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MetaLandSim

Metapopulations Persistence and Range Expansion Simulation MetaLandSim is intended to provide a virtual environment, enabling the experimentation and simulation of processes at two scales: landscape and range. The simulation approach, taken by MetaLandSim, presents several advantages, like allowing the test of several alternatives and the knowledge of the full system. The role of simulation in landscape ecology is fundamental due to the spatial and temporal scale of the studied phenomena, which frequently hinders experimentation.

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Version

Install

install.packages('MetaLandSim')

Monthly Downloads

311

Version

1.0.10

License

GPL (>= 2)

Maintainer

Frederico Mestre

Last Published

June 30th, 2022

Functions in MetaLandSim (1.0.10)

accept.calculate

Calculate acceptance rates in MCMC chains
coda.create

Create files for use with R-package coda.
calcmode

Function for mode estimation of a continuous variable
combine.chains

Combines two chains into a single chain.
addpoints

Add a given number of patches to a landscape
cluster.id

Classify patches in clusters
cabrera

Modified patch occupancy data of Cabrera vole
cluster.graph

Delivers the number of patches per cluster
components.graph

Number of components of a landscape
convert.graph

Convert data frame to landscape
expansion

Class 'expansion'
MetaLandSim.GUI

Graphic User Interface
MetaLandSim-package

Landscape And Range Expansion Simulation
create.parameter.df

Create parameter data frame
import.shape

Import a shapefile
extract.graph

Extract landscape from span.graph generated list
ifm.robust.MCMC

Estimate the robust design incidence function model
ifm.naive.MCMC

Estimate the naive design incidence function model
ifm.missing.MCMC

Estimate the 'missing' design incidence function model
edge.graph

Produce an edge (links) data frame
landscape_change

Landscape loosing 5% of patches per time step
matrix.graph

Returning a matrix with information on connections between patches
list.stats

Returning information on a dynamic landscape list
manage_expansion_sim

Simulate range expansion simulation
manage_landscape_sim

Batch landscape simulation
iterate.graph

Simulate landscape series occupation
landscape

Class 'landscape'
plot_expansion

Graphical display of the expansion
plot_graph

Graphical display of the landscape
metrics.graph

Computes landscape connectivity metrics
min_distance

Computes topological distance
MetaLandSim-internal

Internal functions for the MetaLandSim package.
metapopulation

Class 'metapopulation'
occ.landscape

Sample landscape with one simulated occupancy snapshot
occ.landscape2

Sample landscape with 10 simulated occupancy snapshots
mc_df

Modified patch occupancy data of Cabrera vole as a data frame
parameter.estimate

Estimate parameters
param1

Sample parameter data frame number 1
param2

Sample parameter data frame number 2
plotL.graph

Plot one landscape of the list created by span.graph
range_expansion

Produce a range expansion model
range_raster

Probability of occupancy, dispersal model
removepoints

Remove a given number of patches from the landscape
remove.species

Remove the species occupancy from the landscape
sim.det.20

Array corresponding to nsites x nyears x nvisits
sim.distance

Distance matrix between sampling sites (nsite x nsite).
rland.graph

Creates random landscape graph
summary_landscape

Summarize 'landscape' class objects
spom

Stochastic Patch Occupancy Model
summary_metapopulation

Summarize 'metapopulation' class objects
z.sim.20

Occupancy data generated with perfect detection with approximately 20% of data missing at random.
z.sim

Occupancy data generated with perfect detection.
z.sim.20.fa

Occupancy data containing false absences
span.graph

Simulate landscape dynamics over a number of time steps
species.graph

Simulate landscape occupation
rg_exp

List with range.expansion output
rland

Random landscape
simulate_graph

Simulate species occupancy in one dynamic landscape
sim.area

Vector of the areas for each site; here, 100 sites
simulatedifm

Set of simulated data to use with the IFM parameter estimation functions. The data were generated using the code provided in "details".