Set an objective to maximize the integral index of connectivity

Specify that a restoration problem (restopt_problem()) should the integral index of connectivity (IIC).

Usage

set_max_iic_objective(problem, distance_threshold = -1, unit = "m")

Arguments

problem: restopt_problem() Restoration problem object.
distance_threshold: numeric greater than 0. Minimum distance (in unit) between two patches to consider them connected in the computation of the IIC. The default value -1 causes the function to use 1 aggregated cell as the distance threshold.
unit: unit object or a character that can be coerced to a distance unit (see unit package), or "cells" for cell width of aggregated habitat raster. Units of the distance_threshold parameter. If the input habitat raster does not use a projected coordinate system, only "cells" is available. Meters by default, expected if distance_threshold is set to its default value (-1), which causes the function to use 1 cell by default.

Value

An updated restoration problem (restopt_problem() object.

Details

The integral index of connectivity (IIC) is a graph-based inter-patch connectivity index based on a binary connection model (Pascual-Hortal & Saura, 2006). Its maximization in the context of restoration favours restoring the structural connectivity between large patches. IIC is unitless and comprised between 0 (no connectivity) and 1 (all the landscape is habitat, thus fully connected). The distance_threshold parameter indicates to the solver how to construct the habitat graph, i.e. what is the minimum distance between two patches to consider them as connected. Note that, as the computation occurs on aggregated cells, if distance_threshold is used with a different unit than "cells", it will be rounded to the closest corresponding number of cells.

References

Pascual-Hortal, L., & Saura, S. (2006). Comparison and development of new graph-based landscape connectivity indices: Towards the priorization of habitat patches and corridors for conservation. Landscape Ecology, 21(7), 959‑967. https://doi.org/10.1007/s10980-006-0013-z

Examples

# \donttest{
# load data
habitat_data <- rast(
  system.file("extdata", "habitat_hi_res.tif", package = "restoptr")
)

locked_out_data <- rast(
 system.file("extdata", "locked_out.tif", package = "restoptr")
)

# create problem with locked out constraints
p <- restopt_problem(
    existing_habitat = habitat_data,
    aggregation_factor = 16,
    habitat_threshold = 0.7
  ) %>%
  set_max_iic_objective() %>%
  add_restorable_constraint(
    min_restore = 5,
    max_restore = 5,
  ) %>%
  add_locked_out_constraint(data = locked_out_data) %>%
  add_settings(time_limit = 1)

# print problem
print(p)
#> ----------------------------------------------------------------- 
#>                          Restopt                          
#> ----------------------------------------------------------------- 
#> original habitat:     habitat_hi_res.tif 
#> aggregation factor:   16 
#> habitat threshold:    0.7 
#> existing habitat:     in memory 
#> restorable habitat:   in memory 
#> ----------------------------------------------------------------- 
#> objective:            Maximize integral index of connectivity 
#> ----------------------------------------------------------------- 
#> constraints:          
#>   -  restorable (min_restore = 5, max_restore = 5, min_proportion = 1, unit = ha) 
#>   -  locked out (data = in memory) 
#> ----------------------------------------------------------------- 
#> settings: 
#>   - precision = 4
#>   - time_limit = 1
#>   - nb_solutions = 1
#>   - optimality_gap = 0
#>   - solution_name_prefix = Solution  
#> ----------------------------------------------------------------- 

# solve problem
s <- solve(p)
#> Note: The current solution is the best that the solver could find within the time limit. However, the solver had not enough to prove whether it is optimal or not. Consider increasing the time limit if you need a better solution (solving time = 1.02 s)

# plot solution
plot(s)

# }