime)) ``` ```{r summary totals} # Summarize to total biomass per tow: #### WORKING HERE #### # 1. station_totals = biomass and abundance from each tow for all species # Totals up the abundance and biomass across sex* station_totals <- trawldat %>% group_by(est_year, survey_area, stratum, tow, est_towdate, avgdepth, bottemp, decdeg_beglat, decdeg_beglon, temp_regime, comname) %>% summarise( biomass_kg = sum(biomass_kg, na.rm = T), .groups = "drop") # 2. yrly_avgs = average and sd of biomass year_avgs <- station_totals %>% group_by(temp_regime, comname, est_year) %>% summarise( total_biomass = sum(biomass_kg), avg_biomass = mean(biomass_kg), biomass_sd = sd(biomass_kg), avg_depth = weightedMean(avgdepth, w = biomass_kg, na.rm = T), avg_temp = weightedMean(bottemp, w = biomass_kg, na.rm = T), avg_lat = weightedMean(decdeg_beglat, w = biomass_kg, na.rm = T), avg_lon = weightedMean(decdeg_beglon, w = biomass_kg, na.rm = T), depth_sd = weightedSd(avgdepth, w = biomass_kg, na.rm = T), temp_sd = weightedSd(bottemp, w = biomass_kg, na.rm = T), lat_sd = weightedSd(decdeg_beglat, w = biomass_kg, na.rm = T), lon_sd = weightedSd(decdeg_beglon, w = biomass_kg, na.rm = T), .groups = "drop" ) # Variance across all years within each species global_benchmarks <- station_totals %>% group_by(comname) %>% summarise( avg_biomass = mean(biomass_kg), biomass_sd = sd(biomass_kg), avg_depth = weightedMean(avgdepth, w = biomass_kg, na.rm = T), avg_temp = weightedMean(bottemp, w = biomass_kg, na.rm = T), avg_lat = weightedMean(decdeg_beglat, w = biomass_kg, na.rm = T), avg_lon = weightedMean(decdeg_beglon, w = biomass_kg, na.rm = T), depth_sd = weightedSd(avgdepth, w = biomass_kg, na.rm = T), temp_sd = weightedSd(bottemp, w = biomass_kg, na.rm = T), lat_sd = weightedSd(decdeg_beglat, w = biomass_kg, na.rm = T), lon_sd = weightedSd(decdeg_beglon, w = biomass_kg, na.rm = T), .groups = "drop" ) ``` ## Regional surface Temperatures Using OISST data for the region, and a 30-year climatology, we know that the region has experienced the following trend in sea surface temperature anomalies: ```{r} #| fig.height = 3 # What were temps doing over the whole region: oisst_all <- oisst_access_timeseries("nmfs_trawl_regions", poly_name = "inuse strata", mac_os = "mojave") # Make Yearly averages oisst_yrs <- oisst_all %>% mutate(time = as.Date(time)) %>% group_by(year = lubridate::year(time)) %>% summarise( temp = mean(area_wtd_sst), clim = mean(area_wtd_clim), anom = mean(area_wtd_anom))# %>% #filter(between(year, 2000, 2019)) ggplot(oisst_yrs, aes(year, anom)) + geom_line(linetype = 2, size = 0.75) + geom_point(size = 2, color = gmri_cols("gmri blue")) + labs(title = "Survey Area Temperature Anomalies", x = "Year", y = "Temperature Anomaly °C") ``` ## Yearly-Averaged Preferences The following interactive plots display the biomass weighted averages for what temperatures, depths, and latitudes Each species was caught at as part of the groundfish survey. ```{r} # Standardize all the actual measurements z_data <- map_dfr(saa_species, function(filter_spec){ glob_metrics <- filter(global_benchmarks, comname == filter_spec) year_avgs %>% filter(comname == filter_spec) %>% mutate( depth_z = (avg_depth - glob_metrics$"avg_depth") / glob_metrics$"depth_sd", temp_z = (avg_temp - glob_metrics$"avg_temp") / glob_metrics$"temp_sd", lat_z = (avg_lat - glob_metrics$"avg_lat") / glob_metrics$"lat_sd", lon_z = (avg_lon - glob_metrics$"avg_lon") / glob_metrics$"lon_sd" ) }) ``` For anything that is standardized, it is done so against the average value for that species using data from all years. ```{r observable export} # Reformat years as a date - messes with slider # year_avgs <- mutate(year_avgs, est_year = as.Date(str_c(est_year, "-06-01"))) # Define data to use for js ojs_define(pref_data = z_data) ``` Now we write the filtering function that will transform the data passed to observable using the values of `est_year` and `comname`. ```{ojs} // Filtering Function filtered = transpose(pref_data).filter(function(trawldat) { return year_min < trawldat.est_year && comname.includes(trawldat.comname); }) ``` To use our filter we’ll need some inputs, and we’ll want to be able to use the values of these inputs in our filtering function. To do this, we use the viewof keyword and with some standard Inputs: ## Plot Controls The following controls will update the data for all plots simultaneously allowing direct comparison across multiple variables and species simultaneously. ```ojs viewof year_min = Inputs.range( [1970, 2019], {value: 1970, step: 1, label: "Starting Year:"} ) viewof comname = Inputs.checkbox( ["acadian redfish", "american plaice", "atlantic cod", "atlantic herring", "atlantic mackerel", "black sea bass", "butterfish", "haddock", "pollock", "red hake", "scup", "silver hake", "summer flounder", "white hake", "winter flounder", "witch flounder", "yellowtail flounder"], { value: ["white hake", "black sea bass", "yellowtail flounder", "silver hake"], label: "Species:" } ) ``` Finally, we’ll plot the filtered data using Observable Plot (an open-source JavaScript library for quick visualization of tabular data): ::: {.panel-tabset} ## Latitude ```ojs Plot.plot({ style: "overflow: visible;", y:{grid : true, label: "↑ Latitude (N)"}, x:{grid : false, label: "Year →", tickFormat: d3.format(".1d")}, marks: [ Plot.line(filtered, { x: "est_year", y: "avg_lat", stroke: "comname", marker: "circle"}), Plot.text(filtered, Plot.selectLast({ x: "est_year", y: "avg_lat", z: "comname", text: "comname", textAnchor: "start", dx: 3 })) ] } ) ``` ## Depth ```ojs Plot.plot({ style: "overflow: visible;", y:{grid : true, reverse: true, label: "↓ Depth (m)"}, x:{grid : false, label: "Year →", tickFormat: d3.format(".1d")}, marks: [ Plot.line(filtered, { x: "est_year", y: "avg_depth", stroke: "comname", marker: "circle"}), Plot.text(filtered, Plot.selectLast({ x: "est_year", y: "avg_depth", z: "comname", text: "comname", textAnchor: "start", dx: 3 })) ] } ) ``` ## Bottom Temperature ```ojs Plot.plot({ style: "overflow: visible;", y:{grid : true, label: "↑ Temperature (C)"}, x:{grid : false, label: "Year →", tickFormat: d3.format(".1d")}, marks: [ Plot.line(filtered, { x: "est_year", y: "avg_temp", stroke: "comname", marker: "circle"}), Plot.text(filtered, Plot.selectLast({ x: "est_year", y: "avg_temp", z: "comname", text: "comname", textAnchor: "start", dx: 3 })) ] } ) ``` ::: # Standardized Shifts: The following figures all display how a given variable has shifted relative to the overall variance seen for that variable x species. This allows for easier comparison across species. ::: {.panel-tabset} ## Latitude Change ```ojs Plot.plot({ style: "overflow: visible;", y:{grid : true, label: "↑ Latitude (z)"}, x:{grid : false, label: "Year →", tickFormat: d3.format(".1d")}, marks: [ Plot.line(filtered, { x: "est_year", y: "lat_z", stroke: "comname", marker: "circle"}), Plot.text(filtered, Plot.selectLast({ x: "est_year", y: "lat_z", z: "comname", text: "comname", textAnchor: "start", dx: 3 })) ] } ) ``` ## Depth Change ```ojs Plot.plot({ style: "overflow: visible;", y:{grid : true, reverse: true, label: "↓ Depth (z)"}, x:{grid : false, label: "Year →", tickFormat: d3.format(".1d")}, marks: [ Plot.line(filtered, { x: "est_year", y: "depth_z", stroke: "comname", marker: "circle"}), Plot.text(filtered, Plot.selectLast({ x: "est_year", y: "depth_z", z: "comname", text: "comname", textAnchor: "start", dx: 3 })) ] } ) ``` ## Temperature Change ```ojs Plot.plot({ style: "overflow: visible;", y:{grid : true, label: "↑ Temperature (z)"}, x:{grid : false, label: "Year →", tickFormat: d3.format(".1d")}, marks: [ Plot.line(filtered, { x: "est_year", y: "temp_z", stroke: "comname", marker: "circle"}), Plot.text(filtered, Plot.selectLast({ x: "est_year", y: "temp_z", z: "comname", text: "comname", textAnchor: "start", dx: 3 })) ] } ) ``` ::: # Change Pathways Over the course of several years the mechanism by which a species may seek relief may change. This may be due to some sort of geographical restrictions that may limit the accessibility to deeper water, or some physical barriers that may prevent northward movements. By displaying how both these axes have shifted through time it may help disclose how a species is moving and during which periods. ```ojs // Filtering Function path_filtered = transpose(pref_data).filter(function(trawldat) { return year_start < trawldat.est_year && species.includes(trawldat.comname); }) // Species view controls viewof year_start = Inputs.range( [1970, 2019], {value: 1990, step: 5, label: "Starting Year:"} ) viewof species = Inputs.checkbox( ["acadian redfish", "american plaice", "atlantic cod", "atlantic herring", "atlantic mackerel", "black sea bass", "butterfish", "haddock", "pollock", "red hake", "scup", "silver hake", "summer flounder", "white hake", "winter flounder", "witch flounder", "yellowtail flounder"], { value: ["black sea bass"], label: "Species:" } ) ``` ```ojs Plot.plot({ style: "overflow: visible;", x:{grid : false, label: "↑ Latitude (z)"}, y:{grid : true, reverse: true, label: "↓ Depth (z)"}, marks: [ Plot.line(path_filtered, { x: "lat_z", y: "depth_z", curve: "catmull-rom", stroke: "comname", marker: "circle"}), Plot.text(path_filtered, { filter: d => d.est_year % 5 == 0, x: "lat_z", y: "depth_z", z: "est_year", text: d => `${d.est_year}`, dy: -8 }) ], // Include a legend for the color channel color: { legend: true } }) ```