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Adds an adaptability constraint to a scenario object

Source: R/add_constraint.R
add_constraint_adaptability.Rd

Adaptability constraints assume that suitable habitat for species in (future) projections might be unsuitable if it is outside the range of conditions currently observed for the species.

Usage

add_constraint_adaptability(
  mod,
  method = "nichelimit",
  names = NULL,
  approach = "thresh",
  value = 1,
  value_min = NULL,
  value_max = NULL,
  increment = 0,
  ...
)

# S4 method for class 'BiodiversityScenario'
add_constraint_adaptability(
  mod,
  method = "nichelimit",
  names = NULL,
  approach = "thresh",
  value = 1,
  value_min = NULL,
  value_max = NULL,
  increment = 0,
  ...
)

Arguments

mod

A BiodiversityScenario object with specified predictors.

method

A character indicating the type of constraints to be added to the scenario. See details for more information.

names

A character vector with names of the predictors for which an adaptability threshold should be set (Default: NULL for all).

approach

character on whether thresholds or hinges are to be calculated (Default: 'thresh'). For 'fixedlimit' this controls how strongly the limits are enforced (e.g. abrupt or linearly).

value

A numeric value in units of standard deviation (Default: 1) or alternatively as prefered value for "fixedlimit".

value_min

A numeric minimum value used for method "fixedlimit".

value_max

A numeric maximum value used for method "fixedlimit".

increment

A numeric constant that is added to value at every time step (Default: 0). Allows incremental widening of the niche space, thus opening constraints.

...

passed on parameters. See also the specific methods for adding constraints.

Details

Currently implemented are the following approaches:

* 'nichelimit' = This adds a simple constrain on the predictor parameter space, which can be defined through the "value" parameter. For example by setting it to 1 (Default), any projections are constrained to be within the range of at maximum 1 standard deviation from the range of covariates used for model training. The parameter "increment" furthermore allows to step-wise increase the value range by a certain amount per time step.

* 'fixedlimit' = Here we can supply a fixed limit for a given variable, provided as a minimum ("value_min"), maximum ("value_max") and preferred ("value") range in which a biodiversity feature exist. Common applications include for example known thermal limits with regards to temperature. Internally this function applies a normalized hinge transform based on the supplied values.

See also

Other constraint: add_constraint(), add_constraint_MigClim(), add_constraint_boundary(), add_constraint_connectivity(), add_constraint_dispersal(), add_constraint_minsize(), add_constraint_threshold(), simulate_population_steps()

Examples

if (FALSE) { # \dontrun{
scenario(fit) |>
 add_constraint_adaptability(value = 1)
} # }