Converts the outputs from Rpath into rates for use in Rsim.
Usage
rsim.params(
Rpath,
mscramble = 2,
mhandle = 1000,
preyswitch = 1,
scrambleselfwt = 0,
handleselfwt = 0,
steps_yr = 12,
steps_m = 1
)
Arguments
- Rpath
R object containing a static
Rpath
model.- mscramble
Base value for vulnerability in functional response; default = 2 (mixed response).
- mhandle
Base value for handling time in functional response; default = 1000 (off).
- preyswitch
Exponent for prey switching in functional response; default = 1 (off).
- scrambleselfwt
Value of 1 indicates all predators overlap in the foraging arena while 0 treats predators individually.
- handleselfwt
Value of 1 indicates all prey overlap in the arena and contribute to saturation while 0 treats prey individually.
- steps_yr
Number of time steps per year; default = 12.
- steps_m
Number of time steps per month; default = 1.
Value
Returns an object of class Rsim.params
, a list of 39 objects that is passed to the rsim.run
function via the rsim.scenario
function.
NUM_GROUPS
, number of total model groupsNUM_LIVING
, number of living model groupsNUM_DEAD
, number of detritus model groupsNUM_GEARS
, number of fishery model groupsNUM_BIO
, number of living + detritus model groupsspname
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing names of all model groupsspnum
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" numbered 0, containing numbers of all model groupsB_BaseRef
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing Rpath base biomass of all model groupsMzeroMort
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing unaccounted mortality, calculated as PB * (1.0 - EE), of all model groupsUnassimRespFrac
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing the proportion of consumption that goes to detritus of all model groupsActiveRespFrac
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing the proportion of consumption that is "lost to heat" for all model groupsFtimeAdj
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing rate of change of feeding time, currently set to 0 for all model groupsFtimeQBOpt
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing base QB for all consumer model groups, or base PB for primary producersPBopt
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing base PB for all model groupsNoIntegrate
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing flag set to 0 for high turnover model groups and set tospnum
for all othersHandleself
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing flag for handling time influence, set for all model groups with function argumenthandleselfwt
, default 0 for individual prey handling timeScrambleself
, namedNUM_GROUPS
+1 length character vector beginning with "Outside" containing flag for predator density influence, set for all model groups with function argumentscrambleselfwt
, default 0 for individual predator density dependent predationPreyFrom
, numeric vector lengthNumPredPreyLinks
+1, spnum of prey for each predator prey interaction pair in the modelPreyTo
, numeric vector lengthNumPredPreyLinks
+1, spnum of predator for each predator prey interaction pair in the modelQQ
, numeric vector lengthNumPredPreyLinks
+1, base consumption rate for each predator prey interaction pair in the modelDD
, numeric vector lengthNumPredPreyLinks
+1, handling time effect on functional response for each predator prey pair, set for all model groups with function argumentmhandle
, default = 1000VV
, numeric vector lengthNumPredPreyLinks
+1, vulnerability effect on functional response for each predator prey pair, set for all model groups with function argumentmscramble
, default = 2HandleSwitch
, numeric vector lengthNumPredPreyLinks
+1, prey density dependence effect on functional response for each predator prey pair, set for all model groups with function argumentpreyswitch
, default = 1PredPreyWeight
, numeric vector lengthNumPredPreyLinks
+1, relative weight of individual predator to total predators for each predator prey pair, used ifscrambleselfwt
>0PreyPreyWeight
, numeric vector lengthNumPredPreyLinks
+1, relative weight of individual prey to total prey for each predator prey pair, used ifhandleselfwt
>0NumPredPreyLinks
, number of predator to prey linkages over all groups in the modelFishFrom
, numeric vector lengthNumFishingLinks
+1, spnum of landing and discard for each fishery interaction in the modelFishThrough
, numeric vector lengthNumFishingLinks
+1, spnum of gear type for each fishery interaction in the modelFishQ
, numeric vector lengthNumFishingLinks
+1, landings or discards relative to base fished group biomass for each fishery interaction in the modelFishTo
, numeric vector lengthNumFishingLinks
+1, spnum of sink for each fishery interaction in the model ("Outside" for landings or detritus group for discards)NumFishingLinks
, number of model group landings and discards to fishery links over all groups in the modelDetFrac
, numeric vector lengthNumDetLinks
+1, fraction of detritus going to DetTo for each living and detritus group in the modelDetFrom
, numeric vector lengthNumDetLinks
+1, spnum flowing to detritus for each living and detritus group in the modelDetTo
, numeric vector lengthNumDetLinks
+1, spnum of detritus sink for each living and detritus group in the modelNumDetLinks
, number of model group links to detritus over all groups in the modelBURN_YEARS
, number of model run burn-in (spin up) years, default value -1COUPLED
, number to control species interactions, value of 0 allows density dependent non-interacting species, default value 1RK4_STEPS
, number of RK4 integration steps per month, default value 4SENSE_LIMIT
, numeric vector of multipliers on biomass determining acceptable range for continuing a model run withinBURN_YEARS
See also
Other Rsim functions:
extract.node()
,
rsim.plot()
,
rsim.run()
,
rsim.scenario()
,
rsim.step()
,
write.Rsim()
Examples
# Read in Rpath parameter file and generate model object
Rpath <- rpath(AB.params)
# Create default dynamic parameters from Rpath model
Rsim.params <- rsim.params(Rpath)