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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.

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.

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.

Uniting diverse stakeholders through communication, education or building a collaborative 'common vision' for biodiversity management is a recommended approach for enabling effective conservation in regions with multiple uses. However, socially focused strategies such as building a collaborative vision can require sharing scarce resources (time and financial resources) with the on-ground management actions needed to achieve conservation outcomes. Here we adapt current prioritisation tools to predict the likely return on the financial investment of building a stakeholder-led vision along with a portfolio of on-ground management strategies. Our approach brings together and analyses expert knowledge to estimate the cost-effectiveness of a common vision strategy and on-ground management strategies, before any investments in these strategies are made. We test our approach in an intensively-used Australian biodiversity hotspot with 179 threatened or at-risk species. Experts predicted that an effective stakeholder vision for the region would have a relatively low cost and would significantly increase the feasibility of on-ground management strategies. As a result, our analysis indicates that a common vision is likely to be a cost-effective investment, increasing the expected persistence of threatened species in the region by 9 to 52%, depending upon the strategies implemented. Our approach can provide the maximum budget that is worth investing in building a common vision or another socially focused strategy for building support for on-ground conservation actions. The approach can assist with decisions about whether and how to allocate scarce resources amongst social and ecological actions for biodiversity conservation in other regions worldwide.

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.

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.

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.

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.

1. Spatial management of invasive species is more likely to be successful when multiple locations are treated simultaneously. However, selecting the best locations to act is difficult due to the many options available at any time.2. We design a near-optimal policy for applying multiple actions simultaneously for faster invasive species control within a network. Our method uses a recent optimisation tool, the graph-based Markov decision process (GMDP). Since the policy can be difficult to interpret, we extracted a simpler policy using classification trees. We applied our approach to the eradication of invasive mosquitofish Gambusia holbrooki from the habitat of the red-finned blueeye Scaturiginichthys vermeilipinnis, a critically endangered fish with a global population that is restricted to seven artesian springs in Queensland, Australia.3. The policy returned by the GMDP was to manage springs occupied by mosquitofish and their connected neighbours, unless the neighbours were occupied by red-finned blue-eyes.4. Simultaneous management resulted in rapid declines in simulated mosquitofish occupancy even if eradication effectiveness was low; however, the cost of simultaneous eradication was high and sustained eradication effort was necessary to maintain low mosquitofish occupancy.5. Synthesis and applications. Our paper finds a near-optimal, multi-action control policy to remove an invasive species from a multi-species spatial network. We introduce the graphbased Markov decision process and apply it to a real case study – eradication of invasive mosquitofish from the habitat of the red-finned blue-eye. We find that the graph-based Markov decision process can generate policies for networks with extremely large state spaces; however, it works best when nodes have fewer than five neighbours. We conclude that simultaneous eradications are effective for rapid control of invasive species; however, managers should consider the cost and time required for an effective eradication program.

Mitigating the impacts of global anthropogenic change on species is conservation's greatest challenge. Forecasting the effects of actions to mitigate threats is hampered by incomplete information on species' responses. We develop an approach to predict community restructuring under threat management, which combines models of responses to threats with network analyses of species co-occurrence. We discover that contributions by species to network co-occurrence predict their recovery under reduction of multiple threats. Highly connected species are likely to benefit more from threat management than poorly connected species. Importantly, we show that information from a few species on co-occurrence and expected responses to alternative threat management actions can be used to train a response model for an entire community. We use a unique management dataset for a threatened bird community to validate our predictions and, in doing so, demonstrate positive feedbacks in occurrence and co-occurrence resulting from shared threat management responses during ecosystem recovery.

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.

Changed fire regimes have led to declines of fire-regime-adapted species and loss of biodiversity globally. Fire affects population processes of growth, reproduction, and dispersal in different ways, but there is little guidance about the best fire regime(s) to maintain species population processes in fire-prone ecosystems. We use a process-based approach to determine the best range of fire intervals for keystone plant species in a highly modified Mediterranean ecosystem in southwestern Australia where current fire regimes vary. In highly fragmented areas, fires are few due to limited ignitions and active suppression of wildfire on private land, while in highly connected protected areas fires are frequent and extensive. Using matrix population models, we predict population growth of seven Banksia species under different environmental conditions and patch connectivity, and evaluate the sensitivity of species survival to different fire management strategies and burning intervals. We discover that contrasting, complementary patterns of species life-histories with time since fire result in no single best fire regime. All strategies result in the local patch extinction of at least one species. A small number of burning strategies secure complementary species sets depending on connectivity and post-fire growing conditions. A strategy of no fire always leads to fewer species persisting than prescribed fire or random wildfire, while too-frequent or too-rare burning regimes lead to the possible local extinction of all species. In low landscape connectivity, we find a smaller range of suitable fire intervals, and strategies of prescribed or random burning result in a lower number of species with positive growth rates after 100 years on average compared with burning high connectivity patches. Prescribed fire may reduce or increase extinction risk when applied in combination with wildfire depending on patch connectivity. Poor growing conditions result in a significantly reduced number of species exhibiting positive growth rates after 100 years of management. By exploring the consequences of managing fire, we are able to identify which species are likely to disappear under a given fire regime. Identifying the appropriate complementarity of fire intervals, and their species-specific as well as community-level consequences, is crucial to reduce local extinctions of species in fragmented fire-prone landscapes.

International audience; 1. Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most relevant tool for determining an optimal sequence of decisions over time. 2. Despite an increasing number of applications in ecology, SDP still suffers from a lack of widespread understanding. The required mathematical and programming knowledge as well as the absence of introductory material provide plausible explanations for this. 3. Here, we fill this gap by explaining the main concepts of SDP and providing useful guidelines to implement this technique, including R code. 4. We illustrate each step of SDP required to derive an optimal strategy using a wildlife management problem of the French wolf population. 5. Stochastic dynamic programming is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.

Background. Migratory animals comprise a significant portion of biodiversity worldwide with annual investment for their conservation exceeding several billion dollars. Designing effective conservation plans presents enormous challenges. Migratory species are influenced by multiple events across land and sea–regions that are often separated by thousands of kilometres and span international borders. To date, conservation strategies for migratory species fail to take into account how migratory animals are spatially connected between different periods of the annual cycle (i.e. migratory connectivity) bringing into question the utility and efficiency of current conservation efforts. Methodology/Principal Findings. Here, we report the first framework for determining an optimal conservation strategy for a migratory species. Employing a decision theoretic approach using dynamic optimization, we address the problem of how to allocate resources for habitat conservation for a Neotropical-Nearctic migratory bird, the American redstart Setophaga ruticilla, whose winter habitat is under threat. Our first conservation strategy used the acquisition of winter habitat based on land cost, relative bird density, and the rate of habitat loss to maximize the abundance of birds on the wintering grounds. Our second strategy maximized bird abundance across the entire range of the species by adding the constraint of maintaining a minimum percentage of birds within each breeding region in North America using information on migratory connectivity as estimated from stable-hydrogen isotopes in feathers. We show that failure to take into account migratory connectivity may doom some regional populations to extinction, whereas including information on migratory connectivity results in the protection of the species across its entire range. Conclusions/ Significance. We demonstrate that conservation strategies for migratory animals depend critically upon two factors: knowledge of migratory connectivity and the correct statement of the conservation problem. Our framework can be used to identify efficient conservation strategies for migratory taxa worldwide, including insects, birds, mammals, and marine organisms.