'RAMAS Metapop' < https://www.ramas.com/ramas.htm#metapop> is a popular software package for performing spatially-explicit population viability analysis. It is primarily GUI-driven, but can benefit from integration into an R workflow, wherein model results can be subjected to further analysis. 'RAMAS Metapop' stores metapopulation model parameter settings and population dynamics simulation results in plain text files (.mp files). This package facilitates access, summary and visualisation of 'RAMAS Metapop 5' outputs in order to better integrate 'RAMAS' analyses into an R workflow.
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.
||Extract management action details from .mp files|
||Extract carrying capacity times series from .kch files|
||Plot the carrying capacity and abundance dynamics of one or more populations|
||Extract population details from .mp files|
||Extract coordinates for populations from .mp files, and transform them to the original coordinate reference system|
||Animate temporal habitat and abundance dynamics on a gridded landscape|
||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|
||Extract total habitat suitability from .ptc files.|
First of all, we install and load the
mptools package. This requires the
devtools package to be present, which can be installed with
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')```To extract the simulation results, we use the `results` function, pointing it to the .mp file.``` rres <- results(mp=mp)res
## results: ## --- ## Untitled model ## --- ## Simulation completed at 6/19/2013 08:44:31 ## 263 populations; 100 time steps; 1000 iterations ## ## EMA Extinction risk ## 38167.98 0.00
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_min), maximum (
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 predetermined threshold,
qe_prob); the mean of minimum abundance (
EMA; McCarthy & Thompson 2001) 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:
## mean sd min max ## [1,] 1762330 108409 1172861 1836649 ## [2,] 1780789 104213 1211457 1847632 ## [3,] 1753311 133856 1192633 1858674 ## [4,] 1721145 159946 1042715 1869636 ## [5,] 1700337 178770 998090 1880646 ## [6,] 1723829 175547 970144 1891940
The same could be extracted for population 40, referring to it by name:
head(res$results[,, 'Pop 40'])
## mean sd min max ## [1,] 30680 2434 18046 31986 ## [2,] 30979 2249 19782 32148 ## [3,] 30486 2907 19104 32309 ## [4,] 29892 3291 15157 32471 ## [5,] 29690 3606 15225 32677 ## [6,] 30024 3662 13546 32839
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)
## popName xMetapop yMetapop initN ddType Rmax K Ksd allee kch ## 1 Pop 1 199.500 279.300 0 EX 1 4234 NA NA example1.kch ## 2 Pop 2 189.525 289.275 0 EX 1 6896 NA NA example2.kch ## 3 Pop 3 199.500 289.275 0 EX 1 37355 NA NA example3.kch ## 4 Pop 4 269.325 289.275 0 EX 1 1024 NA NA example4.kch ## 5 Pop 5 279.300 289.275 0 EX 1 5335 NA NA example5.kch ## 6 Pop 6 289.275 289.275 0 EX 1 2471 NA NA example6.kch ## ddDispSourcePopN cat1LocalMulti cat1LocalProb includeInTotal stageMatrix ## 1 NA 1 0 TRUE 1 ## 2 NA 1 0 TRUE 1 ## 3 NA 1 0 TRUE 1 ## 4 NA 1 0 TRUE 1 ## 5 NA 1 0 TRUE 1 ## 6 NA 1 0 TRUE 1 ## relFec relSurv localThr cat2LocalMulti cat2LocalProb sdMatrix ## 1 1 1 0 1 0 1 ## 2 1 1 0 1 0 1 ## 3 1 1 0 1 0 1 ## 4 1 1 0 1 0 1 ## 5 1 1 0 1 0 1 ## 6 1 1 0 1 0 1 ## ddDispTargetPopK tSinceCat1 tSinceCat2 relDisp relVarFec relVarSurv ## 1 1 0 0 1 1 1 ## 2 1 0 0 1 1 1 ## 3 1 0 0 1 1 1 ## 4 1 0 0 1 1 1 ## 5 1 0 0 1 1 1 ## 6 1 0 0 1 1 1
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 included with
mptools. We identify the file path of one of these, and pass it to
## Loading required package: sp
r <- system.file('example_001.tif', package='mptools')xy <- mp2xy(mp=mp, r=r, cell.length=9.975)
## pop x_mp y_mp x y ## 1 Pop 1 199.500 279.300 1326093 -4044288 ## 2 Pop 2 189.525 289.275 1316118 -4054263 ## 3 Pop 3 199.500 289.275 1326093 -4054263 ## 4 Pop 4 269.325 289.275 1395918 -4054263 ## 5 Pop 5 279.300 289.275 1405893 -4054263 ## 6 Pop 6 289.275 289.275 1415868 -4054263
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 coordinate system.
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 simulation.
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
CRS objects rather than as PROJ.4 strings.)
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')
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 with
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 84.
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)
## num [1:100, 1:263] 4234 4271 4351 4388 4426 ... ## - attr(*, "dimnames")=List of 2 ## ..$ : NULL ## ..$ : chr [1:263] "Pop 1" "Pop 2" "Pop 3" "Pop 4" ...
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 step.
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, we can create a gif animation showing temporal dynamics in habitat suitability and mean abundance. 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 create a
RasterStack rasters included with
mptools, and pass it to
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='README_files/dynamics.gif',zlim=c(0, 800), width=630, height=615)
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.
This is the initial CRAN release of
mptools, a package to extract, summarise,
and visualise the results of RAMAS Metapop model simulations. See the
readme for further
details and examples.