Introduction to BioTIMEr

Load the required libraries

# Install and load the latest version of BioTIMEr
library(BioTIMEr)

When loading the BioTIMEr package, a subset of BioTIME data is loaded together with its metadata. This subset includes 12 studies from marine, terrestrial and freshwater time series across different taxonomic groups. The whole BioTIME database is accessible here

# you can run the following commands to retrieve more information about the subsets.
?BTsubset_meta
?BTsubset_data

# you can also see a full list of BioTIMEr functions and help pages by:
??BioTIMEr

How to use BioTIMEr?

1. Grid BioTIME data into a discrete global grid based on the samples’ location (latitude/longitude) to standardize the spatial extent between different studies using:
   • gridding()
     
2. Apply sample-based rarefaction to standardize the number of samples per year within each cell-level time series using:
   • resampling()
3. Calculate a set of standard alpha and/or beta diversity metrics using:
   • getAlphaMetrics()
   • getBetaMetrics()
4. Fit linear regression models to step 3 outputs as a function of time with:
   • getLinearRegressions ()
5. Visualise the results.

Below is a detailed run through these different steps, breaking down some of the tasks and the reasoning behind our suggestions for best practice when analysing BioTIME data.

We use the ‘pipe’ operator (%>%) to make our code more efficient and streamlined, and we use ggplot2 library for most visualisations. This and other libraries required for BioTIMEr to run will be, if needed, installed at the same time BioTIMEr is installed.

Why bother with separating observations using a global grid?

Typically, a BioTIME study contains distinct samples collected with a consistent methodology for a given period of time. However, the spatial extent and sample distribution among studies can vary substantially depending on the study and the taxon being sampled (e.g. forest quadrants, bird transects, fish tows, etc.; which can be implemented within smaller or larger areas). Because the spatial extent varies among studies, an advisable step is to grid those studies that have large extents and multiple sampling locations into smaller equal-sized subsets. The gridding() function is designed to perform such task using a global grid of hexagonal cells derived from dggridR (see vignette(‘dggridR’)). By implementing this gridding, the large scales studies are split into multiple cell-level assemblages, while the small scale studies are assigned to a single cell.

Let’s apply gridding() to a subset of BioTIME data and see what happens:

grid_samples <- gridding(meta = BTsubset_meta, btf = BTsubset_data, res = 12, resByData = FALSE) 

# Get a look at the output
# grid_samples %>% head() %>% kable()

You can see that each sample is assigned a different combination of study ID and grid cell (based on its latitude and longitude) resulting in a unique identifier that determines our assemblage time series (assemblageID). This assemblageID can now be used as the study unit for analysis of biodiversity change based on similar spatial extent between them.

Let’s take a look and see how many studies/cells/assemblages time series are there in this subset:

check_res_1 <- grid_samples %>% 
  group_by(STUDY_ID, StudyMethod) %>% 
  summarise(n_cell = n_distinct(cell), n_aID = n_distinct(assemblageID), res = "res12")

check_res_1 %>% head(10) %>% kable()

# How many samples were there in each study?
ggplot(data = check_res_1) +
  geom_bar(mapping = aes(x = as.character(STUDY_ID), y = n_aID, fill = res),
           stat = "identity") +
  scale_fill_discrete(type = c("#155f49")) +
  xlab("StudyID") + ylab("Number of assemblages in a study") +
  theme_bw() +
  theme(legend.position = "none")

You can see that several studies are not partitioned, and that’s because all their observations were already contained within a single cell.

# define an alternative resolution of 14 (~10.7 km2 cells)
grid_samples_14 <- gridding(meta = BTsubset_meta, btf = BTsubset_data, res = 14, resByData = FALSE) 

# allow the spatial extent of the data to define the resolution
grid_samples_auto <- gridding(meta = BTsubset_meta, btf = BTsubset_data, res = 12, resByData = TRUE)
# this option also returns a message with the automatically picked resolution:

## Resolution: 15, Area (km^2): 3.55473501726709, Spacing (km): 1.86210705534755, CLS (km): 2.12744663988395

check_res_2 <- grid_samples_14 %>% 
  group_by(StudyMethod, STUDY_ID) %>% 
  summarise(n_cell = n_distinct(cell), n_aID = n_distinct(assemblageID), res = "res14")

check_res_3 <- grid_samples_auto %>% 
  group_by(StudyMethod, STUDY_ID) %>% 
  summarise(n_cell = n_distinct(cell), n_aID = n_distinct(assemblageID), res = "res15") 

checks <- rbind(check_res_1, check_res_2, check_res_3)

ggplot(data = checks) +
  geom_bar(mapping = aes(x = as.character(STUDY_ID), y = n_aID, fill = res), 
           stat = "identity", position = "dodge") +  
  scale_fill_discrete(type = c("#155f49","#66c1d1","#d9d956")) +
  xlab("StudyID") + ylab("Number of assemblages in a study") +
  theme_bw()

## The following example is built on the demonstrations in
## https://cran.r-project.org/web/packages/dggridR/vignettes/dggridR.html.

# First we build the ~96 km2 global grid
dgg_12 <- dggridR::dgconstruct(res = 12)

# To simplify, we only map the grid cell boundaries for cells which 
# have observations.  
# NOTE: if you are working with the full BioTIME database, this step may take some time. 
grid_12 <- dggridR::dgcellstogrid(dgg_12, grid_samples$cell)

# Now let's follow the same steps and build a ~10.7 km2 global grid:
dgg_14 <- dggridR::dgconstruct(res = 14)
grid_14 <- dggridR::dgcellstogrid(dgg_14, grid_samples_14$cell)

# And we get some polygons for each country of the world, to create a background:
countries <- ggplot2::map_data("world")

# Now you could map the whole world, but let's just zoom in the UK and have a look at 
# STUDY 466 (Marine Fish):
map_uk_locations <- ggplot() +
  geom_polygon(data = countries, aes(x = long, y = lat, group = group), fill = NA, color = "grey") +
  geom_sf(data = grid_12, aes(), color = alpha("blue", 0.4)) +
  coord_sf(xlim = c(-20, 10), ylim = c(50, 60)) +
  geom_rect(aes(xmin = -11, xmax = -0.7, ymin = 57.2, ymax = 59), colour = "red", fill = NA) +
  labs(x = NULL, y = NULL) +
  theme_bw() + theme(text = element_text(size = 8))

zoom_in_map <- ggplot() +
  geom_polygon(data = countries, aes(x = long, y = lat, group = group), fill = NA,
               color = "grey") +
  geom_sf(data = grid_12, aes(), color = alpha("blue", 0.4)) +
  geom_sf(data = grid_14, aes(), color = alpha("red", 0.4)) +
  coord_sf(xlim = c(-11, -0.7), ylim = c(57.2, 59)) +
  theme_bw()

grid::grid.newpage()
main <- grid::viewport(width = 1, height = 1, x = 0.5, y = 0.5)  # the main map
inset <- grid::viewport(width = 0.4, height = 0.4, x = 0.82, y = 0.45)  # the inset in bottom left

# The resulting distribution of different size cells appears as follows:
print(zoom_in_map, vp = main)
print(map_uk_locations, vp = inset)

Why rarefaction is a must!

Now we will deal with another common issue when working with time series data: the fact that the number of samples may vary over time. When evaluating changes through time it’s important to be able to quantify change independently of the number of samples, i.e. we want to be able to assess whether e.g. alpha diversity has truly increased or decreased over time and that such signal is not a sampling artifact due to uneven sampling effort – i.e. resulting from having more or fewer samples in different years or periods of the time series being compared.

Probably the most common and widely used approach to prevent temporal variation in sampling effort from affecting diversity estimate is to apply sample-based rarefaction. Here (add link) is a great introduction to it. BioTIMEr provides the resampling() function that implements sample-based rarefaction, already adapted to BioTIME data and designed to run smoothly over the previously gridded dataset as done above. The function identifies the minimum number of samples across all years in a time series and then uses this minimum to randomly resample each year down to that number.

Let’s continue with our BioTIME example subset dataset:

# First, if you are not sure you need this step, 
# you can always check how many samples there are in every year of the different time series:
check_samples <- grid_samples %>% 
  group_by(STUDY_ID, assemblageID, YEAR) %>% 
  summarise(n_samples = n_distinct(SAMPLE_DESC), n_species = n_distinct(Species))

check_samples %>% head(10) %>% kable() 

Looking at the n_samples column we can already see for the few assemblages in this subset that there are substantial differences in sampling effort (i.e. number of samples) through time. Thus, we should standardize for sampling effort:

# Let's apply resampling() then, using the data frame of the gridded data:
grid_rare <- resampling(x = grid_samples, measure = "ABUNDANCE",
                       resamps = 1, conservative = FALSE)

The function returns a single long-form data frame containing the total currency of interest (i.e. summed across all samples) for each species in each year within each assemblage time series. You can specify what the currency field is: Abundance, Biomass or both (BioTIME studies can include records for either or both). Note that resampling() also tests and removes any NAs within the selected currency field(s) before implementing the sample-based rarefaction. Thus, if you choose to use both currency fields, only observations (when conservative = FALSE) or full sampling events (when conservative = TRUE) in which BOTH Abundance and Biomass were recorded are retained and a warning is issued as follows:

# Keep only observations with both abundance and biomass
grid_rare_ab <- resampling(x = grid_samples, measure = c("ABUNDANCE", "BIOMASS"),
    resamps = 1, conservative = FALSE)

## Warning in resampling(x = grid_samples, measure = c("ABUNDANCE", "BIOMASS"), : NA values found and removed.
## Only a subset of `x` is used.

# Keep only sampling events where all observations within had both abundance
# and biomass to start with
grid_rare_abT <- resampling(x = grid_samples, measure = c("ABUNDANCE", "BIOMASS"),
    resamps = 1, conservative = TRUE)

## Warning in resampling(x = grid_samples, measure = c("ABUNDANCE", "BIOMASS"), : NA values found and whole samples removed since `conservative` is TRUE.
## Only a subset of `x` is used.

# What is the number of samples in the year with the lowest sampling effort?
ggplot(data = check_samples[check_samples$assemblageID == "18_335699",], aes(x = YEAR, y = n_samples)) +
  geom_col(aes(x = YEAR, y = n_samples), fill = "red", alpha = 0.5) +
  geom_segment(aes(x = 1926, y = min(n_samples) + 3,
                   xend = 1927, yend = min(n_samples)),
               arrow = arrow(length = unit(0.2, "cm"))) +
  xlab("Year") + ylab("Number of samples") +
  theme_bw()

## Warning in geom_segment(aes(x = 1926, y = min(n_samples) + 3, xend = 1927, : All aesthetics have length 1, but the data has 29 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
##   a single row.

# In this case,the year 1927 had the lowest sampling effort, with 3 samples (arrow).

# Let's implement the sample-based rarefaction by resampling the dataset 10 times.
grid_rare_n10 <- resampling(x = grid_samples, measure = "ABUNDANCE", resamps = 10,
                            conservative = FALSE)

# Note that you may want to resample many more times (e.g. at least 30-100+ times, but up to 199
# if e.g. working with the whole BioTIME data), depending on how many iterations you want a
# subsequent bootstrap analysis to have.
# This may also take some computation time, so if you are working with a big subset of BioTIME
# is advisable to break it down in smaller subsets.

# Each resampling iteration will be identified as resamp = 1:n, in this case 1:10.
# Now we can check if there are differences across the first 3 of these iterations:

check_resamps <- grid_rare_n10[grid_rare_n10$resamp < 4,] %>%
  group_by(STUDY_ID, assemblageID, resamp) %>%
  summarise(n_obs = n(), n_species = n_distinct(Species), n_year = n_distinct(YEAR))

check_resamps %>% head(10) %>% kable()

Calculate diversity estimates

Now the following functions are pretty straightforward. getAlphaMetrics() and getBetaMetrics() estimate a set of common alpha and beta diversity metrics for each year within each time series in your BioTIME data. Let’s apply them to our now processed BioTIME example dataset:

# Get alpha metrics estimates:
alpha_metrics <- getAlphaMetrics(x = grid_rare, measure = "ABUNDANCE")
# see also help("getAlphaMetrics") for more details on the metrics

# Have a quick look at the output
alpha_metrics %>% head(6) %>% kable()

# Get beta metrics estimates:
beta_metrics <- getBetaMetrics(x = grid_rare, measure = "ABUNDANCE")
#see also help("getBetaMetrics") for more details on the metrics

# Have a quick look at the output
beta_metrics %>% head(6) %>% kable()

# NOTE the functions used the rarefied data with only one resampling iteration

There are many metrics for measuring biodiversity in community ecology. Here, we focused on the more commonly used metrics when measuring temporal changes in biodiversity. getAlphaMetrics() estimates nine alpha diversity metrics: Species richness (S), numerical abundance (N), maximum numerical abundance (maxN), Shannon index, Simpson index, inverse Simpson, McNaughton dominance, probability of intraspecific encounter (PIE) and exponential Shannon. getBetaMetrics() estimates three beta diversity metrics: Jaccard dissimilarity, Bray-Curtis dissimilarity and Morisita-Horn dissimilarity, assuming the first year in the time series as the baseline. Note that N and maxN metrics return numerical abundance when measure=ABUNDANCE, and biomass values when measure=BIOMASS.

Investigating biodiversity trends

Finally, when looking to measure biodiversity change using temporal data, it is common to estimate temporal trends in alpha diversity and beta diversity by fitting a simple linear regression to the diversity estimates over the duration of the time series. The getLinearRegressions() function implements linear models as a function of year and is designed to run smoothly over an output of either the getAlphaMetrics() or getBetaMetrics() functions. Specifically, it provides the results of simple linear regressions (slope, p-value, significance, intercept) using lm(), and fit individually to the yearly diversity estimates of each metric within each assemblage time series in your dataset.

# Let's apply it then:
alpha_slopes <- getLinearRegressions(x = alpha_metrics, divType = "alpha",
                                     pThreshold = 0.05) #for alpha metrics


# Have a quick look at the output 
alpha_slopes %>% head(6) %>% kable()

beta_slopes <- getLinearRegressions(x = beta_metrics, divType = "beta",
                                    pThreshold = 0.05)  #for beta metrics

# Have a quick look at the output
# beta_slopes %>% head(6) %>% kable()

You can change the default P-value threshold for statistical significance (i.e. the measure of the strength of evidence against H0). For easy sorting, plotting and interpretation, a column called significance is also provided, where 1 indicates the threshold is met, i.e. the estimated slope is significant at the specified p-value level.

Also note that some biodiversity analysis may benefit from additional transformations and scaling of data before modelling (check a nice tutorial on the subject here).

Summarize and visualize biodiversity trends

Now that we have quantified change through time, we are ready to make some plots! The next section provides only a few examples of queries and data visualization, but it’s not a comprehensive exploration of the results, which will also vary depending on your own research question.

Right, let’s start will some overall summaries and trends:

# First, how many assemblages in our dataset show a moderate evidence (P < 0.05) of change in alpha diversity?
check_alpha_trend <- alpha_slopes %>%
  group_by(metric) %>% 
  filter(significance == 1) %>%   # or use filter(pvalue<0.05)
  summarise(n_sig = n_distinct(assemblageID), mean(slope))

check_alpha_trend %>% kable() 

# We can see that only a few (<40) of the assemblage time series actually show a significant
# trend of change over time, independently of the metric used. This indicates that in most
# time series in the studies we analysed alpha diversity is not really changing through time.

Let’s do some summary plots showing the variation of alpha diversity change:

# Get a slope per assemblageID and metric
alpha_slopes_simp <- alpha_slopes %>% 
  group_by(assemblageID, metric, pvalue) %>% 
  filter(significance == 1) %>%   #select only the assemblages with significant trends
  summarise(slope = unique(slope))

# Calculate the mean slope and CIs 
stats_alpha <- alpha_slopes_simp %>%  
  group_by(metric) %>%
  summarise(mean_slope = mean(slope), #mean
            ci_slope = qt(0.95, df = length(slope) - 1) * (sd(slope, na.rm = TRUE) / sqrt(length(slope)))) #margin of error

# Let's put it all together
ggplot(data = alpha_slopes_simp) +
  geom_histogram(aes(x = slope, fill = metric), bins = 25) +
  #geom_density(alpha=0.5)+ #in case you what a density plot instead
  geom_vline(aes(xintercept = 0), linetype = 3, colour = "grey",linewidth = 0.5) + #add slope=0 line
  geom_vline(data = stats_alpha, aes(xintercept = mean_slope), linetype = 1,
             linewidth = 0.5,colour = "black") + #mean
  geom_vline(data = stats_alpha, aes(xintercept = mean_slope - ci_slope),
             linetype = 2, linewidth = 0.5, colour = "black") + #lower confidence interval
  geom_vline(data = stats_alpha, aes(xintercept = mean_slope + ci_slope),
             linetype = 2, linewidth = 0.5, colour = "black") + #upper confidence interval
  facet_wrap(~metric, scales = "free") +
  scale_fill_biotime() +  #using the customize BioTIME colour scale. See help("scale_color_biotime") for more options.
  ggtitle("Alpha diversity change") +
  theme_bw() +
  theme(legend.position = "none",plot.title = element_text(size = 11, hjust = 0.5))

Now the same for beta diversity change:

# Hint: If you wish to plot all metrics together, simply merge you alpha_slopes and 
# beta_slopes dataframes beforehand.

That’s great! While overall trends are important, you may also want to evaluate the patterns for a specific taxa or climate, and look at both sets of trends (i.e. significant and non-significant) together. For example, let’s say you want to have a look at changes in Shannon diversity for the two more represented taxon in our example BioTIME dataset.

# First, we need to check the meta file and retrieve the information for the studies of interest
#head(BTsubset_meta)
meta <- select(BTsubset_meta, STUDY_ID, TAXA, REALM, CLIMATE)

# Get a slope per assemblageID and metric
alpha_slopes_simp <- alpha_slopes %>%
  group_by(assemblageID, metric,'pvalue', significance) %>%
  summarise(slope = unique(slope))

# Get back the Study ID by separating our assemblageID column into multiple other columns.
alpha_slopes_simp <- as.data.frame(alpha_slopes_simp %>%
                                     separate(., assemblageID, 
                                              into = c("STUDY_ID", "cell"),
                                              sep = "_", remove = FALSE))
# Merge it all
alpha_slopes_meta <- merge(alpha_slopes_simp, meta, by = "STUDY_ID")

# Select relevant data for plotting
alpha_slopes_set1 <- subset(alpha_slopes_meta, alpha_slopes_meta$metric == "Shannon" & (alpha_slopes_meta$TAXA == "Fish" | alpha_slopes_meta$TAXA == "Birds"))

# Let's put it all together
ggplot(data = alpha_slopes_set1, aes(x = slope, fill = TAXA)) +
  geom_histogram(fill = "grey", bins = 25) + #all assemblages
  geom_histogram(data = alpha_slopes_set1[alpha_slopes_set1$significance == 1, ], bins = 25) + #only significant change assemblages
  geom_vline(aes(xintercept = 0), linetype = 3, colour = "black", linewidth = 0.5) + #add slope=0 line
  facet_wrap(~TAXA, scales = "free") +
  scale_fill_biotime() +
  ggtitle("Shannon-Weiner Species Diversity Index") +
  theme_bw() + 
  theme(plot.title = element_text(size = 11, hjust = 0.5))

# Now we see the full distribution of slopes of change for the Shannon index for the birds 
# and fish time series in our subset: all slopes are shown in grey, while in color we show 
# the subset of assemblages for which evidence (P < 0.05) of change was detected.

We might also want to explore the different trends for a given time series, allowing us to visualise the data and the trend line:

# Let's go back to the data frame with the yearly values and select our relevant data for plotting
alpha_metrics_set <- subset(alpha_metrics, assemblageID == "18_335699")

# Transform data
alpha_metrics_set_long <- pivot_longer(alpha_metrics_set,
                                   cols = c("S", "N", "maxN", "Shannon", "expShannon",
                                   "Simpson", "invSimpson", "PIE", "DomMc"),
                                   names_to = "metric",
                                   names_transform = as.factor)

# Get assemblage ID slope of change
alpha_slopes_set2 <- subset(alpha_slopes, assemblageID == "18_335699")

# Get assemblage ID
name_assemblage <- unique(alpha_slopes_set2$assemblageID)

# Merge info
alpha_metr_slop<- left_join(alpha_slopes_set2, alpha_metrics_set_long,
                                join_by(assemblageID, metric))

# Let's put it all together
ggplot(data = alpha_metr_slop, aes(x = YEAR, y = value)) +
    geom_point(colour = "#155f49", size = 1) +   #plot the yearly estimates
    stat_smooth(method = "lm", se = FALSE, linetype = 2, colour = "grey") + #draw all regression line
    stat_smooth(data = subset(alpha_metr_slop, alpha_metr_slop$significance == 1),
                aes(x = YEAR, y = value), method = "lm", se = FALSE, 
                linetype = 2, colour = "#155f49") + #color only trends that are significant
    facet_wrap(~metric, scales = "free") +
    scale_fill_biotime() +
    ggtitle(paste("Assemblage", name_assemblage)) + ylab("Diversity") +
    theme_bw() + theme(plot.title = element_text(size = 11, hjust = 0.5))

We can see that results vary depending on the dataset and the metric of change used. It is important to remember that biodiversity metrics are just a tool used to measure changes, and each have diverse abilities to detect changes in different biodiversity dimensions and under different scenarios e.g.They thus provide different pieces of a complex puzzle and may strongly affect our interpretation of biodiversity change.

Credits and Disclaimer

This R package was developed by the BioTIME team. The functions and documentation were developed predominantly by Alban Sagouis, Faye Moyes and Inês S. Martins. For optimal use, the package should operate in the manner described here. Feedback is appreciated, particularly if you come across any errors or would like to suggest changes. Updates will be made sporadically.

How to cite BioTIMEr

Sagouis A, Moyes F, Martins IS, Blowes SA, Brambilla V, Chow CFY, Fontrodona-Eslava A, Antão L, Chase JM, Dornelas M, Magurran AE (2024). BioTIMEr: tools to use and explore the BioTIME database. https://github.com/bioTIMEHub/BioTIMEr