| Title: | Spatial Subsampling of Biodiversity Occurrence Data |
|---|---|
| Description: | Divide taxonomic occurrence data into geographic regions of fair comparison, with three customisable methods to standardise area and extent. Calculate common biodiversity and range-size metrics on subsampled data. Background theory and practical considerations for the methods are described in Antell and others (2023) <doi:10.31223/X5997Z>. |
| Authors: | Gawain Antell [aut, cre, cph]
|
| Maintainer: | Gawain Antell <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.0 |
| Built: | 2024-07-22 06:11:15 UTC |
| Source: | CRAN |
bandit subsamples spatial point data to a specified number of sites
within bins of equal latitude
bandit( dat, xy, iter, nSite, bin, centr = FALSE, absLat = FALSE, maxN = 90, maxS = -90, crs = "epsg:4326", output = "locs" )bandit( dat, xy, iter, nSite, bin, centr = FALSE, absLat = FALSE, maxN = 90, maxS = -90, crs = "epsg:4326", output = "locs" )
dat |
A |
xy |
A vector of two elements, specifying the name or numeric position
of columns in |
iter |
The number of times to subsample localities within each latitudinal band. |
nSite |
The quota of unique locations to include in each subsample. |
bin |
A positive numeric value for latitudinal band width, in degrees. |
centr |
Logical: should a bin center on and cover the equator
( |
absLat |
Logical: should only the absolute values of latitude be
evaluated? If |
maxN |
Optional argument to specify the northmost limit for subsampling, if less than 90 degrees. |
maxS |
Optional argument to specify the southmost limit for subsampling, if not -90 degrees. Should be a negative value if in the southern hemisphere. |
crs |
Coordinate reference system as a GDAL text string, EPSG code,
or object of class |
output |
Whether the returned data should be two columns of
subsample site coordinates ( |
bandit() rarefies the number of spatial sites within latitudinal ranges
of specified bin width. (Compare with cookies() and clustr(), which spatially
subsample to a specified extent without regard to latitudinal position.)
Cases where it could be appropriate to control for latitudinal spread of localities
include characterisations of latitudinal diversity gradients (e.g. Marcot 2016)
or comparisons of ecosystem parameters that covary strongly with
latitude (e.g. diversity in reefal vs. non-reefal habitats). Note that
the total surface area of the Earth within equal-latitudinal increments
decreases from the equator towards the poles; bandit() standardises only
the amount of sites/area encompassed by each subsample, not the total area
that could have been available for species to inhabit.
As with all divvy subsampling functions, sites within a given
regional/latitudinal subsample are selected without replacement.
To calculate an integer number of degrees into which a given latitudinal
range divides evenly, the palaeoverse package (v 1.2.1) provides the
palaeoverse::lat_bins() function with argument fit = TRUE.
A list of subsamples, each a data.frame containing
coordinates of subsampled localities (if output = 'locs')
or the subset of occurrences from dat associated with those coordinates
(if output = 'full'). The latitudinal bounds of each subsample
are specified by its name in the list. If there are too few localities
in a given interval to draw a subsample, that interval is omitted from output.
Allen BJ, Wignall PB, Hill DJ, Saupe EE, Dunhill AM (2020). “The latitudinal diversity gradient of tetrapods across the Permo–Triassic mass extinction and recovery interval.” Proceedings of the Royal Society B, 287(1929), 20201125. doi:10.1098/rspb.2020.1125.
Marcot JD, Fox DL, Niebuhr SR (2016). “Late Cenozoic onset of the latitudinal diversity gradient of North American mammals.” Proceedings of the National Academy of Sciences, 113(26), 7189-7194. doi:10.1073/pnas.1524750113.
# load bivalve occurrences to rasterise library(terra) data(bivalves) # initialise Equal Earth projected coordinates rWorld <- rast() prj <- 'EPSG:8857' rPrj <- project(rWorld, prj, res = 200000) # 200,000m is approximately 2 degrees # coordinate column names for the current and target coordinate reference system xyCartes <- c('paleolng','paleolat') xyCell <- c('centroidX','centroidY') # project occurrences and retrieve cell centroids in new coordinate system llOccs <- vect(bivalves, geom = xyCartes, crs = 'epsg:4326') prjOccs <- project(llOccs, prj) cellIds <- cells(rPrj, prjOccs)[,'cell'] bivalves[, xyCell] <- xyFromCell(rPrj, cellIds) # subsample 20 equal-area sites within 10-degree bands of absolute latitude n <- 20 reps <- 100 set.seed(11) bandAbs <- bandit(dat = bivalves, xy = xyCell, iter = reps, nSite = n, output = 'full', bin = 10, absLat = TRUE, crs = prj ) head(bandAbs[[1]]) # inspect first subsample names(bandAbs)[1] # degree interval (absolute value) of first subsample #> [1] "[10,20)" unique(names(bandAbs)) # all intervals containing sufficient data #> [1] "[10,20)" "[20,30)" "[30,40)" "[40,50)" # note insufficient coverage to subsample at equator or above 50 degrees # subsample 20-degree bands, where central band spans the equator # (-10 S to 10 N latitude), as in Allen et al. (2020) # (An alternative, finer-grain way to divide 180 degrees evenly into an # odd number of bands would be to set 'bin' = 4.) bandCent <- bandit(dat = bivalves, xy = xyCell, iter = reps, nSite = n, output = 'full', bin = 20, centr = TRUE, absLat = FALSE, crs = prj ) unique(names(bandCent)) # all intervals containing sufficient data #> [1] "[-50,-30)" "[10,30)" "[30,50)"# load bivalve occurrences to rasterise library(terra) data(bivalves) # initialise Equal Earth projected coordinates rWorld <- rast() prj <- 'EPSG:8857' rPrj <- project(rWorld, prj, res = 200000) # 200,000m is approximately 2 degrees # coordinate column names for the current and target coordinate reference system xyCartes <- c('paleolng','paleolat') xyCell <- c('centroidX','centroidY') # project occurrences and retrieve cell centroids in new coordinate system llOccs <- vect(bivalves, geom = xyCartes, crs = 'epsg:4326') prjOccs <- project(llOccs, prj) cellIds <- cells(rPrj, prjOccs)[,'cell'] bivalves[, xyCell] <- xyFromCell(rPrj, cellIds) # subsample 20 equal-area sites within 10-degree bands of absolute latitude n <- 20 reps <- 100 set.seed(11) bandAbs <- bandit(dat = bivalves, xy = xyCell, iter = reps, nSite = n, output = 'full', bin = 10, absLat = TRUE, crs = prj ) head(bandAbs[[1]]) # inspect first subsample names(bandAbs)[1] # degree interval (absolute value) of first subsample #> [1] "[10,20)" unique(names(bandAbs)) # all intervals containing sufficient data #> [1] "[10,20)" "[20,30)" "[30,40)" "[40,50)" # note insufficient coverage to subsample at equator or above 50 degrees # subsample 20-degree bands, where central band spans the equator # (-10 S to 10 N latitude), as in Allen et al. (2020) # (An alternative, finer-grain way to divide 180 degrees evenly into an # odd number of bands would be to set 'bin' = 4.) bandCent <- bandit(dat = bivalves, xy = xyCell, iter = reps, nSite = n, output = 'full', bin = 20, centr = TRUE, absLat = FALSE, crs = prj ) unique(names(bandCent)) # all intervals containing sufficient data #> [1] "[-50,-30)" "[10,30)" "[30,50)"
A dataset containing the (palaeo)coordinates and genus identifications of 8,000 marine bivalves from the Pliocene (ca. 5.3-2.6 Ma). Records with uncertain or unaccepted taxonomic names, non-marine palaeo-environments, or missing coordinates are excluded from the original download (24 June 2022).
bivalvesbivalves
A data frame with 8095 rows and 9 variables:
Latin genus identification. Subgenera are not elevated.
Coordinates of an occurrence, rotated to its palaeogeographic location with the tectonic plate model of GPlates
Unique identifiers for the collection and published reference containing the occurrence
One of 23 marine environment categories
Bounds of the age estimate for an occurrence
Original identification, including subgenus and species epithet if applicable, according to the latest PBDB accepted taxonomy at time of download
Given point occurrences of environmental categories, classRast generates
a raster grid with cell values specifying the majority environment therein.
classRast(grid, dat = NULL, xy, env, cutoff)classRast(grid, dat = NULL, xy, env, cutoff)
grid |
A |
dat |
Either a |
xy |
A vector specifying the name or numeric position of columns
in |
env |
The name or numeric position of the column in |
cutoff |
The (decimal) proportion of incidences of an environmental
category above which a cell will be assigned as that category.
|
The cutoff threshold is an inclusive bound: environmental incidence
proportions greater than or equal to the cutoff will assign cell values
to the majority environmental class. For instance, if category A represents
65% of occurrences in a cell and cutoff = 0.65, the returned value for the
cell will be A. If no single category in a cell meets or exceeds the
representation necessary to reach the given cutoff, the value returned
for the cell is indet., indeterminate.
Cells lacking environmental occurrences altogether return NA values.
The env object can contain more than two classes, but in many cases it will
be less likely for any individual class to attain an absolute majority the
more finely divided classes are. For example, if there are three classes,
A, B, and C, with relative proportions of 20%, 31%, and 49%, the cell value
will be returned as indet. because no single class can attain a cutoff
above 50%, despite class C having the largest relative representation.
Missing environment values in the point data should be coded as NA,
not e.g. 'unknown'. classRast() ignores NA occurrences when tallying
environmental occurrences against the cutoff. However, NA occurrences
still count when determining NA status of cells in the raster: a cell
containing occurrences of only NA value is classified as indet., not NA.
That is, any grid cell encompassing original point data is non-NA.
Antell and others (2020) set a cutoff of 0.8, based on the same threshold
Nürnberg and Aberhan (2013) used to classify environmental preferences for taxa.
The coordinates associated with points should be given with respect to the
same coordinate reference system (CRS) of the target raster grid, e.g. both
given in latitude-longitude, Equal Earth projected coordinates, or other CRS.
The CRS of a SpatRaster object can be retrieved with terra::crs()
(with the optional but helpful argument describe = TRUE).
A raster of class SpatRaster defined by the terra package
Antell GT, Kiessling W, Aberhan M, Saupe EE (2020). “Marine biodiversity and geographic distributions are independent on large scales.” Current Biology, 30(1), 115-121. doi:10.1016/j.cub.2019.10.065.
Nürnberg S, Aberhan M (2013). “Habitat breadth and geographic range predict diversity dynamics in marine Mesozoic bivalves.” Paleobiology, 39(3), 360-372. doi:10.1666/12047.
library(terra) # work in Equal Earth projected coordinates prj <- 'EPSG:8857' # generate point occurrences in a small area of Northern Africa n <- 100 set.seed(5) x <- runif(n, 0, 30) y <- runif(n, 10, 30) # generate an environmental variable with a latitudinal gradient # more habitat type 0 (e.g. rock) near equator, more 1 (e.g. grassland) to north env <- rbinom(n, 1, prob = (y-10)/20) env[env == 0] <- 'rock' env[env == 1] <- 'grass' # units for Equal Earth are meters, so if we consider x and y as given in km, x <- x * 1000 y <- y * 1000 ptsDf <- data.frame(x, y, env) # raster for study area at 5-km resolution r <- rast(resolution = 5*1000, crs = prj, xmin = 0, xmax = 30000, ymin = 10000, ymax = 30000) binRast <- classRast(grid = r, dat = ptsDf, xy = c('x', 'y'), env = 'env', cutoff = 0.6) binRast # plot environment classification vs. original points plot(binRast, col = c('lightgreen', 'grey60', 'white')) points(ptsDf[env=='rock', ], pch = 16, cex = 1.2) # occurrences of given habitat points(ptsDf[env=='grass',], pch = 1, cex = 1.2) # classRast can also accept more than 2 environmental classes: # add a 3rd environmental class with maximum occurrence in bottom-left grid cell newEnv <- data.frame('x' = rep(0, 10), 'y' = rep(10000, 10), 'env' = rep('new', 10)) ptsDf <- rbind(ptsDf, newEnv) binRast <- classRast(grid = r, dat = ptsDf, xy = c('x', 'y'), env = 'env', cutoff = 0.6) plot(binRast, col = c('lightgreen', 'grey60', 'purple', 'white'))library(terra) # work in Equal Earth projected coordinates prj <- 'EPSG:8857' # generate point occurrences in a small area of Northern Africa n <- 100 set.seed(5) x <- runif(n, 0, 30) y <- runif(n, 10, 30) # generate an environmental variable with a latitudinal gradient # more habitat type 0 (e.g. rock) near equator, more 1 (e.g. grassland) to north env <- rbinom(n, 1, prob = (y-10)/20) env[env == 0] <- 'rock' env[env == 1] <- 'grass' # units for Equal Earth are meters, so if we consider x and y as given in km, x <- x * 1000 y <- y * 1000 ptsDf <- data.frame(x, y, env) # raster for study area at 5-km resolution r <- rast(resolution = 5*1000, crs = prj, xmin = 0, xmax = 30000, ymin = 10000, ymax = 30000) binRast <- classRast(grid = r, dat = ptsDf, xy = c('x', 'y'), env = 'env', cutoff = 0.6) binRast # plot environment classification vs. original points plot(binRast, col = c('lightgreen', 'grey60', 'white')) points(ptsDf[env=='rock', ], pch = 16, cex = 1.2) # occurrences of given habitat points(ptsDf[env=='grass',], pch = 1, cex = 1.2) # classRast can also accept more than 2 environmental classes: # add a 3rd environmental class with maximum occurrence in bottom-left grid cell newEnv <- data.frame('x' = rep(0, 10), 'y' = rep(10000, 10), 'env' = rep('new', 10)) ptsDf <- rbind(ptsDf, newEnv) binRast <- classRast(grid = r, dat = ptsDf, xy = c('x', 'y'), env = 'env', cutoff = 0.6) plot(binRast, col = c('lightgreen', 'grey60', 'purple', 'white'))
Spatially subsample a dataset based on minimum spanning trees connecting points within regions of set extent, with optional rarefaction to a site quota.
clustr( dat, xy, iter, nSite = NULL, distMax, nMin = 3, crs = "epsg:4326", output = "locs" )clustr( dat, xy, iter, nSite = NULL, distMax, nMin = 3, crs = "epsg:4326", output = "locs" )
dat |
A |
xy |
A vector of two elements, specifying the name or numeric position
of columns in |
iter |
The number of spatial subsamples to return |
nSite |
The quota of unique locations to include in each subsample. |
distMax |
Numeric value for maximum diameter (km) allowed across locations in a subsample |
nMin |
Numeric value for the minimum number of sites to be included in
every returned subsample. If |
crs |
Coordinate reference system as a GDAL text string, EPSG code,
or object of class |
output |
Whether the returned data should be two columns of
subsample site coordinates ( |
Lagomarcino and Miller (2012) developed an iterative approach of aggregating
localities to build clusters based on convex hulls, inspired by species-area
curve analysis (Scheiner 2003). Close et al. (2017, 2020) refined the approach and
changed the proximity metric from minimum convex hull area to minimum spanning
tree length. The present implementation adapts code from Close et al. (2020)
to add an option for site rarefaction after cluster construction and to grow
trees at random starting points iter number of times (instead of a
deterministic, exhaustive iteration at every unique location).
The function takes a single location as a starting (seed) point; the seed
and its nearest neighbour initiate a spatial cluster. The distance between
the two points is the first branch in a minimum spanning tree for the cluster.
The location that has the shortest distance to any points already within the
cluster is grouped in next, and its distance (branch) is added to the sum
tree length. This iterative process continues until the largest distance
between any two points in the cluster would exceed distMax km.
In the rare case multiple candidate points are tied for minimum distance
from the cluster, one point is selected at random as the next to include.
Any tree with fewer than nMin points is prohibited.
In the case that nSite is supplied, nMin argument is ignored,
and any tree with fewer than nSite points is prohibited.
After building a tree as described above, a random set of nSite points
within the cluster is taken (without replacement).
The nSite argument makes clustr() comparable with cookies()
in that it spatially standardises both extent and area/locality number.
The performance of clustr() is designed on the assumption iter
is much larger than the number of unique localities. Internal code first
calculates the full minimum spanning tree at every viable starting point
before it then samples those trees (i.e. resamples and optionally rarefies)
for the specified number of iterations. This sequence means the total
run-time increases only marginally even as iter increases greatly.
However, if there are a large number of sites, particularly a large number
of densely-spaced sites, the calculations will be slow even for a
small number of iterations.
A list of length iter. Each element is a data.frame
(or matrix, if dat is a matrix and output = 'full').
If nSite is supplied, each element contains nSite observations.
If output = 'locs' (default), only the coordinates of subsampling
locations are returned.
If output = 'full', all dat columns are returned for the
rows associated with the subsampled locations.
Antell GT, Kiessling W, Aberhan M, Saupe EE (2020). “Marine biodiversity and geographic distributions are independent on large scales.” Current Biology, 30(1), 115-121. doi:10.1016/j.cub.2019.10.065.
Close RA, Benson RB, Upchurch P, Butler RJ (2017). “Controlling for the species–area effect supports constrained long-term Mesozoic terrestrial vertebrate diversification.” Nature Communications, 8(1), 1–11. doi:10.1038/ncomms15381.
Close RA, Benson RB, Saupe EE, Clapham ME, Butler RJ (2020). “The spatial structure of Phanerozoic marine animal diversity.” Science, 368(6489), 420-424. doi:10.1126/science.aay8309.
Lagomarcino AJ, Miller AI (2012). “The relationship between genus richness and geographic area in Late Cretaceous marine biotas: Epicontinental sea versus open-ocean-facing settings.” PloS One, 7(8), e40472. doi:10.1371/journal.pone.0040472.
Scheiner SM (2003). “Six types of species–area curves.” Global Ecology and Biogeography, 12(6), 441-447. doi:10.1046/j.1466-822X.2003.00061.x.
# generate occurrences: 10 lat-long points in modern Australia n <- 10 x <- seq(from = 140, to = 145, length.out = n) y <- seq(from = -20, to = -25, length.out = n) pts <- data.frame(x, y) # sample 5 sets of 4 locations no more than 400km across clustr(dat = pts, xy = 1:2, iter = 5, nSite = 4, distMax = 400)# generate occurrences: 10 lat-long points in modern Australia n <- 10 x <- seq(from = 140, to = 145, length.out = n) y <- seq(from = -20, to = -25, length.out = n) pts <- data.frame(x, y) # sample 5 sets of 4 locations no more than 400km across clustr(dat = pts, xy = 1:2, iter = 5, nSite = 4, distMax = 400)
A dataset containing the (palaeo)coordinates and recorded marine environment of 8,000 PBDB fossil collections from the Silurian, formatted and downloaded from the Paleobiology Database on 24 June 2022.
collSilurcollSilur
A data frame with 8345 rows and 7 variables:
Coordinates of a collection, rotated to its palaeogeographic location with the tectonic plate model of GPlates
Unique identifier for the collection and its published reference
One of 23 marine environment categories
Bounds of the age estimate for a collection
Spatially subsample a dataset to produce samples of standard area and extent.
cookies( dat, xy, iter, nSite, r, weight = FALSE, crs = "epsg:4326", output = "locs" )cookies( dat, xy, iter, nSite, r, weight = FALSE, crs = "epsg:4326", output = "locs" )
dat |
A |
xy |
A vector of two elements, specifying the name or numeric position
of columns in |
iter |
The number of spatial subsamples to return |
nSite |
The quota of unique locations to include in each subsample. |
r |
Numeric value for the radius (km) defining the circular extent of each spatial subsample. |
weight |
Whether sites within the subsample radius should be drawn
at random ( |
crs |
Coordinate reference system as a GDAL text string, EPSG code,
or object of class |
output |
Whether the returned data should be two columns of
subsample site coordinates ( |
The function takes a single location as a starting (seed) point and
circumscribes a buffer of r km around it. Buffer circles that span
the antemeridian (180 degrees longitude) are wrapped as a multipolygon
to prevent artificial truncation. After standardising radial extent, sites
are drawn within the circular extent until a quota of nSite is met.
Sites are sampled without replacement, so a location is used as a seed point
only if it is within r km distance of at least nSite-1 locations.
The method is introduced in Antell et al. (2020) and described in
detail in Methods S1 therein.
The probability of drawing each site within the standardised extent is
either equal (weight = FALSE) or proportional to the inverse-square
of its distance from the seed point (weight = TRUE), which clusters
subsample locations more tightly.
For geodetic coordinates (latitude-longitude), distances are calculated along great circle arcs. For Cartesian coordinates, distances are calculated in Euclidian space, in units associated with the projection CRS (e.g. metres).
A list of length iter. Each list element is a
data.frame or matrix (matching the class of dat)
with nSite observations. If output = 'locs'
(default), only the coordinates of subsampling locations are returned.
If output = 'full', all dat columns are returned for the
rows associated with the subsampled locations.
If weight = TRUE, the first observation in each returned subsample
data.frame corresponds to the seed point. If weight = FALSE,
observations are listed in the random order of which they were drawn.
Antell GT, Kiessling W, Aberhan M, Saupe EE (2020). “Marine biodiversity and geographic distributions are independent on large scales.” Current Biology, 30(1), 115-121. doi:10.1016/j.cub.2019.10.065.
# generate occurrences: 10 lat-long points in modern Australia n <- 10 x <- seq(from = 140, to = 145, length.out = n) y <- seq(from = -20, to = -25, length.out = n) pts <- data.frame(x, y) # sample 5 sets of 3 occurrences within 200km radius cookies(dat = pts, xy = 1:2, iter = 5, nSite = 3, r = 200)# generate occurrences: 10 lat-long points in modern Australia n <- 10 x <- seq(from = 140, to = 145, length.out = n) y <- seq(from = -20, to = -25, length.out = n) pts <- data.frame(x, y) # sample 5 sets of 3 occurrences within 200km radius cookies(dat = pts, xy = 1:2, iter = 5, nSite = 3, r = 200)
A dataset containing the (palaeo)coordinates and genus identifications of
13,500 marine brachiopods from the Silurian (443.1-419 Ma). Records with
uncertain or unaccepted taxonomic names, non-marine palaeo-environments,
or missing coordinates are excluded from the original download (29 July 2022).
Taxonomic synonymisation and removal of stratigraphic outliers
follows the fossilbrush vignette example of cross-correlation with the
Sepkoski range-through database [fossilbrush::sepkoski()].
occSiluroccSilur
A data frame with 13502 rows and 11 variables:
Latin order, family, and genus name, as synonymised against Sepkoski database
Coordinates of an occurrence, rotated to its palaeogeographic location with the tectonic plate model of GPlates
Unique identifiers for the collection and published reference containing the occurrence
One of 23 marine environment categories
Bounds of the age estimate for an occurrence, according to the ICS 2013 geologic time scale.
Original identification, including subgenus and species epithet if applicable, according to the latest PBDB accepted taxonomy at time of download
Calculate occurrence count, centroid coordinates, latitudinal range (degrees), great circle distance (km), mean pairwise distance (km), and summed minimum spanning tree length (km) for spatial point coordinates.
rangeSize(coords, crs = "epsg:4326")rangeSize(coords, crs = "epsg:4326")
coords |
2-column |
crs |
Coordinate reference system as a GDAL text string, EPSG code,
or object of class |
Coordinates and their distances are computed with respect to the original
coordinate reference system if supplied, except in calculation of latitudinal
range, for which projected coordinates are transformed to geodetic ones.
If crs is unspecified, by default points are assumed to be given in
latitude-longitude and distances are calculated with spherical geometry.
Duplicate coordinates will be removed. If a single unique point is supplied,
all distance measures returned will be NA.
A 1-row, 7-column matrix
# generate 20 occurrences for a pseudo-species # centred on Yellowstone National Park (latitude-longitude) # normally distributed with a standard deviation ~110 km set.seed(2) x <- rnorm(20, 110.5885, 2) y <- rnorm(20, 44.4280, 1) pts <- cbind(x,y) rangeSize(pts)# generate 20 occurrences for a pseudo-species # centred on Yellowstone National Park (latitude-longitude) # normally distributed with a standard deviation ~110 km set.seed(2) x <- rnorm(20, 110.5885, 2) y <- rnorm(20, 44.4280, 1) pts <- cbind(x,y) rangeSize(pts)
Summarise the geographic scope and position of occurrence data, and optionally estimate diversity and evenness
sdSumry( dat, xy, taxVar, crs = "epsg:4326", collections = NULL, quotaQ = NULL, quotaN = NULL, omitDom = FALSE )sdSumry( dat, xy, taxVar, crs = "epsg:4326", collections = NULL, quotaQ = NULL, quotaN = NULL, omitDom = FALSE )
dat |
A |
xy |
A vector of two elements, specifying the name or numeric position
of columns in |
taxVar |
The name or numeric position of the column containing
taxonomic identifications. |
crs |
Coordinate reference system as a GDAL text string, EPSG code,
or object of class |
collections |
The name or numeric position of the column containing unique collection IDs, e.g. 'collection_no' in PBDB data downloads. |
quotaQ |
A numeric value for the coverage (quorum) level at which to perform coverage-based rarefaction (shareholder quorum subsampling). |
quotaN |
A numeric value for the quota of taxon occurrences to subsample in classical rarefaction. |
omitDom |
If |
sdSumry() compiles metadata about a sample or list of samples,
before or after spatial subsampling. The function counts the number
of collections (if requested), taxon presences (excluding repeat incidences
of a taxon at a given site), and unique spatial sites;
it also calculates site centroid coordinates, latitudinal range (degrees),
great circle distance (km), mean pairwise distance (km), and summed
minimum spanning tree length (km).
Coordinates and their distances are computed with respect to the original
coordinate reference system if supplied, except in calculation of latitudinal
range, for which projected coordinates are transformed to geodetic ones.
If crs is unspecified, by default points are assumed to be given in
latitude-longitude and distances are calculated with spherical geometry.
The first two diversity variables returned are the raw count of observed taxa
and the Summed Common species/taxon Occurrence Rate (SCOR). SCOR reflects
the degree to which taxa are common/widespread and is decoupled from
richness or abundance (Hannisdal et al. 2012). SCOR is calculated as the
sum across taxa of the log probability of incidence, .
For a given taxon, ,
where is estimated as the fraction of occupied sites.
Very widespread taxa make a large contribution to an assemblage SCOR,
while rare taxa have relatively little influence.
If quotaQ is supplied, sdSumry() rarefies richness at the
given coverage value and returns the point estimate of richness (Hill number 0)
and its 95% confidence interval, as well as estimates of evenness (Pielou's J)
and frequency-distribution sample coverage (given by iNEXT$DataInfo).
If quotaN is supplied, sdSumry() rarefies richness to the given
number of occurrence counts and returns the point estimate of richness
and its 95% confidence interval.
Coverage-based and classical rarefaction are both calculated with
iNEXT::estimateD() internally. For details, such as how diversity
is extrapolated if sample coverage is insufficient to achieve a specified
rarefaction level, consult Chao and Jost (2012) and Hsieh et al. (2016).
A matrix of spatial and optional diversity metrics. If dat is a
list of data.frame objects, output rows correspond to input elements.
Chao A, Jost L (2012). “Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size.” Ecology, 93(12), 2533–2547. doi:10.1890/11-1952.1.
Hannisdal B, Henderiks J, Liow LH (2012). “Long-term evolutionary and ecological responses of calcifying phytoplankton to changes in atmospheric CO2.” Global Change Biology, 18(12), 3504–3516. doi:10.1111/gcb.12007.
Hsieh TC, Ma KH, Chao A (2016). “iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers).” Methods in Ecology and Evolution, 7(12), 1451–1456. doi:10.1111/2041-210X.12613.
# generate occurrences set.seed(9) x <- sample(rep(1:5, 10)) y <- sample(rep(1:5, 10)) # make some species 2x or 4x as common abund <- c(rep(4, 5), rep(2, 5), rep(1, 10)) sp <- sample(letters[1:20], 50, replace = TRUE, prob = abund) obs <- data.frame(x, y, sp) # minimum sample data returned sdSumry(obs, c('x','y'), 'sp') # also calculate evenness and coverage-based rarefaction diversity estimates sdSumry(obs, xy = c('x','y'), taxVar = 'sp', quotaQ = 0.7)# generate occurrences set.seed(9) x <- sample(rep(1:5, 10)) y <- sample(rep(1:5, 10)) # make some species 2x or 4x as common abund <- c(rep(4, 5), rep(2, 5), rep(1, 10)) sp <- sample(letters[1:20], 50, replace = TRUE, prob = abund) obs <- data.frame(x, y, sp) # minimum sample data returned sdSumry(obs, c('x','y'), 'sp') # also calculate evenness and coverage-based rarefaction diversity estimates sdSumry(obs, xy = c('x','y'), taxVar = 'sp', quotaQ = 0.7)
Subset a dataset to unique spatial localities or locality-taxon combinations.
uniqify(dat, xy, taxVar = NULL, na.rm = TRUE)uniqify(dat, xy, taxVar = NULL, na.rm = TRUE)
dat |
A |
xy |
A vector of two elements, specifying the name or numeric position
of columns in |
taxVar |
The name or numeric position of the column containing
taxonomic identifications. |
na.rm |
Should records missing information be removed? Default is yes. |
The na.rm argument applies to coordinate values and, if taxVar
is supplied, to taxon values. If na.rm = FALSE, any NA values will be
retained and treated as their own value. Note that divvy ignores any rows
with missing coordinates for the subsampling functions cookies(),
clustr(), and bandit().
An object with the same class and columns as dat, containing the
subset of rows representing unique coordinates (if only xy supplied)
or unique taxon-site combinations (if taxVar is also supplied).
The first record at each spatial locality is retained,
or if taxVar is specified, the first record of each taxon at a locality.
# generate occurrence data x <- rep(1, 10) y <- c(rep(1, 5), 2:6) sp <- c(rep(letters[1:3], 2), rep(letters[4:5], 2)) obs <- data.frame(x, y, sp) # compare original and unique datasets: # rows 4 and 5 removed as duplicates of rows 1 and 2, respectively obs uniqify(obs, taxVar = 3, xy = 1:2) # using taxon identifications or other third variable is optional uniqify(obs, xy = c('x', 'y')) # caution - data outside the taxon and occurrence variables # will be lost where associated with duplicate occurrences obs$notes <- letters[11:20] uniqify(obs, 1:2, 3) # the notes 'n' and 'o' are absent in the output data# generate occurrence data x <- rep(1, 10) y <- c(rep(1, 5), 2:6) sp <- c(rep(letters[1:3], 2), rep(letters[4:5], 2)) obs <- data.frame(x, y, sp) # compare original and unique datasets: # rows 4 and 5 removed as duplicates of rows 1 and 2, respectively obs uniqify(obs, taxVar = 3, xy = 1:2) # using taxon identifications or other third variable is optional uniqify(obs, xy = c('x', 'y')) # caution - data outside the taxon and occurrence variables # will be lost where associated with duplicate occurrences obs$notes <- letters[11:20] uniqify(obs, 1:2, 3) # the notes 'n' and 'o' are absent in the output data