Geology topics

Christopher J. Fonnesbeck

Publications and source records attributed to Christopher J. Fonnesbeck.

8 recordsLinked to original sources

USGS2017

Essential information: Uncertainty and optimal control of Ebola outbreaks

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Proceedings of the National Academy of Sciences of↗

USGS2016

Decision-making for foot-and-mouth disease control: Objectives matter

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Epidemics↗

USGS2015

Status and threats analysis for the Florida manatee ( Trichechus manatus latirostris ), 2012

The endangered West Indian manatee ( Trichechus manatus ), especially the Florida subspecies (T. m. latirostris), has been the focus of conservation efforts and extensive research since its listing under the Endangered Species Act. On the basis of the best information available as of December 2012, the threats facing the Florida manatee were determined to be less severe than previously thought, either because the conservation efforts have been successful, or because our knowledge of the demographic effects of those threats is increased, or both. Using the manatee Core Biological Model, we estimated the probability of the Florida manatee population on either the Atlantic or Gulf coast falling below 500 adults in the next 150 years to be 0.92 percent. The primary threats remain watercraft-related mortality and long-term loss of warm-water habitat. Since 2009, however, there have been a number of unusual events that have not yet been incorporated into this analysis, including several severely cold winters, a severe red-tide die off, and substantial loss of seagrass habitat in Brevard County, Fla. Further, the version of the Core Biological Model used in 2012 makes a number of assumptions that are under investigation. A revision of the Core Biological Model and an update of this quantitative threats analysis are underway as of 2015.

Open-File Report↗

USGS2014

Adaptive management and the value of information: learning via intervention in epidemiology

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

PLoS Biology↗

USGS2011

An adaptive decision framework for the conservation of a threatened plant

Mead's milkweed Asclepias meadii , a long-lived perennial herb of tallgrass prairie and glade communities of the central United States, is a species designated as threatened under the U.S. Endangered Species Act. Challenges to its successful management include the facts that much about its life history is unknown, its age at reproductive maturity is very advanced, certain life stages are practically unobservable, its productivity is responsive to unpredictable environmental events, and most of the known populations occur on private lands unprotected by any legal conservation instrument. One critical source of biological uncertainty is the degree to which fire promotes growth and reproductive response in the plant. To aid in its management, we developed a prototype population-level state-dependent decision-making framework that explicitly accounts for this uncertainty and for uncertainties related to stochastic environmental effects and vital rates. To parameterize the decision model, we used estimates found in the literature, and we analyzed data from a long-term monitoring program where fates of individual plants were observed through time. We demonstrate that different optimal courses of action are followed according to how one believes that fire influences reproductive response, and we show that the action taken for certain population states is informative for resolving uncertainty about competing beliefs regarding the effect of fire. We advocate the use of a model-predictive approach for the management of rare populations, particularly when management uncertainty is profound. Over time, an adaptive management approach should reduce uncertainty and improve management performance as predictions of management outcome generated under competing models are continually informed and updated by monitoring data.

Journal of Fish and Wildlife Management↗

USGS2010

Simultaneous modeling of habitat suitability, occupancy, and relative abundance: African elephants in Zimbabwe

The recent development of statistical models such as dynamic site occupancy models provides the opportunity to address fairly complex management and conservation problems with relatively simple models. However, surprisingly few empirical studies have simultaneously modeled habitat suitability and occupancy status of organisms over large landscapes for management purposes. Joint modeling of these components is particularly important in the context of management of wild populations, as it provides a more coherent framework to investigate the population dynamics of organisms in space and time for the application of management decision tools. We applied such an approach to the study of water hole use by African elephants in Hwange National Park, Zimbabwe. Here we show how such methodology may be implemented and derive estimates of annual transition probabilities among three dry-season states for water holes: (1) unsuitable state (dry water holes with no elephants); (2) suitable state (water hole with water) with low abundance of elephants; and (3) suitable state with high abundance of elephants. We found that annual rainfall and the number of neighboring water holes influenced the transition probabilities among these three states. Because of an increase in elephant densities in the park during the study period, we also found that transition probabilities from low abundance to high abundance states increased over time. The application of the joint habitat–occupancy models provides a coherent framework to examine how habitat suitability and factors that affect habitat suitability influence the distribution and abundance of organisms. We discuss how these simple models can further be used to apply structured decision-making tools in order to derive decisions that are optimal relative to specified management objectives. The modeling framework presented in this paper should be applicable to a wide range of existing data sets and should help to address important ecological, conservation, and management problems that deal with occupancy, relative abundance, and habitat suitability.

Hwange National Park↗

USGS2007

A core stochastic population projection model for Florida manatees (Trichechus manatus latirostris)

A stochastic, stage-based population model was developed to describe the life history and forecast the population dynamics of the Florida manatee (Trichechus manatus latirostris) in four separate regions of Florida. This population model includes annual variability in survival and reproductive rates, demographic stochasticity, effects of changes in warm-water capacity, and catastrophes. Further, the model explicitly accounts for uncertainty in parameter estimates. This model is meant to serve as a flexible tool for use in assessments relevant to management decision making, and was used in the State of Florida's recent biological status review. The parameter estimates and model structure described herein reflect our understanding of manatee demography at the time that this status review was completed. In the Northwest and Upper St. Johns regions, the model predicts that the populations will increase over time until warm-water capacity is reached, at which point growth will taper off. In the Atlantic region, the model predicts a stable or slightly increasing population over the next decade or so, and then a decrease as industrial warm-water capacity is lost. In the Southwest region, the model predicts a decline over time, driven by high annual mortality in the short-term and exacerbated by loss of industrial warm-water winter refuges over the next 40 years. Statewide, the likelihood of a 50% or greater decline in three manatee generations was 12%; the likelihood of a 20% or greater decline in two generations was 56%. These declines are largely driven by the anticipated loss of warm-water capacity, especially in the Atlantic and Southwest regions. The estimates of probability of extinction within 100 years were 11.9% for the Southwest region, 0.6% for the Northwest, 0.04% for the Atlantic, and <0.02% for the Upper St. Johns. The estimated probability that the statewide population will fall below 1000 animals within 100 years was 2.3%. Thus, while the estimated probability of extinction is low, the model predicts that current and emerging threats are likely to result in a long-term decline in the statewide population and a change in the regional distribution of manatees. Analyses of sensitivity and variance contribution highlight the importance of reducing uncertainty in some life-history parameters, particularly adult survival, temporal variance of adult survival, and long-term warm-water capacity. This core biological model is expected to evolve over time, as better information becomes available about manatees and their habitat, and as new assessment needs arise. We anticipate that this core model will be customized for other state and federal assessments in the near future.

Open-File Report↗

USGS2007

A Quantitative Threats Analysis for the Florida Manatee ( Trichechus manatus latirostris )

The Florida manatee (Trichechus manatus latirostris) is an endangered marine mammal endemic to the southeastern United States. The primary threats to manatee populations are collisions with watercraft and the potential loss of warm-water refuges. For the purposes of listing, recovery, and regulation under the Endangered Species Act (ESA), an understanding of the relative effects of the principal threats is needed. This work is a quantitative approach to threats analysis, grounded in the assumption that an appropriate measure of status under the ESA is based on the risk of extinction, as quantified by the probability of quasi-extinction. This is related to the qualitative threats analyses that are more common under the ESA, but provides an additional level of rigor, objectivity, and integration. In this approach, our philosophy is that analysis of the five threat factors described in Section 4(a)(1) of the ESA can be undertaken within an integrated quantitative framework. The basis of this threats analysis is a comparative population viability analysis. This involves forecasting the Florida manatee population under different scenarios regarding the presence of threats, while accounting for process variation (environmental, demographic, and catastrophic stochasticity) as well as parametric and structural uncertainty. We used the manatee core biological model (CBM) for this viability analysis, and considered the role of five threats: watercraft-related mortality, loss of warm-water habitat in winter, mortality in water-control structures, entanglement, and red tide. All scenarios were run with an underlying parallel structure that allowed a more powerful estimation of the effects of the various threats. The results reflect our understanding of manatee ecology (as captured in the structure of the CBM), our estimates of manatee demography (as described by the parameters in the model), and our characterization of the mechanisms by which the threats act on manatees. As an example of the type of results generated, we estimated that the probability of the manatee population falling to less than 250 adults on either the Atlantic or Gulf coasts (from a current statewide population size of near 3300) within 100 years is 8.6%. Complete removal of the watercraft threat alone would reduce this risk to 0.4%; complete removal of the warm-water threat to 4.2%; removal of both threats would reduce the risk to 0.1%. The modeling approach we have taken also allows us to consider partial removal of threats, as well as removal of multiple threats simultaneously. We believe the measure we have proposed (probability of quasi-extinction over y years, with quasi-extinction defined as dropping below a threshold of z on either coast) is a suitable measure of status that integrates a number of the elements that are relevant to interpretation under the ESA (it directly integrates risk of extinction and reduction of range, and indirectly integrates loss of genetic diversity). But the identification of the time frame of interest and the tolerable risk of quasi-extinction are policy decisions, and an ecology-based quasi-extinction threshold has not yet been determined. We have endeavored to provide results over a wide range of these parameters to give decision-makers useful information to assess status. This assessment of threats suggests that watercraft-related mortality is having the greatest impact on manatee population growth and resilience. Elimination of this single threat would greatly reduce the probability of quasi-extinction. Loss of warm-water is also a significant threat, particularly over the long-term. Red tide and entanglement, while noticeable threats, have had less of an impact on the manatee population. The effect of water control structures may have already been largely mitigated. We did not, however, consider an exhaustive list of threats. Other threats (e.g., reduction of food resources due to storms and development) may play a

Open-File Report↗