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Amoroso, R., Lomonico, S., Snouffer, B. & Gutierrez, N.L. 2024. Data preparation to inform assessment and management approaches in data-limited fisheries – A practical manual. FAO Fisheries and Aquaculture Technical Paper, No. 706. Rome, FAO. https://doi.org/10.4060/cd0366en

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Preparation of this document

The preparation of this document was motivated by a growing need for practical guidance on the critical review and exploration of fishery data in data-limited settings. Specifically, in working across a variety of data-limited fisheries in Chile, Costa Rica, Mexico, Peru, Thailand, the Eastern Mediterranean, the Western Indian Ocean and West Africa, the authors received feedback that a series of similar steps were often needed to inventory, synthesize, explore and curate fishery data sources. But due to limited guidance on these steps and limited emphasis on their importance, they could be overlooked. In response, this manual aims to document and share steps for data preparation and exploration, highlighting their essential role in ensuring fishery data is used correctly for data-limited assessment and management. While it is not intended as a fully comprehensive roadmap for data exploration, the manual provides a set of clear and actionable guidelines, based on applied experiences and real-world practices in data-limited fisheries around the world.

Ricardo Amoroso and Serena Lomonico prepared the initial draft, with guidance and input from Nicolas Gutierrez. Brian Snouffer led the development of FishualizeR and its integration into the manual. The first stage of feedback was provided by the following colleagues at the Food and Agriculture Organization of the United Nations (FAO): Pedro Barros, James Geehan, John Valbo-Jorgensen and Stefania Vannuccini. Subsequent feedback was then provided by a panel of experts with experience in the practical realities of data-limited fisheries: Kevern Cochrane, Jason Cope, Dawn Dougherty, Bill Harford, Dan Ovando, Ana Parma, Sui Chian Phang and Paul Tuda. Rich Bell provided technical input and writing, and the manual was edited by Andi Shiraz.

Abstract

“On two occasions I have been asked, ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.”

Charles Babbage, originator of the modern analytic computer, from “The Life of a Philosopher” (1864)

In fisheries science and management, it is not uncommon that fishery data are used at “face value”, as inputs into data-limited assessments or empirical indicator-based frameworks for management, without first conducting a thorough exploration and critical review of the data. This practice may lead to biases in results and misdirected fishery management actions. To address intermediate steps between data collection and any analysis used to inform stock status, this manual provides guidance on how to prepare, explore and critically review fishery data in data-limited situations. With increasing development and use of data-limited stock assessment methods, it is imperative that data inputs be reviewed and thoroughly understood. This manual contains stepwise guidelines and lessons from real fishery settings that can be tailored to diverse contexts. Regarding scope, the manual does not focus on the design and implementation of data collection programmes, nor the numerous types of data-limited stock assessment methods. Rather, it fills an often-overlooked step by focusing on how to inventory existing data sources, conduct basic data preparation exercises, detect and resolve biases, and perform exploratory data analysis (EDA).

The manual has seven chapters: (1) Introduction, (2) Understanding key data types, (3) Inventorying and organizing data, (4) Database set-up and management, (5) Overview of data preparation and exploratory data analysis, (6) Data preparation, and (7) Exploratory data analysis.

Throughout the manual, guidance and sample data are provided primarily in Microsoft Excel or in comma separated value (CSV) file formats, as well as through FishualizeR, a publicly available, web-based, R Shiny app that was developed to support the manual. Instructions in this manual are not intended to present a single, prescriptive path, but rather to provide guidance that may be further tailored to each individual context. It is the authors’ hope and intent that the guidance contained in this manual will allow users to better understand their data, make corrections, and gain a deeper understanding of the data’s utility in assessment and management of data-limited fisheries.

Acknowledgements

We are grateful to the group of expert reviewers who generously provided their time, detailed attention and technical input in reviewing this manual. Thank you to Rich Bell, Kevern Cochrane, Jason Cope, Dawn Dougherty, Bill Harford, Dan Ovando, Ana Parma, Sui Chian Phang and Paul Tuda. We also thank The Nature Conservancy, which supported a portion of time for Serena Lomonico on this project. And we are grateful to Anna Rita Colagrossi, Cristiana Fusconi, and Marianne Guyonnet at FAO, who helped us navigate the publishing process. Lastly, thank you to Andi Shiraz for helping us get this manual over the finish line with a detailed eye and incredibly thoughtful editing approach.

Contents

Preparation of this document

Abstract

Acknowledgements

Preface

Abbreviations

Chapter 1: Introduction

1.1 Assessing and managing data-limited fisheries

1.2 Data preparation and exploration

Chapter 2: Understanding key data types

2.1 Biological data

2.2 Catch and effort data

2.3 Local knowledge and alternative data sources

Chapter 3: Inventorying and organizing data

3.1 Data inventory

3.1.1 Filling out the data inventory template

3.2 Timeline of influential events

3.2.1 Filling out the timeline template

3.3 Interpreting the data inventory and timeline

3.4 Life-history parameter inventory

3.4.1 Filling out the template

3.5 Understanding the life-history parameter inventory

Chapter 4: Database set-up and management

4.1 Databases

4.2 Data management

4.3 Data formats

Chapter 5: Overview of data preparation and exploratory data analysis

5.1 Workflow

5.2 Common tools

5.2.1 Summary statistics

5.2.2 Univariate plots

5.2.3 Multivariate plots

Chapter 6: Data preparation

6.1 Initial data review

6.1.1 Cleaning text or categorical data

6.1.2 Cleaning numerical data

6.2 Identifying and resolving outliers

6.2.1 Outlier identification

6.2.1.1 Utilizing summary statistics and quantitative indicators to identify outliers

6.2.1.2 Utilizing plots to identify outliers

6.2.1.3 Utilizing FishualizeR to identify outliers

6.2.2 Handling outliers

6.2.2.1 Removing or correcting error outliers: examples

6.2.3 Understanding interesting outliers

Chapter 7: Exploratory data analysis

7.1 Data coverage

7.1.1 Interpretation

7.1.1.1 Temporal trends

7.1.1.2 Spatial trends, species and fishery characteristics

7.1.1.3 Dealing with gaps

7.1.1.4 Different data types

7.2 Biological databases

7.2.1 Length composition data

7.2.1.1 Information content

7.2.1.2 Exploratory data analysis

7.2.1.3 Other considerations

7.2.2 Reproductive information

7.2.2.1 Gonadosomatic index over time by sex

7.2.2.2 Sex proportions in catch over time or area

7.2.2.3 Maturity proportions in catch over time or area

7.3 Catch and effort databases

7.3.1 Information content

7.3.2 EDA for catch data

7.3.3 EDA for effort data

7.4 Indices of abundance

7.4.1 Information content

7.4.2 Exploratory data analysis

References

Excel templates and sample datasets

Boxes

1.   Accessing FishualizeR and uploading files

2.   Visualizing the data inventory and timeline in FishualizeR

3.   Visualizing the life-history parameter inventory in FishualizeR

4.   Data validation in Excel

5.   Documenting changes and corrections, resaving data, and version control

6.   Cleaning text or categorical data in FishualizeR

7.   Creating a summary table by using a pivot table in Excel

8.   Identifying outliers with numerical univariate analysis in FishualizeR

9.   Identifying outliers for date data in FishualizeR

10. Visualizing and understanding multivariate numerical data in FishualizeR

11. Analysing data coverage in FishualizeR

Figures

1.   Overview of manual chapters presenting stepwise guidance on reviewing and exploring fishery data

2.   Spectrum of data requirements for empirical and stock assessment methods

3.   View of “Excel_Template1_DataInventory_Events” Excel file, showing “DataInventory” tab/sheet

4.   View of “Excel_Template1_DataInventory_Events” Excel file, showing “Timeline” tab/worksheet

5.   Visualization in FishualizeR of data inventory and timeline for temporal span of sample dataset

6.   Visualization of length composition data for sample dataset, showing frequency distribution (2011– 2018)

7.   Views of “Excel_Template2_LifeHistoryInventory”, showing sections from four key tabs/worksheets

8.   View of “Aux_References” worksheet in “Excel_Template2_LifeHistoryInventory” Excel file

9.   View of “vonBert” worksheet in “Excel_Template2_LifeHistoryInventory” Excel file, showing drop-down list

10. Visualization in FishualizeR of sex-specific differences in life-history parameters

11. Visualization in FishualizeR of distribution of estimations of natural mortality (from empirical methods)

12. Using the “=LEN” function in Excel to count total number of characters in a cell

13. Schematic representation of sample dataset in long format (left) vs wide format (right)

14. Conceptual view of workflow and tools for data preparation and exploratory data analysis

15. Using the “=UNIQUE” function in Excel to list unique entries/values for a given column or set of rows

16. Using “Find and Replace” in Excel to locate and correct each individual occurrence of an error

17. Schematic representation for outliers identification using the interquartile range method

18. Simplified view of data faceting, showing sample length frequency data by gear type and sex

19. Histogram in FishualizeR, showing count (y-axis) of fishing trip durations in days (x-axis)

20. Histogram in FishualizeR, showing count (y-axis) of individual fish of a certain length (x-axis)

21. Histogram in FishualizeR, showing the weight (y-axis) of individual fish of a certain length (x-axis)

22. Stacked histogram in FishualizeR, showing the number of records (y-axis) of individual trips of a certain duration (x-axis)

23. Scatter plot showing relationship between interdorsal length (x-axis) and total length (y-axis)

24. Scatter plot showing relationship between interdorsal length and total length, faceted by sex

25. Scatter plot of relationship between interdorsal length and total length, faceted by year, coloured by sex

26. Density plot of the catch per trip in a univariate framework

27. Box plot showing catch per trip, faceted by fleet (artisanal and industrial)

28. Box plot showing catch per trip, faceted by fleet (artisanal and industrial) and area

29. Box plot showing trip duration, faceted by fleet (artisanal and industrial)

30. Scatter plot of relationship between catch per trip and trip duration, faceted by area, coloured by fleet

31. Conceptual view of data coverage analysis

32. Comparing age and length composition of stock when unexploited/unfished (top) vs exploited/fished (bottom)

33. Length composition of population and catch across different selectivity patterns

34. Tile plot showing number of samples (by colour), by area (x-axis) and year (y-axis)

35. Histograms showing length composition data by year (each plot) and area (by colour)

36. Tile plot showing number of samples (by colour), per gear type (x-axis) and year (y-axis)

37. Histograms showing length composition by gear for four gear types (all years aggregated)

38. Density plots showing length composition for each gear (by colour), across multiple years

39. Density plot showing length composition by sex (by colour)

40. Plot of gonadosomatic index over time, by sex

41. Plot of gonadosomatic index for different areas over time, by sex (left and right). Legend indicating the area has been ommited

42. Plot of maturity proportions in catch by area

43. Fishery development phases classified by catch time series

44. Discrepancies in catch data from official statistics (printed yearbooks in blue, electronic database in red)

45. Temporal changes in export price, export volume, permits operating in a given year, and reported landings

46. Distribution of trip duration (y-axis) by year (x-axis) and by gear (red and blue)

47. Comparing fishing effort over time, calculated using different units (number of days, tows and trips)

48. Expected relationship between abundance and an index of abundance or CPUE (left); and of abundance and CPUE over time (right)

49. Departures of linearity between abundance and index of abundance

50. Histogram showing total catch per fleet (by colour), per year

51. Box plot showing catch rates per fleet for period in which data overlapped (2000–2005)

52. Time series plots showing CPUE by gear type and by unit of effort

53. An example of CPUE declining for one gear type (red) and increasing for a second gear type (blue) for the same species

54. Box plots showing CPUE (y-axis) by month (x-axis), for two geographic zones

55. Faceted box plots showing CPUE (y-axis) by year (each plot), and by zone (within each plot)

56. Plot comparing time series of official CPUE (blue line) with alternative sources of CPUE (red dots)

Tables

1.   Filling out the “DataInventory” sheet of the “Excel_Template1_DataInventory_Events” Excel file

2.   Filling out the “Timeline” worksheet of the “Excel_Template1_DataInventory_Events” Excel file

3.   Methodologies for the “Excel_Template2_LifeHistoryInventory” Excel file

4.   Overview of common summary statistics

5.   Overview of common univariate diagnostic plots

6.   Overview of common multivariate analyses and plots

7.   Overview of text or categorical data as compared to numerical data

Preface

Scope and structure

This manual focuses on the critical, intermediate steps between data collection and any analysis to inform stock status – how to inventory existing data sources, format existing datasets, conduct basic data preparation exercises, detect and resolve biases, and perform exploratory data analysis (EDA). It provides practical guidelines for data preparation and EDA that can be tailored to unique situations and contexts, with an explicit focus on data-limited situations. Importantly, this manual does not dive into the intricacies of data-limited stock assessment methods, but rather focuses on the important steps of data organization that must precede the application of any stock assessment or empirical indicator-based approach aimed at informing fisheries management. The manual does not focus on how to elicit or gather fishery data, nor on how to design effective data collection programmes; however, the exercise of critically reviewing existing data may highlight the need for changes and improvements in existing data collection protocols.

The manual is structured so as to provide sequential guidance on understanding available sources of fishery data, conducting preliminary explorations and detecting challenges, and exploring available information. As such, it is organized into the following seven chapters: (1) Introduction, (2) Understanding key data types, (3) Inventorying and organizing data, (4) Database set-up and management, (5) Overview of data preparation and exploratory data analysis, (6) Data preparation, and (7) Exploratory data analysis.

The sections in each chapter provide practical guidance on data types, and on specific activities that can be undertaken to explore available data streams, address challenges and refine data. Stepwise guidance and user-friendly instructions are also provided throughout, to organize, review and prepare data – primarily in Microsoft Excel or in comma separated value (CSV) file format. In addition, guidance on data exploration and visualization is presented within “FishualizeR”, a publicly available, web-based, R Shiny app. FishualizeR provides a user-friendly interface for conducting EDA and producing graphical outputs, allowing users to easily visualize trends and detect and resolve problems more efficiently than when using Excel or generating code in R. Notably, the R software environment is not covered by this manual, but many resources for fishery data analysis in R are available online (see for example, SFG, 2019). The scope and focus on Excel and FishualizeR were intentionally defined to reflect and address the practical needs of the manual’s target audience.

Audience and use

The manual is intended for fisheries management professionals, fisheries scientists, and fishery managers who are engaged in data collection, database management, evaluations of stock status, and provision of management advice. It is also intended for use in training or capacity-building contexts, or when relevant within university fisheries science courses. As there is no one-size-fits-all recipe for data preparation and exploration, the steps and examples provide potential analyses that should be tailored to each unique context and dataset. The authors therefore recommend that in using this manual, readers first review and consider carefully the steps presented, and then customize the process to apply to and fit each unique setting.

The manual assumes a baseline understanding of fisheries science and management, and fisheries science terminology is used accordingly throughout. While efforts have been made to explain many terms in the text, the manual is not intended to provide full, detailed definitions for all terms, nor does it include a glossary. For more information on specific terms, please refer to the FAO Fisheries Glossary.[1]

Supporting tools

The manual itself provides steps for data review, organization and exploration. The primary supporting tool is FishualizeR,[2] a publicly available, web-based, R Shiny app. Guidance is also provided for certain exercises and examples using Microsoft Excel.

[1] See: http://www.fao.org/faoterm/collection/fisheries/en

[2] See: http://apps.upwellsolutionsllc.com/app/FishualizeR

Abbreviations

CPUE

catch per unit effort

CSV

comma separated value

EDA

exploratory data analysis

EEZ

exclusive economic zone

FAO

Food and Agriculture Organization of the United Nations

FL

fork length

IQR

interquartile range

IUU

illegal, unreported and unregulated fishing

LEK

local ecological knowledge

MAD

median absolute deviation

nei

not elsewhere included

NGO

non-governmental organization

QA

quality assurance

QC

quality control

SL

standard length

SPR

spawning potential ratio

TEK

traditional ecological knowledge

TL

total length

Chapter 1: Introduction

1.1 Assessing and managing data-limited fisheries

Across the globe, wild capture marine and inland fisheries contribute to the livelihoods of 10–12 percent of the world’s population; they also drive thriving national and international trade for aquatic products, provide cultural ties to aquatic ecosystems, and serve as a source of food security and nutrition for millions of people (FAO, 2020). But despite their economic, ecological and cultural importance, many fisheries remain unassessed and unmanaged – endangering not only fish populations and entire aquatic ecosystems, but also the human communities that depend on them. A fundamental challenge therefore lies in improving management resources and practical tools for the world’s unassessed and unmanaged fisheries This is an essential requirement – both for individual fisheries and for many countries – to meet international agreements and national goals related to the sustainable exploitation of fishery resources for food security, and to ensure economic and social development in the context of ecosystem limits.

It is well accepted that when an assessment of stock status is conducted, the design and implementation of management approaches tend to be more successful (Hilborn et al., 2020; Melnychuk et al., 2021; Hilborn and Ovando, 2014). Unfortunately, most fish stocks are currently unassessed, and their status is generally perceived to be worse than those for which assessments have been conducted (Hilborn et al., 2020; Costello et al., 2012). While this trend is not as evident across the more well-managed, large-scale, industrial fisheries in higher governance areas, it is particularly prevalent in smaller-scale, coastal fisheries, often located in developing countries or regions with limited management capacity (Costello and Ovando, 2019; Ye and Gutierrez, 2017). Lack of adequate data, as well as lack of capacity to curate and analyse available data and information, are key factors in understanding why the vast majority of global fisheries are unassessed and/or unmanaged. These data limitations are often correlated with other compounding attributes, such as limited institutional resources, lack of transparency in fisheries governance processes, and limited staff expertise in fisheries science and management.

In response to these realities, significant efforts have been made in recent years to improve and develop cost-effective methodologies to determine the status of data-limited fisheries, with the understanding that better knowledge of stock status results in a more appropriate management response. As a result, the last decade has seen enormous advances in a growing suite of assessment methods for data-limited contexts. Nevertheless, the use and application of these methods must be undertaken with caution – while data-limited assessments may seem to have undergone a boom in simplification, development, and implementation, many of them are not quite so “simple” (Dowling et al., 2019). Most are relatively complex, require high-quality information, and use model-based approaches that require both quantitative and modelling skills. Moreover – and despite these considerations – it is not uncommon for scientists and practitioners to perform data-limited assessments by using the data at “face value”, without thorough exploration, organization and critical review of the available information. This practice can lead to severe biases in the results, and in misdirected management actions. Even in the most data-rich and complex assessments, issues of data quality can seriously impact the robustness of results.

When model-based stock assessment is not an option, empirical approaches may offer an even more simplified approach to data-limited fisheries management. Although they still require a thorough exploration and understanding of available data, these approaches can be effective, and can serve as stepping stones towards advancing management objectives or conducting more detailed assessments in the future (Harford et al. 2021). As such, a lack of model-based stock assessments should not be a reason to avoid making management decisions. However, as with data-limited assessment methods, a common challenge exists around the need to critically evaluate and organize available information streams before using them in assessment or in decision-making. This stage is crucial, as many long-term, resource-intensive data collection programmes end up underutilized – either due to the poor quality of the data itself, and/or the lack of capacity to manage, curate and prepare the data to inform assessment and management approaches.

To advance understanding of the world’s unassessed fisheries and, where appropriate, correctly apply data-limited methods, the practical realities of data and capacity limitations must therefore be addressed (Ovando et al., 2021). In this context, understanding data quality and biases, providing guidance on properly interpreting fishery and biological data, and effectively detecting and resolving challenges, is of utmost importance.

1.2 Data preparation and exploration

Across seven chapters, this manual provides practical, stepwise guidance on data preparation, review and exploration to inform assessment and management in data- limited fisheries (see Figure 1), with a focus on detecting and resolving problems while maintaining a clear database structure.

FIGURE 1
Overview of manual chapters presenting stepwise guidance on reviewing and exploring fishery data

While Chapter 1 (the present chapter) serves as an introduction, Chapter 2, “Understanding key data types”, provides a general overview of major fishery data types, along with key characteristics and challenges. It is not intended as a fully comprehensive review of each data type (as numerous resources exist to describe fishery data types in greater detail), but to provide overall context for the data types used in the manual.

Chapter 3, “Inventorying and organizing data”, provides detailed instructions for inventorying available data sources and building a timeline of influential events that may impact data interpretation. Thoroughly reviewing and cataloguing all available data, documenting spatial and temporal coverage, and understanding data quality allows pieces of valuable data to be used in analyses, while at the same time identifying information gaps. In particular, the timeline of influential events also serves to document environmental, historical or anthropogenic impacts on the fishery and surrounding system that may impact data interpretation.

Chapter 4, “Database set-up and management”, focuses on how to compile and organize data streams for use in preliminary exploration and analysis. Database management is explained in the context of data review and preparation, but does not cover full database construction and maintenance.

Chapter 5, “Overview of data preparation and exploratory data analysis”, presents a high-level overview of these processes, and of terms, concepts and analyses that are then covered in detail in Chapter 6 and Chapter 7. The chapter also introduces a range of simple statistical and visual tools that are used throughout the rest of the manual.

Chapter 6, “Data preparation”, presents a series of initial steps to explore, visualize and explain or correct data anomalies – the main goals being to better understand the available data and to identify errors and outliers. This process – also known as data cleaning, data quality assurance (QA) or quality control (QC) – is carried out on the raw data before conducting any data aggregation or summaries. It is a highly iterative phase, in which the emphasis is on detecting and correcting basic errors in the available data (e.g. obviously incorrect units or dates). Illustrative examples are used from real fisheries in order to demonstrate the types of diagnostics that can be performed.

Chapter 7, “Exploratory data analysis”, involves a process of looking at and learning from the data available. The general process and techniques used in exploratory data analysis (EDA) are common to data preparation for statistical analysis across a variety of fields (Cox, 2017). EDA involves investigating the data to find recurrent patterns that require explanation, cross-checking and comparing multiple data sources, and testing violations of key assumptions in order to interpret fishery patterns to be used in informing management, and/or before formal statistical modelling is carried out (or before deciding which variables to include in modelling). Graphical outputs and visualizations are used to detect and understand interactions between variables. A thorough EDA can be time-consuming, as it requires conducting several filtering processes, transformations and summaries, and producing a large amount of graphical output. Importantly, EDA is often an iterative process, where changes in data must be made to correct issues.

Chapter 2: Understanding key data types

This manual focuses on three broad categories of fishery data: (i) biological data, (ii) catch and effort data, and (iii) local ecological knowledge and alternative data sources. This chapter provides a brief introduction to each category, but does not aim to fully cover the depth and breadth of data types within each. For more information on terms, please refer to the FAO Fisheries Glossary.[3]

Within each of these categories, data may be either fishery-dependent or fishery- independent. Fishery-dependent data are collected directly from the fishery, and may include information about landings, discards and the fishing trip or fishing effort itself (e.g. time spent fishing or fishing gear used). In contrast, fishery-independent data are mostly collected by scientists or other researchers, with a view to supporting the development of unbiased indices of abundance, or to gathering information on age, growth and other life-history characteristics. Fishery-independent data are often collected through long-term monitoring programmes or surveys, and their design and collection often require more resources and capacity as compared to fishery-dependent data.

2.1 Biological data

Biological fishery data represent an extensive category of information relating to the biological characteristics of fish populations in a specific area. Biological data are those related to the biological or biometric characteristics of the target species, including length, weight, age (for example, as determined via otolith samples), sex, fecundity and maturation from the gonad stage. This type of data may serve two main purposes: first, to estimate population parameters and life-history information such as growth rate, maturity or morphometric relationships; and second (as explored in this manual), to generate information that may be used as an indicator of stock status – either alone or in combination with other sources of information such as total catch, total effort or index of abundance.

Biological data may be collected via a variety of methods – both fishery-dependent and fishery-independent. Fishery-dependent biological data are usually collected by trained personnel conducting sampling at landing sites, markets or processing plants. Such data may also be gathered by fishers in communities that are strongly involved in data collection or management processes. Fishery-independent biological data collected via research studies are designed to be independent of the catch, and often focus on growth and maturity.

Length composition data are often one of the key types of biological data available. Sources of length composition data may be very diverse (such as from formal fisheries monitoring protocols, survey data, opportunistic sampling, market sampling and samples inside marine protected areas). In particular, length composition data may be influenced by the way the data are collected (examples include: because different types of gear may vary in selectivity, market samples do not include non-commercial sizes, and the location and the time of year of data collection can influence the size ranges sampled). A detailed biological data inventory is important, in order to assess the extent to which the data available can inform the status of the fishery. For example, if there are data for gear that catch juveniles only, the length composition data collected for this gear will be truncated. For another example, if there are length samples from inside and outside a marine protected area, their comparison can, under the right conditions, provide information on the effects of fishing on the stock.

With very little data processing, biological information may be used to generate simple indicators of stock status. For example, length composition data combined with maturity data can be used to track the percentage of juveniles and mega-spawners in the catch, and the sex ratio can be used to understand whether females are targeted more actively than males. Length composition data, in combination with life-history parameters, also serve as the main inputs for a variety of data-limited stock assessments. Some life-history parameters may be estimated directly from these samples (such as growth or maturity curves), but different sampling designs may be needed for different objectives. For example, efforts may be made to collect a wide range of lengths for a growth study; but if those length composition data are then used in an assessment (where the data must represent the true length frequency in the population and not necessarily the wide range of the population), they may lead to severely biased results.

Good quality biological data from the target species, combined with basic fishery information and proper interpretation of the observed patterns, can also provide sufficient information to adjust or draft interim management measures – such as adjusting size limits for example, or implementing seasonal/spatial closures.[4]

2.2 Catch and effort data

Time series catch and effort data provide crucial information for understanding fishery resource exploitation history. These data serve as basic inputs in most stock assessment techniques, and can be used to compute an abundance index (such as catch per unit of effort or CPUE). Types of information included with catch and effort data may include location or area, gear type, soak or tow time, fleet, species and date.

Care must be taken to distinguish between total catches and landings, especially in fisheries with high levels of at-sea discards, as these may skew results for catch data as representative of total removals. Catches generally refer to the total amount caught (potentially including discards), whereas landings refer to the amount actually retained. Total catch is preferable to landings, particularly if discard mortality is high. In some fisheries, catch or landing data are collected differently from effort data: effort may be inferred from frame surveys, which are conducted biannually or depending on the availability of funds. In some cases, this may lead to situations where catch levels may vary, but effort – because of a reliance on previous frame survey data – appears relatively (and misleadingly) stable.

Records collected at the national or regional level are usually the main source of catch and effort data. If such records are incomplete (for example in terms of undeclared fishing and discards), the interpretation of total catch/landings and effort trends may prove difficult (for more information, see Section 7.2.2.). However, if records are available for a representative portion of the fleet, indices of abundance may be constructed to assess stock status. In addition to official catch records that are collected and maintained by regional or national governments, other entities such as universities, research centres and non-governmental organizations (NGOs) may also collect and maintain catch and effort databases.

The information required to understand stock status is not always collected in a systematic way or centralized by a single institution, and rarely spans the timeframe for which the fishery has been operating. Establishing a baseline sometimes requires compiling old data from reports, scientific papers, or student theses. Moreover, it should be noted that the information that can prove useful for management and assessment purposes is not limited to data collected for scientific or statistical purposes. For example, length data collected in recreational fishing competitions can serve to track changes in the observed maximum sizes of the fish that are caught. As such, local knowledge or anecdotal information should not be disregarded, as it can provide insight on major changes in the stock being analysed. In general, it is a good practice to create an inventory of all the data that can potentially inform the status of a fished stock and/or interim management measures. A data inventory can also guide the selection of the most appropriate assessment options for the fishery. (For more on data inventorying, see Chapter 3.)

Larger fishing operations typically have higher value and volume of catches – and because they are more centralized nature – these characteristics can facilitate the collection of accurate catch data. On the other hand, collecting catch and effort data from small-scale fisheries are complicated by a range of factors, including for example:

- The catch may be landed across a large and/or remote area, and sold/consumed in local communities where formal sampling is challenging.

- The presence of multi-species, multi-gear, and multi-landing site fisheries can create challenges for data collection.

- There is overlap between fishers in recreational, commercial and subsistence or small-scale sectors, and a lack of clarity on participation per sector.

- Many fishers may operate informally, without a license or without reporting to national authorities. As a result, they are not always sought out to participate in reporting mechanisms. Additionally, they may simply lack incentives to report accurately (for example due to lack of trust in management systems).

- Discards are not usually reported, and some small-scale fisheries do not have discards at all, as everything may be sold or consumed.

- Illegal, unreported and unregulated fishing (IUU) is difficult to capture overall.

Government agencies often strive to incrementally improve their catch and effort data collection systems, either by using new technology, devoting more staff to monitoring duties, or formally recognizing and legitimizing data collected by fishers (e.g. through community- based data collection programmes) (Gutierrez, 2017). While improvements in data collection are always welcome, practitioners should be aware that changes in data collection protocols may create spurious patterns that are not necessarily related to fishery or ecological dynamics. For example, total landings in a particular year may increase due to better data collection coverage or to adjustments made by a central government agency before official records are shared, and not due to increased abundance or fishing effort. As databases improve over time, it is also common to add more resolution and nuance, or improve units of fishing effort. For example, the way in which effort is recorded may change over time: it may start out in terms of the number of permits, but later change to the number of fishing trips or fishing days; this may complicate the integration of all available data into one time series.

Indices of abundance

Total catch and effort time series data are basic inputs in most stock assessment techniques, and can be used to compute an index of abundance (such as CPUE), as well as to provide a scaling factor that allows models to estimate the absolute abundance of the fish population under study. Catch and effort data can also be used in empirical indicator frameworks, where trends in CPUE and/or rate of change in CPUE can be to adjust harvest control rules (Harford et al., 2021).

An index of abundance is a key piece of information for tracking abundance trends, and can prove very important in stock assessments. The advantage of an abundance index is that the input data do not need to be from a complete census of the fishery, but can be calculated from a representative sample of one or more fleets. Compiling meaningful measures of fishing effort is challenging, as it requires quantifying the time that the given fishing gear was deployed in the water. Measurements of fishing effort will vary depending on the gear deployed, including for example the total dive time for dive fisheries, the swept area for trawling, the number of hooks multiplied by hours in the water for longlines, and the number or area of panels multiplied by hours submerged for gillnets.

Obtaining such granular measurements of effort in data-limited fisheries is difficult and uncommon. Coarser metrics can be used, such as the number of trips, the number of fishing days or the number of active licenses. A challenge with coarser metrics, however, is that the efficiency of a given unit – for example a day of fishing – can vary over time due to the addition of new technologies, or to changes in the fishing operation or crew expertise. While several methodologies exist to account for such factors (including a standardized index of abundance [Maunder and Punt, 2004]), they are beyond the scope of this manual.

2.3 Local knowledge and alternative data sources

Many fisheries may be categorized as “data-limited” while also being “information-rich” – due to a wealth of local ecological or traditional knowledge sources. In settings with limited biological and catch and effort information, local knowledge or local ecological knowledge (LEK) may be used to inform assessments and provide informative indicators. In this manual, local knowledge is treated as a mechanism to enhance understandings of catch, effort, and biological data, or to derive this information when formal data is not available.

In general, LEK refers to the knowledge, practices and beliefs of people in local communities regarding ecological systems and functions, as gained through extensive and long-term interaction with the environment. In fisheries, it may also refer to knowledge of broader fishery dynamics or history, and may be referred to as traditional ecological knowledge or TEK (Berkes, Colding and Folke, 2000). Information from people familiar with a given fishery can be used as a complement or, when appropriate, in place of quantitative data (Berkström et al., 2019). Consultation with fishers, community members, scientific experts and other potential sources of information may be used to:

- gain or improve understanding of the ecology or biology of a species – for example, to identify reproductive seasons and the location of spawning grounds, or to map juvenile habitats;

- gain understanding of the operation of the fleet – for example, to track changes in the distribution of fishing effort or fishing practices;

- generate quantitative time series data for fisheries performance indicators – for example, by using questionnaires to generate catch rates or track changes in sizes of individuals;

- understand potentially varying perceptions on the status of the stock and avoid shifting baselines; and

- validate existing or sparse catch records and support and improved understanding of historical catch patterns or catch reconstructions.

Although key challenges exist in its use (including with regard to selecting respondents and assessing data consistency or robustness across respondents), LEK offers an extremely beneficial and necessary mechanism for gathering rich and valuable information about a fishery in data-limited situations. Further resources and sample methodologies for eliciting and using LEK may be found in Moller et al. (2004), Martins et al. (2018), and Berkström et al. (2019).

A variety of methods may be used to elicit LEK, including informal interviews, structured and semi-structured interviews, expert elicitation methods and participatory mapping (Mclean, Forrester and García-Quijano, 2022). These types of interviews can help to uncover important variables that may have been overlooked in previous research. For example, fishers may inform the interviewer of changes in discarding practices, or of operations that may have been declared illegal, but which the fleet is continuing to conduct.

More formal or semi-structured interviews can be used in LEK contexts to obtain information on catch and effort, or to validate patterns seen in existing data sources. In order to obtain reliable and comparable data from such interviews, it is necessary to design surveys and plan implementation with care. This includes the preparation of relevant and meaningful questions and the selection of respondents to be interviewed, so as to minimize the potential for sampling bias. For instance, respondents may be asked about their perceptions of the status of the fishery as compared to previous decades, about the distances they need to travel to find fish as compared to previous years, or whether they have had to seek out new fishing sites because of the depletion of traditional older sites.

In the absence of formal data or in the case of limited time series, structured interviews may be used to obtain semi-quantitative data, and to gain an understanding of the history of the fishery. For example, respondents may be asked about the average or maximum catch per trip at different points in time, or about the average or maximum weight or size of fish at different points in time. Other types of “alternative” data sources (or information that may be used to confirm sparse or unreliable catch and effort records) may include export records or employment statistics for the fishing sector; however, caution should also be exercised when interpreting these data sources.

[3] See: http://www.fao.org/faoterm/collection/fisheries/en

[4] For more information on biological data collection, see FAO’s Marine species biological data collection manual (2016), as well as Arreguín Sánchez (2021).

Chapter 3: Inventorying and organizing data

The assessment and management of data-limited fisheries benefits from the review, analysis and interpretation of all sources of information available, even if these sources may differ in quality or if data collection protocols have changed over time. An important first step is therefore to create a data inventory or list of all the information that may potentially be used. Along with a data inventory, it is equally important to build a timeline of influential events : this should consist of a list of historical or recent events that could affect the interpretability or usability of the data. In both processes, collaboration with stakeholders is essential.

Furthermore, most assessments of stock status require life-history information (to derive reference point proxies, to be combined with other data in order to generate indicators of stock health, or to be used for drivers to model population dynamics). Although practitioners often rely on a single set of life-history parameters when conducting stock assessments, life-history parameter estimates can be highly variable, due to differences in methodologies or sampling protocols, or due to sex- specific differences. When confronted with the question of which parameter values better describe the life history of the species, it is important to compile all available parameters in a life-history parameter inventory. This should draw from published papers, university theses, global databases and other such sources.

This chapter provides instructions on how to build a data inventory (Section 3.1), document a timeline of influential fishery events (Section 3.2), understand examples of the inventory and timeline (Section 3.3), and create an inventory of available life- history parameters (Section 3.4 and Section 3.5).

To support the user in compiling a data inventory and life-history parameter inventory, the following Microsoft Excel files or workbooks are provided as templates within the FishualizeR[5] app that accompanies this manual:

- The first Excel file/workbook, titled “Excel_Template1_DataInventory_Events” may be used to compile the data inventory and timeline of influential events.

- The second Excel file/workbook, titled “Excel_Template2_LifeHistoryInventory” may be used to compile life-history parameters.

Beginning with this chapter, the manual uses a series of diverse fisheries example datasets to convey concepts, provide instructions and demonstrate how to build visuals and plots. While these fisheries are not meant to represent all global fisheries, they serve to provide tangible examples, allowing for clearer and more concrete instructions. This enables users to better understand both FishualizeR and the data exploration process overall.

3.1 Data inventory

The inventory of key fishery data can be thought of as a high-level, overarching process to list all available data streams. It serves as a fundamental step for a variety of reasons:

1. It indicates which data are readily available and may serve to highlight where efforts should be devoted to access and share existing databases. For example, old catch records may only be available in printed, hard-copy versions of fishery yearbooks, and using this information would require gathering and compiling the data into an electronic format. If data have been collected by different institutions, arrangements should be made to confirm or ensure such data can be shared and utilized.

2. It allows for the identification of both data gaps and periods where overlapping information is available. For example, catch data may be available in both electronic format and in hard-copy fishery yearbooks. As a rule, electronic records typically have more detailed information than high-level yearbooks – they allow for data analysis at different levels of resolution (catch per port, per gear, per month, etc.), whereas yearbooks generally report total catch at country level only.

3. It highlights the existence of multiple pieces of data to inform the same variable. For example, export records may be contrasted with catch reports to assess consistency in reporting of removals. Length composition data may also be available from multiple sources for overlapping periods (scientific surveys, observer data from the catch, market samples, processor data, etc.). These different data streams will be particularly important during the exploratory analysis phase, when the analyst should check consistency across different pieces of information and make decisions on how to treat them.

4. It allows for the identification of potential methods of assessment based on data availability. The synoptic picture provided by the data inventory can be contrasted with the data requirements of different stock assessment methods or empirical approaches, to generate an a priori list of potential techniques that may be implemented to assess stock status and/or inform management measures (see Figure 2). While data availability is a prerequisite to conduct certain assessment methods, issues with data quality or representation should also be taken into account. While this manual does not explore the intricacies of selecting data- limited stock assessment methods, the FishPath Tool (Dowling et al., 2022) is available online as a more comprehensive resource that uses a questionnaire about available data types and quality, as well as other fishery characteristics, to guide the selection of appropriate data-limited assessment methods.

FIGURE 2
Spectrum of data requirements for empirical and stock assessment methods

When creating the data inventory, special focus is required on sources of catch, effort, and length composition time series – these data may be available from diverse sources, with varying degrees of resolution or data quality. It is also important to consult with a variety of diverse stakeholders, since databases are often decentralized and analysts may not be aware of the existence of potentially useful data that are not automatically shared with fisheries management institutions. This can include the unpublished thesis work of university students for example, or data collected by NGOs. In some cases, a source may consist of data that are not immediately available (e.g. from decentralized paper records that need to be digitized); but these may still be gathered and used to inform management. It is also important to document and understand the temporal coverage or time frame of each data source in the data inventory.

3.1.1 Filling out the data inventory template

The following instructions relate to the “Data Inventory” worksheet of the Excel file/workbook entitled “Excel_Template1_DataInventory_Events”, available via the FishualizeR app. The template stores metadata (i.e. information about data types/sources), and does not contain any raw data itself. The use of this specific template is not required – if preferred, users are free to follow the prompts to create a data inventory in the format of their choice.

The template consists of multiple tabs or worksheets (see Figure 3); these include a worksheet for the data inventory (“DataInventory”), a worksheet for the timeline of influential events (“Timeline”, as discussed in Section 3.2), and several auxiliary worksheets (with the prefix “Aux_”), which are used to create drop-down lists to prevent typos and errors in data entry. These drop-down lists are populated with pre- defined, “valid” values for a given column, so as to ensure that of each auxiliary sheet corresponds to a column name on the “DataInventory” sheet (for example, the sheet “Aux_Gear” contains a list of the potential values for the “Gear” variable). Although common options are included for each variable, the lists are not necessarily exhaustive. The user may create additional “valid” values, by simply adding new entries as needed in the corresponding auxiliary sheet. Once a new value has been added in an auxiliary sheet, it is automatically added to the drop-down list in the main “DataInventory” sheet.

FIGURE 3
View of “Excel_Template1_DataInventory_Events” Excel file, showing “DataInventory” tab/sheet

Notes: The black rectangle indicates the data inventory tab or worksheet. The green rectangle indicates the auxiliary worksheets. Italic text in red denotes sample information from a hypothetical fishery (and may be deleted).

To complete the data inventory, the user should fill out the columns in the “DataInventory” sheet, as explained in Table 1. Consultation with several stakeholders is strongly encouraged. The following questions and considerations may be useful in building a comprehensive inventory of available information streams:

- Begin by thinking through each major data category and available data streams.

- Data are often collected by fleet and gear type. Note the major fleet and gear types and consider the data available for each.

- For some time periods, data may be aggregated, whereas for others, more detailed information may be available. List each individual data stream as a unique entry in the data inventory sheet. This may include catch data, effort data and length data. Each row or entry should correspond to a specific, individual data source. For each major data category (e.g. for catch or effort) there may typically be multiple, diverse data sources.

- If the data source is not within a consecutive time period, include separate, individual rows (if a data stream was discontinued and later in time data was collected again, each period corresponds to a row).

- Data quality is important to document. Use the “Data Quality Notes” column to add information on specific aspects such as spatial or temporal coverage, or fishery coverage.

Table 1. Filling out the “DataInventory” sheet of the “Excel_Template1_DataInventory_Events” Excel file

Column

Explanation and guidance

Data type category

This column specifies the type of data. Four values are listed in the template: “Catch”, “Effort”, “Length composition”, and “Abundance” and are available from a drop-down list. Note that for each one of these data types, multiple sources of information may be available, and each one should be listed as a new entry. For example, catch data may consist of estimations of total catch as reported by national agencies, or a partial sample gathered as part of a local monitoring programme. Moreover, a single data type can have different levels of readiness for analysis. For example, a part of the data may still be on paper while another part already exists in electronic format. In this case, each part should be considered as a different entry in the worksheet.

Data type subcategory

For each data type category, several subcategories are available. For example, there may be data available for reported catch, but also for estimates of unreported catch. Similarly, the length composition may be a sample of the catch, or a survey of the population or samples within a marine protected area. For each data type, default options have been added in the “Aux_Subcategory” sheet.

Data source

Similar datasets may be obtained in different ways. For example, length composition samples of the catch can be gathered at the market, a processing plant or onboard a vessel. For each data type subcategory, pre-defined values are provided in the “Aux_Source” sheet. However, the user may need to add a new type of data source for a given data type subcategory. This can be done by adding a new row in the “Aux_Source” sheet, in the corresponding column for the given data type subcategory. For example, local ecological knowledge studies may be included as “Interviews”, or the sheet may be edited to include a new type of data source.

Sector

Each data type may be available by Fleet or Sector. The template allows for 6 pre-defined values as included in the “Aux_FleetOrSector” sheet: “Artisanal”, “Recreational”, “Industrial”, “Aggregated”, “Subsistence” and “Semi- Industrial”. Note that different countries define each of these sectors differently.

Gear

Each data type may be available by gear. The template allows the user to define the gear used via the “Aux_ Gear” sheet. If multiple gear types are used in the fishery or reported together, use “Aggregated”.

Units

This column is used to specify the system of measurement for the given data type.

Area

This column is used to indicate the spatial coverage or area of the data. Some fishery data may cover the entire geographic extent of the stock or fishery, whereas other data only represent portions (e.g. as collected by different management jurisdictions). If this is not relevant, the value should be left blank.

Start year

This column is used to indicate the first year of the data. As this is meant to be a high-level data inventory template, more specific measures (such as month or day) need not be recorded.

End year

This column is used to indicate the last year of data. If the data are only available for one year, this column should be set to “NA” (not available) or left blank.

Available (yes/no)

This column indicates if the data are in hand electronically and ready to be analysed via computer. For example, some data may be in paper format, and may need to be digitized or converted to electronic format. Similarly, some data may be in electronic format, but – having been collected by different institutions – may not be usable until the relevant institutional arrangement is formalized. In other cases, data may not be available for immediate use, and reconstruction or validation may be needed.

Institution responsible

This column indicates who has ownership of the data. This information is important to ensure that the proper stakeholders are consulted in relation to the data’s access and use.

Data quality

This column is used to capture a subjective categorization of the credibility/quality of the data. It is essential to obtain a perspective on the degree of “trust” in the data from stakeholders, as this may serve as an important piece of information in assessing data use, and in assigning the appropriate weight to different data sources in decision- making.

Data quality notes

This column may be used to capture any additional notes regarding the data source. Important characteristics of the data to be noted include reliability, consistency and any gaps in coverage during the given time period.

3.2 Timeline of influential events

In addition to building a data inventory of fishery data, it is important to document the events that may impact data interpretation. These include any external circumstance, new development or event that may generate patterns in the data and/or affect their interpretation. While it may seem challenging to create an exhaustive list of all the events that can affect the interpretation of the year-to-year variation in a time series, building a timeline of influential events that impact the fishery and/or monitoring process provides a useful mechanism to interpret anomalies or sudden changes in the data.

3.2.1 Filling out the timeline template

In addition to the “DataInventory” tab or worksheet (as discussed previously in Section 3.1), the Excel workbook entitled “Excel_Template1_DataInventory_Events” also contains a “Timeline” worksheet, to list influential events (see Figure 4). The instructions, questions and table in this section will support the user in considering the most common external factors that can affect the interpretation of fishery data, in order to populate an appropriate timeline.

FIGURE 4
View of “Excel_Template1_DataInventory_Events” Excel file, showing “Timeline” tab/worksheet

Notes: The black rectangle indicates the timeline tab or worksheet. Italic text in red denotes sample information from a hypothetical fishery and should be deleted.

To complete the timeline, the user should fill out the columns in the “Timeline” sheet, as explained in Table 2. As with the “DataInventory” sheet, consultation with several stakeholders is strongly encouraged. The following questions may also be useful during this process, and will support a comprehensive listing of events that may impact the interpretation of the data:

Fishery and fishery management events

- When did the fishery start? This is a critical event to include in the fishery timeline. If not known, an approximation may be used.

- At what point did landings peak? If not known, an approximation may be used.

- Were there any changes in the operational characteristics of the fleet/s? For example, were there any changes in mesh size or in the area where the fleet operates?

- Were there any changes in fishing regulations? For example, did the minimum legal size change over time and if so, at what points? Was a quota system ever implemented?

- Were there changes in the market? For example, did a market develop for smaller fish or fish of a certain size fish, as motivated by a desirable “plate size”?

- Did any changes in sampling protocols take place? For example, did length composition sampling occur before or after discarding?

Environmental and anthropogenic events

- Have there been any significant environmental events? For example, was there an El Niño event or a hurricane?

- Have there been any significant anthropogenic events or impacts? For example, was a seabed where fishing occurs dredged – either periodically or at a single point in time?

Other

- Have any protected areas that regulate or limit fishing been implemented?

- Have there been any major projects or funding sources that have influenced data collection? For example, were there any regional or collaborative projects that collected data – when did these begin and end?

- Were there any changes in data collection protocols, such as from paper-based to digital systems?

- Have there been any major political or governance events that influenced fishery data collection funding, or changes in government agency fisheries staff?

- Are there any events that may have increased or decreased fishing? For example, a popular documentary film about spearfishing may have contributed to a dramatic increase in fishing effort.

- Did a foreign fleet ever operate illegally in national waters? At what times?

- Were there any other external drivers that may have influenced fishing effort or catches, such as the COVID-19 pandemic?

Table 2. Filling out the “Timeline” worksheet of the “Excel_Template1_DataInventory_Events” Excel file

Column

Explanation and guidance

Influential event or change

This refers to any external circumstance, development or event that may generate patterns in the data and/or affect their interpretation. As a rule, the user should always indicate when the fishery started and when the catches peaked, as key points of reference within the fishery. The first two rows of the template are reserved to store those data. The use of event titles that are both descriptive and short is recommended, as these titles may subsequently be plotted and visualized in FishualizeR (see Box 2), and will thus allow for clearer and easier interpretation of the graphs.

Start year

This is the year the event started or occurred. If the event occurred over multiple years, it is the first year that the event took place.

End year

This is the final year for which the event took place. If an event occurred only for one year, this column should be set to “NA” (not available) or left blank.

Notes on significance or how this event impacts data interpretation

Careful consideration should be given to each event, and how it may impact data interpretation. The user should be able to hypothesize the influence of a given event on the data (some examples are provided in the template). At a later stage, each hypothesis may be contrasted with the available data.

3.3 Interpreting the data inventory and timeline

The process of inventorying data and building a timeline of influential events forces researchers to hypothesize and think critically about how a particular event may impact data interpretation. Once the data inventory and timeline worksheets have been completed as indicated in Section 3.1 and Section 3.2, respectively, they can be uploaded with ease into FishualizeR, in order to create graphical outputs (see Box 1 and Box 2).

Box 1. Accessing FishualizeR and uploading files

The FishualizeR app consists of several sections, all of which are listed in the main navigation (i.e. the left-hand menu/panel, as shown in Figure A). Each of the sections provides brief instructions on how to upload the relevant files for that section (i.e. Excel or CSV) and create graphs or other visuals of the data for exploratory purposes.

Figure A. View of FishualizeR home page, showing the left-hand menu/panel of available sections

In order to function, the app requires the user to upload data files. The “Data Inventory” and “Life History Parameters” sections require the user to use the specific templates as provided within these sections (i.e. the “Excel_Template1_DataInventory_Events” and “Excel_Template2_LifeHistoryInventory”, respectively). The remaining sections require the user to upload a CSV file, for which there are minimal requirements: the data must be stored in the wide format (see Section 4.3), and the first row must consist of the column headers (i.e. names).

To upload a data file, the user should first navigate to the FishualizeR app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR/), select the relevant section from the left-hand menu/panel (i.e. “Data Inventory”, “Life History Parameters” or “Upload Data File”), and then:

1. Click “Browse” (see Figure B).

2. Use the file navigation system to select the appropriate file.

3. Click “Open”.

Once opened, the data are automatically loaded into the app for use.

Figure B. View of “Upload data file” section of FishualizeR

The Excel workbooks that are uploaded in the “Data inventory” and “Life history parameters” sections are only used in those sections, whereas the CSV file uploaded in the “Upload data file” section is used in other explorations throughout the rest of the app.

Box 2. Visualizing the data inventory and timeline in FishualizeR

After the “Excel_Template1_DataInventory_Events” Excel template is populated with a complete data inventory (using the “DataInventory” worksheet, as described in Section 3.1) and timeline of influential events (using the “Timeline” worksheet, as described in Section 3.2), both may be visualized together in FishualizeR. To do so, the user should navigate to the app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR/), select the “Data Inventory” section from the left-hand menu/panel, and upload the Excel file.

Once the file is uploaded, the resulting graphical output presents a visualization of the available data and influential events (see Figure A). In viewing the output, the following should be noted:

- The start of the fishery and catch peak are indicated with a solid and dashed line, respectively.

- The two checkboxes allow the user to hide the data sources that have been marked as not available, and/or hide the events timeline.

- The data category drop-down list allows for more detailed exploration of each data category. When “Summary” is selected, the timeline for the data inventory is consolidated by combining rows that have the same data category, data source and sector. When “Individual” is selected, the timeline provides more detail by only combining rows that have the same data category, data source, sector, gear and area.

Figure A. Visualization in FishualizeR of “Summary” output for sample data inventory and timeline

Notes: This plot displays a summary of the temporal coverage of all the data sources and major events. The labels on the right indicate what data category each colour represents (e.g. blue for catch data). The labels on the left provide the details for each row.

Chapter 7 of this manual – which focuses on EDA – will provide further detail on critically evaluating the actual influence of a single event on the data, as a basis for discussing the consequences of using the data to inform fisheries management decisions. In this section however, brief examples are provided to demonstrate the value of the data inventory, and of cross-checking perceived trends with influential events.

As a first example, Figure 5 shows FishualizeR output for the data inventory and timeline of influential events for a hypothetical industrial fleet (i.e. a sample dataset), for which catch information is available in two different formats (digital or electronic, and paper-based). In this case, building a visual timeline clearly demonstrates that the time series available in electronic format is very short compared to that which is available in paper yearbooks. Moreover, as shown by the orange bars in the figure, the digital data source alone would not provide sufficient data with which to understand the history of removals.

FIGURE 5
Visualization in FishualizeR of data inventory and timeline for temporal span of sample dataset

As a second example, Figure 6 shows a visualization of length composition data from 2011 to 2018, for the same hypothetical industrial fleet as used in Figure 5. If the data are taken at face value, the figure suggests a sudden change in the frequency distribution of length composition of the population in 2016, after which smaller individuals are no longer present. This could be interpreted, for example, as a series of consecutive recruitment failures. However, when we combine this information with the timeline visualization (see Figure 5), we know that there was a change in mesh size in 2016 – when a fishery management regulation for a minimum mesh size of 80 mm was implemented. As such, the change in length composition is likely due to changes in gear selectivity.

FIGURE 6
Visualization of length composition data for sample dataset, showing frequency distribution (2011–2018)

Notes: In this example, a regulation for a minimum mesh size of 80 mm was implemented in 2016 (as indicated by a blue line in the years for which the regulation was in place).

3.4 Life-history parameter inventory

Life-history parameters are essential in fisheries management, and may be used either in combination with fishery data in model-based stock assessments to derive indicators of population status, or in empirical indicator-based frameworks to inform management. For example, if in a given catch the proportion of individual fish below size at maturity is higher than a certain predetermined percentage, or if the mean size of individuals is consistently below size at maturity, a management response may include a reduction in fishing effort or a move to a different area (Harford et al., 2021; Froese, 2004).

The most commonly used life-history parameters include the von Bertalanffy growth parameters, natural mortality, size-at-maturity and weight-at-length relationships. Although fecundity information may exist (i.e. the number of eggs likely to be laid by an individual – often counted in ovaries [Murua et al., 2003]), it is not explicitly considered in this inventory, as this type of data tends to be less commonly available. However, if fecundity data are available, the user should include it, as it may be relevant for certain types of assessments.

The aim of the life-history parameter inventory is to serve as a repository of plausible values for the stock under study. In circumstances where parameters are not available for the stock under study, parameters may be borrowed from other geographies or related species (Punt, Smith and Smith, 2011).

This manual focuses on compiling existing parameters from studies or scientific literature; it does not provide guidance on how to estimate life-history parameters from biological data. In this regard, the following key online resources may offer guidance, in addition to published scientific literature: FishBase,[6] which compiles parameter values from the literature at the species level; and FishLife,[7] which uses published information to derive parameter estimates at different taxonomic levels. In the absence of published life-history parameter estimates, these may be obtained using empirical relationships (see for example, FishBase, 2023), but caution is recommended when using empirically derived values.

Once parameters have been compiled, the life-history parameter inventory allows for the assessment of the level of uncertainty around key parameters (for more information, see Section 3.5), and for the identification of information gaps or areas for future improvement (for example, conducting a maturity study in situ for the target species). In some cases, expert judgement will be needed to decide whether the parameters estimated in a given study are reliable (for example, studies conducted with a very small sample size may be unreliable), or whether they accurately reflect the life history of the stock under study (for example, growth parameters may vary between geographic regions).

3.4.1 Filling out the template

The following instructions relate to the Excel workbook entitled “Excel_Template2_ LifeHistoryInventory”, as provided via the FishualizeR app (Figure 7). However, the use of this template is not required, and users are free to follow the prompts to create a life-history parameter inventory in the format of their choice.

FIGURE 7
Views of “Excel_Template2_LifeHistoryInventory”, showing sections from four key tabs/worksheets

Notes: The four tabs/worksheets reflect the four main types of parameters for the life-history parameters inventory. Each one (“vonBert”, “Mortality”, “Maturity” and “LW”) contains specific columns to record parameter values.

The template file or workbook is organized into eight separate tabs or worksheets. These include four main worksheets for values related to growth curve (“vonBert”), natural mortality (“Mortality”), length at maturity (“Maturity”) and length-weight relationship (LW) parameters, respectively (see Figure 7), as well as several auxiliary worksheets (with the prefix “Aux_”), which use drop-down lists to help prevent typos and errors in data entry.

As shown in Figure 7, the four main worksheets are similar in structure, with columns for references, sex, geographic area, methodology (as used to estimate the parameters), and notes. The “Mortality” sheet also includes a column to specify age at mortality. Further detail on the columns is provided as follows:

Reference: This column specifies where the information was extracted from. When this involves theses, reports or other published material, a short name and date may be used (e.g. “Smith_et_al_1996”). However, in cases where the data have not been published, but parameters have been estimated by local researchers, the name of the person in charge of the analysis may be used as a reference (e.g. “Smith_unpublished”).

Sex: This column specifies whether the estimated parameters were sex-specific, or if they were estimated using aggregated samples.

Area: This column specifies the geographic area in which samples were collected to perform the analysis. This is important, as spatial heterogeneity in parameter values for many species may be caused by environmental conditions or gradients, and should be taken into account when deciding suitable parameter values for the stock under study, as well as when interpreting results. The analyst should decide the appropriate level of spatial resolution that is relevant to interpret the data. For example, for a species with a home range that crosses several national jurisdictions, the country name may be appropriate. But for a sedentary species, this level of resolution may not be granular enough, and the area should be split into smaller subunits.

Methodology: This column specifies the method used to estimate the parameter values, and requires extracting information from the original publication or study. Parameter variability can arise due to methodological differences in data collection and processing. For example, a maturity curve may be constructed either by using histological samples or by determining the maturity state of an individual through visual grading. Growth parameters may be estimated using approaches such as otolith readings, scale readings, or by fitting a model to the length composition of the catch. Specifying the methodology used can help the practitioner to assess the level of variability between methods, and decide which methodologies may be more reliable.

The “Excel_Template2_LifeHistoryInventory” Excel file is designed to support the data review and input process, in particular by preventing data entry errors and typos. Except for the numerical values for the actual parameters, all information in the main worksheets can be filled in using drop-down lists. These lists restrict what may be entered in a given cell to a set of pre-defined values. In order to populate the drop- down lists with these values, the corresponding information should first be entered in the auxiliary worksheets. For example, to populate the drop-down list of “References” in the main worksheets, entries may be added to Column A of the “Aux_References” sheet, starting from Cell A2, as shown in Figure 8.

FIGURE 8
View of “Aux_References” worksheet in “Excel_Template2_LifeHistoryInventory” Excel file

Once these references have been entered in the auxiliary sheet, they will automatically appear for selection in the corresponding drop-down lists across the main worksheets (see Figure 9).

FIGURE 9
View of “vonBert” worksheet in “Excel_Template2_LifeHistoryInventory” Excel file, showing drop-down list

A given life-history parameter may be estimated via multiple different methods; in these cases, the chosen methodology may explain the observed variability. Each life-history type admits a set of pre-specified methodologies in the template, as shown in Table 3. If a new methodology is required, the user adds a new entry to the corresponding column in the “Aux_Methodology” worksheet.

Table 3. Methodologies for the “Excel_Template2_LifeHistoryInventory” Excel file

Von Bertalanffy

Natural Mortality

Maturity

Length–weight

Methodology

Otoliths

Empirically derived

Histology

Total weight

Scales

Catch curve

Visual grading

Eviscerated weight

Modal components

Bhattacharya

Notes: If needed, the user can add new methodologies to the corresponding column; this metadata can then be read along with the rest of the Excel file when uploaded to FishualizeR for visualization.

Each of the main worksheets consists of a number of columns for corresponding parameter values:

Von Bertalanffy: To describe the somatic growth pattern, the three parameters of the Von Bertalanffy equation are required (Linf, k and t_0), as follows:

where the L is length, the subscript t is the age, Linf is the asymptotic size, k is the growth constant and t_0 is a scale parameter. When a reference provides sex-specific growth parameters, each set must be entered separately. Be sure to note if the estimate of Linf was made using total length or fork length. This is relevant when the length composition data are not consistent with the estimations of the growth model, since measurements need to be comparable.

Maturity: The maturity curve can be described as a logistic curve with two parameters, L_50 and L_95. These parameters represent the length at which 50 percent and 95 percent of the individuals are sexually mature.

The above equation is one possible parametrization of the logistic curve, where M is the percentage of mature individuals, the subscript l is the length, l_50 is the length at which 50 percent of the individuals are mature and l_95 is the length at which 95 percent of the individuals are mature. However, there are several possible parametrizations for the maturity curve, and the practitioner should convert parameter values to conform to the previous equation. The parameter values can also be extracted visually from the best fit, if numerical values are not available. And when only information related to l_50 is available, l_95 may be approximated using empirical relationships (for example, 1.1 × l_50)

Length-Weight: These parameters describe the relationship between the body weight and length of an individual. Note that for different studies, the value of the parameter a may differ due to the unit of weight used (kilograms, grams, etc.); in order to be comparable, this needs to be standardized among references.

where W is the weight, L is the length and a and b are parameters.

Natural mortality: For natural mortality, only one parameter is needed, which is the estimated value of M. Natural mortality is rarely measured with data, and empirical estimates are used instead. Several empirical methods to estimate natural mortality exist (Then et al., 2015), but practitioners typically tend to pick the value of a preferred method. Exploring the distribution of natural mortality as estimated by several different methods is recommended, for example with the Natural Mortality Tool (Cope and Hamel, 2022), which helps in calculating natural mortality using the most common empirical methods.[8]

3.5 Understanding the life-history parameter inventory

Once the life-history parameter inventory has been completed as indicated in Section 3.4, it can be uploaded into FishualizeR (see Box 3), in order to conduct a preliminary exploration of any uncertainty in the life-history parameters.

Box 3. Visualizing the life-history parameter inventory in FishualizeR

After the “Excel_Template2_LifeHistoryInventory” file is populated with a life-history parameters inventory (as described in Section 3.4), the data can be visualized in FishualizeR. To do so, the user should navigate to the app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR), select the “Life History Parameters” section from the left-hand menu/panel, and upload the Excel file.

Once the file is uploaded, the resulting graphical output presents a visualization of the variability in the main life-history parameter estimates (see Figure A and Figure B).

Figure A. Visualization in FishualizeR of life-history parameter inventory for von Bertalanffy growth curves

Figure B. Visualization of life-history parameter inventory for maturity, length-weight relationship, and natural mortality

Both the inventory process and its visualization in FishualizeR enable a quick visual assessment of availability and variability in key inputs for the most commonly used data-limited assessment methods and empirical approaches. Variability among parameter values can arise due to (i) a random sampling process, (ii) true parameter differences among sexes or geographic areas, or (iii) due to biases introduced by flawed sampling or methodology, as follows:

Variation due to random sampling: Some level of variability is expected among studies, even if conducted under very similar circumstances (e.g. same area, year and sampling gear), due to the random nature of the sampling process. When dealing with this type of uncertainty, it is common practice to examine the extent to which the results of an assessment are affected by changes in the input parameters (i.e. by running the same analysis multiple times with different parameter values). This process is referred to as a sensitivity analysis, and while it is not fully covered in this manual, the practitioner should keep in mind that assessing uncertainty due to parameter values is a critical process (for more detail, see Cope, forthcoming). The life-history parameter inventory provides a range of plausible parameter values that should be explored during a sensitivity analysis.

Variation due to true parameter differences: Some life-history parameters may truly differ by sex or location. For example, some species exhibit sexual dimorphism, and key parameter values, like the asymptotic length, vary greatly among sexes. In these cases, the practitioner should explicitly account for these differences for assessment purposes. For example, Figure 10 shows a species where females tend to reach a greater maximum size than males. This is relevant, as some assessment methods use the spawning potential of the population as a descriptor of stock status, which is mainly driven by the female fraction of the stock. In those cases, it is suggested to use only data and parameters from females. Some complex modelling approaches (such as age-structured integrated models) allow for specific accounting for sex differences, but usually require a large amount of data and technical skills. It is common in many data-limited fisheries for no local life-history parameters estimates to have been generated, and to use existing data from other parts of the world. However, when large differences in life-history parameters are detected among different areas, it is important for researchers to carefully assess which set of parameters may be reasonable for the stock under study. Even within the same country, growth parameters for the same species may differ. These differences may arise either due to a bias in the size structure used for the analysis (due to selectivity) or because of true site-specific differences. The life-history parameter inventory allows for an easy assessment of the level of variation across sex or area, by visual inspection.

FIGURE 10
Visualization in FishualizeR of sex-specific differences in life-history parameters

Notes: Females (left) tend to reach greater maximum sizes than males (right).

Variation due to flawed sampling or methodology: Some parameter values may be outside a plausible range and should be discarded. Although the rationale to identify unreliable estimates will depend highly on each set of parameters, the simplest way to begin identifying outliers is to visualize the parameter distribution in FishualizeR. Some outliers can be easily discarded if they do not conform to the biology of the species, but most cases require a review of the original studies and attention to the sampling protocols and methodology used. For instance, if the maximum observed length is substantially larger than the estimated asymptotic size, this is an indication that the estimation may be unreliable (e.g. because it may have been estimated using data from a severely overfished stock, hence lacking in large individuals). And a methodological difference may arise, for example, when using otolith studies vs scale readings to determine age in finfish (Abecasis et al., 2018). Some methodologies use data that directly describe the process of interest (e.g. by using length-at-age to estimate growth), while others try to describe the process from aggregated data (e.g. by slicing the length frequency distribution into modal components to estimate growth). Expert judgement should be used to assess which methodology can more accurately describe the process of interest. Generally, recent publications should be favoured over older ones, as it is safe to assume that sampling methodology and methods have improved over time. However, this may not always be the best choice, as samples from older papers may be more representative of an undisturbed population than recent ones, and may better describe key parameters such as the asymptotic length (for example, large individuals may be absent in more recent samples of an overexploited population). Some parameters are rarely estimated using data, and are instead calculated using empirical relationships (e.g. natural mortality). When empirical relationships are used however, it is not uncommon for a large range of parameter values to arise. For example, Figure 11 shows a bimodal distribution for all estimations of natural mortality as obtained by several empirical methods. In such cases, deciding which mode better represents the natural mortality of the species can be challenging. The practitioner should review the input data used by each empirical relationship (including longevity, growth, maturity and temperature), or consider each mode as a different hypothesis and conduct a sensitivity analysis (for more detail, see Cope and Hamel, 2022).

FIGURE 11
Visualization in FishualizeR of distribution of estimations of natural mortality (from empirical methods)

[5] See: http://apps.upwellsolutionsllc.com/app/FishualizeR

[6] See: https://www.fishbase.se/search.php

[7] See: https://james-thorson.shinyapps.io/FishLife

[8] See: https://connect.fisheries.noaa.gov/natural-mortality-tool

Chapter 4: Database set-up and management

After inventorying available data streams, understanding the timeline of influential events and compiling life-history information (see Chapter 3), an important step before diving into data cleaning and EDA is proper data management. Although this manual is not focused on database design, it does explore key points on available databases and best practices for maintaining organized datasheets, in preparation for exploratory analysis.

4.1 Databases

A wide range of software and formats may be used for fishery data storage. Ideally, centralized relational databases – or types of databases that store and provide access to data points that are related to one another – are best suited for fishery data storage and management. Relational databases provide many advantages: they avoid data repetition; ensure the validity, integrity and internal consistency of the data; and securely maintain primary data. In terms of platforms, Microsoft Access, MySQL, and SQL Server are among the most popular for hosting relatively small databases, and often include some limited reporting and data analysis capabilities as well. Most are proprietary, but free and open-source options may also be used, such as MySQL’s open-source version and Google Sheets. As always when adopting any new proprietary data storage or management system however, caution should be exercised. For example, before committing to any proprietary vendor, users should assess whether the given platform will allow for future migration to a new system if needed.

In the data-limited fisheries space, there is often no dedicated team available to design and maintain a data management system. Instead, these responsibilities are typically taken on by a small group of people who are also in charge of a broad spectrum of other tasks, such as collecting data in the field, performing data entry, analysing data, and providing management advice to authorities. For this reason, the manual does not explore the specifics of relational databases. Instead, it focuses on the use of simple tabular data (i.e. CSV) files. This file format is commonly available, widely used and well supported by Microsoft Excel (as well as other free alternatives such as Google Sheets), and therefore serves as a practical choice for storing and analysing data.

4.2 Data management

Spreadsheets (e.g. Microsoft Excel and Google Sheets) are an easy-to-use, flexible and popular way to store, analyse and visualize data. But, their significant flexibility also increases the chances of error and inaccuracies. For example, certain character combinations may be automatically converted into dates, dates may be stored differently in different operating systems, different regional settings may denote and interpret decimal places differently, different data formats (text vs numbers) may be stored under the same variable, and data entry errors and typos may occur. When using spreadsheets, researchers should therefore design them to be as “error-proof” as possible. While there is no definitive guide to designing the perfect spreadsheet for fishery data, the following recommendations may prove useful:

- Be consistent with naming: Pick a naming convention and stick with it. From individual sheet names to file names, decide for example how names will be structured and how dates will be denoted, and make this consistent. If necessary, consult online resources for additional advice and tips on file naming.[9]

- Name each sheet: If it is necessary to keep multiple sheets or tables in the same file (for example, to implement data validation), provide a succinct, clear name for each one.

- Maintain a rectangular design: Each column should be represent a single variable and each row should represent a single record. The first row should consist of the column headers or labels.

- Use meaningful headers: Choose names for variables that can be easily understood and interpreted by other users (e.g. “Catch_tons” instead of “CT”).

- Avoid special characters, spaces and commas: Some special characters (e.g. “ñ”, “/” and “´”) may not be easily interpreted by some programming languages when the data are imported. The use of special characters should therefore be avoided, particularly in headers. Spaces should also be avoided in column headers (e.g. “CatchTons” or “Catch_Tons” instead of “Catch Tons”). And commas (“,”) should also be avoided, as some programming languages may interpret them as column separators when importing data.

- Avoid mixing data types: Each column should contain only one type of data (either characters, numbers, dates, etc.).

- Avoid the use of formulae: Databases should only contain raw data. All calculations should be done later in the workflow.

- Keep data spreadsheets separated and clean: Although tempting, no graphs or analysis should be included within files where raw data are stored.

- Distinguish zeros from no data: It is important to distinguish between true zeros (informative) and no data (non-informative). Typing “NA” (not available) is an efficient way to identify cells with no data or missing data. When dealing with cells with no data, the most important thing is to be consistent. Use either “NA” or a blank, as opposed to any other notation (such as “NaN”, “missing”, “not available”, etc.).

- Avoid colour coding: Colour coding specific cells to add meaning is not advisable. If needed, add a new column to include the information that the colours would indicate, and store it explicitly.

- Be consistent with the date format: Depending on the operating system, Microsoft Excel may handle date data differently. A best practice is to always store dates in three separate columns (for year, month and day) and convert to dates later in the workflow, or to consistently enter dates as YYYY-MM-DD. The “LEN” function in Excel (see Figure 12) counts the number of characters in a cell. Having a different number of characters in a date column is an indicator that the date format is not consistent.

FIGURE 12
Using the “=LEN” function in Excel to count total number of characters in a cell

SOURCE: Exceljet. 2023. Excel LEN function. In: Exceljet. Salt Lake City, USA. [Cited 2023]. https://exceljet.net/functions/len-function

- Think carefully about “Comments” or “Notes”: Lengthy notes may make sense while collecting data, but are usually difficult to track and use efficiently when stored in a database. Some information in a notes field may count as its own variable (e.g. price of catch and weather conditions), and should be stored separately. A note should always explain the specific row of data it pertains to, for instance to explain an outlier (such as a specific fishing vessel having caught an anomalous or record amount on a certain day, in relation to an abnormally high catch value).

- Create a data dictionary or metadata sheet: It is very helpful to have a plain- text file that enumerates each column in the database, provides a description of the data stored, and potentially includes detail on how data were collected. This sheet can help ensure consistency among individual staff who are tasked with data collection or data entry, and may also be extremely useful in training new staff on consistency with data entry protocols.

- Use data validation: Spreadsheets allow all kinds of typos and inconsistent entries for the same value (e.g. “Trawling”, “Tawling”, “TRAWLING” and “Trawlng”). This variation in the way a value is stored may not seem a major problem at first, but can impact any filtering or classification operations. Excel provides a data validation tool that allows users to control what may be entered in a cell, thus reducing the chances of errors (see Box 4).

- Use data and variable coding: When inputting categorical or factor variables (such as gear type), it is useful to define the categories before entering the data, to avoid having more than one name for the same entry (e.g. “pole and line” and “handline” may be grouped). Conversely, the use of local dialect to name gear often leads to having multiple gear types that are in fact the same (e.g. “gillnet” may have up to six different name variations, either because of different mesh size or the way the gear is deployed).

- Use consistent species names: Avoid using common or colloquial names under which multiple species may be grouped, or names that may have a different interpretation depending on the region. A best practice is to record both scientific and common names.

Box 4. Data validation in Excel

The data validation feature in Excel allows users to control what can be entered in a given cell. For example, values for a cell can be selected from a drop-down list, thus avoiding typos. This could be used to pre-define a list of species names or port names, which can then be selected by clicking the appropriate option or value from the drop-down list (see Figure A).

Figure A. Use of the Excel data validation feature, shown with the drop-down list

To activate data validation in Excel, select the column of interest, select the “Data” tab from the main menu, and click the “Data Validation” button in the “Data Tools” group (see Figure B).

Figure B. Accessing data validation tools in Excel (orange arrows)

When the “Data Validation” button is activated, a popup will appear. In the “Settings” tab, the user can specify the type of data that can be entered in a cell. When the “List” option is chosen, the user can specify the range of cells from which the values will be selected, as previously prepared for this purpose (see Figure C).

Figure C. Specifying drop-down list values in Excel data validation

The tool also allows for the creation of customized validation criteria (such as a range of values) and customized error messages for incorrect entries.

Figure D. Setting up customized data validation error alerts in Excel

(Keep in mind that Excel versions may vary depending on the operating system. For more information and instructions on using the Data Validation Tool, see the Microsoft Support website – for example: https://support.microsoft.com/en-us/office/apply-data-validation-to-cells-29fecbcc-d1b9-42c1-9d76-eff3ce5f7249)

4.3 Data formats

There are usually two ways of storing data for spreadsheet-based fishery databases: long and wide format. In the long format, as suggested in Section 4.2, each row represents a single record and each column represents a single variable (for example, each row reflects the catch for a given species in a given trip, as in Figure 13, left). In the wide format, each row represents a data point and each column represents an attribute of interest (for example, each data point is a trip and the catch for a given species is specified in the columns, as in Figure 13, right). FishualizeR handles data in long format only, but data in Excel can be transposed or converted from wide to long format (or vice versa) as needed.[10]

FIGURE 13
Schematic representation of sample dataset in long format (left) vs wide format (right)

Both data formats in Figure 13 contain the same information, and each has certain advantages and disadvantages. The wide format makes data entry easier – as some information does not need to be duplicated – but can result in a large and unwieldy number of columns, and may need to be updated if new instances of the same variable appear in the data. The long format requires more time-consuming data entry, but is better suited for efficient use of filtering and summary tables or statistics. All else being equal however, the long format is the better way to store data for use in analysis.

[9]    For example, see: https://www.youtube.com/watch?v=ES1LTlnpLMk

[10] For example, see: https://www.geeksforgeeks.org/how-to-convert-data-from-wide-to-long-format-in-excel/

Chapter 5: Overview of data preparation and exploratory data analysis

5.1 Workflow

Data preparation and exploratory data analysis (EDA) are iterative processes in which data sources are thoroughly examined to correct any errors and typos, to detect outliers; and to understand the data before conducting formal statistical analysis. The two processes are explored in detail in Chapter 6 and Chapter 7, respectively, whereas this chapter serves to provide an initial introduction and overview.

Data preparation ensures that the database is organized, free of errors and ready for analysis. At this stage, it is necessary to detect faulty or inaccurate entries and replace, modify or delete such records accordingly. This process is usually time-consuming and iterative (see Figure 14).

FIGURE 14
Conceptual view of workflow and tools for data preparation and exploratory data analysis

EDA begins after a curated database has been obtained, with the goal of learning from the data before conducting any formal analysis. Saving and setting aside a “raw version” of the original database is strongly recommended in this context (as are regular backups), given the high risk of accidental alteration or deletion of the original data.

Preparing a database and performing EDA requires extensive use of quantitative, non-graphical techniques (such as summary tables or statistics), as well as graphical techniques. Quantitative techniques involve the calculation of key summary statistics on the data. Graphical techniques involve summarizing the data via basic visual or diagrammatic outputs. The types of graphical outputs available will depend on the type of data in hand (e.g. numerical, categorical or date data), and on whether the analysis being performed is univariate or multivariate.

5.2 Common tools

Commonly employed tools for data preparation and EDA include summary statistics or tables, univariate plots and multivariate plots. Each of these tools are defined here and explored further in the following two chapters.

5.2.1 Summary statistics

Summary statistics are the simplest form of data analysis, in which the data are typically analysed in terms of a single variable. Their main purpose is to describe the data in a succinct manner, so as to support the detection of outliers and errors. Table 4 provides an overview of the most common univariate calculations used when analysing a single variable in this regard.

Table 4. Overview of common summary statistics

Statistic

Data type

Definition and usage

Count

Numerical, categorical, date

Counting the number of records provides a quantitative measure of how many records are available for analysis. Counting may provide information on sample size.

Number of zeros or missing values

Numerical, date, categorical

Counting the number of records with a value of zero or with a missing value allows for the detection of blanks. Some records cannot be included in an analysis if data are missing (for example, catch per year requires the year). Zeros may be acceptable values for some variables (e.g. catch), but not for others (e.g. fish length).

Unique values

Can be used on numerical or date data, but most useful for categorical variables

Listing the unique values observed allows for the detection of typing errors in the database. For example, the same gear can be entered as “Trawlng” and “trawling”.

Min/max

Numerical, date

Displaying the minimum and maximum values observed in the dataset allows for the identification of possible errors or outliers in the database.

Mean, median, mode, and standard deviation

Numerical

The mean, median and mode are estimates of the “middle” of the dataset. These elements are commonly referred to as “measures of central tendency” in statistics, and are useful in characterizing the average value of a variable. The standard deviation is a measure of how variable the data are, and is defined as the average distance between the data and the mean.

5.2.2 Univariate plots

While the use of text-based summary statistics or tables provides a simple mechanism to describe the data and hint at potential issues with outliers, the use of visual aids is almost mandatory to fully capture the overall nature of the data distribution for data preparation and EDA. The most common graphical tools for univariate analysis include histograms, density plots, box plots and violin plots, as described in Table 5.

Table 5. Overview of common univariate diagnostic plots

5.2.3 Multivariate plots

Similarly to the univariate case, summary statistics and graphs can also be used in multivariate analyses. The type of visual tools that are available will depend on the type of data in hand, and the number of variables included in the analysis. Common types of multivariate analyses and plots are summarized in Table 6. (For guidance on generating these plots in FishualizeR, see Box 10.)

Table 6. Overview of common multivariate analyses and plots

Chapter 6: Data preparation

The process of data preparation, also known as data cleaning, is aimed at gaining familiarity with the data, identifying outliers, and ultimately correcting errors. Data cleaning is performed on raw data before any data aggregation or summaries have been conducted, and should be done by experts familiar with the fishery as well as with how the data were collected. Most importantly, it is an iterative process that involves error detection, resolution, and repetition in order to check and validate corrections.

If data are only available in aggregated format (for example, total catch per year), users should have a clear idea of how the aggregations were performed, and of any type of data modification or cleaning that was previously conducted. It is essential that the practitioner has access to the “rawest” data possible, because once data have been aggregated it is nearly impossible to detect faulty records.

In each iteration of the data preparation process, sequential corrections to the raw database will be performed. As such, it is critical to have a version control system in place to track all changes, in case it is necessary to revert to a previous version of the database. (For more on version control, including a logical way to organize and name different versions of the database in Excel, see Box 5.) Version control is most easily done by creating a computer program that reads in the raw data, corrects it, and saves a “clean” version of the database (using, for example, R or Python), but requires training in specific programming languages.

Box 5. Documenting changes and corrections, resaving data, and version control

When performing data preparation and cleaning, version control is essential. Each time a correction is completed, the file should be resaved – using “Save As” in Excel (see Figure A) – to maintain version control. All such versions should be kept separate from the raw or original data file.

Figure A. Using the “Save As” function in Excel

For example, the user may simply start with “Data_X_raw”, and resave as “Data_X_ corrected_date.csv”. As corrections are completed, it is important to keep a log of changes that have been made between the raw and corrected data files.

If a new batch of data is added (such as a new year of data collection), it is advisable to work with the file and format of “Data_X_corrected_date.csv” as the new “raw data”, so that the cleaning and correction process will not have to be repeated for the older records. However, after adding the new batch of data, the correction process should be performed again to identify and correct any errors that may have occurred during the most recent data entry process. (For additional instructions and best practices on using R for resaving cleaned data, see SFG, 2019.)

For practical purposes, some users prefer to keep data batches separate – for example, by year(s) or location. In this case, another approach may be to perform the data cleaning process for each batch of data, applying the same log of corrections, and then combine the corrected datasets into a single, consolidated file (i.e. by combining all the rows from each separate, corrected dataset). Regardless of approach, all original data files should always be safely saved.

Although this manual does not focus on data collection programme design or data entry protocols, it should be noted that the process of logging errors may be used to improve broader data collection protocols. If any changes or improvements are made, these should be carefully noted in the timeline of influential events (see), as these changes may impact data interpretation. (See also Section 4.2 for brief guidance on setting up databases and database management.)

Throughout this chapter, specific examples from real fishery applications, with real datasets, are used to demonstrate the types of diagnostics that may be performed in order to prepare and clean data sources. Confidential information has been removed in all examples. This is not an exhaustive selection of diagnostics, but rather a sample of common, stepwise procedures that may be followed to explore, clean, correct and better understand a dataset. Instructions are included on how to perform data exploration using Excel, and several boxes provide guidelines on how to visualize information in FishualizeR.

6.1 Initial data review

A best practice when starting the data preparation process is to sequentially check each column of the database (it is advised to proceed from left to right). As corrections are made, they should be logged, and the data should be resaved as suggested in Box 5. Instructions in the following sections are specific to either text data or numerical data (Table 7).

Table 7. Overview of text or categorical data as compared to numerical data

Text or categorical data

Numerical data

Categorical or text data consist of names – such as names of geographic areas, vessel names and species – as well as notes and/or comments. Summary statistics or tables are typically used to explore and clean text data. Common errors include misspelling and inconsistent capitalization or codes. For example, a species may be spelled correctly as “Huachinango”, and misspelled as “Huachinago”.

Numerical data are elements in a dataset, such as catch values. They include both date data and geographic location data (such as latitude and longitude).

6.1.1 Cleaning text or categorical data

If the column includes text or categorical data, the goal is to check for and correct text errors and typos; these include spelling mistakes, capitalization errors, duplicate entries and blank or missing values. The following steps may be applied when cleaning text or categorical data in Excel (for instructions in FishualizeR, see Box 6):

1. Check for inconsistencies

Use the “=UNIQUE”’ function in Excel to return a list of unique entries or names from the data. In Figure 15 for example, this function is being used to list all the unique values in the “Area” column (i.e. Column A, from row A1 to A3489).

FIGURE 15
Using the “=UNIQUE” function in Excel to list unique entries/values for a given column or set of rows

Notes: In this example, there are a total of five repeating entries or values used in the “Area” column (i.e. Column A): “A”, “Area A”, “AreaA”, “B” and “Area B”.

Applying the “=UNIQUE” function allows the user to check for any (i) capitalization errors; (ii) typos; and (iii) abnormal characters or spaces. If the data are categorical, the user can also ensure that all entries correspond to one of the predefined categories or names. Once errors are identified, they can be corrected in Excel using the “Find and Replace” function (i.e. by clicking on the “Home” tab, then “Find & Select”, then “Replace”), as shown in Figure 16. This check may be conducted more efficiently by using FishualizeR (see Box 6).

FIGURE 16
Using “Find and Replace” in Excel to locate and correct each individual occurrence of an error

Notes: In this example, the function will find every occurrence of “Area A” and replace it with “A”.

2. Check for blank cells, missing data and “NA” values

There may be some records among text or categorical data for which the data were not collected by the enumerator, or entered into the database. If categorical or text data cells have no data (for example, if there is no name for the sampling location or no name for the vessel), what should be done? In such cases, original data sources should be consulted to look for the values in question. If the value cannot be located, type “NA” (i.e. not available) to indicate each instance of missing data. It is a best practice to use the exact form “NA” consistently, as opposed to any other notation (such as “NaN”, “missing”, “not available”, etc.), in order to distinguish between true zeros (informative) and no data (non-informative). These can later be filtered out during subsequent analyses as needed.

3. Consider any notes

Datasets often have “Notes” columns, which provide important information for data interpretation. “Notes” should be treated carefully – although they may be removed when conducting quantitative explorations or analyses, they should be referenced to interpret the data.

4. Check for commas

It is a best practice to ensure there are no commas in text or categorical data, as they will cause errors when the data are stored in CSV file format, or read in FishualizeR. Particular attention is recommended for “Notes” and similar fields, where commas are often found. For example, the phrase “Estimated using length frequency, only from females” will be interpreted as two different columns by most software and may complicate data reading. If needed, commas can be replaced with other delimiters such as spaces or semicolons (for example, use “Traps; Pots” instead of “Traps, Pots”).

5. Document the process and log corrections

The issues discussed here are likely to repeat in the future, as they commonly stem from errors in the original data collection protocol, or from human errors inherent to the data entry process. In order to address and offset this risk, it is essential to document the process for data cleaning and to record any corrections or issues. This can allow for the data collection protocol to be reviewed and updated, with the aim of minimizing future errors. And if the analysis is repeated in the subsequent year(s) for example, the same cleaning process may be followed.

Box 6. Cleaning text or categorical data in FishualizeR

To clean text or categorical data in FishualizeR, the user should navigate to the app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR), select the “Upload Data File” section from the left-hand menu/panel, and upload the data file to be cleaned (see Box 1).

Alternatively, to test this process, a sample or mock dataset is available as part of the downloadable zip file within the “Introduction” section of FishualizeR. Simply download the zip file from the link provided, extract the “Mock_length_freq_area.csv” file and upload it via the “Upload Data File” section, as previously indicated.

Once the file is uploaded:

1. Navigate to the “Text/Categorical Data” subsection under the “Univariate Data Exploration” section of the left-hand menu/panel.

2. At the top of the main screen, select the column of interest to see its unique values, along with the count for each unique value as displayed in the table. FishualizeR detects all unique values, for example, separating out blank or unfilled cells from cells with spaces. In case of the latter, the area between the single quotes (‘ ‘) indicates the number of spaces.

3. Selecting for each column as needed, check for any (i) capitalization errors; (ii) typos; and (iii) abnormal characters or spaces.

4. To correct any errors, return to the data file, make corrections, and resave.

Figure A. View of FishualizeR, showing a summary table of unique values for a chosen column in a data file

Notes: In this example, the unique values for all entries in the “Area” column are shown, along with the counts for each (under “Count”).

6.1.2 Cleaning numerical data

If the column includes numerical data, the goal is to check for and correct outliers, incorrect units, and blank or missing values. The following steps are recommended:

1. Make sure all data are numerical

First, check to ensure there are no text or special characters. In cases where there are typos such as “12*” instead of “12”, correct as needed. The “AutoFilter” feature in Excel provides a useful tool for detecting these types of problems. Once “AutoFilter” is activated (click “Data”, then “Filter”), a filter control is added to each column of the table as a header arrow. Clicking on a header arrow will open a filtering menu with a list of all the unique values for the column, sorted first by numerical value and then by character value. Check the bottom of the list to see if character values are mixed in with numerical values.

2. Check for inconsistencies

Check for inconsistencies in format (for example, thousands indicated by commas instead of decimals, or vice versa). In general, if the same version of Excel was used throughout data entry and storage, this should not be an issue. But if data were stored in different versions (for example, in English and Spanish versions) and combined later, this may cause inconsistencies.

3. Check for blank cells, missing data, 0s and “NA” values

It is important to consider the differences between a blank cell or white space, and an intentional value of zero – as usage can often be confusing and inconsistent. How should cells with missing or blank values be treated? For example, what if there is no year, or no value for catches? In such cases, original data sources should be consulted to look for the values in question. If the value cannot be located, type “NA” (i.e. not available) to indicate each instance of missing data. It is a best practice to use the exact form “NA” consistently, as opposed to any other notation (such as “NaN”, “missing”, “not available”, etc.), in order to distinguish between true zeros (informative) and no data (non-informative). For example, a particular trip for which the enumerator was not able to record the catch should be indicated/noted with “NA”, while a trip in which 0 (zero) kg of catch was recorded should be noted with a “0”. These can later be filtered out during subsequent analyses as needed.

4. Review date data

Date data often require special, careful attention. Analysts from different countries may also interpret date data differently (for example, depending on the country, “03/10/1989” may be interpreted as 3 October 1989 or as 10 March 1989). A best practice is to use separate columns for the day, month, and year, which can then be combined later in a consistent date format. Unfortunately, date formats are often already mixed in the raw database, presenting an issue that is difficult to resolve. The functions “Year”, “Month” and “Day” in Excel will extract the corresponding values from a date. For example, using the function “Month” on the date 12/14/2001 will return the number 12 if the date format is mm/dd/yyyy, but will return an error if the format is dd/mm/yyyy (14 is not a valid value for a month). This diagnostic allows for the identification of some problematic records but will not work in cases like 03/02/2001 (as both 3 and 2 are valid month values). However, mixed formats usually come in blocks, as data are often sequentially added to the database in batches. Detecting a few problematic records may therefore serve to identify the broader data segment around them that requires date format review.

For records referring to trips, it is common to record the start and end date of each trip. Logically, the latter (i.e. the return or arrival date) should not precede the departure or start date. A good way to check this is to subtract the departure date from the start date and directly analyse the trip duration. Trip duration cannot be negative, and should not exceed expert-derived thresholds for plausible trip durations (for example, a trip duration of 400 days is not practical for a small-scale, coastal fishery). If only certain fields are available (e.g. year), a best practice is to enter NA in the other columns (i.e. month and/or day).

5. Review location data

Location data may be stored in a variety of numeric formats; for example, latitude may be stored as degrees, minutes and seconds (e.g. “-421008”) or in decimal format (e.g. “-42.16”). Moreover, it is not uncommon for location values to be entered as strings of characters (e.g. “42º10' 08" S”) rather than numbers; such data cannot be analysed properly unless converted to numerical values. It is therefore important to ensure all location data are in a consistent format ( wherever possible, decimal degrees are preferred). If location values were entered as character strings, the “MID” function in Excel can help to extract meaningful quantities (i.e. degrees, minutes and seconds). For example if a value was entered as “42º10' 08" S”, then “MID (cell,1,2)” will extract the first two values (i.e. 42). Location may occasionally be entered not as a numerical field, but as text (e.g. “area 1”, “area 2” or “area 3”). In this case, follow the guidelines for cleaning text or categorical data as provided in Section 6.1.1.

6.2 Identifying and resolving outliers

Outliers are commonly understood to be data points that exhibit major deviations from other data points. However, standard guidelines for how to deal with outliers are limited. For example, based on a literature review, Aguinis, Gottfredson and Joo (2013) found 14 different definitions of what constitutes an outlier, most of which were based on criteria that require a complex, post hoc analysis after the statistical model has been fitted applied – for example in the case of deviations from a fitted curve (analysis of the residual values) or the influence of a data point on parameter estimation (computation of the leverage).

As the focus of this manual is to prepare and understand the data before conducting any statistical tests or fitting of models, attention is focused on the detection and handling of outliers that do not require a statistical model. In this context, the following sections concentrate on two types of outliers as defined by Aguinis, Gottfredson and Joo (2013) – error outliers and interesting outliers. Error outliers are the result of improper data entry, computation or measurement. Interesting outliers on the other hand, result from accurate data, and in deviating significantly from other data points, they may reveal surprising information or knowledge. While similar techniques can be used to detect either kind of outliers, they should be handled differently. Error outliers are illegitimate observations and should either be corrected or removed from the database. But interesting outliers should be studied in detail to better understand the possible causes for their deviation from the bulk of the data. Failure to do so may result in lost opportunities to develop new insights and understanding of the variables under study.

Dealing with outliers is a two-step process that involves (i) identification, and (ii) handling. While a thorough data cleaning process (see Section 6.1) should minimize error outliers in the database, some may still remain undetected. The following sections therefore provide guidelines on the use of visual aids and simple statistical techniques to identify outliers and study them in greater detail. The techniques for outlier identification can be classified either as univariate or multivariate. Univariate analyses focus on detecting unusually large departures for a single given variable, whereas multivariate analysis requires examining the joint distribution of two or more variables, and identifying significant departures from the bulk of the combined data. Fisheries data is inherently multivariate, reflecting the complex and interconnected relationships among biological, environmental and the nature of fishing operations. As a first step in the outlier identification process, each variable can be addressed as an univariate case. However, a multivariate approach is mandatory follow up process: while univariate diagnostics may give the impression that no anomalous records are included in the database, issues may become evident when critically scrutinizing the relationship between two or more variables. Similarly, clusters of data that seem anomalous when analysed by one variable may have a logical explanation when explored using another. For example, for a given species, the dataset of lengths and the dataset of total weights may seem free of anomalies when considered separately, but analysing the two variables together may reveal records for which the weight-at-length is anomalous – for example, a fish of 1 cm in length that weighs as much as a fish of 100 cm in length.

Summary tables and quantitative indicators are valuable tools for uncovering outliers in the univariate case, but their application becomes more intricate in a multivariate setting. Using quantitative indicators in to identify outliers involving more than one variable requires advanced modelling techniques such as clustering and principal component analysis. These approaches are beyond the scope of this manual and the use of summary statistics and quantitative indicators will be only addressed for the univariate case (6.2.1.1). Visualization techniques are useful to detect outliers both in the univariate and multivariate case (6.2.1.2).

Once outliers are identified, they need to be classified either as errors or as interesting outliers. Depending on the type, different courses of action are then possible: to “correct” or “delete” in the case of error outliers, or to “explain” in the case of interesting outliers. If an outlier cannot be classified as an error outlier (i.e. it cannot be corrected or deleted), it should be considered an interesting outlier, emphasizing the importance of exploring and understanding these data points for richer insights.

6.2.1 Outlier identification

After the data cleaning phase, obvious issues with text and numerical data should have been fixed, however, some problems may remain. For example, the year “2010”, maybe entered incorrectly as “2001”, or part of the data may have been entered in centimetres (cm) and part of it in millimetres (mm) in the same column. The following sections use simple calculations and graphical tools to provide guidelines and examples for identifying problematic records.

6.2.1.1 Utilizing summary statistics and quantitative indicators to identify outliers

Simple summary tables can be created both using Excel or FishualizeR (see Box 8 for numerical data or, if working specifically with dates, see Box 9). In Section 5.2.1 the most common summary quantities have been described (Table 4). Once a summary table has been constructed outliers can be identified by:

1. Looking at the minimum, maximum, and quartiles in a basic summary table

This is particularly useful to quickly detect obvious error outliers in a dataset. For example, if negative values exist for a variable that cannot take values lower than zero, those points can immediately be classified as error outliers. Similarly, if the maximum length of a fish is 70 mm, and far higher values are present (for example 800 mm), they can also be classified as error outliers. (To create a summary table in Excel, see Box 7; if working with numerical data in FishualizeR, see Box 8; or if working specifically with dates, see Box 9.)

2. Use simple quantitative indicators

Summary statistics of the data can be combined to calculate cut-off values, so as to identify potential outliers (i.e. by calculating the distance of each point to some measure of the central tendency of the data). While these methods are useful for sorting through large amounts of data and identifying observations that warrant extra attention, it should be noted that outliers detected in this manner should not be automatically or immediately eliminated.

The Z score : If the variable under study is expected to be symmetrical (i.e. normal), a Zscore can be calculated. The Zscore is defined as:

where x is the data point, and sd is the standard deviation.

Data points with Zscore values higher than 3 can be considered outliers, the rationale being that if a variable is normally distributed, 99.7 percent of the data will lie within 3 standard deviations. However, the Zscore does not perform well for flagging outliers in the case of very extreme values, or of small sample size (for example, it will never detect an outlier if the sample size is smaller than 12). In such cases, a more robust approach involves the use the modified Zscore. This requires first calculating the median absolute deviation (MAD) of the data as:

and the modified Zscore as:

Interquartile range: Another robust method for outlier detection involves the interquartile range (IQR), which is calculated as: IQR = Q3 − Q1.

Q1 is the first quartile (i.e. the values within which the first 25 percent of the data points are found, when arranged in increasing order) and Q3 is the third quartile (i.e. the values within which 75 percent of the data points are found). Outliers are defined as those points below the range of [[Q1−(1.5 *IQR)] – [Q3 + 1(.5 *IQR)]] (see Figure 17).

FIGURE 17
Schematic representation for outliers identification using the interquartile range method

Notes: The interquartile range is indicated by “IQR”. The whiskers are indicated by “Q1 − 1.5 *IQR” and “Q3 + 1.5 *IQR”.

Box 7. Creating a summary table by using a pivot table in Excel

Pivot tables allow for the grouping of data in diverse ways, thereby enabling quick comparisons. This example uses an extract of a logbook dataset (many fields and/or rows have been removed for ease of visualization). The dataset contains 93 368 rows. Each row records trip attributes (see Figure A), along with the catch for each species (one row per species).

Figure A. View of sample logbook dataset in Excel

1. In this example, the goal is to calculate the mean and maximum catch per region per year, by gear type. The first step is to select the entire table; then, select the “Insert” tab from the main menu, and click “PivotTable” in the “Tables” group (see Figure B).

Figure B. Accessing pivot tables in Excel (orange arrow)

2. When “PivotTable” is activated, a pop-up will appear, asking the user to specify where to store the new table.

Figure C. Pop-up menu to select data for a pivot table and where to store it

3. To keep summary statistics separate from actual data, it is advisable to store the pivot table in a new and different worksheet.

4. The user may then drag and drop the different variables into columns and rows. In the “Values” area (see Figure D), the summary statistic to report is specified.

Figure D. Pivot table fields

5. The result is a comprehensive data summary (see Figure E).

Figure E. Pivot table as created from sample logbook dataset

In addition to data comparison, pivot tables may also be used to aggregate data – by selecting the appropriate function (e.g. “Sum” or “Average”) for the desired categories (e.g. total catch per area per year, or mean size per year).

6.2.1.2 Utilizing plots to identify outliers

Plots are widely regarded as superior tools for identifying outliers compared to summary tables. A visual representation allows the analyst to intuitively grasp the distribution of the data and identify patterns, especially those involving significant deviations from the mean. Visual intuition tends to be more immediate than interpreting numerical values presented in summary tables. Moreover, plots are more effective tools for communicating results to a broader audience. In the univariate context, histograms, boxplots, density plots and violin plots (Table 5) offer an insightful glimpse into the central tendency, spread and potential outliers of the data. Multivariate datasets exist in a high-dimensional space, where each variable represent one dimension. Combining multiple dimensions in a single plot may help to identify anomalous records. However, the number of dimensions that can be included in a single plot is limited. Faceting is a valuable technique for dealing with high-dimensional data. It involves stratifying the data by one or more variables, calculating the same statistics or performing the same plot for each of several such strata, and then comparing the resulting smaller charts side-by-side in a grid format. The multivariate data are thus split into a suite of smaller visualizations, each having a similar format, so as to better detect and understand patterns or trends. For example, as shown in Figure 18, length frequency data may be faceted by gear type and then by sex, using different colours to plot the sex against each facet of gear type. Other examples of faceting may include catch per trip by fleet and by fishing area, and more complex faceting may be performed by splitting across additional variables.

FIGURE 18
Simplified view of data faceting, showing sample length frequency data by gear type and sex

Notes: In this example, a plot of length frequency data (top left) is faceted by gear type and sex, allowing the original plot to be split and replicated across gear type (blue boxes) and sex (red and green lines). With faceting, it becomes clear that anomalous records may be present for Gear 3.

6.2.1.3 Utilizing FishualizeR to identify outliers

While Excel is a widely uses software with many capabilities, it may have some limitations when it comes to creating and customize faceted plots, especially in comparison to specialized data visualization tools. FishualizeR allows for easy identification of outliers by simultaneously creating summary tables. The tool´s capabilities extend to both univariate (see Box 8; and for identifying outliers in date data, see Box 9.) or multivariate diagnostic plots (see Box 10). It’s important to note that the current version of FishualizeR does not have a filtering and data replacement function. However, once issues are identified through the visualizations generated, Excel’s filter function can be employed to isolate unreliable records. Analysts can then inspect these records more closely, take corrective action if necessary, and subsequently re-upload the refined file to FishualizeR for further analysis.

By combining the strengths of FishualizeR and Excel, users can efficiently identify and address outliers in their datasets. FishualizeR's visualization capabilities complement Excel's data manipulation functionalities, providing a comprehensive approach to outlier detection and data refinement.

Box 8. Identifying outliers with numerical univariate analysis in FishualizeR

To use FishualizeR to conduct numerical univariate analysis, including outlier identification, the user should navigate to the app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR), select the “Upload Data File” section from the left-hand menu/panel, and upload the data file to be analysed (see Box 1).

Alternatively, to test this process, a sample or mock dataset is available as part of the downloadable zip file within the “Introduction” section of FishualizeR. Simply download the zip file from the link provided, extract the “Mock_biometric_area_trip_duration.csv” file and upload it via the “Upload Data File” section, as previously indicated.

Once the file is uploaded:

1. Navigate to the “Numerical Data” subsection under the “Univariate Data Exploration” section of the left-hand menu/panel (see Figure A).

2. Use the first drop-down list in the main screen to select the column of interest. (Note that only the columns composed strictly of numbers will appear as options. If a column is not shown as an option, it likely has text for at least one data point – for example, “unknown”. This must be remedied before using the column to conduct numerical analysis in FishualizeR.)

3. Use the second drop-down list to select the type of plot:

1. histogram;

2. box plot or violin plot (to visualize data distribution/mean); or

3. density plot (i.e. histogram + smoother; for example, as for plotting life-history parameters).

4. For each case or combination, summary table information (including on missing data rows) is shown above the resulting graphical output (see Figure A).

Figure A. View of FishualizeR, showing histogram for numerical univariate analysis

Box 9. Identifying outliers for date data in FishualizeR

To use FishualizeR to generate summary tables and plots that help with date data cleaning, outlier identification and exploratory analysis, the user should navigate to the app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR), select the “Upload Data File” section from the left-hand menu/panel, and upload the data file to be analysed (see Box 1).

Alternatively, to test this process, a sample or mock dataset is available as part of the downloadable zip file within the “Introduction” section of FishualizeR. Simply download the zip file from the link provided, extract the “Mock_biometric_area_trip_duration.csv” file and upload it via the “Upload Data File” section, as previously indicated.

Note that FishualizeR requires dates to be formatted either as three separate columns (e.g. Year, Month and Day) or as six separate columns (e.g. if start and end dates are provided, Start Year, Start Month, Start Day, End Year, End Month and End Day). While a separate numerical analysis of each column may be performed to detect errors/outliers, properly identifying these variables as dates makes the process both simpler and more powerful.

Once the file is uploaded:

1. Navigate to the “Date Data” subsection under the “Univariate Data Exploration” section of the left-hand menu/panel (see Figure A).

2. If viewing a date range (i.e. if start and end dates are provided), check the box “Is this a date range?”.

3. Use the drop-down lists to select the columns containing the date’s year, month and day.

4. If viewing a date range, select the columns containing the end date’s year, month and day.

5. Click “Plot Dates”.

6. Depending on the columns selected, a summary table and histogram are displayed (see Figure A and Figure B).

Figure A. View of FishualizeR, showing date analysis for dataset containing start and end dates

Figure B. Histograms showing start and end dates in years (top left and right), and date range in days (bottom left)

Box 10. Visualizing and understanding multivariate numerical data in FishualizeR

To visualize and explore multivariate numerical data in FishualizeR, the user should navigate to the app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR/), select the “Upload Data File” section from the left-hand menu/panel, and upload the data file to be analysed see Box 1. Alternatively, to test this process, a sample or mock dataset is available as part of the downloadable zip file within the “Introduction” section of FishualizeR. Simply download the zip file from the link provided, extract the files, upload via the “Upload Data File” section, as previously indicated.

Once the file is uploaded:

1. Navigate to the “Multivariate Data Exploration” section of the left-hand menu/panel.

2. Use the relevant drop-down lists in the main screen to select a first variable of interest to plot and, if desired, a second variable to plot against (see Figure A).

3. Select the type of plot:

1. histogram (available when plotting one variable);

2. density plot (available when plotting one variable);

3. box plot (available when plotting one variable);

4. violin plot (available when plotting one variable); or

5. scatter plot (available when plotting two variables).

4. Select from the available grouping options as follows:

1. colour – i.e. to colour the data based on the given attribute;

2. factor for the x-axis (available for box and violin plots) – i.e. to subset of data based on the given attribute, and plot each subset on the same graph;

3. facet – i.e. to subset the data based on the given attribute, and plot each subset on its own graph; and

4. facet scales – i.e. to set the scales for faceted plots.

5. Click “Plot Data”.

6. Depending on the parameters selected, the resulting graphical output presents a visualization of the multivariate numerical data (see Figure B). If the data inventory file was also previously uploaded, the timeline of influential events will also be automatically displayed below it, so as to enable better interpretation of the multivariate visualization.

Figure A. View of FishualizeR, showing drop-down lists for visualizing multivariate numerical data

Figure B. View of FishualizeR, showing multivariate visualization (top) and timeline of influential events (below)

Notes: The timeline can be referenced when interpreting the data.

Caution is recommended when visualizing binned or frequency data in FishualizeR. For example, if the dataset is in bins where frequency is counted, FishualizeR will count the number of individual rows, and not the number of actual observations.

6.2.2 Handling outliers

Identifying outliers can employ various approaches, as described above, yet handling them lacks a one-size-fits-all prescription. Once outliers are pinpointed, the subsequent steps depend on the nature of the data and the classification of outliers.

If certain data points are classified as error outliers, the appropriate course of action involves either removing them from the dataset (6.2.2.1 ·Example 1) or correcting those values (6.2.2.1 Example 2). Correcting error outliers can be a time-consuming process, particularly if it necessitates revisiting original paper records (Example 3). However, in instances where the source of error is easily discernible, bulk correction of outliers may be feasible (Example 2). In contrast, there are situations where decisions on how to handle outliers cannot be made without seeking expert consultation (6.2.2.1 Example 4). Expert input becomes crucial for cases where the context, domain-specific knowledge, or the nature of the data requires a nuanced understanding. Experts can provide valuable insights into whether an outlier represents a genuine anomaly or if there are legitimate reasons for its existence.

6.2.2.1 Removing or correcting error outliers: examples

Univariate approach

Example 1. A first example looks at fishing trip data, which are usually recorded by initial departure and return or arrival dates, such that the duration of the trip may be calculated from these data. The histogram in Figure 19 uses a sample dataset to plot the count of trips (y-axis) of varying trip duration in days (x-axis) and indicates the presence of negative values for trip duration. Obviously trips with a negative value for duration are outliers and require correction. Beyond some data entry issues, some data protocols record both the date and time for trip departures but record only the time for returns. If in these cases there are trips that return after midnight, the calculated trip duration will be negative, since the hour of return is less than the hour/time of departure. Recording both the departure and return dates is therefore strongly recommended in any monitoring protocol.

FIGURE 19
Histogram in FishualizeR, showing count (y-axis) of fishing trip durations in days (x-axis)

Notes: In this example, there are trip durations with negative values (i.e. less than 0 on the x-axis), indicating the presence of error outliers in the dataset.

Ideally, the original records should be consulted to correct the data in the database. However, if the amount of data lost by removing the obvious error outliers is low, the analyst can decide to eliminate those records from the database without much loss of information or impact on later analysis. For instance, truncating the data within a range of plausible values may be a pragmatic and reasonable option.

Example 2. This example involves a hypothetical finfish species for which the maximum known length of an individual is 90 cm, but the maximum value in the database is 897 cm. This could be due to a typo in a single record, or it could indicate that multiple records were entered by an enumerator who may have assumed different units (e.g. mm instead of cm). Plotting a histogram of the length frequency distribution clearly illustrates that there are two groups of data – one consistent with the biology of the species and another that is far larger than the maximum expected length (see Figure 20). This points to an obvious problem with units (a mix of cm and mm) that should be confirmed with experts and can then be corrected in bulk by a simple conversion of the anomalous records. Note that using a truncation approach in this situation may have eliminated perfectly good data (for example, by removing all records having a length greater than 150 cm) that could be used for analysis if corrected.

FIGURE 20
Histogram in FishualizeR, showing count (y-axis) of individual fish of a certain length (x-axis)

Notes: In this example, there is a bimodal distribution, with values that deviate significantly from the maximum length recorded for the species, indicating that different units (mm and cm) have likely been used in the data entry process.

Multivariate approach

Example 3. The following example considers the length-weight relationship of a species: a univariate analysis focusing on the length or weight variable alone may not reveal any apparent anomalies. Yet, when these two variables are examined together in a multivariate context, unexpected patterns or deviations from the norm become apparent (Figure 21). It appears there are individuals in the dataset exhibiting excessive weight for their corresponding length, and vice versa. These deviations can be safely classified as error outliers. In this scenario, distinguishing whether there is an error in the recorded weight or length becomes challenging. Correcting such data may prove to be excessively time-consuming, as it necessitates revisiting the original paper records. The removal of outliers, while potentially altering the overall distribution, is often a pragmatic approach when the correction of individual data points is impractical or resource-intensive.

FIGURE 21
Histogram in FishualizeR, showing the weight (y-axis) of individual fish of a certain length (x-axis)

Example 4: Adding dimensions to the analysis can indeed provide valuable insights and help identify outliers, even when a univariate approach seems satisfactory. In the following example, the distribution of fishing trip duration may initially appear to have no discernible issues when examined in isolation. However, introducing an additional dimension, such as gear type, reveals nuances that might otherwise go unnoticed (Figure 22). Upon including the gear type as a dimension (Figure 22), it becomes evident that some records associated with gear type 2 exhibit unusually high trip durations. While this anomaly may not be apparent in a univariate analysis, the multivariate approach highlights potential outliers specific to certain gear types. In such cases, seeking consultation with experts becomes pivotal to determine the appropriate course of action. If fishers indicate that the fleet using gear type 2 never spends more than seven days at sea, a cut-off value can be decided to identify error outliers. Conversely, if fishers indicate that occasionally longer trips take place, those data points should be retained.

FIGURE 22
Stacked histogram in FishualizeR, showing the number of records (y-axis) of individual trips of a certain duration (x-axis)

Notes: In this example, the overall distribution would suggest the absence of outliers. The inclusion of new dimension (Gear) reveals problems present in the data entry process. Trips longer than 10 days are very unlikely for the fleet that operates Gear 2.

6.2.3 Understanding interesting outliers

Interesting outliers arise from genuine and accurate data. They represent observations that deviate significantly from the norm but carry valuable information or insights. The process of discovering and interpreting interesting outliers often follows an iterative hypothesis-driven approach, where analysts pose hypotheses about how these outliers may have been generated and then test these hypotheses using the data. This iterative process commonly involves the generation of multiple plots to visually explore and understand the patterns associated with interesting outliers. The visualization process is significantly enhanced by the use of specialized tools like FishualizeR compared to more general tools like Excel.

The process of understanding interesting outliers involves a case-by-case approach, with steps that vary depending on the specific dataset and context. However, the fundamental approach and framework remain consistent: identifying anomalies, posing questions about how those anomalies were generated, testing hypotheses with relevant data and, if needed, identifying new questions based on findings.

In this section, two examples are presented to illustrate the process of understanding interesting outliers, showcasing scenarios where it enables a decision about data usage (Example 1) or primarily contributes to gaining insights into the dataset (Example 2).

Example 1. The following example demonstrates the application of multivariate graphical analysis to enhance the understanding of a dataset within a fishery with multiple fleets. In this scenario, the dataset comprises data from the largest vessels, where fish are fully measured by onboard observers, and the coastal fleet, where samples are measured only at landing sites, with fish already processed (no head, no tail, and eviscerated). For the coastal fleet, measurements are limited to the interdorsal length (distance between the first and second dorsal fin). In contrast, the observers on the largest vessels record multiple measurements, including both the total length and the interdorsal length. To conduct an analysis of the length frequency distribution for the species, it becomes necessary to combine data from both fleets. However, achieving this integration requires predicting the total length from the available interdorsal length measurements. efore proceeding with the formal parameter estimation to predict total length, a simple scatter plot is generated to assess the linear relationship between the two variables (see Figure 23). Upon review, significant deviations from the bulk of the data are noticeable, suggesting potential errors in measurement or data entry. Simultaneously, there is a considerable dispersion of data, hinting at a hidden pattern.

FIGURE 23
Scatter plot showing relationship between interdorsal length (x-axis) and total length (y-axis)

Notes: The data show some points that can be classified as errors but also a exhibits a large dispersion that hint to the existence of an underlying pattern.

While data suspected to be errors can be corrected or removed, the dispersion in the data prompts further investigation. A hypothesis is proposed that the relationship between the variables may differ for males and females. However, a faceted plot by sex (Figure 24) indicates that the observed pattern persists regardless of sex.

FIGURE 24
Scatter plot showing relationship between interdorsal length and total length, faceted by sex

Notes: For both females (left) and males (right), interdorsal length is plotted on the x-axis and total length is plotted on the y-axis. The large dispersion of the data persists, regardless of the sex.

Other variables may be used for additional faceting (such as fleet and geographic area) if there is a plausible explanation for the data. In this data set faceting by year (and colouring by sex), as shown in Figure 25, revealed a reduced variability after the third year. No biological hypothesis can explain this pattern, but consultation with experts revealed that the protocol to measure the interdorsal length was standardized only from the fourth year onwards, explaining the prior high variability. This was therefore the main driver for the high variability observed before that point, and highlights the importance of properly documenting the events that can affect data interpretation (as discussed in Section 3.2). A possible decision regarding data usage is to include only records collected by trusted observers using the accepted protocol for estimating linear regression parameters. This approach ensures the reliability of the data and aligns with the established protocol, contributing to more accurate analyses.

FIGURE 25
Scatter plot of relationship between interdorsal length and total length, faceted by year, coloured by sex

Notes: Interdorsal length (x-axis) is plotted against total length (y-axis), faceted by year (for each plot), and coloured by sex (females in red and males in blue). Dispersion of the data is greater before the fourth year.

Example 2. The following example provides a step-by-step illustration to understand interesting outliers, transitioning from simple plots of single variables to multivariate visualizations. The dataset involves catch records per trip or catch per unit of effort (CPUE) for a fishery with two fleets operating in different areas. Fishing trip duration (in days) is also recorded for some records. Before conducting the analysis, a crucial step is to consider available data and how it can be strategically broken down for effective data interpretation. Large datasets may contain numerous variables (temperature, depth, hour of the day, presence of clouds, sea conditions), and analyzing all relationships may not be meaningful. Best practices for organized data exploration include:

- Identify the main categorical variables that can explain the largest fraction of the variability in a dataset. For example, the fleet and area are natural candidates to explain a large part of the variation.

- Assess if there are potential confounding variables. In contrast to an experimental design, where the effects of an individual factor can be assessed, the nature of fishery data makes confounding variables very common. For example, if the fleets have similar efficiencies (i.e. similar catch rates), but operate in different areas, the data won’t allow us to compare the fleet effect.

- Assess if there is a quantitative variable that can be used instead of a categorical variable, to better explain the data. For example, can engine power be used instead of a broader fleet categorization?

Figure 26 shows the distribution of the CPUE using a density plot. The distribution of the catch per trip is expected to have a long tail to the right (very few trips with very large catches); hence those records cannot automatically be classified as outliers, and deeper exploration is required. However, in some cases some cut-off values can also be used based on expert knowledge to remove outliers. Fishers may, for example, inform that catching more than two tons per trip is unrealistic.

FIGURE 26
Density plot of the catch per trip in a univariate framework

Notes: The distribution of catch per trip exhibits a long tail to the right.

To gain a deeper understanding, visualizing catch per trip by fleet (Figure 27) is crucial to further understand the pattern behind the long tail in the data set.

FIGURE 27
Box plot showing catch per trip, faceted by fleet (artisanal and industrial)

Notes: In this example, the data on catch per trip are faceted by fleet; i.e. artisanal (left) and industrial (right). With faceting, it becomes apparent that anomalies are largely related to the industrial fleet (a pattern that was masked in the univariate analysis).

As seen in Figure 27, the highest values in the catch rates appear to be associated with the industrial fleet (right-hand box plot). However, the artisanal fleet also shows a cluster of data that fall far from the mean (left-hand box plot). While those could be considered outliers, it is necessary to test whether the area might explain the observed patterns – for example, and as in Figure 28, by plotting the catch per trip by fleet and area.

FIGURE 28
Box plot showing catch per trip, faceted by fleet (artisanal and industrial) and area

Notes: In this example, the data are faceted by fleet (i.e. artisanal on the left, industrial on the right), and coloured by area (red, green and blue).

As illustrated by Figure 28, faceting by area in this case does not yield any new insight on whether the anomalous records are outliers. However, it does provide the following deeper insights on the data:

- The industrial fleet does not operate in Area 3.

- The mean values in Area 2 are consistently higher than in Area 1, for both fleets.

- When there are data for both fleets in both areas, the catch per trip is higher for the industrial fleet.

This raises the following question: Is the higher catch per trip a function of fleet efficiency, or are the trips made by the industrial fleet longer? Fortunately, the trip duration in days is available for some records. This question can therefore be addressed by using the data, as illustrated in Figure 29.

FIGURE 29
Box plot showing trip duration, faceted by fleet (artisanal and industrial)

Notes: In this example, the data on trip duration are faceted by fleet; i.e. artisanal (left) and industrial (right).

As illustrated by the figure, there appear to be minor differences in median trip duration (represented by the middle line of the box plot) between the two fleets. However, there is also significant variation in trip durations within each fleet. This raises the question of how trip duration may be affecting the catch per trip. Figure 30 therefore explores the relationship between the catch per trip and trip duration, for each area and for each fleet.

FIGURE 30
Scatter plot of relationship between catch per trip and trip duration, faceted by area, coloured by fleet

Notes: In this example, the data are faceted by area and coloured by fleet (i.e. artisanal in red and industrial in blue). The red ovals indicate potential outliers.

Figure 30 indicates that catch per trip does not relate linearly with trip duration (as the lowest catches per trip seem to occur for both very long and short trip durations). In Area 2, where most of the fleet overlap takes place, it is clear that the industrial fleet has larger catches per trip than the artisanal fleet.

This extended example/scenario illustrates the use of a series of graphical approaches as part of the data cleaning and preparation process. Admittedly, the necessary steps may change on a case-by-case basis, but the general approach and framework remain fundamental – to ask a question, test with appropriate data, identify new questions, and continue exploring. It is also important to emphasize that while this manual presents data preparation and EDA as separate chapters (i.e. Chapter 6 and Chapter 7), both processes are iterative and interwoven. Moreover, the steps undertaken during data cleaning and preparation contribute to a deeper understanding of the data, thereby setting a strong foundation for and reinforcing further EDA.

Chapter 7: Exploratory data analysis

Once the user has a curated, cleaned database, it is possible to begin exploratory data analysis (EDA), in order to continue learning from the data before conducting any formal analysis or data-limited stock assessment. This chapter is divided by major data types: (i) biological data, (ii) catch and effort data, and (iii) indices of abundance. Due to the importance of using and applying local knowledge across all data types (in order to validate and augment existing information – which may be limited), local knowledge is not treated as a specific category, but rather a type of information that may be used to supplement either biological or catch and effort data, or improve its interpretation.

Data exploration, preparation and cleaning are iterative processes. When using data in hand for stock assessment or to provide of management advice, it is essential to carefully assess (i) if the data have enough information to understand stock status, and (ii) how the sampling protocol may be impacting data interpretation and affecting the perception of stock status.

A thorough exploration of the data can also highlight any information gaps, and guide decisions on allocating available economic and human resources to reduce biases in sampling protocols, or to make other sampling improvements. In the following subsections on EDA, techniques are presented with an emphasis on how to interpret patterns, address data gaps, and use this information to improve data collection protocols.

EDA can be conducted either on a corrected database, a transformed database or an aggregated database. These operations are defined as follows:

- Entire database: The use of the entire database involves using variables without transformations or combinations – for example, by analysing changes in the length frequency distribution of the catch, the length-weight relationship for a species, or the location of individual fishing trips.

- Data transformation: Certain exploratory analyses require the creation of new variables from the raw data – the most common transformations of original data being data discretization and log transformation. Data discretization is the process of transforming continuous data into categorical data – known as “bins” – to improve or facilitate analysis. For example, the user may be interested in analysing changes in CPUE in deep and shallow waters, but each record has an associated, specific numerical depth for the given fishing operation. In this case, the continuous numerical values for “depth” need to be partitioned into discrete, categorical bins based on threshold values for what is considered “deep” or “shallow” waters. One of the most common cases of discretization in fishery data involves classifying the length of a fish into categorical length bins. This process does not create a new database, but incorporates a new categorical column into the existing database.

- Data aggregation: Certain analyses require exploring the data in a summarized manner, by aggregating records. For example, the user may be interested in analysing the mean size of the catch by region and year, or the total catch or effort by fleet and year. The process of aggregating data will create a new database that has fewer rows than the original database. This step is usually necessary after data discretization – for example, because the user may want to count the number of individuals that belong to each length bin so as to create a length frequency distribution plot, or calculate the average length of fish at different depth strata. (Box 7).

Although confronting the limitations of the data may seem overwhelming at first, careful EDA can provide insight on selecting the most informative and least biased datasets to feed an analysis. In addition, after potential sources of biases are identified, the EDA process can also provide insight on how the data collection or monitoring scheme may be modified to improve the robustness of future assessments. Finally, EDA can also support the identification of fishery patterns that can be used in empirical, indicator-based approaches aimed at providing management recommendations, in line with a precautionary approach (e.g. when the size of landed fish has been continuously decreasing for years and most are below size at maturity, management measures such as size limits may need to be implemented or revised).

7.1 Data coverage

Data coverage refers to the extent of a dataset, and is an important concept when conducting EDA. Analysing data coverage involves counting the number of records for a given variable by categories, in order to visualize the spread or coverage of records (see Figure 31). This is particularly useful when analysing how the sampling protocol or the nature of fishing activities can potentially impact data interpretation. FishualizeR calculates and produces a visualization for data coverage, allowing the user to better understand patterns in data availability and distribution (see Box 11). In order to calculate and visualize data coverage in Excel on the other hand, pivot tables may be used to calculate the number of observations by category (see Box 7). Understanding data coverage should be the first step in any EDA.

FIGURE 31
Conceptual view of data coverage analysis

Notes: Data coverage analyses provide information on the number of individual data records across variables such as year and region. These are often shown against a colour scale, which represents a gradient of sample coverage.

7.1.1 Interpretation

In order to be meaningful, data coverage plots require thoughtful interpretation. For example, consider that the total number of records is not necessarily a good indication of the representativeness or quality of a sample – it may be more representative to measure 1 000 fish from 10 boats, than to measure 10 000 fish from the same boat or the same area. The following subsections explore a series of considerations to be made in analysing data coverage patterns – in particular to gain a deeper understanding of coverage trends and to understand how potential bias in the interpretation of actual stock patterns may be avoided. (For guidance on producing data coverage plots in FishualizeR, see Box 11.)

7.1.1.1 Temporal trends

Over time, the number of records for a given variable (for example, catch reported in logbooks) may change – either due to monitoring effort or actual changes in the fishery (for example, if total effort decreases, a corresponding decrease in the number of catch records may be expected). In order to gain a deeper understanding of temporal trends in data coverage plots, the following questions may be asked: Do data records exist for every year/month? If there are gaps or missing years in the data, do these gaps have plausible explanations? (For example, did the data collection programme lose funding for a certain period?) If there are gaps or missing years in the data, are there known influential events that took place during those years that would therefore not be accurately captured by the dataset? For example, if the year 2010 had no data, does that mean the catch was truly equal to zero in 2010, or that the data records for the catch are missing? If the number of records across different years is very variable, is it due to changes in monitoring effort, or actual changes in the fishery?

When plotting data coverage, temporal resolution should be defined in line with the purpose of the final analysis. For example, length composition data for a fast-growing, short-lived species change dramatically from month to month. Missing months in particular years can therefore lead to severe biases in the interpretation of the length composition when aggregated and visualized on a yearly basis.

7.1.1.2 Spatial trends, species and fishery characteristics

Analysing spatial trends in data coverage can provide valuable insights on the consistency of data collection locations over time. This analysis helps identify potentially spurious patterns that may arise due to spatially heterogeneous life histories, or changes in the spatial operations of fleets. It is crucial to consider the life-history characteristics of species – such as their migratory behaviour or sedentary nature – as well as the ways in which these attributes intersect with data coverage. Certain species exhibit geographic gradients in their life history, particularly in terms of growth. In such cases, if sampling is conducted in different areas each year (e.g. in areas with high and low growth rates), temporal changes in characteristics such as mean length may be the result of a non-representative sampling protocol, rather than a true change in length structure. Furthermore, data collected from a highly migratory species sampled within a limited range of their distribution may only capture a specific life-history stage. This may lead to incomplete or biased information (e.g. regarding juveniles only). Fishing practices can also be spatially different – for example, trawl gear may be used in deep areas, whereas hook-and-line gear may be used in shallow waters. When the effects of gear and area interact, changes in variables such as length composition may be the result of sampling from gear with different selectivity (for a concrete example, see Section 7.2.1.2). Finally, changes in area coverage may reflect a true change in the spatial distribution of the fleet (for example, when fishers move to a different area due to the overexploitation of certain fishing grounds).

7.1.1.3 Dealing with gaps

When addressing data gaps in the context of fisheries management, it is crucial to acknowledge that it is not possible to alter the past or recreate missing data. Nevertheless, certain steps may be taken to derive meaning from existing data, or to fill in such gaps. For instance, areas with strong or complete data coverage can potentially serve as proxies for other areas with weaker data coverage or gaps. The use of data as proxies for years other than the years for which the data were originally collected is not generally recommended. However, in the absence of reliable data or in the case of poorly reported years, local knowledge and qualitative estimates can serve as valuable resources. Combined with expert judgement, these can support qualitative estimates of the scale of catch or effort for the years with missing or inadequate reporting.

Based on expert judgement, practitioners may choose to interpolate between trusted years or to replace missing values with mean values. These approaches can help in creating more complete and meaningful datasets and provide a basis for informed decision-making. It is important to exercise caution however, and ensure that any extrapolations or estimations are not only justified, but based on sound scientific principles.

7.1.1.4 Different data types

Different data types (such as biological, catch or CPUE data) require different ways of interpreting data coverage patterns. In order to be meaningfully used in assessment and management, data must be representative of the fishery and its fishing patterns. For example, if lengths are collected to be used as indicators, these must be collected by a representative sampling programme.

Box 11. Analysing data coverage in FishualizeR

The “Data Coverage” section of FishualizeR assesses the number of records available for analysis under different grouping criteria. To access this feature, the user should navigate to the FishualizeR app in their browser (http://apps.upwellsolutionsllc.com/app/FishualizeR), select the “Upload Data File” section from the left-hand menu/panel, and upload the data file to be analysed (see Box 1).

Alternatively, to test this process, a sample or mock dataset is available as part of the downloadable zip file within the “Introduction” section of FishualizeR. Simply download the zip file from the link provided, extract the “Mock_length_freq_area_gear.csv” file and upload it via the “Upload Data File” section, as previously indicated.

Once the file is uploaded:

1. Navigate to the “Data Coverage” section of the left-hand menu/panel.

2. Use the first drop-down list in the main screen to select the variable to assess coverage (see Figure A).

3. Use the second and third lists to select the grouping criteria (for example, to count the number or records by by Area and Month).

4. By default, the number of rows in each category are counted. However, this metric may be misleading when there are missing values in the database. For example, if during the sampling process all individuals were measured but only a few were sexed, the variable “sex” will have as many records as the variable “length”, but many of those records will have a missing value. In FishualizeR, the user can specify that only the records with data must be counted, by selecting the option “# of Non-Missing Observations” in the fourth drop-down list (which appears after the initial choices are made, as in the steps above). FishualizeR also provides the option to report the percentage of missing values in each category (“% of Observations Missing”) or the percentage of the total observations in each category (“% of Total Observations”).

Figure A. View of “Data Coverage” section of FishualizeR, showing drop-down lists

5. Once selections have been made across all drop-down lists, a data coverage plot is produced (see Figure B). In this example, the plot indicates the number of individuals for which the sex was recorded per area and per month. It also indicates that for area A (shown on the left), there were no individuals for which sex was recorded, while for area B (shown on the right), there were records for which sex was recorded every month, and the maximum number of such records was for September.

Figure B. View of FishualizeR, showing tile plot of data coverage

7.2 Biological databases

Biological data collected as part of fisheries monitoring protocols serve multiple purposes: they generate information about the life history of the species (such as growth, maturity and morphometric relationships); they generate data that may be used as an indicator of stock status – whether alone or in combination with other sources of information (such as total catch, an abundance index and total effort); and they may also provide information about recruitment events that are important for management considerations. This manual focuses on the EDA that should be carried out when using biological data to learn about the status of the stock.

In the context of ensuring informed management decisions, these data can be used in the form of simple indicators, such as the mean size of catch or the time of spawning. Alternatively, they can be used to derive meaningful quantities, such as the spawning potential ratio (SPR) or fishing mortality. This section focuses on conducting EDA on (i) length composition data, and (ii) reproductive (i.e. gonadosomatic) or sex information.

7.2.1 Length composition data

Length composition data may be used to generate indicators of stock status, and are relatively inexpensive to collect for most commercial fisheries. In an exploited or fished area, fishing disproportionately affects the older, larger individuals in a population, which become less abundant as fishing pressure increases. As an example, Figure 31 demonstrates the differences in the age and length composition between an unexploited population and an exploited population, showing the relative abundance of older or larger fish as compared to smaller or younger fish in the former. Thus, although fishing produces changes in abundance that cannot be tracked using biological data alone, analysing the length composition of a population can be very informative with regard to the fishing mortality exerted on the stock.

Ideally, samples of length composition data for the catch should be representative of the stock. In other words, data collection programmes should prioritize collecting samples that maintain a high precision over a large geographical area, in rough proportion to the density in different locations, and with a high temporal resolution that includes all types of fishing gear. In reality however, budget and staff limitations commonly result in less than optimal sampling. In these cases – for example if data are only collected in one particular location, and/or for one particular gear or landing site – the representativeness of the data must be recognized when interpreting any results. Samples covering the full range of lengths are rare, as most commonly, data collection programmes obtain length composition from the catch (and catches may not represent the full length structure in a population).

7.2.1.1 Information content

When trying to extract and analyse information about the status of the stock from length composition data, a clear understanding a clear understanding of how the samples originated is essential, as it directly influences the amount and quality of information available for assessing stock status. In particular, certain biases or characteristics may impact the interpretation of the data, detailed as follows:

Effect of fishing selectivity

The length composition of the catch will be different than that of the true population due to the fishery and the gear selectivity process. Selectivity refers to the probability of catching an animal of a given size or age, and its curve often takes one of two main shapes, as shown in Figure 32: S-shaped (logistic), or dome-shaped. In both cases, smaller animals generally have a lower probability of being caught due to the nature of fishery behavior and gear design, often missing younger or smaller fish. Various factors, such as passing through nets, preferences for specific hook sizes, bait attractions, or inhabiting less targeted areas, contribute to the lower catch probability for smaller individuals.

FIGURE 32
Comparing age and length composition of stock when unexploited/unfished (top) vs exploited/fished (bottom)

The logistic and dome-shaped selectivity curves share a similar pattern for small and medium-sized fish, with small fish having a low catch probability that increases with size. In logistic selectivity, the catch probability reaches a maximum at a larger size, typically around 100 percent, where all fish at or above that size have the highest probability of being caught. Conversely, the dome-shaped curve attains a maximum at intermediate to large sizes, followed by a decrease in the catch probability for the largest fish. This phenomenon may occur in highly size-selective fisheries where medium-sized fish are more valuable, and fishers intentionally avoid capturing large individuals by adjusting gear or avoiding specific areas. Additionally, large fish may elude certain gear types or inhabit less-targeted offshore or deeper waters.

Figure 33 shows the composition of the catch for the same population across two different selectivity patterns, and indicates that the relative proportion of large individuals in the catch is lower for the dome-shaped selectivity curve, despite the two samples coming from the same population. As a result, if the proportion of larger or older individuals is an indicator of fishing pressure, and if the samples are collected from a fishery with a dome-shaped selectivity pattern, the analyst may incorrectly deduce that the population is subject to higher fishing mortality than it actually is. Determining whether the lack of larger or older fish in the catch is the result of a dome- shaped selectivity pattern or of overfishing of the population can be challenging – it requires an understanding of the fishery, the fishers’ behaviour and the life history of the species.

FIGURE 33
Length composition of population and catch across different selectivity patterns

Effects of market sampling

Sampling the catch at landing sites may be logistically difficult, and in response, some sampling protocols are designed to collect data at the point of sale – otherwise known as market sampling. However, collecting data at markets does not allow for gear information to be obtained; this may complicate the analysis if multiple types of gear are used in the fishery (particularly if the proportion of the catch varies by gear). Moreover, the length composition of the catch will be an aggregation from all gear types. Other sources of bias may also arise if fishers use part of the landings for personal consumption, and if they do so by selecting according to size (for example when smaller and less profitable fish are eaten at home, while larger fish are brought to market). However, if only the fraction of small fish is under-represented in market samples, good information on total mortality may still be extracted. On the other hand, a possible absence of large fish could lead to an overestimation of fishing pressure. This may occur in settings when samples need to be purchased, but where, in some cases (for example due to budget limitations), it may be prohibitive to purchase large animals for biological sampling. Overall, market or point-of-sale sampling schemes need to be carefully designed and periodically reviewed. As with all sampling methods, the goal is to obtain a sample that is representative of the fishery and the fished population with regard to gear, location and size of fish.

Effects of study design or sampling protocol

Samples of length composition data may also be a product or goal of a specific study or sampling protocol, and in these cases they can impact the information content of the data. For example, studies designed to determine life-history parameters typically attempt to sample from the full size range of the species, and often target sizes that are difficult to find, such as the very smallest and largest individuals. Spanning the full range of sizes is important for growth and reproductive studies, but the sampling protocols may not sample the lengths of the fish in proportion to the population or their distribution in the catch. Similarly, if length data come from a growth study whose protocol required sampling the same number of fish in every size bin, these data should not be used to evaluate stock status, because the distribution of fish sizes will not reflect the distribution in the population. Data collection efforts designed to estimate stock status should sample randomly from the population, and should not actively try to collect for certain size classes simply because smaller numbers of them have been measured. Finally, fishing gear and fishing location can also impact the size, sex and abundance of fish sampled, and should be considered when evaluating how representative the length data are for the given population.

Effects of short-lived, fast-growing species

Despite having an optimal sampling design, length composition data may be of limited use for inferring stock status for certain life histories. For example, for short-lived species that are dominated by recruitment pulses (such as octopus or shrimp), length composition data obtained throughout the course of a year (or several years) may be a good indicator of growth (if monitored with regularity), but would not provide any information on fishing pressure. By following modes over time, growth can be estimated with relatively good results, but in the absence of an index of abundance that allows for proper weighting of monthly samples, extracting information on stock status is questionable.

Effects of trends in recruitment

Most assessment methods based on length composition data rely on the assumption that recruitment has remained relatively constant over time. Recruitment variability is always present, but some species experience higher levels of fluctuation than others (for example, anchovies or squid). Averaging data over a short period of time may help minimize the effect of recruitment variation, but when temporal trends exist, they present a serious problem for inferring stock status from length composition data. For example, if recruitment is increasing over time, the proportion of small animals in the sample will also increase over time. While an upward trend in recruitment is a good sign of the status of the stock, a stock assessment based solely on the length composition data may yield the opposite finding – as the proportion of large animals decreases over time, this may be interpreted as a sign of increased fishing pressure. Even simple indicators like the mean length of the catch can be affected, because a recruitment pulse could decrease the mean length – not because of increased fishing, but because of an increase in the proportion of small individuals in the water. It is often difficult to detect these trends in recruitment without additional data such as larvae or post-settlement abundance surveys, but these are typically only available in data-rich settings or from fishery-independent surveys. A best practice is to therefore consider alternative explanations for trends of this kind (i.e. when the number of large fish has decreased or the number of small fish has increased), and refrain from taking the data immediately at face value.

Effects of patchy distribution

Length composition data may also be misleading for species with strong spatial structure, such as benthic animals, where population density and recruitment can be highly variable in space. Length composition in different patches may be highly variable (even at short distances), providing different stock status estimates depending on the number and the location of samples collected. In these cases, both the design of the data collection programme and the analysis itself must ensure that the spatial extent of the samples is large enough to cover the entire distribution of the stock, and that it is correctly distributed to sample patches in proportion to the species density.

Effects of serial depletion

Another issue that may complicate the analysis and use of length composition samples to understand stock status is serial depletion. Serial depletion occurs when fishers reduce the size of local aggregations and – once catch rates dimmish – move to other local aggregations. This produces a sequential depletion of local patches, but with little change in the length composition data of the combined stocks, as each new fishing event is in a relatively unexploited location. This is difficult to detect without additional information on spatial locations and patterns of fishing operations. Local knowledge is therefore crucial in detecting the presence of serial depletion.

7.2.1.2 Exploratory data analysis

An ultimate goal of length composition data analysis in the context of fisheries management is to detect changes in fishing pressure over time. As previously discussed however, the data used to determine stock status may be affected by multiple variables and effects (see Section 7.2.1.1). Therefore, instead of using the data at face value, an in-depth evaluation should be performed on how the data may introduce biases. This will allow for more informed decisions on how to use the data in an analysis or stock assessment.

In this context, the following steps provide key guidelines for conducting EDA on length composition data:

1. List potential variables that may affect length composition data: The most common candidate variables that may introduce noise in the data include the area where catches occurred, the time when samples were collected, the proportion of landing sites or catch that is sampled or recorded, the gear selectivity, the sampling protocol, and the sex of the individuals.

2. Assess data coverage: Understanding data coverage is useful to anticipate problems with data interpretation and provide a basis for decisions on how to make the best use of the data in hand. Ideally, monthly samples in all areas and for all fleets should be available. Unbalanced sampling may lead to year-to-year differences in length composition that are not the result of fishing pressure, but an artifact of how data were collected. (See also Section 7.1 and Box 11.)

3. Plot the length composition data, discriminating by the categories of the variable under study: In general, if the length composition data look stable when separated by category (for example, gear type or area) for a given year, it is safe to move on and ignore its potential effects. However, if significant differences are encountered, it is necessary to dig deeper. For example, if the length composition data are markedly different between two areas, it could be the result of a different population structure in each area, or an artifact introduced by the use of different gear in different regions.

4. Explore interactions between variables: Although time-consuming, if there are enough data, the analyst should explore multiple combinations of variables, discriminate effects, and decide on a proper course of action (for example, to exclude an entire fleet, to use only female data or to use only the months that were consistently sampled).

5. Recognize the limitations of the dataset: Issues with uninformative data or unbalanced sampling force the analyst to think about the implications of using such data to determine stock status. Examples of uninformative data include (i) highly size-selective fisheries in which fishers actively target specific length ranges (for example, fish that are neither too big nor small – i.e. dome-shaped selectivity); and (ii) short-lived, pulse recruitment species (where only the growth of a cohort can be tracked over time). In these cases, accurately estimating fishing pressure from length composition data alone is hopeless, and management actions should prioritize caution. If the lack of large individuals in the samples is due to highly size-selective fisheries (as in the first case, above), the resulting estimate may indicate a “worst case” scenario for stock status that is ultimately imprecise. Unbalanced sampling on the other hand, usually requires making decisions on how to use the data available. For example, if not all months are sampled each year, but the length composition data do not change much seasonally, the data may be aggregated, or only those months that are consistently represented in all years may be used. As another example, if juveniles concentrate in coastal areas and adults in deeper waters, but the deeper zone is not sampled each year, the analyst may decide to use only those years where samples come from the full bathymetric range, and eliminate the years in which the samples are not representative of the population as a whole. Although it can be discouraging, recognizing the gaps and limitations of the data may be very useful for improving future data collection.

As the analyst progresses through these steps, there are three common effects to check for: (i) area effects, (ii) gear effects, and (iii) sex effects. The following sections illustrate examples of these effects, and discuss how EDA may be used to minimize bias in length composition data analysis.

Area effects

If catch is sampled from different geographic areas, it is important to check how many samples per area exist per year (for data coverage assessment in FishualizeR, see Box 11). For example, the sampling protocol in Figure 34 demonstrates an imbalance, as samples from both regions only exist for one year. If the length composition between areas is similar, this may not constitute a problem, and the data may be aggregated and the entire time series used for analysis. However, if the mean length between areas is consistently different, it may lead to improper interpretation of fishing pressure over time.

FIGURE 34
Tile plot showing number of samples (by colour), by area (x-axis) and year (y-axis)

Notes: Samples exist for both Area A and Area B only in the year 2018, demonstrating an imbalance.

Comparing length composition data across areas for overlapping time periods provides insight on how much the data coverage may affect results. For example, in Figure 35, the length composition data for the year 2018 is markedly different between Area A (in red) and Area B (in blue).

FIGURE 35
Histograms showing length composition data by year (each plot) and area (by colour)

Notes: The year 2018 is the only year in which both Area A (red) and Area B (blue) were sampled.

These differences may be due to the use of different gear, a spatial gradient in the size distribution, growth differences between areas, or different market preferences between areas. The analyst should further investigate the sources of the differences and decide on a proper course of action before using the data. Answers to the following questions may prove useful in this context:

- Are the differences in length composition due to gear selectivity or market preferences? If so, is the length composition of one area more representative of the actual proportion of large animals in the population?

- Are the differences due to differential growth among areas? Are those differences large enough that the samples should be considered from different stocks? (Keep in mind that both simple indicators and more complex models make use of life- history parameters – such as asymptotic size, size at maturity and optimum size of the catch – to infer stock status.)

Gear effects

If several types of gear are used to target the species, analysing data coverage by gear is important, particularly if the different gear have markedly different selectivities. For example, in Figure 36, the sampling is unbalanced across gear types, and the gear type with the greatest number of samples is “Gear 3”.

FIGURE 36
Tile plot showing number of samples (by colour), per gear type (x-axis) and year (y-axis)

Figure 37 considers length composition by gear and year, and indicates that samples from Gear 3 and Gear 4 tend to have larger sample sizes over the entire period. However, this in itself is not enough evidence that gear selectivity is solely responsible for the differences observed in the length composition data, as this could be the result of a few anomalous years (i.e. with a large number of samples).

FIGURE 37
Histograms showing length composition by gear for four gear types (all years aggregated)

Further inspection, as in Figure 38, shows that during the first six years of data, almost all information came from Gear 3, and only in recent years is information from other gear types available. Moreover, with the exception of the final year, the entire period includes samples from Gear 3 or Gear 4, which tend to capture large individuals.

FIGURE 38
Density plots showing length composition for each gear (by colour), across multiple years

This example illustrates a scenario in which the analyst is forced to make decisions about how the data should be used for analysis. In such cases, it is always advisable to consult local experts with knowledge of the fishery, for their insights on the gear and fishing practices that may account for these results. In addition, and given these gear effects, some plausible courses of action and respective considerations might include:

- Aggregating all data. It should be noted however, that this may lead to an overestimation of fishing pressure for recent years, due to a decrease in the mean size of the catch resulting from the inclusion of data from Gear 1 and Gear 2.

- Weighing the length frequency distribution of each year, using approximate percentages of the catch composition per gear. In this case however, this does not appear to be a valid option, since there is no data from all gear types in all years.

- Using only Gear 3, since it provides the longest time series and has a good representation of large individuals. But on the other hand, the lack of samples for Gear 3 in recent years would render any results to be of little use for management.

- Analysing only Gear 3 and Gear 4, comparing the results for those years where samples from both are present, and checking for consistency.

Sex effects

When data on the sex of individuals are available, it is also recommended to assess if the length composition data are markedly different between males and females. As an example, Figure 39 illustrates a case in which females are larger than males. In these situations, it is always advisable to work with the female fraction of the data, since females are considered in most species to be the limiting component for egg production.

FIGURE 39
Density plot showing length composition by sex (by colour)

Notes: The red area indicate females and the blue area indicate males.

7.2.1.3 Other considerations

While some key sources of variability in the data can be understood through EDA, attention should be paid to events and/or variables that are not formally recorded by fisheries sampling protocols. (For more on the importance of documenting influential events that may affect data interpretation, see Section 3.2.) When analysing results, it is important to review whether such events, as noted by experts, have an actual impact on the perception of stock status. For example, if a reduction in mesh size was imposed at a certain point during the time series, it could explain observed changes in length frequency data that would otherwise be ascribed to changes in fishing pressure. Although such a change (i.e. in mesh size) will likely have an impact on stock status as well, it is important to note the specific reasons that drive changes in length frequency data. Similarly, if a new and profitable market for a certain size class developed, it can drive fisher behaviour to actively target those sizes by changing their fishing practices. Changes in fishing gear, fishing location, market, and regulations can also affect length-frequency data, and the reasons for such changes are often not evident from the data alone.

Data from formal sampling protocols are usually available for a much shorter period than the lifespan of the fishery, resulting in a poor baseline for comparing current stock status with a nearly unexploited state. While methods that use length composition data make use of life-history parameters to estimate a virgin population for comparison, it is always a good idea to dig through the literature for old records that may help validate or contradict the results.

7.2.2 Reproductive information

In data-limited situations where length data may be unavailable or unreliable, information on spawning time or on the presence/absence of gravid females may provide a useful indicator on which to base precautionary temporal or spatial management measures in empirical indicator-based frameworks. For example, a precautionary measure may include the closure of fishing activity during the months of the year with the highest proportion of gravid females – a measure otherwise known as a reproductive seasonal closure.

7.2.2.1 Gonadosomatic index over time by sex

Visualizing a gonadosomatic index (i.e. the weight of the gonads as a fraction of the total weight) over time by sex allows for temporal spawning trends to be understood. This may help inform on the times of the year when spawning is occurring, and allow for better protection during such times. As an example, Figure 40 illustrates a dataset of samples collected opportunistically over different areas and different years. The gonadosomatic index shows consistent reproductive seasons for both males and females.

FIGURE 40
Plot of gonadosomatic index over time, by sex

Even if the samples come from a large number of areas, if the pattern appears consistent (as in Figure 41), the information may be used to implement a management measure that protects the reproductive period.

FIGURE 41
Plot of gonadosomatic index for different areas over time, by sex (left and right). Legend indicating the area has been ommited

Notes: The sex/area combination is represented by different colours. The area legend has been removed to improve readability.

7.2.2.2 Sex proportions in catch over time or area

If the gonadosomatic index is not known, the proportions of males and females per year should be plotted and visualized. This may help understand if there is a certain time or area in which more females are being captured than males (or if only females are being captured), as a potential cause for concern. Similarly, if protecting females is a concern, and if the proportion of females is consistently higher than males in certain areas, this may be a rationale for closing such areas.

7.2.2.3 Maturity proportions in catch over time or area

Similarly to the use of sex proportions, the location of areas with a consistent presence of mature or immature individuals can inform a management action, even in the absence of a proper understanding of the stock status. For example, in the case of Figure 42, opportunistic samples of the catch were obtained in different areas, with individuals classified as sexually mature or immature. As the figure illustrates, mature individuals are present in all of the areas, and the catch in Area 2 and Area 4 is composed almost entirely of mature individuals. This information could be used to trigger or inform spatial closures in order to protect juveniles.

FIGURE 42
Plot of maturity proportions in catch by area

7.3 Catch and effort databases

In the absence of a comprehensive understanding of stock status, gaining insights into temporal and spatial changes in fishing pressure and catches becomes crucial for implementing interim management measures. Catch and effort databases are frequently compiled as aggregated values per year at the national level for statistical purposes. However, this compilation process often involves multiple corrections to the original data, which are frequently inadequately documented, and the raw data may not always be accessible to fisheries scientists. Regrettably, aggregated data have the potential to obscure vital patterns related to the dynamics of the resource or the fleet, which holds significance for effective management. Therefore, it is imperative to secure the raw data or, at the very least, documentation outlining the processing steps and assumptions made during data aggregation.

The application of Exploratory Data Analysis (EDA) to catch and effort data is an essential step in preparing fishery data for use in assessment and management.

However, it is crucial to recognize that, while EDA provides a robust foundation for extracting insights from the data, expert judgment is equally critical for interpretation. This ensures that catch and effort patterns align with the realities of the fishery and its context.

7.3.1 Information content

As with biological data, understanding the information content of catch and effort data requires a deeper analysis. It is important to understand how samples of catch and effort originated, and how much information about the stock status they may contain.

In the 1990s, observations made by Grainger and Garcia (1996) indicated that trends in the catch of a fish stock may reveal something about the status of the stock. In particular, they looked at the trends in catch for stocks that were classified by other methods (such as full stock assessment models) as overexploited or depleted, and showed that the catch for those stocks followed the same patterns of increase and subsequent decline as the stock was depleted. As a general rule, if it is known that fishing pressure has increased, a decline in catches would suggest a significant decline in stock abundance. Some methods therefore use catch time series – with several variations – to classify a stock in different phases of fishery development; i.e. (as in Figure 43), with catch being low in the undeveloped phase, growing rapidly during the developing phase, peaking and then starting to decline in the mature phase, and continuing its decline in the senescent phase.

FIGURE 43
Fishery development phases classified by catch time series

When more data are available, catch time series are used to drive population dynamic models and estimate the status of the stock. The magnitude of the removals are informative of the size of population.

While there is active debate on the use of catch time series to infer stock status (Pauly et al., 2013), there is consensus that catch data should be used with care, and that using catch data alone should be avoided when better information is available (Ovando et al. 2022). ). Conversely, effort data are seldom employed in isolation to assess stock status; instead, they are typically utilized in conjunction with other data types, such as catch, to derive meaningful insight.

Whether utilizing catch in isolation to assess stock status or relying on effort trends to depict changes in fishing pressure, it is imperative to recognize that both types of data can be influenced by various factors unrelated to stock abundance. Some of the most relevant are described in following sections.

Effects of catch reporting

Ideally, a catch time series should be as long as possible (i.e. covering a substantial time frame since the beginning of the fishery), complete (i.e. including discards and illegal fishing), and of high quality (i.e. minimizing misreported and unreported catches). However, most available databases report landings, and the levels of unreported catch may need to be estimated. In some cases, it may be possible to fill in missing data or extend the time series by reconstructing catch data from historical reports and records. But in general, catch and landings records often have issues that need to be addressed before using the data for assessing stock and/or informing management. Common issues may include over- or under-reporting, missing records, lack of spatial coverage and species identification, and the presence of aggregated catch data rather than individual species data.

Spatial patterns in effort

Spatial patterns in effort can reveal important issues or concerns in the fishery. For example, the shifting of effort concentrations over time may indicate serial depletion, as certain patches or areas of the fishery are depleted and fishers shift to exploit new areas. Alternatively however, these trends may simply indicate changes in fishing patterns and seasonality. It is therefore always advisable to cross-check and explore these hypotheses with local experts or fishers, who will have deep insight into spatial patterns in effort. Lack of representation across all fishing areas can also be a concern, and should be evaluated prior to using the data for analyses.

Effects of sampling protocols and data sources

Discrepancies between data sources are common, and can even occur between different fisheries agencies within the same country. Landings data are not always compiled and processed by the same institution, and records may be kept in different formats or processed in different ways. For example, a fisheries science agency may maintain a thorough, representative sampling protocol for fishery-dependent data, while a fisheries monitoring and compliance agency samples the same fleets at a higher level, and maintains a general dataset of total catches. If these agencies do not have interoperable systems or codes that can match trips, the data they have in common may not be resolvable.

While official statistics are generally considered to be the most reliable source of data, at least in terms of declared landings, it is not uncommon for fisheries regulations to create incentives for over- or under-reporting. When more than one source of data is available, data should be compared by plotting and visualizing each source over time. This is especially important if using multiple sources of catch records, such as import or export records, dealer tickets, logbooks and official statistics.

Furthermore, the sampling protocols, species life-history characteristics and taxonomy all need to be properly considered when using and interpreting the data. For example, sampling protocols to record catch and effort from fleets targeting highly migratory species should look different from those developed for benthic invertebrates. Consistency in sampling protocols is also important. It is essential to evaluate whether sampling protocols – such as how fishing effort is recorded – is the same across areas, fleets and time. For example, if fishing effort is recorded as days at sea in one location and meters of gillnet in another, this will substantially impact the analysis. Similarly, if catch is recorded for an aggregate such as “lobster” in the first part of the time series, and is then broken out by species in the later part of the time series, this too will impact the interpretation of the data.

Effects of incomplete time series

It is important to recognize the limitations of an incomplete time series, even if it may initially appear to be long. For example, if part of a dataset has been recorded digitally (e.g. from 1980 to 2020), but paper catch records go back much further (from 1950 to 1979), an analyst using only the digital time series may get an incorrect impression of the actual maximums, minimums and overall trends in catch for the fishery, as it is quite possible that catches were much higher during the earlier period. If the electronic catch time series is used in isolation, a misleading baseline or picture of the fishery may therefore result – whereas if a truly complete time series is available (i.e. from 1950 onwards), a more complete picture of the fishery may be obtained. At the same time however, it should be noted that historical data may also have issues of representation, and of over- or under-reporting, simply due to the limited data and data collection methods available at the time. For example, sharp rises have been observed in the catches of species of elasmobranch, particularly in the last 20–30 years, but these are partly a function of improvements in the monitoring of catches.

Other drivers and trends

Understanding the main drivers behind a catch trajectory is very important when this is the only source of data, and when the analyst relies on “catch-only” methods to estimate stock status. Assessment methods that rely solely on catch history assume that the catch can provide some information about stock abundance. In particular, an increase followed by a decreasing trajectory in landings is interpreted as a sign of stock depletion. However, factors other than abundance may be the main drivers of a catch history pattern, and if not properly identified, this can lead to a misinterpretation of the data. Unfortunately, EDA works with the data available, and those drivers can be challenging to identify with data. However, if the timeline of influential events was conducted properly (see Section 3.2), the analysis of the catch trajectory can consider and incorporate the timeline information. This combined analysis can then assess and/or support the assumption of the catch history as reflective of stock abundance. For example, some influential events that may drive catch history patterns include price changes that discourage a fleet’s operation, market losses or reductions in demand, regulations that mandate reductions in fishing effort, changes in fishing boundaries due to legislation (for example as related to the exclusive economic zone or EEZ), environmental factors (such as shifts in productivity cycles), and changes in the documentation of origin of the catch (for example reporting where it arrives vs where it was landed).

An interesting case may arise when authorities increase the rate of reporting by providing permits to fishers that were previously operating without a license. In this case, the official catch records may increase as a result of the mandate that the “new” or newly licensed fishers now have to report. In such a scenario, a retrospective catch reconstruction would be required to accurately interpret historic catches. Another underlying factor that may drive catch patterns that are not representative of true changes in stock status may be the inconsistent use of common names or taxonomic classification over time (for example, reporting catch at aggregate level vs individual species level). Again, this issue is difficult to detect from the catch data alone, and requires consultation with local experts.

For a related example from fishery databases managed by FAO, the proportion of catches which are reported under higher-level species aggregations may change over time in terms of the granularity of the data available at species level (such as with marine fishes nei [not elsewhere included], or freshwater fishes nei). These changes should be considered when modelling longer-term catch trends.

7.3.2 EDA for catch data

Based on the potential sources of bias as previously discussed, key steps for exploring catch data are suggested as follows. Although not fully comprehensive, these steps provide guidelines for conducting EDA on catch data.

1. List potential factors or variables that may affect the catch. In this step, cross- reference the timeline of influential events (see Section 3.2). Typical factors to consider may include changes in regulations, changes in sampling protocol, changes in spatial or temporal coverage that may influence reporting, changes in fished area, gear or vessel changes, and market forces that influence total catches.

2. Assess data coverage. For each source of catch data, visualize and plot catch data coverage by both area and time period (see Section 7.1 and Box 11). Samples should be representative of both the geographic range of the species and the spatial range of fishing activity.

3. Plot and visualize all sources of catch data. Visualizing and comparing catch data sources is one of the most important aspects of EDA. This allows the user to deeply understand any patterns, overlap or discrepancies between catch data sources. Reconciling differences among multiple sources of catch is often an important part of this step.

As an example of visualizing multiple sources of catch data, Figure 44 shows discrepancies between two catch sources, both reported as official statistics at the national level. The blue dots correspond to data collected from printed yearbooks, while the red dots correspond to the total catch recorded in a national electronic database. While the trends are similar, the absolute catch reported in the yearbooks is up to 2.5 times higher than the records in the electronic database. Understanding the sources of these discrepancies may require discussions with the staff in charge of the data processing (for example, it may be that the yearbooks already calculate discards, or that they also estimate the catch of foreign flagged vessels).

FIGURE 44
Discrepancies in catch data from official statistics (printed yearbooks in blue, electronic database in red)

In fisheries where most of the product is exported, comparing export volumes with official statistics can highlight under-reporting in landings, along with incentives for increased catch. Figure 45 compares the time series of export price, export volume, number of permits (i.e. vessels in operation) for the year, and reported landings at the national level. Comparing all these data from independent sources enables a more in-depth understanding of the catch time series. As seen in the figure, as reported landings increased, so did exports and effort – most likely driven by an increase in price. In such cases, using the catch data at face value may be problematic, and instead requires a clearer understanding of the trends involved across these other factors.

FIGURE 45
Temporal changes in export price, export volume, permits operating in a given year, and reported landings

Notes: Export price is shown per kg of fish.

If issues with official reported data are found or suspected, a catch reconstruction may be required. Catch reconstructions use official reported data along with additional fragmentary pieces of information to estimate the trajectory of landings (Pauly and Zeller, 2015). While they should not be undertaken without considerable caution and care, they can be valuable in highlighting or attempting to quantify particular sectors that are known to be under-reported or missing entirely from official catch statistics – for example subsistence fisheries, and contexts with high levels of illegal, unreported and unregulated fishing (IUU).

7.3.3 EDA for effort data

While trends in effort may be an indicator of fishing pressure in cases where catch records are not reliable, effort data alone cannot typically be used to conduct a stock assessment. However, when used in combination with catch data, effort data can be extremely valuable. The main source of effort time series are usually official records collected by fisheries agencies, but other actors such as academia or civil society organizations may also collect effort data. As with catch time series, a large number of irregular patterns or a short time series may indicate that the official data are incomplete, and may need to be reconstructed – for example by using interviews or alternative sources such as employment statistics.

Key steps for exploring effort data are suggested as follows. Although not fully comprehensive, these steps provide guidelines for conducting EDA on effort data.

1. List all sources of effort data and the units of effort for each source, along with any variables that may impact effort. Defining the proper units for each source is essential when compiling effort time series. Official data sources typically record the catch per fishing trip and the duration of the trip, but these measures can prove too coarse. For example, a single fleet may change the number of days at sea, or increase or reduce the number of hooks carried. Consultation with experts should be conducted to make sure that the units of effort used are meaningful for comparison among years and vessels. As for variables that may impact the effort, the timeline of influential events should be referenced – for example, for information on gear or technology improvements (see Section 3.2) – and any changes in the units of effort or sampling procedure should be recorded.

2. Assess data coverage. For each source of effort data, visualize and plot effort data coverage by both area and time period (see Section 7.1 and Box 11). In addition, plot the total number of records and effort data against all potential variables associated with the fishery or with effort information.

3. Plot and visualize all definitions or units of total effort. Visualizing each source of effort data with different units of effort will help identify any inconsistencies. Plot effort in all units available, such as trip duration (number of days) or number of hooks used per set. In particular, look for consistency in effort trends across different sources of units.

As an example, Figure 46 shows the temporal changes in the distribution of trip length or trip duration by gear. In the absence of any other information, it can be inferred that the fishers are conducting longer trips. This may be due to an increase in the size of vessels, improved cold storage over time, and/or an increase in the amount of time it takes to find the target species. In this example, the unit of effort – a fishing trip – is not the same at the end of the time series as it was at the beginning of the time series. This is called effort creep, and may require further analysis in order to be changed to the actual number of days spent fishing, as opposed to the number of trips taken.

FIGURE 46
Distribution of trip duration (y-axis) by year (x-axis) and by gear (red and blue)

When multiple units of effort are recorded, it is advisable to compare the shape of the time series for each unit. As an example, Figure 47 demonstrates a case of inconsistency between effort units. The total number of trips increased over time, as did the total number of days at sea, but the total number of tows was dramatically reduced after 2010. At first glance, it may seem more appropriate to use the total number of tows as a measure of total effort, however it is hard to interpret the decrease in the number of tows. One possibility is that the duration of the tows increased after 2010, needing less tows to cover the same area. Alternatively, the number of tows may have decreased because the vessels are now traveling further to find better or different fishing grounds, suggesting that closer areas may be depleted. The amount of time looking for fish or steaming should be included as a measure of effort. Such an abrupt change in a variable may also suggest that an external factor was primarily responsible, rather than a gradual change in fishing practices. This is another case where the data may not be properly interpreted without a solid understanding of the events that impacted the fishery.

FIGURE 47
Comparing fishing effort over time, calculated using different units (number of days, tows and trips)

Misleading units of effort may also occur when fleets are mixed (for example, artisanal and industrial). Multi-gear settings, which are particularly common in the case of small-scale fisheries in developing countries, can also complicate (gear-specific) estimates of catch and effort. It is therefore important to separate effort by fleet and gear whenever possible, in order to

The ultimate objective of Exploratory Data Analysis (EDA) on catch and effort data is to obtain reliable and representative time series, whether derived from multiple sources or various data types. Time series of catches typically demand completeness, with a focus on scale, as the total removals in a population are crucial for stock assessment. Conversely, for effort time series, capturing the trend accurately is often sufficient for usability. Thus, when examining multiple effort datasets, if the overall pattern is the same across all of them – regardless of the absolute magnitude – it may be possible to analyse the overall pattern without having to deal with the differences in magnitude.

7.4 Indices of abundance

An index of abundance is a relative measure that tracks with the size of a population. Ideally, an index of abundance should be based on scientifically designed data collection protocols such as an acoustic or trawl survey (generally referred to as fishery- independent data). However, the associated costs of scientific surveys are prohibitive for many fisheries around the world, and data collected by the fleet must instead be used to assess the status of the population (fishery-dependent data). The most common fishery- dependent data used as an index of abundance are catch rates – i.e. catch and effort information expressed as CPUE. This manual focuses on the use of fishery-dependent data as an index of abundance. The main assumption behind using catch rates as an index of abundance is that the index is proportional to the total abundance or density. The coefficient that relates the total abundance to the index is called catchability (q), and represents the proportion of the abundance caught by a unit of effort:

7.4.1 Information content

CPUE is a reliable tracker of temporal changes in abundance, if q is a constant and the relationship between CPUE and abundance is linear (Figure 48). That is, if the abundance of the stock declines by a certain percentage, the index of abundance should decline in the same proportion.

FIGURE 48
Expected relationship between abundance and an index of abundance or CPUE (left); and of abundance and CPUE over time (right)

A major problem in interpreting an index of abundance arises when catchability (q) is not constant at different biomass levels. This may occur if the proportion of the stock removed by a unit of effort changes depending on whether the population is large or small. There are two potential scenarios for a density-dependent q: hyperstability and hyperdepletion. As shown in Figure 49, hyperstability occurs when the index of abundance remains high while the stock is rapidly declining, whereas hyperdepletion occurs when the index shows faster decline than the population. Both phenomena lead to an incorrect interpretation of stock status, and hence to misguided management actions. If hyperstability occurs, there is an optimistic perception of stock status and corrective actions to avoid overfishing will only be implemented when it is too late. On the other hand, if hyperdepletion occurs, unnecessary mitigation measures will be implemented, with undesired consequences for the fishing sector.

FIGURE 49
Departures of linearity between abundance and index of abundance

The mechanisms and processes leading to either hyperdepletion or hyperstability are the results of interactions between biological traits and fishing behaviour. Hyperstability may be the result of serial depletion of low-mobility individuals (as fishers move sequentially from one fishing ground to another, experiencing high catch rates in each area while the population in total is being depleted), or of fishing over spawning aggregations (which are highly predictable in space and time and yield high catch rates while the population is being depleted). Hyperdepletion is less commonly documented, but can also result from fishing-induced changes in fish behaviour (for example, certain fish species may become more “timid” after harvesting activities and as a consequence, catch rates may diminish faster than the population). It can also result from fishers moving from core areas to lower-density areas as depletion takes place in mobile species (in this case, catch rates are overinflated in the core area, and as fishers move the index rapidly diminishes, giving the impression of large drop in abundance). Unfortunately, the presence of either hyperdepletion or hyperstability is hard to characterize without a careful experimental design that allows for simultaneously collecting CPUE data and reliable estimates of total abundance. Nevertheless, data on changes in catch rates at different spatial and temporal scales can provide clues to indicate that either of these processes is taking place.

Even in the absence of hyperdepletion or hyperstability, fishery-dependent abundance indexes should be interpreted with care. If estimates of total catch and total effort are available over time, a CPUE time series can be constructed by calculating the index for the year i as:

This CPUE index is commonly known as the raw CPUE. In many data-limited situations, where aggregated estimates of the total catch and some measure of the total effort (number of vessels, fishing days, etc.) are available, this may be the only option for generating an index of abundance. However, the interpretation of temporal changes in raw CPUE can be problematic for several reasons:

- The calculation of raw CPUE integrates information from a diverse fishing fleet, composed of vessels with different “fishing power” or efficiency.

- The catchability (q) may differ by area or by the time of year when fishing takes place.

- Technological improvements or fishers’ experience can lead to an increase in catchability over time (effort creep).

- Environmental factors can change catchability over time.

- The units of effort may be misleading.

For these reasons, raw CPUE is rarely used to assess the status of a stock. Instead, a statistical procedure is conducted to account for all those factors that affect catchability, but are not the result of a change in abundance. This process is known as CPUE standardization, and involves the use of several different statistical methods. While the process itself is beyond the scope of this manual, the following sections provide examples on assessing the potential effects of different variables when interpreting the abundance index.

Fleet effects

When very heterogeneous fleets target the same species (for example, artisanal vs industrial and/or purse seine vs gillnet), the use of an aggregated index of abundance is problematic, particularly if the relative composition of the fleets changes over time or if the fleet’s coverage of the sampling protocol changes. For example, if effort is measured as fishing days, the catchability of an industrial vessel in one day is presumably much higher than that of an artisanal boat. Similarly, a reduction in the number of industrial vessels operating, or an increase in the proportion of artisanal boats that are being sampled, will lead to a drop in CPUE that is not the result of a reduction in abundance. A solution may be to calculate the nominal CPUE by vessel gear type (if available), rather than across all fishing vessels.

Changes in fleet composition that are the result of real changes in the fishery or in the sampling protocol will also impact the interpretation of year-to-year variation in an index of abundance. As an example, Figure 50 shows total catch per fleet per year, and indicates a marked change in fleet composition over time. There is a complete lack of activity for the industrial fleet by the end of the period, suggesting that the interpretation of an aggregated index of abundance may be problematic. However, there is also a period where overlapping data from the artisanal and industrial fleet are available. These data are used in Figure 51 to further investigate and assess the differences in catch rates between the two fleets, and as shown in the figure, confirm that – at least for the period when data overlapped – they tended to have different catch rates.

FIGURE 50
Histogram showing total catch per fleet (by colour), per year

FIGURE 51
Box plot showing catch rates per fleet for period in which data overlapped (2000–2005)

Spatial–temporal effects

A fishery-dependent index of abundance only tracks the changes of the fraction of the population under exploitation (i.e. that is vulnerable to fishing. If the fishery operates in a reduced area compared to the distribution of the stock, there may be a limited relationship between CPUE and total abundance.

If temporal changes in the spatial distribution of the fleet are not being accounted for, serial depletion may be occurring. As a result, a hyperstable index of abundance will lead to an overly optimistic interpretation of the stock status. Fish migration or spawning seasons may also affect catchability within a year. If sampling is not balanced, trends in CPUE can be misleading. For example, if catchability increases during the spawning season, and for some reason the sampling protocol in one year was discontinued during that season, the CPUE will drop, while the abundance may have remained constant. Similarly, if different areas present markedly different catch rates, an unbalanced sampling between years will lead to spurious changes in the abundance index.

Effort creep

When fishing efficiency increases over time – due to the implementation of new fishing practices or technology – and when this effect is not accounted for, catch rates will appear inflated over time, giving the impression that the stock is in much better shape than it actually is.

Environmental factors

Environmental factors can change the vertical or horizontal distribution and/or the level of aggregation of fish stocks, producing changes in catchability. If these factors are not explicitly considered, changes in CPUE that are unrelated to abundance will impact the perception of stock status. For example, if factors like El Niño contribute to a large fraction of the stock leaving jurisdictional waters, a CPUE reduction at the national level will be interpreted as a reduction in abundance.

Subsetting in multispecies fisheries

For multi-species fisheries in particular, where several species may be targeted at different points, the selection of the data to be used to construct an index of abundance is an important decision. If the analysis includes trips in which the species under study was not actively targeted, low catch rates may be interpreted as an indication of low abundance resulting from decreased catchability. A typical approach to address this problem involves conducting some analysis on the catch composition per trip, to create a subset of data with a high probability of targeting the species under study. Usually, this process involves a series of subjective decisions based on expert judgement, such as selecting vessels that record catches of the species for a minimum period, or ensuring that the total/average/mean catch of the species per vessel per year is higher than a given threshold. In this context, different decisions and rules on vessel selection can lead to markedly variable CPUE trends.

Effects of zero catch

When calculating CPUE in multi-species fisheries, a common practice involves selecting only the records with positive values for catch for the species under study. This process does not acknowledge that there may be trips where the catch for the given species (i.e. the species being actively targeted) was zero. Ignoring trips with zero catch can have major implications for the information content of the CPUE time series. For example, if a vessel goes fishing two days in a row, with fish “X” as a potential target species for both days, and catches 100 kg of fish X on the first day and 0 kg of fish X on the second: if only positive records are used, the CPUE would be calculated incorrectly as 100 kg/day, when the correct calculation is (100 kg + 0 kg) ÷ 2 days = 50 kg/day. Including zeros when no individuals were actually caught on a fishing trip can dramatically change the data and affect the estimated status of the stock. It is therefore critical to determine what species are generally targeted on a trip (i.e. for or during one unit of effort), and ensure that targeted species are given a numerical value even if the value is zero because none were caught. An intertidal octopus harvester does not need to record zero catch for tuna, but if a diver is harvesting only lobster and catches no lobster on a given trip, that trip must still be recorded with zero catch, in order to ensure an accurate CPUE. For each fishing event, the range of species that are likely to be harvested should be recorded and given a catch value, even if zero. Only by accurately tracking the catch and effort in this way can CPUE be used to estimate stock status.

7.4.2 Exploratory data analysis

Calculating a reliable index of abundance is probably the most informative tool for tracking stock status. However, the interpretation of year-to-year variation in the index may be affected by multiple variables (see Section 7.4.1). Although a CPUE standardization process is strongly advised, a preliminary and in-depth evaluation of the available data will allow for deeper understanding of the data in hand. This will ultimately improve the interpretation of any statistical model.

The following steps provide guidelines for conducting EDA on trip-by-trip datasets to be used to construct an index of abundance. The ability to learn from the data will depend on which co-variables associated with a fishing trip are available (such as month, area, depth, type of hook, fleet, environmental parameters, number of hooks and net size).

1. Use expert judgement and all available data to assess whether hyperdepletion or hyperstability is occurring. Although hard to test with data, expert judgement can prove very useful to assess the chances that the index is not linearly related to abundance. When working with sedentary resources in particular, the practitioner should pay special attention to the potential for serial depletion. If high-resolution data on the fishing area are available, they can be used to create an index of abundance per area, and to assess if the fleet moved sequentially between areas of high catch rates.

2. Assess data coverage. For each source of effort data, visualize and plot data coverage by both area and time period. Understanding data coverage is useful to anticipate problems with data interpretation when sampling is unbalanced, or when systematic changes in the fleet composition have occurred. Unbalanced sampling may lead to year-to-year variations in the index that are unrelated to abundance. (See also Section 7.1 and Box 11.)

3. Subset or filter the data. It is particularly important to select data that are informative and that contain relevant information for the species under study. Large databases of logbook data usually contain catch records of different species, habitats, gear and fleets, and some of these records may not be relevant for the calculation of CPUE. For example, a trip that contains a small catch of an octopus species in a trawl fishery that targets hake should not be used to infer the status of the octopus population (the CPUE will be artificially low, as the species is not being actively targeted). The subsetting process usually relies on subjective criteria. However, several statistical methods that analyse the catch composition of the data have been developed to infer targeting and should be used whenever possible (see for example, Winker, Kerwath and Attwood, 2014; Campbell, 2016).

4. Analyse different effort definitions and subsetting criteria. In general, if trends in the abundance index are similar regardless of the effort unit definition or the data subset, this indicates that the interpretation of changes in stock status are robust. However, if major discrepancies are encountered, it is necessary to dig deeper and make informed decisions on the best data or units to use in further analysis.

5. Explore the relationship between CPUE and as many variables as possible. Although time-consuming, exploring the relationship between the abundance index and any candidate variables that may affect catchability is a critical process. This analysis will be key to providing more detail on the index of abundance, and will also identify candidate co-variables for any further statistical analysis. Common co-variables include:

- characteristics of the fleet (such as horsepower or vessel size, and fishing efficiency advances such as fishfinders);

- environmental variables (such as temperature, El Niño events, and known presences of upwelling or phytoplankton blooms); note that caution should be exercised when utilizing environmental data to interpret trends in abundance; and

- fishing operations (such as combinations of area and month that may dramatically impact the catchability coefficient).

To assess consistency between different CPUE time series, separate time series of CPUE by gear, area (and if possible, using different effort units) should be created. Figure 52 shows how the CPUE trend changes across different units of effort such as number of trips, number of fishing days, and number of hooks multiplied by number of days. Using the catch per trip as an index of abundance suggests that the population remains relatively stable or that it grows. But when using more refined units of effort, a drop in abundance is suggested. If high-resolution effort data exist for the entire time series, those should be preferred over coarser metrics of effort (e.g. hooks × days > days > trips).

FIGURE 52
Time series plots showing CPUE by gear type and by unit of effort

A more difficult case arises if the different CPUE time series have different trajectories (Figure 53). For example, if CPUE is declining for one gear type and increasing for another, it suggests one of the following: (i) fishers are not targeting the same population, in which case assessment and management should reflect this stock structure; or (ii) there are some technological issues that should be taken explicitly into account.

FIGURE 53
An example of CPUE declining for one gear type (red) and increasing for a second gear type (blue) for the same species

A natural next step in exploring CPUE data is to plot CPUE against potential variables that may affect catchability. In the following example (see Figure 54), CPUE data are plotted by month for two geographic regions. This type of faceting can be extremely useful in detecting patterns and discrepancies. For example, this analysis indicates that there is an interaction between the catch rates and zone per month, and that these should be considered as candidate variables for CPUE standardization.

FIGURE 54
Box plots showing CPUE (y-axis) by month (x-axis), for two geographic zones

As an alternate example, Figure 55 plots CPUE per year per region, and suggests that during the first years of the fishery, the catch rates were similar between areas, but that in recent years, the CPUE in Zone 2 is much lower. This could indicate a shift in the resource distribution or a local depletion in Zone 2.

FIGURE 55
Faceted box plots showing CPUE (y-axis) by year (each plot), and by zone (within each plot)

An overarching goal of conducting EDA on CPUE data is to obtain a representative time series that accurately tracks changes in abundance, which can then be used either in a more complex stock assessment model or in an empirical indicator-based framework. For the latter however – i.e. using CPUE in empirical indicator-based frameworks – caution is recommended. For example, if CPUE is available but total catch is not, defining a reliable reference point by which to interpret the CPUE becomes difficult. In this case, local knowledge is essential to understanding whether the CPUE is reliable or not (and whether the fishery is in a “good” or “poor” state). Another alternative may be to use a survey to understand how CPUE has changed over time.

Effect of short time series

As shown in Figure 56, there may be cases when the time series of reliable indices of abundance are short, compared to the actual history of exploitation of the fishery. In such cases, it is advisable to use the existing data as much as possible, and to complement the data with historical information from LEK and/or alternative sources of information on catch and effort (for example, by using export records or vessel registries to obtain rough estimates of catch and effort, respectively). Expert knowledge is particularly important in these situations, as it can reveal patterns that do not arise from quantitative analysis. In his example, the timeseries of official records does not capture the large drop in abundance the occurred during the firsts decades of exploitation.

FIGURE 56
Plot comparing time series of official CPUE (blue line) with alternative sources of CPUE (red dots)

Notes: In this example, the time series of the official CPUE (blue line) is significantly short compared to the alternative sources of CPUE (red dots and black line). These therefore allow for a more complete understanding of the exploitation history of the fishery than if the official CPUE data stream had been used in isolation.

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Excel templates and sample datasets

Throughout the manual, a variety of Microsoft Excel and comma separated value (CSV) files are referenced, in particular, to execute the practice scenarios and examples of FishualizeR as illustrated in Boxes 1–11. These templates and mock/sample datasets are listed below, and are available for download within the “Introduction” section of FishualizeR (http://apps.upwellsolutionsllc.com/app/FishualizeR/).

- Excel_Template1_DataInventory_Events

- Excel_Template2_LifeHistoryInventory

- Mock_length_freq.csv

- Mock_length_freq_area.csv

- Mock_biometric_area_trip_duration.csv

- Mock_length_freq_area_gear.csv