Allocating conservation resources between areas where persistence of a species is uncertain

Research on the allocation of resources to manage threatened species typically assumes that the state of the system is completely observable; for example whether a species is present or not. The majority of this research has converged on modeling problems as Markov decision processes (MDP), which give an optimal strategy driven by the current state of the system being managed. However, the presence of threatened species in an area can be uncertain. Typically, resource allocation among multiple conservation areas has been based on the biggest expected benefit (return on investment) but fails to incorporate the risk of imperfect detection. We provide the first decision-making framework for confronting the trade-off between information and return on investment, and we illustrate the approach for populations of the Sumatran tiger (Panthera tigris sumatrae) in Kerinci Seblat National Park. The problem is posed as a partially observable Markov decision process (POMDP), which extends MDP to incorporate incomplete detection and allows decisions based on our confidence in particular states. POMDP has previously been used for making optimal management decisions for a single population of a threatened species. We extend this work by investigating two populations, enabling us to explore the importance of variation in expected return on investment between populations on how we should act. We compare the performance of optimal strategies derived assuming complete (MDP) and incomplete (POMDP) observability. We find that uncertainty about the presence of a species affects how we should act. Further, we show that assuming full knowledge of a species presence will deliver poorer strategic outcomes than if uncertainty about a species status is explicitly considered. MDP solutions perform up to 90% worse than the POMDP for highly cryptic species, and they only converge in performance when we are certain of observing the species during management: an unlikely scenario for many threatened species. This study illustrates an approach to allocating limited resources to threatened species where the conservation status of the species in different areas is uncertain. The results highlight the importance of including partial observability in future models of optimal species management when the species of concern is cryptic in nature

Adaptive Management of Migratory Birds Under Sea Level Rise

International audienceThe best practice method for managing ecological systems under uncertainty is adaptive management (AM), an iterative process of reducing uncertainty while simultaneously optimizing a management objective. Existing solution methods used for AM problems assume that the system dynamics are stationary, i.e., described by one of a set of pre-defined models. In reality ecological systems are rarely stationary and evolve over time. Importantly, the effects of climate change on populations are unlikely to be captured by stationary models. Practitioners need efficient algorithms to implement AM on real-world problems. AM can be formulated as a hidden model Markov Decision Process (hmMDP), which allows the state space to be factored and shows promise for the rapid resolution of large problems. We provide an ecological dataset and performance metrics for the AM of a network of shorebird species utilizing the East Asian-Australasian flyway given uncertainty about the rate of sea level rise. The non-stationary system is modelled as a stationary POMDP containing hidden alternative models with known probabilities of transition between them. We challenge the POMDP community to exploit the simplifications allowed by structuring the AM problem as an hmMDP and improve our benchmark solutions

Les POMDP: une solution pour modéliser des problèmes de gestion adaptative en biologie de la conservation

National audienceEn biologie de la conservation, la gestion adaptative est un processus itératif d'amélioration de la gestion par la réduction de l'incertitude à travers une surveillance. La gestion adaptative est l'outil principal pour la conservation d'espèces menacées par les changements planétaires, toutefois les problèmes de gestion adaptative souffrent d'un ensemble pauvre de méthodes de résolution. L'approche courante employée pour résoudre un problème de gestion adaptative est de faire l'hypothèse que l'état du système est connu et que sa dynamique est dans un ensemble de modèles pré-définis. La méthode de résolution utilisée n'est pas satisfaisante parce qu'elle emploie l'algorithme d'itération sur la valeur sur un belief MDP discrétisé qui restreint l'étude à de très petits problèmes. Nous montrons comment dépasser cette limitation en modélisant un problème de gestion adaptative par un type particulier de processus de décision markovien partiellement observable (POMDP) appelé MDP à observabilité mixte (MOMDP). Nous montrons comment simplifier la fonction de valeur, l'opérateur de mise à jour de la fonction de valeur et le calcul de mise à jour de l'état de croyance. Ceci ouvre la voie à des améliorations des algorithmes de résolution des POMDP. Nous illustrons l'utilisation de notre MOMDP "adaptatif" à la gestion d'une population de pinsons diamants de Gould, une espèce d'oiseaux endémique de l'Australie du nord. Notre approche de modélisation simple est une grande avancée pour la résolution de problèmes de gestion adaptative pour la conservation en utilisant des méthodes efficaces pour les POMDP

MOMDPs: a Solution for Modelling Adaptive Management Problems

International audienceIn conservation biology and natural resource management, adaptive management is an iterative process of improving management by reducing uncertainty via monitoring. Adaptive management is the principal tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a restricted Mixed Observability MDP called hidden model MDP (hmMDP). We demonstrate how to simplify the value function, the backup operator and the belief update computation. We show that, although a simplified case of POMDPs, hmMDPs are PSPACE-complete in the finite-horizon case. We illustrate the use of this model to manage a population of the threatened Gouldian finch, a bird species endemic to North- ern Australia. Our simple modelling approach is an important step towards efficient algorithms for solving adaptive management problems

Setting conservation priorities for migratory networks under 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

Which States Matter? An Application of an Intelligent Discretization Method to Solve a Continuous POMDP in Conservation Biology

When managing populations of threatened species, conservation managers seek to make the best conservation decisions to avoid extinction. Making the best decision is difficult because the true population size and the effects of management are uncertain. Managers must allocate limited resources between actively protecting the species and monitoring. Resources spent on monitoring reduce expenditure on management that could be used to directly improve species persistence. However monitoring may prevent sub-optimal management actions being taken as a result of observation error. Partially observable Markov decision processes (POMDPs) can optimize management for populations with partial detectability, but the solution methods can only be applied when there are few discrete states. We use the Continuous U-Tree (CU-Tree) algorithm to discretely represent a continuous state space by using only the states that are necessary to maintain an optimal management policy. We exploit the compact discretization created by CU-Tree to solve a POMDP on the original continuous state space. We apply our method to a population of sea otters and explore the trade-off between allocating resources to management and monitoring. We show that accurately discovering the population size is less important than management for the long term survival of our otter population

Optimal Conservation of Migratory Species

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

Threat management priorities for conserving Antarctic biodiversity

Antarctic terrestrial biodiversity faces multiple threats, from invasive species to climate change. Yet no large-scale assessments of threat management strategies exist. Applying a structured participatory approach, we demonstrate that existing conservation efforts are insufficient in a changing world, estimating that 65% (at best 37%, at worst 97%) of native terrestrial taxa and land-associated seabirds are likely to decline by 2100 under current trajectories. Emperor penguins are identified as the most vulnerable taxon, followed by other seabirds and dry soil nematodes. We find that implementing 10 key threat management strategies in parallel, at an estimated present-day equivalent annual cost of US$23 million, could benefit up to 84% of Antarctic taxa. Climate change is identified as the most pervasive threat to Antarctic biodiversity and influencing global policy to effectively limit climate change is the most beneficial conservation strategy. However, minimising impacts of human activities and improved planning and management of new infrastructure projects are cost-effective and will help to minimise regional threats. Simultaneous global and regional efforts are critical to secure Antarctic biodiversity for future generations