A will-o’-the wisp? On the utility of voluntary contributions of data and knowledge from the fishing industry to marine science

For future sustainable management of fisheries, we anticipate deeper and more diverse information will be needed. Future needs include not only biological data, but also information that can only come from fishers, such as real-time ‘early warning’ indicators of changes at sea, socio-economic data and fishing strategies. The fishing industry, in our experience, shows clear willingness to voluntarily contribute data and experiential knowledge, but there is little evidence that current institutional frameworks for science and management are receptive and equipped to accommodate such contributions. Current approaches to producing knowledge in support of fisheries management need critical re-evaluation, including the contributions that industry can make. Using examples from well-developed advisory systems in Europe, United States, Canada, Australia and New Zealand, we investigate evidence for three interrelated issues inhibiting systematic integration of voluntary industry contributions to science: (1) concerns about data quality; (2) beliefs about limitations in useability of unique fishers’ knowledge; and (3) perceptions about the impact of industry contributions on the integrity of science. We show that whilst these issues are real, they can be addressed. Entrenching effective science-industry research collaboration (SIRC) calls for action in three specific areas; (i) a move towards alternative modes of knowledge production; (ii) establishing appropriate quality assurance frameworks; and (iii) transitioning to facilitating governance structures. Attention must also be paid to the science-policy-stakeholder interface. Better definition of industry’s role in contributing to science will improve credibility and legitimacy of the scientific process, and of resulting management.

Introduction

Science-industry research collaboration (SIRC) in fisheries is at a crossroads. SIRC, in our experience, is driven by a clear willingness on the part of the fishing industry to voluntarily collect and provide information to science in support of management, and by a growing interest within the scientific community to collaborate with fishers and their associated organizations (‘the fishing industry’) (Holm et al., 2020a; Steins et al., 2020a; Mackinson, 2022). For many, SIRC is seen as the way forward for cost-effective, improved data collection (Johnson and Van Densen, 2007; Wendt and Starr, 2009; Kraan et al., 2013; Stephenson et al., 2016; Mangi et al., 2018; Thompson et al., 2019). Experience shows that SIRC done well, can also increase transparency and communication, build capacity amongst fishers and scientists, improve societal relevance of research, and build a collaborative rationale for durable solutions (Karp et al., 2001; Johnson and Van Densen, 2007; Johnson, 2009; Innes and Booher, 2010; Doerner et al., 2015; Mackinson and Middleton, 2018; Mangi et al., 2018; Thompson et al., 2019; Holm et al., 2020a; Steins et al., 2020a; Mackinson, 2022).

In the field of gear technology, SIRC goes back many years (Feekings et al., 2019). However, in fish stock assessment and ecosystem science, input to science from the fishing industry has generally taken the form of recording and submitting a narrow range of fisheries-dependent types of data, such as statutory data on landings and fleet effort generated by industry, and data from sampling on board of fishing vessels by scientific observers. Since the turn of the 21^st century some individual scientists and research projects have been more receptive to the benefits of direct participation of industry in gathering scientific data and exchanging knowledge (Stanley and Rice, 2007; Hind, 2015). Furthermore, openness to Indigenous knowledge became required in some regions of the world. Also, the need for active stakeholder involvement is now explicitly acknowledged in international policy frameworks such as the United Nations’ Sustainable Development Goals (UN, 2015), and its guidelines for small-scale fisheries (FAO, 2015). As part of its criteria for research grants, Europe’s Responsible Research and Innovation policy and actions (Owen et al., 2012) demands partnerships with industry and delivery of outcomes that address societal issues. These criteria encourage researchers to gather and access data through engagement with the fishing industry (Johnson and Van Densen, 2007; Doerner et al., 2015; Hind, 2015; Dubois et al., 2016; Stephenson et al., 2016; Mackinson and Middleton, 2018; Nursey-Bray et al., 2018; Baker et al., 2019; Bentley et al., 2019; Thompson et al., 2019; Holm et al., 2020a; Raicevich et al., 2020; Steins et al., 2020a).

In addition to the ability to collect and share quantitative data, the fishing industry also possesses important “experiential knowledge” (Stephenson et al., 2016) that can give context, and help with the interpretation of quantitative scientific and industry data and findings. Nevertheless, experiential knowledge routinely gets little consideration, often being qualified as biased or ‘anecdotal’ (Johannes and Neis, 2007) and thus not fit for purpose in science for management advice. Both quantitative and experiential information sources can be unique in the evidence and insight they offer and are relevant for enhancing scientific understanding of the marine environment (Neis et al., 1999; Bentley et al., 2019). They can inform the development of responsive management systems, as fishers are often the first to notice changes at sea. They can also contribute to the inclusion of social and economic considerations in fisheries management frameworks (Stephenson et al., 2018; Foley et al., 2020; Stephenson et al., 2021), hypothesis testing (Stanley and Rice, 2007) and coping with uncertainty (Dankel et al., 2012).

Despite a rich global literature on different forms of SIRC, there are only a few peer-reviewed publications where SIRC projects made a difference in scientific assessments as part of advisory processes (Melvin et al., 2002; Röckmann et al., 2015; Hesp et al., 2017; Duplisea, 2018; Bentley et al., 2019; Chagaris et al., 2020; Clegg et al., 2021; Howell et al., 2021). This indicates that while the value of using industry data and knowledge contributions and SIRC partnerships is increasingly recognized, there are still challenges about how to engage in SIRC in a way that delivers good quality information considered trustworthy within the constraints of established, evidence-based decision-making processes. These challenges relate to both the mechanics of the scientific advisory system and opinions on how to govern its integrity (Linke et al., 2020).

This paper addresses questions about the utility of voluntary data and knowledge contributions from the fishing industry to enhance the evidence base used to inform fisheries science and ultimately, management. We combine insights from a literature review, our own experiences, and findings from structured expert discussions in regional workshops in Australia/New Zealand, the Americas and Europe, to investigate and characterize the conditions that determine whether voluntary data and knowledge contributions from the fishing industry are, should, or could be considered useful; or not. Our objective is to disentangle challenging, intertwined issues related to personal and institutional perceptions and practices around using industry information in assembling ‘the best available information’ for science.

We focus on ‘voluntary contributions’ – as opposed to ‘statutory requirements’ – including situations where the fishing industry by its own initiative or choice engages in SIRC as active contributors of their data and knowledge. Voluntary contributions may include situations that are more transactional in nature, but still characterized by deliberate choices over the extent to which fishers contribute. This would include, for example, the chartering of commercial vessels by scientific institutes to undertake fish stock surveys and responding to questionnaires and researcher requests for interviews. In this paper, the term ‘fishing industry’ encompasses both fishers, i.e., those who fish – whether it be small-scale, large-scale, independent, contractual, and irrespective of their gender, and fishing organizations, i.e., those higher-level entities such as alliances, associations, companies, cooperatives and unions, that represent fishers, fleets, or sectors. Fishers and fishing organizations are present in many science and management forums, making it hard to separate their voluntary contributions. That said, we recognize that they may encompass groups with different value systems, including around how they share their knowledge and with whom, and that the fishing industry is extremely heterogeneous around the world depending on the types of fisheries and even within the same métiers (Schadeberg et al., 2021), governance systems and cultures. Where separation of contributions by fishers and fishing organizations is important to the discussion, we make that distinction. The same thinking applies to the words ‘scientists’ and ‘science’ and the groups and phenomena they describe. Our focus on voluntary industry contributions is explicitly directed at regions with well-developed scientific advisory systems because this is where issues about the transition in governance and participatory approaches in fisheries are matters of debate rather than necessity (Stilgoe et al., 2013; García et al., 2016; Holm et al., 2020b; Linke et al., 2020; Macher et al., 2021).

In the next section, we outline our approach to identifying three key issues inhibiting systematic integration of voluntary industry contributions to science: (1) concerns about data quality; (2) beliefs about limitations in useability of unique fishers’ knowledge; and (3) perceptions about the impact of industry contributions on the integrity of science. We will then review these issues. In particular, we focus on understanding the utility of voluntary contributions in specific applications and how they might affect confidence in the integrity of information, processes and science organizations. In summing up, we expose the dilemmas associated with using voluntary industry contributions and what it means for how the future of fisheries science is best conducted in the emerging frameworks for responsible research and innovation.

Method

Investigative critique

This research triangulates findings from a literature review, causal explanation (Nachmias and Frankfort-Nachmias, 1976), and expert judgement. The first five authors (Steins, Mackinson, Stephenson, Mangi, and Pastoors) originally set out to develop a comparative analysis of international experiences of SIRC projects using a review of the literature to arrive at tangible recommendations for sustained integration of knowledge generated through co-creation between fishers and scientists within the institutional frameworks for science and management. While conducting the review, these authors found few published examples where SIRC projects had made a difference in science or management, prompting them to confront their beliefs and wonder whether they were perhaps being led astray by a will-o’-the wisp¹. This resulted in a new approach towards developing an investigative critique of the evidence around SIRC, formulating explanations in the form of the most vexatious and thorny questions related to involvement of fishers and the fishing industry in the provision of data and knowledge for science. A deliberately provocative approach was adopted, rooted in experiential observations of implicit bias against voluntary contributions. This enabled us, as confessed proponents of SIRC, to confront ourselves and the readers with the difficult questions that we might otherwise be expected to avoid.

The original authors drew on their own knowledge of the literature and experience in SIRC or scientific advisory settings to identify five key elements associated with resistance to the use of voluntary knowledge contributions in scientific evidence to support management: (i) threats to quality; (ii) lack of reliability; (iii) threats to the integrity of science; (iv) concerns about the uniqueness or lack of added- value in SIRC; and (v) inconsistent availability. We then articulated potential explanations for these elements in five provocative statements intended to expose ‘the elephant in the room². We then carried out a literature review to assess the evidence to support or refute these statements (see Supplementary Materials). We focused on voluntary data and knowledge contributions, rather than statutory requirements, because they demonstrate an appeal from the fishing industry for engagement with science beyond that which is mandatory. Furthermore, it is here where questions about conflict of interest, trustworthiness and reliability make some scientists and receivers of scientific advice start to feel concerned. However, we did not neglect the importance of contributions linked to statutory reporting in the critique. In some contexts contributions linked to statutory reporting are also subject to the same issues of trust or conflict of interest and reliability as such data is also the industry’s responsibility The evidence was drawn from referenced case studies from regions with well-developed fisheries assessment and scientific advisory processes.

Regional workshops

Having collated our evidence, the original team then identified a group of scientific experts with backgrounds in natural and social sciences and in fisheries research, advice and management, SIRC, and science-policy interfaces and invited them to participate in expert panels. Twenty-eight international colleagues agreed to meet to discuss whether arguments for and against the five provocative statements were justified. The meetings took the form of three online workshops for the following regions: Europe, Australia/New Zealand and the Americas. Each workshop was facilitated by a chair recruited from outside the original author group (authors Ballesteros, Brooks, and McIsaac). Chairs also prepared the resulting meeting reports (see Supplementary Materials). Participants received an evidence document ahead of the meeting and were asked to fill out a short assessment report and point to additional references or observed evidence relevant to the discussion. During the workshops, the original five authors introduced each of the 5 elements with a provocative statement to prompt discussion (see Supplementary Materials). They subsequently participated as observers as participants discussed each statement. Following the regional workshops, participants who could commit to an active role in the writing process joined the author group. All authors were involved in analyzing emerging themes from each workshop, as well as the separate evidence supplied by individual participants. Analysis took place via two online author meetings, joint working documents and by email correspondence, resulting in a rich and substantial volume of documented information (see Supplementary Materials).

Workshop discussions resulted in the merger of the original five key elements into three main issues relating to (1) the quality of voluntary contributions by the fishing industry; (2) the uniqueness of fishers’ knowledge; and (3) the integrity of science (Figure 1; definitions in Table 1). Using this refined ‘lens’ for our approach, in the remainder of this paper, we look to identify with confidence, what, where and when there is utility in including the data and knowledge products of SIRC as evidence in assessment and scientific advisory processes, and the utility of the SIRC process itself in achieving this. We use ‘quality’ and ‘uniqueness’ as the key metrics of utility. We also explore important issues about notions of ‘integrity’ because they are linked with perceptions about utility.

FIGURE 1

Figure 1 Analytical framework: Three key issues and associated provocative statements.

TABLE 1

Table 1 Operational definitions of terminology (in alphabetical order).

Definitions

Beyond the clarification already made in the introduction regarding definitions for ‘voluntary’ and ‘fishing industry’, during discussions and subsequent analysis, it became clear that a common vocabulary was needed. For example, participating social scientists used the term ‘data’ to refer to all information and knowledge, whereas natural scientists generally referred to data as quantified information and were inclined to link experiential knowledge to ‘anecdotal’ information. For this reason, we developed a number of operational definitions for the main terminology used in this paper (Table 1).

Findings: Utility of fishing industry contributions to science

Compared to statutory fisheries data, there is limited evidence that either voluntary data from the fishing industry or experiential knowledge are systematically used in well-developed systems for fisheries assessment and management advice. This observation is in contrast to the keenness routinely expressed by industry in various fora to get involved in supporting the provision of scientific evidence (Graham et al., 2011; Doerner et al., 2015; ICES, 2019d), and with the growing interest by the scientific community to collaborate to improve the knowledge base for fisheries management (Holm et al., 2020a; Steins et al., 2020a; Mackinson, 2022). This lack of use of voluntary data persists despite clear drivers in policy frameworks and funding mechanisms to facilitate stakeholder contributions to science [e.g., (Bradley et al., 2019; ICES, 2021c; ICES, 2021b)]. Our analysis suggests this is rooted in perceptions about the quality of voluntary industry contributions, uniqueness of fishers’ knowledge and integrity of science (Figure 1).

Issue 1: Quality of voluntary industry contributions

Provocative debate prompt: Use of data collected by industry poses a threat to the quality of the evidence for science-based decision-making (Figure 1).

This statement is rooted in beliefs that voluntary contributions from industry cannot live up to the quality standards and consistent availability that should be expected of scientific data; additionally, that voluntary contributions are driven by opportunistic motives implying bias, or that information provided is ‘anecdotal’ and therefore not suitable. Evidence shows that concerns about quality issues related to industry data are indeed legitimate. Work on observer programs has shown disparity between data collected by fishers and observers, where the former are keen to record data on species they exploit or are more familiar with, while ignoring other species in the catch (Mangi et al., 2016). Similarly, positive bias has been observed in fishers’ sampling data versus scientists’ in stock surveys where both were using the same methodology (Mayfield et al., 2011). In this regard, fishers may not be equipped with the necessary professional education, skills and understanding of sampling design to collect data that meet scientific standards (Calderwood et al., 2021a), or have received instructions from scientists that were unclear or open to interpretation (Kraan et al., 2013; Stenevik et al., 2020). Also, self-sampling schemes may suffer from low sampling rates thereby increasing uncertainty in results (Starr, 2010; Clegg et al., 2022). Another quality concern is that the process of engaging in SIRC is associated with bias in relation to who participates and thus, where and which data are collected, as voluntary data collection often involves the same group of selected or motivated fishers (Kraan et al., 2013; Raicevich et al., 2020; Steins et al., 2020a).

Quality may also be affected when industry is constrained from engaging in SIRC due to limited finances or available time. They may underestimate the extent of the commitment and continuity of resources required for sustained research (Starr, 2010), making it difficult to ensure that data provision persists (Lordan et al., 2011; Mangi et al., 2015; Mangi et al., 2018; Raicevich et al., 2020; Steins et al., 2020a; Van Helmond et al., 2020). Continuity may also be affected when committed fishers leave the fishery (Jones et al., 2022) or when there are trust issues, for instance, when participating fishers or their peers fear the information they provide will be used against them, serving only to punish efforts of collaboration (Carruthers and Neis, 2011; Kraan et al., 2013; Mangi et al., 2015; Röckmann et al., 2015; Barz et al., 2020; Wätjen and Ramírez-Monsalve, 2020; Calderwood et al., 2021b; Cvitanovic et al., 2021; Ford and Stewart, 2021). Finally, data may be withdrawn because of opportunistic motives, as was the case for a stock assessment by the International Council for the Exploration of the Sea (ICES), where “a fishing industry offered survey data they had funded but withdrew the information when the inclusion resulted in a lower Total Allowable Catch [advice]” (ICES, 2014).

Evidence also shows, however, that concerns about the quality of industry data can and have been successfully addressed through a variety of methods including training, development of sampling standards, interviewing, and systems of verification and validation (Neis et al., 1999; Stephenson et al., 1999; Johannes et al., 2000; MinPI, 2011; Kraan et al., 2013; Mion et al., 2015; Fry et al., 2018; Mangi et al., 2018; ICES, 2019d; Keane et al., 2019; Thompson et al., 2019; Flores Martin, 2020; Raicevich et al., 2020; Stenevik et al., 2020; Suuronen and Gilman, 2020; Van Helmond et al., 2020). Further, concerns about data quality are not unique to industry but also apply to science (Liggins et al., 1997; Benoit and Allard, 2009; Cartwright, 2019; Gismondi et al., 2020; Fernandes et al., 2021).

Using fishers to collect data allows for dealing with time, cost and spatial and temporal restrictions associated with scientific catch sampling programs (Poos et al., 2013; Jones et al., 2022). When many vessels of a fleet are involved in sampling, geographical coverage can be extensive and fully representative of the area fished by this fleet. The number of trips sampled can outweigh limitations of small sample sizes from each vessel (Bjørkan, 2011; Pennington and Helle, 2011; Kraan et al., 2013; Gawarkiewicz and Malek Mercer, 2019; Jones et al., 2022). Voluntary fishing industry data, including from fish processors, may exceed that collected by government or third-party sampling schemes in amount and distribution (Mackinson et al., under review; Power et al., 2007; Rochette et al., 2018; Dunn, 2020; Kenyon et al., 2022). Such sampling can go beyond providing data on catches, and can contribute to surveying parts of the stock not targeted in the same way that fisheries-independent scientific surveys do (Gerlotto et al., 2012; Schram et al., 2021). In addition to fish stock assessments, industry data have also contributed to an improved knowledge base or validation of by-catch data regarding seabirds, marine mammals and Endangered, Threatened and Protected species (Bjørkan, 2011; Fangel et al., 2015; Fry et al., 2018; Moan et al., 2020). Such voluntary industry contributions, provided they are done well, all contribute to improving quality of data collected by science institutions.

To overcome concerns about consistent, long-term availability of voluntary industry data, most long-term SIRC initiatives rely on an assortment of remuneration options, such as remuneration for haul specific logbook data or discard samples (Kraan et al., 2013; Jones et al., 2022); payment of net income differences with comparable vessels not involved in research (Schram et al., 2021); additional quota allocations (Kindt-Larsen et al., 2011; Van Helmond et al., 2016); or payment for ship and crew hire (Ressler et al., 2009; Gallaudet, 2021; Hoff et al., 2021). In this context, it is important to note that concerns about long-term availability of data and information are not exclusive to industry contributions. There is a perception that fisheries-independent data collection has a high level of funding security, because it is usually embedded in (inter)national agreements. But this is not a guarantee that such scientific sampling schemes will continue. For example, the ICES stock assessment for North East Atlantic mackerel was impacted by Norway’s decision to step out of the egg survey to pursue a swept-area survey (Spijkers and Boonstra, 2017). In Canada, the 4WX larval herring survey was eliminated after 22 years (Stephenson et al., 2015) during a time of fiscal restraint within government. Also in New Zealand, statutory data systems have suffered from continuity of quality (Langley, 2014; Middleton, 2021). Equally, major changes in commercial fisheries due to policy decisions, such as fishery closures, can result in the termination of long-term Catch per Unit of Effort (CPUE) series used in assessments. Finally, there are continuity issues in scientific data collection in relation to a changing world. For example, in the context of climate change, scientific surveys that are standardized to allow for time-series development of relative changes in fish stock populations may miss important changes in stock dynamics (Karp et al., 2022). Here, fishers’ knowledge contributions can assist evaluation of the need for potential changes in survey design.

In considering the quality of voluntary industry contributions, concerns about the trustworthiness and reliability of such data, as well as conflict of interest (see issue 3), make some scientists and receivers of scientific advice worry. However, issues with data quality and reliability are not unique to industry. In this context, it is worth reflecting on experiences with existing statutory data collection, which share related concerns about reliability. For instance, catch misreporting or aggregation of species into generic groups may lead to incorrect interpretations of fishing pressure on stocks and affect the quality of assessments and the advice upon which they are based (Patterson, 1998; Bradley et al., 2019). Despite these concerns, statutory catch data collection schemes remain a cornerstone of information for assessments. Another example is observer bias, a known issue, even when highly trained scientific observers collect data (Liggins et al., 1997; Benoit and Allard, 2009; Kraan et al., 2013; Suuronen and Gilman, 2020). Observer bias is accepted implicitly, whereas information provided by industry is more heavily scrutinized (Kraan et al., 2013; Berg et al., 2022; Clegg et al., 2022).

When specific concerns about the quality of data contributions from industry have been addressed, as is best practice for any source of data, there is no reason why such information should not be used in scientific research. Indeed, fisheries-dependent data are often the only available source for assessments of commercially important fish stocks when there are financial, spatial or temporal limitations to fisheries-independent data collection. This is the case for Alaska, Australia, Canada and New Zealand. Potential shortcomings of voluntary industry contributions can be mitigated by co-design of sampling schemes and putting in place necessary quality control measures in the same way as implemented by most scientific institutes or science organizations. But the presence of such quality control systems does not mean that issues and concerns regarding quality can always be fully eliminated. There are numerous examples in well-developed scientific advisory systems where data and methodological errors were detected after scientific advice was given (Hilborn and Peterman, 1996; Spijkers and Boonstra, 2017; ICES, 2019c; SWFPA and SFA, 2021; ICES, 2022). Mostly, the evidence trail for these is transparent, but not always. For example, in the ICES assessment for blue whiting, the 2010 survey estimate was initially included in the assessment (ICES, 2010) and later withdrawn because it was considered to be an outlier, i.e., quality issue, without a clear explanation (ICES, 2012).

There is evidence that fishers’ motives for data collection can be short-term and motivated by a combination of objectives such as deriving immediate financial gain, improving fishing opportunities, and providing evidence to impact decision-making (Woo et al., 2013; Dubois et al., 2016). This will also influence their decisions to be involved in long-term data collection. While scientists’ objectives for data collection are unlikely to be driven by opportunities for personal financial gain, scientists do have interests to consider, such as research grants, project objectives and publication track records. Scientists also often share fishers’ motivations to influence others’ views. This is a reality of the competition for scientific knowledge and differences in opinion that exists in science; even if the ‘scientific approach’ seeks to be neutral, treating different viewpoints as testable hypotheses, without a stake in any particular result. However, SIRC experiences also show that it is wrong to assume that by default the industry has only short-term, opportunistic motivations with regards to voluntary data collection. The industry also recognizes the importance of long-term data collection, including funding these schemes (Starr, 2010; Mackinson and Middleton, 2018; Pastoors, 2021). The motivations for SIRC projects are often founded upon a desire to contribute knowledge and data to a continuous improvement process for stock assessment and advice, and also to provide internal business intelligence information relevant to skippers and owners. Examples are European pelagic industries that work on the development of standardized commercial CPUE series for data-limited stocks (Pastoors and Hintzen, 2020; Quirijns and Pastoors, 2020). They have also implemented self-sampling data collection schemes to clarify biological questions on the duration of the spawning period of mackerel and the linkage between populations in the western area and the North Sea (Pastoors, 2021; Kenyon et al., 2022; Mackinson, 2022). The Dutch demersal industry initiated a dedicated survey for North Sea turbot and brill in response to ICES advice that highlights the need for such a survey (ICES, 2019b; Schram et al., 2021). Other motivations for funding voluntary data collection include, for example, requirements to provide evidence in support of sustainability certification schemes or providing information for developing fisheries management plans, including harvest control rules and protection of spawning and nursery areas (Steenbergen et al., 2017; Holm et al., 2020a).

Increasing recognition and experience of the benefits of industry participation in catch sampling and surveys (Poos et al., 2013; Doerner et al., 2015; Mangi et al., 2018; Gawarkiewicz and Malek Mercer, 2019; Holm et al., 2020a; Steins et al., 2020a; De Boois et al., 2021; Jones et al., 2022; Mackinson, 2022), combined with government budget declines, and increasing data and information demands to service ecosystem-based advice and management (Ballesteros et al., 2018; Bradley et al., 2019), have led to increased delegation of responsibilities and costs of sampling from government to industry. Such delegation can also contribute to the development of trust relations. It is widely recognized that SIRC can contribute to developing mutual trust (e.g., St. Martin et al., 2007; Holm et al., 2020a; Steins et al., 2020a; Ford and Stewart, 2021; Macher et al., 2021; Köpsel, 2022) and to industry’s perceptions about the legitimacy of science (Murray et al., 2008b; Johnson and McCay, 2012; Röckmann et al., 2015; Stephenson et al., 2016; De Boois et al., 2021; Su et al., 2021). Once trust has been established the degree of integration of industry data (Stephenson et al., 2016; Cvitanovic et al., 2021) and unique experiential knowledge (Steins et al., 2020a) may evolve. However, in our experience, acceptance of this experiential knowledge in well-developed scientific advisory systems for fisheries and ecosystem management tends to be problematic.

Issue 2: Uniqueness of fishers’ knowledge

Provocative debate prompt: Industry has limited unique knowledge that is usable (and therefore useful) as evidence to support management beyond that already known or available from science institutions (Figure 1).

Most SIRC projects have focused narrowly on working with fishers on gear and selectivity research, using fishers to help collect basic biological and catch information or using industry vessels for science-directed survey observation platforms. Whereas engagement with measurable industry data is growing, experiential knowledge seems to be overlooked. Globally, fisheries scientists struggle to include information that is not quantitative or is considered to be ‘anecdotal’ (Johannes and Neis, 2007) and, while potentially beneficial to providing important context, is not regarded as fit-for-purpose in quantitative science or usable in receiving systems that prefer fisheries-independent information and independent sampling. Using experiential knowledge also suffers from even more pronounced suspicions than measurable industry data regarding opportunistic motives and efforts to frame alternative explanations to scientific findings (Issue 1, previous section). These perceptions persist despite the existence of policy frameworks in regions with well-developed scientific advisory systems that prescribe or include binding requests to use the best available information, including fishers’ knowledge (MinPI, 2011; Owen et al., 2012; Lynch et al., 2018; ICES, 2020a). In these contexts, the question is whether and under what conditions experiential knowledge, due to its unique nature, can be used and is therefore useful as evidence to inform management beyond that already known or available from science institutions.

Some experiential observations are perceived as relevant and fit readily into assessment-related evaluation, e.g. the impact of tide conditions on catchability. But often experiential information does not fit easily into the established assessment structure and therefore is perceived as unsubstantiated evidence used in an attempt to influence management. One example is where fishers' knowledge of fish distribution and how it changes leads them to question the utility of random sampling as a scientific research design (De Boois et al., 2021). This is one of the contexts where experiential knowledge is overlooked or often dismissed as being ‘anecdotal’, even when the collective experience of individual fishers or fishing communities point to changes in a stock that may affect the appropriateness of a particular survey design. What makes ‘anecdotal’ information considered to be less true useful for monitoring change is not necessarily that it is less true, but that it is regarded as not ‘systematic’ (Wilson, 2009). For example, stock assessment science tends to be based on large spatial scale units, discrete sampling techniques, and standardized sampling protocols, whereas experiential knowledge is often more localized and is based on different and often variable temporal scales and continuous sampling practices and technologies (Perry and Ommer, 2003; Wilson, 2009; Karp et al., 2022). These are some of the reasons why experiential knowledge is often considered unusable in fish stock or ecological assessment models; particularly those that are already data-rich (Mackinson and Nøttestad, 1998). Most stock assessment protocols lack the flexibility to incorporate experiential knowledge in a meaningful way.

Dismissing fishers’ experiential knowledge as ‘anecdotal’ and thus not useable may have serious unintended consequences. Several examples illustrate that dismissing information from fishers came at a high price. These include the cod stock collapse in Canada (Finlayson, 1994; Neis et al., 1999; Rose and Kulka, 1999) and the ICES Northeast Atlantic mackerel assessment (Spijkers and Boonstra, 2017; ICES, 2019c). Indeed, where scientists have made efforts to include experiential knowledge in quantitative fisheries science, it can make significant contributions. In Canada, systematically collected information from experiential knowledge furthered understanding of relevant variables in the northern cod assessments such as stock structure, identification of spawning areas, technological creep and spatial dynamics (Neis, 1992; Neis et al., 1999; Murray et al., 2008a; Murray et al., 2008b; Johnsen et al., 2009). Within ICES, fishers’ experiential knowledge was successfully used to improve the Irish Sea ecosystem model for informing the fisheries stock assessment process (ICES, 2020b). The model that included experiential information performed the best overall statistical fit, capturing the biomass trends of commercial stocks. It also replicated the increase in landings of benthos and epifauna, which were poorly simulated in the model that only used scientific data (Bentley et al., 2019). These examples also highlight that, like fishers, scientists also make assumptions about how fisheries and ecosystem dynamics work.

While scientists’ assumptions may be rooted in existing science findings, their assumptions are also based on their own experiences or perceptions, and therefore it must be noted that scientists’ assumptions may be flawed, just like fishers’ assumptions can be. For example, fishers’ knowledge successfully challenged scientists’ assumptions about past landings by providing insights on discards and high-grading (Palmer and Sinclair, 1997; Duplisea, 2018). In Alaska, scientists’ assumptions for a ‘safe counting protocol’ for bowhead whale migration led to meaningless scientific estimates. Eventually, after 14 years, a revised and more satisfactory monitoring program based on Inuit fishers’ knowledge was adopted (Johannes et al., 2000).

It is true that experiential knowledge may not always be available in a form that is fit for quantitative assessmentsor evaluating the options among alternative management approaches, but there are also alternative ways of incorporating it into science and management. The private Marine Stewardship Council certification program developed a qualitative, risked-based assessment methodology for the evaluation of data-limited fisheries against its standard for sustainable fisheries (MSC, 2022). This precautionary methodology for stock and ecosystem assessment relies heavily on qualitative appraisal of fishers’ experiential knowledge. Also, science organizations have been known to use experiential information from fishers in validating or cross-checking scientific findings. For example, between 2002 and 2014, the international Fishers’ North Sea Stock Survey (FNSS) provided a qualitative assessment of fishers’ perceptions on relative changes in abundance, fish size, discards and recruitment of eight species compared to the previous year (Napier, 2014). Although the relevant ICES stock assessment group could not use the FNSS results in their quantitative models and the survey was considered to be “non-quantitative and subjective [in] nature” (Napier, 2014; Stange, 2017), the group responsible for drafting ICES advice occasionally used the results of the FNSS qualitatively for sense-checking of stock assessment results. The survey was discontinued due to declining fishers’ participation, possibly caused by frustration about lack of uptake by ICES (Stange, 2017). In Australia and New Zealand, fishers’ experiential knowledge has also proven to be useful in understanding changes in CPUE trends, particularly when no or limited fisheries-independent data are available. After all, CPUE deviations do not necessarily have to be related to changes in stock abundance but can be related to technological changes, changes in fisher behavior resulting from market/economic drivers or regulatory changes (Johnsen et al., 2009), or changes in fish behavior (Fernö et al., 2011).

Experiential knowledge from fishers is also useful for guiding assumptions and interpreting results from Management Strategy Evaluations (MSEs). Most MSEs are largely based on relatively complex simulation tools that have a very simplistic representation of fishers’ decision-making and behavior. The same applies to mixed fisheries models and displacement models (Nielsen et al., 2018; Wijermans et al., 2020). Information from the fishing industry can be used to test assumptions and generate more realistic expectations on the type of changes that may be assumed when management measures change, thus making fisheries and ecosystem models and MSEs more informative for management (Steenbergen et al., 2015; Pope et al., 2019; Wijermans et al., 2020; Schadeberg et al., 2021). One example is the development of a harvest strategy for the Australian Southern and Eastern Scale Fish and Shark Fishery. Separate quantitative and qualitative stakeholder information-based MSEs were done, including projecting the same set of indicators under the same set of alternative harvest strategies. Both showed very similar results. The qualitative MSE was, however, “instrumental in helping industry confront a range of systemic problems and issues in the fishery, and was used in part as the basis for a successful call for assistance in restructuring the fishery to achieve the changes that were identified as needed” (Smith et al., 2007). Information on fishing strategies, including economic and social aspects is also key in evaluation of ‘full spectrum sustainability’ (Foley et al., 2020), which extends the traditional focus of MSEs on ecological and economic to include social, cultural and institutional considerations (Stephenson et al., 2018). As attention to these ‘human dimensions’ of fisheries management increases, the need for, and role of, fishers’ experiential knowledge will also increase (Stephenson et al., 2021).

Scientists also have used experiential knowledge to find alternative explanations for scientific observations. For example, in the case of a camera monitoring scheme as part of North Sea cod management, the apparent behavior of the fleet did not follow scientific predictions, which were based on incorrect assumptions. Subsequent interviews with fishers resulted in a logical, but unconsidered, explanation for these changes (Van Helmond et al., 2016). Engaging with fishers in research and, by extension, with their experiences does not always result in changes to science but can add to the sum total of knowledge on both sides, as with the collaboration between commercial fishers and government scientists related to Pacific rockfish off British Columbia that ranged from hypothesis formulation through data analysis (Stanley and Rice, 2007).

Other areas of fisheries science where fishers’ experiential knowledge has been used is in the documentation of new or invasive species (Azzurro et al., 2019), reporting of ecological change (Keane et al., 2019), enhancing the underpinning of the science base for protecting vulnerable species and habitats (Gass and Willison, 2005; Colpron et al., 2010; Bjørkan, 2011; Kraan, 2015), establishing the relationship between vessel size, gear size and catching capacity (Reid et al., 2011), and in survey gear technology (Cotter, 2004; Reid et al., 2007; DeCelles et al., 2012; Johnson and McCay, 2012; De Boois et al., 2021; Jones et al., 2021).

Even where fishers’ experiential knowledge is very relevant, linking it to phenomena at a broader spatial and temporal scale such as those used to study stock dynamics, requires that it is systematically collected, structured and made available. This is often not the case when changes are being observed in real-time and require management action (Wilson, 2009). The unique nature of experiential knowledge means that ‘interdisciplinary expertise’ (Tress et al., 2005) is needed to make experiential knowledge systematically accessible in forms that are useable to aid in knowledge transfer to bridge gaps between fishers and natural scientists. Gathering experiential data and information through interviews, for example, not only requires skills in interview techniques but also scientific rigor around sampling to help ensure those sampled are considered by their peers to be most knowledgeable (Davis and Wagner, 2003). Since fishers’ experiential knowledge is socially distributed and shared by actors involved, also crew, shore-based personnel and processors need to be included, depending on the research question at hand (Palsson, 2000). Using this knowledge also requires consideration of variability in experiential knowledge related to ecological patchiness and change over time. This is crucial to research that seeks to use experiential information for historical reconstruction of fisheries and changing fish ecology, for understanding shifting effort, for documenting migration patterns, stock structure, spawning areas including those now extinct, the location of deep-water corals, endangered species abundance, and changing fishing strategies and dynamics (Neis et al., 1999; Murray et al., 2008a; Colpron et al., 2010; Dawe and Neis, 2012; Paterson et al., 2018; Bentley et al., 2019). Furthermore, researchers have to be aware that information providers, for all kinds of reasons, may deliberately provide erroneous information, may suffer from “personal or generational amnesia” (Papworth et al., 2009), or experiential bias (Shackeroff and Campell, 2007; Raicevich et al., 2009; Slooten et al., 2017).

Attention to both ecological and social variability in fishers’ experiential knowledge is critical in research design, including ensuring participants’ information collectively captures the full temporal, spatial and technological scale of relevant fisheries, to ensure appropriate contextualization. This concern bridges both interview-based research and participatory or collaborative research design. However, most marine institutes and science organizations do not have sufficient social science capacity, and marine social scientists often reside in academia instead of applied science organizations. Moreover, the institutions involved in science for advice tend to get by on fisheries-independent and fisheries-dependent statutory data. Where attempts are being or have been made to incorporate contributions from industry, receiving science systems focus on basic biological data provided by industry and are slow in expanding research design and science capacity. They tend to lack capacity to deal with data and information beyond the natural sciences, even when it concerns quantifiable socio-economic data. Incorporation of experiential knowledge requires involvement of social scientists and substantial financial resources; fisheries scientists would have to be trained in social science epistemology and methods to foster interdisciplinary approaches and social scientists would have to be structurally included in fisheries research frameworks including funding (DePiper et al., 2017; Macher et al., 2021; Moon et al., 2021). Too often, fishers’ experiential knowledge is considered to be ‘nice-to-know’, something to be documented in academia so that it does not get lost to mankind (Johannes, 1981), instead of as an important source of information that can be applied to further understanding of fisheries and marine ecosystem dynamics, due to the costs and governance changes needed to incorporate it.

Issue 3: Integrity of science

Provocative debate prompt: Involving fishers, representatives, or industry-scientists in fish stock assessments and research poses a threat to the professional integrity and credibility of science institutions, and perception of the legitimacy of their contributions to clients or society (Figure 1).

This statement seems paradoxical in view of global efforts by science-policy systems to make the required adaptations to accommodate industry information as part of using ‘best available information’ policies. But nonetheless the point of view is still prevalent. Examples of science institutions where the goal of industry participation is operationalized are the New Zealand system with its Research Science and Information Standard (MinPI, 2011; Mackinson and Middleton, 2018), NOAA fisheries science (Lynch et al., 2018; Link et al., 2021), the Canadian science advisory peer review process (CSAS, 2021) and various stakeholder engagement initiatives within ICES (Dickey-Collas and Ballesteros, 2019; ICES, 2019c; ICES, 2021c). Such efforts have a strong focus on quality assurance including Conflict of Interest (COI) and Code of Conduct policies. Preconceptions remain however that opportunistic motives may lead to ‘tainted’ data contributions from industry (Issue 1 section) and that fishers’ knowledge, as well as input from industry-employed scientists, should not be trusted because of a perceived threat to the integrity of the science profession and the credibility and even legitimacy of science in support of management. These claims might sound overly dramatic, but such lines of thought pervade scientific and management arenas, even if rarely explicitly articulated.

For example, such thinking may have informed the narrative directing to events in New Zealand that led to the dissolution of Trident Systems, an industry-led, not-for-profit organization established as a research provider in 2012 (Middleton, 2018). Trident was founded with the support of the Ministry of Primary Industries and worked collaboratively with government research providers and industry. In debates associated with the New Zealand fisheries management system (Melnychuk et al., 2017; Slooten et al., 2017), there appears to be some lack of understanding that ‘data provided by the industry’ are diverse. For example, catch, effort and landings data are provided by industry but as part of statutory obligations and are subject to verification by government. However, other data are voluntarily provided by the industry. Trident was considered to be an example of how use of both statutory data and industry contributions to science in support of management could be organized (Mackinson and Middleton, 2018). Nevertheless, Trident’s integrity was publicly questioned. This appeared to be triggered when Trident became engaged in the development of video monitoring for fisheries observation, which in an NGO press release was confused with the government’s responsibility of fisheries compliance (Greenpeace-NZ, 2017; Johnston, 2017; Middleton and Guard, 2021). Subsequently, the New Zealand fisheries research system was subjected to a COI review (Jenkins and Wallace, 2019). Trident decided in 2019 “that it was not possible to meet their objectives of improving the efficiency of fisheries data collection and extracting greater value from fisheries data in an environment in which Trident’s industry ownership had become a barrier to its participation in Government funded or supported research” (Middleton and Guard, 2021). It took several years to establish a role for an industry-led research provider, but little time to have its foundations pulled away.

Other examples are found in the US and the ICES context. In the US, legal mandates in relation to ‘best available scientific knowledge’ constrained use of proven experiential knowledge (Lynch et al., 2018; Link et al., 2021). In the case of Atlantic bluefish, use of fishers’ experiential knowledge was blocked by preventing the scientists from using what they felt was their best scientific judgement (Wilson and Degnbol, 2002). Events around the US Trawl Survey Advisory Panel, set up to integrate scientists’ and fishers’ expertise in developing a new and improved survey trawl net and gear, show how notions about ‘objectivity’ resulted in the demise of the panel (Johnson and McCay, 2012). Established to increase the credibility and legitimacy of science, industry members left the panel when the Science Centre unilaterally decided to order trawl doors, as in relation to “something as important as a resource survey [they could not] allow themselves to be seen as fully cooperative with the industry” (Johnson and McCay, 2012). Such thinking was also exposed in high level discussions in the ICES council and, to a lesser extent, in the advisory committee, where not all members agreed on the merits of opening up science work to be more inclusive of contributions from industry or other stakeholders. Some perceive this as not appropriate, despite many good governance measures ICES has put in place to maintain the professional integrity and credibility of its expert groups (Dickey-Collas and Ballesteros, 2021) and workshops that have sought to foster dialogue about transparency and objectivity on the quality of science (Doerner et al., 2015; ICES, 2019c; ICES, 2019d; ICES, 2021c).

Preconceptions about industry involvement in generating scientific evidence or about the personal integrity of industry-employed scientists may encourage beliefs that such involvement potentially jeopardizes the credibility of the science organizations involved in the advisory process. This is a particular concern in cases where industry disagrees with specific management actions, does not have confidence in their scientific basis, or mistrusts the science or management institutions or processes (cf. (Dubois et al., 2016). Whilst there are only a few documented cases where scientists and scientific consultants employed by the fishing industry or other stakeholders have ‘bent’ scientific evidence in favor of the industry or conservation purposes, or have contested the scientific process (Starr et al., 1998; Loring, 2017; Moore et al., 2018; Le Manach et al., 2019; Kraan et al., 2020; O’Brien, 2022), such cases have contributed to the perception that stakeholder-employed scientists should be regarded with suspicion. However, there are also cases where scientists from marine institutes or academia, using their institutional credentials in the name of the scientific advice committee they are a member of, have acted as advocacy scientists in support of stakeholder views (Rice, 2011; Steins et al., 2020b; Mossler, 2021; Harris, 2022; Hutchings, 2022) or have selectively used information in science communications as a commodity seeking to polarize views to highlight debate and garner readership, instead of promoting understanding (for example, Pauly et al., 2013; Harris, 2022). Finally, there are also (mostly un-documented) examples from Europe and Canada where government, not industry or conservation stakeholders, has put pressure on scientists to advocate specific positions (e.g., Hutchings, 2022). Related to this, in most scientific advisory systems there tends to be a rather close link between government-employed scientists and the clients of advice: policy makers (e.g., Wilson, 2009; Dankel et al., 2016). This close science-policy relation, while it also pertains to issues of integrity and independence, is usually taken for granted. Inclusion of industry contributions in the scientific process in and of itself does not necessarily compromise credibility. Institutional credibility is based on the capability to create authoritative, replicable, and trusted information (Cash et al., 2002). As long as data and information used meet scientific quality standards and process, origin does not matter.

In ICES, discussion about industry participation in expert groups started when it was decided to include all names of expert group and workshop participants as authors in reports. For a number of years, ICES assessment groups had already included scientists employed by industry (Dickey-Collas and Ballesteros, 2021). Despite initial reservations, the data and information industry-employed scientists bring to the table is considered useful, and in many cases innovative (Mackinson, 2022). There have been no signals from these groups to the advisory committee or the council that this led to bias in assessment results. However, when it comes to participating in post-assessment stages, there are different procedures for stakeholder-employed scientists compared to scientists from marine institutes or academia. This is due to concerns about how clients and stakeholders will perceive the independence of the advice. This is also why in ICES, engagement with stakeholder interest groups is limited to workshops and advice drafting groups (Dickey-Collas and Ballesteros, 2019).

In Canada, diverse members of the fishing industry have been participating more actively in the provision of information and for some time in the assessment peer reviews of the Canadian Science Advisory Secretariat (Stephenson et al., 1999; Winter and Hutchings, 2020; CSAS, 2021). In cases where scientific consultants or industry-employed scientists critically reviewed or even contested government stock assessments, this brought benefits including intensive peer-review, the ability to bring data from all parties to the process, and improved understanding and trust. It has been shown that this contributed to substantially improved assessments (Starr et al., 1998) and therefore to scientific credibility. Also, in New Zealand, the assessment system reinforces healthy scrutiny of data and assessments (Mackinson and Middleton, 2018).

The credibility of science is closely linked to its legitimacy (Röckmann et al., 2015; Su et al., 2021). It is widely acknowledged that industry-employed scientists, can contribute to increasing the legitimacy of science within industry in their role as ‘boundary spanners’ who recognize the value of fishers’ knowledge and are able to communicate on both sides of the boundary between scientific and fishers’ knowledge (Johnson, 2011). Where industry and the scientific community consider the integration of industry contributions to be a way forward to increasing legitimacy of science, other stakeholders may perceive this differently. The earlier example of Trident Systems, where NGOs have successfully questioned the legitimacy of science from industry-management partnerships, is a case in point. Equity and fairness principles are obvious issues of concern that call for reflection on more inclusive participatory approaches to evidence-building based on stakeholders’ capability and availability. Obviously, the fishing industry is linked with vessels out at sea with the possibility of making and contributing observations, and many industry organizations and fishers are also acutely aware that fisheries-dependent and fisheries-independent data are required for stock assessments as a basis for management. But experience-based knowledge comes from diverse sources, and the scientific process needs to be open to accepting and using industry observations as part of ‘best available information’. For example, in Australia, Resource Assessment Groups provide peer-review of scientific data and information and advice on stock status, economic status of the fishery and ecosystem impact. They include members from science, industry and, where relevant, members from conservation interests and recreational and Indigenous fisheries (AFMA, 2021). In Canada, assessments and peer review under the Canadian Science Advisory Secretariat process increasingly include NGOs (CSAS, 2021). In ICES, NGO representatives can be invited as workshop member or obtain observer status for advice drafting groups (Dickey-Collas and Ballesteros, 2019). It is also conceivable that like industry-employed scientists, scientists employed by NGOs will become involved in assessment working groups. The ICES national delegates have the discretionary power to nominate experts on the basis of their reputation and scientific credibility, not on behalf of a specific employer. However, adding scientists employed by stakeholders to scientific expert groups in itself is unlikely to solve potential legitimacy challenges of scientific advice, as perceived credibility of science is also part of the equation (Röckmann et al., 2015). Quality assurance, transparency and accountability are key aspects of the integrity of the processes and procedures governing the production of scientific advice. In this context, stakeholder engagement throughout the scientific advisory process contributes to dialogue and improved understanding, and hence to perceptions about its credibility and saliency. However, as is shown in a comparison of Canadian and EU scientific advisory processes, it is important to clearly distinguish between the science, irrespective of the source, and ‘interest driven’ input and be transparent about this (Winter and Hutchings, 2020).

Discussion

Is sea-change upon us?

Despite the normative calls for participatory research and consistent evidence of SIRC benefits in the literature, fisheries science in regions with well-developed scientific advisory systems remains firmly rooted in traditional ‘Mode 1’ knowledge production (Hessels and van Lente, 2008) and associated approaches and beliefs (cf. (Su et al., 2021). In our evidence, we recognize important contributions of SIRC, but we do not yet see overwhelming evidence of a sea-change towards the systematic integration of industry contributions and more transdisciplinary fisheries science as part of ‘Mode 2’ (Hessels and van Lente, 2008) approaches.

Any such sea-change is hindered by three interrelated issues that are embedded in traditional Mode 1 ways of thinking about science: (1) concerns about the quality of industry contributions; (2) beliefs about limitations in the useability of unique fishers’ knowledge; and (3) perceptions about the impact of industry contributions on the integrity of science. Our assessment of each of these issues suggests that the first and second can easily be addressed through a combination of mechanisms. The third issue, which entails perceptions from a variety of stakeholders with different belief systems, is more difficult to tackle. It is an important inhibiting factor for ‘mainstreaming’ knowledge co-production in fisheries science, even when the first and second concerns have been successfully addressed. We will discuss ways forward following a brief summary assessment of each of the three concerns.

Summary assessment of the three statements

The first issue, that use of industry data and information poses a threat to the evidence for science-based decision-making, can be addressed through recognizing that such contributions do not pose a threat, but rather raise challenges and have limitations related to the conditions of their application. Concerns about industry’s independence and conformity with the same procedural standards of collecting data, formatting, verification and submitting information as other data sources are certainly legitimate. Quality concerns and underlying beliefs about opportunistic motives that result in bias and lack of consistent availability are not exclusive to industry or other non-scientific stakeholders, but equally apply to all participants, including scientists. Voluntary industry contributions are most often made on the basis of good will and, when this is done in collaboration with scientists, usually adhere to basic agreed data collection standards. We need to appreciate that industry data have their limitations, and that industry is not monolithic, with significant variability in types of fisheries, vulnerability to overfishing, resources to apply to contributing to science and influencing management, and often competitive interests within and across fleet sectors. This has implications for data collection, its use, and for motivations to participate in SIRC or full industry-driven research programs. The same applies to science, and this also affects if and how SIRC are set up, including consideration of power and how data and information are used.

The second issue, that industry has limited unique knowledge that is useable (and therefore useful) as evidence to support management beyond that already known or available from science institutions, is flawed. Doubt has been cast on fishers’ experiential knowledge because of mis-perceptions that it is inherently decontextualized and local, hence ‘anecdotal’, or pressing a particular agenda, but these characteristics are variable. It is undeniable that experiential knowledge can be valuable and when drawn systematically from a range of fishers with careful attention to sampling, can be structured and applied quantitatively and qualitatively. Qualifying experiential knowledge as unusable entails an inherent risk that management will not be based on the best available information, particularly when fishers see in real time what is happening on the grounds and scientific assessments (forecasts) show a delay in appreciating the actual situation. The paradigm in well-developed scientific advisory systems that an assessment is only a ‘good’ assessment if it is fully quantitative, is thus not only problematic but results in limited input to management decisions. Moreover, it is associated with social justice issues: from this perspective fishers, fisheries or nations having limited access to quantitative data and assessment models would never be able to evaluate the status of their fisheries resource and the effectiveness of management measures. Our evidence shows that, where interdisciplinary efforts were made to systematically make experiential knowledge available in regions with well-developed science systems, this contributed to improving the scientific knowledge base and understanding of variability in stock and ecosystem dynamics and impacts of these on fisheries.

For both the first and second issues, it is important to acknowledge that not all data and information are the same and these should be used in a way appropriate to the source and the intrinsic limitations therein. While some voluntary industry contributions may be suitable for use in traditional stock assessments or as structured evidence to support management decisions, others may be more useful in interpreting and validating model outcomes or (re)setting model parameters, or for full spectrum sustainability evaluations (see Supplementary Materials for an overview of applications for industry contributions in fisheries and marine ecosystem science). In cases where information from the fishing industry has been sought for inclusion in scientific analysis, we found it has also served as a mechanism to open dialogue, benefiting both fishers and scientists. This is particularly important considering prevailing trust issues, which relate, for both sides, to the trustworthiness of the data and the scientific process, as well as to trust by fishers that their contributions are not being used against them in the translation from scientific advice to management measures.

Trust is also an underlying theme in the third issue identified: the concern that involving the fishing industry in science poses a threat to the professional integrity and independence of the scientific institutions and, hence, perceptions of the legitimacy and credibility of their advice. We found some evidence for these concerns, but even more examples were identified where industry involvement benefited the scientific process. There may be some documentation bias here, as published evidence for misbehavior is difficult to find; perhaps this is also illustrative of the discomfort of addressing this issue. Where industry or other stakeholders criticize scientific work, this should be embraced rather than merely dismissed as being politically motivated; all scientists should welcome critical review of their work, including when it is not from their disciplinary peers. Furthermore, there is an irony in ‘condemning’ the situation where the industry, by being actively involved in the scientific process, becomes more ‘literate’ and subsequently uses the knowledge they obtained to criticize the scientific advice or influence management discussions. Advocacy by stakeholders is inextricably bound to the governance domain. This does not mean that these stakeholders cannot be part of producing credible, quality assured science. Indeed, there is ample evidence that credible science contributes to increasing the legitimacy of science.

Collaborative research has had an impact in terms of building trust between fishers and scientists in improving research findings, in creating a situation where fishers are more willing to cooperate, and in capacity building for fishers and scientists. But much of this impact is limited to the domain of dedicated research projects, many of which are useful for science and policy but are not really being structurally integrated into routine scientific processes. Hence, SIRC tends to remain limited to successes at local or regional levels. It has proven difficult to change science and management systems that are based on routines. Examples where SIRC really made an impact on the science that informs fisheries management are either largely invisible or scarce, but the opportunities have been equally scarce. Without a doubt, “it is easier to organize collaborative research than to make it count” (Holm et al., 2020a). This is problematic, because many projects that use fishers’ knowledge are aimed at making an impact on the science that informs management. There are, of course, various reasons for this. ICES, for example, has only recently started to think about how to integrate industry data, while at the same time there are still many problems with the data from scientific institutes which need to be sorted out (ICES, 2019d; ICES, 2021b; ICES, 2021c). The bottom line is that if findings from fishers’ knowledge projects aimed at improving the knowledge base for management are not used, research collaborations will be eroded along with carefully built trust (Johnson and McCay, 2012; Steins et al., 2020a). This, in turn, will impact trust in management.

Ways forward

The scope of fisheries evaluation in the modern context of sustainability is becoming more comprehensive (Foley et al., 2020; Stephenson et al., 2021) making explicit the limitations of conventional research. There is increasing need to integrate ecological with economic and social factors. Further, addressing the increased uncertainties associated with climate change and other factors, as well as the potential of introducing additional uncertainty to assessments, means that the traditional systems of data gathering and assessment will need to be adapted for this purpose. Current assessment and management structures will no longer be able to get by with the statutory and fisheries-independent data that has been available from within government departments or science institutions. Information on fishing strategies, economic and social aspects in fishery evaluation is key information, most of which has not traditionally been collected by government and scientific institutions. Industry is better able to contribute such information. In addition to bridging social, economic, and fishing behavioral knowledge gaps, industry contributions of quantitative data and experiential knowledge are relevant for a broad spectrum of fisheries science applications (see Table in Supplementary Materials). For responsive management “we must tap from a diversity of sources and we must find ways to use this knowledge to build a complete picture” (Wilson, 2009). We anticipate that the future of fisheries evidence will be based on much the same principles as held now, but with a broader range of data, information and knowledge providers, and more transparent agreed processes. Its credibility and legitimacy rely upon (a) respecting and making the most of different sources of knowledge to learn as much as we can, and (b) the need to verify the knowledge through evidence or reasoned argument and carefully balancing and assessing the strengths and weaknesses of different types of knowledge, as we have undertaken to do here.

We aimed to identify with confidence, what, where and when there is utility in including the data, information and knowledge contributions of science-industry research collaboration as evidence in regions with well-developed scientific advisory systems, and the utility of the SIRC process itself in achieving this. The answer is not breaking news: SIRC is context-dependent and shaped by the institutional framework within which it takes place, so the utility ‘depends’ on the case. In addressing these questions, we provide systematic and robust evidence for: a) practitioners to assess the suitability of SIRC on a case-by-case basis; b) researchers to explore the implications for theoretical developments in knowledge production; c) policymakers to gain a better understanding of what SIRC entails for scientific support and management performance.

The evidence shows SIRC’s potential contributions, limitations and constraints. The analysis details associated challenges and reviews the methods to cope with them, illustrated with examples. While no panaceas apply, entrenching SIRC calls for action in three specific areas:

i. Knowledge production has to advance towards alternative science modes that ensure effective SIRC, fostering accountability of both scientists and industry in the process.

ii. Quality Assurance frameworks, including COI provisions, need to become part of the institutional context to tackle objective and perceived pitfalls, generating credibility and transparency.

iii. Governance structures should facilitate the move towards alternative science modes that rely on plural sources of information, by providing arenas for continuous dialogue, building trust to manage real and perceived threats to the integrity and independence of scientific advice, and financial support.

Move towards alternative modes of knowledge production

The integration of fishers’ knowledge requires current scientific assessment and advisory systems to actively embrace and facilitate transdisciplinary modes of knowledge production (Tress et al., 2005; Hessels and van Lente, 2008; Stephenson et al., 2016). Consequently, besides industry expertise, expertise from a broader range of scientific disciplines must be mobilized. Many scientific advisory systems do not yet include expertise from the social sciences to assist in making fishers’ experiential knowledge systematically accessible and available. This is not necessarily because they are unwilling to do so, but may be because their clients, including governments, do not ask for ‘full spectrum advice’ (Foley et al., 2020; Stephenson et al., 2021). A way forward is to demonstrate to recipients of advice what full spectrum advice could look like, as ICES has recently done in its Aquaculture Overview for the Norwegian Sea Ecoregion (ICES, 2021a) or NOAA Fisheries in the context of integrated ecosystem assessments for marine regions in the USA (Levin et al., 2016). Operational advances towards alternative modes of knowledge production requires: (a) funding for full spectrum advice; (b) effective learning across disciplines (epistemology, developing joint methodology, training and developing interdisciplinary trust (DePiper et al., 2017; Thompson et al., 2019; Macher et al., 2021; Moon et al., 2021); and (c) addressing potential ethical issues (Carruthers and Neis, 2011), power imbalances and related threats to social justice that could be affected by uneven SIRC initiatives.

Appropriate quality assurance frameworks

Moving towards alternative modes of knowledge production will require agreement on appropriate processes for validation and quality control. Acknowledging the challenges to credibility, integrity and independence posed by the use of measurable industry data and experiential knowledge, we argue that there is a suite of methods and processes able to cope with them. Formalized and transparent quality assurance systems for all data contributions, irrespective of their source, will be needed to ensure rigor in design and quality of data collection and verification and in its use for analysis. These should include: (a) documented sampling designs, methods and quality controls applied through the data chain; (b) documentation of the source(s) of data and information, by whom it was collected and when and where; (c) documentation of any assumptions, hypotheses and data inconsistencies, as part of a risk assessment with regards to data quality; (d) development of data sharing agreements that define rationale for sharing these data and information and constraints on their use; (e) transparent, documented coding systems for data; (f) independent validation and peer-review. We refer to the report of the ICES Workshop on Standards and Guidelines for Fisheries-dependent Data for a comprehensive overview of international examples of quality assurance processes (ICES, 2021c). A particular challenge here is that meeting the same standards could be difficult when fish and fisheries straddle multiple jurisdictions.

Agreed processes for quality assurance should, as much as possible, be in place before data are collected and delivered³. Many scientific organizations already have some form of quality assurance in place. Adapting these to be applicable and receptive for contributions by the fishing industry and other non-scientific actors will therefore be a gradual, iterative process. It is important that all non-scientific actors who may be contributing data and information are informed with the appropriate data collection processes (ICES, 2021c). Training and communication are key here, as well as having ‘boundary spanners’ (Johnson, 2011). Scientists employed by stakeholder groups and who work closely with colleagues in science organizations should be well-equipped for this role (Mackinson, 2022). In this context, concerns about professional integrity can be a sensitive topic, but one that is nonetheless important to address. Joint reflection on “whose hat [scientists] are wearing” (Dankel et al., 2016) is likely to be more effective in overcoming such concerns.

Conflict of Interest protocols are a formal way of organizing transparency about who participates in scientific processes. Conflicts of interest related to data collection and knowledge contributions are different from other situations where COI may occur, such as scientific meetings and review panels. In the latter case, COI may be handled by balancing representation of participants and adoption of well-established review protocols (e.g., ICES, 2020a; CSAS, 2021; NPRB, 2021). While standards for managing COI in scientific meetings are not directly applicable to data and knowledge contributions, the underlying principles are relevant, and have a direct relation to quality assurance. Standards for COI management should be extended to include managing perceived or actual COI in the collection and application of data for use by scientific advisory systems. The purpose should be to protect the legitimacy of advice when data-collectors with potential conflicts of interest are involved (ICES, 2021c). In this context, we note that the European fishing industry has voluntarily established a Code of Conduct for industry observers attending ICES meetings (NPWG, 2016b) and for industry-affiliated scientists (NPWG, 2016a) to allay potential COI concerns, which seems testament to their willingness to engage in SIRC. Development of implementable standards for managing COI should not only address the additional legitimacy-risks introduced by third-party participation in data collection, but also manage the risks that may already be associated with the data collection performed by scientific institutions. A standard for managing conflicts of interest in data collection should therefore clearly address requirements for transparency and documentation.

Facilitating governance structures for alternative modes of knowledge production

Guidelines for SIRC stress the importance of communication (Johnson and Van Densen, 2007; Mackinson et al., 2017; Mangi et al., 2018; Steins et al., 2020a; De Boois et al., 2021; Jones et al., 2022). This includes communicating about the purpose of data collection, why it is done in a certain way and its limitations. It also includes communicating about preliminary and final results and how these have been used. Expectations management is key here, particularly when fishers are contributing towards development of time-series or when use of fishers’ experiential knowledge is not yet part of established routines. Communicating about things that went wrong is also essential. Both expectations management and being open about mistakes are closely linked to building trust relations (Mangi et al., 2018; Cvitanovic et al., 2021). Communication should not be limited to those directly involved in collaborative research, but also to a wider stakeholder audience. After all, once the collaborative science gets into the policy and societal domain, trust in its quality and integrity is key. Trust is “a critical precondition underpinning successful knowledge exchange [and] evidence-informed decision-making” (Cvitanovic et al., 2021). Trust issues do not resolve themselves by merely setting up appropriate scientific quality assurance systems. These also require continuous dialogue between all parties involved to manage real and perceived threats to integrity and independence of scientific advice, though this is by no means a panacea (e.g., Delaney et al., 2022). Extensive stakeholder-oriented communication does not necessarily come naturally to many scientists and science organizations and is often at the bottom of research budgets or not seen as a priority task. Fundamental change is needed, for example by allocating specific roles and budgets to boundary spanners in transdisciplinary science. Trust-building strategies are a crucial part of ways forward in integrating industry contributions in science; proposals on how to do so have been made in a recent publication by Cvitanovic and colleagues (Cvitanovic et al., 2021).

Enabling scientific advisory systems to move towards collaborative approaches also requires financial support. This includes facilitation of balanced voluntary industry contributions to science. It would be naive to think that an industry-led data collection program can run indefinitely on the good will of fishers, particularly when science-led programs are government-funded. Direct funding is an obvious route, but financial support can also take indirect forms, such as additional quota allocations. In areas where responsibilities for data collection are increasingly delegated to industry, we also see that costs are downloaded to industry with potential negative impacts on younger, less established fishing enterprises and on opportunities to expand the research disciplinary focus to include social science. Expecting industry to fully pay for data collection also brings along equity issues, as not all industries have sufficient financial means and human capital to organize data collection. The impacts of this became clear in Australia, where cost recovery policies for fishery-independent data collection have been introduced in some jurisdictions and vary considerably in what costs are attributed to industry and to public good (Cox, 2001). For smaller-scale, lower value fisheries cost of these programs are more burdensome given they do not gain the advantages of efficiencies of scale (MFA, 2020). The implication is that the evidence-base for management of these latter fisheries has to rely on less fishery-independent data and therefore higher uncertainty and more precautionary harvest settings as a result. In cases where profitability of fisheries declines due to decreasing fishing opportunities or increasing costs, the result may be that the industry has a perverse incentive to cease or narrow the scope of data collection. Thus, while one could argue that acceptance and use of voluntary industry contributions is likely to be the greatest reward to fishers for engaging in science support, there are limits to what can be expected on the basis of fisheries’ scale and profitability. These must be well-considered. As part of ways forward, we recommend a review of current funding and alternative support mechanisms for fisheries data collection involving the industry and the development of best support practices.

In our search for explanations for why fisheries advisory systems in well-developed regions only make limited use of observational and experiential data, information and knowledge from SIRC and our exploration of ways forward, we found the exchange of experiences between different regions in our regional workshops to be incredibly insightful. Some regions had already developed solutions for challenges experienced in others or were experiencing positive or negative impacts from changes. A problem or solution in one region does not of course have to play out similarly in other regions in view of contextual, cultural and institutional differences. But looking at issues from different angles is very helpful. As part of ways forward in integrating voluntary industry contributions in regional scientific advisory systems, we therefore recommend organizing regional exchanges of experiences.

Final reflections and perspectives

We believe the growing momentum for using voluntary industry contributions in science is linked to a generational change where scientists who embrace more inclusive and transdisciplinary ways of thinking about science are now at the point in their careers where they can make a difference. Well-meaning efforts to enable the use of ‘best available information’ are, however, confronted with legitimate concerns regarding perceived and real risks that it might be detrimental to the credibility of scientific advice – particularly when science evidence becomes an object of negotiation in management decisions (Winter and Hutchings, 2020). Safeguarding against this requires transparent quality assurances systems for the processes intended to deliver ‘best available information’, as well as objective evaluation of the performance of the information for its intended purpose. To differing degrees across the world, achieving this will involve adaptations to current fisheries governance frameworks toward new cultures of cooperation. Proposals for possible avenues have been suggested in a number of recent publications (Gómez and Köpsel, 2023; Bradley et al., 2019; Holm et al., 2020a; Fulton, 2021; Hart, 2021; Macher et al., 2021; Stephenson et al., 2021; Su et al., 2021; Strand et al., 2022). Better definition of industry’s role in contributing to science will improve credibility and legitimacy of the scientific process, and of resulting management. As part of progressing towards integration of voluntary industry contributions into science for advice, further analysis of the receiving systems that have been more receptive of fishers’ and other sources of knowledge is needed. Carrying out a performance evaluation of fisheries managed on the basis of fisheries-dependent data or voluntary industry contributions versus fisheries managed (mostly) on the basis of fisheries-independent data, may help rationalize the debate about the utility of voluntary industry contributions. The best evidence for utility of industry data, after all, lies in its performance.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

NAS, SM, SCM, MP and RLS conceived of the idea for this manuscript, collated the initial evidence document and prepared the regional workshops. MB, KB and JAM chaired the regional workshops and prepared their reports. All authors participated in the regional workshops and subsequent analysis. NAS prepared the manuscript with significant contributions from all other authors. Authorship is in alphabetical order after considering the aforementioned types of contributions. All authors contributed to the article and approved the submitted version.

Funding

NAS received funding from the European Maritime and Fisheries Fund (Dutch Operational Program, Partnerships Science and Fisheries) and the Knowledge Base Statutory Research Tasks Program of the Dutch Ministry of Agriculture, Nature and Food Quality; JC from the Science Foundation Ireland through a Starting Investigator Research Grant No 18/SIRG/5554; and BN from the Ocean Frontier Institute funded through the Canada Research Excellence Fund.

Acknowledgments

We thank all participants of the regional workshops who could not commit to co-authorship for this paper for their valuable contributions. In the Americas: Ashleen Benson (CA), Mariano Guitierrez T. (PE), Bonnie McCay (US), John Manderson (US) and Bob Rangeley (CA), as well as Erin Carruthers (CA) who could not attend but provided information. In Australia and New Zealand: Kate Barclay (AU), Ian Cartwright (AU), David Middleton (NZ), Eva Plaganyi (AU), Nick Rayns (AU), Keith Sainsbury (AU), Tony Smith (AU) and Kevin Stokes (NZ). In Europe: Dorothy Dankel (NO), Poul Degnbol (DK), Mark Dickey-Collas (DK), Marloes Kraan (NL), Colm Lordan (IE), Stefan Neuenfeldt (DK), Saša Raicevich (IT) and Tom Williams (NO). We would also like to thank the reviewers.

Conflict of interest

Author SCM is employed by MRAG Ltd. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2022.954959/full#supplementary-material

Footnotes

1. ^ Will-o’-the-wisp is an atmospheric ghost light seen by travelers at night, especially over bogs, swamps or marshes, and is said to mislead travelers by resembling a flickering lamp or lantern. In literature, will-o'-the-wisp metaphorically refers to a hope or goal that leads one on but is impossible to reach, or something one finds sinister and confounding. https://en.wikipedia.org/wiki/Will-o%27-the-wisp and https://historydaily.org/will-o-the-wisp-deadly-fairy-lights.
2. ^ The elephant in the room’ is a metaphor to refer to an obvious major problem of issue that people avoid discussing or acknowledging because it makes at least some of them uncomfortable or is personally, socially, or politically embarrassing, controversial, inflammatory, or dangerous. https://en.wikipedia.org/wiki/Elephant_in_the_room.
3. ^ We note challenges associated with unplanned or rare events on the water (e.g., superpod convergence). Information on such events is imperative in understanding ecosystem function, yet it would be difficult for fishers to guarantee quality assurance before collecting data on these occurrences.

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