StoX: An open source software for marine survey analyses

Espen Johnsen,Atle Totland,Nils Olav Handegard,Gjert Endre Dingsør,Arne Johannes Holmin,Åsmund Skålevik,Edvin Fuglebakk,Edward Codling

doi:10.1111/2041-210x.13250

Espen Johnsen, Atle Totland + Show 6 more

Open Access

https://doi.org/10.1111/2041-210x.13250

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Abstract

Abstract Scientists across the globe conduct survey programs to monitor and characterize abundance, population structure, biodiversity and geographical distributions. To assess the state of marine fish and zooplankton, population surveys are often repeated annually using standardized sampling protocols and analysis techniques to establish trustworthy stock status. However, although transparency and repeatability are recognised as important principles of this process, it is often difficult to obtain comprehensive documentation of metadata and data processing steps. This is particularly challenging for workflows that include manual processing steps. StoX was principally built to process research‐vessel survey data, and we have included several standard survey estimation models. The software was developed to be robust and versatile and aimed at the open source community, such that users could easily build their own models. StoX is fully integrated with R to utilize the large number of R‐packages and enable any StoX function and stock estimation model to be controlled using R. There has been a large need for a freely available software for research–vessel survey estimation, and StoX is tested in surveys carried out in four continents and is the official tool for many important fish stock surveys. The basic workflow and transparency principles of StoX, together with a customizable GUI, makes StoX applicable for any geographically coded surveys. Future versions of StoX will include statistical models to estimate the catch composition in commercial fisheries. In fields such as conservation management, there is also a need to document the estimation methods, and additional estimation and analyses models, including biodiversity indices are currently implemented. In parallel, we envision a closer web service integration with existing international and national data centres.

Highlights

To monitor change in abundance, population structure, geographical distribution (Gunderson, 1993) and biodiversity (Johannesen, Høines, Dolgov, & Fossheim, 2012) of fish and zooplankton populations (Dalpadado, Ingvaldsen & Hassel, 2003), scientific acoustic and swept area trawl surveys are conducted worldwide
There has been a large need for a freely available software for research–vessel survey estimation, and StoX is tested in surveys carried out in four continents and is the official tool for many important fish stock surveys
In parallel with the development of StoX, ICES has established databases to store acoustic and biotic trawl data to support data input files for StoX. This provides complete transparency from data input to the survey estimates used in stock assessment models, and StoX will be integrated into the new Transparent Assessment Framework that “is a framework to organize data, methods, and results used in ICES assessments, so that they are easy to reference and re‐run

Summary

Introduction

To monitor change in abundance, population structure, geographical distribution (Gunderson, 1993) and biodiversity (Johannesen, Høines, Dolgov, & Fossheim, 2012) of fish and zooplankton populations (Dalpadado, Ingvaldsen & Hassel, 2003), scientific acoustic and swept area trawl surveys are conducted worldwide These surveys are often repeated annually following standard protocols to produce time series, which are used to assess the state of fish stocks (Gunderson, 1993) and the health of ecosystems (Kirkman et al, 2016). The lack of streamlined data structures, documentation of parameter settings and information regarding subjective user decisions, make it nearly impossible to recalculate long time series with updated parameter settings, to test the effect of new methods and carry out sensitivity tests. This bottle neck in development, and lack of transparency in the data and methods used to produce time series, slows down the implementation of new methods, especially in multinational surveys

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