Scenarios for valuing sample information in natural resources

Qualitative value of information provides a transparent and repeatable method for identifying critical uncertainty

Conservation decisions are often made in the face of uncertainty because the urgency to act can preclude delaying management while uncertainty is resolved. In this context, adaptive management is attractive, allowing simultaneous management and learning. An adaptive program design requires the identification of critical uncertainties that impede the choice of management action. Quantitative evaluation of critical uncertainty, using the expected value of information, may require more resources than are available in the early stages of conservation planning. Here, we demonstrate the use of a qualitative index to the value of information (QVoI) to prioritize which sources of uncertainty to reduce regarding the use of prescribed fire to benefit Eastern Black Rails (Laterallus jamaicensis jamaicensis), Yellow Rails (Coterminous noveboracensis), and Mottled Ducks (Anas fulvigula; hereafter, focal species) in high marshes of the U.S. Gulf of Mexico. Prescribed fire has been used as a management tool in Gulf of Mexico high marshes throughout the last 30+ years; however, effects of periodic burning on the focal species and the optimal conditions for burning marshes to improve habitat remain unknown. We followed a structured decision-making framework to develop conceptual models, which we then used to identify sources of uncertainty and articulate alternative hypotheses about prescribed fire in high marshes. We used QVoI to evaluate the sources of uncertainty based on their Magnitude, Relevance for decision-making, and Reducibility. We found that hypotheses related to the optimal fire return interval and season were the highest priorities for study, whereas hypotheses related to predation rates and interactions among management techniques were lowest. These results suggest that learning about the optimal fire frequency and season to benefit the focal species might produce the greatest management benefit. In this case study, we demonstrate that QVoI can help managers decide where to apply limited resources to learn which specific actions will result in a higher likelihood of achieving the desired management objectives. Further, we summarize the strengths and limitations of QVoI and outline recommendations for its future use for prioritizing research to reduce uncertainty about system dynamics and the effects of management actions.

Four conservation challenges and a synthesis

Conservation and management of biological systems involves decision-making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue-specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences.

Optimizing conservation in species-specific agricultural landscapes

Recovery of grassland birds in agricultural landscapes is a global imperative. Agricultural landscapes are complex, and the value of resource patches may vary substantially among species. The spatial extent at which landscape features affect populations (i.e., scale of effect) may also differ among species. There is a need for regional-scale conservation planning that considers landscape-scale and species-specific responses of grassland birds to environmental change. We developed a spatially explicit approach to optimizing grassland conservation in the context of species-specific landscapes and prioritization of species recovery and applied it to a conservation program in Kentucky (USA). We used a hierarchical distance-sampling model with an embedded scale of effect predictor to estimate the relationship between landscape structure and abundance of eastern meadowlarks (Sturnella magna), field sparrows (Spizella pusilla), and northern bobwhites (Colinus virginianus). We used a novel spatially explicit optimization procedure rooted in multi-attribute utility theory to design alternative conservation strategies (e.g., prioritize only northern bobwhite recovery or assign equal weight to each species' recovery). Eastern meadowlarks and field sparrows were more likely to respond to landscape-scale resource patch adjacencies than landscape-scale patch densities. Northern bobwhite responded to both landscape-scale resource patch adjacencies and densities and responded strongly to increased grassland density. Effects of landscape features on local abundance decreased as distance increased and had negligible influence at 0.8 km for eastern meadowlarks (0.7-1.2 km 95% Bayesian credibility intervals [BCI]), 2.5 km for field sparrows (1.5-5.8 km 95% BCI), and 8.4 km for bobwhite (6.4-26 km 95% BCI). Northern bobwhites were predicted to benefit greatly from future grassland conservation regardless of conservation priorities, but eastern meadowlark and field sparrow were not. Our results suggest similar species can respond differently to broad-scale conservation practices because of species-specific, distance-dependent relationships with landscape structure. Our framework is quantitative, conceptually simple, customizable, and predictive and can be used to optimize conservation in heterogeneous ecosystems while considering landscape-scale processes and explicit prioritization of species recovery.

Recursive Bayesian computation facilitates adaptive optimal design in ecological studies

Optimal design procedures provide a framework to leverage the learning generated by ecological models to flexibly and efficiently deploy future monitoring efforts. At the same time, Bayesian hierarchical models have become widespread in ecology and offer a rich set of tools for ecological learning and inference. However, coupling these methods with an optimal design framework can become computationally intractable. Recursive Bayesian computation offers a way to substantially reduce this computational burden, making optimal design accessible for modern Bayesian ecological models. We demonstrate the application of so-called prior-proposal recursive Bayes to optimal design using a simulated data binary regression and the real-world example of monitoring and modeling sea otters in Glacier Bay, Alaska. These examples highlight the computational gains offered by recursive Bayesian methods and the tighter fusion of monitoring and science that those computational gains enable.