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table4alt_recruit-models.qmd
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table4alt_recruit-models.qmd
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---
editor:
mode: source
crossref:
tbl-labels: alpha 4
---
## Table 4: Input data structure for AGEPRO Recruitment Models {.unnumbered}
\u2116
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Model | Recruitment | Recruitment Model |
| Number | Model | Input Description |
+=========+=================================================================+=======================================================================================================================================================================================================================================================================+
| 1 | Markov Matrix | - Input the number of Recruitment States: $K$ |
| | | - On the next line, input the recruitment values: $R_1,R_2,...,R_K$ |
| | | - On the next line, input number of spawning biomass states: $J$ |
| | | - On the next line, input $J-1$ cut points : $B_{S,1},B_{S,2},...,B_{S,J-1}$ |
| | | - On the next $J$ lines, input the conditional recruitment probabilities for the spawning biomass states: |
| | | - $\begin{matrix} P_{1,1} \quad P_{1,2} \quad \dots \quad P_{1,K} \\ P_{2,1} \quad P_{2,2} \quad \dots \quad P_{2,k} \\ \quad \vdots \quad\quad \vdots \qquad\quad \vdots \qquad\quad \vdots \\ P_{J,1}, \quad P_{J,2}, \quad \dots, \quad P_{J,K} \end{matrix}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2 | Empirical Recruits Per | - Input the number of stock recruitment data points: $T$ |
| | Spawning Biomass | - On the next line, input the recruitments: $R_1,R_2,...,R_T$ |
| | Distribution | - On the next line, input the spawning biomasses: $B_{S,1}, B_{S,2}, ..., B_{S,T}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 3 | Empirical Recruitment | - Input the number of recruitment data points: $T$ |
| | Distribution | - On the next line, input the recruitment: $R_1,R_2,...,R_T$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 4 | Two-Stage Empirical | - Input the number of low and high recruits per spawning biomass data points: $T_{Low} \cdot T_{High}$ |
| | Recruits Per Spawning | - On the next line, input the cutoff level of spawning biomass: $B^*_S$ |
| | Biomass Distribution | - On the next line, input the low state recruitment: $R_1,R_2,...,R_{T_{Low}}$ |
| | | - On the next line, input the low state spawning biomass: $B_{S,1}, B_{S,2}, ..., B_{S,T_{Low}}$ |
| | | - On the next line, input the high state recruitment: $R_1,R_2,...,R_{T_{High}}$ |
| | | - On the next line, input the high state spawning biomass: $B_{S,1}, B_{S,2}, ..., B_{S,T_{High}}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 5 | Beverton-Holt Curve with | - Input the stock-recruitment parameters: $\alpha, \beta, \sigma^2_w$ |
| | Lognormal Error | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 6 | Ricker Curve with | - Input the stock-recruitment parameters: $\alpha, \beta, \sigma^2_w$ |
| | Lognormal Error | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 7 | Shepherd Curve with | - Input the stock-recruitment parameters: $\alpha, \beta, k, \sigma^2_w$ |
| | Lognormal Error | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 8 | Lognormal Distribution | - Input the log-scale mean and standard deviation: $\mu_{\log(r)},\sigma_{\log(r)}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 10 | Beverton-Holt Curve with | - Input the stock-recruitment parameters: $\alpha, \beta, \sigma^2_w$ |
| | Autocorrected Lognornal | - On the next line, input the autoregressive parameters: $\phi, \varepsilon_{0}$ |
| | Error | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 11 | Ricker Curve with | - Input the stock-recruitment parameters: $\alpha, \beta, \sigma^2_w$ |
| | Autocorrected Lognormal | - On the next line, input the autoregressive parameters: $\phi, \varepsilon_{0}$ |
| | Error | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 12 | Shepherd Curve with | - Input the stock-recruitment parameters: $\alpha, \beta, k, \sigma^2_w$ |
| | Autocorrected Lognormal | - On the next line, input the autoregressive parameters: $\phi, \varepsilon_{0}$ |
| | Error | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 13 | Autocorrected Lognormal | - Input the log-scale mean and standard deviation: $\mu_{\log(r)},\sigma_{\log(r)}$ |
| | Distribution | - On the next line, input the autoregressive parameters: $\phi, \varepsilon_{0}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 14 | Empirical Cumulative | - Input the number of recruitment data points: $T$ |
| | Distribution Function of | - On the next line, input the recruitments $R_1,R_2,...,R_T$ |
| | Recruitment | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 15 | Two-Stage Empirical | - Input the number of low and high recruits per spawning biomass data points: $T_{Low} \cdot T_{High}$ |
| | Cumulative Distribution | - On the next line, input cutoff level of spawning biomass: $B^*_S$ |
| | Function of Recruitment | - On the next line, input the low state recruitment: $R_1,R_2,...,R_{T_{Low}}$ |
| | | - On the next line, input the high state recruitment: $R_1,R_2,...,R_{T_{High}}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 16 | Linear Recruits Per | - Input the predictors: $N_P$ |
| | Spawning Biomass Predictor | - On the next line, input the intercept coefficient: $\beta_0$ |
| | with Normal Error | - On the next line, input the slope coefficient for each predictor: $\beta_1, \beta_2, ..., \beta_{N_p}$ |
| | | - On the next line, input the error variance: $\sigma^2$ |
| | | - On the next $N_P$ lines, input the expected value of the predictor through projection time horizon: |
| | | - $\begin{matrix} X_1(1) \quad\ \dots \quad\ X_1(Y) \\ X_2(1) \quad\ \dots \quad\ X_2(Y) \\ \ \vdots \qquad\quad \ \vdots \qquad\quad\ \ \vdots \\ X_P(1) \quad\ \dots \quad\ X_P(Y) \end{matrix}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 17 | Linear Recruits Per | - Input the number of predictors: $N_P$ |
| | Spawning Biomass Predictor | - On the next line, input the intercept: $\beta_0$ |
| | with Lognormal Error | - On the next line, input the linear coefficient for each predictor: $\beta_1, \beta_2, ..., \beta_{N_P}$ |
| | | - On the next line, input the log-scale error variance: $\sigma^2$ |
| | | - And on the next $N_P$ lines, input the expected predictor values over the forecast time horizon |
| | | $1, ..., Y$ |
| | | - $\begin{matrix} X_1(1) \quad X_1(2) \quad \dots \quad X_1(Y) \\ X_2(1) \quad X_2(2) \quad \dots \quad X_2(Y) \\ \quad \vdots \qquad\quad\ \vdots \qquad\quad \vdots \qquad\quad \vdots \\ X_P(1) \quad X_P(2) \quad \dots \quad X_P(Y) \end{matrix}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 18 | Linear Recruitment | - Input the number of predictors: $N_P$ |
| | Predictor with Normal Error | - On the next line, input the intercept: $\beta_0$ |
| | | - On the next line, input the linear coefficient for each predictor: $\beta_1, \beta_2, ..., \beta_{N_P}$ |
| | | - On the next line, input the error variance: $\sigma^2$ |
| | | - And on the next $N_P$ lines, input the expected predictor values over the forecast time horizon |
| | | $1, ..., Y$ |
| | | - $\begin{matrix} X_1(1) \quad X_1(2) \quad \dots \quad X_1(Y) \\ X_2(1) \quad X_2(2) \quad \dots \quad X_2(Y) \\ \quad \vdots \qquad\quad\ \vdots \qquad\quad \vdots \qquad\quad \vdots \\ X_P(1) \quad X_P(2) \quad \dots \quad X_P(Y) \end{matrix}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 19 | Loglinear Recruitment | - Input the number of predictors: $N_P$ |
| | Predictor with Lognormal | - On the next line, input the intercept: $\beta_0$ |
| | Error | - On the next line, input the linear coefficient for each predictor: $\beta_1, \beta_2, ..., \beta_{N_P}$ |
| | | - On the next line, input the log-scale error variance: $\sigma^2$ |
| | | - And on the next $N_P$ lines, input the expected predictor values over the forecast time horizon |
| | | $1, ..., Y$ |
| | | - $\begin{matrix} X_1(1) \quad X_1(2) \quad \dots \quad X_1(Y) \\ X_2(1) \quad X_2(2) \quad \dots \quad X_2(Y) \\ \quad \vdots \qquad\quad\ \vdots \qquad\quad \vdots \qquad\quad \vdots \\ X_P(1) \quad X_P(2) \quad \dots \quad X_P(Y) \end{matrix}$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 20 | Fixed Recruitment | - Input the number of recruitment data points: $T$ |
| | | - On the next line, input the Recruitment: $R_1,R_2,...,R_T$ |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 21 | Empirical Cumulative | - Input the number of number of observed recruitment values: $T$ |
| | Distribution Function of | - On the next line, input the recruitment values: $R_1, R_2, ..., R_T$ |
| | Recruitment with Linear | - And on the next line, input spawning biomass threshold: $B^*_S$ |
| | Decline to Zero | |
+---------+-----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
: Required input data for AGEPRO recruitment models, where spawning biomass and recruitment inputs are measured in units of `RECRUIT` the
conversion factors **SSBFac** and **RecFac** respectively, which typically have units of **SSBFac**=**RecFac**=1000. {#tbl-4}