rescomp

rescomp is an R package that supports the definition, simulation and visualization of ODE models of ecological consumer-resource interactions. In essence, it is a consumer-resource modelling focused interface to the excellent deSolve package.

Installation

You can install rescomp from GitHub with:

# install.packages("devtools")
devtools::install_github("andrewletten/rescomp")

Example

The primary user function in rescomp is spec_rescomp(), which facilitates: i) the definition and parameterisation of a desired consumer-resource model, and ii) the specification of simulation parameters. The default output from spec_rescomp() is a list defining a model for a single type I consumer (linear functional response) and a single continuously supplied resource (e.g., in a chemostat).

pars <- spec_rescomp()

plot_funcresp() plots the functional response for easy visualistion prior to running a simulation.

The model is then simulated via sim_rescomp() (effectively a wrapper for deSolve::ode() with convenient defaults).

m1 <- sim_rescomp(pars)

Output dynamics can be visualised with plot_rescomp().

Note, the core rescomp functions are compatible with pipes. For example spec_rescomp() |> sim_rescomp() |> plot_rescomp() will output the plot above.

The main utility of rescomp comes with specifying more elaborate models and simulation dynamics. Features/options include (but are not limited to):

  • Unlimited number of consumers/resources
  • Consumer functional response (type I, II or III)
  • Resource dynamic (chemostat, logistic and/or pulsed)
  • Resource type (substitutable or essential)
  • Continuous or intermittent mortality (e.g. serial transfer)
  • Time dependent growth and consumption parameters
  • Delayed consumer introduction times

See ?spec_rescomp for all argument options.

The following two examples demonstrate how to build and simulate a model for: i) two consumers with type II functional responses on a single logistically growing resources; and ii) two consumers with type III functional responses with pulsed resources and time dependent growth parameters. A wide range of other examples can be found in the package vignettes.

Example 1 

pars <- spec_rescomp(
  spnum = 2,
  resnum = 1,
  funcresp = funcresp_monod(
    mumax = crmatrix(0.7, 0.05),
    ks = crmatrix(2, 0.015)
  ),
  rinit = 0.2,
  ressupply = ressupply_logistic(
    r = 3,
    k = 0.2
  ),
  totaltime = 2000
)
plot_funcresp(pars, maxx = 0.2)

m2 <- sim_rescomp(pars)
plot_rescomp(m2)

Example 2 

pars <- spec_rescomp(
  spnum = 2,
  resnum = 2,
  funcresp = funcresp_hill(
    mumax = rescomp_coefs_lerp(
      crmatrix(
        0.2, 0.1,
        0.5, 0.3
      ),
      crmatrix(
        0.4, 0.1,
        0.05, 0.02
      ),
      "env_state"
    ),
    ks = crmatrix(1),
    n = crmatrix(2)
  ),
  params = rescomp_param_list(
    env_state = rescomp_param_square(period = 80)
  ),
  ressupply = ressupply_constant(0),
  events = list(
    event_schedule_periodic(
      event_res_add(1),
      period = 40
    )
  ),
  totaltime = 1000
)
plot_funcresp(pars, maxx = 1)

m3 <- sim_rescomp(pars)