# Introduction to fb4package

#### Hans Ttito

#### 2026-05-04

## Overview

`fb4package` implements Fish Bioenergetics 4.0 (Deslauriers et al. 2017) in R. The model allocates consumed energy among metabolism, waste, and growth to produce daily estimates of fish consumption and weight gain.

Working with the package follows a three-step pattern: build a `Bioenergetic` object that holds species parameters, temperature, diet, and initial conditions; pass it to `run_fb4()` to run a simulation or fit a feeding level; then extract or plot the results.

## 1. Species parameters

The package ships with a built-in database covering 105 models (73 species).

    data(fish4_parameters)
    cat("Species in database:", length(fish4_parameters), "\n")
    #> Species in database: 73
    head(names(fish4_parameters), 8)
    #> [1] "Alosa pseudoharengus"           "Gadus morhua"
    #> [3] "Brevoortia tyrannus"            "Thymallus arcticus baicalensis"
    #> [5] "Clupea harengus"                "Anchoa mitchilli"
    #> [7] "Hypophthalmichthys nobilis"     "Coregonus hoyi"

Retrieve Chinook salmon (Oncorhynchus tshawytscha) and inspect one life stage:

    chinook_db  <- fish4_parameters[["Oncorhynchus tshawytscha"]]
    stage       <- names(chinook_db$life_stages)[1]
    cat("Life stage used:", stage, "\n")
    #> Life stage used: adult

    sp_params   <- chinook_db$life_stages[[stage]]
    sp_info     <- chinook_db$species_info

    # Key consumption parameters
    cat("CEQ:", sp_params$consumption$CEQ,
        "  CA:", sp_params$consumption$CA,
        "  CB:", sp_params$consumption$CB, "\n")
    #> CEQ: 3   CA: 0.303   CB: -0.275

## 2. Environmental and diet data

Temperature and diet are supplied as data frames with a `Day` column.

    days <- 1:100

    # Seasonal temperature (°C)
    temp_data <- data.frame(
      Day         = days,
      Temperature = round(10 + 5 * sin(2 * pi * (days - 60) / 365), 2)
    )

    # Two-prey diet (constant proportions, J/g energy densities)
    diet_data <- list(
      proportions = data.frame(Day = days, Alewife = 0.7, Shrimp = 0.3),
      prey_names  = c("Alewife", "Shrimp"),
      energies    = data.frame(Day = days, Alewife = 4900, Shrimp = 3200)
    )

    head(temp_data, 3)
    #>   Day Temperature
    #> 1   1        5.75
    #> 2   2        5.80
    #> 3   3        5.84

## 3. Building a `Bioenergetic` object

    bio <- Bioenergetic(
      species_params      = sp_params,
      species_info        = sp_info,
      environmental_data  = list(temperature = temp_data),
      diet_data           = diet_data,
      simulation_settings = list(initial_weight = 1800, duration = 100)
    )

    # Required when PREDEDEQ = 2 (energy density as function of weight)
    bio$species_params$predator$ED_ini <- 4500
    bio$species_params$predator$ED_end <- 5500

    print(bio)
    #> FB4 Bioenergetic Model
    #> =========================
    #> Species: Oncorhynchus tshawytscha (Chinook salmon (adult))
    #> Setup: 1800 g -> 100 days
    #>
    #> Components:
    #>   [OK] Parameters: 41 params (consumption, respiration, activity, sda, egestion, excretion, predator, source, notes)
    #>   [OK] Temperature: 100 days (5.8-13.2°C)
    #>   [OK] Diet: 2 prey species, 100 days
    #>
    #> Status: Ready for fitting

## 4. Running simulations

### 4a. Direct simulation — fixed feeding level

Run the model with a constant proportion of maximum consumption (p = 0.8):

    res_direct <- run_fb4(bio, strategy = "direct", p_value = 0.8)
    print(res_direct)
    #> FB4 Simulation Results
    #> =========================
    #> Species: Oncorhynchus tshawytscha
    #> Method: Direct Execution
    #> Duration: 100 days
    #>
    #> RESULTS:
    #>   Initial weight: 1800 g
    #>   Final weight: 2647.37 g
    #>   Growth: 47.1%
    #>   Total consumption: 4821.23 g
    #>   P_value: 0.8
    #>
    #> FITTING: [OK] Successful

### 4b. Binary search — fit to a target final weight

Find the p that produces a target weight of 3 000 g after 100 days:

    res_bs <- run_fb4(bio,
                      strategy  = "binary_search",
                      fit_to    = "Weight",
                      fit_value = 3000)

    cat("Estimated p-value :", round(res_bs$summary$p_value, 4), "\n")
    #> Estimated p-value : 0.9095
    cat("Final weight      :", round(res_bs$summary$final_weight, 1), "g\n")
    #> Final weight      : 3000 g
    cat("Total consumption :", round(res_bs$summary$total_consumption_g, 1), "g\n")
    #> Total consumption : 5771.2 g

### 4c. MLE — uncertainty on p from observed weights

When multiple fish weights are available, `strategy = "mle"` returns a point estimate of p together with its standard error and 95% confidence interval:

    set.seed(42)
    obs_weights <- rnorm(30, mean = 3000, sd = 80)   # 30 observed final weights

    res_mle <- run_fb4(bio,
                       strategy         = "mle",
                       fit_to           = "Weight",
                       observed_weights = obs_weights)

    cat("p-value (MLE)  :", round(res_mle$summary$p_value, 4), "\n")
    #> p-value (MLE)  : 0.9106
    cat("SE             :", round(res_mle$summary$p_se,    4), "\n")
    #> SE             : NA
    cat("Converged      :", res_mle$summary$converged, "\n")
    #> Converged      : TRUE

### 4d. Bootstrap — non-parametric uncertainty

    set.seed(42)
    res_boot <- run_fb4(bio,
                        strategy         = "bootstrap",
                        fit_to           = "Weight",
                        observed_weights = obs_weights,
                        upper            = 1,
                        n_bootstrap      = 100,
                        parallel         = FALSE)

    cat("p mean (bootstrap):", round(res_boot$summary$p_mean, 4), "\n")
    #> p mean (bootstrap): 0.911
    cat("p SD              :", round(res_boot$summary$p_sd,   4), "\n")
    #> p SD              : 0.0057

## 5. Comparing strategies

All three methods converge on similar p estimates for the same data — binary search gives a point estimate, while MLE and bootstrap additionally quantify uncertainty:

All strategies use the same bio object (Chinook salmon, 100-day simulation). MLE and bootstrap fit to obs_weights; binary search fits to a target weight.
Strategy | p estimate | SE / SD | Uncertainty type
--- | --- | --- | ---
binary_search | 0.9095 | NA | none (point estimate)
mle | 0.9106 | NA | SE (asymptotic)
bootstrap | 0.9110 | 0.0057 | SD (non-parametric)

## 6. Analysing results

    growth   <- analyze_growth_patterns(res_bs)
    feeding  <- analyze_feeding_performance(res_bs)
    energy   <- analyze_energy_budget(res_bs)

    cat(sprintf("Final weight              : %.1f g\n",
                growth$final_weight$estimate))
    #> Final weight              : 3000.0 g
    cat(sprintf("Specific growth rate      : %.4f g/g/day\n",
                growth$specific_growth_rate$estimate))
    #> Specific growth rate      : 0.5108 g/g/day
    cat(sprintf("Total consumption         : %.1f g\n",
                feeding$total_consumption$estimate))
    #> Total consumption         : 5771.2 g
    cat(sprintf("Gross conversion efficiency: %.3f\n",
                feeding$gross_conversion_efficiency$estimate))

## 7. Visualising results

    plot(res_bs, type = "growth")

Daily growth trajectory from binary search fit.

    plot(res_bs, type = "energy")

Daily energy budget partitioning.

    plot(res_bs, type = "dashboard")

Full dashboard (growth, consumption, temperature, energy).

## 8. Temperature × p sensitivity

Explore how final weight varies across temperatures and feeding levels:

    plot(bio,
         type         = "sensitivity",
         temperatures = seq(6, 20, by = 2),
         p_values     = seq(0.3, 1.0, by = 0.1))
    #> === GROWTH SENSITIVITY ANALYSIS ===
    #> Mode: SEQUENTIAL
    #> Temperature range: 6 - 20 °C ( 8 values)
    #> P-value range: 0.3 - 1 ( 8 values)
    #> Total combinations: 64
    #>
    #> Processing temperature: 6 °C
    #> Processing temperature: 8 °C
    #> Processing temperature: 10 °C
    #> Processing temperature: 12 °C
    #> Processing temperature: 14 °C
    #> Processing temperature: 16 °C
    #> Processing temperature: 18 °C
    #> Processing temperature: 20 °C
    #>
    #> === ANALYSIS COMPLETE ===
    #> Successful simulations: 64 / 64 ( 100 %)
    #> Maximum growth rate: 0.598 %/d
    #> Optimal conditions: 10 °C, 100 % feeding (p = 1.00 )

Temperature and p-value sensitivity grid.

## 9. Experimental features

The following modules are implemented as standalone functions but are not yet integrated into the main `run_fb4()` loop. They can be called directly for custom analyses and will be connected automatically in a future release.

### Contaminant bioaccumulation

Three bioaccumulation models (dietary uptake, gill exchange, bioenergetics-based):

- `calculate_contaminant_accumulation()` — daily bioaccumulation
- `process_contaminant_params()` — parameter pre-processing

### Nutrient regeneration

Daily nitrogen and phosphorus fluxes (ingestion, retention, excretion):

- `calculate_nutrient_balance()` — daily N/P budget
- `calculate_nutrient_efficiencies()` — N and P use efficiencies
- `process_nutrient_params()` — parameter pre-processing

### Mortality rates

Natural, fishing, and predation mortality rates can be processed via `process_mortality_params()` and computed step-by-step with `calculate_mortality_reproduction()`, but mortality rates are not yet applied automatically inside `run_fb4()`.

> Note: spawning energy loss (reproductive cost) is already integrated and applies automatically when `reproduction_data` is supplied to the `Bioenergetic` object.

## References

Deslauriers D, Chipps SR, Breck JE, Rice JA, Madenjian CP (2017). Fish Bioenergetics 4.0: An R-Based Modeling Application. Fisheries 42(11):586–596. https://doi.org/10.1080/03632415.2017.1377558