This documents outlines how a multi-taxon indicator (Community temperature index for Finnish Birds in Winter) can be calculated in R.

Load packages

The following packages are required. All packages are available on CRAN apart from {fbi} which can be installed from GitHub.

library(dplyr)
library(fbi)
library(finbif)
library(ggplot2)
library(lubridate)
library(lme4)
library(arm)

Survey data

These five fields are required for the survey data.

select <- c("document_id", "location_id", "year", "month", "day")

These filters restrict the survey data to the Dec-Jan “Winter Bird Census” monitoring dataset from December 1958 onwards where the selected data fields have no missing data.

filter <- list(
  collection = "Winter Bird Census",
  date_range_ymd = c("1958-12-01", ""),
  date_range_md= c("12-01", "01-31"),
  has_value = select
)

The survey data can now downloaded from FinBIF.

surveys <- finbif_occurrence(
  filter = filter,
  select = select,
  aggregate = "events",
  aggregate_counts = FALSE,
  n = "all",
  quiet = TRUE
)

A single processing function is applied to the survey data to limit each site to the first survey of the year (where year is the period Dec-Jan).

surveys <- pick_first_survey_in_winter(surveys)

Count data

Count data requires two fields to be selected: the survey identifier (document_id) and the measure of abundance (abundance_interpreted).

select <- c("document_id", abundance = "abundance_interpreted")

The count data requires the same filters as the survey data (though the filter has_value needs to be redefined).

filter[["has_value"]] <- select

A set of taxa and their species temperature index values is defined.

sti <- c(
  "Poecile montanus" = -7.51,
  "Regulus regulus" = -4.23,
  "Lophophanes cristatus" = -5.90,
  "Certhia familiaris" = -6.89
)

The count data for these taxa can now be downloaded from FinBIF.

counts <- lapply(
  names(sti),
  finbif_occurrence,
  filter = filter,
  select = select,
  n = "all",
  quiet = TRUE
)

The count data is then combined with the survey data and the species temperature indices.

counts <- lapply(counts, combine_with_surveys, surveys)

counts <- mapply(mutate, counts, sp = names(sti), SIMPLIFY = FALSE)

counts <- do.call(rbind, counts)

counts[["sti"]] <- sti[counts[["sp"]]]

Create Index

A community temperature index for each year is created by fitting a linear mixed effects regression model to the combined data.

cti_data <- summarise(
  group_by(counts, location_id, year),
  cti = sum(abundance * sti) / sum(abundance), .groups = "drop"
)

cti <- lmer(cti ~ (1 | location_id) + (1 | year), cti_data)

cti <- data.frame(index = coef(cti)[["year"]], se = se.coef(cti)[["year"]])

cti[["time"]] <- as.integer(row.names(cti))

names(cti) <- c("index", "se", "time")

ggplot(cti) +
aes(
  x = parse_date_time(time, "Y"),
  y = index,
  ymin = index - se,
  ymax = index + se
) +
geom_ribbon(alpha = .2) +
geom_line() +
ylab(NULL) +
xlab(NULL) +
theme_minimal()

Community Temperature Index for Winter Birds in Finland