library(knitr) opts_chunk$set(dev="png", fig.path='figs/') library(mptools)
In recent years, spatially-explicit, coupled habitat-demographic models have been developed to acknowledge the importance of population processes when evaluating the impacts of habitat change (Keith et al. 2008, 2014; Southwell et al. 2008; Fordham et al. 2012; Cadenhead et al. 2015). Frequently, these coupled models use the Metapop module of RAMAS GIS (Akçakaya & Root 2005) to relate spatially-dynamic habitat suitability to carrying capacity, and then simulate metapopulation dynamics. Metapop saves settings, parameters values, and simulation results to a text file (with .mp extension). Unfortunately, there is limited flexibility within Metapop regarding presentation of results, and extraction of results via the GUI is awkward.
mptools package provides several useful functions for extracting and
processing data from RAMAS Metapop .mp files.
Function | Description
--------- | -------------------------------------------------------------
actions() | Extract management action details from .mp files
kch() | Extract carrying capacity times series from .kch files
knt() | Plot the carrying capacity and abundance dynamics of one or more populations
meta() | Extract population details from .mp files
mp2sp() | Create a
SpatialPointsDataFrame representing the centroid of each population, with attributes
pop (population name),
time (the time step), and
N (the mean population size)
mp2xy() | Extract coordinates for populations from .mp files, and transform them to the original coordinate reference system
mp_animate()| Animate temporal habitat and abundance dynamics on a gridded landscape
results() | Extract simulation results (mean, sd, min and max of population size), as well as expected minimum abundance (EMA) and its standard deviation, from .mp files
ths() | Extract total habitat suitability from .ptc files.
mptools comes packaged with some data relating to a RAMAS Metapop model. The
model is of a metapopulation of a hypothetical species, comprising 263
populations, the population dynamics of which have been simulated over 100
years. In reality, the number of populations, iterations, and time steps are
only limited by RAMAS Metapop itself, and the amount of memory available to R.
Most functions in the mptools package operate on RAMAS Metapop .mp files that
have been run and contain simulation results. An example .mp file representing
our hypothetical metapopulation is included in the package. The path to this
file is given by
system.file('example.mp', package='mptools'). Here we assign
that path to the object
mp <- system.file('example.mp', package='mptools')
results(): extract simulation results
To extract the simulation results, we use the
results function, pointing it to
the .mp file. This extracts time series of the mean, minimum, maximum and
standard deviation (across iterations) of abundance for each population; the
minimum, maximum and terminal metapopulation abundance for each iteration; and
calculates the expected minimum abundance (EMA; McCarthy & Thompson 2001) and
the standard deviation of minimum abundance.
res <- results(mp=mp) res
Summary information is printed by default, but the resulting list comprises
elements containing: the mean, minimum, maximum and standard deviation of
population size for each time step, given for each patch and for the
metapopulation as a whole (in element
results); the minimum (
iter_max) and final (
iter_terminal) abundance across the time
horizon, for each iteration; the probability of quasi-extinction (i.e., the
proportion of iterations during which total abundance fell beneath a
qe_prob); the mean (
EMA) and standard deviation
SDMA) of minimum abundance. Additionally, some metadata about the simulation
is provided: the date and time that the model was run (
timestamp); and the
number of populations (
n_pops), time steps (
duration), and iterations
For example, the mean, minimum, maximum and standard deviation of total population size at each time step (first six time steps shown here) is given by:
The same could be extracted for population 40, referring to it by name:
head(res$results[,, 'Pop 40'])
meta(): extract Metapop settings
meta function returns a
data.frame containing information about RAMAS
Metapop settings for each population, including their names, initial abundances,
density dependence types, carrying capacities, and maximum growth rates. Here
the first few rows are shown for brevity.
met <- meta(mp=mp) head(met)
mp2xy(): extract patch coordinates
RAMAS Metapop simulations are often based on spatial grids describing the
distribution of habitat (i.e., when using the Spatial Data module to identify
patch structure). In these cases, spatial coordinates are converted by RAMAS
such that they describe the position relative to the top left corner of the
grid. Such coordinates are returned by the
meta function in the columns
yMetapop. In order to relate simulation results to the true
landscape, the original (untransformed) coordinates can be recovered with
mp2xy. This requires one of the original grids used by Spatial Data (or its
Raster* object representation), and knowledge of the cell length setting
passed to that module. By default,
mp2xy creates a plot of the points,
overlaid upon the provided raster data.
The raster grids that were originally used to define the patch structure are
mptools. We pass one of them to
library(raster) r <- system.file('example_001.tif', package='mptools') xy <- mp2xy(mp=mp, r=r, cell.length=9.975) head(xy)
Above we see that the
data.frame returned by
mp2xy includes the populations'
names, their RAMAS coordinates, and their original coordinates. In this case,
the original coordinates were defined by the Australian Albers equal area
mp2sp(): create a
SpatialPointsDataFramedescribing population dynamics
The coordinates returned by
mp2xy, and the abundance at the corresponding
patches at each time step of the simulation, can be used to create a spatial
object that enables further analysis and visualisation. This is done with
mp2sp, which creates a
SpatialPointsDataFrame with attributes containing the
average (across iterations) abundance at each patch, for each time step of the
In the following example, we set
start to 2000, indicating that the first time
step of the simulation corresponds to the year 2000. This controls labelling of
the shapefile's attributes---subsequent time steps are labelled with sequential
integers. Below we also pass the source and target PROJ.4
strings describing the coordinate
reference system (CRS) for the input coordinates, and the output
spdf <- mp2sp(mp=mp, coords=xy, start=2000, s_p4s='+init=epsg:3577', t_p4s='+init=epsg:4326')
Above, we indicated that the source CRS is Australian Albers (EPSG 3577) and the
target CRS is WGS 84 (EPSG 4326). (These could also be passed as
objects rather than as
We can plot the output with
sp::spplot, which can colour points according to
the value of an attribute:
library(sp) library(viridis) spplot(subset(spdf, time==2000), zcol='N', cuts=c(-Inf, 0, 10^(0:6)), key.space='right', col.regions=c('gray80', viridis(100)))
We can then write these data out to an ESRI shapefile or KML for use in, e.g., a GIS or Google Earth. For example, to write out as a shapefile, we can use:
library(rgdal) writeOGR(spdf, dsn=tempdir(), layer='mp', driver='ESRI Shapefile', overwrite_layer=TRUE)
The shapefile was written out to a file called "mp.shp" (along with its
accessory files) within the current temporary directory, which can be viewed
To write out to KML, we include the output file name in the string passed to the
dsn argument, and specify the KML driver. Note that below, we subset the data
to the first time step (2000), to reduce the size of the output.
library(rgdal) writeOGR(subset(spdf, time==2000), dsn=file.path(tempdir(), 'mp.kml'), layer='', driver='KML')
If Google Earth is installed on your system, and if .kml files are associated with it, you can open the new .kml file with:
Note that to display correctly in Google Earth, the data should be in the WGS 84
datum. When using
mp2xy above, we ensured that
spdf was transformed to WGS
kch(): extract carrying capacity dynamics from .kch files
If a model involves carrying capacity dynamics, RAMAS stores information about
these in .kch files. Changes in carrying capacity can be extracted from these
files with the
kch function. Rather than show this as text output, we see
below how these carrying capacity sequences are used by
knt, along with the
mean abundances returned by
k <- kch(meta=met, path=dirname(mp)) str(k)
knt(): visualise carrying capacity and abundance dynamics
knt function plots the change in carrying capacity and mean abundance
through time, for each population, or for a given subset of populations. Below,
we show these dynamics for four chosen populations. Black lines show mean total
abundance, and grey lines show carrying capacity. Populations whose names are
shown in bold on a blue background had positive abundance at the first time
knt(meta=met, kch=k, pops=c('Pop 169', 'Pop 170', 'Pop 174', 'Pop 175'), show_N=TRUE, results=res, samelims=TRUE, layout=c(2, 2))
mp_animate(): animate habitat and abundance dynamics
mp_animate, we can create a gif animation showing temporal dynamics in
habitat suitability and in carrying capacity. This can reveal lags in response
to changing habitat suitability.
The function requires a
RasterBrick with layers, describing
habitat change, in temporal order. The interval between successive rasters (i.e.,
the interval between time steps) is assumed to be constant. Below, we pass a
mp_animate, comprising the rasters included with
library(raster) tifs <- list.files(system.file(package='mptools'), '\\.tif$', full.names=TRUE) spdf <- mp2sp(mp=mp, coords=xy, start=2000) mp_animate(spdf, habitat=stack(tifs), outfile='dynamics.gif', zlim=c(0, 800), width=630, height=615, overwrite=TRUE)
Points indicate populated patches, with colours scaled linearly from white (mean abundance = 1) to black (mean abundance is equal to the maximum mean abundance across populations and years). Points are not shown when mean abundance is zero. The raster is coloured according to the carrying capacity of the cell.
Akçakaya, H. & Root, W. (2005). RAMAS GIS: Linking spatial data with population viability analysis (version 5.0). Applied Biomathematics, Setauket, New York.
Cadenhead, N.C., Kearney, M.R., Moore, D., McAlpin, S. & Wintle, B.A. (2015). Climate and fire scenario uncertainty dominate the evaluation of options for conserving the great desert skink. Conservation Letters.
Fordham, D.A., Resit Akçakaya, H., Araújo, M.B., Elith, J., Keith, D.A., Pearson, R., Auld, T.D., Mellin, C., Morgan, J.W. & Regan, T.J. (2012). Plant extinction risk under climate change: Are forecast range shifts alone a good indicator of species vulnerability to global warming? Global Change Biology, 18, 1357–1371.
Keith, D.A., Akçakaya, H.R., Thuiller, W., Midgley, G.F., Pearson, R.G., Phillips, S.J., Regan, H.M., Araújo, M.B. & Rebelo, T.G. (2008). Predicting extinction risks under climate change: Coupling stochastic population models with dynamic bioclimatic habitat models. Biology Letters, 4, 560.
Keith, D.A., Mahony, M., Hines, H., Elith, J., Regan, T.J., Baumgartner, J.B., Hunter, D., Heard, G.W., Mitchell, N.J. & Parris, K.M. (2014). Detecting extinction risk from climate change by IUCN red list criteria. Conservation Biology, 28, 810–819.
McCarthy, M.A. & Thompson, C. (2001). Expected minimum population size as a measure of threat. Animal Conservation, 4, 351–355.
Southwell, D., Lechner, A., Coates, T. & Wintle, B. (2008). The sensitivity of population viability analysis to uncertainty about habitat requirements: Implications for the management of the endangered southern brown bandicoot. Conservation Biology, 22, 1045–1054.
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