WCS Vibrant Oceans Baseline Coral Reef Monitoring • mermaidr

This case study follows an analysis of baseline coral reef studies for Vibrant Oceans, completed by the Wildlife Conservation Society (WCS). The original survey and analysis covered 168 sites across Tanzania, Fiji, and Indonesia, surveying underwater information across a diversity of management, habitat types, and other environmental characteristics. This analysis is a small subset, only using publicly available data, and intended to illustrate the usage of mermaidr for such a project.

First, we load mermaidr and search for projects tagged with “Vibrant Oceans”:

library(mermaidr)

vo_projects <- mermaid_search_projects(tags = "Vibrant Oceans")

vo_projects
#> # A tibble: 18 × 15
#>    id          name  countries num_sites tags  notes status data_policy_beltfish
#>    <chr>       <chr> <chr>         <int> <chr> <chr> <chr>  <chr>               
#>  1 1277efc2-d… Taka… Indonesia        46 WCS … ""    Open   Private             
#>  2 1e982dc7-b… Aceh… Indonesia        12 WCS … "SAP… Open   Public Summary      
#>  3 207ea040-7… Aceh… Indonesia        36 WCS … "SAP… Open   Private             
#>  4 2f6db63d-6… 2022… Fiji             11 WCS … "Thi… Open   Public Summary      
#>  5 3a9ecb7c-f… Aceh… Indonesia        18 WCS … ""    Open   Private             
#>  6 507d1af9-e… Kari… Indonesia        43 WCS … ""    Open   Private             
#>  7 60c77c7e-6… ACEH… Indonesia         9 WCS … ""    Open   Public Summary      
#>  8 95e0ffc7-3… 2019… Fiji             44 WCS … ""    Open   Private             
#>  9 a3521a0e-7… VOI … Tanzania         25 WCS … ""    Open   Public Summary      
#> 10 a93b43f1-7… VOI … Tanzania         25 WCS … ""    Open   Public Summary      
#> 11 ac586c72-a… Taka… Indonesia        39 WCS … ""    Open   Private             
#> 12 b24c070f-d… 2022… Fiji             67 WCS … "Thi… Open   Public Summary      
#> 13 b2d1d706-d… Pula… Indonesia        22 WCS … "TWA… Open   Private             
#> 14 bb4899a8-5… ACEH… Indonesia         9 WCS … ""    Open   Public Summary      
#> 15 bcb1f115-8… Taka… Indonesia        39 WCS … ""    Open   Private             
#> 16 c314adef-8… Taka… Indonesia        39 WCS … ""    Open   Private             
#> 17 e9649e0d-7… BAF … Kenya, T…        15 WCS … ""    Open   Private             
#> 18 ea85fea0-c… Aceh… Indonesia        18 WCS … ""    Open   Public Summary      
#> # ℹ 7 more variables: data_policy_benthiclit <chr>,
#> #   data_policy_benthicpit <chr>, data_policy_benthicpqt <chr>,
#> #   data_policy_habitatcomplexity <chr>, data_policy_bleachingqc <chr>,
#> #   created_on <chr>, updated_on <chr>

For this analysis, WCS field teams accessed ecological condition using underwater surveys to assess two key indicators of coral reef health: live hard coral cover and reef fish biomass. This data is available from mermaidr via the benthic PIT and fishbelt methods, respectively. We’ll focus on projects that have summary data publicly available for these methods.

We are able to see the data policy of projects and methods by looking at the data_policy_* columns of vo_projects. For example, focusing on benthic PIT and fishbelt, we can see that the Taka Bonerate NP-2019 project has summary data publicly available for fishbelt and benthic PIT, while the 2019 Dama Bureta Waibula and Dawasamu-WISH ecological survey has public data available for only benthic PIT.

library(tidyverse)

vo_projects %>%
  select(name, data_policy_beltfish, data_policy_benthicpit)
#> # A tibble: 18 × 3
#>    name                              data_policy_beltfish data_policy_benthicpit
#>    <chr>                             <chr>                <chr>                 
#>  1 Taka Bonerate 2015                Private              Private               
#>  2 Aceh East Coast 2019              Public Summary       Public Summary        
#>  3 Aceh (Weh & Aceh Besar) 2019      Private              Private               
#>  4 2022 DamaWISH endline survey      Public Summary       Public Summary        
#>  5 Aceh Jaya MPA 2020                Private              Private               
#>  6 Karimunjawa NP 2019               Private              Private               
#>  7 ACEH_EAST COAST_2022              Public Summary       Public Summary        
#>  8 2019_Dama Bureta Waibula and Daw… Private              Public Summary        
#>  9 VOI - 20/22                       Public Summary       Public Summary        
#> 10 VOI - 2019                        Public Summary       Public Summary        
#> 11 Taka Bonerate NP-2024             Private              Private               
#> 12 2022_BAF and WISH coral reef sur… Public Summary       Public Summary        
#> 13 Pulau Weh 2022                    Private              Private               
#> 14 ACEH_EAST COAST_2022-Delete       Public Summary       Public Summary        
#> 15 Taka Bonerate NP-2019             Private              Private               
#> 16 Taka Bonerate NP-2021             Private              Private               
#> 17 BAF 1 - 2020                      Private              Public Summary        
#> 18 Aceh Jaya MPA 2022                Public Summary       Private

Live hard coral cover

We’ll focus on hard coral (benthic PIT) data first. We can get this data by filtering for projects that have publicly available summary data for it (returning both the Taka Bonerate and Dama Bureta Waibula and Dawasamu projects):

projects_public_benthic <- vo_projects %>%
  filter(data_policy_benthicpit == "Public Summary")

projects_public_benthic %>%
  select(name)
#> # A tibble: 9 × 1
#>   name                                                        
#>   <chr>                                                       
#> 1 Aceh East Coast 2019                                        
#> 2 2022 DamaWISH endline survey                                
#> 3 ACEH_EAST COAST_2022                                        
#> 4 2019_Dama Bureta Waibula and Dawasamu-WISH ecological survey
#> 5 VOI - 20/22                                                 
#> 6 VOI - 2019                                                  
#> 7 2022_BAF and WISH coral reef surveys in Tailevu_Ovalau      
#> 8 ACEH_EAST COAST_2022-Delete                                 
#> 9 BAF 1 - 2020

And then by querying for public summary data, using mermaid_get_project_data(), specifying the “benthicpit” method with “sampleevents” data. The key to accessing public summary data is to set our token to NULL - this makes it so that mermaidr won’t try to authenticate us, and instead just returns the data if the data policy allows it.

benthic_data <- projects_public_benthic %>%
  mermaid_get_project_data("benthicpit", "sampleevents", token = NULL)

head(benthic_data)
#> # A tibble: 6 × 62
#>   project             tags  country site  latitude longitude reef_type reef_zone
#>   <chr>               <chr> <chr>   <chr>    <dbl>     <dbl> <chr>     <chr>    
#> 1 2022 DamaWISH endl… Vibr… Fiji    DamC1    -16.8      179. patch     back reef
#> 2 2022 DamaWISH endl… Vibr… Fiji    DamC3    -16.8      179. patch     back reef
#> 3 2022 DamaWISH endl… Vibr… Fiji    DamC4    -16.9      179. patch     back reef
#> 4 2022 DamaWISH endl… Vibr… Fiji    DamC5    -16.9      179. patch     fore reef
#> 5 2022 DamaWISH endl… Vibr… Fiji    DamC8    -16.8      179. patch     fore reef
#> 6 2022 DamaWISH endl… Vibr… Fiji    DamE1    -16.9      179. patch     back reef
#> # ℹ 54 more variables: reef_exposure <chr>, tide <chr>, current <chr>,
#> #   visibility <chr>, management <chr>, management_secondary <chr>,
#> #   management_est_year <dbl>, management_size <dbl>, management_parties <chr>,
#> #   management_compliance <chr>, management_rules <chr>, sample_date <date>,
#> #   depth_avg <dbl>, depth_sd <dbl>,
#> #   percent_cover_benthic_category_avg_sand <dbl>,
#> #   percent_cover_benthic_category_avg_rubble <dbl>, …

At a high level, this returns the aggregations for a survey at the sample event level; roughly, it provides a summary of all observations for all transects at a given site and date, giving us information like the average percent cover for each benthic category. Let’s get the data for the latest date for each site, then focus in on the country, site, and percent_cover_benthic_category_avg_hard_coral columns.

benthic_data_latest <- benthic_data %>%
  group_by(site) %>%
  filter(sample_date == max(sample_date)) %>%
  ungroup() %>%
  select(country, site, management, hard_coral = percent_cover_benthic_category_avg_hard_coral)

We’d like to summarise hard coral coverage across these sites. WCS considers a 10% cover as a minimum threshold of carbonate production and reef growth, while 30% cover may be more related to a threshold for biodiversity and fisheries production, so we will aggregate and visualize the coverage.

First, let’s categorize each value of hard_coral according to whether it’s below 10%, between 10% and 30%, or above 30%:

benthic_data_latest <- benthic_data_latest %>%
  mutate(threshold = case_when(
    hard_coral < 10 ~ "Below 10% cover",
    hard_coral >= 10 & hard_coral < 30 ~ "Midrange 10-30% cover",
    hard_coral >= 30 ~ "Above 30% hard coral cover"
  ))

We can count how many fall into each category - it looks like the bulk of sites (49.4%) have above 30% hard coral cover, a small number () are below 10%, and just over a third (50.6%) of surveyed sites are in the midrange of 10-30% cover.

benthic_data_latest %>%
  count(threshold) %>%
  mutate(prop = n / sum(n))
#> # A tibble: 2 × 3
#>   threshold                      n  prop
#>   <chr>                      <int> <dbl>
#> 1 Above 30% hard coral cover    40 0.494
#> 2 Midrange 10-30% cover         41 0.506

We’d also like to see the distribution of these values - for example, what do those 10 - 30% coverages actually look like?

Let’s visualize the data, using ggplot2:

library(ggplot2)

ggplot(benthic_data_latest) +
  geom_col(aes(x = hard_coral, y = site, fill = threshold))

A bar chart with hard coral cover on the x axis (titled hard_coral) and sites on the y axis (titled site). The bars are colored in pink, green, and blue, according to whether they have above 30% hard coral cover, below 10% cover, or 10-30% cover, respectively. The sites are ordered alphabeticaly.

This is a good start, but it would probably be much more useful with a few changes. We can recorder the sites so that they go in descending order, from highest to lowest hard coral coverage. We can also rearrange the threshold legend, so that it follows a logical order, from highest to lowest coverage (instead of alphabetical). It would also be helpful to add a line at the 10% and 30% points, to visualize those thresholds more explicitly.

benthic_data_latest <- benthic_data_latest %>%
  mutate(
    threshold = fct_reorder(threshold, hard_coral, .desc = TRUE),
    site = fct_reorder(site, hard_coral)
  )

hard_coral_plot <- ggplot(benthic_data_latest) +
  geom_col(aes(x = hard_coral, y = site, fill = threshold), alpha = 0.8) +
  geom_vline(xintercept = 10, linetype = "dashed", linewidth = 0.25) +
  geom_vline(xintercept = 30, linetype = "dashed", linewidth = 0.25)

hard_coral_plot

A bar chart with hard coral cover on the x axis (titled hard_coral) and sites on the y axis (titled site). The bars are colored in pink, green, and blue, according to whether they have above 30% hard coral cover, 10-30% cover, or below 10% cover. The sites are ordered by their hard coral coverage, from highest to lowest. There is a dashed vertical line at 10% and 30% hard coral.

Finally, we can make some visual changes, like updating the theme, removing the site names; we care more about the distribution than which site they correspond to (which is too small to read anyways!) for this plot, adding axis labels for every 10%, cleaning up labels, and some other fiddly bits to create a beautiful plot!

hard_coral_plot <- hard_coral_plot +
  scale_x_continuous(name = "Hard coral cover, %", breaks = seq(0, 80, 10)) +
  scale_y_discrete(name = NULL) +
  scale_fill_manual(name = "Threshold", values = c("#7F7F7F", "#F1A83B", "#E73F25")) +
  labs(title = "A sample of WCS Vibrant Oceans survey sites, ordered by hard coral cover") +
  theme_minimal() +
  theme(
    axis.text.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank()
  )

hard_coral_plot

That looks much better!

For further analysis, we can also calculate the average hard coral cover (and its standard error) then show them in a beautiful graph. Since the highest level in the data after site is country, in this example, we will calculate the average hard coral cover per country and treat sites as the replicates per country.

hard_coral_by_country <- benthic_data_latest %>%
  group_by(country) %>%
  summarise(
    n_sites = n_distinct(site),
    hard_coral_average = mean(hard_coral),
    hard_coral_sd = sd(hard_coral),
    hard_coral_se = hard_coral_sd / sqrt(n_sites)
  )

hard_coral_by_country
#> # A tibble: 4 × 5
#>   country   n_sites hard_coral_average hard_coral_sd hard_coral_se
#>   <chr>       <int>              <dbl>         <dbl>         <dbl>
#> 1 Fiji           57               29.9         13.7           1.82
#> 2 Indonesia       9               49.1          7.03          2.34
#> 3 Kenya           5               28.1          7.17          3.21
#> 4 Tanzania       10               40.3         17.4           5.51

Take a look at the hard_coral_by_country data frame. There are only five columns with four rows of data.

You can also use the code to calculate the average hard coral cover percentage based on other columns. For example, if you want to average per management regime, you can group by management instead:

benthic_data_latest %>%
  group_by(management) %>%
  summarise(
    n_sites = n_distinct(site),
    hard_coral_average = mean(hard_coral),
    hard_coral_sd = sd(hard_coral),
    hard_coral_se = hard_coral_sd / sqrt(n_sites)
  )
#> # A tibble: 20 × 5
#>    management             n_sites hard_coral_average hard_coral_sd hard_coral_se
#>    <chr>                    <int>              <dbl>         <dbl>         <dbl>
#>  1 Bu_LMMA                      4               21.8          7.73          3.86
#>  2 Core Zone                    2               50.9         15.7          11.1 
#>  3 Dama_LMMA                    8               23.1          9.47          3.35
#>  4 Dama_Tabu                    3               36.6          9.86          5.69
#>  5 Dawa_LMMA                   12               29.3         12.9           3.72
#>  6 Diani Marine Reserve         1               29.2         NA            NA   
#>  7 Fisheries                    4               46.6          4.85          2.43
#>  8 Jimbo tengefu                1               17.9         NA            NA   
#>  9 Kisite-Mpunguti Marin…       1               29.2         NA            NA   
#> 10 Open Access                  1               53.8         NA            NA   
#> 11 Ovalau_LMMA                 16               36.0         17.0           4.26
#> 12 Ovalau_Tabu                  8               35.6          7.68          2.72
#> 13 Tanga Coelacanth Mari…       1               40           NA            NA   
#> 14 Tanga Coelacanth Mari…       1               27.1         NA            NA   
#> 15 Tanga Marine Reserves…       4               38.2         13.0           6.50
#> 16 Utility                      2               50.2          4.72          3.34
#> 17 Vanga tengefu                1               26.2         NA            NA   
#> 18 Wai_LMMA                     6               18.4          9.98          4.08
#> 19 community reef               4               45.7         25.3          12.6 
#> 20 open access                  1               37.9         NA            NA

You can also calculate the average and standard error for other benthic attribute, such as soft coral, macroalgae, etc.

Now that we’ve calculated the average hard coral cover and standard error, the next step is to put the result in a nice graph. We will do this using the ggplot2 as we did it previously, using geom_col() to create the bars and geom_errorbar() to show the standard errors.

ggplot(hard_coral_by_country, aes(x = country, y = hard_coral_average)) +
  geom_col(aes(fill = country)) +
  geom_errorbar(
    aes(
      x = country, y = hard_coral_average,
      ymin = hard_coral_average - hard_coral_se,
      ymax = hard_coral_average + hard_coral_se
    ),
    width = 0.2
  ) +
  labs(
    x = "Country", y = "Average Hard Coral Cover (%)\n\u00B1 Standard Error",
    title = "Average Hard Coral Cover by Country"
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold")
  )

A bar chart with country on the x axis and average hard coral cover on the y axis. The bars are colored in according to country. The bar chart has a standard error for each country, using site as the replicates. The plot now has a title: Average Hard Coral Cover by Country. The x axis is titled: Country and the y axis is titled: Average Hard Coral Cover (%) plus or minus standard error.

It might also be interesting to see the changes in hard coral cover over time. To make this graph, first, we need to go back to the original benthic_data data set which contains sample date.

benthic_data <- benthic_data %>%
  select(country, site, sample_date, hard_coral = percent_cover_benthic_category_avg_hard_coral)

The next step is to separate it into year, month, and day. To do this, we’re going to use the separate() function.

benthic_data <- benthic_data %>%
  separate(sample_date,
    into = c("year", "month", "day"),
    sep = "-"
  )

benthic_data
#> # A tibble: 119 × 6
#>    country site  year  month day   hard_coral
#>    <chr>   <chr> <chr> <chr> <chr>      <dbl>
#>  1 Fiji    DamC1 2022  04    23          27.3
#>  2 Fiji    DamC3 2022  04    26          26.3
#>  3 Fiji    DamC4 2022  04    26          46  
#>  4 Fiji    DamC5 2022  04    22          37.3
#>  5 Fiji    DamC8 2022  04    23          22  
#>  6 Fiji    DamE1 2022  04    25          24  
#>  7 Fiji    DamE2 2022  04    25          39  
#>  8 Fiji    DamE4 2022  04    20          30.3
#>  9 Fiji    DamE5 2022  04    22          20  
#> 10 Fiji    DamE6 2022  04    23          10.3
#> # ℹ 109 more rows

Now we have all of the columns that we need, we can continue by calculating the average hard coral cover by year by country.

hard_coral_by_country_by_year <- benthic_data %>%
  group_by(country, year) %>%
  summarise(hard_coral_average = mean(hard_coral)) %>%
  ungroup()

hard_coral_by_country_by_year
#> # A tibble: 5 × 3
#>   country   year  hard_coral_average
#>   <chr>     <chr>              <dbl>
#> 1 Fiji      2019                22.1
#> 2 Fiji      2022                30.9
#> 3 Indonesia 2022                49.1
#> 4 Kenya     2020                28.1
#> 5 Tanzania  2020                40.7

In the hard_coral_by_country_by_year data frame, you can see that only Fiji and Indonesia have more than one year data. So, we’re going to remove countries that only have one year of data.

hard_coral_by_country_by_year <- hard_coral_by_country_by_year %>%
  group_by(country) %>%
  mutate(n_years = n_distinct(year)) %>%
  filter(n_years > 1) %>%
  ungroup() %>%
  select(-n_years)

Now, we are ready to present the data in a line plot.

ggplot(
  hard_coral_by_country_by_year,
  aes(x = year, y = hard_coral_average, group = country, color = country)
) +
  geom_line() +
  geom_point() +
  labs(
    x = "Year", y = "Average Hard Coral Cover (%)",
    title = "Trend of Average Hard Coral Cover by Country"
  ) +
  scale_color_discrete(name = "Country") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

A line chart with year on the x axis and average hard coral cover on the y axis. The lines are colored in red and blue to differentiate per country. The plot now has a title: Trend Average Hard Coral Cover per Country. The x axis is titled: Year and the y axis is titled: Average Hard Coral Cover (%).

Looks great! You can save the plot and use it in your report!

Reef fish biomass

Next, let’s turn to reef fish biomass. Similarly to the step above, we can get this data by filtering for projects that have publicly available summary data for it.

projects_public_fishbelt <- vo_projects %>%
  filter(data_policy_beltfish == "Public Summary")

projects_public_fishbelt %>%
  select(name)
#> # A tibble: 8 × 1
#>   name                                                  
#>   <chr>                                                 
#> 1 Aceh East Coast 2019                                  
#> 2 2022 DamaWISH endline survey                          
#> 3 ACEH_EAST COAST_2022                                  
#> 4 VOI - 20/22                                           
#> 5 VOI - 2019                                            
#> 6 2022_BAF and WISH coral reef surveys in Tailevu_Ovalau
#> 7 ACEH_EAST COAST_2022-Delete                           
#> 8 Aceh Jaya MPA 2022

Next, we query for public summary data, again using mermaid_get_project_data to get “sampleevents” data, this time specifying the “fishbelt” method:

fishbelt_data <- projects_public_fishbelt %>%
  mermaid_get_project_data("fishbelt", "sampleevents", token = NULL)

head(fishbelt_data)
#> # A tibble: 6 × 138
#>   project             tags  country site  latitude longitude reef_type reef_zone
#>   <chr>               <chr> <chr>   <chr>    <dbl>     <dbl> <chr>     <chr>    
#> 1 Aceh East Coast 20… WCS … Indone… Anoi…     5.83      95.4 fringing  fore reef
#> 2 Aceh East Coast 20… WCS … Indone… Anoi…     5.84      95.4 fringing  fore reef
#> 3 Aceh East Coast 20… WCS … Indone… Bent…     5.85      95.4 fringing  fore reef
#> 4 Aceh East Coast 20… WCS … Indone… Beur…     5.78      95.3 fringing  fore reef
#> 5 Aceh East Coast 20… WCS … Indone… Ie M…     5.90      95.3 fringing  fore reef
#> 6 Aceh East Coast 20… WCS … Indone… Inti…     5.83      95.4 fringing  fore reef
#> # ℹ 130 more variables: reef_exposure <chr>, tide <chr>, current <chr>,
#> #   visibility <chr>, management <chr>, management_secondary <chr>,
#> #   management_est_year <dbl>, management_size <dbl>, management_parties <chr>,
#> #   management_compliance <chr>, management_rules <chr>, sample_date <date>,
#> #   depth_avg <dbl>, depth_sd <dbl>, biomass_kgha_avg <dbl>,
#> #   biomass_kgha_sd <dbl>, biomass_kgha_trophic_group_avg_omnivore <dbl>,
#> #   biomass_kgha_trophic_group_avg_piscivore <dbl>, …

Again, this summarises all observations for all transects at a given site and date, and gives us information like the average biomass at that site, and average biomass across groupings like trophic group or fish family.

We’ll just focus on the average biomass, at the latest sample date for each site:

fishbelt_data_latest <- fishbelt_data %>%
  group_by(site) %>%
  filter(sample_date == max(sample_date)) %>%
  ungroup() %>%
  select(country, site, biomass = biomass_kgha_avg) %>%
  distinct()

WCS considers 500 kg/ha as a threshold where below this biomass, ecosystems may pass critical thresholds of ecosystem decline, and often seek to maintain reef fish biomass above 500 kg/ha as a management target. Let’s categorize each value of biomass according to whether it’s above or below 500 kg/ha, and reorder both the sites and the threshold factor from highest to lowest biomass:

fishbelt_data_latest <- fishbelt_data_latest %>%
  mutate(
    threshold = case_when(
      biomass >= 500 ~ "Above 500 kg/ha",
      biomass < 500 ~ "Below 500 kg/ha"
    ),
    threshold = fct_reorder(threshold, biomass, .desc = TRUE),
    site = fct_reorder(site, biomass)
  )

Then, we can visualize the distribution of average biomass in sites, similar to our visualization before:

ggplot(fishbelt_data_latest) +
  geom_col(aes(x = biomass, y = site, fill = threshold), alpha = 0.8) +
  geom_vline(xintercept = 500, linetype = "dashed", size = 0.25) +
  scale_x_continuous(name = "Total reef fish biomass, kg/ha") +
  scale_y_discrete(name = NULL) +
  scale_fill_manual(name = "Threshold", values = c("#7F7F7F", "#E73F25")) +
  labs(title = "A sample of WCS Vibrant Oceans survey sites, ordered by reef fish biomass") +
  theme_minimal() +
  theme(
    axis.text.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank()
  )

We can also calculate the average fish biomass per country with standard errors and fish biomass trend through time, just like we did for benthic. First, let’s calculate the average fish biomass bt country and present it in a bar plot.

fish_biomass_by_country <- fishbelt_data_latest %>%
  group_by(country) %>%
  summarise(
    n_sites = n_distinct(site),
    fish_biomass_average = mean(biomass),
    fish_biomass_sd = sd(biomass),
    fish_biomass_se = fish_biomass_sd / sqrt(n_sites)
  )

fish_biomass_by_country
#> # A tibble: 3 × 5
#>   country   n_sites fish_biomass_average fish_biomass_sd fish_biomass_se
#>   <chr>       <int>                <dbl>           <dbl>           <dbl>
#> 1 Fiji           27                 889.            555.           107. 
#> 2 Indonesia      12                 512.            131.            37.7
#> 3 Tanzania       36                 256.            238.            39.6

ggplot(fish_biomass_by_country, aes(x = country, y = fish_biomass_average)) +
  geom_col(aes(fill = country)) +
  geom_errorbar(
    aes(
      x = country, y = fish_biomass_average,
      ymin = fish_biomass_average - fish_biomass_se,
      ymax = fish_biomass_average + fish_biomass_se
    ),
    width = 0.2
  ) +
  labs(
    x = "Country", y = "Average Fish Biomass (kg/ha)\n\u00B1 Standard Error",
    title = "Average Fish Biomass by Country"
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold")
  )

A bar chart with country on the x axis and average fish biomass on the y axis. The bars are colored in red, green, and blue to differentiate per country. The plot now has a title: Average Fish Biomass per Country. The x axis is titled: Country and the y axis is titled: Average Fish Biomass (kg/ha).

We have our average fish biomass per country plot. Let’s continue with visualizing the changes in fish biomass per country. Again, we’ll go back to the original fishbelt_data data set which has the sample date.

fishbelt_data <- fishbelt_data %>%
  select(country, site, sample_date, biomass = biomass_kgha_avg)

fishbelt_data <- fishbelt_data %>% separate(sample_date,
  into = c("year", "month", "day"),
  sep = "-"
)

fishbelt_biomass_by_country_by_year <- fishbelt_data %>%
  group_by(country, year) %>%
  summarise(fish_biomass_average = mean(biomass)) %>%
  ungroup()

fishbelt_biomass_by_country_by_year
#> # A tibble: 6 × 3
#>   country   year  fish_biomass_average
#>   <chr>     <chr>                <dbl>
#> 1 Fiji      2022                 889. 
#> 2 Indonesia 2019                 805. 
#> 3 Indonesia 2022                 479. 
#> 4 Tanzania  2019                 357. 
#> 5 Tanzania  2022                 368. 
#> 6 Tanzania  2023                  75.0

Looking at the fishbelt_biomass_by_country_by_year data frame, we can see that there are only two countries that have more than one year of fish data, which are Indonesia and Tanzania. So, we are going to only use data from countries with more than one year of data, then visualize it in a line plot.

fishbelt_biomass_by_country_by_year <- fishbelt_biomass_by_country_by_year %>%
  group_by(country) %>%
  mutate(n_years = n_distinct(year)) %>%
  filter(n_years > 1) %>%
  ungroup() %>%
  select(-n_years)

ggplot(
  fishbelt_biomass_by_country_by_year,
  aes(x = year, y = fish_biomass_average, group = country, color = country)
) +
  geom_line() +
  geom_point() +
  labs(
    x = "Year", y = "Average Fish Biomass (kg/ha)",
    title = "Trend of Average Fish Biomass by Country"
  ) +
  scale_color_discrete(name = "Country") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

A line chart with year on the x axis and average fish biomass on the y axis. The lines are colored in red and tosca to differentiate per country. The plot now has a title: Trend of Average Fish Biomass by Country. The x axis is titled: Year and the y axis is titled: Average Fish Biomass (kg/ha).