Geology topics

Ellie Brown

Publications and source records attributed to Ellie Brown.

10 recordsLinked to original sources

USGS2025

Economic costs of invasive carps in the United States: Case study and management implications

Biological invasions can have far-reaching impacts and incur enormous monetary costs. Economic considerations play an important role in management decision-making. We used the invasion of U.S. waterways by silver ( Hypophthalmichthys molitrix ) and bighead ( H. nobilis ) carp as a case study of the costs of aquatic invasive species. Although these carps are well-known invaders, published reports on their economic costs are lacking. Our study included market values for commercial fisheries, non-market values for recreational fisheries, and management costs. Our results showed that by 2020, U.S. federal and state agencies had spent nearly $592 million in cumulative management costs. A difference-in-difference model testing for the effect of invasive carp on commercial harvest in invaded versus uninvaded reaches of the Mississippi and Illinois Rivers showed no statistical significance. A benefit transfer analysis of invasion effects on total economic value of recreational fishing, an important ecosystem service, in a heavily invaded section of the Illinois River estimated a total loss of more than $10 million over 10 years. While there are other known impacts on ecosystem services, including alteration of aquatic food webs, plankton communities, and native fish communities, these could not be quantified in economic terms in our analysis.

conterminous United States↗

USGS2024

Societal benefits of cyanobacteria harmful algal bloom management in Lake Okeechobee in Florida—Potential damages avoided during the 2018 event under U.S. Army Corps of Engineers Harmful Algal Bloom Interception, Treatment, and Transformation System scenarios

Freshwater harmful algal blooms (HABs) formed by blue-green algae, or cyanobacteria, have emerged as a global environmental problem. Their negative impacts on aquatic ecosystems can affect the benefits nature provides to human society by reducing water quality; inhibiting aquatic recreation; killing fish, wildlife, and pets; and posing a risk to human health. To manage harmful algal blooms, the Engineer Research and Development Center of the U.S. Army Corps of Engineers is developing an advanced technology called the Harmful Algal Bloom Interception, Treatment, and Transformation System (HABITATS), which has been tested in pilot demonstrations upstream of spillways at HAB-affected waterbodies in Florida. The U.S. Geological Survey and cooperators from the U.S. Department of the Interior Office of Policy Analysis investigated the societal benefits of HABITATS technology by using data from an actual 2018 harmful algal bloom in Lake Okeechobee to characterize the observed societal impacts and then comparing observed effects to hypothetical scenarios of HABITATS deployment. This study estimated an economic value of $5.5 million in foregone recreation as a result of closed boating ramp facilities and other restrictions on aquatic recreation such as fishing and swimming during the 2018 cyanobacteria harmful algal bloom outbreak. The change in housing sales prices that could have resulted from murky water or bad odor during that outbreak was estimated as $2.3 million. The team also investigated drinking water contamination and human illness but did not find significant societal impacts in this case. If HABITATS had been deployed, the avoided losses less the cost of management could have provided net societal benefits that ranged between negative $2.1 million and positive $0.8 million, depending on the vertical distribution of algae in the water column and the HABITATS version used. The study’s estimated societal benefit is undoubtedly a lower bound estimate because current scientific knowledge is inadequate to characterize, or monetize, all the impacts.

Florida↗

USGS2024

Managing ecosystems with resist-accept-direct (RAD)

In recent years considerable interest has been generated in a new approach known as resist – accept – direct , or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed system toward novel ecological conditions. In particular, the potential for integrating RAD and adaptive management has been broadly considered, though absent a decision-making framework needed for implementation. We propose a hierarchical decision scheme for RAD that accounts for strategy selection among the three RAD options, as well as adaptive decision making within each option. We use stochastic models and uncertainties about ecosystem processes to account for the dynamics of climate-transformed ecosystems, and show how these features can be used to inform RAD strategies. Operationally, the approach involves decisions at two levels: one level involves choosing a policy for each strategy, and the second level involves deciding which strategy has the greatest policy value. The structure described here extends recent work in climate change adaptation, by including Markovian decisions under climate change, strategy-specific policies, and value functions for assessing and selecting RAD strategies. We provide a hierarchical accounting of decisions and responses, and develop rules for the timing of those decisions. Combining RAD and adaptive management can help to organize thinking about ecological conservation under climate change, and focus attention on mechanisms for making decisions. We believe the structure presented here can facilitate conservation efforts under the non-stationary climate conditions we are sure to face for the foreseeable future.

Methods in Ecology and Evolution↗

USGS2023

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.

Ecology and Evolution↗

USGS2022

Partial observability and management of ecological systems

The actual state of ecological systems is rarely known with certainty, but management actions must often be taken regardless of imperfect measurement (partial observability). Because of the difficulties in accounting for partial observability, it is usually treated in an ad hoc fashion, or simply ignored altogether. Yet incorporating partial observability into decision processes lends a realism that has the potential to improve ecological outcomes significantly. We review frameworks for dealing with partial observability, focusing specifically on dynamic ecological systems with Markovian transitions, i.e., transitions among system states that are influenced by the current system state and management action over time. Fully observable states are represented in an observable Markov decision process (MDP), whereas obscure or hidden states are represented in a partially observable process (POMDP). POMDPs can be seen as a natural extension of observable MDPs. Management under partial observability generalizes the situation for complete observability, by recognizing uncertainty about the system's state and incorporating sequential observations associated with, but not the same as, the states themselves. Decisions that otherwise would depend on the actual state must be based instead on state probability distributions (“belief states”). Partial observability requires adaptation of the entire decision process, including the use of belief states and Bayesian updates, valuation that includes expectations over observations, and optimal strategy that identifies actions for belief states over a continuous belief space. We compare MDPs and POMDPs and highlight POMDP applications to some common ecological problems. We clarify the structure and operations, approaches for finding solutions, and analytic challenges of POMDPs for practicing ecologists. Both observable and partially observable MDPs can use an inductive approach to identify optimal strategies and values, with a considerable increase in mathematical complexity with POMDPs. Better understanding of POMDPs can help decision makers manage imperfectly measured ecological systems more effectively.

Ecology and Evolution↗

USGS2020

Scenarios for valuing sample information in natural resources

Uncertainty is ubiquitous in natural resource systems, science and management. Sample data are obtained in order to reduce uncertainty, thereby increasing knowledge and improving resource management, but sampling always comes at a cost of some sort. Is that cost worthwhile? Analysis of the value of sample information ( VSI ) addresses this question. In this paper we develop the valuation of sample information in terms of five elements: (a) a system whose attributes are the focus of analysis; (b) a range of management actions that affect the system's status; (c) uncertainty about system status or structure, as characterized by initial (prior) probabilities of possible system states or structures; (d) an experiment or other information source that produces new data points and updated (posterior) probabilities; and (e) a value measure that is a function of the management action taken, conditional on either the system state or structure. We describe five scenarios for analysing the VSI under uncertainty about system structure and state. Scenarios 1–3 comprise analyses of conditional, expected and optimal expected values of sample information. They focus primarily on choice of management adaptations with new information. Scenarios 4 and 5 involve pre-selected management actions, and are useful for comparing designs of data collection rather than for choosing a management action. These last scenarios expand the framework for VSI to include actions that have been selected independently of the updating of uncertainty. We discuss other extensions of VSI analysis, which include spatial applications, hybrid scenarios, applications involving dynamic systems, and a focus on costs rather than net benefits. Value of sample information analysis holds promise in emerging areas of ecology such as ecological forecasting and the use of remote sensing in conservation, where potential new data from models and satellites can be evaluated in advance, thereby allowing more efficient prioritization of scientific efforts. More generally, VSI can contribute to better ecological understanding and more effective management in a wide range of ecological situations.

Methods in Ecology and Evolution↗

USGS2019

Sampling and analysis frameworks for inference in ecology

1. Reliable statistical inference is central to ecological research, much of which seeks to estimate population attributes and their interactions. The issue of sampling design and its relationship to inference has become increasingly important due to rapid proliferation of modeling methodology (line transect modeling, capture-recapture, estimation of occurrence, model selection procedures, hierarchical modeling) and new sampling approaches (adaptive sampling, other specialized designs). It is important for ecologists using these advanced methods to be aware of how the linkages between sample selection and data analysis can potentially affect inference. 2. We examine design-based and model-based inference frameworks for ecological data collected randomly, purposively, or opportunistically. We elucidate differences in the probability structures for data arising from these frameworks, clarify the assumptions that underlie them, and demonstrate their differences. 3. Design-based inference builds on a probability structure inherited from randomized data collection, whereas model-based inference relies on an assumed stochastic model of the data. By itself, a design-based approach is of limited value for inferences about causal hypotheses. In contrast, model-based inference is dependent on a conditionality principle that can seldom be shown to be met for an ecological system. We describe the conditions under which one can safely ignore sampling design in model-based analysis, along with inferential implications if these conditions are not met. The special case of opportunistic sampling is discussed. 4. We present a combined framework that takes advantage of both approaches to inference, and provides a robust methodology that can deal with the modeling of sampling problems such as nondetection and misclassification, as well as the exploration of causal hypotheses. The combined framework can be useful for identifying optimal sampling strategies. 5. Each approach to inference has its strengths and weaknesses, and practitioners should be aware of these in order to tailor designs and analyses to specific questions. We use the approaches and their underlying rationales to provide guidelines for choosing designs and estimators for reliable inference.

Methods in Ecology and Evolution↗

USGS2018

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference(designbased,modelbased),andscientificparadigms(confirmatory,exploratory).Wedistinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking samplingdesign,andexaminedoverarchingobjectives,design,analysis,volunteertraining,andperformance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

Conservation Biology↗

USGS2018

Double loop learning in adaptive management: the need, the challenge, and the opportunity

Adaptive management addresses uncertainty about the processes influencing resource dynamics, as well as the elements of decision making itself. The use of management to reduce both kinds of uncertainty is known as double-loop learning. Though much work has been done on the theory and procedures to address structural uncertainty, there has been less progress in developing an explicit approach for institutional learning about decision elements. Our objective is to describe evidence-based learning about the decision elements, as a complement to the formal “learning by doing” framework for reducing structural uncertainties. Adaptive management is described as a multi-phase approach to management and learning, with a set-up phase of identifying stakeholders, objectives, and other decision elements; an iterative phase that uses these elements in an ongoing cycle of technical learning about system structure and management impacts; and an institutional learning phase involving the periodic reconsideration of the decision elements. We describe a framework for institutional learning that is complementary to that of technical learning, including uncertainty metrics, propagation of change, and mechanisms and consequences of change over time. Operational issues include ways to recognize when the decision elements should be revisited, which elements should be adjusted, and how alternatives can be identified and incorporated based on experience and management performance. We discuss the application of this framework in decision making for renewable natural resources. As important as it is to learn about the processes driving resource dynamics, learning about the elements of the decision architecture is equally, if not more, important.

Environmental Management↗

USGS2017

A proposal for amending administrative law to facilitate adaptive management

In this article we examine how federal agencies use adaptive management. In order for federal agencies to implement adaptive management more successfully, administrative law must adapt to adaptive management, and we propose changes in administrative law that will help to steer the current process out of a dead end. Adaptive management is a form of structured decision making that is widely used in natural resources management. It involves specific steps integrated in an iterative process for adjusting management actions as new information becomes available. Theoretical requirements for adaptive management notwithstanding, federal agency decision making is subject to the requirements of the federal Administrative Procedure Act, and state agencies are subject to the states' parallel statutes. We argue that conventional administrative law has unnecessarily shackled effective use of adaptive management. We show that through a specialized 'adaptive management track' of administrative procedures, the core values of administrative law—especially public participation, judicial review, and finality— can be implemented in ways that allow for more effective adaptive management. We present and explain draft model legislation (the Model Adaptive Management Procedure Act) that would create such a track for the specific types of agency decision making that could benefit from adaptive management.

Environmental Research Letters↗