Load required packages



In [ ]:

    
using DataFrames, Distributions, Gadfly, FishABM

Specify stock assumptions:

Age specific mortality
Age at 50% maturity
Age specific fecundity
Carrying capacity (total adults)
Compensatory strength - fecundity
Compensatory strength - age at 50% maturity
Compensatory strength - adult natural mortality
Age specific catchability



In [ ]:

    
s_a = StockAssumptions([0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50],
                       5,
                       [2500, 7500, 15000, 20000, 22500, 27500, 32500],
                       2,
                       0.25,
                       1,
                       [0.00001, 0.00002, 0.000025, 0.000025, 0.000025, 0.000025, 0.000025])

Specify environment assumptions:

Spawning areas
Habitat types
Risk areas



In [ ]:

    
e_a = EnvironmentAssumptions(readdlm(Pkg.dir("FishABM")"/examples/LakeHuron_1km_spawning.csv", ',', Bool)[150:end, 200:370],
                             readdlm(Pkg.dir("FishABM")"/examples/LakeHuron_1km_habitat.csv", ',', Int)[150:end, 200:370],
                             readdlm(Pkg.dir("FishABM")"/examples/LakeHuron_1km_risk.csv", ',', Bool)[150:end, 200:370])

PadEnvironmentAssumptions!(e_a)

Specify agent assumptions:

Weekly natural mortality rate (by habitat type in the rows, and stage in the columns)
Weekly risk mortality (by stage)
Stage length (in weeks)
Movement weight matrices
Movement autonomy



In [ ]:

    
a_a = AgentAssumptions([[0.90 0.10 0.10]
                        [0.10 0.10 0.10]
                        [0.90 0.10 0.10]
                        [0.90 0.90 0.10]
                        [0.90 0.90 0.90]
                        [0.90 0.90 0.90]],
                        [0.0, 0.0, 0.0],
                        [19, 52, 104],
                        Array[[[0. 0. 0.]
                               [0. 1. 0.]
                               [0. 0. 0.]]
                              [[2. 4. 2.]
                               [1. 5. 2.]
                               [1. 1. 2.]]
                              [[2. 4. 2.]
                               [1. 4. 2.]
                               [1. 1. 2.]]],
                        [0., 0.2, 0.8])

Initialize stock database:

Initial population distribution
Empty harvest dataset



In [ ]:

    
s_db = StockDB(DataFrame(age_2=100000,
                         age_3=50000,
                         age_4=40000,
                         age_5=30000,
                         age_6=20000,
                         age_7=10000,
                         age_8=100000),
               DataFrame(age_2=Int[],
                         age_3=Int[],
                         age_4=Int[],
                         age_5=Int[],
                         age_6=Int[],
                         age_7=Int[],
                         age_8=Int[]))

Begin life cycle simulation, specifying:

Year specific carrying capacity (vector length determines simulation length)
Annual fishing effort
Population bump

And indicating the previously specified objects:

Stock database
Stock assumptions
Agent assumptions
Environment assumptions



In [ ]:

    
k = rand(Normal(500000, 50000),5)
a_db = simulate(k, [0], [100000], s_db, s_a, a_a, e_a, false, false)