Article

Decision support tools in conservation: a workshop to improve user-centred design

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  • Berks Bucks and Oxon Wildlife Trust
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... Digital communication platforms, (e.g., websites and web applications), are common solutions to bridge this gap and often fall under the umbrella of decision support tools (DSTs), software designed to fill knowledge gaps and support decision-making by analyzing and communicating information. Despite the ever-improving technological capabilities and functionality of DSTs, there continues to be a lack of uptake from practitioners, stakeholders, managers, and policy makers due to usability issues [1], misaligned policy priorities [2], lack of training or trust [3], and lack of relevance [4]. Surveys of end users highlight ways to improve DST adoption, like building long-term partnerships [4] and facilitating multidirectional dialogues between researchers, collaborators, and end users [5]. ...
... Despite the ever-improving technological capabilities and functionality of DSTs, there continues to be a lack of uptake from practitioners, stakeholders, managers, and policy makers due to usability issues [1], misaligned policy priorities [2], lack of training or trust [3], and lack of relevance [4]. Surveys of end users highlight ways to improve DST adoption, like building long-term partnerships [4] and facilitating multidirectional dialogues between researchers, collaborators, and end users [5]. These efforts can help generate high-quality end user feedback and help frame decisions, especially in the early phases of the DST development cycle. ...
... Acknowledging and incorporating constructive feedback from end users is critical for the iterative improvement of DSTs. These interactions help ensure that end-products are relevant and meaningful to potential users, an important stepping stone to long-term adoption [4]. Additionally, collaborative design can help establish a community understanding of the standard for credible information between scientists and practitioners [28]. ...
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Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT's credibility, utility, and relevance.
... There is also an increased demand from businesses for biodiversity information to inform their decision-making (inter alia, Cooper and Trémolet 2019; IBAT Annual Report 2019). Bennun et al. (2017) outlined the value of the IUCN Red List of Threatened Species for business decision-making and illustrated how the range of data within the IUCN Red List has many uses across a development project's life cycle. This paper demonstrated the data-information-knowledge-wisdom journey (Baskarada and Koronios 2013) that biodiversity data go through from its raw format as data that are used within the IUCN Red List process to generate a species assessment (i.e., information) to a point in time where a decision-maker interacts with that information to understand the whole biodiversity situation at a location at one point in time in order to make a decision. ...
... It is claimed that the latter system has over 6000 users across 182 countries. A further suite of systems was the subject of a user testing workshop by Rose et al. (2017), andMcIntosh et al. (2011) identified a number of different systems for environmental management. Furthermore, a team at the University of Queensland have built a decision support system to help policy-makers with biodiversity offsetting, and there is evidence that this collaboration between researchers and government has been successful (see http://www.uq.edu.au/research/impact/stories/a-calculatedapproach/). ...
... • Lack of system relevance for decision-makers-for example, a system does not help policy-makers address key policy objectives (Addison et al. 2013;Gibson et al. 2017;Weatherdon et al. 2017). • Limited trust between designer and user-noted, for example, in studies by McIntosh et al. (2011), Addison et al. (2013), and Gibson et al. (2017). ...
Book
Complete summary of the scientific knowledge currently available on closing of the knowledge-implementation gap in both terrestrial and aquatic ecosystems. Describes interdisciplinary and innovative uses of knowledge sources and knowledge mobilization practices to halt biodiversity loss under human-driven global environmental change. Essential reading for graduate students, researchers, practitioners, and policy-makers working across sectors with biodiversity knowledge and natural resource management around the world. Available here: https://link.springer.com/book/10.1007/978-3-030-81085-6
... UCD originated in the product and software development field, and is a process whereby designers consider explicitly the characteristics of a product's end-users, those who will eventually use a product, throughout the whole design process [36,37]. When a UCD process is followed end-users' intellectual, emotional, experiential and moral characteristics are taken into account, improving the likelihood that products will be considered useful, understandable and useable by end-users [38][39][40]. ...
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Visualisations are powerful communication tools that have the potential to help societies assess and manage natural hazard and disaster risks. However, the diversity of risk management contexts and user characteristics is a challenge to develop understandable and useable visualisations. We conducted a systematic literature review to understand the current state developing disaster risk visualisations following design best practices and accounting for the heterogeneity between end-users and disaster risk contexts. We find that, despite being widely recommended, tailoring visualisations to users through the process of user-centred design remains a relatively unexplored topic within disaster risk. To address this, we present a unifying user-centred design framework for disaster risk visualisation, based on existing visualisation frameworks. The framework contains three phases: the Define phase, which aims to define and characterise the disaster risk management context and end-user group who will benefit from a visualisation; the Design phase, which is highly iterative and presents an opportunity to test how users interpret different design elements; and the Refine phase, which focuses on evaluating how users understand, respond to, and make decisions based on the visualisation. The framework is sufficiently flexible to be applied to any disaster risk management and natural hazard context to identify challenges and design effective disaster risk visualisations that are understandable and useable.
... Conservation decision support system (DSS) is a computerised system that provides a set of decision support tools to organise geographical, physical, and biological data for modelling species distribution and conservation planning (Prato, 1999). Inspite of realising the importance of DSS or decision support tools in conserving the birds (Alexander et al., 2008;Rose et al., 2017;Wszola et al., 2017), DSS and decision support tools are utilised by very few researchers (Downs and Horner, 2008;Poirazidis et al., 2011;Sutti et al., 2017) for conservation of birds and their habitats. Moreover, authors generally focus either on selecting the priority areas, or analysing the bird population only. ...
... Conservation decision support system (DSS) is a computerised system that provides a set of decision support tools to organise geographical, physical, and biological data for modelling species distribution and conservation planning (Prato, 1999). Inspite of realising the importance of DSS or decision support tools in conserving the birds (Alexander et al., 2008;Rose et al., 2017;Wszola et al., 2017), DSS and decision support tools are utilised by very few researchers (Downs and Horner, 2008;Poirazidis et al., 2011;Sutti et al., 2017) for conservation of birds and their habitats. Moreover, authors generally focus either on selecting the priority areas, or analysing the bird population only. ...
Chapter
Decision support systems (DSS) aim to provide evidence in a usable format for decision-makers, thereby improving the prospects for evidence-informed conservation policy and practice. These systems are usually software-based either in computer or app-form, but may exist in other formats such as on paper. Conservation decision-makers are typically faced with complex socio-environmental landscapes, competing stakeholder interests, and irreducible uncertainty. Consequently, conservation has been the focus for numerous decision support systems, which can help users to face the challenge of making trade-offs. Despite the many systems designed for conservation, there is not an accepted framework for how to develop systems that make an impact in practice. There is evidence, however, to suggest that some systems are failing to make an impact in practice. This chapter draws on lessons learned from conservation and related disciplines on how to design good decision support systems that are desirable to intended end users. To this end, we suggest a five-stage process for participatory user-centred design—(1) identifying the user, (2) proving system value, (3) assessing available infrastructure and focusing on ease of use, (4) adopting a good marketing plan, and (5) establishing a long-term legacy—a process which could be used by researchers and funders alike to ensure that systems will be used by their intended audiences. Above all, we need to change our own design behaviour to increase the relevance and usefulness of the systems we are building. Acknowledging the reality that decision support systems will be implemented in complex and potentially data-sparse environments, we also reflect on how decision support systems can help decision-makers to deal with uncertain information. This final element seeks to establish the value both of quantifying uncertainty and communicating it in accessible ways to decision-makers.
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Making the reasoning and evidence behind conservation management decisions clear and transparent is a key challenge for the conservation community. Similarly, combining evidence from diverse sources (e.g., scientific and local knowledge) into decision‐making is also difficult. Our group of conservation researchers and practitioners has co‐produced an intuitive tool and template (Evidence‐to‐Decision [E2D] tool: www.evidence2decisiontool.com) to guide practitioners through a structured process to transparently document and report the evidence and reasoning behind decisions. The tool has three major steps: (1). Define the Decision Context; (2). Gather Evidence; and (3). Make an Evidence‐Based Decision. In each step, practitioners enter information (e.g., from the scientific literature, practitioner knowledge and experience, and costs) to inform their decision‐making and document their reasoning. The tool packages this information into a customized downloadable report (or is documented if using the offline template), which we hope can stimulate the exchange of information on decisions within and between organizations. By enabling practitioners to revisit how and why past decisions were made, and integrate diverse forms of evidence, we believe our open‐access tool's template can help increase the transparency and quality of decision‐making in conservation. Here we present the Evidence‐to‐Decision tool, which aims to make the evidence and reasoning behind conservation and natural resource management decisions more transparent. This article summarizes the tool and its potential to help make practical management decisions more clear and evidence‐based.
Preprint
Making the reasoning and evidence behind conservation decisions clear and transparent is a key challenge for the conservation community. Similarly, combining evidence from diverse sources (e.g., scientific vs non-scientific information) into decision-making is also difficult. Our group of conservation researchers and practitioners has co-produced an intuitive tool and template (Evidence-to-Decision (E2D) tool: www.evidence2decisiontool.com) to guide practitioners through a structured process to transparently document and report the evidence and reasoning behind decisions. The tool has three major steps: 1. Define the Decision Context; 2. Gather Evidence; and 3. Make an Evidence-Based Decision. In each step, practitioners enter information (e.g., from the scientific literature, practitioner knowledge and experience, and costs) to inform their decision-making and document their reasoning. The tool packages this information into a customised downloadable report (or is documented if using the offline template), which we hope can stimulate the exchange of information on decisions within and between organisations. By enabling practitioners to revisit how and why past decisions were made, and integrate diverse forms of evidence, we believe our open-access tool’s template can help increase the transparency and quality of decision-making in conservation.
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We are grateful to the AHDB, SARIC and the NERC administrators who have provided context and advice during this research. We also want to thank John Williams and Lizzie Sagoo of ADAS for their expert advice on nutrient research throughout the project. Finally, the research team would like to thank all participants of the workshop that took place on the 5th December 2017 at Lancaster University. The results of that workshop have fed into this report and its recommendations. Executive Summary Slurry is a significant by-product of livestock agriculture in the United Kingdom. In 2016 an estimated 83 million tonnes fresh weight of livestock manure was produced during the housing period. Of this, 67 Mt (80%) came from cattle: 47% of this was estimated to be undiluted slurry and 53% solid, mainly straw-based farmyard manure (FYM) (Smith and Williams 2016). Regional differences in cattle manure management mean that there is a predominance of slurry in South West Scotland, Northern Ireland and Wales, compared to the relative importance of straw-based FYM systems in England. Within England, slatted floor housing systems producing cattle slurry are more often found in the North and NorthWest. It is well recognised that the way in which government and industry support good practice for handling and utilising slurry across the UK is important for business efficiencies as well as for farm, catchment and wider environmental sustainability. The SLURRY-MAX research project was formed and designed in response to a problem statement put forward to SARIC by the Agriculture and Horticulture Development Board (AHDB): they were interested in understanding more about the 'provision of decision-support on organic slurry storage and treatment techniques'. The project was partly framed around the idea, prevalent among advisers and the agricultural industry, that 'there are really good decision support tools out there, but a significant number of people don't engage' (Interview with Farm Adviser, 2017). That is, despite much advice being available, farmers are still not managing slurries and manures effectively. Aware that this 'information behaviour gap' is part of a broader set of issues around the effective storage and management of livestock, and particularly cattle, manures , the researchers designed the SLURRY-MAX project to evaluate and assess cattle slurry storage and treatment practices in a multidisciplinary , holistic manner. During the research, the 'management' of slurry became a more important focus than 'treatment' per se. Focusing specifically on the beef sector in the UK, the research found that: • Most surveyed beef farmers do not currently use decision support tools • By examining farm practices it is possible to understand why tools are not suited for the needs of farmers in the beef sector The researchers have used their understanding of farm practices and the shortfalls of existing decision support tools to design a farmer-friendly prototype tool for slurry and nutrient management. No matter how good they might become, however, the researchers recommend that decision support tools should be seen as only one of many ways to support good slurry and nutrient management practices across the industry 1 .
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This article differentiates between descriptive and explanatory factors to develop a typology and a theory of stakeholder and public engagement. The typology describes different types of public and stakeholder engagement, and the theory comprises four factors that explain much of the variation in outcomes (for the natural environment and/or for participants) between different types of engagement. First, we use a narrative literature search to develop a new typology of stakeholder and public engagement based on agency (who initiates and leads engagement) and mode of engagement (from communication to coproduction). We then propose a theory to explain the variation in outcomes from different types of engagement: (1) a number of socioeconomic, cultural, and institutional contextual factors influence the outcomes of engagement; (2) there are a number of process design factors that can increase the likelihood that engagement leads to desired outcomes, across a wide range of sociocultural, political, economic, and biophysical contexts; (3) the effectiveness of engagement is significantly influenced by power dynamics, the values of participants, and their epistemologies, that is, the way they construct knowledge and which types of knowledge they consider valid; and (4) engagement processes work differently and can lead to different outcomes when they operate over different spatial and temporal scales. We use the theoretical framework to provide practical guidance for those designing engagement processes, arguing that a theoretically informed approach to stakeholder and public engagement has the potential to markedly improve the outcomes of environmental decision-making processes.
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p>The pernicious problem of evidence complacency, illustrated here through conservation policy and practice, results in poor practice and inefficiencies. It also increases our vulnerability to a ‘post-truth’ world dealing with ‘alternative facts’.</p
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Spatial decision support systems (SDSS) represent a step forward in efforts to account for the spatial dimension in environmental decision-making. The aim of SDSS is to help policymakers and practitioners access, interpret and understand information from data, analyses and models, and guide them in identifying possible actions during a decision-making process. Researchers, however, report difficulties in up-take of SDSS by the intended users. Some suggest that this field would benefit from investigation of the social aspects involved in SDSS design, development, testing and use. Borrowing insights from the literature on science-policy interactions, we explore two key social processes: knowledge integration and learning. Using a sample of 36 scientific papers concerning SDSS in relation to environmental issues, we surveyed whether and how the selected papers reported on knowledge integration and learning. We found that while many of the papers mentioned communication and collaboration with prospective user groups or stakeholders, this was seldom underpinned by a coherent methodology for enabling knowledge integration and learning to surface. This appears to have hindered SDSS development and later adoption by intended users.
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The management requirements for protected areas are frequently complex and urgent; as a result, managers often need to act quickly and make decisions with limited supporting evidence at their disposal. Despite demands for high-quality information, it is unclear how much of this evidence conservation practitioners use to assist with their decision making. We investigated the information used to manage protected areas, based on the evidence reported by practitioners when evaluating their management performance. We examined the management of over 1000 protected areas run by two Australian conservation agencies – Parks Victoria and the New South Wales Department of Environment and Climate Change – an unprecedented scope for this type of study. We found that very few conservation practitioners use evidence-based knowledge to support their management. The evidence used varies with the management issue, reserve type, and reserve size. Around 60% of conservation management decisions rely on experience-based information, and many practitioners report having insufficient evidence to assess their management decisions. While experience plays an important role in conservation management, the apparent lack of evidence-based information to support decision making in the reserves has the potential to compromise outcomes and jeopardize the investment made in protected areas for conservation.
Integrated environmental modeling: A vision and roadmap for the future Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies
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