Introduction to mptools"

library(knitr)
opts_chunk$set(dev="png", fig.path='figs/')
library(mptools)

Why 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.

The mptools package provides several useful functions for extracting and processing data from RAMAS Metapop .mp files.

Function overview:

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.

Examples

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.

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_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 (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 (n_iters).

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:

head(res$results[,, 'ALL'])

The same could be extracted for population 40, referring to it by name:

head(res$results[,, 'Pop 40'])

meta(): extract Metapop settings

The 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 xMetapop and 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 pass one of them to mp2xy, below.

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 coordinate system.

mp2sp(): create a SpatialPointsDataFrame describing 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 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 SpatialPointsDataFrame, respectively.

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', 
         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 with browseURL(tempdir()).

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:

file.show(file.path(tempdir(), 'mp.kml'))

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.

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 results.

k <- kch(meta=met, path=dirname(mp))
str(k)

knt(): visualise carrying capacity and abundance dynamics

The 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(): animate habitat and abundance dynamics

With 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 RasterStack or 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 RasterStack to mp_animate, comprising the rasters included with mptools.

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.

References

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.

[//]: # (Citations styled using the Methods in Ecology and Evolution style written by Xiaodong Dang and provided by the Citation Style Language project under the Creative Commons Attribution-ShareAlike 3.0 Unported license [http://creativecommons.org/licenses/by-sa/3.0/ license] - http://citationstyles.org/)



Try the mptools package in your browser

Any scripts or data that you put into this service are public.

mptools documentation built on May 29, 2017, 8:43 p.m.