Contents
Why to subsample spatially
Prepare Paleobiology Database records
Subsampling examples
Circular subsampling
PBDB collections, references, and duplicate entries
Nearest-neighbour subsampling
Latitudinal subsampling
Analysis on subsampled data
References
This document introduces rationale for spatial subsampling and covers
common use cases for divvy functions, including example R
code. Sections below discuss ways to format data, implement three
different subsampling routines, and calculate common biodiversity
metrics.
For a more comprehensive review of spatial subsampling theory,
applications in paleobiology and ecology, and implementation in the
divvy package, refer to Antell,
Benson, and Saupe (accepted), ‘Spatial standardization of taxon
occurrence data—a call to action’.
Why to subsample spatially
Biodiversity measures, whether quantified from fossil or extant data,
reflect the outcome of three types of processes: 1. the biological
processes the measurements aim to capture, 2. stochastic processes that
generate random error, and 3. observation processes, such as the
distribution of collecting effort intensity. Compared to neontological
data, observation densities of palaeontological data reflect the
additional contributions of taphonomy (decay and preservation processes)
and sedimentation (burial and lithification processes). The term
‘sampling’ is used herein to refer to the full suite of influences on
how many of the fossil taxa that lived in a place are recorded as fossil
occurrences there. Given the large number of processes involved in
sampling fossils, it is unsurprising that for many palaeo-occurrence
datasets, the combined effect size of unexplained error and variable
observation probabilities on biodiversity parameter estimates is greater
than that of true biological processes from the past.
The relative strength of sampling structure compared to biological
structure is a well-known issue in quantitative palaeobiology and
therefore has received considerable attention. The most mainstream
method of standardising sampling in biodiversity data (fossil or extant)
is rarefaction, a genre of statistical method that resamples taxon
occurrences to a given threshold of sampling completeness. ‘Classical’
rarefaction uses cumulative specimen count as a proxy of sampling
completeness, while ‘coverage-based’ rarefaction estimates the
cumulative coverage of a taxon frequency distribution curve as a proxy
(Alroy 2010; Chao and Jost 2012). Examples
of both types of rarefaction abound in recent palaeobiology literature.
Rarefaction largely succeeds in producing diversity estimates corrected
for sampling differences between sites, i.e. diversity estimates at
discrete sites (alpha diversity) that can be compared fairly.
However, challenges arise in comparing diversity between times or
regions that contain multiple sites.
Taxonomic composition turns over from one geographic location to
another. The amount of this turnover (beta diversity) generally
increases with distance, a relationship known as the species–area
effect. In practical terms, the greater the spatial dispersion or area
of sampling, the more taxa will tend to be recovered. Sampling processes
control both the dispersion and area of localities with fossil data,
which makes it essential for palaeobiologists to account for spatial
coverage of sampling when conducting biodiversity analyses, regardless
of the use of rarefaction (Benson et al.
2021). Without standardising sampling in a spatial context, any
estimates of biodiversity parameters will be an indiscernible mix of
true historic values conflated with the amount of observation of those
values.
divvy implements several spatial subsampling methods
that control both the geographic dispersion and area of taxon
occurrences, so analysts can make fairer estimates of biodiversity
parameters between regions or through time. Each method is iterative,
drawing many, comparable spatial replicates.
Prepare Paleobiology Database records
We begin by loading the divvy package itself, and note
its geospatial package dependencies units, sf,
terra, and vegan, and the iNEXT
package for taxonomic richness rarefaction.
library(divvy)
library(units)
library(sf)
library(terra)
library(vegan)
library(iNEXT)
divvy includes the attached dataset
bivalves, a download of fossil occurrences from the Paleobiology Database (PBDB) that has
been subset to a few relevant columns and minimally cleaned.
bivalves contains ca. 8,000 (palaeo)coordinates and
identifications for ca. 500 marine bivalve genera from the Pliocene. For
more information about the dataset, query ?bivalves.
The attached dataset is provided only as an example for working with
PBDB-structured occurrence records; formal analyses would be wise to vet
downloaded data more rigorously, including revising taxonomy, time bin
assignments, and environmental classifications. The
fossilbrush package provides a palette of tools to help
clean PBDB data.
data(bivalves)
head(bivalves)
#> genus paleolng paleolat collection_no reference_no
#> 1 Lima 176.85 -42.69 34849 9315
#> 2 Nemocardium 176.85 -42.69 34849 11706
#> 3 Ostrea 176.85 -42.69 34849 11706
#> 4 Chlamys 176.85 -42.69 34849 9315
#> 5 Pycnodonte -114.37 33.76 34857 9338
#> 6 Euvola -114.37 33.76 34857 9338
#> environment max_ma min_ma accepted_name
#> 1 basin reef 5.333 3.6 Lima
#> 2 basin reef 5.333 3.6 Nemocardium (Pratulum)
#> 3 basin reef 5.333 3.6 Ostrea chilensis
#> 4 basin reef 5.333 3.6 Chlamys
#> 5 marginal marine indet. 5.333 3.6 Pycnodonte heermanni
#> 6 marginal marine indet. 5.333 3.6 Euvola keepi
#> [1] 8095
length(unique(bivalves$genus))
#> [1] 550
The latitude-longitude coordinates in the PBDB come from many
different studies, which means the precision and accuracy vary across
records. Fossils collected from the same locality may be reported with
different coordinates due purely to different GPS equipment, mapping, or
decimal rounding, for example. The spatial subsampling procedures below
put great weight on the number of unique localities in a region, so it
is important to avoid inflating the site number artificially.
One standard way to smooth over slight discrepancies in occurrence
positions is to convert point coordinates (‘vector’ spatial data) to
grid cells (‘raster’ spatial data). Effectively, one lays a web over a study
region and records the position of polygons (‘grid cells’) in which
points fall, rather than the original xy point coordinates. An
additional advantage of raster over vector data is the tremendous
increase in efficiency of spatial computations.
Palaeobiologists often convert abundance counts to binary
presence-absence data for taxon occurrences in grid cells. This practice
is especially common for PBDB data because abundance information is
usually non-standard, if it is recorded at all. (Read below for further
notes about duplicate taxon occurrences.)
Many PBDB analyses are also global in extent, which makes the choice
of coordinate
reference system for the raster grid important. The areas and shapes
of grid cells at the poles vs. the equator can differ widely with
certain reference systems or map projections. The code below converts
latitude-longitude coordinates to the Equal Earth reference system, an
equal-area projection for world maps. Another option of friendly raster
grid system is the is the icosa package, developed by
palaeobiologist Ádám Kocsis, which creates tessellations of pentagons
and hexagons. The polygons are approximately equal in area and shape
across the globe. The spatial resolution of the grid is controlled by
arguments in the hexagrid function.
# initialise Equal Earth projected coordinates
rWorld <- rast()
prj <- 'EPSG:8857'
rPrj <- project(rWorld, prj, res = 200000) # 200,000m is approximately 2 degrees
values(rPrj) <- 1:ncell(rPrj)
# coordinate column names for the current and target coordinate reference system
xyCartes <- c('paleolng','paleolat')
xyCell <- c('cellX','cellY')
# retrieve coordinates of raster cell centroids
llOccs <- vect(bivalves, geom = xyCartes, crs = 'epsg:4326')
prjOccs <- project(llOccs, prj)
bivalves$cell <- cells(rPrj, prjOccs)[,'cell']
bivalves[, xyCell] <- xyFromCell(rPrj, bivalves$cell)
We can inspect the spatial distribution of data by converting
occurrence points to spatial features (a class that
contains coordinate system information) and plotting them against a
basic world map, supplied here from the rnaturalearth and
rnaturalearthdata packages.
occUniq <- uniqify(bivalves, xyCell)
ptsUniq <- st_as_sf(occUniq, coords = xyCell, crs = prj)
library(rnaturalearth)
library(rnaturalearthdata)
library(ggplot2) # for plot visualisation
world <- ne_countries(scale = "medium", returnclass = "sf")
worldP <- ggplot(data = world) +
theme_bw() +
geom_sf() +
geom_sf(data = ptsUniq, shape = 17, color = 'blue')
plot(worldP)
Subsampling examples
divvy offers three approaches to spatially subsample
data:
cookies: Imposes a radial constraint on the spatial
bounds of a subsample and standardises area by rarefying the number of
localities
clustr: Aggregates sites that are nearest neighbours
(connecting them with a minimum spanning tree) to impose a maximum
diameter on the spatial bounds of a subsample, and optionally rarefies
localities
bandit: Rarefies the number of localities within
bands of equal latitude
Circular subsampling
First let’s apply circular subsampling, which both constrains the
spatial bounds of a sample to a specified radius from a random start
point and standardises the spatial area of a sample to a specified
number of sites. (Recall that sites were allocated to equal-area
polygons in the preceding section.) The radius (1500 km) and the numbers
of sites (n = 12) and iterations (n = 500) are specified here to match
the subsampling parameters in the global analysis of Antell et al. (2020).
set.seed(1)
circLocs <- cookies(dat = bivalves,
xy = xyCell,
iter = 500,
nSite = 12,
r = 1500, # radial distance in km
weight = TRUE, # probabilistically aggregate subsampling sites
crs = prj, # Equal Earth projection
output = 'locs')
length(circLocs)
#> [1] 500
#> cellX cellY
#> 1019 -6543959 4739440
#> 56 -7343959 3539440
#> 7586 -7143959 3939440
#> 1278 -7343959 3939440
#> 1049 -6543959 4539440
#> 2921 -7143959 3539440
#> 1054 -6743959 4339440
#> 1076 -6743959 4539440
#> 122 -7743959 3939440
#> 1251 -7543959 3939440
#> 2065 -7543959 3739440
#> 911 -6543959 4339440
Subsamples are returned as elements in a list of length
iter. If output = "locs" (default), each
element is a data.frame of coordinates for the
nSite sites included in a subsample. This output may be
useful as an intermediate object on which to run custom functions that
calculate ecological parameters of interest, for instance metrics of
spatial connectedness among fossil sites. We can also use location
output to explore where the subsample plots on our earlier map. This
subsample happens to fall along the East and Gulf Coasts of North
America.
# over-plot the subsample locations
smplPts <- st_as_sf(circLocs[[1]], coords = xyCell, crs = prj)
worldP +
geom_sf(data = smplPts, shape = 17, color = 'red')
Now note that the first code chunk in this section set the
weight argument of cookies() to
TRUE. Weighting is designed to cluster subsample sites more
compactly than random selection; that is, distant points will tend to be
left out. Weighting is achieved by increasing the probability of drawing
a site the closer it falls to the central occurrence point (‘seed cell’)
in a given subsample. The seed cell is always included in weighted
subsamples (but not necessarily in unweighted ones) and corresponds to
the first row listed in the output. To visibilise the weighted subsample
method further, let’s extract the seed location and manually plot the
circular constraint around it.
cntr <- smplPts[1,]
# distances inferred to be meters based on lat-long coord system
r <- 1500
buf <- st_buffer(cntr, dist = r*1000)
# over-plot subsample boundary region and included sites
worldP +
geom_sf(data = smplPts, shape = 17, color = 'red') +
geom_sf(data = buf, fill = NA, linewidth = 1, color = 'red')
This inspection also reveals that there happen to be 14 sites in the
region from which to draw a subsample of 12.
inBuf <- st_intersects(ptsUniq, buf, sparse = FALSE)
sum(inBuf)
#> [1] 14
As demonstrated above, the location-type output of divvy
subsampling functions can be useful in certain cases; however, more
often researchers will want to retrieve taxon records. Changing
output from "locs" to "full",
each element of the returned object now contains the subset of
occurrence rows from bivalves located at the sites in a
subsample. This output will be useful for analysis in the final vignette
section below.
set.seed(7)
# same parameter values as above except for 'output'
circOccs <- cookies(dat = bivalves,
xy = xyCell,
iter = 500,
nSite = 12,
r = 1500,
weight = TRUE,
crs = prj,
output = 'full')
head( circOccs[[8]] )
#> genus paleolng paleolat collection_no reference_no environment
#> 83 Anadara -69.82 10.25 41552 11132 marine indet.
#> 409 Iliochione -79.18 0.54 51887 13814 offshore
#> 410 Undulostrea -79.18 0.54 51887 13814 offshore
#> 411 Argopecten -79.18 0.54 51887 13814 offshore
#> 412 Argopecten -79.18 0.54 51881 13814 coastal indet.
#> 413 Arca -79.18 0.54 51881 13814 coastal indet.
#> max_ma min_ma accepted_name cell cellX cellY
#> 83 5.333 3.600 Anadara (Larkinia) notabilis 6074 -6543959 1339439.8
#> 409 5.333 2.588 Iliochione callistoides 7101 -7543959 139439.8
#> 410 5.333 2.588 Undulostrea megodon 7101 -7543959 139439.8
#> 411 5.333 2.588 Argopecten ventricosus 7101 -7543959 139439.8
#> 412 5.333 2.588 Argopecten ventricosus 7101 -7543959 139439.8
#> 413 5.333 2.588 Arca 7101 -7543959 139439.8
PBDB collections, references, and duplicate entries
Data fed into divvy subsampling functions can contain
duplicate taxon–location records, which are common from the
collections-based format of PBDB data entry. For instance, the subsample
output printed above shows Argopecten twice at the same
coordinates. The duplicates stem from different collections (#51881 and
#51887). In the previous section we standardised spatial dispersion and
area, but at this point we could rarefy the number of collections or
references, too, as a standardisation for sampling effort.
The study that developed the circular buffer approach for regional
subsampling (implemented with cookies()) avoided rarefying
collections/references, as this step would be largely redundant with
rarefying sites/raster grid cells (Antell et al.
2020). The number of PBDB reference counts for marine
invertebrate occurrences correlates nearly perfectly with grid cell
counts (Alroy et al. 2008). Applying
rarefaction to both grid cells and collections or references would
compress the distribution of observed values, which could reduce the
statistical power of analysis and heighten the risk of overlooking a
true biological signal (type 2 error).
In contrast, the study that developed the nearest-neighbour
subsampling approach (described below) rarefied collections within
references, following Alroy (2014), but
avoided rarefying sites/cells (Close et al.
2017). The richness estimation procedure involved drawing PBDB
references, drawing up to three collections within each of those
references, and evaluating only the taxon occurrences within those
subsampled collections. The divvy diversity summary
function (sdSumry()) and the most recent study to use
nearest-neighbour subsamples (Close et al.
2020) call on the iNEXT implementation of
coverage-based richness estimation, which ignores the PBDB
collections/references data structure. Therefore, users wishing to to
rarefy collections and/or references should write custom richness
estimation scripts or adapt Alroy’s Perl scripts.
To filter out duplicate taxon–location records from a datset, thereby
reducing object size and saving memory, divvy offers the
uniqify() function. Omitting duplicate occurrences from
bivalves removes more than 5,000 rows.
bivUniq <- uniqify(bivalves, taxVar = 'genus', xyCell)
nrow(bivUniq)
#> [1] 3061
Nearest-neighbour subsampling
As mentioned in the preceding section, papers to date that analysed
spatial subsamples constructed with the nearest-neighbour method
constrained only the spatial dispersion and not cumulative area or
number of sites (Close et al. 2017, 2020).
For backwards compatibility with these originator publications, the
clustr() function contains an option to turn off site
rarefaction (argument nSite = NULL). However, depending on
study design it may well be prudent to standardise the area/number of
sites when using the nearest-neighbour method, as is mandated in the
circular subsampling method. To set the site quota for either method,
use the nSite argument.
set.seed(2)
nnLocs <- clustr(dat = bivalves,
xy = xyCell,
iter = 500,
distMax = 3000, # diameter = 2x the circular radius set above
nSite = 12,
crs = prj
)
nnLocs[[1]]
#> cellX cellY
#> 81 -6343959 1339439.78
#> 1891 -7343959 -60560.22
#> 5884 -7343959 539439.78
#> 6477 -7743959 -460560.22
#> 1491 -5743959 1339439.78
#> 2415 -7943959 1139439.78
#> 409 -7543959 139439.78
#> 5916 -6543959 1539439.78
#> 1936 -6743959 2339439.78
#> 3887 -7743959 1339439.78
#> 74 -5943959 1339439.78
#> 5751 -7143959 1339439.78
If we skip site rarefaction and instead include all locations within
a cluster of maximum diameter deterministically, a subsample built on a
given starting location could include any number of sites above the
minimum threshold (here, nMin = 3, the default). The first
replicate in this example contains 17 sites.
set.seed(2)
nnAllSites <- clustr(dat = bivalves,
xy = xyCell,
iter = 500,
distMax = 3000, # diameter = 2x the circular radius set above
nMin = 3,
crs = prj
)
nrow( nnAllSites[[1]] )
#> [1] 17
Latitudinal subsampling
Many biological and environmental variables of interest vary
characteristically with latitude. Hence, depending on research question
it may be exigent to control for latitudinal differences between items
of comparison, e.g. occurrence data from a time step with predominantly
low-latitude fossil localities vs. a time step with more mid-latitude
localities.
The bandit() function returns subsamples of a given site
quota within latitudinal bands of a given bin resolution/width.
Optionally, the function will ignore hemisphere differences and consider
absolute latitude. The iter argument in
bandit() specifies the number of subsamples to take within
each band, rather than the total number globally. No subsamples are
returned in bands containing fewer than nSite
localities.
bandLocs <- bandit(dat = bivalves,
xy = xyCell,
iter = 100, nSite = 12,
bin = 20, # interval width in degrees
crs = prj
# ,absLat = TRUE # optional
)
nrow(bandLocs[[1]]) # number of sites in one subsampled band
#> [1] 12
length(bandLocs) # number of subsamples, tallied across all bands
#> [1] 500
unique(names(bandLocs)) # latitudinal degrees of subsampled intervals
#> [1] "[-50,-30)" "[-10,10)" "[10,30)" "[30,50)" "[50,70)"
Analysis on subsampled data
The sdSumry() function returns a summary of the spatial
characteristics of a dataset/subsample: number of unique locations,
centroid coordinates, latitudinal range (degrees), great circle distance
(km), and summed minimum spanning tree length (km) for occurrences.
sdSumry() also tallies taxa in a sample and performs
coverage-based rarefaction (if quotaQ supplied) and
classical rarefaction (if quotaN supplied). Rarefied
estimates are returned along with their associated 95% confidence
interval (estimated by iNEXT).
When coverage-based rarefaction is applied, the specified coverage
level (quotaQ) should be appropriate for the estimated
coverage of the original sample. Any requested quota greater than
empirically available will require diversity extrapolation.
sdSumry() returns the estimated sample coverage whenever
coverage-based rarefaction is applied. Also note, homogeneous evenness
is an underlying assumption for fair comparisons of coverage-based
rarefaction diversity estimates. To help check this assumption,
sdSumry() also returns Pielou’s J evenness metric whenever
coverage-based rarefaction is applied. Evenness metrics are an area of
ongoing theoretical debate and methodological development in ecology,
and the use of Pielou’s J in divvy is a reflection of this
metrics’ widespread use rather than a singular endorsement. If J
estimates vary widely between time intervals or other categories of
comparison, it is worth investigating evenness differences in more
nuanced detail, for instance by calculating a variety of metrics.
Compare the summary data from the original dataset vs. a single
spatial subsample. First consider the summary of spatial metrics. The
geographic dispersion of bivalves occurrences is
enormous—this in itself indicates spatial standardisation is necessary
to derive meaningful biological metrics. When we compare diversity
estimates from the global vs. spatially-subsampled dataset, richness
estimates are much higher for the global dataset, regardless of
rarefaction method. This result demonstrates the species–area effect
described in the introduction: even when rarefaction is applied to the
same quota or coverage level, diversity estimates are larger for
datasets with greater spatial coverage, because rarefaction fails to
control spatial turnover in diversity.
unsamp <- sdSumry(dat = bivalves,
taxVar = 'genus',
collections = 'collection_no',
xy = xyCell,
quotaQ = 0.8, quotaN = 100,
omitDom = TRUE,
crs = prj)
unsamp
#> nColl nOcc nLoc centroidX centroidY latRange greatCircDist meanPairDist
#> 1 863 3061 157 -434404.9 1647720 137.4159 28917.12 11375.07
#> minSpanTree SCOR nTax evenness coverage SQSdiv SQSlow95 SQSupr95
#> 1 93349.91 20.64401 550 0.9061343 0.9414 323.015 310.6638 335.3662
#> CRdiv CRlow95 CRupr95
#> 1 470.9424 454.3334 487.5515
samp1 <- sdSumry(dat = circOccs[[1]],
taxVar = 'genus',
collections = 'collection_no',
xy = xyCell,
quotaQ = 0.8, quotaN = 100,
omitDom = TRUE,
crs = prj)
samp1
#> nColl nOcc nLoc centroidX centroidY latRange greatCircDist meanPairDist
#> 1 134 442 12 -7027292 4139440 10.25901 1612.452 779.4877
#> minSpanTree SCOR nTax evenness coverage SQSdiv SQSlow95 SQSupr95
#> 1 2648.528 46.01033 164 0.9572183 0.8704 136.402 123.5963 149.2078
#> CRdiv CRlow95 CRupr95
#> 1 219.8183 190.056 249.5806
Iterative spatial subsampling addresses this issue in that it can
control for beta diversity in global occurrence datasets. Even
though any individual subsample includes only a fraction of the total
occurrences, all data can contribute to analyses through repeated
subsampling. Ecological variables calculated on any given subsample are
directly comparable to those from other subsamples on the same dataset
or a comparison dataset (e.g. a different time step or habitat type).
The code chunk below demonstrates how to calculate summary statistics
over all subsamples of Pliocene bivalve occurrences. The median taxon
count in a 1500km-radius subsample is 162, with an interquartile range
of 126–183 taxa. This range can be interpreted loosely as primary
regional variation in estimated palaeodiversity.
# warning - it's slow to rarefy diversity for hundreds of subsamples!
# this code chunk skips it for quick demonstration purposes
sampsMeta <- sdSumry(dat = circOccs,
taxVar = 'genus',
collections = 'collection_no',
xy = xyCell,
crs = prj
)
quantile(sampsMeta$nTax, c(0.25, 0.5, 0.75))
#> 25% 50% 75%
#> 126 162 183