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A simple wrapper for msi_tool to make it easier to use in R. Multi-Species Indicators (MSI) are biodiversity indicators that combine the population development of species into a single indicator. The MSI-tool calculates an MSI, confidence intervals for the MSIs and linear and flexible (smoothed) trends. The trends are classified in terms like "moderate increase", "strong decrease" or "stable". A number of additional analyses can be performed like testing for changepoints, comparison of trends before and after a changepoint and the calculation and testing of the total change in a time series.

Usage

msi(data, jobname = "MSI_job", ...)

Arguments

data

a data.frame with 4 columns in this order: 'species', 'year', 'index', 'se' (standard error). The index value in the base year (which need not be the first year), should be set to 100, with se of 0.

jobname

Generic name for output files

...

other parameters to pass to msi_tool

Value

Returns a dataframe with 4 columns: Year, Index, lower2.5, upper97.5. The last two columns are the credible intervals

Examples


# Create some example data in the format required
nyr = 20
species = rep(letters, each = nyr)
year = rev(rep(1:nyr, length(letters)))

# Create an index value that increases with time
index = rep(seq(50, 100, length.out = nyr), length(letters))
# Add randomness to species
index = index * runif(n = length(index), 0.7, 1.3)
# Add correlated randomness acrosss species, to years
index = index * rep(runif(0.8, 1.2, n = nyr), length(letters))

se = runif(n = nyr * length(letters), min = 10, max = 20)

data <- data.frame(species, year, index, se)

# Our species are decreasing
plot(data$year, data$index)


# Species index values need to be 100 in the base year. Here I use
# the first year as my base year and rescale to 100. The standard error
# in the base year should be 0.
min_year <- min(data$year)

for(sp in unique(data$species)){
  
  subset_data <- data[data$species == sp, ]
  multi_factor <- 100 / subset_data$index[subset_data$year == min_year]
  data$index[data$species == sp] <- data$index[data$species == sp] * multi_factor
  data$se[data$species == sp] <- data$se[data$species == sp] * multi_factor
  data$se[data$species == sp][1] <- 0
  
}

# Run the MSI function
msi_out <- msi(data, plot = FALSE)

# Plot the resulting indicator
plot(msi_out)