bboutools

bboutools is an R package to estimate the annual survival, recruitment and population growth for boreal caribou populations using Bayesian and Maximum Likelihood models with fixed and random effects. It was developed to facilitate direct comparison of estimates across jurisdictions.

Installation

To install the latest development version:

# install.packages("remotes")
remotes::install_github("poissonconsulting/bboutools")

Introduction

Survival

bboutools accepts survival data for a single population in a specific format.

library(tibble) # for printing data frames
library(ggplot2) # for plotting

library(bboudata) # for boreal caribou datasets
library(bboutools)

surv_data <- bboudata::bbousurv_a
head(surv_data)
#> # A tibble: 6 × 6
#>   PopulationName  Year Month StartTotal MortalitiesCertain MortalitiesUncertain
#>   <chr>          <int> <int>      <int>              <int>                <int>
#> 1 A               1986     1          0                  0                    0
#> 2 A               1986     2          8                  0                    0
#> 3 A               1986     3          8                  0                    0
#> 4 A               1986     4          8                  0                    0
#> 5 A               1986     5          8                  0                    0
#> 6 A               1986     6          8                  0                    0

The survival model, which includes a random effect of month, is fitted using bb_fit_survival(). To achieve convergence increase nthin.

set.seed(99)
surv_fit <- bb_fit_survival(surv_data, quiet = TRUE)
glance(surv_fit)
#> # A tibble: 1 × 8
#>       n     K nchains niters nthin   ess  rhat converged
#>   <int> <int>   <int>  <int> <dbl> <int> <dbl> <lgl>    
#> 1   363     5       3   1000    10   111  1.03 FALSE

Predictions can then be generated using bb_predict_survival().

surv_pred <- bb_predict_survival(surv_fit)
surv_pred
#> # A tibble: 32 × 6
#>    PopulationName CaribouYear Month estimate lower upper
#>    <chr>                <int> <int>    <dbl> <dbl> <dbl>
#>  1 A                     1985    NA    0.872 0.762 0.947
#>  2 A                     1986    NA    0.881 0.791 0.952
#>  3 A                     1987    NA    0.841 0.645 0.908
#>  4 A                     1988    NA    0.887 0.813 0.96 
#>  5 A                     1989    NA    0.874 0.785 0.943
#>  6 A                     1990    NA    0.872 0.785 0.936
#>  7 A                     1991    NA    0.877 0.801 0.936
#>  8 A                     1992    NA    0.889 0.822 0.956
#>  9 A                     1993    NA    0.87  0.793 0.927
#> 10 A                     1994    NA    0.849 0.756 0.909
#> # ℹ 22 more rows

And plotted using bb_plot_year()

bb_plot_year_survival(surv_pred) +
  expand_limits(y = c(0, 1))

The survival in each month can be plotted using bb_plot_month()

bb_plot_month_survival(surv_fit)

These estimates represent the annual survival rate if that month lasted the whole year.

Recruitment

The equivalent functions for recruitment data are

recruit_data <- bboudata::bbourecruit_a
head(recruit_data)
#> # A tibble: 6 × 9
#>   PopulationName  Year Month   Day  Cows Bulls UnknownAdults Yearlings Calves
#>   <chr>          <int> <int> <int> <int> <int>         <int>     <int>  <int>
#> 1 A               1990     3     9     1     1             0         0      0
#> 2 A               1990     3     9     5     1             0         0      0
#> 3 A               1990     3     9     4     1             0         0      0
#> 4 A               1990     3     9     2     0             0         0      0
#> 5 A               1990     3     9     6     0             0         0      0
#> 6 A               1990     3     9     4     1             0         0      0

set.seed(99)
recruit_fit <- bb_fit_recruitment(recruit_data, quiet = TRUE)

bb_plot_year_recruitment(recruit_fit)

Population Growth

The function bb_predict_growth() predicts the annual population growth (lambda) rate from the survival and recruitment models.

lambda <- bb_predict_growth(surv_fit, recruit_fit)
bb_plot_year_growth(lambda)

bb_predict_population_change() calculates the population change (%) with uncertainty as the cumulative product of population growth.

change <- bb_predict_population_change(surv_fit, recruit_fit)
bb_plot_year_population_change(change)

Information

Additional information is available from the bboutools website, which includes the following articles:

• Get Started.
• Analytic Methods for Estimation of Boreal Caribou Survival, Recruitment and Population Growth.
• Prior Selection and Influence.

There is also a user-friendly graphical interface, called bboushiny, for non-R users.

bbou Suite

bboutools is part of the bbou suite of tools. Other packages in this suite include:

• bboudata
• bbouretro
• bboushiny
• bbousims

Contribution

Please report any issues.

Pull requests are always welcome.

Code of Conduct

Please note that the bboutools project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

The code is released under the Apache License 2.0

Copyright 2022 Environment and Climate Change Canada

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.