Implications of Spatially Variable Costs and Habitat Conversion Risk in Landscape-Scale Conservation Planning

‘‘Strategic habitat conservation’’ refers to a process used by the U.S. Fish and Wildlife Service to develop cost-efficient strategies for conserving wildlife populations and their habitats. Strategic habitat conservation focuses on resolving uncertainties surrounding habitat conservation to meet specific wildlife population objectives (i.e., targets) and developing tools to guide where conservation actions should be focused on the landscape. Although there are examples of using optimization models to highlight where conservation should be delivered, such methods often do not explicitly account for spatial variation in the costs of conservation actions. Furthermore, many planning approaches assume that habitat protection is a preferred option, but they do not assess its value relative to other actions, such as restoration. We developed a case study to assess the implications of accounting for and ignoring spatial variation in conservation costs in optimizing conservation targets. We included assumptions about habitat loss to determine the extent to which protection or restoration would be necessary to meet an established population target. Our case study focused on optimal placement of grassland protection or restoration actions to influence bobolink Dolichonyx oryzivorus populations in the tallgrass prairie ecoregion of the north central United States. Our results show that not accounting for spatially variable costs doubled or tripled the cost of meeting the population target. Furthermore, our results suggest that one should not assume that protecting existing habitat is always a preferred option. Rather, our results show that the balance between protection and restoration can be influenced by a combination of desired targets, assumptions about habitat loss, and the relative cost of the two actions. Our analysis also points out how difficult it may be to reach targets, given the expense to meet them. We suggest that a full accounting of expected costs and benefits will help to guide development of viable management actions and meaningful conservation plans

Understanding and Finding Solutions to the Problem of Sedimentation in the National Wildlife Refuge System

The National Wildlife Refuge System (Refuge System) is a collection of public lands maintained by the U.S. Fish and Wildlife Service for migratory birds and other wildlife. Wetlands on individual National Wildlife Refuges (Refuges) may be at risk of increased sedimentation because of land use and water management practices. Increased sedimentation can reduce wetland habitat quality by altering hydrologic function, degrading water quality, and inhibiting growth of vegetation and invertebrates. On Refuges negatively affected by increased sedimentation, managers have to address complex questions about how to best remediate and mitigate the negative effects. The best way to account for these complexities is often not clear. On other Refuges, managers may not know whether sedimentation is a problem. Decision makers in the Refuge System may need to allocate resources to studying which Refuges could be at risk. Such analyses would help them understand where to direct support for managing increased sedimentation. In this paper, we summarize a case study demonstrating the use of decision-analytic tools in the development of a sedimentation management plan for Agassiz National Wildlife Refuge, Minnesota. Using what we learned from that process, we surveyed other Refuges in U.S. Fish and Wildlife Service Region 3 (an area encompassing the states of Illinois, Indiana, Iowa, Ohio, Michigan, Minnesota, Missouri, and Wisconsin) and Region 6 (an area encompassing the states of Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming) about whether they experience sediment-related impacts to management. Our results show that cases of management being negatively affected by increased sedimentation are not isolated. We suggest that the Refuge System conduct a comprehensive and systematic assessment of increased sedimentation among Refuges to understand the importance of sedimentation in context with other management problems that Refuges face. The results of such an assessment could guide how the Refuge System allocates resources to studying and managing widespread stressors

Optimizing historic preservation under climate change: Decision support for cultural resource adaptation planning in national parks

Climate change poses great challenges for cultural resource management, particularly in coastal areas. Cultural resources, such as historic buildings, in coastal areas are vulnerable to climate impacts including inundation, deterioration, and destruction from sea-level rise and storm-related flooding and erosion. However, research that assesses the trade-offs between actions for protecting vulnerable and valuable cultural resources under budgetary constraints is limited. This study focused on developing a decision support model for managing historic buildings at Cape Lookout National Seashore. We designed the Optimal Preservation Decision Support (OptiPres) model to: (a) identify optimal, annual adaptation actions for historic buildings across a 30-year planning horizon, (b) quantify trade-offs between different actions and the timing of adaptation actions under constrained budgets, and (c) estimate the effectiveness of budget allocations on the resource value of historic buildings. Our analysis of the model suggests that: (1) funding allocation thresholds may exist for national parks to maintain the historical significance and use potential of historic buildings under climate change, (2) the quantitative assessment of trade-offs among alternative adaptation actions provides generalizable guidance for decision makers about the dynamics of their managed system, and (3) the OptiPres model can identify cost-efficient approaches to allocate funding to maintain the historical value of buildings vulnerable to the effects of climate change. Therefore, the OptiPres model, while not designed as a prescriptive decision tool, allows managers to understand the consequences of proposed adaptation actions. The OptiPres model can guide park managers to make costeffective climate adaptation decisions for historic buildings more transparently and robustly

On the Role of Budget Sufficiency, Cost Efficiency, and Uncertainty in Species Management

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an infogap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one’s budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance

Adaptive Management of Bull Trout Populations in the Lemhi Basin

The bull trout Salvelinus confluentus, a stream-living salmonid distributed in drainages of the northwestern United States, is listed as threatened under the Endangered Species Act because of rangewide declines. One proposed recovery action is the reconnection of tributaries in the Lemhi Basin. Past water use policies in this core area disconnected headwater spawning sites from downstream habitat and have led to the loss of migratory life history forms. We developed an adaptive management framework to analyze which types of streams should be prioritized for reconnection under a proposed Habitat Conservation Plan. We developed a Stochastic Dynamic Program that identified optimal policies over time under four different assumptions about the nature of the migratory behavior and the effects of brook trout Salvelinus fontinalis on subpopulations of bull trout. In general, given the current state of the system and the uncertainties about the dynamics, the optimal policy would be to connect streams that are currently occupied by bull trout. We also estimated the value of information as the difference between absolute certainty about which of our four assumptions were correct, and a model averaged optimization assuming no knowledge. Overall there is little to be gained by learning about the dynamics of the system in its current state, although in other parts of the state space reducing uncertainties about the system would be very valuable. We also conducted a sensitivity analysis; the optimal decision at the current state does not change even when parameter values are changed up to 75% of the baseline values. Overall, the exercise demonstrates that it is possible to apply adaptive management principles to threatened and endangered species, but logistical and data availability constraints make detailed analyses difficult

Review of \u3ci\u3eEstimation of Parameters for Animal Populations: A Primer for the Rest of Us\u3c/i\u3e by Larkin A. Powell and George A. Gale

“Me? A modeler? Never!” This is the opening challenge of the book, Estimation of Parameters for Animal Populations: A Primer for the Rest of Us, by Larkin Powell and George Gale. I say “challenge” because I think this book attempts to challenge the misconception that quantitative methods are out of reach for most biologists and wildlife scientists. When many of us attend college or graduate school to study wildlife science there’s a sense that, at some point, there will be math. But it appears that the attitude of many students toward this reality is to simply suffer through the math, quickly forget it, and then move on. That is, until they encounter it again on the job. As a person who started out their professional life in a non-quantitative way, but later learned how to think quantitatively, I assure you that it is possible to learn this stuff. You just need the right resources. For students, this might mean seeking out the right teachers, whereas practicing wildlife professionals may have to rely more on books. The problem is that there aren’t many easily accessible books available on this topic for those who want to learn, but really cannot devote their time to a Ph.D. in statistics. Powell and Gale’s book sets out to remedy this. The first part of the book is dedicated to understanding many of the basic quantitative estimation concepts that a wildlife professional might encounter. These include concepts such as maximum likelihood and variance estimation. While these may seem like esoteric topics to those who do not think of themselves as modelers, many of these concepts underlay the process of estimating fundamental population parameters, like abundance or survival

Pseudorandom walks in ecological analysis: Capturing uncertainty for better estimation and decision making

As scientists, we have to familiarize ourselves with ways of measuring both what we don\u27t know and our confidence in what we do know. In a broad sense, we refer to these measures as uncertainty. The presumption is the more knowledge we have the less uncertain we are and the more confident we become in what we know. In this document I have tried to show a few ways in which uncertainty can manifest itself in the ecology of a species and how that impacts our ability to make decisions about wildlife management. There are two main types of uncertainty that plague ecological analysis: epistemic uncertainty and linguistic uncertainty. Epistemic uncertainty can generally be thought of as uncertainty in what we know. Linguistic uncertainty, on other hand, arises from the imprecision of language. I dealt with epistemic uncertainty in the first three chapters. First, I confronted uncertainty that arises in wildlife surveys due to imperfect detection and from spatial variation in where animals are found. To deal with this, I developed a statistical model that is capable of estimating detection rates and capturing spatial variation in count data. In the first chapter I applied this model to estimating the abundance of endangered bird species in western Nebraska. Next, I addressed the idea of reducing parametric uncertainty about endangered species abundances and distributions by designing better surveys. I analyzed the tradeoff between investing localized effort in a few locations and spreading effort out among more locations. I also considered whether going to the same sites year after year provided better information than choosing new sites where we expect to find individuals. I also dealt with a kind of uncertainty that is not very common to ecologists: Knightian uncertainty. This kind of uncertainty arises in situations where uncertainty is not measurable because we know too little about a system. I used an information gap approach to analyze a set of conservation decisions that must be made despite great uncertainty. Finally, I addressed the notion of linguistic uncertainty in conservation criteria and how this potentially impacts the allocation of money in conservation programs. My hope is that these tools and analyses provide some use to ecologists and wildlife managers

Review of \u3ci\u3eEstimation of Parameters for Animal Populations: A Primer for the Rest of Us\u3c/i\u3e by Larkin A. Powell and George A. Gale

“Me? A modeler? Never!” This is the opening challenge of the book, Estimation of Parameters for Animal Populations: A Primer for the Rest of Us, by Larkin Powell and George Gale. I say “challenge” because I think this book attempts to challenge the misconception that quantitative methods are out of reach for most biologists and wildlife scientists. When many of us attend college or graduate school to study wildlife science there’s a sense that, at some point, there will be math. But it appears that the attitude of many students toward this reality is to simply suffer through the math, quickly forget it, and then move on. That is, until they encounter it again on the job. As a person who started out their professional life in a non-quantitative way, but later learned how to think quantitatively, I assure you that it is possible to learn this stuff. You just need the right resources. For students, this might mean seeking out the right teachers, whereas practicing wildlife professionals may have to rely more on books. The problem is that there aren’t many easily accessible books available on this topic for those who want to learn, but really cannot devote their time to a Ph.D. in statistics. Powell and Gale’s book sets out to remedy this. The first part of the book is dedicated to understanding many of the basic quantitative estimation concepts that a wildlife professional might encounter. These include concepts such as maximum likelihood and variance estimation. While these may seem like esoteric topics to those who do not think of themselves as modelers, many of these concepts underlay the process of estimating fundamental population parameters, like abundance or survival