Developing a decision support tool for Atlantic salmon management

Overview

In recent years, the use and application of models has become widespread and indispensable in ecology and conservation science. Such models cover a broad spectrum of applications ranging from improved understanding of the likely effects of climate change on biodiversity, to supporting the decisions made in natural resource management. Given the continued rapid global loss of biodiversity, understanding the mechanisms and consequences of management decisions is vital for long-term sustainability. Although a number of drivers of biodiversity loss have been identified, one of the most prevalent and widespread is human exploitation of habitats and natural resources (e.g. through hunting, fishing or habitat loss). Because natural resource use is fundamentally driven by humans, accurately predicting future sustainability is reliant as much on understanding human decision-making as it is on understanding ecological dynamics themselves. Thus, the development of social-ecological models and decision support tools that account for the interaction between natural resource dynamics and human decision making is becoming increasingly urgent.

Cutting-edge modelling approaches have made significant progress towards modelling complex social-ecological systems, but their increased complexity poses two interlinked challenges. First, models are often difficult to communicate clearly to non-specialist audiences, such as resource managers and other stakeholders. Thus, frequently the evidence for practical uptake of many models is limited. Second, their complexity implies the need for extensive data to parameterise them effectively and to build the linkages between the ecological and the decision-making component. In terms of social-ecological management systems, while data to parameterise the ecological components are often relatively easily available from the literature, the human decision-making and engagement components are often based on limited theory and lack a general empirical basis. To maximise the adoption and use of complex social-ecological models as resource management and decision support tools (DSTs), both appropriate representation of human decision-making, and effective communication, are therefore key.

This PhD project will use management of wild Atlantic salmon populations as a case study with the support of the Atlantic Salmon Trust, a key partner in the Missing Salmon Alliance ( https://missingsalmonalliance.org/ ). The flagship project of this alliance is to improve management of salmon populations and their habitats.

With declines in abundance, and the myriad effects of climate change, managers of wild Atlantic salmon populations face growing challenges when directing their conservation actions. Shortcomings remain in the standardisation and mobilization of large data sets, and how to provide this information in the right way to end-users and stakeholders. Often the highly technical outputs from salmon research do not adequately deliver this information in a form that encourages active use. Important science guidance can therefore remain relatively inaccessible to managers and mechanisms that enable an exchange of knowledge between scientists and decision-makers is therefore becoming increasingly necessary to promote the development of effective tools and guidance for helping to tackle complex environmental management challenges. The sporadic integration or preferential application of science output is likely to lead to biases in salmon management strategies, ineffective resource allocation and poor management outcomes.

Data is at the heart of salmon management, as this provides the evidence upon which to base management decisions. An equally important deliverable is to provide the evidence in an appropriately accessible format to maximise its integration into future salmon management and thus provide wider access to the data. The challenge is to match the management needs with the appropriate technology, and the technology to the modelling outputs and datasets.

The aim of this PhD project is to evaluate the information needs of salmon stakeholders, establish the links between management actions and the expected responses of the salmon population and their habitats, co-develop a prototype decision support tool and evaluate its impact on sustainability.

Methodology

This PhD project has a strong focus on stakeholder engagement. To bridge the gap between existing data and user-friendly systems to support effective decision-making requires a ‘human-centric’ approach. As a first step the project will determine what kind of information the stakeholders need to make better decisions. The PhD will use stakeholder workshops and focus group discussions to produce a compendium of stakeholder needs (sometimes common needs, sometimes conflicting across stakeholder groups) and to create a narrative and storyboard for a DST.

The next step in the PhD project will be to explore the information available, the influence of management and how it interacts with the salmon populations and their habitat using Bayesian Belief Networks and Management Strategy Evaluation. These approaches will tackle the need to link different underpinning models of existing salmon and habitats as well as the social and economic drivers of the system.

The next step for the second part of the PhD will consist of a process of co-development of a DST together with the stakeholders, which will build on steps 1 and 2 but focus on the development of a tool. This represents a departure from tool development conducted in isolation by technical experts, which can subsequently result in poor uptake by end-users because of complex and inaccessible design, to one of joint ownership in the design of an engaging and user-friendly tool. Furthermore, by involving stakeholders in the process of designing and developing the DST, is likely to result in greater trust in the DST outputs, which in turn helps to promote the acceptance and uptake of the resulting DST. A prototype of this tool will take the form of an interactive R Shinyapp.

As a final step, this PhD project will explore and reflect on the pros and cons, as well the usability and experience, of the development of DSTs using salmon as a case study. The Theory of Change will be used to evaluate the impact of the study together with key stakeholders (focus group approach).

This project will bring together ecology, social science, technology and stakeholder engagement in an interdisciplinary way that will improve the link between state-of-the-art scientific modelling and real-world management of natural resources.

Project Timeline

Year 1

Review of the literature on decision making, DSTs, social-ecological systems, Atlantic salmon, freshwater and marine resource management. Organising stakeholder workshops to identify the information needs and knowledge base for a decision support tool.

Year 2

Second round of stakeholder workshops to build the wishlist for and co-development of decision support tools in general and for Atlantic salmon specifically. Qualitative and quantitative analysis of the stakeholder workshops.

Year 3

Development of prototype of DST and evaluation of the prototype tool, using stakeholder workshops, capturing data from end-users. Start of thesis write up.

Year 3.5

Writing up

Training
& Skills

Qualitative research methods: stakeholder workshops, focus groups.
Quantitative research methods: questionnaires and surveys.
Participatory quantitative modelling: Bayesian Belief Networks and Management Strategy Evaluation
Technology: Interactive DST development using R Shinyapps

References & further reading

Bunnefeld, Nicholson, Milner-Gulland (2017) Decision-making in conservation and natural resource management: models for interdisciplinary approaches. Cambridge University Press

Stosch KC, Quilliam RS, Bunnefeld N & Oliver DM (2019). Quantifying stakeholder understanding of an ecosystem service trade-off, Science of the Total Environment, 651, 2524-2534.

Missing Salmon Alliance https://missingsalmonalliance.org/

Further Information

Prof Nils Bunnefeld, nils.bunnefeld@stir.ac.uk

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