| add_max_phylo_div_objective {prioritizr} | R Documentation |
Add maximum phylogenetic diversity objective
Description
Set the objective of a conservation planning problem to
maximize the phylogenetic diversity of the features represented in the
solution subject to a budget. This objective is similar to
add_max_features_objective() except
that emphasis is placed on representing a phylogenetically diverse set of
species, rather than as many features as possible (subject to weights).
This function was inspired by Faith (1992) and Rodrigues et al.
(2002).
Usage
add_max_phylo_div_objective(x, budget, tree)
Arguments
x |
|
budget |
|
tree |
|
Details
The maximum phylogenetic diversity objective finds the set of
planning units that meets representation targets for a phylogenetic tree
while staying within a fixed budget. If multiple solutions can meet all
targets while staying within budget, the cheapest solution is chosen.
Note that this objective is similar to the maximum
features objective (add_max_features_objective()) in that it
allows for both a budget and targets to be set for each feature. However,
unlike the maximum feature objective, the aim of this objective is to
maximize the total phylogenetic diversity of the targets met in the
solution, so if multiple targets are provided for a single feature, the
problem will only need to meet a single target for that feature
for the phylogenetic benefit for that feature to be counted when
calculating the phylogenetic diversity of the solution. In other words,
for multi-zone problems, this objective does not aim to maximize the
phylogenetic diversity in each zone, but rather this objective
aims to maximize the phylogenetic diversity of targets that can be met
through allocating planning units to any of the different zones in a
problem. This can be useful for problems where targets pertain to the total
amount held for each feature across multiple zones. For example,
each feature might have a non-zero amount of suitable habitat in each
planning unit when the planning units are assigned to a (i) not restored,
(ii) partially restored, or (iii) completely restored management zone.
Here each target corresponds to a single feature and can be met through
the total amount of habitat in planning units present to the three
zones.
Value
An updated problem() object with the objective added to it.
Mathematical formulation
This objective can be expressed mathematically for a set of planning units
(I indexed by i) and a set of features (J
indexed by j) as:
\mathit{Maximize} \space \sum_{i = 1}^{I} -s \space c_i \space x_i +
\sum_{j = 1}^{J} m_b l_b \\
\mathit{subject \space to} \\
\sum_{i = 1}^{I} x_i r_{ij} \geq y_j t_j \forall j \in J \\
m_b \leq y_j \forall j \in T(b) \\
\sum_{i = 1}^{I} x_i c_i \leq B
Here, x_i is the decisions variable (e.g.,
specifying whether planning unit i has been selected (1) or not
(0)), r_{ij} is the amount of feature j in planning
unit i, t_j is the representation target for feature
j, y_j indicates if the solution has meet
the target t_j for feature j. Additionally,
T represents a phylogenetic tree containing features j
and has the branches b associated within lengths l_b.
The binary variable m_b denotes if
at least one feature associated with the branch b has met its
representation as indicated by y_j. For brevity, we denote
the features j associated with branch b using
T(b). Finally, B is the budget allocated for the
solution, c_i is the cost of planning unit i, and
s is a scaling factor used to shrink the costs so that the problem
will return a cheapest solution when there are multiple solutions that
represent the same amount of all features within the budget.
Notes
In early versions, this function was named as the
add_max_phylo_div_objective function.
References
Faith DP (1992) Conservation evaluation and phylogenetic diversity. Biological Conservation, 61: 1–10.
Rodrigues ASL and Gaston KJ (2002) Maximising phylogenetic diversity in the selection of networks of conservation areas. Biological Conservation, 105: 103–111.
See Also
See objectives for an overview of all functions for adding objectives.
Also, see targets for an overview of all functions for adding targets, and
add_feature_weights() to specify weights for different features.
Other objectives:
add_max_cover_objective(),
add_max_features_objective(),
add_max_phylo_end_objective(),
add_max_utility_objective(),
add_min_largest_shortfall_objective(),
add_min_set_objective(),
add_min_shortfall_objective()
Examples
## Not run:
# load ape package
require(ape)
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_phylogeny <- get_sim_phylogeny()
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# plot the simulated phylogeny
par(mfrow = c(1, 1))
plot(sim_phylogeny, main = "phylogeny")
# create problem with a maximum phylogenetic diversity objective,
# where each feature needs 10% of its distribution to be secured for
# it to be adequately conserved and a total budget of 1900
p1 <-
problem(sim_pu_raster, sim_features) %>%
add_max_phylo_div_objective(1900, sim_phylogeny) %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# solve problem
s1 <- solve(p1)
# plot solution
plot(s1, main = "solution", axes = FALSE)
# find out which features have their targets met
r1 <- eval_target_coverage_summary(p1, s1)
print(r1, width = Inf)
# plot the phylogeny and color the adequately represented features in red
plot(
sim_phylogeny, main = "adequately represented features",
tip.color = replace(
rep("black", terra::nlyr(sim_features)),
sim_phylogeny$tip.label %in% r1$feature[r1$met], "red"
)
)
# rename the features in the example phylogeny for use with the
# multi-zone data
sim_phylogeny$tip.label <- feature_names(sim_zones_features)
# create targets for a multi-zone problem. Here, each feature needs a total
# of 10 units of habitat to be conserved among the three zones to be
# considered adequately conserved
targets <- tibble::tibble(
feature = feature_names(sim_zones_features),
zone = list(zone_names(sim_zones_features))[
rep(1, number_of_features(sim_zones_features))],
type = rep("absolute", number_of_features(sim_zones_features)),
target = rep(10, number_of_features(sim_zones_features))
)
# create a multi-zone problem with a maximum phylogenetic diversity
# objective, where the total expenditure in all zones is 5000.
p2 <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_max_phylo_div_objective(5000, sim_phylogeny) %>%
add_manual_targets(targets) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# solve problem
s2 <- solve(p2)
# plot solution
plot(category_layer(s2), main = "solution", axes = FALSE)
# find out which features have their targets met
r2 <- eval_target_coverage_summary(p2, s2)
print(r2, width = Inf)
# plot the phylogeny and color the adequately represented features in red
plot(
sim_phylogeny, main = "adequately represented features",
tip.color = replace(
rep("black", terra::nlyr(sim_features)), which(r2$met), "red"
)
)
# create a multi-zone problem with a maximum phylogenetic diversity
# objective, where each zone has a separate budget.
p3 <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_max_phylo_div_objective(c(2500, 500, 2000), sim_phylogeny) %>%
add_manual_targets(targets) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# solve problem
s3 <- solve(p3)
# plot solution
plot(category_layer(s3), main = "solution", axes = FALSE)
# find out which features have their targets met
r3 <- eval_target_coverage_summary(p3, s3)
print(r3, width = Inf)
# plot the phylogeny and color the adequately represented features in red
plot(
sim_phylogeny, main = "adequately represented features",
tip.color = replace(
rep("black", terra::nlyr(sim_features)), which(r3$met), "red"
)
)
## End(Not run)