Package: metaRange 1.2.0

Stefan Fallert

metaRange: Framework to Build Mechanistic and Metabolic Constrained Species Distribution Models

Build spatially and temporally explicit process-based species distribution models, that can include an arbitrary number of environmental factors, species and processes including metabolic constraints and species interactions. The focus of the package is simulating populations of one or multiple species in a grid-based landscape and studying the meta-population dynamics and emergent patterns that arise from the interaction of species under complex environmental conditions. It provides functions for common ecological processes such as negative exponential, kernel-based dispersal (see Nathan et al. (2012) <doi:10.1093/acprof:oso/9780199608898.003.0015>), calculation of the environmental suitability based on cardinal values ( Yin et al. (1995) <doi:10.1016/0168-1923(95)02236-Q>, simplified by Yan and Hunt (1999) <doi:10.1006/anbo.1999.0955> see eq: 4), reproduction in form of an Ricker model (see Ricker (1954) <doi:10.1139/f54-039> and Cabral and Schurr (2010) <doi:10.1111/j.1466-8238.2009.00492.x>), as well as metabolic scaling based on the metabolic theory of ecology (see Brown et al. (2004) <doi:10.1890/03-9000> and Brown, Sibly and Kodric-Brown (2012) <doi:10.1002/9781119968535.ch>).

Authors:Stefan Fallert [aut, cre, cph], Lea Li [aut, cph], Juliano Sarmento Cabral [aut, cph, ths], Tyler Morgan-Wall [ctb, cph], Bavarian Ministry of Science and Arts [fnd], Deutsche Bundesstiftung Umwelt [fnd]

metaRange_1.2.0.tar.gz
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metaRange_1.2.0.tgz(r-4.6-x86_64)metaRange_1.2.0.tgz(r-4.6-arm64)metaRange_1.2.0.tgz(r-4.5-x86_64)metaRange_1.2.0.tgz(r-4.5-arm64)
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metaRange_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
metaRange/json (API)

# Install 'metaRange' in R:
install.packages('metaRange', repos = c('https://metarange.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/metarange/metarange/issues

Pkgdown/docs site:https://metarange.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

6.36 score 12 stars 38 scripts 218 downloads 16 exports 6 dependencies

Last updated from:d643acb92f. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK199
linux-devel-x86_64OK213
source / vignettesOK255
linux-release-arm64OK194
linux-release-x86_64OK199
macos-release-arm64OK206
macos-release-x86_64OK329
macos-oldrel-arm64OK196
macos-oldrel-x86_64OK366
windows-develOK208
windows-releaseOK181
windows-oldrelOK170
wasm-releaseOK131

Exports:calculate_dispersal_kernelcalculate_normalization_constantcalculate_suitabilitycreate_simulationdispersalmetabolic_scalingmetaRangeEnvironmentmetaRangePriorityQueuemetaRangeProcessmetaRangeSimulationmetaRangeSpeciesnegative_exponential_functionricker_allee_reproduction_modelricker_reproduction_modelsave_speciesset_verbosity

Dependencies:backportscheckmateR6RcppRcppArmadilloterra

08: Advanced options
Time layer mapping | Default mapping | Static environment | Changing the number of time steps | Burn-in period | Global variables and processes | Interaction with the priority queue | Queuing a process | Dequeue a process | Ending the simulation early

Last update: 2024-03-26
Started: 2023-11-16

02: Multiple species
Setting up the simulation | Adding more species to the simulation | Adding traits to (multiple) species | Adding processes | Reproduction | Executing the simulation | References:

Last update: 2024-03-26
Started: 2023-11-16

03: Niche suitability
Setup | Creating the simulation and species | Adding traits to species | Adding processes | Calculate the suitability | Suitability | Reproduction | Results | References

Last update: 2024-03-26
Started: 2023-11-16

04: Dispersal
Basic setup | Unweighted dispersal | Weighted dispersal | Comparison of the results | References

Last update: 2024-03-26
Started: 2023-11-16

05: Species interactions
Competition | Trophic interactions | Final notes

Last update: 2024-03-26
Started: 2023-11-16

06: Metabolic scaling
Calculating the normalization constant | Example | References

Last update: 2024-03-26
Started: 2023-11-16

10: Saving results
Saving a time series of traits

Last update: 2024-03-26
Started: 2024-03-25

09: Troubleshooting / caveats
Debugging | General caveats

Last update: 2024-03-26
Started: 2023-11-16

01: Introduction to metaRange
Setting up a simulation | Loading the landscape | Pre-setup | Creating the simulation | Adding species to the simulation | Adding traits to species | Adding processes | Reproduction | Executing the simulation | Plotting the results | Saving the simulation | References

Last update: 2024-03-25
Started: 2023-11-16

07: Age structured populations
Basic setup | Traits | Processes | More processes | Result

Last update: 2024-03-25
Started: 2023-11-16

Readme and manuals

Help Manual

Help pageTopics
Calculate 2D dispersal kernel.calculate_dispersal_kernel
Normalization constant calculationcalculate_normalization_constant
Calculate (estimate) environmental suitabilitycalculate_suitability
Create a simulationcreate_simulation
Dispersal processdispersal
Metabolic scalingmetabolic_scaling
metaRangeEnvironment objectmetaRangeEnvironment
Process priority queuemetaRangePriorityQueue
metaRangeProcess objectmetaRangeProcess
metaRangeSimulation objectmetaRangeSimulation
metaRangeSpecies objectmetaRangeSpecies
Negative Exponential kernelnegative_exponential_function
Plotting functionplot.metaRangeEnvironment
Plotting functionplot.metaRangeSimulation
Plotting functionplot.metaRangeSpecies
Print traits or globalsprint.metaRangeVariableStorage
Ricker reproduction model with Allee effectsricker_allee_reproduction_model
Ricker reproduction modelricker_reproduction_model
Save functionsave_species
Set verbosity of metaRange simulationset_verbosity
Summary for metaRange simulationsummary.metaRangeSimulation
Summary for metaRange speciessummary.metaRangeSpecies