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.