# 1 Introduction

In this context, fishery data includes any information that can be used for one of three purposes:

1. to estimate the status of an exploited stock (e.g., stock assessment or a similar analysis),
2. to condition an operating model (OM) for evaluating the performance of alternative management approaches using closed-loop simulation testing (e.g., Management Strategy Evaluation; MSE)
3. to be used by a management procedure (MP) to provide management advice.

This document describes the standardized fishery data format for the DLMtool and MSEtool R packages, and the browser-based interface for these packages: MERA.

Using a standardized format for fishery data has the advantage that a the data can easily be applied for these three purposes without any requiring any re-organizing of the fishery data.

For example, the data file described in this document can be imported in MERA (or the R packages) and used to:

1. estimate stock status with the 10+ stock assessment methods,
2. condition an operating model for quantitative risk assessment or MSE,
3. provide management advice from the 100+ available management procedures.

## 1.1 Data Files

There are two input files for the fishery data: a data input file and a data documentation file.

Once imported into MERA, the two files are combined to provide a report document including the contents of the documentation file and graphical display of the quantitative data in the data input file.

### 1.1.1 Data Input File

The Data Input File is a standardized file format for fishery data that uses a standardized spreadsheet format: either a CSV (comma separated file; file extension .csv’) or a MS Excel file (file extension .xlsx or .xls). Support for Google Sheets may be added later. Users enter all available fishery data into the data input file, which can then be imported into the R packages or MERA and used for the variety of purposes described above.

### 1.1.2 Data Documentation File

The Data Documentation file is a companion document that contains supporting information for the quantitative data in the input file. For example, the data documentation file can include a written description of the sources of the various data, the methods used to obtain the data, and relevant references to data sources or other information relating to the data preparation.

The Data Documentation file can be opened in any ordinary text editor (e.g., Notepad, Wordpad, or MS Word). It must be saved as an ordinary text file with extension ‘.md’ (i.e., do not save as ‘.doc’ or ‘.docx’).

We suggest having the Data Input and Data Documentation files both open and documenting the data sources in the Documentation file while entering the quantitative data in the Input file.

The Data Documentation file uses the Markdown text formatting syntext. This means that all entries should be plain text. Markdown is very powerful, and it is straightforward to include additional formating such as equations, bullet points, or ordered lists. See the Markdown cheat sheet for more information.

It is not necessary to be familiar with Markdown to enter information in the Data Documentation file. The main thing to be aware of is that headings are indicated with the # symbol; for example ‘#’ is a first level heading, ‘##’ is a second level heading, and so on.

## 1.2 Templates

Templates for the Data Input and Data Documentation files can be downloaded from an online repository.

Note that the links to the Data Input CSV File and Data Documentation File will open in the web browser. Save these files to your machine (usually by right-clicking in the browser) will file extensions ‘.csv’ and ‘.md’ respectively.

If using the DLMtool or MSEtool R packages in a R session, the template files can be generated by typing the following commands in the R console:

library(DLMtool)
DataInit("Example") # create example populated Input and Documentation files
# or create blank templates:
DataInit("MyDataFile")
# replace "MyDataFile" with the name of the data files you wish to create

# 2 Populating the Fishery Data Files

This section of the guide describes how to enter data in the Data Input file and document the data in the Data Documentation file.

Important Note 1: It is important to note that, in most cases, the data input file allows only one entry for each data type. For example, multiple catch-at-age data sets may be available (e.g., from a commercial fishery and from a fishery-independent survey). However, to be used in stock assessment or analysis, the two data sets must be combined in some way (or one data set ignored if it is not considered representative or reliable). Consequently, the data included in the Data Input file must represent the best available data. That is, the data sets must be combined in some manner (ideally described in the Data Documentation File) and the single data set entered into the data input file so it can be used in assessment or by management procedures.

This same principle applies to other data entered into the data input file. The only exception is for indices of abundance where multiple indices are allowed. This is explained in more detail in later sections of this guide.

Important Note 2: It is important that the text in the first column of the input file (column A) is not modified at all. These names are used to import the data file into MERA or the R packages.

Important Note 3: The data input file requires both character string (i.e., text) and numeric inputs. The data format for each entry is described below. It is important that no text is entered into the entries that require numeric inputs. For example, “Previous TAC” is a numeric input. A value of ‘1000’ (without the quotations) is acceptable, while an input of ‘about 1000’ is not.

Important Note 4: Do not use any thousands separators. For example 1 000 000 and 1,000,000 may introduce errors during the data import. Entries like 1000000 are preferable.

The fishery data are grouped into 7 categories:

2. Biological parameters
3. Selectivity parameters
4. Time-series information
5. Catch-at-age data
6. Catch-at-length data
7. Reference points and other metrics

The Data Documentation file has seven headings (indicated with the ## symbols) that correspond to these 7 categories. Do not delete or modify these headings. Enter all documentation under the relevant section in the Data Documentation file.

The first three lines of the Data Documentation file ask for information on the title, author, and date. Replace all text after the “:” symbol with the relevant information. Do not delete or modify the text title, author, and date.

The following sub-sections describe the data inputs for each of data category.

The metadata section has 9 entries (see Figure 2.1 for example). All entries must go in the second column (column B if using a spreadsheet program such as MS Excel).

The Metadata section in the Data Documentation file is indicated with “## Metadata”. Enter any relevant supporting information for the information in the Metadata section of the input file here.

Use in-text citations where possible and include references under the “## Reference List” section.

### 2.1.1 Name

Text entry. A unique name for this data file.

### 2.1.2 Common Name

Text entry. The common name of the species.

The example data file is for data from a fishery for cobia.

### 2.1.3 Species

Text entry. The scientific name of the species.

The example data file includes the species name for cobia.

### 2.1.4 Region

Text entry. The region of the fishery.

The example data file assumes the fishery is in the Western Atlantic.

### 2.1.5 Last Historical Year

Numeric entry. The calendar year of either:

1. when the most recent time-series data was collected, or
2. in cases where an MSE has already been conducted for this species and new data has been collected since, the last historical year when the MSE was run. For example, if an MSE was conducted for this fishery in 2016 and new data has been collected since then, the last historical year is 2016.

The last historical year was 2011 in the example (Figure 2.1).

### 2.1.6 Previous TAC

Numeric entry. The most recent total allowable catch (TAC). Leave blank if no TAC exists.

There was no existing TAC for the example cobia fishery.

### 2.1.7 Units

Text entry. The units of the TAC and catch data, e.g., ‘thousand tonnes’. Leave blank if no TAC or catch data exists.

The catch data in the example fishery is in units of ‘1000 lbs’.

### 2.1.8 Previous TAE

Numeric entry. The most recent total allowable effort limit (TAE). Leave blank if no TAE exists.

There was no existing TAE for the example cobia fishery.

### 2.1.9 nareas

Numeric entry. The number of spatial areas used in management. Only used for management procedures that set spatial closures. Leave blank if no spatial management is used or proposed. Don’t enter 0 or 1.

The default value is 2 areas, which will be used if no value is entered here.

There is no spatial management for the example cobia fishery.

## 2.2 Biology

The next section contains mean and uncertainty values for the biological parameters of the species. Leave any entry blank if the parameter is unknown.

The Biology section in the Data Documentation file is indicated with “## Biology”. Enter any relevant supporting information for the information in the Biology section of the input file here.

Ideally a short paragraph or two with supporting information for each type of biological data entered in the Data Input file.

Use in-text citations where possible and include references under the “## Reference List” section.

Do not delete or modify the “## Biology” heading.

### 2.2.1 Maximum age

Numeric entry. The maximum age of the species. The catch-at-age data entries must match this value (see the Catch-at-Age section for more details).

The cobia example has a maximum age of 16 (Figure 2.2).

### 2.2.2 M and CV M

Numeric entries. A point estimate for the (adult) natural mortality rate (M) and a coefficient of variation (CV) associated with this estimate (assuming a log-normal distribution).

The cobia example has an estimate of M of 0.26 and an associated CV of 0.3 (Figure 2.2).

### 2.2.3 von Bertalanffy Linf parameter and CV

Numeric entries. The estimated mean asymptotic length from a fitted von Bertalanffy growth model and the associated CV.

The units of the Linf parameter are not important, but all length parameters and data (e.g., length-at-maturity and catch-at-length) must be in the same units.

The cobia example has an estimate of Linf of 1324.4 and an associated CV of 0.23 (Figure 2.2).

### 2.2.4 von Bertalanffy K parameter and CV

Numeric entries. The estimated von Bertalanffy growth parameter (K) and the associated CV.

The K parameter must be in the same units as M, usually $$\text{year}^{-1}$$.

The cobia example has an estimate of K of 0.27 and an associated CV of 0.07 (Figure 2.2).

### 2.2.5 von Bertalanffy t0 parameter and CV

Numeric entries. The estimated age when mean length is zero (t0) and the associated CV.

The t0 parameter must be in the same units as “Maximum age” (usually years).

The cobia example has an estimate of t0 of -0.47 and an associated CV of 0.05 (Figure 2.2).

### 2.2.6 Length-weight parameters

Numeric entries. Estimates of the a and b parameters (and associated CVs) from a fitted length-weight model of the form:

$W=aL^b$

This data is not available for the example cobia data (Figure 2.2).

### 2.2.7 Recruitment parameters

Numeric entries. Mean estimates and associated CVs.

The steepness parameter is the expected fraction of virgin recruitment when the spawning biomass has been reduced to 20% of the unfished level. This is an important parameter for determining the productivity of the stock, especially at low levels of spawning biomass. However, the parameter is difficult to estimate and not well known for many species.

The sigmaR parameter describes the variance around the expected stock-recruitment relationship.

This data is not available for the example cobia data (Figure 2.2).

### 2.2.8 Length-at-Maturity parameters

Numeric entries. Mean estimates and associated CVs.

The Length at 50% maturity and Length at 95% maturity parameters are estimated by fitting a logistic model to maturity-at-length data. The parameters refer to the expected length where 50% and 95% respectively of the population are mature. The CV of length at 95% maturity is assumed to be the same as the CV of length at 50% maturity.

The example cobia data has estimates of the length at 50% and 95% maturity of 644 and 850 mm respectively, and a CV of 0.05.

### 2.2.9 Variability of length-at-age

Numeric entry. The expected variability of length-at-age; that is, the distribution of length-at-age around the mean growth curve described by the von Bertalanffy growth model.

The example cobia data assumed a coefficient of variability of length-at-age of 0.1.

## 2.3 Selectivity

There are five parameters relating to selectivity at length. Leave any entry blank if the parameter is unknown.

The Selectivity section in the Data Documentation file is indicated with “## Selectivity”. Enter any relevant supporting information for the information in the Selectivity section of the input file here.

Ideally a short paragraph or two with supporting information for data entered in the Data Input file for the selectivity parameters.

Use in-text citations where possible and include references under the “## Reference List” section.

Do not delete or modify the “## Selectivity” heading.

### 2.3.1 Length at first capture

Numeric entries.

The Length at first capture is an estimate of first length class that is vulnerable to the fishery (and the associated CV).

The example cobia data file assumes a length of first capture of 130 and a CV of 0.2 (Figure 2.3).

### 2.3.2 Length at full selection

Numeric entries.

The Length at full selection is the first length class that is fully vulnerable to fishing (and the associated CV).

No information is available for the length at full selection for the example cobia data file (Figure 2.3).

### 2.3.3 Vulnerability at asymptotic length

Numeric entry.

The Vulnerability at asymptotic length describes that shape of the selectivity curve. Dome-shaped selectivity patterns (Vulnerability at asymptotic length < 1) occurs vulnerability to fishing begins to decrease after reaching a maximum value at some intermediate length.

The example cobia data file assumes asymptotic selectivity (Vulnerability at asymptotic length = 1) (Figure 2.3).

## 2.4 Time-Series

The time-series section includes data sources such as annual catches, annual abundance indices, and other annual indices such as recruitment, and mean length.

The Time-Series section in the Data Documentation file is indicated with “## Time-Series”. Enter any relevant supporting information for the information in the Time-Series section of the input file here.

Ideally a short paragraph or two with supporting information for data entered in the Data Input file for the Time-Series data.

Use in-text citations where possible and include references under the “## Reference List” section.

Do not delete or modify the “## Time-Series” heading.