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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.

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.

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.

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.

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 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.

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?

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.

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.

To choose among conservation actions that may benefit many species, managers need to monitor the consequences of those actions. Decisions about which species to monitor from a suite of different species being managed are hindered by natural variability in populations and uncertainty in several factors: the ability of the monitoring to detect a change, the likelihood of the management action being successful for a species, and how representative species are of one another. However, the literature provides little guidance about how to account for these uncertainties when deciding which species to monitor to determine whether the management actions are delivering outcomes. We devised an approach that applies decision science and selects the best complementary suite of species to monitor to meet specific conservation objectives. We created an index for indicator selection that accounts for the likelihood of successfully detecting a real trend due to a management action and whether that signal provides information about other species. We illustrated the benefit of our approach by analyzing a monitoring program for invasive predator management aimed at recovering 14 native Australian mammals of conservation concern. Our method selected the species that provided more monitoring power at lower cost relative to the current strategy and traditional approaches that consider only a subset of the important considerations. Our benefit function accounted for natural variability in species growth rates, uncertainty in the responses of species to the prescribed action, and how well species represent others. Monitoring programs that ignore uncertainty, likelihood of detecting change, and complementarity between species will be more costly and less efficient and may waste funding that could otherwise be used for management.

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.

When managing endangered species the consequences of making a poor decision can be extinction. To make a good decision, we must account for the stochastic dynamic of the population over time. To this end stochastic dynamic programming (SDP) has become the most widely used tool to calculate the optimal policy to manage a population over time and under uncertainty. However, as a result of its prohibitive computational complexity, SDP has been limited to solving small dimension problems, which results in SDP models that are either oversimplified or approximated using greedy heuristics that only consider the immediate rewards of an action. We present a heuristic sampling (HS) method that approximates the optimal policy for any starting state. The method is attractive for problems with large state spaces as the running time is independent of the size of the problem state space and improves with time. We demonstrate that the HS method out-performs a commonly used greedy heuristic and can quickly solve a problem with 33million states. This is roughly 3 orders of magnitude larger than the largest problems that can currently be solved with SDP methods. 5.We found that HS out-performs greedy heuristics and can give near-optimal policies in shorter timeframes than SDP. HS can solve problems with state spaces that are too large to optimize with SDP. Where the state space size precludes SDP, we argue that HS is the best technique. © 2010 The Authors. Methods in Ecology and Evolution

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.

International audience; Stochastic dynamic programming (SDP) or Markov decision processes (MDP) are increasingly being used in ecology to find the best decisions over time and under uncertainty so that the chance of achieving an objective is maximised. To date, few programs are available to solve SDP/MDP. We present MDPtoolbox, a multi-platform set of functions to solve Markov decision problems (MATLAB, GNU Octave, Scilab and R). MDPtoolbox provides state-of-the-art and ready to use algorithms to solve a wide range of MDPs. MDPtoolbox is easy to use, freely available and has been continuously improved since 2004. We illustrate how to use MDPtoolbox on a dynamic reserve design problem.

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.

Failure to account for interactions between endangered species may lead to unexpected population dynamics, inefficient management strategies, waste of scarce resources, and, at worst, increased extinction risk. The importance of species interactions is undisputed, yet recovery targets generally do not account for such interactions. This shortcoming is a consequence of species-centered legislation, but also of uncertainty surrounding the dynamics of species interactions and the complexity of modeling such interactions. The northern sea otter (Enhydra lutris kenyoni) and one of its preferred prey, northern abalone (Haliotis kamtschatkana), are endangered species for which recovery strategies have been developed without consideration of their strong predator-prey interactions. Using simulation-based optimization procedures from artificial intelligence, namely reinforcement learning and stochastic dynamic programming, we combined sea otter and northern abalone population models with functional-response models and examined how different management actions affect population dynamics and the likelihood of achieving recovery targets for each species through time. Recovery targets for these interacting species were difficult to achieve simultaneously in the absence of management. Although sea otters were predicted to recover, achieving abalone recovery targets failed even when threats to abalone such as predation and poaching were reduced. A management strategy entailing a 50% reduction in the poaching of northern abalone was a minimum requirement to reach short-term recovery goals for northern abalone when sea otters were present. Removing sea otters had a marginally positive effect on the abalone population but only when we assumed a functional response with strong predation pressure. Our optimization method could be applied more generally to any interacting threatened or invasive species for which there are multiple conservation objectives.