Importance scores (also known as irreplaceability scores) can be used to assess the relative importance of planning units selected in a solution to a conservation planning problem().

Details

The following methods are available for calculating importance scores:

eval_replacement_importance()

Calculate importance scores using replacement costs (based on Cabeza and Moilanen 2006). These scores quantify the change in the objective function (e.g. additional costs required to meet feature targets) of the optimal solution if a given planning unit in a solution cannot be acquired. They can (i) account for the cost of different planning units, (ii) account for multiple management zones, (iii) apply to any objective function, and (iv) identify truly irreplaceable planning units (denoted with infinite values).

eval_ferrier_importance()

Calculate importance scores following Ferrier et al. (2000). These scores measure importance based on how critical planning units are for meeting targets. They can only be applied to conservation problems with a minimum set objective and a single zone (i.e. the classic Marxan-type problem). Furthermore---unlike the replacement cost scores---these scores provide a score for each feature within each planning unit, providing insight into why certain planning units are more important than other planning units.

eval_rare_richness_importance()

Calculate importance scores using the rarity weighted richness metric (based on Williams et al. 1996). These score are simply a measure of biodiversity. They do not account for planning costs, multiple management zones, objective functions, or feature targets (or weightings). They merely describe the spatial patterns of biodiversity, and do not account for many of the factors needed to quantify the importance of a planning unit for achieving conservation goals.

Generally speaking, we recommend using replacement cost scores for small and moderate sized problems (e.g. less than 30,000 planning units) when it is feasible to do so. It can take a very long time to compute replacement cost scores, and so it is simply not feasible to compute these scores for particularly large problems. For large sized problems (e.g. more than 100,000 planning units), we recommend using the rarity weighted richness scores simply because there is no other choice available. It has been known for decades that such measures of biodiversity lead to poor conservation plans (Kirkpatrick 1983). We do not currently recommend using the Ferrier scores because the code requires further testing to verify correctness.

References

Cabeza M and Moilanen A (2006) Replacement cost: A practical measure of site value for cost-effective reserve planning. Biological Conservation, 132: 336--342.

Ferrier S, Pressey RL, and Barrett TW (2000) A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement. Biological Conservation, 93: 303--325.

Kirkpatrick, JB (1983) An iterative method for establishing priorities for the selection of nature reserves: an example from Tasmania. Biological Conservation, 25: 127--134.

Williams P, Gibbons D, Margules C, Rebelo A, Humphries C, and Pressey RL (1996) A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds. Conservation Biology, 10: 155--174.

See also

Examples

# \dontrun{ # load data data(sim_pu_raster, sim_pu_polygons, sim_features) # build minimal conservation problem with raster data p1 <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_default_solver(gap = 0, verbose = FALSE) # solve the problem s1 <- solve(p1) # plot solution plot(s1, main = "solution", axes = FALSE, box = FALSE)
# calculate importance scores using replacement cost scores ir1 <- eval_replacement_importance(p1, s1) # calculate importance scores using Ferrier et al 2000 method, # and extract the total importance scores ir2 <- eval_ferrier_importance(p1, s1)[["total"]] # calculate importance scores using rarity weighted richness scores ir3 <- eval_rare_richness_importance(p1, s1) # plot importance scores plot(stack(ir1, ir2, ir3), axes = FALSE, box = FALSE, main = c("replacement cost", "Ferrier score", "rarity weighted richness"))
# }