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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# paleopop <img src='man/figures/logo.png' align="right" height="125" />
<!-- badges: start -->
[](https://doi.org/10.1111/geb.13601)
[](https://github.com/GlobalEcologyLab/paleopop/actions)
[](https://CRAN.R-project.org/package=paleopop)
[](https://CRAN.R-project.org/package=paleopop)
[](https://github.com/GlobalEcologyLab/paleopop/commits/master)
<!-- badges: end -->
`paleopop` is part of the poemsverse. `poems` is a spatially-explicit, process-explicit, pattern-oriented framework for modeling population dynamics. This extension adds functionality for modeling large populations at generational time-steps over paleontological time-scales.
## Installation
You can install the latest release of `paleopop` from CRAN with:
```{r cran}
install.packages("paleopop")
```
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("GlobalEcologyLab/paleopop")
```
## About R6 classes
`poems` and `paleopop` are based on [R6](https://r6.r-lib.org/articles/Introduction.html "R6 reference") class objects. R is primarily a *functional* programming language; if you want to simulate a population, you might use the `lapply` or `replicate` functions to repeat a generative function like `rnorm`. R6 creates an *object-oriented* programming language inside of R, so instead of using functions on other functions, in these packages we simulate populations using methods attached to objects. Think of R6 objects like machines, and methods like switches you can flip on the machines.
## Example
One of the major additions in `paleopop` is the `PaleoRegion` R6 class, which allows for regions that change over time due to ice sheets, sea level, bathymetry, and so on. The plots below show the temporal mask functionality of the `PaleoRegion` object. The temporal mask indicates cells that are occupiable at each time step with a 1 and unoccupiable cells with a `NA`. In this example, I use the `temporal_mask_raster` method to show how "Ring Island" changes at time step 10 due to a drop in sea level.
```{r paleoregion_example}
library(poems)
library(paleopop)
coordinates <- data.frame(x = rep(seq(-178.02, -178.06, -0.01), 5),
y = rep(seq(19.02, 19.06, 0.01), each = 5),
z = rep(1, 25))
template_raster <- raster::rasterFromXYZ(coordinates,
crs = "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
sealevel_raster <- template_raster
template_raster[][c(7:9, 12:14, 17:19)] <- NA # make Ring Island
sealevel_raster[][c(7:9, 12:14, 17:18)] <- NA
raster_stack <- raster::stack(x = append(replicate(9, template_raster), sealevel_raster))
region <- PaleoRegion$new(template_raster = raster_stack)
raster::plot(region$temporal_mask_raster()[[1]], main = "Ring Island (first timestep)",
xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
colNA = "blue")
raster::plot(region$temporal_mask_raster()[[10]], main = "Ring Island (last timestep)",
xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
colNA = "blue")
```
`paleopop` also includes the `PaleoPopModel` class, which sets up the population model structure. Here I show a very minimalist setup of a model template using this class.
```{r paleopopmodel}
model_template <- PaleoPopModel$new(
region = region, # makes the simulation spatially explicit
time_steps = 10, # number of time steps to simulate
years_per_step = 12, # years per generational time-step
standard_deviation = 0.1, # SD of growth rate
growth_rate_max = 0.6, # maximum growth rate
harvest = F, # are the populations harvested?
populations = 17, # total occupiable cells over time
initial_abundance = seq(9000, 0, -1000), # initial pop. sizes
transition_rate = 1.0, # transition rate between generations
carrying_capacity = rep(1000, 17), # static carrying capacity
dispersal = (!diag(nrow = 17, ncol = 17))*0.05, # dispersal rates
density_dependence = "logistic", # type of density dependence
dispersal_target_k = 10, # minimum carrying capacity to attract dispersers
occupancy_threshold = 1, # lower than this # of pops. means extinction
abundance_threshold = 10, # threshold for Allee effect
results_selection = c("abundance") # what outputs do you want in results?
)
```
The `paleopop_simulator` function accepts a PaleoPopModel object or a named list as input to simulate populations over paleo time scales, and the `PaleoPopResults` class stores the outputs from the paleo population simulator.
```{r paleosimulator_example}
results <- paleopop_simulator(model_template)
results # examine
raster::plot(region$raster_from_values(results$abundance[,10]),
main = "Final abundance", xlab = "Longitude (degrees)",
ylab = "Latitude (degrees)", colNA = "blue")
```
A practical example of how to use `paleopop`, with more complex parameterization, can be found in the vignette.
## Citation
You may cite `paleopop` in publications using our software paper in *Global Ecology and Biogeography*:
Pilowsky, J. A., Haythorne, S., Brown, S. C., Krapp, M., Armstrong, E., Brook, B. W., Rahbek, C., & Fordham, D. A. (2022). Range and extinction dynamics of the steppe bison in Siberia: A pattern‐oriented modelling approach. *Global Ecology and Biogeography*, *31*(12), 2483-2497.