From Climate Change to Pandemics: Decision Science Can Help Scientists Have Impact

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. However, there is a perpetual challenge in translating scientific insight into policy. Many articles explain how to better bridge the gap through improved communication and engagement, but we believe that communication and engagement are only one part of the puzzle. There is a fundamental tension between science and policy because scientific endeavors are rightfully grounded in discovery, but policymakers formulate problems in terms of objectives, actions and outcomes. Decision science provides a solution by framing scientific questions in a way that is beneficial to policy development, facilitating scientists’ contribution to public discussion and policy. At its core, decision science is a field that aims to pinpoint evidence-based management strategies by focussing on those objectives, actions, and outcomes defined through the policy process. The importance of scientific discovery here is in linking actions to outcomes, helping decision-makers determine which actions best meet their objectives. In this paper we explain how problems can be formulated through the structured decisionmaking process. We give our vision for what decision science may grow to be, describing current gaps in methodology and application. By better understanding and engaging with the decision-making processes, scientists can have greater impact and make stronger contributions to important societal problems.Christopher M. Baker, Patricia T. Campbell, Iadine Chades, Angela J. Dean, Susan M. Hester, Matthew H. Holden, James M. McCaw, Jodie McVernon, Robert Moss, Freya M. Shearer, and Hugh P. Possingha

General rules for environmental management to prioritise social ecological systems research based on a value of information approach

Globally, billions of dollars are invested each year to help understand the dynamics of social ecological systems (SES) in bettering both social and environmental outcomes. However, there is no scientific consensus on which aspect of an SES is most important and urgent to understand; particularly given the realities of limited time and money. Here we use a simulation-based “value of information” approach to examine where research will deliver the most important information for environmental management in four SESs representing a range of real-life environmental issues. We find that neither social nor ecological information is consistently the most important: instead, researchers should focus on understanding the primary effects of their management actions. Thus, when managers are undertaking social actions the highest research priority should be understanding the dynamics of social groups. Alternatively, when manipulating ecological systems it will be most important to quantify ecological population dynamics. Synthesis and applications. Our results provide a standard assessment to determine the uncertain social ecological systems (SES) component with the highest expected impact for management outcomes. First, managers should determine the structure of their SES by identifying social and ecological nodes. Second, managers should identify the qualitative nature of the network, by determining which nodes are linked, but not the strength of those interactions. Finally, managers should identify the actions available to them to intervene in the SES. From these steps, managers will be able to identify the SES components that are closest to the management action(s), and it is these nodes and interactions that should receive priority research attention to achieve effective environmental decision making

Conservation decision-making in large state spaces

For metapopulation management problems with small state spaces, it is typically possible to model the problem as a Markov decision process (MDP), and find an optimal control policy using stochastic dynamic programming (SDP). SDP is an iterative procedure that seeks to optimise a value function at each timestep by trying each of the actions defined in the MDP

A comparison of adaptive management and real options approaches for environmental decisions under uncertainty

Two approaches to sequential decisions under uncertainty in the environmental management adaptive management and real options analysis -have evolved independently over the last decades

Should I spread my risk or concentrate my efforts: Is triage of a subpopulation ever the best decision?

Threatened species often exist in a small number of isolated subpopulations. Given limitations on conservation spending, we must ask the question: should we put all our eggs in one basket and manage the best quality subpopulation or the subpopulation most likely to benefit from management, or should we hedge our bets and manage both subpopulations? A further complexity arises when we consider that most threatened species are cryptic and their presence in an area can be uncertain as a result of the imperfect nature of most detection methods. Managers of cryptic species thus face several dilemmas: if they are unsure whether a species is present in an area or has been extirpated, should they continue to manage the species in that area or instead invest some of their limited resources in surveying to determine if the species is still present (and viable)? How much negative evidence do they need in order to give up and make the decision to cease management? The ecology and conservation literature present little guidance on how to approach such problems, though some analogous problems have been tackled within a decision theory framework (Gerber et al. 2005; Regan et al. 2006; Wilson et al. 2006). Here we build on lessons from these studies and others investigating optimal conservation decision making (Possingham et al. 2001; Dorazio & Johnson 2003) to develop a coherent decision framework for allocating resources between two subpopulations of a threatened species where we are uncertain about the persistence of the species in our management areas. In this problem we must make a decision about how to allocate finite resources to three separate actions in each subpopulation; management, surveying and doing nothing. Management reduces a subpopulation's risk of extinction. Surveying, while not reducing extinction risk, improves our knowledge about whether the species is present, therefore avoiding costly unnecessary expenditure. Both management and surveying cost money and thus the decision to perform either of these actions in a subpopulation will alter the resources available and therefore the success of the action implemented in the other subpopulation. At any point in time managers will have a belief about whether a subpopulation is still extant. In this paper we assess how our optimal decisions change as a function of those beliefs and the time remaining in the management period. The goal of efficient conservation planning and management is to find a resource allocation strategy, or set of actions, that maximises the net expected long-term benefit. Here the optimal strategy involves a trade-off between the persistence of our subpopulations at the end of the management period, and the impact of our decisions on the probability of subpopulation extinction. We pose this problem as a Partially Observable Markov Decision Process (POMDP) and solve a multi time-step scenario using the incremental pruning algorithm (Cassandra et al. 1997). The POMDP algorithm finds an optimal resource allocation each year given the current belief about the state of the species (extant or extinct) in each subpopulation. This paper has two major aims; (i) to extend the framework proposed by Chades et al. (in review) to incorporate two subpopulations of a threatened species, addressing the issue of triage in conservation management, and (ii) to introduce more ecological complexity and realism to the problem by considering subpopulationws of differing habitat quality

Win-wins for biodiversity and ecosystem service conservation depend on the trophic levels of the species providing services

Confronted by significant impacts to ecosystems world-wide, decision makers face the challenge of maintaining both biodiversity and the provision of ecosystem services (ES). However, the objectives of managing biodiversity and supplying ES may not always be in concert, resulting in the need for trade-offs. Understanding these potential trade-offs is crucial for identifying circumstances under which conservation strategies designed to maximise either biodiversity or ES will result in win-win or win-lose outcomes. One important factor that may influence these outcomes are species interactions and the structure of the networks in which they are embedded. We combine optimisation and network theory to investigate the difference in species prioritisation and management outcomes when targeting biodiversity or ES, by considering trophic interactions between species. We analyse 360 simulated ecosystem networks with different ecosystem structures, including the trophic level of the species providing the ES, the number of ES considered, and the food web connectivity. We then illustrate the framework on a saltmarsh case study. We find that trade-offs between biodiversity and ES depend on the network structure of the ecosystem being managed. The trophic level of the species providing the ES is an important determinant of optimal species protection priorities and the biodiversity-ES trade-offs. A strategy targeting ES leads to similar levels of biodiversity conservation (a win-win situation) only when basal species provide the services. In contrast, food web connectivity and the number of services considered have little impact on biodiversity-ES trade-offs. Synthesis and applications. Our research provides the first optimisation model to examine trade-offs between a biodiversity- or ecosystem service-based approach for managing a network of interacting species that provide services. Importantly, results from considering species-services interactions in ecosystem network dynamics can provide managers with quantitative insights to identify opportunities for win-wins and or to avoid win-loss outcomes, by focusing on the trophic level of the species providing services. Future research could build on our model to add multiple interaction types among species, ecosystem functions, and ecosystem services to analyse optimal ecosystem management for multiple conservation objectives

Key considerations and challenges in the application of social-network research for environmental decision making

Attempts to better understand the social context in which conservation and environmental decisions are made has led to increased interest in human social networks. To improve the use of social-network analysis in conservation, we reviewed recent studies in the literature in which such methods were applied. In our review, we looked for problems in research design and analysis that limit the utility of network analysis. Nineteen of 55 articles published from January 2016 to June 2019 exhibited at least 1 of the following problems: application of analytical methods inadequate or sensitive to incomplete network data; application of statistical approaches that ignore dependency in the network; or lack of connection between the theoretical base, research question, and choice of analytical techniques. By drawing attention to these specific areas of concern and highlighting research frontiers and challenges, including causality, network dynamics, and new approaches, we responded to calls for increasing the rigorous application of social science in conservation

Migratory connectivity magnifies the consequences of habitat loss from sea-level rise for shorebird populations

Sea-level rise (SLR) will greatly alter littoral ecosystems, causing habitat change and loss for coastal species. Habitat loss is widely used as a measurement of the risk of extinction, but because many coastal species are migratory, the impact of habitat loss will depend not only on its extent, but also on where it occurs. Here, we develop a novel graph-theoretic approach to measure the vulnerability of a migratory network to the impact of habitat loss from SLR based on population flow through the network. We show that reductions in population flow far exceed the proportion of habitat lost for 10 long-distance migrant shorebirds using the East Asian-Australasian Flyway. We estimate that SLR will inundate 23-40% of intertidal habitat area along their migration routes, but cause a reduction in population flow of up to 72 per cent across the taxa. This magnifying effect was particularly strong for taxa whose migration routes contain bottlenecks-sites through which a large fraction of the population travels. We develop the bottleneck index, a new network metric that positively correlates with the predicted impacts of habitat loss on overall population flow. Our results indicate that migratory species are at greater risk than previously realized

Prioritizing recovery funding to maximize conservation of endangered species

The absence of a rigorous mechanism for prioritizing investment in endangered species management is a major implementation hurdle affecting recovery. Here, we present a method for prioritizing strategies for endangered species management based on the likelihood of achieving species' recovery goals per dollar invested. We demonstrate our approach for 15 species listed under Canada's Species at Risk Act that co-occur in Southwestern Saskatchewan. Without management, only two species have >50% probability of meeting recovery objectives; whereas, with management, 13 species exceed the >50% threshold with the implementation of just five complementary strategies at a cost of $126m over 20 years. The likelihood of meeting recovery objectives rarely exceeded 70% and two species failed to reach the >50% threshold. Our findings underscore the need to consider the cost, benefit, and feasibility of management strategies when developing recovery plans in order to prioritize implementation in a timely and cost-effective manner