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As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state‐dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small‐scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species‐poor sites are subsets of species‐rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small‐scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un‐nested and when social networks are likely to be centralized., Article impact statement: The value of collecting social network information for conservation planning depends on both species distributions and social network structure.

Managers often have incomplete information to make decisions about threatened species management, and lack the time or funding needed to obtain complete information. Value of information (VOI) analysis can assist managers in deciding whether to manage using current information or monitor to reduce uncertainty before managing. However, VOI analysis has not yet been applied to spatial allocation of monitoring resources across a landscape.Here, we demonstrate how to make the best use of data from species distribution models (SDMs) and VOI analysis to assess the value of land protection decisions for single and multiple-species objectives across a heterogeneous landscape. Our method determines the situations where one should monitor before protecting the land, and those where one should act based on current incomplete information. Further, we prioritize land planning units based on cost-effectiveness (expected number of occurrences protected per dollar spent) and identify properties to target for monitoring or immediate conservation.In a single species case study, we found that the optimal decision was to act based on current information when the prior probability of detecting an occurrence in a survey was low. When probability of detection was high, it was most effective to monitor the majority of units. In a multi-species case study, monitoring was only optimal in 50% of cases, due to high inferred probability of at least one occurrence of a threatened species in many units. When compared to a simulation where units were monitored by default, using VOI to determine which units were monitored or prioritized for immediate conservation led to an increase in the expected number of occurrences protected.Synthesis and applications. Using a combination of species distribution models and value of information analysis can assist managers in efficiently distributing limited resources for protected area allocation. Our results suggest that if managers can use value of information to monitor more efficiently, it can lead to protecting a greater number of threatened species occurrences.

PCR testing is a crucial capability for managing disease outbreaks, but it is also a limited resource and must be used carefully to ensure the information gain from testing is valuable. Testing has two broad uses for informing public health policy, namely to track epidemic dynamics and to reduce transmission by identifying and managing cases. In this work we develop a modelling framework to examine the effects of test allocation in an epidemic, with a focus on using testing to minimise transmission. Using the COVID-19 pandemic as an example, we examine how the number of tests conducted per day relates to reduction in disease transmission, in the context of logistical constraints on the testing system. We show that if daily testing is above the routine capacity of a testing system, which can cause delays, then those delays can undermine efforts to reduce transmission through contact tracing and quarantine. This work highlights that the two goals of aiming to reduce transmission and aiming to identify all cases are different, and it is possible that focusing on one may undermine achieving the other. To develop an effective strategy, the goals must be clear and performance metrics must match the goals of the testing strategy. If metrics do not match the objectives of the strategy, then those metrics may incentivise actions that undermine achieving the objectives.

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.Las decisiones sobre la conservación de la biodiversidad son difíciles de tomar, especialmente cuando involucran diferentes valores, objetivos multidimensionales complejos, recursos limitados, urgencia y una incertidumbre considerable. Las ciencias de la decisión incorporan una teoría sobre cómo tomar decisiones difíciles y una variedad extensa de marcos de trabajo y herramientas que transforman esa teoría en práctica. Buscamos mejorar la claridad conceptual y la aplicación práctica de las ciencias de la decisión para ayudar al órgano decisorio a aplicar estas ciencias a los problemas de conservación. Nos enfocamos en las barreras para la aceptación de las ciencias de la decisión, incluyendo la falta de capacitación y de conciencia por estas ciencias; la confusión por la terminología común y cuáles herramientas y marcos de trabajo aplicar; y la impresión errónea de que la aplicación de estas ciencias consume tiempo y debe ser costosa y compleja. Para asistir en la navegación de la literatura extensa y dispar de las ciencias de la decisión, aclaramos el significado de varios términos comunes: ciencias de la decisión, teoría de la decisión, análisis de decisiones, toma estructurada de decisiones y herramientas de apoyo para las decisiones. La aplicación de las ciencias de la decisión no tiene que ser compleja ni debe llevar mucho tiempo; de hecho, todo comienza con saber cómo pensar detenidamente en los componentes de una decisión mediante el análisis de decisiones (es decir, definir el problema, producir objetivos, desarrollar alternativas, estimar consecuencias y realizar compensaciones). Lo anterior se logra de mejor manera mediante la aplicación de una estrategia prototipos rápidos. En cada paso, las herramientas de apoyo para las decisiones pueden proporcionar visión y claridad adicionales, mientras que los marcos de apoyo para las decisiones (p.ej.: gestión de amenazas prioritarias y planeación sistemática de la conservación) pueden asistir en la navegación de los diferentes pasos de un análisis de decisiones para contextos particulares. Resumimos los marcos de trabajo y las herramientas más importantes de apoyo para las decisiones y describimos el paso, y el contexto, del análisis de decisiones para el que es más útil aplicarlos. Nuestra introducción a las ciencias de la decisión apoyará en la contextualización de las estrategias actuales y los nuevos desarrollos, y ayudarán al órgano decisorio a comenzar a aplicar estas ciencias en los problemas de conservación.

Innovation has the potential to enable conservation science and practice to keep pace with the escalating threats to global biodiversity, but this potential will only be realized if such innovations are designed and developed to fulfill specific needs and solve well-defined conservation problems. We propose that business-world strategies for assessing the practicality of innovation can be applied to assess the viability of innovations, such as new technology, for addressing biodiversity conservation challenges. Here, we outline a five-step, "lean start-up" based approach for considering conservation innovation from a business-planning perspective. Then, using three prominent conservation initiatives - Marxan (software), Conservation Drones (technology support), and Mataki (wildlife-tracking devices) - as case studies, we show how considering proposed initiatives from the perspective of a conceptual business model can support innovative technologies in achieving desired conservation outcomes.

Advancing technology represents an unprecedented opportunity to enhance our capacity to conserve the Earth's biodiversity. However, this great potential is failing to materialize and rarely endures. We contend that unleashing the power of technology for conservation requires an internationally coordinated strategy that connects the conservation community and policy-makers with technologists. We argue an international conservation technology entity could (1) provide vision and leadership, (2) coordinate and deliver key services necessary to ensure translation from innovation to effective deployment and use of technology for on-the-ground conservation across the planet, and (3) help integrate innovation into biodiversity conservation policy from local to global scales, providing tools to monitor outcomes of conservation action and progress towards national and international biodiversity targets. This proposed entity could take the shape of an international alliance of conservation institutions or a formal intergovernmental institution. Active and targeted uptake of emerging technology can help society achieve biodiversity conservation goals.

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.

Threats to biodiversity and the integrity of ecological systems are escalating globally, both within and outside of protected areas. Decision makers have inadequate resources to manage all threats and typically lack information on the likely outcomes and cost‐effectiveness of possible management strategies. Priority Threat Management (PTM) is an emerging approach designed to address this challenge, by defining and appraising cost‐effective strategies for mitigating threats to biodiversity across regions. The scientific and practical impacts of PTM are increasing, with a growing number of case study applications across the globe. Here, we provide guidance and resource material for conducting the PTM process based on our experience delivering six large‐scale projects across Australia and Canada. Our handbook describes the four stages of PTM: scoping and planning; defining and collecting key elements; analysing the cost‐effectiveness of strategies; and communicating and integrating recommendations. We summarise critical tips, strengths, and limitations and scope for possible enhancements of the approach. Priority Threat Management harnesses scientific and expert‐derived information to prioritise management strategies based on their benefit to biodiversity, management costs and feasibility. The approach involves collaboration with key experts and stakeholders in a region to improve knowledge sharing and conservation support. The PTM approach identifies sets of regional level strategies that together provide the greatest benefits for multiple species under a limited budget, which can be used to inform existing processes for decision‐making. The PTM approach applies some generalisations in management strategies and resolution, in order to address complex challenges. Further developments of the approach include testing in a greater range of socioecological systems with adaptations that cater for multiobjective decisions. Synthesis and applications. Priority Threat Management is a decision science approach that brings people together to define and prioritise strategies for managing threats to biodiversity across broad regions. It delivers a prospectus for investment in the biodiversity of a region that is transparent, repeatable, participatory, and based on the best available information. Our handbook provides the necessary guidance and resources for expanding the Priority Threat Management approach to new locations, contexts, and challenges.

1. Making effective management decisions is challenging in multi-species, multi-threat systems because of uncertainty about the effects of different threats on different species. To inform management decisions, we often monitor species to detect spatial or temporal trends that can help us learn about threatening processes. However, which species to monitor and how to monitor to inform the management of threats can be difficult to determine.2. Value of information (VOI) analysis is an approach for quantifying the value of monitoring to inform management decisions. We developed a novel method that applies VOI analysis to quantify the benefits of different species monitoring strategies in multi-threat, multi-species systems. We applied the approach to compare the effectiveness of surveillance monitoring (monitoring species without experimentation) to targeted monitoring (monitoring species with experimentation to learn about a specific threat), and how prior information drives the benefits of these two different strategies and the species to monitor. We also illustrate the approach by applying it to two contrasting case studies for monitoring and managing declining mammals in Western Australia.3. Our approach shows that surveillance monitoring generally provides far lower benefits than targeted monitoring for managing threats in multi-species, multi-threat systems under economic constraints.4. Our approach also informs the choice of species to monitor and which threats to manage experimentally to most improve threat management outcomes. We show that the key parameters driving these choices include: the budget available for management, prior understanding of which threats cause declines in which species, the relative cost of managing these threats, and the background probability of decline.5. Our new VOI approach allows the evaluation of monitoring decisions in multi-species, multi-threat systems in the face of uncertainty, while explicitly accounting for the improvement in management outcomes. We recommend that managers need to explicitly consider a range of decision parameters when selecting which species to monitor to inform management. Our framework provides an objective way to do this.

The question of when to monitor and when to act is fundamental to applied ecology and notoriously difficult to answer. Value of information (VOI) theory holds great promise to help answer this question for many management problems. However, VOI theory in applied ecology has only been demonstrated in single-decision problems and has lacked explicit links between monitoring and management costs. Here, we present an extension of VOI theory for solving multi-unit decisions of whether to monitor before managing, while explicitly accounting for monitoring costs. Our formulation helps to choose the optimal monitoring/management strategy among groups of management units (e.g. species, habitat patches) and can be used to examine the benefits of partial and repeat monitoring. To demonstrate our approach, we use case-simulated studies of single-species protection that must choose among potential habitat areas, and classification and management of multiple species threatened with extinction. We provide spreadsheets and code to illustrate the calculations and facilitate application. Our case studies demonstrate the utility of predicting the number of units with a given outcome for problems with probabilities of discrete states and the efficiency of having a flexible approach to manage according to monitoring outcomes. Synthesis and applications. The decision to act or gather more information can have serious consequences for management. No decision, including the decision to monitor, is risk-free. Our multi-unit expansion of Value of Information theory can reduce the risk in monitoring/acting decisions for many applied ecology problems. While our approach cannot account for the potential value of discovering previously unknown threats or ecological processes via monitoring programmes, it can provide quantitative guidance on whether to monitor before acting, and which monitoring/management actions are most likely to meet management objectives.

1.Species management requires decision-making under uncertainty. Given a management objective and limited budget, managers need to decide what to do, and where and when to do it. A schedule of management actions that achieves the best performance is an optimal policy. A popular optimisation technique used to find optimal policies in ecology and conservation is stochastic dynamic programming (SDP). Most SDP approaches can only accommodate actions of equal durations. However, in many situations, actions take time to implement or cannot change rapidly. Calculating the optimal policy of such problems is computationally demanding and becomes intractable for large problems. Here, we address the problem of implementing several actions of different durations simultaneously. 2.We demonstrate analytically that synchronising actions and their durations provide upper and lower bounds of the optimal performance. These bounds provide a simple way to evaluate the performance of any policy, including rules of thumb. We apply this approach to the management of a dynamic ecological network of Aedes albopictus, an invasive mosquito that vectors human diseases. The objective is to prevent mosquitoes from colonising mainland Australia from the nearby Torres Straits Islands where managers must decide between management actions that differ in duration and effectiveness. 3.We were unable to compute an optimal policy for more than eight islands out of 17, but obtained upper and lower bounds for up to 13 islands. These bounds are within 16% of an optimal policy. We used the bounds to recommend managing highly populated islands as a priority. 4.Our approach calculates upper and lower bounds for the optimal policy by solving simpler problems that are guaranteed to perform better and worse than the optimal policy, respectively. By providing bounds on the optimal solution, the performance of policies can be evaluated even if the optimal policy cannot be calculated. Our general approach can be replicated for problems where simultaneous actions of different durations need to be implemented.

Many migratory shorebird species are undergoing severe population declines due to habitat loss. Selecting sites for protection along migratory shorebird flyways requires accounting for connectivity between sites and representing all migratory cycle stages within a protected area network. Site protection decisions often additionally account for the economic value of habitat-related ecosystem services, as shorebird habitats often provide services such as fisheries and recreational opportunities. In this study, we develop a new approach which combines graph theory, decision science, and stochastic dynamic optimization, to investigate whether habitat protection strategies aimed at safeguarding ecosystem services can also deliver efficient protection for migratory shorebirds. We use graph theory to empirically model migration routes for two shorebird species, the Curlew Sandpiper (Calidris ferruginea) and Eastern Curlew (Numenius madagascariensis), linking their migratory networks with data on habitat-related ecosystem services within the East Asian - Australasian Flyway (EAAF). Using dynamic optimization, we calculate the optimal habitat protection strategy for both ecosystem services and shorebird conservation objectives for both species. We further investigate if, and how, these two strategies can converge to achieve both objectives, and whether there is a trade-off between strategies. Our results show that the biodiversity outcome from strategies maximizing ecosystem services provided by shorebird habitat could align closely with a species-focused conservation plan. More specifically, the optimal ecosystem services oriented strategy maintains a similar level of shorebird populations as the optimal conservation strategy, while protecting an additional 45% (Curlew Sandpiper) and 42% (Eastern Curlew) of ecosystem services throughout the network. Conservation budget also matters, as small budgets would lead to inefficient protection for biodiversity. Our research shows that conservation planning can simultaneously achieve conservation and ecosystem services objectives for migratory species. By accounting for the value of ecosystem services within shorebird habitats, we provide quantitative evidence of a win-win scenario in which both shorebird conservation and ecosystem service provision can be achieved. Our model could be applied to other migratory species to improve spatially explicit planning for both biodiversity and ecosystem service targets.

Determining the best management actions is challenging when critical information is missing. However, urgency and limited resources require that decisions must be made despite this uncertainty. The best practice method for managing uncertain systems is adaptive management, or learning by doing. Adaptive management problems can be solved optimally using decision-theoretic methods; the challenge for these methods is to represent current and future knowledge using easy-to-optimize representations. Significant methodological advances have been made since the seminal adaptive management work was published in the 1980s, but despite recent advances, guidance for implementing these approaches has been piecemeal and study-specific. There is a need to collate and summarize new work. Here, we classify methods and update the literature with the latest optimal or near-optimal approaches for solving adaptive management problems. We review three mathematical concepts required to solve adaptive management problems: Markov decision processes, sufficient statistics, and Bayes’ theorem. We provide a decision tree to determine whether adaptive management is appropriate and then group adaptive management approaches based on whether they learn only from the past (passive) or anticipate future learning (active). We discuss the assumptions made when using existing models and provide solution algorithms for each approach. Finally, we propose new areas of development that could inspire future research. For a long time, limited by the efficiency of the solution methods, recent techniques to efficiently solve partially observable decision problems now allow us to solve more realistic adaptive management problems such as imperfect detection and non-stationarity in systems.

Conservation of endangered species increasingly envisages complex strategies that integrate captive and wild management actions. Management decisions in this context must be made in the face of uncertainty, often with limited capacity to collect information. Adaptive management (AM) combines management and monitoring, with the aim of updating knowledge and improving decision-making over time. We provide a guide for managers who may realize the potential of AM, but are unsure where to start. The urgent need for iterative management decisions, the existence of uncertainty, and the opportunity for learning offered by often highly-controlled captive environments create favorable conditions for AM. However, experiments and monitoring may be complicated by small sample sizes, and the ability to control the system, including stochasticity and observability, may be limited toward the wild end of the spectrum. We illustrate the key steps to implementing AM in threatened species management using four case studies, including the management of captive programs for cheetah ( Acinonyx jubatus ) and whooping cranes ( Grus americana ), of a translocation protocol for Arizona cliffroses Purshia subintegra and of ongoing supplementary feeding of reintroduced hihi ( Notiomystis cincta ) populations. For each case study, we explain (1) how to clarify whether the decision can be improved by learning (i.e. it is iterative and complicated by uncertainty) and what the management objectives are; (2) how to articulate uncertainty via alternative, testable hypotheses such as competing models or parameter distributions; (3) how to formally define how additional information can be collected and incorporated in future management decisions.

With many conservation issues requiring urgent action, determining how much data are needed to inform good decisions is a common problem. We examine this problem in relation to the protection of critical habitat, the habitat required for species’ recovery and persistence. The protection of critical habitat is an essential step in the threatened species recovery process. It is also one of the most contentious and protracted decisions faced by environmental agencies. Uncertainty about what constitutes critical habitat, and the challenges of balancing competing societal objectives and of protecting critical habitat once identified are stalling the recovery process. We offer insight into this challenge by investigating how long we can afford to spend identifying critical habitat before opportunities to recover a species are lost. We illustrate our decision model using Canada's threatened northern abalone (Haliotis kamtschatkana). Our method delivers the stopping time at which habitat protection must begin, despite uncertainty, in order to avoid an unacceptable risk of extinction.

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.Consideraciones y Retos Importantes en la Aplicación de la Investigación por medio de Redes Sociales para la Toma de Decisiones Ambientales Resumen Los intentos por tener un mejor entendimiento del contexto social en el que se toman las decisiones ambientales y de conservación han derivado en un incremento en el interés por las redes sociales humanas. Para mejorar el uso del análisis de redes sociales en la conservación, buscamos en la literatura los estudios recientes que hayan aplicado dichos métodos y los sometimos a una revisión. En esta revisión, examinamos los problemas en el diseño de la investigación y del análisis que limitan la utilidad del análisis de redes. Diecinueve de los 55 artículos publicados entre enero 2016 y junio 2019 exhibieron al menos uno de los siguientes problemas: aplicación de métodos analíticos inadecuados o sensibles a la información incompleta sobre las redes; aplicación de estrategias estadísticas que ignoran la dependencia en la red; o falta de conexión entre la base teórica, la pregunta de investigación y la selección de técnica analítica. Al llamar la atención hacia estas áreas específicas de interés y resaltar las fronteras y retos de la investigación, incluyendo la causalidad, las dinámicas de redes y las estrategias nuevas, respondimos a la necesidad de incrementar la aplicación rigurosa de las ciencias sociales en la conservación.

Invasive non-native plants have negatively impacted on biodiversity and ecosystem functions world-wide. Because of the large number of species, their wide distributions and varying degrees of impact, we need a more effective method for prioritizing control strategies for cost-effective investment across heterogeneous landscapes. Here, we develop a prioritization framework that synthesizes scientific data, elicits knowledge from experts and stakeholders to identify control strategies, and appraises the cost-effectiveness of strategies. Our objective was to identify the most cost-effective strategies for reducing the total area dominated by high-impact non-native plants in the Lake Eyre Basin (LEB). We use a case study of the 120 million ha Lake Eyre Basin that comprises some of the most distinctive Australian landscapes, including Uluru-Kata Tjuta National Park. More than 240 non-native plant species are recorded in the Lake Eyre Basin, with many predicted to spread, but there are insufficient resources to control all species. Lake Eyre Basin experts identified 12 strategies to control, contain or eradicate non-native species over the next 50 years. The total cost of the proposed Lake Eyre Basin strategies was estimated at AU$1·7 billion, an average of AU$34 million annually. Implementation of these strategies is estimated to reduce non-native plant dominance by 17 million ha - there would be a 32% reduction in the likely area dominated by non-native plants within 50 years if these strategies were implemented. The three most cost-effective strategies were controlling Parkinsonia aculeata, Ziziphus mauritiana and Prosopis spp. These three strategies combined were estimated to cost only 0·01% of total cost of all the strategies, but would provide 20% of the total benefits. Over 50 years, cost-effective spending of AU$2·3 million could eradicate all non-native plant species from the only threatened ecological community within the Lake Eyre Basin, the Great Artesian Basin discharge springs. Synthesis and applications. Our framework, based on a case study of the 120 million ha Lake Eyre Basin in Australia, provides a rationale for financially efficient investment in non-native plant management and reveals combinations of strategies that are optimal for different budgets. It also highlights knowledge gaps and incidental findings that could improve effective management of non-native plants, for example addressing the reliability of species distribution data and prevalence of information sharing across states and regions. Our framework, based on a case study of the 120 million ha Lake Eyre Basin in Australia, provides a rationale for financially efficient investment in non-native plant management and reveals combinations of strategies that are optimal for different budgets. It also highlights knowledge gaps and incidental findings that could improve effective management of non-native plants, for example addressing the reliability of species distribution data and prevalence of information sharing across states and regions.

Invasive species pose a substantial risk to native biodiversity. As distributions of invasive species shift in response to changes in climate so will management priorities and investment. To develop cost-effective invasive species management strategies into the future it is necessary to understand how species distributions are likely to change over time and space. For most species however, few data are available on their current distributions, let alone projected future distributions. We demonstrate the benefits of Bayesian Networks (BNs) for projecting distributions of invasive species under various climate futures, when empirical data are lacking. Using the introduced pasture species, buffel grass (Cenchrus ciliaris) in Australia as an example, we employ a framework by which expert knowledge and available empirical data are used to build a BN. The framework models the susceptibility and suitability of the Australian continent to buffel grass colonization using three invasion requirements; the introduction of plant propagules to a site, the establishment of new plants at a site, and the persistence of established, reproducing populations. Our results highlight the potential for buffel grass management to become increasingly important in the southern part of the continent, whereas in the north conditions are projected to become less suitable. With respect to biodiversity impacts, our modelling suggests that the risk of buffel grass invasion within Australia’s National Reserve System is likely to increase with climate change as a result of the high number of reserves located in the central and southern portion of the continent. In situations where data are limited, we find BNs to be a flexible and inexpensive tool for incorporating existing process-understanding alongside bioclimatic and edaphic variables for projecting future distributions of species invasions.

Summary Applied ecologists continually advocate further research, under the assumption that obtaining more information will lead to better decisions. Value of information (VoI) analysis can be used to quantify how additional information may improve management outcomes: despite its potential, this method is still underused in environmental decision-making. We provide a primer on how to calculate the VoI and assess whether reducing uncertainty will change a decision. Our aim is to facilitate the application of VoI by managers who are not familiar with decision-analytic principles and notation, by increasing the technical accessibility of the tool. Calculating the VoI requires explicit formulation of management objectives and actions. Uncertainty must be clearly structured and its effects on management outcomes evaluated. We present two measures of the VoI. The expected value of perfect information is a calculation of the expected improvement in management outcomes that would result from access to perfect knowledge. The expected value of sample information calculates the improvement in outcomes expected by collecting a given sample of new data. We guide readers through the calculation of VoI using two case studies: (i) testing for disease when managing a frog species and (ii) learning about demographic rates for the reintroduction of an endangered turtle. We illustrate the use of Bayesian updating to incorporate new information. The VoI depends on our current knowledge, the quality of the information collected and the expected outcomes of the available management actions. Collecting information can require significant investments of resources; VoI analysis assists managers in deciding whether these investments are justified.

Spatial representations of threatening processes – “threat maps” – can identify where biodiversity is at risk, and are often used to identify priority locations for conservation. In doing so, decision makers are prone to making errors, either by assuming that the level of threat dictates spatial priorities for action or by relying primarily on the location of mapped threats to choose possible actions. We show that threat mapping can be a useful tool when incorporated within a transparent and repeatable structured decision-making (SDM) process. SDM ensures transparent and defendable conservation decisions by linking objectives to biodiversity outcomes, and by considering constraints, consequences of actions, and uncertainty. If used to make conservation decisions, threat maps are best developed with an understanding of how species respond to actions that mitigate threats. This approach will ensure that conservation actions are prioritized where they are most cost-effective or have the greatest impact, rather than where threat levels are highest.

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.

Under limited time and resources, ecological managers are under increasing pressure to demonstrate tangible impact of monitoring activities. Value of Information (VOI) has been advocated as an ideal tool to evaluate whether more data is required to improve expected management outcomes. Yet, despite several recent works explaining its value, VOI remains seldom used in practice. Here we provide an example of a successful ecological application of VOI. We apply VOI to a novel multi-objective freshwater management problem and show how to make the best use of expert data through a robust sensitivity analysis. Unlike previous VOI approaches, our analysis provides statistical confidence to our recommendations. We apply our approach to the recovery of Moira grass (Pseudoraphis spinescens) plains, a threatened vegetation community at the Ramsar-listed Barmah Forest on the Murray River, Australia. Working closely with managers, we discovered that although many threats may impede Moira grass recovery, reducing grazing pressure and applying ideal depth and duration of flooding were most likely to lead to recovery. We found that learning from monitoring can significantly increase the existing extent of Moira grass, although these gains are modest compared to immediate management action. Our study shows how VOI can be used to demonstrate efficient use of limited environmental water to maximise ecological impact and increase transparency when making monitoring or management decisions. More broadly, the study methods will be of interest to any environmental manager who needs to prioritise monitoring and evaluation activities subject to a limited research budget. At a time where researchers and managers are asked to be more accountable for their decision-making, VOI provides a very accessible tool that can speed up the decision of whether to wait and collect more data or act immediately despite uncertainty.

For nearly a decade, environmental scientists have deplored the paradox of needing to fly to conferences1–3 and have increasingly called for sustainable conferencing4,5. Have conferences responded by reducing their environmental impact?

Antarctic terrestrial biodiversity occurs almost exclusively in ice-free areas that cover less than 1% of the continent. Climate change will alter the extent and configuration of ice-free areas, yet the distribution and severity of these effects remain unclear. Here we quantify the impact of twenty-first century climate change on ice-free areas under two Intergovernmental Panel on Climate Change (IPCC) climate forcing scenarios using temperature-index melt modelling. Under the strongest forcing scenario, ice-free areas could expand by over 17,000 km2 by the end of the century, close to a 25% increase. Most of this expansion will occur in the Antarctic Peninsula, where a threefold increase in ice-free area could drastically change the availability and connectivity of biodiversity habitat. Isolated ice-free areas will coalesce, and while the effects on biodiversity are uncertain, we hypothesize that they could eventually lead to increasing regional-scale biotic homogenization, the extinction of less-competitive species and the spread of invasive species.

Conserving migratory species requires protecting connected habitat along the pathways they travel. Despite recent improvements in tracking animal movements, migratory connectivity remains poorly resolved at a population level for the vast majority of species, thus conservation prioritization is hampered. To address this data limitation, we developed a novel approach to spatial prioritization based on a model of potential connectivity derived from empirical data on species abundance and distance traveled between sites during migration. We applied the approach to migratory shorebirds of the East Asian-Australasian Flyway. Conservation strategies that prioritized sites based on connectivity and abundance metrics together maintained larger populations of birds than strategies that prioritized sites based only on abundance metrics. The conservation value of a site therefore depended on both its capacity to support migratory animals and its position within the migratory pathway; the loss of crucial sites led to partial or total population collapse. We suggest that conservation approaches that prioritize sites supporting large populations of migrants should, where possible, also include data on the spatial arrangement of sites.

Adaptive management is applied in conservation and natural resource management, and consists of making sequential decisions when the transition matrix is uncertain. Informally described as ’learning by doing’, this approach aims to trade off between decisions that help achieve the objective and decisions that will yield a better knowledge of the true transition matrix. When the true transition matrix is assumed to be an element of a finite set of possible matrices, solving a mixed observability Markov decision process (MOMDP) leads to an optimal trade-off but is very computationally demanding. Under the assumption (common in adaptive management) that the true transition matrix is stationary, we propose a polynomial-time algorithm to find a lower bound of the value function. In the corners of the domain of the value function (belief space), this lower bound is provably equal to the optimal value function. We also show that under further assumptions, it is a linear approximation of the optimal value function in a neighborhood around the corners. We evaluate the benefits of our approach by using it to initialize the solvers MO-SARSOP and Perseus on a novel computational sustainability problem and a recent adaptive management data challenge. Our approach leads to an improved initial value function and translates into significant computational gains for both solvers.

Conservation decision tools based on cost-effectiveness analysis are used to assess threat management strategies for improving species persistence. These approaches rank alternative strategies by their benefit to cost ratio but may fail to identify the optimal sets of strategies to implement under limited budgets because they do not account for redundancies. We devised a multiobjective optimization approach in which the complementarity principle is applied to identify the sets of threat management strategies that protect the most species for any budget. We used our approach to prioritize threat management strategies for 53 species of conservation concern in the Pilbara, Australia. We followed a structured elicitation approach to collect information on the benefits and costs of implementing 17 different conservation strategies during a 3-day workshop with 49 stakeholders and experts in the biodiversity, conservation, and management of the Pilbara. We compared the performance of our complementarity priority threat management approach with a current cost-effectiveness ranking approach. A complementary set of 3 strategies: domestic herbivore management, fire management and research, and sanctuaries provided all species with >50% chance of persistence for $4.7 million/year over 20 years. Achieving the same result cost almost twice as much ($9.71 million/year) when strategies were selected by their cost-effectiveness ranks alone. Our results show that complementarity of management benefits has the potential to double the impact of priority threat management approaches.

International audience; The management of introduced species that are both invasive and commercially valuable is contentious. While such species provide substantial economic benefits to some, they pose considerable costs to others due to negative impacts on ecosystems. We propose a decision framework to help balance conflicting objectives and support the management of commercially valuable invasive species. We illustrate our framework using the buffel grass (Pennisetum ciliare syn. Cenchrus ciliaris L.) invasion in Australia. In rangelands worldwide, buffet grass is amongst a suite of commercially valuable invasive species, highly valued by some graziers as a pasture species but widely unpopular among those concerned with the threat it poses for native biodiversity. The framework comprises four components. First we develop a state and transition model to represent the invasion dynamics of buffel grass and the effect of management actions. Second we construct utility functions to represent the relative values of buffel grass cover in terms of production and biodiversity utilities (indicative of grazier's and conservationist's relative 'happiness' regarding buffel grass cover). We draw on expert and empirical data for the construction of the model and utility functions. Third, we present management strategies that minimize losses in production and biodiversity utilities in the absence of budget constraints. We use stochastic dynamic programming and multi-criteria decision analysis which explicitly account for trade-offs between production and biodiversity conservation concerns. Finally, we conduct a sensitivity analysis to assess the impact of assumptions of the model and utility functions. Management complexity arises from biodiversity-production trade-off situations in which the desirable state of buffel grass cover and effective actions to achieve this state depend on the utility functions. Management solutions were particularly sensitive to the shape of the utility functions for biodiversity. Solutions were less sensitive to uncertainty surrounding the effectiveness of management actions. We found in most biodiversity-production tradeoff situations a compromise solution led to management for an intermediate level (>0-50%) of buffel grass cover. However, when maximal values for both biodiversity and production are high, it may be more practical to manage for one value rather than find a compromise solution where concessions from both sides are high. Our decision framework does not attempt to optimize economic outcomes but provides a guide for formulating trade-offs between opposing views, something that is relevant to the management of any natural resource management problem where there are conflicting objectives.

Regrowing forests on cleared land is a key strategy to achieve both biodiversity conservation and climate change mitigation globally. Maximizing these co-benefits, however, remains theoretically and technically challenging because of the complex relationship between carbon sequestration and biodiversity in forests, the strong influence of climate variability and landscape position on forest development, the large number of restoration strategies possible, and long time-frames needed to declare success. Through the synthesis of three decades of knowledge on forest dynamics and plant functional traits combined with decision science, we demonstrate that we cannot always maximize carbon sequestration by simply increasing the functional trait diversity of trees planted. The relationships between plant functional diversity, carbon sequestration rates above-ground and in the soil are dependent on climate and landscape positions. We show how to manage ‘identities’ and ‘complementarities’ between plant functional traits in order to achieve systematically maximal co-benefits in various climate and landscape contexts. We provide examples of optimal planting and thinning rules that satisfy this ecological strategy and guide the restoration of forests that are rich in both carbon and plant functional diversity. Our framework provides the first mechanistic approach for generating decision-making rules that can be used to manage forests for multiple objectives, and supports joined carbon credit and biodiversity conservation initiatives, such as Reducing Emissions from Deforestation and forest Degradation REDD+. The decision framework can also be linked to species distribution models and socio-economic models in order to find restoration solutions that maximize simultaneously biodiversity, carbon stocks and other ecosystem services across landscapes. Our study provides the foundation for developing and testing cost-effective and adaptable forest management rules to achieve biodiversity, carbon sequestration and other socio-economic co-benefits under global change.