Analyzing Simulated Data on Coral Reef Health
In my study on the impact of ocean acidification on coral reefs, I simulated a large dataset to analyze how increased CO2 levels influence various health indicators of coral ecosystems. This simulated data includes key variables such as calcium carbonate levels, algae cover, fish diversity, coral bleaching incidents, and CO2 concentrations, enabling a robust multivariate analysis to identify patterns and draw insights.
I crafted a dataset with 5000 entries, ensuring a wide range of variability across all key health indicators. This approach allows me to model realistic ecological conditions under varying environmental stress levels.
I introduced a ‘HealthIndex’ to quantify overall reef health based on the simulated indicators. This composite metric helps summarize the multifaceted aspects of reef health into a single, interpretable figure.
By categorizing reef health into ‘Good’, ‘Moderate’, and ‘Poor’ based on the ‘HealthIndex’, I can easily classify and prioritize areas for conservation efforts.
The simulated data analysis reveals critical dependencies between CO2 levels and coral health. For instance, higher CO2 levels correlate with increased bleaching events and decreased overall health indices. Such insights are invaluable for environmental scientists and policymakers aiming to devise strategies to mitigate the adverse effects of ocean acidification.
By leveraging this comprehensive simulated dataset, I can effectively model potential future scenarios and their impacts on coral reefs, guiding better-informed decisions to protect these vital ecosystems.
In my analysis of the simulated coral data, I meticulously reviewed the summary statistics to glean insights into the health and environmental factors affecting coral reefs. Here’s my interpretation based on the data provided:
I noticed that the calcium carbonate levels in my dataset range widely from 243.1 to 572.3, with a median of 399.6. This suggests a varied composition in the reef structures I’m simulating, where higher values might indicate more robust and healthy reefs. Algae Cover:
The algae cover varies from almost none (0.01466) to nearly complete coverage (99.98987), with a mean of around 49.88. This wide range indicates diverse reef conditions in my simulations. A higher algae cover could signify stressed reefs, especially if it trends near the maximum. Fish Diversity:
Fish diversity, measured as the number of species, ranges from 7 to 23 species per sample area, with an average count close to 20 species. This parameter is crucial for my assessment as higher diversity often correlates with healthier reef ecosystems. Coral Bleaching:
Interestingly, coral bleaching data shows a minimal mean bleaching rate of about 0.29, but the maximum reaches 1. This variable, crucial for my study, indicates the percentage of corals affected by bleaching, where 1 represents 100% bleaching. Most observations show no bleaching, which is encouraging, but the presence of maximum values suggests areas of high stress. CO2 Levels:
CO2 levels in the water range from 310.3 to 527.8, with a median right at 414.0. The elevated CO2 levels in some areas could be driving some of the bleaching events and affecting overall reef health, a hypothesis supported by the data’s upper range. Health Index and Health Category:
The health index, a calculated metric from 0.6996 to 3.1134, helps me quantify overall reef health in a single figure. A median of 2.0587 indicates moderately healthy reefs, but the range shows that some reefs are in excellent condition while others are significantly stressed. The health category, a qualitative measure, complements my numerical analysis, allowing me to classify reefs based on observed and derived metrics easily.
# Loading necessary libraries
library(tidyverse) # I utilize the tidyverse for its powerful data manipulation and visualization capabilities.## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Setting the seed for reproducibility
set.seed(123) # I set a seed to ensure that my results are reproducible, which is essential for consistent outputs across different runs.
# Simulating a large dataset with 5000 observations
coral_data <- tibble(
CalciumCarbonate = rnorm(5000, mean = 400, sd = 50), # I'm simulating calcium carbonate levels with a normal distribution because it realistically models natural variation.
AlgaeCover = runif(5000, min = 0, max = 100), # I chose a uniform distribution for algae cover to simulate a varied but evenly distributed percentage range across reefs.
FishDiversity = rpois(5000, lambda = 20), # I'm using a Poisson distribution for fish diversity counts because fish species counts tend to follow a Poisson process in natural ecosystems.
CoralBleaching = rbinom(5000, size = 1, prob = 0.3), # I'm simulating coral bleaching events as a binary outcome (0 or 1) using a binomial distribution, where 30% of observations indicate bleaching.
CO2Levels = rnorm(5000, mean = 415, sd = 30) # I simulate CO2 levels with a normal distribution to reflect natural fluctuations in oceanic CO2 concentrations.
)
# Adding a response variable 'HealthIndex' calculated based on other factors
coral_data <- coral_data %>%
mutate(
HealthIndex = (CalciumCarbonate * 0.5 + (100 - AlgaeCover) * 0.3 + FishDiversity * 0.2) / 100,
# I calculate a HealthIndex to quantitatively assess reef health. It combines key factors weighted by their perceived impact on overall health: calcium levels, algae cover (inverted), and fish diversity.
HealthIndex = if_else(CoralBleaching == 1, HealthIndex * 0.5, HealthIndex),
# I halve the HealthIndex when bleaching is present, reflecting its significant negative impact on reef health.
HealthCategory = if_else(HealthIndex > 0.75, "Good", if_else(HealthIndex > 0.5, "Moderate", "Poor"))
# I categorize health into 'Good', 'Moderate', or 'Poor' based on the HealthIndex to facilitate straightforward assessments and reporting.
)
# Providing an overview of the simulated data
print("Summary of Simulated Coral Data:")## [1] "Summary of Simulated Coral Data:"summary(coral_data) # I display a summary to quickly understand the ranges, averages, and distribution of the simulated data.## CalciumCarbonate AlgaeCover FishDiversity CoralBleaching
## Min. :243.1 Min. : 0.01466 Min. : 7.00 Min. :0.0000
## 1st Qu.:367.2 1st Qu.:24.92396 1st Qu.:17.00 1st Qu.:0.0000
## Median :399.6 Median :49.04930 Median :20.00 Median :0.0000
## Mean :400.0 Mean :49.88182 Mean :20.03 Mean :0.2856
## 3rd Qu.:433.0 3rd Qu.:75.79749 3rd Qu.:23.00 3rd Qu.:1.0000
## Max. :572.3 Max. :99.98987 Max. :37.00 Max. :1.0000
## CO2Levels HealthIndex HealthCategory
## Min. :310.3 Min. :0.6996 Length:5000
## 1st Qu.:393.6 1st Qu.:1.2413 Class :character
## Median :414.0 Median :2.0587 Mode :character
## Mean :414.1 Mean :1.8790
## 3rd Qu.:434.2 3rd Qu.:2.2953
## Max. :527.8 Max. :3.1134# Load necessary libraries
library(tidyverse)
library(DT)
# Setting the seed for reproducibility
set.seed(123)
# Parameters for the simulation
n <- 5000
mean_cc <- 400
sd_cc <- 50
mean_co2 <- 415
sd_co2 <- 30
# Simulating data for coral reef health indicators
coral_data <- tibble(
CalciumCarbonate = rnorm(n, mean = mean_cc, sd = sd_cc),
AlgaeCover = runif(n, min = 0, max = 100),
FishDiversity = rpois(n, lambda = 20),
CoralBleaching = rbinom(n, size = 1, prob = 0.3),
CO2Levels = rnorm(n, mean = mean_co2, sd = sd_co2)
)
# Calculating Health Index
coral_data <- coral_data %>%
mutate(
HealthIndex = (CalciumCarbonate * 0.5 + (100 - AlgaeCover) * 0.3 + FishDiversity * 0.2) / 100,
HealthIndex = ifelse(CoralBleaching == 1, HealthIndex * 0.5, HealthIndex)
)
# Categorizing health based on the HealthIndex
coral_data <- coral_data %>%
mutate(
HealthCategory = case_when(
HealthIndex >= 0.75 ~ "Good",
HealthIndex >= 0.5 ~ "Moderate",
TRUE ~ "Poor"
)
)
# Displaying the data as a colorful table
datatable(
coral_data,
options = list(
pageLength = 10,
autoWidth = TRUE,
searching = FALSE,
ordering = TRUE
),
filter = 'top',
class = 'cell-border stripe'
) %>%
formatStyle(
'HealthIndex',
backgroundColor = styleInterval(c(0.5, 0.75), c('red', 'yellow', 'green'))
)