,
Donald Swiderski [aut],
Miriam Zelditch [aut]| Title: | EcoPhyloMapper |
|---|---|
| Description: | Facilitates the aggregation of species' geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) <doi:10.1111/2041-210X.13914>. |
| Authors: | Pascal Title [aut, cre] ,
Donald Swiderski [aut],
Miriam Zelditch [aut] |
| Maintainer: | Pascal Title <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.2 |
| Built: | 2024-11-15 05:38:06 UTC |
| Source: | https://github.com/ptitle/epm |
Adds a legend to an existing plot, with some additional manual controls.
addLegend( r, params = NULL, direction, side, location = "right", nTicks = 3, adj = NULL, shortFrac = 0.02, longFrac = 0.3, axisOffset = 0, border = TRUE, ramp, isInteger = "auto", ncolors = 64, breaks = NULL, minmax = NULL, locs = NULL, label = "", cex.axis = 0.8, tcl = NA, labelDist = 0.7, minDigits = 2 )addLegend( r, params = NULL, direction, side, location = "right", nTicks = 3, adj = NULL, shortFrac = 0.02, longFrac = 0.3, axisOffset = 0, border = TRUE, ramp, isInteger = "auto", ncolors = 64, breaks = NULL, minmax = NULL, locs = NULL, label = "", cex.axis = 0.8, tcl = NA, labelDist = 0.7, minDigits = 2 )
r |
the epmGrid, rasterLayer, SpatRaster or sf object that has been plotted |
params |
If an epmGrid plot was saved to a variable, provide that here. Contents will override other arguments. |
direction |
direction of color ramp. If omitted, then direction is
automatically inferred, otherwise can be specified as |
side |
side for tick marks, see |
location |
either a location name (see |
nTicks |
number of tick marks, besides min and max. |
adj |
if location is top, left, bottom or right, use this argument to adjust the location of the legend, defined in percent of the figure width. See Details for additional information. |
shortFrac |
Percent of the plot width range that will be used as the short dimension of the legend. Only applies to preset location options. |
longFrac |
Percent of the plot width range that will be used as the long dimension of the legend. Only applies to preset location options. |
axisOffset |
distance from color bar for labels, as a percent of the plot width. |
border |
logical, should the color legend have a black border |
ramp |
either a vector of color names that will be interpolated, or a
color ramp function that takes an integer (see for example
|
isInteger |
If |
ncolors |
grain size of color ramp |
breaks |
If a custom set of color breaks were used in plotting
|
minmax |
min and max values from which the color ramp will be derived.
If left as |
locs |
locations of tick marks, if |
label |
text to plot alongside the legend |
cex.axis |
size of axis labels |
tcl |
length of tick marks (see help for tcl in ?par) |
labelDist |
distance from axis to axis labels (passed to |
minDigits |
minimum number of significant digits for labels |
A number of predefined locations exist in this function to make it easy to add a legend to a plot.
Preset locations are: topleft, topright,
bottomleft, bottomright, left, right,
top and bottom.
If more fine-tuned control is desired,
then a numeric vector of length 4 can be supplied to location,
specifying the min x, max x, min y and max y values for the legend.
Additionally, the adj argument can be used to more intuitively
adjust where the legend is placed. adj is defined as a percentage
of the figure width or height, left to right, or bottom to top,
respectively. For example, if the legend is at the bottom, adj =
0.8 will place the legend 80% of the distance from the top of the
figure, horizontally centered.
If an epmGrid object was plotted with plot.epmGrid, and if
use_tmap = FALSE was specified, and if that plot was assigned to
a variable, then you can supply that variable here to the params
argument, and a number of options will be automatically handed over to
this function.
See examples.
Invisibly returns a list with the following components.
coords: 2-column matrix of xy coordinates for each color bin in the legend.
width: Coordinates for the short dimension of the legend.
pal: the color ramp
tickLocs: the tick mark locations in plotting units
Pascal Title
# create square-cell epmGrid object tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') # need to disable tmap if we want to anything to a plot plot(tamiasEPM2, use_tmap = FALSE, legend = FALSE) addLegend(tamiasEPM2, location = 'right', label = 'richness') addLegend(tamiasEPM2, location = 'top', label = 'richness') # fine-tune placement addLegend(tamiasEPM2, location=c(113281, 1265200, -1500000, -1401898), side = 1) # Using the params option xx <- plot(tamiasEPM2, use_tmap = FALSE, legend = FALSE, col = viridisLite::magma) addLegend(tamiasEPM2, params = xx, location = 'top') # works with hex grids as well xx <- plot(tamiasEPM, use_tmap = FALSE, legend = FALSE, col = viridisLite::magma) addLegend(tamiasEPM, params = xx, location = 'top')# create square-cell epmGrid object tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') # need to disable tmap if we want to anything to a plot plot(tamiasEPM2, use_tmap = FALSE, legend = FALSE) addLegend(tamiasEPM2, location = 'right', label = 'richness') addLegend(tamiasEPM2, location = 'top', label = 'richness') # fine-tune placement addLegend(tamiasEPM2, location=c(113281, 1265200, -1500000, -1401898), side = 1) # Using the params option xx <- plot(tamiasEPM2, use_tmap = FALSE, legend = FALSE, col = viridisLite::magma) addLegend(tamiasEPM2, params = xx, location = 'top') # works with hex grids as well xx <- plot(tamiasEPM, use_tmap = FALSE, legend = FALSE, col = viridisLite::magma) addLegend(tamiasEPM, params = xx, location = 'top')
Add a phylogeny to epmGrid object.
addPhylo(x, tree, replace = FALSE, verbose = FALSE)addPhylo(x, tree, replace = FALSE, verbose = FALSE)
x |
object of class |
tree |
a phylogeny of class |
replace |
boolean; if a tree is already a part of |
verbose |
if TRUE, list out all species that are dropped/excluded, rather than counts. |
If any species in the phylogeny are not found in the epmGrid geographical data, then those species will be dropped from the phylogeny, and a warning will be issued.
If providing a set of trees as a multiPhylo object, it is expected that all trees have the same tips.
object of class epmGrid, with a phylo object as the
list element named phylo.
Pascal Title
tamiasEPM tamiasTree addPhylo(tamiasEPM, tamiasTree)tamiasEPM tamiasTree addPhylo(tamiasEPM, tamiasTree)
Add univariate or multivariate trait data to an epmGrid object.
addTraits(x, data, replace = FALSE, verbose = FALSE)addTraits(x, data, replace = FALSE, verbose = FALSE)
x |
object of class |
data |
named numeric vector, matrix or dataframe with rownames
corresponding to species in |
replace |
boolean; if data is already a part of |
verbose |
if TRUE, list out all species that are dropped/excluded, rather than counts. |
If any species in data are not found in the epmGrid
geographical data, then those species will be dropped from data,
and a warning will be issued.
object of class epmGrid, with trait data as the list element
named data.
Pascal Title
tamiasEPM tamiasTraits addTraits(tamiasEPM, tamiasTraits)tamiasEPM tamiasTraits addTraits(tamiasEPM, tamiasTraits)
Change in morphological disparity is calculating across a
moving window of neighboring grid cells. To implement a custom function,
see customBetaDiv.
betadiv_disparity(x, radius, slow = FALSE, nThreads = 1)betadiv_disparity(x, radius, slow = FALSE, nThreads = 1)
x |
object of class |
radius |
Radius of the moving window in map units. |
slow |
if TRUE, use an alternate implementation that has a smaller memory footprint but that is likely to be much slower. Most useful for high spatial resolution. |
nThreads |
number of threads for parallelization |
For each gridcell neighborhood (defined by the radius), we calculate the proportion of the full disparity contained in those grid cells, and then take the standard deviation of those proportions across the gridcell neighborhood. This way, the returned values reflect how much disparity (relative to the overall total disparity) changes across a moving window.
If the R package spdep is installed, this function should run more quickly.
Returns a sf polygons object (if hex grid) or a SpatRaster object (if square grid).
Pascal Title
Foote M. 1993. Contributions of individual taxa to overall morphological disparity. Paleobiology. 19:403–419.
tamiasEPM tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) z <- betadiv_disparity(tamiasEPM, radius = 150000) plot(z) # using square grid epmGrid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addTraits(tamiasEPM2, tamiasTraits) z2 <- betadiv_disparity(tamiasEPM2, radius = 150000) terra::plot(z2, col = sf::sf.colors(100))tamiasEPM tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) z <- betadiv_disparity(tamiasEPM, radius = 150000) plot(z) # using square grid epmGrid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addTraits(tamiasEPM2, tamiasTraits) z2 <- betadiv_disparity(tamiasEPM2, radius = 150000) terra::plot(z2, col = sf::sf.colors(100))
Multisite phylogenetic community dissimilarity is calculated for
each cell within a circular moving window of neighboring cells. To implement a
custom function, see customBetaDiv.
betadiv_phylogenetic( x, radius, component = "full", focalCoord = NULL, slow = FALSE, nThreads = 1 )betadiv_phylogenetic( x, radius, component = "full", focalCoord = NULL, slow = FALSE, nThreads = 1 )
x |
object of class |
radius |
Radius of the moving window in map units. |
component |
which component of beta diversity to use, can be
|
focalCoord |
vector of x and y coordinate, see details |
slow |
if TRUE, use an alternate implementation that has a smaller memory footprint but that is likely to be much slower. Most useful for high spatial resolution. |
nThreads |
number of threads for parallelization |
For each cell, multisite dissimilarity is calculated for the focal
cell and its neighbors. If focalCoord is specified, then instead of
multisite dissimilarity within a moving window of gridcells, pairwise
dissimilarity is calculated from the cell at the focal coordinates, to all
other cells.
All metrics are based on Sorensen dissimilarity and range from 0 to 1: For each metric, the following components can be specified. These components are additive, such that the full metric = turnover + nestedness.
turnover: species turnover without the influence of richness differences
nestedness: species turnover due to differences in richness
full: the combined turnover due to both differences in richness and pure turnover
If the R package spdep is installed, this function should run more quickly.
Returns a sf polygons object (if hex grid) or a SpatRaster object (if square grid) with multisite community dissimilarity for each grid cell.
Pascal Title
Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21 (2012): 1223–1232.
Leprieur, F, Albouy, C, De Bortoli, J, Cowman, PF, Bellwood, DR & Mouillot, D. Quantifying Phylogenetic Beta Diversity: Distinguishing between "True" Turnover of Lineages and Phylogenetic Diversity Gradients. PLoS ONE 7 (2012): e42760–12.
tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) # phylogenetic turnover beta_phylo_turnover <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'turnover') beta_phylo_nestedness <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'nestedness') beta_phylo_full <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'full') oldpar <- par(mfrow=c(1,3)) plot(beta_phylo_turnover, reset = FALSE, key.pos = NULL) plot(beta_phylo_nestedness, reset = FALSE, key.pos = NULL) plot(beta_phylo_full, reset = FALSE, key.pos = NULL) # using square grid epmGrid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTree) beta_phylo_full <- betadiv_phylogenetic(tamiasEPM2, radius = 70000, component = 'full') beta_phylo_full_slow <- betadiv_phylogenetic(tamiasEPM2, radius = 70000, component = 'full', slow = TRUE) par(mfrow = c(1,2)) terra::plot(beta_phylo_full, col = sf::sf.colors(100)) terra::plot(beta_phylo_full_slow, col = sf::sf.colors(100)) # dissimilarity from a focal cell focalBeta <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'full', focalCoord = c(-1413764, 573610.8)) plot(focalBeta, reset = FALSE) points(-1413764, 573610.8, pch = 3, col = 'white') par(oldpar)tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) # phylogenetic turnover beta_phylo_turnover <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'turnover') beta_phylo_nestedness <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'nestedness') beta_phylo_full <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'full') oldpar <- par(mfrow=c(1,3)) plot(beta_phylo_turnover, reset = FALSE, key.pos = NULL) plot(beta_phylo_nestedness, reset = FALSE, key.pos = NULL) plot(beta_phylo_full, reset = FALSE, key.pos = NULL) # using square grid epmGrid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTree) beta_phylo_full <- betadiv_phylogenetic(tamiasEPM2, radius = 70000, component = 'full') beta_phylo_full_slow <- betadiv_phylogenetic(tamiasEPM2, radius = 70000, component = 'full', slow = TRUE) par(mfrow = c(1,2)) terra::plot(beta_phylo_full, col = sf::sf.colors(100)) terra::plot(beta_phylo_full_slow, col = sf::sf.colors(100)) # dissimilarity from a focal cell focalBeta <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'full', focalCoord = c(-1413764, 573610.8)) plot(focalBeta, reset = FALSE) points(-1413764, 573610.8, pch = 3, col = 'white') par(oldpar)
Multisite taxonomic community dissimilarity is calculated for
each cell within a circular moving window of neighboring cells. To implement
a custom function, see customBetaDiv.
betadiv_taxonomic( x, radius, component = "full", focalCoord = NULL, slow = FALSE, nThreads = 1 )betadiv_taxonomic( x, radius, component = "full", focalCoord = NULL, slow = FALSE, nThreads = 1 )
x |
object of class |
radius |
Radius of the moving window in map units. |
component |
which component of beta diversity to use, can be
|
focalCoord |
vector of x and y coordinate, see details |
slow |
if TRUE, use an alternate implementation that has a smaller memory footprint but that is likely to be much slower. Most useful for high spatial resolution. |
nThreads |
number of threads for parallelization |
For each cell, multisite dissimilarity is calculated from the focal
cell and its neighbors. If focalCoord is specified, then instead of
multisite dissimilarity within a moving window of gridcells, pairwise
dissimilarity is calculated from the cell at the focal coordinates, to all
other cells.
All metrics are based on Sorensen dissimilarity and range from 0 to 1.
For each metric, the following components can be specified. These components
are additive, such that the full metric = turnover + nestedness.
turnover: species turnover without the influence of richness differences
nestedness: species turnover due to differences in richness richness and pure turnover
If the R package spdep is installed, this function should run more quickly.
Returns a grid with multi-site community dissimilarity for each cell.
Pascal Title
Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21 (2012): 1223–1232.
tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # taxonomic turnover beta_taxonomic_turnover <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'turnover') beta_taxonomic_nestedness <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'nestedness') beta_taxonomic_full <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'full') oldpar <- par(mfrow = c(1, 3)) plot(beta_taxonomic_turnover, reset = FALSE, key.pos = NULL) plot(beta_taxonomic_nestedness, reset = FALSE, key.pos = NULL) plot(beta_taxonomic_full, reset = FALSE, key.pos = NULL) # using square grid epmGrid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') beta_taxonomic_full <- betadiv_taxonomic(tamiasEPM2, radius = 70000, component = 'full') beta_taxonomic_full_slow <- betadiv_taxonomic(tamiasEPM2, radius = 70000, component = 'full', slow = TRUE) par(mfrow=c(1,2)) terra::plot(beta_taxonomic_full, col = sf::sf.colors(100)) terra::plot(beta_taxonomic_full_slow, col = sf::sf.colors(100)) # dissimilarity from a focal cell focalBeta <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'full', focalCoord = c(-1413764, 573610.8)) plot(focalBeta, reset = FALSE) points(-1413764, 573610.8, pch = 3, col = 'white') par(oldpar)tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # taxonomic turnover beta_taxonomic_turnover <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'turnover') beta_taxonomic_nestedness <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'nestedness') beta_taxonomic_full <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'full') oldpar <- par(mfrow = c(1, 3)) plot(beta_taxonomic_turnover, reset = FALSE, key.pos = NULL) plot(beta_taxonomic_nestedness, reset = FALSE, key.pos = NULL) plot(beta_taxonomic_full, reset = FALSE, key.pos = NULL) # using square grid epmGrid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') beta_taxonomic_full <- betadiv_taxonomic(tamiasEPM2, radius = 70000, component = 'full') beta_taxonomic_full_slow <- betadiv_taxonomic(tamiasEPM2, radius = 70000, component = 'full', slow = TRUE) par(mfrow=c(1,2)) terra::plot(beta_taxonomic_full, col = sf::sf.colors(100)) terra::plot(beta_taxonomic_full_slow, col = sf::sf.colors(100)) # dissimilarity from a focal cell focalBeta <- betadiv_taxonomic(tamiasEPM, radius = 70000, component = 'full', focalCoord = c(-1413764, 573610.8)) plot(focalBeta, reset = FALSE) points(-1413764, 573610.8, pch = 3, col = 'white') par(oldpar)
For an epmGrid object that contains geometric morphometric shape coordinates, calculate the per-grid-cell mean shape.
calcMeanShape(x)calcMeanShape(x)
x |
object of class |
This function will ignore cells that are empty.
a list with 2 elements: (1) matrix where nrow = number of grid cells and ncol = the number of data columns. Each row is a vector of mean shape coordinates. (2) a matrix of xy coordinates corresponding to those grid cells.
Pascal Title
tamiasEPM tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) meanshape <- calcMeanShape(tamiasEPM) head(meanshape[[1]]) head(meanshape[[2]])tamiasEPM tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) meanshape <- calcMeanShape(tamiasEPM) head(meanshape[[1]]) head(meanshape[[2]])
Return the centroid coordinates for a specified set of grid cells.
coordsFromEpmGrid(x, sites)coordsFromEpmGrid(x, sites)
x |
object of class |
sites |
locations of sites, see details. |
Sites can be cell indices as a numeric vector, or you can specify
sites = 'all' to get all grid cells. If the epmGrid object
is hexagon-based, then all grid cells that are occupied are returned.
If the epmGrid is square-based, then all grid cells, occupied or empty,
are returned.
matrix with x and y coordinates.
Pascal Title
tamiasEPM # from cell indices cells <- c(2703, 90, 3112, 179) coordsFromEpmGrid(tamiasEPM, cells) # for all grid cells dim(coordsFromEpmGrid(tamiasEPM, 'all'))tamiasEPM # from cell indices cells <- c(2703, 90, 3112, 179) coordsFromEpmGrid(tamiasEPM, cells) # for all grid cells dim(coordsFromEpmGrid(tamiasEPM, 'all'))
Creates an epmGrid object from a range of species-specific inputs.
createEPMgrid( spDat, resolution = 50000, method = "centroid", cellType = "hexagon", percentThreshold = 0.25, retainSmallRanges = TRUE, extent = "auto", percentWithin = 0, dropEmptyCells = TRUE, checkValidity = FALSE, crs = NULL, nThreads = 1, template = NULL, verbose = FALSE, use.data.table = "auto" )createEPMgrid( spDat, resolution = 50000, method = "centroid", cellType = "hexagon", percentThreshold = 0.25, retainSmallRanges = TRUE, extent = "auto", percentWithin = 0, dropEmptyCells = TRUE, checkValidity = FALSE, crs = NULL, nThreads = 1, template = NULL, verbose = FALSE, use.data.table = "auto" )
spDat |
a number of possible input formats are possible. See details below. |
resolution |
vertical and horizontal spacing of grid cells, in units of the polygons' or points' projection. |
method |
approach used for gridding. Either |
cellType |
either |
percentThreshold |
the percent that a species range must cover a grid cell to be considered present. Specified as a proportion. |
retainSmallRanges |
boolean; should small ranged species be dropped or preserved. See details. |
extent |
if 'auto', then the maximal extent of the polygons will be
used. If not 'auto', can be a SpatialPolygon, sf object, or raster, in
which case the resulting epmGrid will be cropped and masked with respect to
the polygon; or a spatial coordinates object, from which an extent object
will be generated; or a numeric vector of length 4 with minLong, maxLong,
minLat, maxLat. If 'global', a global extent will be specified.
See |
percentWithin |
The percentage of a species range that must be within
the defined extent in order for that species to be included. This filter
can be used to exclude species whose range barely enters the area of
interest. The default value of 0 will disable this filter. If |
dropEmptyCells |
only relevant for hexagonal grids, should empty cells be excluded from the resulting grid. Default is TRUE. Reasons to set this to FALSE may be if you want to retain a grid of a certain extent, regardless of which cells contain species. |
checkValidity |
if |
crs |
if supplying occurrence records in a non-spatial format, then you
must specify the crs. For unprojected long/lat data, you can simply provide
|
nThreads |
if > 1, then employ parallel computing. This won't necessarily improve runtime. |
template |
a grid (SpatRaster, RasterLayer or sf) that will be directly used as the reference grid, bypassing any inference from the input data. |
verbose |
if TRUE, list out all species that are dropped/excluded, rather than counts. |
use.data.table |
if |
Types of accepted inputs for argument spDat:
a list of polygon objects (sf or sp), named with taxon names.
a list of SpatRaster or RasterLayer grids, named with taxon names.
a multi-layer RasterStack or multi-layer SpatRaster.
a set of occurrence records, multiple accepted formats, see below.
a site-by-taxon presence/absence matrix.
If input data consist of occurrence records rather than polygons, then a couple of formats are possible:
You can provide a list of species-specific spatial point objects.
You can provide a single spatial object, where points have a taxon attribute.
You can provide a list of non-spatial species-specific dataframes.
You can provide a single non-spatial dataframe.
For options (1) and (3), the taxon names must be provided as the list names.
For options (2) and (4), the columns must be 'taxon', 'x' and 'y' (or 'long',
'lat'). For options (3) and (4), as these are non-spatial, you must provide a
crs object to the crs argument, so that the function knows what
projection to use.
It is also possible to supply a matrix with sites as rows and taxa as columns. The contents of this matrix must be either 0 or 1. If this is the case, then a raster grid must be supplied under the template argument. This will be the grid system used for converting this presence/absence matrix to an epmGrid object. It is expected that the index order of the grid is the same as the row order of the matrix.
If input is a set of species-specific grids, then it is expected that all grids belong to the same overall grid system, i.e. that the cells align and that all grids have the same resolution. Grids do not need to have the same extent.
Any SpatialPolygon or SpatialPoints objects are converted to objects of class
sf.
If cellType = 'hexagon', then the grid is made of polygons via the sf
package. If cellType = 'square', then the grid is a raster generated
via the terra package. Hexagonal cells have several advantages, including
being able to be of different sizes (if the grid is in unprojected long/lat),
and may be able to more naturally follow coastlines and non-linear features.
However, the raster-based square cells will be much less memory intensive for
high resolution datasets. Choice of grid type matters more for spatial
resolution (total number of cells), than for number of species.
In the polygon-to-grid conversion process, two approaches are implemented.
For method = 'centroid', a range polygon registers in a cell if the
polygon overlaps with the cell centroid. For method =
'percentOverlap', a range polygon registers in a cell if it covers that cell
by at least percentThreshold fraction of the cell.
If retainSmallRanges = FALSE, then species whose ranges are so small
that no cell registers as present will be dropped. If retainSmallRanges
= TRUE, then the cell that contains the majority of the the small polygon
will be considered as present, even if it's a small percent of the cell.
If retainSmallRanges = TRUE, and an extent is provided, then species
may still be dropped if they fall outside of that extent.
You may see the message Failed to compute min/max, no valid pixels found in
sampling. (GDAL error 1) . This just means that a species did not register in any
grid cells. If you specified retainSmallRanges = TRUE, then those species will
be included in a subsequent step. Therefore, this message can be ignored.
For very large datasets, this function will make a determination as to whether or not there is sufficient memory. If there is not, an alternative approach that uses the data.table package will be employed. Please install this R package to take advantage of this feature.
This function is also enhanced by the installation of the exactextractr R package.
an object of class epmGrid.
Pascal Title
library(sf) # example dataset: a list of 24 chipmunk distributions as polygons head(tamiasPolyList) # hexagonal grid tamiasEPM <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'hexagon', method = 'centroid') tamiasEPM # square grid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 # use of a grid from one analysis for another analysis tamiasEPM <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'hexagon', method = 'centroid') tamiasEPM <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'hexagon', method = 'centroid', template = tamiasEPM[[1]]) ####### # demonstration of site-by-species matrix as input. tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') ## first we will use the function generateOccurrenceMatrix() to get ## a presence/absence matrix pamat <- generateOccurrenceMatrix(tamiasEPM2, sites = 'all') # here, our grid template will be tamiasEPM2[[1]] tamiasEPM2[[1]] xx <- createEPMgrid(pamat, template = tamiasEPM2[[1]]) ####### # demonstration with grids as inputs ## We will first generate grids from the range polygons ## (you normally would not do this -- you would have grids from some other source) # define the extent that contains all range polygons fullExtent <- terra::ext(terra::vect(tamiasPolyList[[1]])) for (i in 2:length(tamiasPolyList)) { fullExtent <- terra::union(fullExtent, terra::ext(terra::vect(tamiasPolyList[[i]]))) } # create raster template fullGrid <- terra::rast(fullExtent, res = 50000, crs = terra::crs(terra::vect(tamiasPolyList[[1]]))) # now we can convert polygons to a common grid system spGrids <- list() for (i in 1:length(tamiasPolyList)) { spGrids[[i]] <- terra::trim(terra::rasterize(terra::vect(tamiasPolyList[[i]]), fullGrid)) } names(spGrids) <- names(tamiasPolyList) createEPMgrid(spGrids) ####### # With point occurrences ## demonstrating all possible input formats # list of sf spatial objects spOccList <- lapply(tamiasPolyList, function(x) st_sample(x, size = 10, type= 'random')) tamiasEPM <- createEPMgrid(spOccList, resolution = 100000, cellType = 'hexagon') # list of coordinate tables spOccList2 <- lapply(spOccList, function(x) st_coordinates(x)) tamiasEPM <- createEPMgrid(spOccList2, resolution = 100000, cellType = 'square', crs = st_crs(tamiasPolyList[[1]])) # single table of coordinates spOccList3 <- spOccList2 for (i in 1:length(spOccList3)) { spOccList3[[i]] <- cbind.data.frame(taxon = names(spOccList3)[i], spOccList3[[i]]) colnames(spOccList3[[i]]) <- c('taxon', 'X', 'Y') } spOccList3 <- do.call(rbind, spOccList3) rownames(spOccList3) <- NULL spOccList3[, "taxon"] <- as.character(spOccList3[, "taxon"]) tamiasEPM <- createEPMgrid(spOccList3, resolution = 100000, cellType = 'square', crs = st_crs(tamiasPolyList[[1]])) # a single labeled spatial object spOccList4 <- st_as_sf(spOccList3[, c("taxon", "X", "Y")], coords = c("X","Y"), crs = st_crs(spOccList[[1]])) tamiasEPM <- createEPMgrid(spOccList4, resolution = 100000, cellType = 'square')library(sf) # example dataset: a list of 24 chipmunk distributions as polygons head(tamiasPolyList) # hexagonal grid tamiasEPM <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'hexagon', method = 'centroid') tamiasEPM # square grid tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 # use of a grid from one analysis for another analysis tamiasEPM <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'hexagon', method = 'centroid') tamiasEPM <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'hexagon', method = 'centroid', template = tamiasEPM[[1]]) ####### # demonstration of site-by-species matrix as input. tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') ## first we will use the function generateOccurrenceMatrix() to get ## a presence/absence matrix pamat <- generateOccurrenceMatrix(tamiasEPM2, sites = 'all') # here, our grid template will be tamiasEPM2[[1]] tamiasEPM2[[1]] xx <- createEPMgrid(pamat, template = tamiasEPM2[[1]]) ####### # demonstration with grids as inputs ## We will first generate grids from the range polygons ## (you normally would not do this -- you would have grids from some other source) # define the extent that contains all range polygons fullExtent <- terra::ext(terra::vect(tamiasPolyList[[1]])) for (i in 2:length(tamiasPolyList)) { fullExtent <- terra::union(fullExtent, terra::ext(terra::vect(tamiasPolyList[[i]]))) } # create raster template fullGrid <- terra::rast(fullExtent, res = 50000, crs = terra::crs(terra::vect(tamiasPolyList[[1]]))) # now we can convert polygons to a common grid system spGrids <- list() for (i in 1:length(tamiasPolyList)) { spGrids[[i]] <- terra::trim(terra::rasterize(terra::vect(tamiasPolyList[[i]]), fullGrid)) } names(spGrids) <- names(tamiasPolyList) createEPMgrid(spGrids) ####### # With point occurrences ## demonstrating all possible input formats # list of sf spatial objects spOccList <- lapply(tamiasPolyList, function(x) st_sample(x, size = 10, type= 'random')) tamiasEPM <- createEPMgrid(spOccList, resolution = 100000, cellType = 'hexagon') # list of coordinate tables spOccList2 <- lapply(spOccList, function(x) st_coordinates(x)) tamiasEPM <- createEPMgrid(spOccList2, resolution = 100000, cellType = 'square', crs = st_crs(tamiasPolyList[[1]])) # single table of coordinates spOccList3 <- spOccList2 for (i in 1:length(spOccList3)) { spOccList3[[i]] <- cbind.data.frame(taxon = names(spOccList3)[i], spOccList3[[i]]) colnames(spOccList3[[i]]) <- c('taxon', 'X', 'Y') } spOccList3 <- do.call(rbind, spOccList3) rownames(spOccList3) <- NULL spOccList3[, "taxon"] <- as.character(spOccList3[, "taxon"]) tamiasEPM <- createEPMgrid(spOccList3, resolution = 100000, cellType = 'square', crs = st_crs(tamiasPolyList[[1]])) # a single labeled spatial object spOccList4 <- st_as_sf(spOccList3[, c("taxon", "X", "Y")], coords = c("X","Y"), crs = st_crs(spOccList[[1]])) tamiasEPM <- createEPMgrid(spOccList4, resolution = 100000, cellType = 'square')
Define your own function for summarizing information across a moving window of grid cells.
customBetaDiv( x, fun, radius, minTaxCount = 1, focalCoord = NULL, metricName = "custom_metric" )customBetaDiv( x, fun, radius, minTaxCount = 1, focalCoord = NULL, metricName = "custom_metric" )
x |
object of class |
fun |
a function to apply to grid cell neighborhoods (see details) |
radius |
Radius of the moving window in map units. |
minTaxCount |
the minimum number of taxa needed to apply the function. For instance, should the function be applied to gridcells with just 1 taxon? |
focalCoord |
vector of x and y coordinate, see details |
metricName |
the name you would like to attach to the output |
This function will identify the neighbors of every cell and will apply the specified function to those sets of cell neighborhoods.
The custom function should have just one input: a list of taxon names, where the list will represent a set of grid cells (focal cell + neighboring cells).
However, if a set of focal coordinates is provided, then rather than apply the function to each neighborhood of cells, the function should have two inputs: the focal cell and another cell, and that function will be applied to every pair defined by the focal cell and another cell. See examples.
Within the function call, the trait data already attached to the epmGrid object
must be referred to as dat, and the phylogenetic tree already attached to the
epmGrid must be referred to as phylo.
If the input epmGrid object contains a set of trees, then this function will
be applied, using each tree in turn, and will return a list of results. This
list can then be passed to summarizeEpmGridList to be summarized.
See examples below.
object of class epmGrid, or list of epmGrid objects
Pascal Title
tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # An example using a multivariate dataset ## For each focal cell + neighbors, calculate the morphological standard deviation ## per grid cell and return the standard deviation. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- max(dist(dat[cellList[[i]], ])) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 2, metricName = 'maxdist') # An example using only the phylogeny. ## Calculate standard deviation of phylogenetic diversity across cell neighborhood. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 1, metricName = 'faithPD') # an example that involves both morphological and phylogenetic data ## nonsensical, but for illustrative purposes: ## ratio of Faith's phylogenetic diversity to morphological range ## the standard deviation of this measure across grid cells ## in a neighborhood. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) / max(dist(dat[cellList[[i]], ])) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 2, metricName = 'ratio_PD_maxdist') # from a focal coordinate to all other sites ## Here, the function has 2 inputs. ## Example: calculate the per grid cell mean and take the distance. f <- function(focalCell, otherCell) { x1 <- colMeans(dat[focalCell, ]) x2 <- colMeans(dat[otherCell, ]) return(as.matrix(dist(rbind(x1, x2)))[1,2]) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 1, focalCoord = c(-1413764, 573610.8), metricName = 'meandist') # Example involving a set of trees tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet, replace = TRUE) ## Calculate standard deviation of phylogenetic diversity across cell ## neighborhood. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) } return(sd(vec, na.rm = TRUE)) } # This time, a list of sf objects will be returned, one for each input tree. xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 1, metricName = 'faithPD') # also works with square grid cells tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTree) tamiasEPM2 <- addTraits(tamiasEPM2, tamiasTraits) f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) / max(dist(dat[cellList[[i]], ])) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM2, fun = f, radius = 70000, minTaxCount = 2, metricName = 'ratio_PD_maxdist')tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # An example using a multivariate dataset ## For each focal cell + neighbors, calculate the morphological standard deviation ## per grid cell and return the standard deviation. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- max(dist(dat[cellList[[i]], ])) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 2, metricName = 'maxdist') # An example using only the phylogeny. ## Calculate standard deviation of phylogenetic diversity across cell neighborhood. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 1, metricName = 'faithPD') # an example that involves both morphological and phylogenetic data ## nonsensical, but for illustrative purposes: ## ratio of Faith's phylogenetic diversity to morphological range ## the standard deviation of this measure across grid cells ## in a neighborhood. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) / max(dist(dat[cellList[[i]], ])) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 2, metricName = 'ratio_PD_maxdist') # from a focal coordinate to all other sites ## Here, the function has 2 inputs. ## Example: calculate the per grid cell mean and take the distance. f <- function(focalCell, otherCell) { x1 <- colMeans(dat[focalCell, ]) x2 <- colMeans(dat[otherCell, ]) return(as.matrix(dist(rbind(x1, x2)))[1,2]) } xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 1, focalCoord = c(-1413764, 573610.8), metricName = 'meandist') # Example involving a set of trees tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet, replace = TRUE) ## Calculate standard deviation of phylogenetic diversity across cell ## neighborhood. f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) } return(sd(vec, na.rm = TRUE)) } # This time, a list of sf objects will be returned, one for each input tree. xx <- customBetaDiv(tamiasEPM, fun = f, radius = 70000, minTaxCount = 1, metricName = 'faithPD') # also works with square grid cells tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTree) tamiasEPM2 <- addTraits(tamiasEPM2, tamiasTraits) f <- function(cellList) { vec <- numeric(length(cellList)) for (i in 1:length(cellList)) { vec[[i]] <- faithPD(phylo, cellList[[i]]) / max(dist(dat[cellList[[i]], ])) } return(sd(vec, na.rm = TRUE)) } xx <- customBetaDiv(tamiasEPM2, fun = f, radius = 70000, minTaxCount = 2, metricName = 'ratio_PD_maxdist')
Define your own function for summarizing information across grid cells.
customGridMetric( x, fun, column = NULL, minTaxCount = 1, metricName = "custom_metric" )customGridMetric( x, fun, column = NULL, minTaxCount = 1, metricName = "custom_metric" )
x |
object of class |
fun |
a function to apply to all grid cells (see details) |
column |
If a univariate morphological metric is specified, and the data
in |
minTaxCount |
the minimum number of taxa needed to apply the function. For instance, should the function be applied to gridcells with just 1 taxon? |
metricName |
the name you would like to attach to the output |
This function allows you to not be limited to the diversity metrics
available via the gridMetrics function.
The custom function should have just one input: a vector of taxon names
that will then be used to subset the trait or phylogenetic data. Within the
function call, the trait data already attached to the epmGrid object must
be referred to as dat, and the phylogenetic tree already attached to the
epmGrid must be referred to as phylo.
If the input epmGrid object contains a set of trees, then this function will
be applied, using each tree in turn, and will return a list of results. This
list can then be passed to summarizeEpmGridList to be summarized.
See examples below.
object of class epmGrid, or list of epmGrid objects
Pascal Title
tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # In the following examples, notice that any mention of the trait data or ## phylogeny that are already attached to the epmGrid object are referred ## to as dat and phylo. # example: calculate morphological disparity ## (already implemented in gridMetrics) f <- function(cells) { sum(diag(cov(dat[cells,]))) } # to calculate disparity, we need at least 2 taxa xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 2, metricName = 'disparity') # In the example above, gridcells with 1 species are left as NA. ## But if we wanted those gridcells to have a value of 0 rather than NA, ## we could do the following: f <- function(sp) { if (length(sp) == 1) { 0 } else { sum(diag(cov(dat[sp,]))) } } # and change minTaxCount to 1 xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 1, metricName = 'disparity') # phylogenetic example: mean patristic distance ## this example doesn't actually involve the phylogeny internally, ## we can just supply what is needed to the function patdist <- cophenetic(tamiasEPM[['phylo']]) patdist[upper.tri(patdist, diag = TRUE)] <- NA f <- function(cells) { mean(patdist[cells, cells], na.rm = TRUE) } xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 1, metricName = 'mean patristic') # an example that involves both morphological and phylogenetic data ## nonsensical, but for illustrative purposes: ## ratio of Faith's phylogenetic diversity to morphological range f <- function(cells) { faithPD(phylo, cells) / max(dist(dat[cells, ])) } xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 2, metricName = 'PD_range_ratio') # Example involving a set of trees tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet, replace = TRUE) # get crown clade age of clade containing taxa present in grid cell f <- function(sp) { ape::branching.times(phylo)[as.character(ape::getMRCA(phylo, sp))] } xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 2, metric = 'nodeAge')tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # In the following examples, notice that any mention of the trait data or ## phylogeny that are already attached to the epmGrid object are referred ## to as dat and phylo. # example: calculate morphological disparity ## (already implemented in gridMetrics) f <- function(cells) { sum(diag(cov(dat[cells,]))) } # to calculate disparity, we need at least 2 taxa xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 2, metricName = 'disparity') # In the example above, gridcells with 1 species are left as NA. ## But if we wanted those gridcells to have a value of 0 rather than NA, ## we could do the following: f <- function(sp) { if (length(sp) == 1) { 0 } else { sum(diag(cov(dat[sp,]))) } } # and change minTaxCount to 1 xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 1, metricName = 'disparity') # phylogenetic example: mean patristic distance ## this example doesn't actually involve the phylogeny internally, ## we can just supply what is needed to the function patdist <- cophenetic(tamiasEPM[['phylo']]) patdist[upper.tri(patdist, diag = TRUE)] <- NA f <- function(cells) { mean(patdist[cells, cells], na.rm = TRUE) } xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 1, metricName = 'mean patristic') # an example that involves both morphological and phylogenetic data ## nonsensical, but for illustrative purposes: ## ratio of Faith's phylogenetic diversity to morphological range f <- function(cells) { faithPD(phylo, cells) / max(dist(dat[cells, ])) } xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 2, metricName = 'PD_range_ratio') # Example involving a set of trees tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet, replace = TRUE) # get crown clade age of clade containing taxa present in grid cell f <- function(sp) { ape::branching.times(phylo)[as.character(ape::getMRCA(phylo, sp))] } xx <- customGridMetric(tamiasEPM, fun = f, minTaxCount = 2, metric = 'nodeAge')
Removes particular species from a epmGrid object.
dropSpecies(x, sp)dropSpecies(x, sp)
x |
object of class |
sp |
a character vector of species names to be dropped. |
If species in sp are not in x, they will be ignored.
new epmGrid object.
Pascal Title
tamiasEPM new <- dropSpecies(tamiasEPM, sp = c('Tamias_alpinus', 'Tamias_bulleri')) setdiff(tamiasEPM[['geogSpecies']], new[['geogSpecies']])tamiasEPM new <- dropSpecies(tamiasEPM, sp = c('Tamias_alpinus', 'Tamias_bulleri')) setdiff(tamiasEPM[['geogSpecies']], new[['geogSpecies']])
Calculates the tip-specific DR statistic for speciation rates
DRstat(tree)DRstat(tree)
tree |
phylogeny of class |
named numeric vector of speciation rates
Pascal Title
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491, 444–448.
tamiasTree DRstat(tamiasTree)tamiasTree DRstat(tamiasTree)
An R package that facilitates the aggregation of species' geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic metacommunity metrics across geography.
A detailed wiki for the R package can be found on the epm github page: https://github.com/ptitle/epm/wiki#table-of-contents
To cite the epm package in publications, please use:
Pascal O. Title, Donald L. Swiderski and Miriam L. Zelditch. 2022. EcoPhyloMapper:
an R package for integrating geographic ranges, phylogeny, and morphology. Methods
in Ecology and Evolution. doi:10.1111/2041-210X.13914
Creating and enhancing an epmGrid object
Use createEPMgrid to create an epmGrid object from species spatial data.
Optionally, you can draw the spatial extent that you would like to use with interactiveExtent.
Add in species attributes with addTraits, and/or a phylogeny with addPhylo.
Use the function reduceToCommonTaxa to reduce the epmGrid object to species that are present for all data types.
Calculating diversity metrics
Calculate various diversity metrics with gridMetrics, or define your own, using customGridMetric.
Calculate moving window turnover metrics with betadiv_taxonomic, betadiv_phylogenetic, betadiv_disparity. You can also define your own beta diversity metric with customBetaDiv.
If you have a posterior set of trees, summarize phylogenetic uncertainty with summarizeEpmGridList.
Plotting epmGrid objects
Plot epmGrid object with plot.epmGrid.
You get finer control over the legend with addLegend.
The function getMultiMapRamp will be helpful if you are trying to plot multiple epmGrid objects on the same color scale.
Use plotDispersionField to plot the assemblage dispersion field for a given site.
Getting derived data from epmGrid objects
Use calcMeanShape to get mean morphological shape per grid cell.
Use coordsFromEpmGrid to get the spatial coordinates of specific grid cells.
Use extractFromEpmGrid to get the species that are found at certain coordinates or within a defined polygon.
Use generateOccurrenceMatrix to produce a species-by-site presence/absence matrix.
Use tableFromEpmGrid to pull data from epmGrids and rasters from a set of random points for statistical analysis.
Writing to disk
You can save an epmGrid with write.epmGrid, and read it back in with read.epmGrid.
You can also write an epmGrid object to a spatial file format for use in GIS software with writeEpmSpatial.
Pascal O. Title, Donald L. Swiderski, Miriam L. Zelditch
Useful links:
Included datasets in epm
tamiasEPM tamiasPolyList tamiasTraits tamiasTree tamiasTreeSettamiasEPM tamiasPolyList tamiasTraits tamiasTree tamiasTreeSet
Included north american chipmunk dataset:
tamiasTree is a phylogeny for chipmunks from Zelditch et al. 2017
tamiasTreeSet is a distribution of 10 phylogenies for chipmunks, extracted
from the mammal tree from Upham et al. 2019
tamiasTraits is a geometric morphometrics dataset of mean values for
chipmunks from Zelditch et al. 2017
tamiasPolyList is a set of geographic ranges for chimpunks from IUCN 2021.
tamiasEPM is an epmGrid object created with createEPMgrid
using these datasets.
Zelditch, M. L., Ye, J., Mitchell, J. S., & Swiderski, D. L.
(2017). Rare ecomorphological convergence on a complex adaptive landscape:
Body size and diet mediate evolution of jaw shape in squirrels (Sciuridae).
Evolution, 1–17. https://doi.org/10.1111/evo.13168
Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree:
Species-level sets of phylogenies for questions in ecology, evolution, and
conservation. PLoS Biology, 17(12), e3000494.
https://doi.org/10.1371/journal.pbio.3000494
IUCN 2021. The IUCN Red List of Threatened Species. 2021-3. https://www.iucnredlist.org. Downloaded on 17 March 2021.
Given specific sites, convert epmGrid to phylocomm matrix, with sites as rows, and species as columns
epmToPhyloComm(x, sites)epmToPhyloComm(x, sites)
x |
object of class |
sites |
locations of sites, see details. |
If sites are site coordinates,
then dataframe or matrix with two columns;
if sites are cell indices, then numeric vector;
if sites = 'all', then all cells will be returned as sites.
community matrix, with sites as rows and species as columns
Pascal Title
tamiasEPM # from cell indices cells <- c(2703, 90, 3112, 179) epmToPhyloComm(tamiasEPM, cells) # from coordinates library(sf) # get the projection of the epmGrid object proj <- summary(tamiasEPM)$crs # define some points pts <- rbind.data.frame( c(-120.5, 38.82), c(-84.02, 42.75), c(-117.95, 55.53)) colnames(pts) <- c('x', 'y') ptsSF <- st_as_sf(pts, coords = 1:2, crs = "epsg:4326") pts <- st_coordinates(st_transform(ptsSF, crs = proj)) epmToPhyloComm(tamiasEPM, pts)tamiasEPM # from cell indices cells <- c(2703, 90, 3112, 179) epmToPhyloComm(tamiasEPM, cells) # from coordinates library(sf) # get the projection of the epmGrid object proj <- summary(tamiasEPM)$crs # define some points pts <- rbind.data.frame( c(-120.5, 38.82), c(-84.02, 42.75), c(-117.95, 55.53)) colnames(pts) <- c('x', 'y') ptsSF <- st_as_sf(pts, coords = 1:2, crs = "epsg:4326") pts <- st_coordinates(st_transform(ptsSF, crs = proj)) epmToPhyloComm(tamiasEPM, pts)
The epmGrid object contains an accounting of species per cell in a condensed format. This function returns a complete list of species per cell.
expandSpeciesCellList(x)expandSpeciesCellList(x)
x |
object of class |
Function to expand condensed species list to full set of cells
list of species for each cell.
Pascal Title
tamiasEPM head(expandSpeciesCellList(tamiasEPM))tamiasEPM head(expandSpeciesCellList(tamiasEPM))
Return species from intersection between spatial points or polygons and a epmGrid object.
extractFromEpmGrid(x, spatial, returnCells = FALSE, collapse = TRUE)extractFromEpmGrid(x, spatial, returnCells = FALSE, collapse = TRUE)
x |
object of class |
spatial |
coordinates as either a spatial points object (sp or sf), a matrix/dataframe with two columns, a numeric vector of c(long, lat), or as a spatial polygon object (sp or sf). |
returnCells |
boolean, if |
collapse |
boolean; if |
If spatial is a spatial object, it will be transformed to
the same projection as x if needed. If spatial is not a
spatial object, it is assumed to be in the same projection as x.
A vector of species if collapse = TRUE, or a list of species
by cell if collapse = FALSE. If returnCells = TRUE, a vector
of cell indices that correspond to the rows in the epmGrid sf object.
Pascal Title
library(sf) # get the projection of the epmGrid object proj <- summary(tamiasEPM)$crs # define some points pts <- rbind.data.frame( c(-120.5, 38.82), c(-84.02, 42.75), c(-117.95, 55.53)) colnames(pts) <- c('x', 'y') ptsSF <- st_as_sf(pts, coords = 1:2, crs = "epsg:4326") pts <- st_coordinates(st_transform(ptsSF, crs = proj)) # extract with table of coordinates extractFromEpmGrid(tamiasEPM, pts) # extract with spatial points object extractFromEpmGrid(tamiasEPM, ptsSF) # extract with spatial polygon hull <- st_convex_hull(st_union(ptsSF)) extractFromEpmGrid(tamiasEPM, hull) # returns each cell's contents extractFromEpmGrid(tamiasEPM, hull, collapse=FALSE) # collapses results to unique set of species extractFromEpmGrid(tamiasEPM, hull, collapse=TRUE)library(sf) # get the projection of the epmGrid object proj <- summary(tamiasEPM)$crs # define some points pts <- rbind.data.frame( c(-120.5, 38.82), c(-84.02, 42.75), c(-117.95, 55.53)) colnames(pts) <- c('x', 'y') ptsSF <- st_as_sf(pts, coords = 1:2, crs = "epsg:4326") pts <- st_coordinates(st_transform(ptsSF, crs = proj)) # extract with table of coordinates extractFromEpmGrid(tamiasEPM, pts) # extract with spatial points object extractFromEpmGrid(tamiasEPM, ptsSF) # extract with spatial polygon hull <- st_convex_hull(st_union(ptsSF)) extractFromEpmGrid(tamiasEPM, hull) # returns each cell's contents extractFromEpmGrid(tamiasEPM, hull, collapse=FALSE) # collapses results to unique set of species extractFromEpmGrid(tamiasEPM, hull, collapse=TRUE)
Calculates Faith's PD for a specific set of tips
faithPD(phy, tips)faithPD(phy, tips)
phy |
phylogeny of class |
tips |
tip names to be included |
Returns the sum of total branch lengths that unite a set of species. The root is always included in these calculations. If tip is just one species, then the root-to-tip distance is returned.
numeric value of summed phylogenetic diversity
Pascal Title
Faith D.P. (1992) Conservation evaluation and phylogenetic diversity. Biological Conservation, 61, 1-10.
tamiasTree faithPD(tamiasTree, c('Tamias_minimus', 'Tamias_speciosus'))tamiasTree faithPD(tamiasTree, c('Tamias_minimus', 'Tamias_speciosus'))
Given specific sites (or all sites), convert epmGrid to a species occurrence matrix, with sites as rows, and species as columns.
generateOccurrenceMatrix(x, sites)generateOccurrenceMatrix(x, sites)
x |
object of class |
sites |
locations of sites, see details. |
If sites are site coordinates,
then this should be a dataframe or matrix with two columns;
if sites are cell indices, then a numeric vector;
if sites = 'all', then all cells will be returned as sites.
To get the associated site coordinates, see coordsFromEpmGrid.
a presence/absence matrix, with sites as rows and species as columns.
Pascal Title
tamiasEPM # from cell indices cells <- c(2703, 90, 3112, 179) generateOccurrenceMatrix(tamiasEPM, cells) # get the associated site coordinates coordsFromEpmGrid(tamiasEPM, cells) # from coordinates library(sf) # get the projection of the epmGrid object proj <- summary(tamiasEPM)$crs # define some points pts <- rbind.data.frame( c(-120.5, 38.82), c(-84.02, 42.75), c(-117.95, 55.53)) colnames(pts) <- c('x', 'y') ptsSF <- st_as_sf(pts, coords = 1:2, crs = "epsg:4326") pts <- st_coordinates(st_transform(ptsSF, crs = proj)) generateOccurrenceMatrix(tamiasEPM, pts)tamiasEPM # from cell indices cells <- c(2703, 90, 3112, 179) generateOccurrenceMatrix(tamiasEPM, cells) # get the associated site coordinates coordsFromEpmGrid(tamiasEPM, cells) # from coordinates library(sf) # get the projection of the epmGrid object proj <- summary(tamiasEPM)$crs # define some points pts <- rbind.data.frame( c(-120.5, 38.82), c(-84.02, 42.75), c(-117.95, 55.53)) colnames(pts) <- c('x', 'y') ptsSF <- st_as_sf(pts, coords = 1:2, crs = "epsg:4326") pts <- st_coordinates(st_transform(ptsSF, crs = proj)) generateOccurrenceMatrix(tamiasEPM, pts)
Given a list of SpatialPolygons, return an extent object that encompasses all items.
getExtentOfList(shapes)getExtentOfList(shapes)
shapes |
a list of SpatialPolygons or simple features |
An object of class bbox.
Pascal Title
getExtentOfList(tamiasPolyList)getExtentOfList(tamiasPolyList)
Extracts the range of values across a list of input objects for use in plotting
getMultiMapRamp(...)getMultiMapRamp(...)
... |
objects of class |
If the user would like to plot multiple epmGrid objects
with a standardized color ramp, then the returned values from this function
can be supplied to plot.epmGrid. Also works with RasterLayer
and sf objects. For sf object, only one attribute can be specified.
a numeric vector of length 2: overall min and max value.
Pascal Title
library(terra) tamiasEPM # create a dummy raster for demonstration purposes. ras <- rast() values(ras) <- runif(ncell(ras), min = 0, max = 40) getMultiMapRamp(tamiasEPM, ras)library(terra) tamiasEPM # create a dummy raster for demonstration purposes. ras <- rast() values(ras) <- runif(ncell(ras), min = 0, max = 40) getMultiMapRamp(tamiasEPM, ras)
Calculate species-specific partial disparity, relative to some group mean.
getSpPartialDisparities(dat, groupMean = NULL)getSpPartialDisparities(dat, groupMean = NULL)
dat |
matrix of multivariate morphological data |
groupMean |
if |
Calculates partial disparities, as in Foote 1993. By default, the group mean is calculated from the full input data.
numeric vector
Pascal Title
tamiasTraits[1:5, 1:5] getSpPartialDisparities(tamiasTraits)tamiasTraits[1:5, 1:5] getSpPartialDisparities(tamiasTraits)
Calculate various morphological and phylogenetic community
metrics for every cell in a epmGrid object. To implement other
metrics not available here, see customGridMetric.
gridMetrics( x, metric, column = NULL, verbose = FALSE, dataType = c("auto", "univariate", "multivariate", "pairwise") )gridMetrics( x, metric, column = NULL, verbose = FALSE, dataType = c("auto", "univariate", "multivariate", "pairwise") )
x |
object of class |
metric |
name of metric to use, see Details. |
column |
If a univariate morphological metric is specified, and the data
in |
verbose |
Print various messages to the console. Default is TRUE. |
dataType |
Specify the type of input data that the metric will be calculated from.
Defaults to |
Univariate trait metrics
mean
median
range
variance
mean_NN_dist: mean nearest neighbor distance
min_NN_dist: minimum nearest neighbor distance
evenness: variance of nearest neighbor distances, larger values imply decreasing evenness
arithmeticWeightedMean (see below)
geometricWeightedMean (see below)
Multivariate trait metrics
mean: mean of pairwise distance matrix derived from multivariate data
median: median of pairwise distance matrix derived from multivariate data
disparity
partialDisparity: contribution of species in each gridcell to overall disparity, returned as the ratio of summed partial disparities to total disparity.
range
mean_NN_dist: mean nearest neighbor distance
min_NN_dist: minimum nearest neighbor distance
evenness: variance of nearest neighbor distances, larger values imply decreasing evenness.
Phylogenetic metrics
pd: Faith's phylogenetic diversity, including the root
meanPatristic
meanPatristicNN: mean nearest neighbor in patristic distance
minPatristicNN: minimum nearest neighbor in patristic distance
phyloEvenness: variance of nearest neighbor patristic distances, larger values imply decreasing evenness
phyloDisparity: sum of squared deviations in patristic distance
PSV: Phylogenetic Species Variability
PSR: Phylogenetic Species Richness
DR: non-parametric estimate of speciation rates
Range-weighted metrics
weightedEndemism: Species richness inversely weighted by range size.
correctedWeightedEndemism: Weighted endemism standardized by species richness
phyloWeightedEndemism: Phylogenetic diversity inversely weighted by range size associated with each phylogenetic branch.
If data slot contains a pairwise matrix, column is ignored. Weighted
mean options are available where, for each cell, a weighting scheme (inverse
of species range sizes) is applied such that small-ranged species are
up-weighted, and broadly distributed species are down-weighted. This can be a
useful way to lessen the influence of broadly distributed species in the
geographic mapping of trait data.
It may be desirable to have metrics calculated for a dataset where only taxa
shared across geography, traits and phylogeny are included. The function
reduceToCommonTaxa does exactly that.
If a set of trees are associated with the input epmGrid object x,
then the metric is calculated for each tree, and a list of epmGrid objects
is returned. This resulting list can be summarized with the function
summarizeEpmGridList. For instance the mean and variance can
be calculated, to show the central tendency of the metric across grid cells,
and to quantify where across geography variability in phylogenetic topography
manifests itself.
To implement other metrics not available here, see
customGridMetric.
object of class epmGrid where the grid represents calculations
of the metric at every cell. The species identities per grid cell are those
that had data for the calculation of the metric. If taxa were dropped from
the initial epmGrid object, then they have been removed from this epmGrid.
If a set of trees was involved, then returns a list of epmGrid objects.
partial disparity
Foote, M. (1993). Contributions of individual taxa to
overall morphological disparity. Paleobiology, 19(4), 403–419.
https://doi.org/10.1017/s0094837300014056
PSV, RSV
Helmus, M. R., Bland, T. J., Williams, C. K., & Ives, A. R.
(2007). Phylogenetic Measures of Biodiversity. The American Naturalist,
169(3), E68–E83. https://doi.org/10.1086/511334
DR
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O.
(2012). The global diversity of birds in space and time. Nature, 491(7424),
444–448. https://doi.org/10.1038/nature11631
weighted endemism
Crisp, M. D., Laffan, S., Linder, H. P., & Monro, A.
(2001). Endemism in the Australian flora. Journal of Biogeography, 28(2),
183–198. https://doi.org/10.1046/j.1365-2699.2001.00524.x
phylo weighted endemism
Rosauer, D., Laffan, S. W., Crisp, M. D.,
Donnellan, S. C., & Cook, L. G. (2009). Phylogenetic endemism: a new approach
for identifying geographical concentrations of evolutionary history.
Molecular Ecology, 18(19), 4061–4072.
https://doi.org/10.1111/j.1365-294x.2009.04311.x
tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # univariate morphological example x <- gridMetrics(tamiasEPM, metric='mean', column='V2') plot(x, use_tmap = FALSE) # multivariate morphological x <- gridMetrics(tamiasEPM, metric='disparity') plot(x, use_tmap = FALSE) # phylogenetic metrics x <- gridMetrics(tamiasEPM, metric='meanPatristic') plot(x, use_tmap = FALSE)tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) # univariate morphological example x <- gridMetrics(tamiasEPM, metric='mean', column='V2') plot(x, use_tmap = FALSE) # multivariate morphological x <- gridMetrics(tamiasEPM, metric='disparity') plot(x, use_tmap = FALSE) # phylogenetic metrics x <- gridMetrics(tamiasEPM, metric='meanPatristic') plot(x, use_tmap = FALSE)
Plots a epmGrid object and allows you to click on the plot to return the species found in the cell you clicked on.
## S3 method for class 'epmGrid' identify(x, returnCell = FALSE, ...)## S3 method for class 'epmGrid' identify(x, returnCell = FALSE, ...)
x |
object of class |
returnCell |
boolean; if FALSE, then species names are returned, if TRUE, then cell indices are returned. |
... |
additional arguments passed to sf::plot |
This is a wrapper function for the identify function
in base graphics. This is primarily intended as a useful function for
data exploration and spot-checking.
A vector of species names or cell indices.
Pascal Title
Given a list of polygons or point occurrences, sets up
an interactive plot to allow the user to draw the desired extent.
This can be used to define the extent in createEPMgrid.
interactiveExtent(polyList, cellType = "square", bb = NULL)interactiveExtent(polyList, cellType = "square", bb = NULL)
polyList |
a list of Simple Feature polygons or points. |
cellType |
either |
bb |
c(xmin, xmax, ymin, ymax) to limit the extent for the interactive plot. |
This function returns both a sf polygon and the same polygon
as a WKT string. Either can be supplied to createEPMgrid
as the extent. A recommended strategy is to use this function to find
your extent, and to copy/paste the WKT string into your R script so that
you can retain it for future use, and maintain reproducibility.
See example.
What is chosen for cellType has no effect on what you might choose in
createEPMgrid. Square cells will probably be fastest. If hexagons
are selected, grid cell points are plotted instead of polygons to speed up plotting.
You may see the message Failed to compute min/max, no valid pixels found in
sampling. (GDAL error 1) . This just means that a species did not register in any
grid cells. This can be ignored.
The basemap is from https://www.naturalearthdata.com/.
A list with a polygon, and its WKT string
Pascal Title
if (interactive()) { ex <- interactiveExtent(tamiasPolyList) # You can use this as the extent in createEPMgrid grid <- createEPMgrid(tamiasPolyList, resolution = 50000, extent = ex$wkt) # One way to make your code reproducible would be to copy/paste the wkt # in your code for future use: ex <- interactiveExtent(tamiasPolyList) ex$wkt customExtent <- "POLYGON ((-2238201 3532133, -2675450 1722657, -2470677 -317634, -1863632 -1854074, -521614.8 -2170280, -349356.8 799040.9, -2238201 3532133))" grid <- createEPMgrid(tamiasPolyList, resolution = 50000, extent = customExtent) }if (interactive()) { ex <- interactiveExtent(tamiasPolyList) # You can use this as the extent in createEPMgrid grid <- createEPMgrid(tamiasPolyList, resolution = 50000, extent = ex$wkt) # One way to make your code reproducible would be to copy/paste the wkt # in your code for future use: ex <- interactiveExtent(tamiasPolyList) ex$wkt customExtent <- "POLYGON ((-2238201 3532133, -2675450 1722657, -2470677 -317634, -1863632 -1854074, -521614.8 -2170280, -349356.8 799040.9, -2238201 3532133))" grid <- createEPMgrid(tamiasPolyList, resolution = 50000, extent = customExtent) }
Plot a epmGrid object. This function uses the tmap package for plotting by default.
## S3 method for class 'epmGrid' plot( x, log = FALSE, legend = TRUE, col, basemap = "worldmap", colorRampRange = NULL, minTaxCount = "auto", zoom = TRUE, ignoredColor = gray(0.9), lwd, borderCol = "black", alpha = 1, includeFrame = FALSE, use_tmap = TRUE, fastPoints = FALSE, title = "", add = FALSE, ... )## S3 method for class 'epmGrid' plot( x, log = FALSE, legend = TRUE, col, basemap = "worldmap", colorRampRange = NULL, minTaxCount = "auto", zoom = TRUE, ignoredColor = gray(0.9), lwd, borderCol = "black", alpha = 1, includeFrame = FALSE, use_tmap = TRUE, fastPoints = FALSE, title = "", add = FALSE, ... )
x |
object of class |
log |
boolean; should the cell values be logged? |
legend |
boolean; should legend be included? |
col |
either a vector of color names that will be interpolated,
or a color ramp function that takes an integer
(see for example |
basemap |
if |
colorRampRange |
numeric vector of min and max value for scaling the
color ramp. Automatically inferred if set to |
minTaxCount |
an integer, or 'auto'. Should cells
containing certain numbers of taxa be grayed out? For example,
should single-taxon cells be ignored because the metric only makes sense
for multi-taxon cells? This is predetermined for all metrics in
|
zoom |
Should plot zoom in on cells with data. Default is TRUE. |
ignoredColor |
color for ignored cells. See details. |
lwd |
grid cell border width |
borderCol |
color for grid cell borders |
alpha |
opacity of all colors and borders, ranging from 0 (fully transparent) to 1 (fully opaque) |
includeFrame |
boolean; include frame around plot? |
use_tmap |
boolean; if FALSE, plotting will be done via sf instead of tmap package |
fastPoints |
Intended for debugging purposes. For hex grids and |
title |
text to add to the plot |
add |
logical, add to existing plot? |
... |
additional arguments that can be passed to sf::plot or terra::plot
if |
If x is a metric as generated with gridMetrics
that returns 0 for single-species cells, then those cells
(that have a value of 0) will be plotted in gray (or any color as specified
with ignoredColor) if minTaxCount = 'auto'. You can specify
other values as well. For instance, if you use the function
customGridMetric to calculate phylogenetic signal, which is
a metric that only makes sense for cells with 3 or more taxa, then you could
then specify minTaxCount = 3. Setting minTaxCount = 1 shows all
cells with data.
If the tmap package is not installed, then this function will default
to plotting with sf::plot.
If you would like more control over the legend, then plot with
tmap = FALSE and legend = FALSE, and then call the function
addLegend.
Nothing is returned if plotting with tmap (the default).
If plotting with use_tmap = FALSE, and if the plot is directed to
a variable, then this variable will contain relevant information to be passed
on to the function addLegend:
Pascal Title
plot(tamiasEPM, use_tmap = FALSE) plot(tamiasEPM, legend = FALSE, use_tmap = FALSE, col = viridisLite::inferno) addLegend(tamiasEPM, location = 'top', ramp = viridisLite::inferno) # Example for how to plot multiple epmGrids on the same color scale # for illustration purposes, we will compare weighted endemism to # phylogenetic weighted endemism library(tmap) tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) epm1 <- gridMetrics(tamiasEPM, metric='weightedEndemism') epm2 <- gridMetrics(tamiasEPM, metric='phyloWeightedEndemism') # get global min and max values minmax <- getMultiMapRamp(epm1, epm2) map1 <- plot(epm1, colorRampRange = log(minmax), log = TRUE, legend = FALSE) map2 <- plot(epm2, colorRampRange = log(minmax), log = TRUE, legend = FALSE) # tmap_arrange(map1, map2) # view your plot in the web-browser as a dynamic plot. plot(tamiasEPM, basemap = 'interactive') # Adding a custom legend, and passing along arguments via params xx <- plot(tamiasEPM, use_tmap = FALSE, legend = FALSE, col = viridisLite::magma) addLegend(tamiasEPM, params = xx, location = 'bottom')plot(tamiasEPM, use_tmap = FALSE) plot(tamiasEPM, legend = FALSE, use_tmap = FALSE, col = viridisLite::inferno) addLegend(tamiasEPM, location = 'top', ramp = viridisLite::inferno) # Example for how to plot multiple epmGrids on the same color scale # for illustration purposes, we will compare weighted endemism to # phylogenetic weighted endemism library(tmap) tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) epm1 <- gridMetrics(tamiasEPM, metric='weightedEndemism') epm2 <- gridMetrics(tamiasEPM, metric='phyloWeightedEndemism') # get global min and max values minmax <- getMultiMapRamp(epm1, epm2) map1 <- plot(epm1, colorRampRange = log(minmax), log = TRUE, legend = FALSE) map2 <- plot(epm2, colorRampRange = log(minmax), log = TRUE, legend = FALSE) # tmap_arrange(map1, map2) # view your plot in the web-browser as a dynamic plot. plot(tamiasEPM, basemap = 'interactive') # Adding a custom legend, and passing along arguments via params xx <- plot(tamiasEPM, use_tmap = FALSE, legend = FALSE, col = viridisLite::magma) addLegend(tamiasEPM, params = xx, location = 'bottom')
For a set of specified coordinates, plot a richness map for the species that are found at those coordinates.
plotDispersionField( x, coords, plotCoords = TRUE, legend = TRUE, col, lwd = 0.5, basemap = "worldmap", borderCol = "black", alpha = 1, includeFrame = FALSE, use_tmap = TRUE, add = FALSE )plotDispersionField( x, coords, plotCoords = TRUE, legend = TRUE, col, lwd = 0.5, basemap = "worldmap", borderCol = "black", alpha = 1, includeFrame = FALSE, use_tmap = TRUE, add = FALSE )
x |
object of class |
coords |
coordinates as either a spatial points object (sp or sf), a matrix/dataframe with two columns or a numeric vector of c(long, lat). |
plotCoords |
boolean; should the coordinates be plotted as well? |
legend |
boolean; should legend be included? |
col |
either a vector of color names that will be interpolated,
or a color ramp function that takes an integer
(see for example |
lwd |
grid cell border width |
basemap |
if |
borderCol |
color for grid cell borders |
alpha |
opacity of all colors and borders, ranging from 0 (fully transparent) to 1 (fully opaque) |
includeFrame |
boolean; include frame around plot? |
use_tmap |
boolean; if FALSE, plotting will be done via sf instead of tmap package |
add |
logical, add to existing plot? |
Assemblage dispersion fields represent an overlapping of geographic ranges for the taxa that occur in the focal grid cells.
Nothing is returned.
Pascal Title
Graves, G. R., & Rahbek, C. (2005). Source pool geometry and the assembly of continental avifaunas. Proceedings of the National Academy of Sciences, 102(22), 7871–7876.
# plotDispersionField(tamiasEPM, c(-1944951, 69588.74)) plotDispersionField(tamiasEPM, c(-1944951, 69588.74), use_tmap = FALSE)# plotDispersionField(tamiasEPM, c(-1944951, 69588.74)) plotDispersionField(tamiasEPM, c(-1944951, 69588.74), use_tmap = FALSE)
Plot one species' geographic range, as encoded in the epmGrid object.
plotSpRange( x, taxon, taxonColor = "orange", basemap = "worldmap", lwd = 0.5, alpha = 1, use_tmap = TRUE, add = FALSE )plotSpRange( x, taxon, taxonColor = "orange", basemap = "worldmap", lwd = 0.5, alpha = 1, use_tmap = TRUE, add = FALSE )
x |
object of class |
taxon |
taxon to plot |
taxonColor |
color for plotting taxon's range |
basemap |
if |
lwd |
grid cell border width |
alpha |
opacity of all colors and borders, ranging from 0 (fully transparent) to 1 (fully opaque) |
use_tmap |
if false, use sf or terra packages for plotting |
add |
logical. If TRUE, adds the gridded taxon range to existing plot. |
nothing is returned
Pascal Title
tamiasEPM plotSpRange(tamiasEPM, 'Tamias_speciosus', use_tmap = FALSE)tamiasEPM plotSpRange(tamiasEPM, 'Tamias_speciosus', use_tmap = FALSE)
Convert a raster to sf polygons object, matching the attributes of the target object.
rasterToGrid(x, target, fun = "mean", crop = TRUE, na.rm = TRUE)rasterToGrid(x, target, fun = "mean", crop = TRUE, na.rm = TRUE)
x |
rasterLayer or rasterStack or SpatRaster |
target |
epmGrid or sf object |
fun |
function for summarizing raster cells to polygons |
crop |
if TRUE, the raster will be cropped to the bounding box of the target |
na.rm |
determines how |
By default, raster cells that overlap with target grid cell polygons
will be averaged. If target is a raster grid, then terra::resample
is used.
sf polygons object, or a list of such objects if input has multiple layers.
Pascal Title
library(terra) # We have a terra grid object (for example, climate data read in as a raster) # Here, we are just generating some random data for demo env <- rast(vect(tamiasEPM[[1]]), resolution = 100000) env[] <- sample(1:100, ncell(env), replace = TRUE) plot(env) # Now, if we are interested in doing analyses of environmental data in relation to # the epmGrid data we have, we want to convert the env data to the same grid structure # where the cells align and where raster grid values are resampled and averaged. newgrid <- rasterToGrid(env, target = tamiasEPM, fun = 'mean') plot(newgrid) # again but this time the input has multiple layers env <- rast(vect(tamiasEPM[[1]]), resolution = 100000, nlyr = 3) values(env[[1]]) <- sample(1:100, ncell(env), replace = TRUE) values(env[[2]]) <- sample(1:200, ncell(env), replace = TRUE) values(env[[3]]) <- sample(1:300, ncell(env), replace = TRUE) newgrid <- rasterToGrid(env, target = tamiasEPM, fun = 'mean')library(terra) # We have a terra grid object (for example, climate data read in as a raster) # Here, we are just generating some random data for demo env <- rast(vect(tamiasEPM[[1]]), resolution = 100000) env[] <- sample(1:100, ncell(env), replace = TRUE) plot(env) # Now, if we are interested in doing analyses of environmental data in relation to # the epmGrid data we have, we want to convert the env data to the same grid structure # where the cells align and where raster grid values are resampled and averaged. newgrid <- rasterToGrid(env, target = tamiasEPM, fun = 'mean') plot(newgrid) # again but this time the input has multiple layers env <- rast(vect(tamiasEPM[[1]]), resolution = 100000, nlyr = 3) values(env[[1]]) <- sample(1:100, ncell(env), replace = TRUE) values(env[[2]]) <- sample(1:200, ncell(env), replace = TRUE) values(env[[3]]) <- sample(1:300, ncell(env), replace = TRUE) newgrid <- rasterToGrid(env, target = tamiasEPM, fun = 'mean')
Load a saved epmGrid object.
read.epmGrid(filename)read.epmGrid(filename)
filename |
filename, with extension |
This function will read in epmGrid objects that
were saved with write.epmGrid.
object of class epmGrid
Pascal Title
#save write.epmGrid(tamiasEPM, paste0(tempdir(), '/tamiasEPM')) # read back in tamiasEPM <- read.epmGrid(paste0(tempdir(), '/tamiasEPM.rds')) # delete the file unlink(paste0(tempdir(), '/tamiasEPM.rds'))#save write.epmGrid(tamiasEPM, paste0(tempdir(), '/tamiasEPM')) # read back in tamiasEPM <- read.epmGrid(paste0(tempdir(), '/tamiasEPM.rds')) # delete the file unlink(paste0(tempdir(), '/tamiasEPM.rds'))
An epmGrid object may contain more taxa with morphological data than taxa with phylogenetic information, or vice versa. This function subsets all epmGrid components to the set of taxa shared across geographic, phenotypic and phylogenetic datasets. This might desirable to ensure that all diversity metrics are based on the same set of taxa.
reduceToCommonTaxa(x)reduceToCommonTaxa(x)
x |
object of class |
new epmGrid object.
Pascal Title
tamiasEPM # randomly drop a few species for demonstration tamiasEPM <- addPhylo(tamiasEPM, ape::drop.tip(tamiasTree, sample(tamiasTree$tip.label, 5))) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits[-(3:5),]) new <- reduceToCommonTaxa(tamiasEPM) tamiasEPM newtamiasEPM # randomly drop a few species for demonstration tamiasEPM <- addPhylo(tamiasEPM, ape::drop.tip(tamiasTree, sample(tamiasTree$tip.label, 5))) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits[-(3:5),]) new <- reduceToCommonTaxa(tamiasEPM) tamiasEPM new
Given a epmGrid object, return the grid cell indices of those cells that have just one species.
singleSpCellIndex(x)singleSpCellIndex(x)
x |
object of class |
This function can be useful when further analyzing epmGrid
objects generated by gridMetrics, as it might
make sense to exclude these single-species cells in further analyses.
numeric vector of grid cell indices.
Pascal Title
singleSpCellIndex(tamiasEPM)singleSpCellIndex(tamiasEPM)
Given a epmGrid object, return the grid cell indices of those cells that have the specified number of taxa.
spCountIndex(x, count)spCountIndex(x, count)
x |
object of class |
count |
number of species to consider (can be a vector of integers) |
This function can be useful when further analyzing epmGrid
objects generated by gridMetrics, as it might
make sense to exclude certain grid cells in further analyses.
numeric vector of grid cell indices.
Pascal Title
spCountIndex(tamiasEPM, count = 1) spCountIndex(tamiasEPM, count = 1:3)spCountIndex(tamiasEPM, count = 1) spCountIndex(tamiasEPM, count = 1:3)
If a diversity metric was calculated for an epmGrid object
that contained a phylogenetic distribution, then a list of resulting
epmGrid objects was returned. This function will take that list, and
apply a summary statistic, returning a single epmGrid object.
If the input list is from betadiv_phylogenetic, then that
list of sf or SpatRaster objects can also be summarized with this function.
summarizeEpmGridList(x, fun = mean)summarizeEpmGridList(x, fun = mean)
x |
a list of objects of class |
fun |
a function to apply to grid cells across the list x. |
It is assumed that across the objects in list x, the only difference is the values for the grid cells.
a single object of class epmGrid or sf or SpatRaster.
Pascal Title
tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) x <- gridMetrics(tamiasEPM, metric='meanPatristicNN') z <- summarizeEpmGridList(x, fun = var) # using a custom function f <- function(y) sum(y) / length(y) z <- summarizeEpmGridList(x, fun = f) # works with square grid epmGrids too tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTreeSet) tamiasEPM2 <- addTraits(tamiasEPM2, tamiasTraits) x <- gridMetrics(tamiasEPM2, metric='meanPatristicNN') z <- summarizeEpmGridList(x, fun = median) # With phylogenetic distribution tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet, replace = TRUE) beta_phylo_turnover <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'turnover') z <- summarizeEpmGridList(beta_phylo_turnover) tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTreeSet) beta_phylo_turnover <- betadiv_phylogenetic(tamiasEPM2, radius = 70000, component = 'turnover') z <- summarizeEpmGridList(beta_phylo_turnover, fun = median)tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) x <- gridMetrics(tamiasEPM, metric='meanPatristicNN') z <- summarizeEpmGridList(x, fun = var) # using a custom function f <- function(y) sum(y) / length(y) z <- summarizeEpmGridList(x, fun = f) # works with square grid epmGrids too tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTreeSet) tamiasEPM2 <- addTraits(tamiasEPM2, tamiasTraits) x <- gridMetrics(tamiasEPM2, metric='meanPatristicNN') z <- summarizeEpmGridList(x, fun = median) # With phylogenetic distribution tamiasEPM <- addPhylo(tamiasEPM, tamiasTreeSet, replace = TRUE) beta_phylo_turnover <- betadiv_phylogenetic(tamiasEPM, radius = 70000, component = 'turnover') z <- summarizeEpmGridList(beta_phylo_turnover) tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') tamiasEPM2 <- addPhylo(tamiasEPM2, tamiasTreeSet) beta_phylo_turnover <- betadiv_phylogenetic(tamiasEPM2, radius = 70000, component = 'turnover') z <- summarizeEpmGridList(beta_phylo_turnover, fun = median)
Generates a summary of a epmGrid object.
## S3 method for class 'epmGrid' summary(object, ...)## S3 method for class 'epmGrid' summary(object, ...)
object |
object of class |
... |
further arguments passed to |
Summary information includes
A list containing the summary information is returned invisibly.
Pascal Title
summary(tamiasEPM) attr <- summary(tamiasEPM) attrsummary(tamiasEPM) attr <- summary(tamiasEPM) attr
Given one or several epmGrid objects, sf objects, rasterLayers, SpatRasters, create a table of values and associated coordinate data.
tableFromEpmGrid(..., n = NULL, minTaxCount = 1, coords = NULL, id = FALSE)tableFromEpmGrid(..., n = NULL, minTaxCount = 1, coords = NULL, id = FALSE)
... |
objects of class |
n |
number of cells to randomly subsample, no subsampling if |
minTaxCount |
integer; cells with at least this many taxa will be included. |
coords |
if NULL, then points are sampled as needed, otherwise, data will be extracted at these specified coordinates. |
id |
boolean, should the grid cell index (of the first item in the inputs) be returned as well? |
A set of cells are identified in the input objects. If
n=NULL, then all cells are used, otherwise cells are randomly
subsampled. Values at those cells are then returned. This table
construction can be particularly useful for subsequent statistical
analyses.
Only cells with data in all inputs are returned. If n is greater than the number of cells with data, then fewer than n cells will be returned.
The first element provided should be a epmGrid object, and that will
be the one used as a template for the sampled grid system.
If coords is provided, then data are extracted at those coordinates,
and no subsetting of those points is done.
data.frame with input variables, as well as "x" and
"y".
Pascal Title
tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) morphoDisp <- gridMetrics(tamiasEPM, metric='disparity') meanPat <- gridMetrics(tamiasEPM, metric='meanPatristic') tableFromEpmGrid(tamiasEPM, morphoDisp, meanPat, n = 100, minTaxCount = 2) # this time request grid cell ID's, which would be useful # for linking this table back to the grid system tableFromEpmGrid(tamiasEPM, morphoDisp, meanPat, n = 100, minTaxCount = 2, id = TRUE) # from predetermined set of coordinates pts <- sf::st_sample(tamiasEPM[[1]], size = 10) tableFromEpmGrid(tamiasEPM, morphoDisp, meanPat, n = 100, minTaxCount = 1, coords = pts)tamiasEPM tamiasEPM <- addPhylo(tamiasEPM, tamiasTree) tamiasEPM <- addTraits(tamiasEPM, tamiasTraits) morphoDisp <- gridMetrics(tamiasEPM, metric='disparity') meanPat <- gridMetrics(tamiasEPM, metric='meanPatristic') tableFromEpmGrid(tamiasEPM, morphoDisp, meanPat, n = 100, minTaxCount = 2) # this time request grid cell ID's, which would be useful # for linking this table back to the grid system tableFromEpmGrid(tamiasEPM, morphoDisp, meanPat, n = 100, minTaxCount = 2, id = TRUE) # from predetermined set of coordinates pts <- sf::st_sample(tamiasEPM[[1]], size = 10) tableFromEpmGrid(tamiasEPM, morphoDisp, meanPat, n = 100, minTaxCount = 1, coords = pts)
Write a epmGrid object to disk.
write.epmGrid(x, filename)write.epmGrid(x, filename)
x |
object of class |
filename |
filename with no extension |
This function writes a .rds file with xz compression.
This file can be read back in with read.epmGrid.
Nothing is returned, but object is written to disk.
Pascal Title
#save write.epmGrid(tamiasEPM, paste0(tempdir(), '/tamiasEPM')) # read back in tamiasEPM <- read.epmGrid(paste0(tempdir(), '/tamiasEPM.rds')) # delete the file unlink(paste0(tempdir(), '/tamiasEPM.rds'))#save write.epmGrid(tamiasEPM, paste0(tempdir(), '/tamiasEPM')) # read back in tamiasEPM <- read.epmGrid(paste0(tempdir(), '/tamiasEPM.rds')) # delete the file unlink(paste0(tempdir(), '/tamiasEPM.rds'))
Writes the grid to disk for use in other GIS applications.
writeEpmSpatial(x, filename, ...)writeEpmSpatial(x, filename, ...)
x |
object of class |
filename |
filename to be written to, with the appropriate file extension |
... |
additional arguments to be passed to |
For hexagonal grid systems, appending .shp to the filename will
result in a shapefile, whereas appending .gpkg results in a geopackage
file. See st_write for additional options. For square
grid cells, appending .tif will result in a GeoTiff file being written to
disk. If no extensions are included with the filename, then this function
will default to geopackage files for hexagonal grids and GeoTiffs for
square grids.
the object is written to disk, nothing is returned.
Pascal Title
tamiasEPM tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') writeEpmSpatial(tamiasEPM, filename = paste0(tempdir(), '/tamiasGrid.shp')) writeEpmSpatial(tamiasEPM, filename = paste0(tempdir(), '/tamiasGrid.gpkg')) unlink(paste0(tempdir(), '/tamiasGrid.gpkg')) # will automatically append .gpkg writeEpmSpatial(tamiasEPM, filename = paste0(tempdir(), '/tamiasGrid')) writeEpmSpatial(tamiasEPM2, filename = paste0(tempdir(), '/tamiasGrid.tif')) unlink(paste0(tempdir(), '/tamiasGrid.tif')) # will automatically append .tif writeEpmSpatial(tamiasEPM2, filename = paste0(tempdir(), '/tamiasGrid')) # remove files generated by example unlink(paste0(tempdir(), '/tamiasGrid', c('.dbf', '.gpkg', '.prj', '.shp', '.shx', '.tif')))tamiasEPM tamiasEPM2 <- createEPMgrid(tamiasPolyList, resolution = 50000, cellType = 'square', method = 'centroid') writeEpmSpatial(tamiasEPM, filename = paste0(tempdir(), '/tamiasGrid.shp')) writeEpmSpatial(tamiasEPM, filename = paste0(tempdir(), '/tamiasGrid.gpkg')) unlink(paste0(tempdir(), '/tamiasGrid.gpkg')) # will automatically append .gpkg writeEpmSpatial(tamiasEPM, filename = paste0(tempdir(), '/tamiasGrid')) writeEpmSpatial(tamiasEPM2, filename = paste0(tempdir(), '/tamiasGrid.tif')) unlink(paste0(tempdir(), '/tamiasGrid.tif')) # will automatically append .tif writeEpmSpatial(tamiasEPM2, filename = paste0(tempdir(), '/tamiasGrid')) # remove files generated by example unlink(paste0(tempdir(), '/tamiasGrid', c('.dbf', '.gpkg', '.prj', '.shp', '.shx', '.tif')))