Introduction

Here we will provide a short tutorial showing how the oppr R package can be used to prioritize funding for conservation projects. This package is a general purpose project prioritization decision support tool. It can generate solutions for species-based project prioritization problems (Joseph et al. 2009; Bennett et al. 2014) and priority threat management problems (Carwardine et al. 2018). To develop a project prioritization, this package requires data for (i) conservation projects, (ii) management actions data, and (iii) biodiversity features.

Briefly, biodiversity features are the biological entities that we wish would persist into the future (e.g. threatened populations, native species, eco-systems). These biodiversity features can (and ideally should) include non-threatened species, but should not include threatening processes that we wish to eradicate (e.g. populations of invasive species). Management actions are acts that can be undertaken to enhance biodiversity (e.g. planting native species, reintroducing native species, trapping pest species). Each action should pertain to a specific location (e.g. a national park) or area (e.g. an entire country), and should be associated with a cost estimate. To guide the prioritization, the management actions are grouped into conservation projects (also termed “strategies”). Typically, management actions are grouped into conservation projects based on spatial (e.g. management actions that pertain to the same area), taxonomic (e.g. management actions that pertain to the same pest species or threatened species), or thematic criteria (e.g. management actions that pertain to pest eradication are grouped into a “pest project”, and actions that pertain to habitat restoration are grouped into a “habitat project”). Additionally, some conservation projects can be combinations of other projects (e.g. a “pest and habitat project”). Each conservation project should be associated with (i) a probability of succeeding if it is implemented (also termed “feasibility”), (ii) information about which management actions are associated with it, and (iii) an estimate of how likely each conservation feature affected by the project is to persist into the future if the project is implemented (often derived using expert elicitation). The conservation projects should also include a “baseline project” that represents a “do nothing scenario” which has a 100% chance of succeeding and is associated with an action that costs nothing to implement. This is important because we can’t find a cost-effective solution if we don’t know how much each project improves a species’ chance at persistence. For more background information on project prioritization, please refer to Carwardine et al. (2018).

To start off, we will initialize the random number generator to ensure reproducibility. Next, we will load the oppr R package. And then we will load the ggplot2, tibble, and tidyr R packages to help with data for visualizations and wrangling.

# set random number generated seed
set.seed(1000)
# load oppr package for project prioritization
library(oppr)
# load ggplot2 package for plotting
library(ggplot2)
# load tibble package for working with tabular data
library(tibble)
# load tidyr package for reshaping tabular data
library(tidyr)

Data simulation

Now we will simulate a dataset to practice developing project prioritizations. Specifically, we will simulate a dataset for a priority threat management exercise that contains 40 features, 70 projects, and 30 actions.

# simulate data
sim_data <- simulate_ptm_data(number_projects = 70, number_actions = 30,
                              number_features = 40)

# extract project, action, feature data
projects <- sim_data$projects
actions <- sim_data$actions
features <- sim_data$features

# manually set feature weights for teaching purposes
features$weight <- exp(runif(nrow(features), 1, 15))

# print data
print(projects) # data for conservation projects

# # A tibble: 71 x 73 # name success F1 F2 F3 F4 F5 F6 F7 F8 # <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #  1 proj



Bennett, J.R., Elliott, G., Mellish, B., Joseph, L.N., Tulloch, A.I., Probert, W.J., Di Fonzo, M.M., Monks, J.M., Possingham, H.P. & Maloney, R. (2014). Balancing phylogenetic diversity and species numbers in conservation prioritizat, using a case study of threatened species in New Zealand. Biological Conservation, 174, 47–54.


Carwardine, J., Martin, T.G., Firn, J., Reyes, R.P., Nicol, S., Reeson, A., Grantham, H.S., Stratford, D., Kehoe, L. & Chadès, I. (2018). Priority Threat Management for biodiversity conservation: A handbook. Journal of Applied Ecology, In press, DOI: 10.1111/1365–2664.13268.


Joseph, L.N., Maloney, R.F. & Possingham, H.P. (2009). Optimal allocation of resources among threatened species: A project prioritization protocol. Conservation Biology, 23, 328–338.