The gdm
package provides functions to fit, plot, summarize, and apply
Generalized Dissimilarity Models.
The gdm package is available on CRAN, development versions are available on GitHub.
install.packages("gdm")
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("fitzLab-AL/gdm")
Fitzpatrick MC, Mokany K, Manion G, Nieto-Lugilde D, Ferrier S. (2022) gdm: Generalized Dissimilarity Modeling. R package version 1.5.
GDM has been used in many published studies. In addition to working through the examples here and those throughout the package documentation, we recommend reading these publications for background information:
Ferrier S, Manion G, Elith J, Richardson, K (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity & Distributions 13: 252-264. https://doi.org/10.1111/j.1472-4642.2007.00341.x
Mokany K, Ware C, Woolley, SNC, Ferrier S, Fitzpatrick MC (2022) A working guide to harnessing generalized dissimilarity modelling for biodiversity analysis and conservation assessment. Global Ecology and Biogeography, 31, 802– 821. https://doi.org/10.1111/geb.13459
The R package gdm implements Generalized Dissimilarity Modeling (Ferrier et al. 2007) to analyze and map spatial patterns of biodiversity. GDM models biological variation as a function of environment and geography using distance matrices – specifically by relating biological dissimilarity between sites to how much sites differ in their environmental conditions (environmental distance) and how isolated they are from one another (geographical distance). Here we demonstrate how to fit, apply, and interpret GDM in the context of analyzing and mapping species-level patterns. GDM also can be used to model other biological levels of organization, notably genetic (Fitzpatrick & Keller 2015), phylogenetic (Rosauer et al. 2014), or function/traits (Thomassen et al. 2010), and the approaches for doing so are largely identical to the species-level case with the exception of using a different biological dissimilarity metric depending on the type of response variable.
The initial step in fitting a generalized dissimilarity model is to combine the biological and environmental data into "site-pair" table format using the formatsitepair
function.
GDM can use several data formats as input. Most common are site-by-species tables (sites in rows, species across columns) for the response and site-by-environment tables (sites in rows, predictors across columns) as the predictors, though distance matrices and rasters also are accommodated as demonstrated below.
The gdm package comes with two example biological data sets and two example environmental data sets in a number of formats. Example data include:
southwest
: A data frame that contains x-y coordinates, 10 columns of predictors (five soil and five bioclimatic variables), and occurrence data for 900+ species of plants from southwest Australia (representing a subset of the data used in [@fitzpatrick_2013]). Note that the format of the southwest
table is an x-y species list (i.e., bioFormat = 2
, see below) where there is one row per species record rather than per site. These biological data are similar to what would be obtained from online databases such as GBIF.
gdmDissim
: A pairwise biological dissimilarity matrix derived from the species data provided in southwest
. gdmDissim
is provided to demonstrate how to proceed when you when you want to fit GDM using an existing biological distance matrix (e.g., pairwise Fst) as the response variable (i.e., bioFormat = 3
, see below). Note however that distance matrices can also be used as predictors (e.g., to model compositional variation in one group as a function of compositional variation in another group (Jones et al 2013).
swBioclims
: a raster stack of the five bioclimatic predictors provided in the southwest
data.
Note that for all input data the rows and their order must match in the biological and environmental data frames and must not include NAs. This is best accomplished by making sure your tables have a column with a unique identifier for each site and that the order of these IDs are the same across all tables.
To build a site-pair table, we need individual tables for the biological
and environmental data, so we first index the southwest
table to
create a table for the species data and a second for the environmental
data:
library(gdm)
# have a look at the southwest data set
str(southwest)
#> 'data.frame': 29364 obs. of 14 variables:
#> $ species : Factor w/ 974 levels "spp1","spp10",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ site : int 1066 1026 1025 1026 1027 1047 1048 1066 1066 1067 ...
#> $ awcA : num 14.5 16.3 23.1 16.3 17 ...
#> $ phTotal : num 546 471 460 471 489 ...
#> $ sandA : num 71.3 68.9 71.5 68.9 74.7 ...
#> $ shcA : num 178.9 105.8 88.4 105.8 147.2 ...
#> $ solumDepth: num 875 928 892 928 952 ...
#> $ bio5 : num 31.4 33.1 32.8 33.1 33.2 ...
#> $ bio6 : num 5.06 4.85 4.82 4.85 4.59 ...
#> $ bio15 : num 40.4 48.2 53.9 48.2 44 ...
#> $ bio18 : int 0 0 43 0 0 0 0 0 0 0 ...
#> $ bio19 : num 133 140 145 140 136 ...
#> $ Lat : num -33 -32 -32 -32 -32.1 ...
#> $ Long : num 119 118 118 118 119 ...
# biological data
# get columns with xy, site ID, and species data
sppTab <- southwest[, c("species", "site", "Long", "Lat")]
# # columns 3-7 are soils variables, remainder are climate
# get columns with site ID, env. data, and xy-coordinates
envTab <- southwest[, c(2:ncol(southwest))]
Because the southwest
data is x-y species list format, we use
bioFormat=2
. Otherwise, we just need to provide the required column
names to create the site-pair table:
# x-y species list example
gdmTab <- formatsitepair(bioData=sppTab,
bioFormat=2, #x-y spp list
XColumn="Long",
YColumn="Lat",
sppColumn="species",
siteColumn="site",
predData=envTab)
#> [1] "Site weighting type: Equal"
#> [1] "Site-pair table created with 4371 rows (94 unique sites) and 26 columns (10 environmental variables)."
#> distance weights s1.xCoord s1.yCoord s2.xCoord s2.yCoord s1.awcA
#> 132 0.4485981 1 115.057 -29.40472 115.5677 -29.46599 23.0101
#> 132.1 0.7575758 1 115.057 -29.40472 116.0789 -29.52556 23.0101
#> 132.2 0.8939394 1 115.057 -29.40472 116.5907 -29.58342 23.0101
#> s1.phTotal s1.sandA s1.shcA s1.solumDepth s1.bio5 s1.bio6 s1.bio15
#> 132 480.3266 83.99326 477.5656 1129.933 34.668 8.908 86.64
#> 132.1 480.3266 83.99326 477.5656 1129.933 34.668 8.908 86.64
#> 132.2 480.3266 83.99326 477.5656 1129.933 34.668 8.908 86.64
#> s1.bio18 s1.bio19 s2.awcA s2.phTotal s2.sandA s2.shcA s2.solumDepth
#> 132 0 267.44 22.3925 494.1225 76.6900 357.7225 1183.9025
#> 132.1 0 267.44 17.0975 415.1275 70.0175 112.4800 985.5300
#> 132.2 0 267.44 17.0300 333.4400 71.5950 165.7250 956.5425
#> s2.bio5 s2.bio6 s2.bio15 s2.bio18 s2.bio19
#> 132 35.50571 7.448572 75.37143 0 228.6572
#> 132.1 36.05000 6.605882 64.52941 0 168.8824
#> 132.2 36.18750 6.131250 58.75000 0 141.1250
The first column of a site-pair table contains a biological distance measure (the default is Bray-Curtis distance though any measure scaled between 0-1 is acceptable). The second column contains the weight to be assigned to each data point in model fitting (defaults to 1 if equal weighting is used, but can be customized by the user or can be scaled to site richness, see below). The remaining columns are the coordinates and environmental values at a site (s1) and those at a second site (s2) making up a site pair. Rows represent individual site-pairs. While the site-pair table format can produce extremely large data frames and contain numerous repeat values (because each site appears in numerous site-pairs), it also allows great flexibility. Most notably, individual site pairs easily can be excluded from model fitting.
A properly formatted site-pair table will have at least six columns
(distance, weights, s1.xCoord, s1.yCoord, s2.xCoord, s2.yCoord) and some
number more depending on how many predictors are included. See
?formatsitepair
and ?gdm
for more details.
What if you already have a biological distance matrix because you are
working with, say, genetic data? In that case, it is simple as changing
the bioFormat
argument and also making sure the rows in your
biological and environmental tables are in the same order. Let’s have
a quick look at gdmDissim
, a pairwise biological distance matrix
prodvided with the package:
# Biological distance matrix example
dim(gdmDissim)
#> [1] 94 94
gdmDissim[1:5, 1:5]
#> V1 V2 V3 V4 V5
#> 1 0.0000000 0.8181818 1.0000000 0.5000000 0.7500000
#> 2 0.8181818 0.0000000 0.9000000 0.7777778 0.6551724
#> 3 1.0000000 0.9000000 0.0000000 1.0000000 0.5757576
#> 4 0.5000000 0.7777778 1.0000000 0.0000000 0.9090909
#> 5 0.7500000 0.6551724 0.5757576 0.9090909 0.0000000
We first need to add a site ID column to gdmDissim
. We already know
the sites are in the correct order, so we do not check here, but you
should confirm for your data.
# get the site column from sppTab
site <- unique(sppTab$site)
# bind to gdmDissim
gdmDissim <- cbind(site, gdmDissim)
gdmDissim[1:5, 1:5]
#> site V1 V2 V3 V4
#> 1 1066 0.0000000 0.8181818 1.0000000 0.5000000
#> 2 1026 0.8181818 0.0000000 0.9000000 0.7777778
#> 3 1025 1.0000000 0.9000000 0.0000000 1.0000000
#> 4 1027 0.5000000 0.7777778 1.0000000 0.0000000
#> 5 1047 0.7500000 0.6551724 0.5757576 0.9090909
Now we are ready to use formatsitepair
:
gdmTab.dis <- formatsitepair(bioData=gdmDissim,
bioFormat=3, #diss matrix
XColumn="Long",
YColumn="Lat",
predData=envTab,
siteColumn="site")
#> [1] "Site weighting type: Equal"
#> [1] "Site-pair table created with 4371 rows (94 unique sites) and 26 columns (10 environmental variables)."
#> distance weights s1.xCoord s1.yCoord s2.xCoord s2.yCoord s1.awcA
#> 132 0.4485981 1 115.057 -29.40472 115.5677 -29.46599 23.0101
#> 132.1 0.7575758 1 115.057 -29.40472 116.0789 -29.52556 23.0101
#> 132.2 0.8939394 1 115.057 -29.40472 116.5907 -29.58342 23.0101
#> s1.phTotal s1.sandA s1.shcA s1.solumDepth s1.bio5 s1.bio6 s1.bio15
#> 132 480.3266 83.99326 477.5656 1129.933 34.668 8.908 86.64
#> 132.1 480.3266 83.99326 477.5656 1129.933 34.668 8.908 86.64
#> 132.2 480.3266 83.99326 477.5656 1129.933 34.668 8.908 86.64
#> s1.bio18 s1.bio19 s2.awcA s2.phTotal s2.sandA s2.shcA s2.solumDepth
#> 132 0 267.44 22.3925 494.1225 76.6900 357.7225 1183.9025
#> 132.1 0 267.44 17.0975 415.1275 70.0175 112.4800 985.5300
#> 132.2 0 267.44 17.0300 333.4400 71.5950 165.7250 956.5425
#> s2.bio5 s2.bio6 s2.bio15 s2.bio18 s2.bio19
#> 132 35.50571 7.448572 75.37143 0 228.6572
#> 132.1 36.05000 6.605882 64.52941 0 168.8824
#> 132.2 36.18750 6.131250 58.75000 0 141.1250
In addition to starting with tablular data, environmental data can be
extracted directly from rasters, assuming the x-y coordinates of sites
are provided in either a site-species table (bioFormat=1
) or as a x-y
species list (bioFormat=2
).
# environmental raster data for sw oz
swBioclims <- raster::stack(system.file("./extdata/swBioclims.grd", package="gdm"))
gdmTab.rast <- formatsitepair(bioData=sppTab,
bioFormat=2, # x-y spp list
XColumn="Long",
YColumn="Lat",
sppColumn="species",
siteColumn="site",
predData=swBioclims) #raster stack
#> [1] "Site weighting type: Equal"
#> [1] "Site-pair table created with 4371 rows (94 unique sites) and 16 columns (5 environmental variables)."
Because some sites might not overlap with the rasters, we should check for and remove NA values from the site-pair table:
sum(is.na(gdmTab.rast))
#> [1] 465
gdmTab.rast <- na.omit(gdmTab.rast)
Note that the formatsitepair
function assumes that the coordinates of
the sites are in the same coordinate system as the rasters. At present,
no checking is performed to ensure this is the case. Note also that if
your site coordinates are longitude-latitude that the calculation of
geographic distances between sites will have errors, the size of which
will depend on the geographic extent and location of your study region.
We hope to deal with this in a later release, but for now you can avoid
these problems by using a projected coordinate system (e.g.,
equidistant).
The ideal biological data for fitting a GDM are occurrence records (presence-absence or abundance) from a network of sites where all species (from one or more taxonomic groups) have been intensively sampled such that compositional dissimilarity can be reliably estimated between sites. However most species data are collected as part of ad hoc surveys and are presence-only. Under these circumstances, there is no systematic surveying and no sites per se, but rather grid cells with some number of occurrence records depending on the number of species observed, with many grid cells having none, a few, or even a single species record. When these data are used to calculate compositional dissimilarity, erroneously high values will result, which will bias the model.
The formatsitepair
function provides a few options for dealing with
this potential bias, including (i) weighting sites relative to the
number of species observed (weightType="richness"
), (ii) removing
sites with few species (e.g., speciesFilter=10
) or (iii) both.
Decisions regarding which approach to use will depend on the nature of
the data and study system. See Ferrier et al. (2007) for further
discussion.
# weight by site richness using weightType="richness"
gdmTab.rw <- formatsitepair(bioData=sppTab,
bioFormat=2,
XColumn="Long",
YColumn="Lat",
sppColumn="species",
siteColumn="site",
predData=envTab,
weightType="richness")
#> [1] "Site weighting type: Richness"
#> [1] "Site-pair table created with 4371 rows (94 unique sites) and 26 columns (10 environmental variables)."
# weights based on richness (number of species records)
gdmTab.rw[1:5, 1:5]
#> distance weights s1.xCoord s1.yCoord s2.xCoord
#> 132 0.4485981 0.2449866 115.057 -29.40472 115.5677
#> 132.1 0.7575758 0.1916207 115.057 -29.40472 116.0789
#> 132.2 0.8939394 0.1635852 115.057 -29.40472 116.5907
#> 132.3 0.9178082 0.1858930 115.057 -29.40472 117.1029
#> 132.4 0.9787234 0.1337957 115.057 -29.40472 117.6156
# remove sites with < 10 species records using
# sppFilter = 10
gdmTab.sf <- formatsitepair(bioData=sppTab,
bioFormat=2,
XColumn="Long",
YColumn="Lat",
sppColumn="species",
siteColumn="site",
predData=envTab,
sppFilter=10)
#> [1] "Site weighting type: Equal"
#> [1] "Site-pair table created with 4095 rows (91 unique sites) and 26 columns (10 environmental variables)."
GDM is a nonlinear extension of permutational matrix regression that uses flexible splines and generalized linear modeling (GLM) to accommodate two types of nonlinearity common in ecological datasets: (1) variation in the rate of compositional turnover (non-stationarity) along environmental gradients, and (2) the curvilinear relationship between biological distance and environmental and geographical distance.
The function gdm
fits generalized dissimilarity models and is simple
to use once the biological and predictor data have been formatted to a
site-pair table. In addition to specifying whether or not the model
should be fit with geographical distance as a predictor variable, the
user has the option to specify (i) the number of I-spline basis
functions (the default is three, with larger values producing more
complex splines) and (ii) the locations of “knots” along the splines
(defaults to 0 (minimum), 50 (median), and 100 (maximum) quantiles when
three I-spline basis functions are used). Even though these option are
available, using the default values for these parameters will work fine
for most applications. In other words, unless you have a good reason,
you should probably use the default settings for splines and knots. The
effects (and significance) of altering the number of splines and knot
locations has not been systematically explored.
Here we fit GDM with geo=T and default settings for all other parameters.
gdm.1 <- gdm(data=gdmTab, geo=TRUE)
The summary
function provides an overview of the model, the most
important items to note are:
summary(gdm.1)
#> [1]
#> [1]
#> [1] GDM Modelling Summary
#> [1] Creation Date: Wed Dec 8 13:15:12 2021
#> [1]
#> [1] Name: gdm.1
#> [1]
#> [1] Data: gdmTab
#> [1]
#> [1] Samples: 4371
#> [1]
#> [1] Geographical distance used in model fitting? TRUE
#> [1]
#> [1] NULL Deviance: 651.914
#> [1] GDM Deviance: 129.025
#> [1] Percent Deviance Explained: 80.208
#> [1]
#> [1] Intercept: 0.277
#> [1]
#> [1] PREDICTOR ORDER BY SUM OF I-SPLINE COEFFICIENTS:
#> [1]
#> [1] Predictor 1: bio19
#> [1] Splines: 3
#> [1] Min Knot: 114.394
#> [1] 50% Knot: 172.416
#> [1] Max Knot: 554.771
#> [1] Coefficient[1]: 0.941
#> [1] Coefficient[2]: 0.868
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for bio19: 1.809
#> [1]
#> [1] Predictor 2: phTotal
#> [1] Splines: 3
#> [1] Min Knot: 277.978
#> [1] 50% Knot: 584.609
#> [1] Max Knot: 1860.37
#> [1] Coefficient[1]: 1.127
#> [1] Coefficient[2]: 0.23
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for phTotal: 1.357
#> [1]
#> [1] Predictor 3: bio5
#> [1] Splines: 3
#> [1] Min Knot: 25.571
#> [1] 50% Knot: 32.16
#> [1] Max Knot: 36.188
#> [1] Coefficient[1]: 0.127
#> [1] Coefficient[2]: 0.453
#> [1] Coefficient[3]: 0.114
#> [1] Sum of coefficients for bio5: 0.694
#> [1]
#> [1] Predictor 4: solumDepth
#> [1] Splines: 3
#> [1] Min Knot: 705.02
#> [1] 50% Knot: 1017.628
#> [1] Max Knot: 1247.705
#> [1] Coefficient[1]: 0.682
#> [1] Coefficient[2]: 0
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for solumDepth: 0.682
#> [1]
#> [1] Predictor 5: awcA
#> [1] Splines: 3
#> [1] Min Knot: 12.975
#> [1] 50% Knot: 22.186
#> [1] Max Knot: 50.7
#> [1] Coefficient[1]: 0
#> [1] Coefficient[2]: 0
#> [1] Coefficient[3]: 0.523
#> [1] Sum of coefficients for awcA: 0.523
#> [1]
#> [1] Predictor 6: Geographic
#> [1] Splines: 3
#> [1] Min Knot: 0.452
#> [1] 50% Knot: 2.46
#> [1] Max Knot: 6.532
#> [1] Coefficient[1]: 0.014
#> [1] Coefficient[2]: 0.372
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for Geographic: 0.386
#> [1]
#> [1] Predictor 7: sandA
#> [1] Splines: 3
#> [1] Min Knot: 56.697
#> [1] 50% Knot: 72.951
#> [1] Max Knot: 83.993
#> [1] Coefficient[1]: 0.092
#> [1] Coefficient[2]: 0
#> [1] Coefficient[3]: 0.139
#> [1] Sum of coefficients for sandA: 0.231
#> [1]
#> [1] Predictor 8: shcA
#> [1] Splines: 3
#> [1] Min Knot: 78.762
#> [1] 50% Knot: 179.351
#> [1] Max Knot: 521.985
#> [1] Coefficient[1]: 0
#> [1] Coefficient[2]: 0.156
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for shcA: 0.156
#> [1]
#> [1] Predictor 9: bio6
#> [1] Splines: 3
#> [1] Min Knot: 4.373
#> [1] 50% Knot: 5.509
#> [1] Max Knot: 9.224
#> [1] Coefficient[1]: 0.121
#> [1] Coefficient[2]: 0
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for bio6: 0.121
#> [1]
#> [1] Predictor 10: bio15
#> [1] Splines: 3
#> [1] Min Knot: 29.167
#> [1] 50% Knot: 55.008
#> [1] Max Knot: 87.143
#> [1] Coefficient[1]: 0.027
#> [1] Coefficient[2]: 0
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for bio15: 0.027
#> [1]
#> [1] Predictor 11: bio18
#> [1] Splines: 3
#> [1] Min Knot: 0
#> [1] 50% Knot: 0
#> [1] Max Knot: 52
#> [1] Coefficient[1]: 0
#> [1] Coefficient[2]: 0
#> [1] Coefficient[3]: 0
#> [1] Sum of coefficients for bio18: 0
The fitted splines represent one of the most informative components of a
fitted GDM and so plotting and scrutinizing the splines is a major part
of interpreting GDM and the analyzed biological patterns. The fitted
model and I-splines can be viewed using the plot
function, which
produces a multi-panel plot that includes two model summary plots
showing (i) the fitted relationship between predicted ecological
distance and observed compositional dissimilarity and (ii) predicted
versus observed biological distance, followed by a series of panels
showing each I-spline with at least one non-zero coefficient (plotted in
order by sum of the I-spline coefficients). Note that in the example
bio18 is not plotted because all three coefficients equaled zero and so
had no relationship with the response.
The maximum height of each spline indicates the magnitude of total biological change along that gradient and thereby corresponds to the relative importance of that predictor in contributing to biological turnover while holding all other variables constant (i.e., is a partial ecological distance). The spline’s shape indicates how the rate of biological change varies with position along that gradient. Thus, the splines provide insight into the total magnitude of biological change as a function of each gradient and where along each gradient those changes are most pronounced. In this example, compositional turnover is greatest along gradients of bio19 (winter precipitation) and phTotal (soil phosphorus) and most rapid near the low ends of these gradients.
length(gdm.1$predictors) # get ideal of number of panels
#> [1] 11
plot(gdm.1, plot.layout=c(4,3))
The fitted model (first two panels) and I-splines (remaining panels).
To allow easy customization of I-spline plots, the isplineExtract
function will extract the plotted values for each I-spline.
gdm.1.splineDat <- isplineExtract(gdm.1)
str(gdm.1.splineDat)
#> List of 2
#> $ x: num [1:200, 1:11] 0.452 0.483 0.513 0.544 0.574 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:11] "Geographic" "awcA" "phTotal" "sandA" ...
#> $ y: num [1:200, 1:11] 0 0.00045 0.00095 0.0015 0.0021 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : NULL
#> .. ..$ : chr [1:11] "Geographic" "awcA" "phTotal" "sandA" ...
plot(gdm.1.splineDat$x[,"bio19"],
gdm.1.splineDat$y[,"bio19"],
lwd=3,
type="l",
xlab="Winter precipitation (mm)",
ylab="Partial ecological distance")
Custom I-spline plot for geographic distance.
The I-splines provide an indication of how species composition (or any
other fitted biological response variable) changes along each
environmental gradient. Beyond these insights, a fitted model also can
be used to (i) predict biological dissimilarity between site pairs in
space or between times using the predict
function and (ii) transform
the predictor variables from their arbitrary environmental scales to a
common biological importance scale using the gdm.transform
function.
The following examples show predictions between site pairs in space and locations through time, and transformation of both tabular and raster data. For the raster example, the transformed layers are used to map spatial patterns of biodiversity.
The predict
function requires a site-pair table in the same format as
that used to fit the model. For demonstration purposes, we use the same
table as that was used to fit the model, though predictions to new sites
(or times) can be made as well assuming the same set of
environmental/spatial predictors are available at those locations (or
times).
gdm.1.pred <- predict(object=gdm.1, data=gdmTab)
head(gdm.1.pred)
#> [1] 0.4720423 0.7133571 0.8710175 0.8534788 0.9777208 0.3996694
plot(gdmTab$distance,
gdm.1.pred,
xlab="Observed dissimilarity",
ylab="Predicted dissimilarity",
xlim=c(0,1),
ylim=c(0,1),
pch=20,
col=rgb(0,0,1,0.5))
lines(c(-1,2), c(-1,2))
Predicted vs. observed compositional dissimilarity.
The predict
function can be used to make predictions through time, for
example, under climate change scenarios to estimate the magnitude of
expected change in biological composition in response to environmental
change [@fitzpatrick_2011]. In this case, rasters must be provided for
two time periods of interest.
First we fit a new model using only the climate variables and then create some fake future climate rasters to use as example data.
# fit a new gdm using a table with climate data only (to match rasters)
gdm.rast <- gdm(gdmTab.rast, geo=T)
# make some fake climate change data
futRasts <- swBioclims
##reduce winter precipitation by 25% & increase temps
futRasts[[3]] <- futRasts[[3]]*0.75
futRasts[[4]] <- futRasts[[4]]+2
futRasts[[5]] <- futRasts[[5]]+3
We again use the predict
function, but with time=TRUE
and provide
the current and future climate raster stacks. Th resulting map shows the
expected magnitude of change in vegetation composition, which can be
interpreted as a biologically-scaled metric of climate stress.
timePred <- predict(gdm.rast, swBioclims, time=T, predRasts=futRasts)
raster::plot(timePred, col=rgb.tables(1000))
Predicted magnitude of biological change through time
Using GDM to transform environmental data rescales the individual
predictors to a common scale of biological importance. Spatially
explicit predictor data to be transformed can be a raster stack or brick
with one layer per predictor. If the model was fit with geographical
distance and raster data are provided to the transform
function, there
is no need to provide x- or y-raster layers as these will be generated
automatically. However, the character names of the x- and y-coordinates
(e.g., “Long” and “Lat”) used to fit the model need to be provided.
First we fit a new model using only the climate variables.
# fit the GDM
gdmRastMod <- gdm(data=gdmTab.rast, geo=TRUE)
We then use the gdm.transform
function to rescale the rasters.
transRasts <- gdm.transform(model=gdmRastMod, data=swBioclims)
raster::plot(transRasts, col=rgb.tables(1000))
Site-pair based biological distances are difficult to visualize.
However, if the transform
function is applied to rasters, the
resulting multi-dimensional biological space can be mapped to reveal
biological patterns in geographic space. Alternatively, a biplot can be
used to depict where sites fall relative to each other in biological
space and therefore how sites differ in predicted biological
composition. In either case, the multi-dimensional biological space can
be most effectively visualized by taking a PCA to reduce dimensionality
and assigning the first three components to an RGB color palette. In the
resulting map, color similarity corresponds to the similarity of
expected plant species composition (in other words, cells with similar
colors are expected to contain similar plant communities).
# Get the data from the gdm transformed rasters as a table
rastDat <- na.omit(raster::getValues(transRasts))
# The PCA can be fit on a sample of grid cells if the rasters are large
rastDat <- raster::sampleRandom(transRasts, 50000)
# perform the principle components analysis
pcaSamp <- prcomp(rastDat)
# Predict the first three principle components for every cell in the rasters
# note the use of the 'index' argument
pcaRast <- raster::predict(transRasts, pcaSamp, index=1:3)
# scale the PCA rasters to make full use of the colour spectrum
pcaRast[[1]] <- (pcaRast[[1]]-pcaRast[[1]]@data@min) /
(pcaRast[[1]]@data@max-pcaRast[[1]]@data@min)*255
pcaRast[[2]] <- (pcaRast[[2]]-pcaRast[[2]]@data@min) /
(pcaRast[[2]]@data@max-pcaRast[[2]]@data@min)*255
pcaRast[[3]] <- (pcaRast[[3]]-pcaRast[[3]]@data@min) /
(pcaRast[[3]]@data@max-pcaRast[[3]]@data@min)*255
# Plot the three PCA rasters simultaneously, each representing a different colour
# (red, green, blue)
raster::plotRGB(pcaRast, r=1, g=2, b=3)
Predicted spatial variation in plant species composition. Colors represent gradients in species composition derived from transformed environmental predictors. Locations with similar colors are expected to contain similar plant communities.
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