MetaLandSim-package: Landscape And Range Expansion Simulation
In MetaLandSim: Landscape and Range Expansion Simulation
MetaLandSim-package R Documentation
Landscape And Range Expansion Simulation
Description
The package MetaLandSim is a simulation environment, allowing the generation of random landscapes, represented as graphs, the simulation of landscape dynamics, metapopulation dynamics and range expansion.
The package was developed as part of the Ph.D. thesis of Frederico Mestre (SFRH/BD/73768/2010), funded by European Social Funds and the Portuguese Foundation for Science and Technology, and included in the project NETPERSIST (PTDC/AAG-MAA/3227/2012), funded by European Regional Development Fund (ERDF) through COMPETE programme and Portuguese national funds through the Portuguese Foundation for Science and Technology.
It 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 (Peck, 2004; Zurell et al. 2009). The role of simulation in landscape ecology is fundamental due to the spatial and temporal scale of the studied phenomena, which frequently hinders experimentation (Ims, 2005).
Here, graph and metapopulation theories are combined, which is a broadly accepted strategy to provide a modelling framework for metapopulation dynamics (Cantwell & Forman, 1993; Bunn et al. 2000; Ricotta et al. 2000; Minor & Urban, 2008; Galpern et al. 2011). Also, several graph-based connectivity metrics can be computed from the landscape graphs. This set of metrics have been proven useful elsewhere (Urban & Keitt, 2001; Calabrese & Fagan, 2004). The graph representation of landscape has one major advantage: it effectively summarizes spatial relationships between elements and facilitates a multi-scale analysis integrating patch and landscape level analysis (Calabrese & Fagan, 2004).
MetaLandSim operates at two scales, providing researchers with the possibility of:
Landscape scale - Simulation of metapopulation occupation on a dynamic landscape, computation of connectivity metrics.
Range scale - Computes dispersal model and range expansion scenario simulation.
The landscape unit, an object of class landscape, is the basic simulation unit at both these scales. At the landscape scale, the persistence of the metapopulation in a dynamic landscape is evaluated through the simulation of landscape dynamics using the function iterate.graph or manage_landscape_sim.
At the range scale the metapopulation is allowed to expand to other, empty, landscape units using range_expansion, producing an object of class expansion. The function range_raster allows the conversion of the dispersal model obtained with the previous function into a raster. Finally, also at the range scale, the user can analyse the outcome of several alternative landscapes in range expansion speed and maximum dispersal distance, using the function manage_expansion_sim.
Since version 1.0 new IFM parameter estimation capabilities are available, which based upon Bayesian statistics, using the functions first developed for the paper Risk et al.(2011).
We thank Dr. Santiago Saura (Universidad Politecnica de Madrid) for the very useful inputs and for the R script which greatly improved the connectivity metrics capabilities of MetaLandSim.
After version 2.0.0 MetaLandSim had a few major changes: 1) There is no Graphic User Interface, the
user will have to resort solely to the usual R user interface; 2) It does not use GRASS, resorting uniquely to R packages to conduct the simulations (mainly terra); 3) It depends on much less packages
(after removing rgrass7, maptools, rgeos, raster, tcltk and fgui); 4) There were some major changes to
the functions range_raster and range_expansion. In what concerns
range_expansion the output, rather than considering distinct dispersal probabilities
in all four cardinal directions (as in previous versions), considers the same probability of dispersal
from a current presence in all directions. This has implications in the range_raster
function, that converts the dispersal probability to a raster. However, this does not change the results
in any meaningfull way given that these kinds of simulations require many iterations in which
the distinctions between the dispersal to all four directions was diluted.
Details
Package: MetaLandSim
Type: Package
Version: 2.0.0
Date: 2022-01-12
License: GPL (>=2)
Author(s)
Frederico Mestre, Fernando Canovas, Benjamin Risk, Ricardo Pita, Antonio Mira and Pedro Beja.
Maintainer: Frederico Mestre <mestre.frederico@gmail.com>
References
Bunn, A. G., Urban, D. L. and Keitt, T. H. (2000). Landscape connectivity: a conservation application of graph theory. Journal of Environmental Management, 59(4), 265-278.
Calabrese, J. M. and Fagan, W. F. (2004). A comparison-shopper's guide to connectivity metrics. Frontiers in Ecology and the Environment, 2(10), 529-536.
Cantwell, M. D. and Forman, R. T. (1993). Landscape graphs: ecological modelling with graph theory to detect configurations common to diverse landscapes. Landscape Ecology, 8(4), 239-255.
Galpern, P., Manseau, M. and Fall, A. (2011). Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biological Conservation, 144(1), 44-55.
Ims, R.A. (2005). The role of experiments in landscape ecology. In: Wiens, J.A., and Moss, M.R. (eds.). Issues and Perspectives in Landscape Ecology. Cambridge University Press. pp. 70-78.
Mestre, F., Pita, R., Pauperio, J., Martins, F. M., Alves, P. C., Mira, A., & Beja, P. (2015). Combining distribution modelling and non-invasive genetics to improve range shift forecasting. Ecological Modelling, 297, 171-179.
Mestre, F., Risk, B. B., Mira, A., Beja, P., & Pita, R. (2017). A metapopulation approach to predict species range shifts under different climate change and landscape connectivity scenarios. Ecological Modelling, 359, 406-414.
Mestre, F., Pita, R., Mira, A., Beja, P. (2020). Species traits, patch turnover and successional dynamics: When does intermediate disturbance favour metapopulation occupancy?. BMC Ecology.
Minor, E. S. and Urban, D. L. (2008). A Graph Theory Framework for Evaluating Landscape Connectivity and Conservation Planning. Conservation Biology, 22(2), 297-307.
Peck, S. L. (2004). Simulation as experiment: a philosophical reassessment for biological modelling. Trends in Ecology & Evolution, 19(10), 530-534.
Ricotta, C., Stanisci, A., Avena, G. C., and Blasi, C. (2000). Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach. Community Ecology, 1(1), 89-94.
Risk, B. B., De Valpine, P., Beissinger, S. R. (2011). A robust design formulation of the incidence function model of metapopulation dynamics applied to two species of rails. Ecology, 92(2), 462-474.
Urban, D. and Keitt, T. (2001). Landscape connectivity: a graph-theoretic perspective. Ecology, 82(5), 1205-1218.
Zurell, D., Berger, U., Cabral, J.S., Jeltsch, F., Meynard, C.N., Munkemuller, T., Nehrbass, N., Pagel, J., Reineking, B., Schroder, B. and Grimm, V. (2009). The virtual ecologist approach: simulating data and observers. Oikos, 119(4), 622-635.
MetaLandSim documentation built on Jan. 13, 2023, 1:11 a.m.
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| MetaLandSim-package | R Documentation |
Landscape And Range Expansion Simulation
Description
The package MetaLandSim is a simulation environment, allowing the generation of random landscapes, represented as graphs, the simulation of landscape dynamics, metapopulation dynamics and range expansion.
The package was developed as part of the Ph.D. thesis of Frederico Mestre (SFRH/BD/73768/2010), funded by European Social Funds and the Portuguese Foundation for Science and Technology, and included in the project NETPERSIST (PTDC/AAG-MAA/3227/2012), funded by European Regional Development Fund (ERDF) through COMPETE programme and Portuguese national funds through the Portuguese Foundation for Science and Technology.
It 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 (Peck, 2004; Zurell et al. 2009). The role of simulation in landscape ecology is fundamental due to the spatial and temporal scale of the studied phenomena, which frequently hinders experimentation (Ims, 2005).
Here, graph and metapopulation theories are combined, which is a broadly accepted strategy to provide a modelling framework for metapopulation dynamics (Cantwell & Forman, 1993; Bunn et al. 2000; Ricotta et al. 2000; Minor & Urban, 2008; Galpern et al. 2011). Also, several graph-based connectivity metrics can be computed from the landscape graphs. This set of metrics have been proven useful elsewhere (Urban & Keitt, 2001; Calabrese & Fagan, 2004). The graph representation of landscape has one major advantage: it effectively summarizes spatial relationships between elements and facilitates a multi-scale analysis integrating patch and landscape level analysis (Calabrese & Fagan, 2004).
MetaLandSim operates at two scales, providing researchers with the possibility of:
Landscape scale - Simulation of metapopulation occupation on a dynamic landscape, computation of connectivity metrics.
Range scale - Computes dispersal model and range expansion scenario simulation.
The landscape unit, an object of class landscape, is the basic simulation unit at both these scales. At the landscape scale, the persistence of the metapopulation in a dynamic landscape is evaluated through the simulation of landscape dynamics using the function iterate.graph or manage_landscape_sim.
At the range scale the metapopulation is allowed to expand to other, empty, landscape units using range_expansion, producing an object of class expansion. The function range_raster allows the conversion of the dispersal model obtained with the previous function into a raster. Finally, also at the range scale, the user can analyse the outcome of several alternative landscapes in range expansion speed and maximum dispersal distance, using the function manage_expansion_sim.
Since version 1.0 new IFM parameter estimation capabilities are available, which based upon Bayesian statistics, using the functions first developed for the paper Risk et al.(2011).
We thank Dr. Santiago Saura (Universidad Politecnica de Madrid) for the very useful inputs and for the R script which greatly improved the connectivity metrics capabilities of MetaLandSim.
After version 2.0.0 MetaLandSim had a few major changes: 1) There is no Graphic User Interface, the
user will have to resort solely to the usual R user interface; 2) It does not use GRASS, resorting uniquely to R packages to conduct the simulations (mainly terra); 3) It depends on much less packages
(after removing rgrass7, maptools, rgeos, raster, tcltk and fgui); 4) There were some major changes to
the functions range_raster and range_expansion. In what concerns
range_expansion the output, rather than considering distinct dispersal probabilities
in all four cardinal directions (as in previous versions), considers the same probability of dispersal
from a current presence in all directions. This has implications in the range_raster
function, that converts the dispersal probability to a raster. However, this does not change the results
in any meaningfull way given that these kinds of simulations require many iterations in which
the distinctions between the dispersal to all four directions was diluted.
Details
| Package: | MetaLandSim |
| Type: | Package |
| Version: | 2.0.0 |
| Date: | 2022-01-12 |
| License: GPL (>=2) | |
Author(s)
Frederico Mestre, Fernando Canovas, Benjamin Risk, Ricardo Pita, Antonio Mira and Pedro Beja.
Maintainer: Frederico Mestre <mestre.frederico@gmail.com>
References
Bunn, A. G., Urban, D. L. and Keitt, T. H. (2000). Landscape connectivity: a conservation application of graph theory. Journal of Environmental Management, 59(4), 265-278.
Calabrese, J. M. and Fagan, W. F. (2004). A comparison-shopper's guide to connectivity metrics. Frontiers in Ecology and the Environment, 2(10), 529-536.
Cantwell, M. D. and Forman, R. T. (1993). Landscape graphs: ecological modelling with graph theory to detect configurations common to diverse landscapes. Landscape Ecology, 8(4), 239-255.
Galpern, P., Manseau, M. and Fall, A. (2011). Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biological Conservation, 144(1), 44-55.
Ims, R.A. (2005). The role of experiments in landscape ecology. In: Wiens, J.A., and Moss, M.R. (eds.). Issues and Perspectives in Landscape Ecology. Cambridge University Press. pp. 70-78.
Mestre, F., Pita, R., Pauperio, J., Martins, F. M., Alves, P. C., Mira, A., & Beja, P. (2015). Combining distribution modelling and non-invasive genetics to improve range shift forecasting. Ecological Modelling, 297, 171-179.
Mestre, F., Risk, B. B., Mira, A., Beja, P., & Pita, R. (2017). A metapopulation approach to predict species range shifts under different climate change and landscape connectivity scenarios. Ecological Modelling, 359, 406-414.
Mestre, F., Pita, R., Mira, A., Beja, P. (2020). Species traits, patch turnover and successional dynamics: When does intermediate disturbance favour metapopulation occupancy?. BMC Ecology.
Minor, E. S. and Urban, D. L. (2008). A Graph Theory Framework for Evaluating Landscape Connectivity and Conservation Planning. Conservation Biology, 22(2), 297-307.
Peck, S. L. (2004). Simulation as experiment: a philosophical reassessment for biological modelling. Trends in Ecology & Evolution, 19(10), 530-534.
Ricotta, C., Stanisci, A., Avena, G. C., and Blasi, C. (2000). Quantifying the network connectivity of landscape mosaics: a graph-theoretical approach. Community Ecology, 1(1), 89-94.
Risk, B. B., De Valpine, P., Beissinger, S. R. (2011). A robust design formulation of the incidence function model of metapopulation dynamics applied to two species of rails. Ecology, 92(2), 462-474.
Urban, D. and Keitt, T. (2001). Landscape connectivity: a graph-theoretic perspective. Ecology, 82(5), 1205-1218.
Zurell, D., Berger, U., Cabral, J.S., Jeltsch, F., Meynard, C.N., Munkemuller, T., Nehrbass, N., Pagel, J., Reineking, B., Schroder, B. and Grimm, V. (2009). The virtual ecologist approach: simulating data and observers. Oikos, 119(4), 622-635.
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