README.md

In poems: Pattern-Oriented Ensemble Modeling System

poems: Pattern-oriented ensemble modeling system (for spatially explicit populations)

The poems package provides a framework of interoperable R6 (Chang, 2020) classes for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al., 2005). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes:

1. Model setup including generated spatial layers and demographic population model parameters.
2. Generating model parameters via Latin hypercube sampling (Iman & Conover, 1980).
3. Running multiple sampled model simulations.
4. Collating summary results metrics via user-defined functions.
5. Validating and selecting an ensemble of models that best match known patterns.

By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002) using the abc package (Csillery et al., 2015). However, alternative user-defined functionality could be employed.

The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for trans-locations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.

Installation

You can install the released version of poems from CRAN with:

install.packages("poems")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("GlobalEcologyLab/poems")

Example

The following simple example demonstrates how to run a single spatially explicit demographic population model using poems:

library(poems)

# Demonstration example region (U Island) and initial abundance
coordinates <- data.frame(x = rep(seq(177.01, 177.05, 0.01), 5),
                          y = rep(seq(-18.01, -18.05, -0.01), each = 5))
template_raster <- Region$new(coordinates = coordinates)$region_raster # full extent
template_raster[][-c(7, 9, 12, 14, 17:19)] <- NA # make U Island
region <- Region$new(template_raster = template_raster)
initial_abundance <- seq(0, 300, 50)
raster::plot(region$raster_from_values(initial_abundance), 
             main = "Initial abundance", xlab = "Longitude (degrees)", 
             ylab = "Latitude (degrees)", zlim = c(0, 300), colNA = "blue")

# Set population model
pop_model <- PopulationModel$new(
  region = region,
  time_steps = 5,
  populations = 7,
  initial_abundance = initial_abundance,
  stage_matrix = matrix(c(0, 2.5, # Leslie/Lefkovitch matrix
                          0.8, 0.5), nrow = 2, ncol = 2, byrow = TRUE),
  carrying_capacity = rep(200, 7),
  density_dependence = "logistic",
  dispersal = (!diag(nrow = 7, ncol = 7))*0.05,
  result_stages = c(1, 2))

# Run single simulation
results <- population_simulator(pop_model)
results # examine
#> $all
#> $all$abundance
#> [1] 1010 1181 1236 1359 1335
#> 
#> $all$abundance_stages
#> $all$abundance_stages[[1]]
#> [1] 589 743 699 858 780
#> 
#> $all$abundance_stages[[2]]
#> [1] 421 438 537 501 555
#> 
#> 
#> 
#> $abundance
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]   54  101  155  192  188
#> [2,]  106  158  175  209  171
#> [3,]  127  127  157  173  197
#> [4,]  172  202  185  212  210
#> [5,]  190  222  200  177  182
#> [6,]  171  177  186  205  185
#> [7,]  190  194  178  191  202
#> 
#> $abundance_stages
#> $abundance_stages[[1]]
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]   34   55   87  128  114
#> [2,]   59  100   91  137  103
#> [3,]   82   74   93  105  119
#> [4,]   83  146   85  140  118
#> [5,]  113  147  116  106  116
#> [6,]  107   99  110  124   99
#> [7,]  111  122  117  118  111
#> 
#> $abundance_stages[[2]]
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]   20   46   68   64   74
#> [2,]   47   58   84   72   68
#> [3,]   45   53   64   68   78
#> [4,]   89   56  100   72   92
#> [5,]   77   75   84   71   66
#> [6,]   64   78   76   81   86
#> [7,]   79   72   61   73   91
raster::plot(region$raster_from_values(results$abundance[,5]),
             main = "Final abundance", xlab = "Longitude (degrees)", 
             ylab = "Latitude (degrees)", zlim = c(0, 300), colNA = "blue")

Further examples utilizing the POM workflow and more advanced features of poems can be found in the accompanying vignettes.

References

Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). ‘Approximate Bayesian computation in population genetics’. Genetics, vol. 162, no. 4, pp, 2025–2035.

Chang, W. (2020). ‘R6: Encapsulated Classes with Reference Semantics’. R package version 2.5.0. Retrieved from https://CRAN.R-project.org/package=R6

Csillery, K., Lemaire L., Francois O., & Blum M. (2015). ‘abc: Tools for Approximate Bayesian Computation (ABC)’. R package version 2.1. Retrieved from https://CRAN.R-project.org/package=abc

Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H. H., Weiner, J., Wiegand, T., DeAngelis, D. L., (2005). ‘Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology’. Science vol. 310, no. 5750, pp. 987–991.

Iman R. L., Conover W. J. (1980). ‘Small sample sensitivity analysis techniques for computer models, with an application to risk assessment’. Commun Stat Theor Methods A9, pp. 1749–1842.

poems documentation built on Oct. 7, 2023, 9:06 a.m.