| Type: | Package |
| Title: | Models for Simulating Lion Populations |
| Version: | 1.0.1 |
| Date: | 2022-04-06 |
| Author: | Guillaume Chapron [aut, cre], Matthew Wijers [ctb], Andrew Loveridge [ctb], David Macdonald [ctb] |
| Maintainer: | Guillaume Chapron <gchapron@carnivoreconservation.org> |
| Description: | Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C. |
| License: | GPL-3 |
| Depends: | parallel, abind, testthat |
| NeedsCompilation: | yes |
| Encoding: | UTF-8 |
| Packaged: | 2022-04-07 10:01:24 UTC; guillaume |
| Repository: | CRAN |
| Date/Publication: | 2022-04-08 14:10:02 UTC |
Lion population models
Description
A package to run simulations of lion populations using an Individual-Based Model compiled in C.
Details
| Package: | pop.lion |
| Type: | Package |
| Version: | 0.2 |
| Date: | 2020-04-28 |
| License: | GPL-3 |
Author(s)
Guillaume Chapron <gchapron@carnivoreconservation.org> with contributions from Matthew Wijers, Andrew Loveridge and David Macdonald.
Plot population projections
Description
Plot population projections
Usage
plot_projection(projection, title)
Arguments
projection |
A list obtained after running the function |
title |
A string indicating which variable should be plotted
|
Details
Plot average projections with 95% confidence interval.
Value
No returned value, plot created
Examples
oldpar <- par(mfrow = c(1,1))
years = 25
survival <- matrix(1, nrow=180, ncol=2)
survival[1:12, 1:2] <- 0.97^(1/12)
survival[13:24, 1:2] <- 0.98^(1/12)
survival[25:96, 1:2] <- 0.99^(1/12)
survival[97:108, 1:2] <- 0.98^(1/12)
survival[109:120, 1:2] <- 0.96^(1/12)
survival[121:132, 1:2] <- 0.94^(1/12)
survival[133:144, 1:2] <- 0.92^(1/12)
survival[145:156, 1:2] <- 0.90^(1/12)
survival[157:168, 1:2] <- 0.87^(1/12)
survival[169:180, 1:2] <- 0.83^(1/12)
litter_distribution <- c(0.10, 0.30, 0.35, 0.20, 0.05)
conflict_age <- array(4*12, dim=c(2), dimnames=list(c("female", "male")))
conflict_mortality <- array(0, dim=c(12*years, 2), dimnames=list(NULL, c("female", "male")))
conflict_mortality[24:36,] <- 15.2
hunting_age <- array(5*12, dim=c(2), dimnames=list(c("female", "male")))
hunting_mortality <- array(0, dim=c(12*years, 2), dimnames=list(NULL, c("female", "male")))
hunting_mortality[72:84,"male"] <- 10
projection <- project(
years = years,
runs = 100,
survival = survival,
litter_distribution = litter_distribution,
pop_initial = 5,
conflict_age = conflict_age,
conflict_mortality = conflict_mortality,
hunting_age = hunting_age,
hunting_mortality = hunting_mortality,
hunter_error = 0,
K_indiv = 400,
K_pride = 20,
K_coali = 20,
K_edged = 10,
seed = 1,
details = FALSE
)
par(mfrow=c(2,2))
plot_projection(projection, "NINDIV")
plot_projection(projection, "NPRIDES")
plot_projection(projection, "NCOALIS")
plot_projection(projection, "LITTERS")
par(oldpar)
Lion population projections
Description
Run stochastic lion population projections.
Usage
project(years,
runs,
survival,
litter_distribution,
pop_initial,
conflict_age,
conflict_mortality,
hunting_age,
hunting_mortality,
hunter_error,
K_indiv,
K_pride,
K_coali,
K_edged,
seed,
details)
Arguments
years |
A number: number of years to simulate the population. |
runs |
A number: number of times (or Monte Carlo runs) to simulate the population. |
survival |
A matrix: average monthly survival for each sex. |
litter_distribution |
A vector: probability distribution of litter sizes (1-5 cubs) in the population. |
pop_initial |
A number: number of prides (and coalitions). A simulation starts with an equal number of prides and coalitions. |
conflict_age |
A vector: the minimum age in months at which lions can be killed by conflict for females and males. |
conflict_mortality |
An array: mortality added at the edge by conflict for every month of the simulation and for females and males. Expressed in percentage, a value of 15.2 will be understood by the model as 15.2 per cent. Values can be double. The array has 12 * years rows. |
hunting_age |
A vector: the minimum age in months at which lions can be killed by trophy hunting for females and males. |
hunting_mortality |
An array: mortality added at the edge by trophy hunting for every month of the simulation and for females and males. Expressed in number of individuals, a value of 15 will be understood by the model as 15 killed every month. A value of 0.5 will be understood as 6 lions killed per year. The array has 12 * years rows. |
hunter_error |
A number: hunter error. |
K_indiv |
A number: maximum number of individuals in the population. |
K_pride |
A number: maximum number of prides in the population. |
K_coali |
A number: maximum number of coalitions in the population. |
K_edged |
A number: number of prides in the population that are located at the edge of the reserve and therefore vulnerabe to hunting and poaching. |
seed |
(optional) A number: seed of the random number generator. |
details |
(optional) A boolean: indicate whether individual events are exported. This can generate large simulation objects. |
Details
Run stochastic lion population projections with an Individual-Based Model (IBM) compiled in C.
Value
runs |
a 3-dimensional array of numbers of individuals with dimension c(years, statistics, runs) |
individuals |
a 2-dimensional array of individuals events |
parameters |
a list of parameters of the projection |
Examples
years = 25
survival <- matrix(1, nrow=180, ncol=2)
survival[1:12, 1:2] <- 0.97^(1/12)
survival[13:24, 1:2] <- 0.98^(1/12)
survival[25:96, 1:2] <- 0.99^(1/12)
survival[97:108, 1:2] <- 0.98^(1/12)
survival[109:120, 1:2] <- 0.96^(1/12)
survival[121:132, 1:2] <- 0.94^(1/12)
survival[133:144, 1:2] <- 0.92^(1/12)
survival[145:156, 1:2] <- 0.90^(1/12)
survival[157:168, 1:2] <- 0.87^(1/12)
survival[169:180, 1:2] <- 0.83^(1/12)
litter_distribution <- c(0.10, 0.30, 0.35, 0.20, 0.05)
conflict_age <- array(4*12, dim=c(2), dimnames=list(c("female", "male")))
conflict_mortality <- array(0, dim=c(12*years, 2), dimnames=list(NULL, c("female", "male")))
conflict_mortality[24:36,] <- 15.2
hunting_age <- array(5*12, dim=c(2), dimnames=list(c("female", "male")))
hunting_mortality <- array(0, dim=c(12*years, 2), dimnames=list(NULL, c("female", "male")))
hunting_mortality[72:84,"male"] <- 10
projection <- project(
years = years,
runs = 100,
survival = survival,
litter_distribution = litter_distribution,
pop_initial = 5,
conflict_age = conflict_age,
conflict_mortality = conflict_mortality,
hunting_age = hunting_age,
hunting_mortality = hunting_mortality,
hunter_error = 0,
K_indiv = 400,
K_pride = 20,
K_coali = 20,
K_edged = 10,
seed = 1,
details = FALSE
)
# Population size at the end of the simulation:
apply(projection$runs[,"NINDIV",], 1, mean)[12*years+1]