Aplicación de la modelación integrada bayesiana y de los métodos Monte Carlo basados en cadenas de Markov para la conservación de una especie recolectada

When endeavoring to make informed decisions, conservation biologists must frequently contend with disparate sources of data and competing hypotheses about the likely impacts of proposed decisions on the resource status. Frequently, statistical analyses, modeling (e.g., for population projection) and optimization or simulation are conducted as separate exercises. For example, a population model might be constructed, whose parameters are then estimated from data (e.g., ringing studies, population surveys). This model might then be used to predict future population states, from current population estimates, under a particular management regime. Finally, the parameterized model might also be used to evaluate alternative candidate management decisions, via simulation, optimization, or both. This approach, while effective, does not take full advantage of the integration of data and model components for prediction and updating; we propose a hierarchical Bayesian context for this integration. In the case of American black ducks (Anas rubripes), managers are simultaneously faced with trying to extract a sustainable harvest from the species, while maintaining individual stocks above acceptable thresholds. The problem is complicated by spatial heterogeneity in the growth rates and carrying capacity of black ducks stocks, movement between stocks, regional differences in the intensity of harvest pressure, and heterogeneity in the degree of competition from a close congener, mallards (Anas platyrynchos) among stocks. We have constructed a population life cycle model that takes these components into account and simultaneously performs parameter estimation and population prediction in a Bayesian framework. Ringing data are used to develop posterior predictive distributions for harvest mortality rates, given as input decisions about harvest regulations. Population surveys of black ducks and mallards are used to obtain stock–specific estimates of population size for both species, for inputs into the population life–cycle model. These estimates are combined with the posterior distributions for harvest mortality, to obtain posterior predictive distributions of future population status for candidate sets of regional harvest regulations, under alternative biological hypotheses for black duck population dynamics. These distributions might then be used for both the exploration of optimal harvest policies and for sequential updating of model posteriors, via comparison of predictive distributions to future survey estimates of stock–specific abundance. Our approach illustrates advantages of MCMC for integrating disparate data sources into a common predictive framework, for use in conservation decision making.En el momento de tomar decisiones bien fundamentadas, es habitual que los biólogos conservacionistas deban enfrentarse a fuentes de datos dispares e hipótesis alternativas acerca de los impactos probables que tendrán las decisiones propuestas en el estado del recurso. A menudo, tanto los análisis estadísticos, como la modelación (para la proyección poblacional, por ejemplo) y la optimización o simulación, se llevan a cabo como ejercicios independientes. Así, es posible que se construya un modelo poblacional, cuyos parámetros se estimen a partir de datos (como estudios de anillamiento y estudios poblacionales). Posteriormente, cabe la posibilidad de que este mismo modelo se emplee para predecir situaciones demográficas futuras a partir de las estimaciones de población actuales, utilizando para ello un sistema de gestión determinado. Por último, el modelo parametrizado también puede emplearse para evaluar posibles decisiones de gestión alternativas, a través de la simulación, la optimización, o ambos procedimientos. Si bien este enfoque resulta eficaz, no aprovecha al máximo la integración de datos y los componentes de los modelos para la predicción y actualización. En este estudio proponemos un contexto bayesiano jerárquico que permite efectuar dicha integración. En el caso del ánade sombrío americano (Anas rubripes), los gestores deben enfrentarse a la labor de intentar extraer una recolección sostenible de la especie, al tiempo que mantienen los stocks de individuos por encima de umbrales aceptables. El problema se ve agravado por la heterogeneidad espacial que presentan las tasas de crecimiento y la carga cinegética de los stocks de ánades sombríos, el movimiento entre los stocks, las diferencias regionales en la intensidad de la presión recolectora y la heterogeneidad en el grado de competencia por parte de un congénere cercano —el ánade real (Anas platyrynchos)— entre los stocks. Hemos formulado un modelo del ciclo vital de la población que toma en consideración estos componentes, al tiempo que permite llevar a cabo una estimación de los parámetros y una predicción de la población en un marco bayesiano. Los datos de anillamiento se emplean para desarrollar distribuciones predictivas posteriores para las tasas de mortalidad durante la recolección, expresadas como decisiones de entrada acerca de la normativa sobre recolecciones. Los estudios poblacionales del ánade sombrío y del ánade real se emplean para obtener estimaciones sobre el tamaño poblacional específicas de los stocks de ambas especies, que se emplearán como entradas para el modelo del ciclo vital de la población. Dichas estimaciones se combinan con las distribuciones posteriores para la mortalidad durante la recolección, con el propósito de obtener distribuciones predictivas posteriores de la situación demográfica futura para posibles conjuntos de normativas regionales acerca de la recolección, de acuerdo con hipótesis biológicas alternativas relativas a la dinámica poblacional del ánade sombrío. En una fase posterior, tales distribuciones pueden utilizarse tanto para la investigación de políticas óptimas en materia de recolección, como para la actualización secuencial de distribuciones posteriores del modelo mediante la comparación de distribuciones predictivas para estimaciones en estudios futuros acerca de la abundancia poblacional presente de forma específica en los stocks. Nuestro enfoque ilustra las ventajas que presentan las técnicas de Montecarlo basadas encadenas de Markov (MCMC) para integrar fuentes de datos dispares en un marco predictivo común, con vistas a su utilización en la toma de decisiones sobre conservación

An Adaptive Decision Framework for the Conservation of a Threatened Plant

This is the publisher's version, also available electronically from http://www.fwspubs.org/.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

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

Adult cognitive outcomes in phenylketonuria:explaining causes of variability beyond average Phe levels

OBJECTIVE: The objective was to deepen the understanding of the causes of individual variability in phenylketonuria (PKU) by investigating which metabolic variables are most important for predicting cognitive outcomes (Phe average vs Phe variation) and by assessing the risk of cognitive impairment associated with adopting a more relaxed approach to the diet than is currently recommended. METHOD: We analysed associations between metabolic and cognitive measures in a mixed sample of English and Italian early-treated adults with PKU (N = 56). Metabolic measures were collected through childhood, adolescence and adulthood; cognitive measures were collected in adulthood. Metabolic measures included average Phe levels (average of median values for each year in a given period) and average Phe variations (average yearly standard deviations). Cognition was measured with IQ and a battery of cognitive tasks. RESULTS: Phe variation was as important, if not more important, than Phe average in predicting adult outcomes and contributed independently. Phe variation was particularly detrimental in childhood. Together, childhood Phe variation and adult Phe average predicted around 40% of the variation in cognitive scores. Poor cognitive scores (> 1 SD from controls) occurred almost exclusively in individuals with poor metabolic control and the risk of poor scores was about 30% higher in individuals with Phe values exceeding recommended thresholds. CONCLUSIONS: Our results provide support for current European guidelines (average Phe value = < 360 μmol/l in childhood; = < 600 μmo/l from 12 years onwards), but they suggest an additional recommendation to maintain stable levels (possibly Phe SD = < 180 μmol/l throughout life). PUBLIC SIGNIFICANCE STATEMENTS: We investigated the relationship between how well people with phenylketonuria control blood Phe throughout their life and their ability to carry out cognitive tasks in adulthood. We found that avoiding blood Phe peaks was as important if not more important that maintaining average low Phe levels. This was particularly essential in childhood. We also found that blood Phe levels above recommended European guidelines was associated with around 30% increase in the risk of poor cognitive outcomes

Application of integrated Bayesian modeling and Markov chain Monte Carlo methods to the conservation of a harvested species

When endeavoring to make informed decisions, conservation biologists must frequently contend with disparate sources of data and competing hypotheses about the likely impacts of proposed decisions on the resource status. Frequently, statistical analyses, modeling (e.g., for population projection) and optimization or simulation are conducted as separate exercises. For example, a population model might be constructed, whose parameters are then estimated from data (e.g., ringing studies, population surveys). This model might then be used to predict future population states, from current population estimates, under a particular management regime. Finally, the parameterized model might also be used to evaluate alternative candidate management decisions, via simulation, optimization, or both. This approach, while effective, does not take full advantage of the integration of data and model components for prediction and updating; we propose a hierarchical Bayesian context for this integration. In the case of American black ducks (Anas rubripes), managers are simultaneously faced with trying to extract a sustainable harvest from the species, while maintaining individual stocks above acceptable thresholds. The problem is complicated by spatial heterogeneity in the growth rates and carrying capacity of black ducks stocks, movement between stocks, regional differences in the intensity of harvest pressure, and heterogeneity in the degree of competition from a close congener, mallards (Anas platyrynchos) among stocks. We have constructed a population life cycle model that takes these components into account and simultaneously performs parameter estimation and population prediction in a Bayesian framework. Ringing data are used to develop posterior predictive distributions for harvest mortality rates, given as input decisions about harvest regulations. Population surveys of black ducks and mallards are used to obtain stock-specific estimates of population size for both species, for inputs into the population life-cycle model. These estimates are combined with the posterior distributions for harvest mortality, to obtain posterior predictive distributions of future population status for candidate sets of regional harvest regulations, under alternative biological hypotheses for black duck population dynamics. These distributions might then be used for both the exploration of optimal harvest policies and for sequential updating of model posteriors, via comparison of predictive distributions to future survey estimates of stock-specific abundance. Our approach illustrates advantages of MCMC for integrating disparate data sources into a common predictive framework, for use in conservation decision making

Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control : Ebola as a case study

Determining how to best manage an epidemiological outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). However, these two uncertainties are rarely addressed concurrently in epidemic studies. We present an approach to simultaneously address both sources of uncertainty, to elucidate which source most impedes decision making. In the case of the 2014 West African Ebola outbreak, epidemiological uncertainty is represented by a large ensemble of published models. Operational uncertainty about three classes of interventions is assessed for a wide range of potential intervention effectiveness. We ranked each intervention by caseload reduction in each model, initially assuming an unlimited budget as a counterfactual. We then assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The improvement in management outcomes to be gained by resolving uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant reduction in caseload could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for management of other diseases for which multiple models and multiple interventions are available

Analysis of the measles vaccination case study.

<p>(A) The number of susceptible individuals in each age class for the three alternative age distribution models for measles. (B) The EVPI as a function of the daily vaccination rate, colors indicate the optimal intervention (blue, vaccinate 0–5 years; green, vaccinate 0–10 years; yellow, vaccinate 0–15 years) for each vaccination rate. (C) The optimal vaccination target age, as a function of the weight on each of the three models of the susceptible population (axes on ternary plots) and the time, in days, at which the vaccination target can be changed to the optimal target conditional on the true susceptible age distribution (colors are as in panel B).</p

The costs for the four strategies (IP only [IP], IP+DC [DC], IP+DC+CP [CP], IP+DC+3 km ring culling [RC], when the FMD model is simulated with the dispersal kernels K1, K2, and K3.

<p>The costs shown are in millions of £. Weighted average costs associated with each management strategy for two different distributions of kernel beliefs (case 1, a stronger belief that the 2001 UK kernel will apply to a novel outbreak; case 2, an equal weighting on each model) are also shown.</p>a<p>Bold numbers highlight the best (lowest cost) outcomes possible.</p>b<p>Italic numbers are the worst outcomes possible.</p><p>The costs for the four strategies (IP only [IP], IP+DC [DC], IP+DC+CP [CP], IP+DC+3 km ring culling [RC], when the FMD model is simulated with the dispersal kernels K1, K2, and K3.</p