Modelo de control viable para el manejo sostenible del stock de anchoveta peruana (Engraulis ringens)

El manejo sostenible de pesquerías es todavía un problema abierto y la teoría de viabilidad ofrece una alternativa para determinar políticas de manejo de los recursos que garanticen la sostenibilidad, una vez definidas las restricciones que determinan los estados sostenibles del sistema. La dinámica poblacional de la anchoveta peruana se modeló usando un modelo estructurado por edades tipo Thomson–Bell con capturas discretas acoplado con el modelo de reclutamiento de Ricker, con pasos semestrales entre los años 1963–1984. Se definió además un conjunto deseable de estados sostenibles, asociado a los niveles del stock y capturas que satisfacen restricciones ecológicas, económicas y sociales previamente definidas. En base a esto se calculó el conjunto de los estados del stock para los que existe un sucesión de capturas que permiten mantenerlo en un estado sostenible (conjunto denominado núcleo de viabilidad) y una familia de conjuntos de capturas viables, que corresponden a todos los niveles de captura que se puedan aplicar sobre cada estado del stock de manera tal que éste se mantenga dentro del núcleo de viabilidad, es decir, permanezca en un estado sostenible. Se encontró una condición suficiente para la existencia de un núcleo de viabilidad no vacío: que la cuota social (captura mínima para mantener en funcionamiento la pesquería) sea menor a un desembarque de 915 800 t semestrales. Se comparó la serie histórica de capturas con las obtenidas a partir de la teoría de viabilidad para el periodo 1963 - 1984, encontrándose que hubo sobrepesca desde finales de 1968, lo que conllevó al colapso de la pesquería durante El Niño de 1972-1973. A partir de los resultados de viabilidad, se definieron 5 estrategias de manejo pesquero (E1–E5) para la anchoveta peruana, concluyéndose que la estrategia precautoria viable media (E5) hubiera podido evitar el colapso de la pesquería de anchoveta, manteniendo además niveles aceptables de pesca. Además, la estrategia precautoria del ICES (E2) no aseguró la sostenibilidad del stock durante los periodos El Niño. Además, se concluye que hubiera sido necesaria una veda de un año después del colapso de la pesquería para que el stock regresara al núcleo de viabilidad, posibilitando un manejo sostenible en adelante. La teoría de la viabilidad, con el núcleo de viabilidad y las capturas viables asociadas, resultaron ser herramientas útiles para el diseño de estrategias de manejo que aseguran la sostenibilidad de los recursos pesqueros.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Startegy selection during exploratory behavior: sex differences, uncertainty and risk managment

This study deals with the psychological processes underlying the selection of appropriate strategy during exploratory behavior. A new device was used to assess sexual dimorphisms in spatial abilities that do not depend on spatial rotation, map reading or directional vector extraction capacities. Moreover, it makes it possible to investigate exploratory behavior as a specific response to novelty that trades off risk and reward. Risk management under uncertainty was assessed through both spontaneous searching strategies and signal detection capacities. The results of exploratory behavior, detection capacities, and decision-making strategies seem to indicate that women's exploratory behavior is based on risk-reducing behavior while men behavior does not appear to be influenced by this variable. This difference was interpreted as a difference in information processing modifying beliefs concerning the likelihood of uncertain events, and therefore influencing risk evaluation.

Testing the bivariate distribution of daily equity returns using copulas: an application to the Spanish stock market

In this paper we deal with the identification of dependencies between time series of equity returns. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several families of copulas are fitted and compared with Spanish stock market data. The results show that the t-copula generally outperforms other dependence structures, and highlight the difficulty in adjusting a significant number of bivariate data series

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Carbon stock and its compartments in a subtropical oxisol under long-term tillage and crop rotation systems

Soil organic matter (SOM) plays a crucial role in soil quality and can act as an atmospheric C-CO2 sink under conservationist management systems. This study aimed to evaluate the long-term effects (19 years) of tillage (CT-conventional tillage and NT-no tillage) and crop rotations (R0-monoculture system, R1-winter crop rotation, and R2- intensive crop rotation) on total, particulate and mineral-associated organic carbon (C) stocks of an originally degraded Red Oxisol in Cruz Alta, RS, Southern Brazil. The climate is humid subtropical Cfa 2a (Köppen classification), the mean annual precipitation 1,774 mm and mean annual temperature 19.2 ºC. The plots were divided into four segments, of which each was sampled in the layers 0-0.05, 0.05-0.10, 0.10-0.20, and 0.20-0.30 m. Sampling was performed manually by opening small trenches. The SOM pools were determined by physical fractionation. Soil C stocks had a linear relationship with annual crop C inputs, regardless of the tillage systems. Thus, soil disturbance had a minor effect on SOM turnover. In the 0-0.30 m layer, soil C sequestration ranged from 0 to 0.51 Mg ha-1 yr-1, using the CT R0 treatment as base-line; crop rotation systems had more influence on soil stock C than tillage systems. The mean C sequestration rate of the cropping systems was 0.13 Mg ha-1 yr-1 higher in NT than CT. This result was associated to the higher C input by crops due to the improvement in soil quality under long-term no-tillage. The particulate C fraction was a sensitive indicator of soil management quality, while mineral-associated organic C was the main pool of atmospheric C fixed in this clayey Oxisol. The C retention in this stable SOM fraction accounts for 81 and 89 % of total C sequestration in the treatments NT R1 and NT R2, respectively, in relation to the same cropping systems under CT. The highest C management index was observed in NT R2, confirming the capacity of this soil management practice to improve the soil C stock qualitatively in relation to CT R0. The results highlighted the diversification of crop rotation with cover crops as a crucial strategy for atmospheric C-CO2 sequestration and SOM quality improvement in highly weathered subtropical Oxisols.

Comparing Basal Area Diameter Distributions Estimated by Tree Species and for the Entire Stock in a Mixed Stand

Summary

SPATIAL UNCERTAINTY OF NUTRIENT LOSS BY EROSION IN SUGARCANE HARVESTING SCENARIOS

The assessment of spatial uncertainty in the prediction of nutrient losses by erosion associated with landscape models is an important tool for soil conservation planning. The purpose of this study was to evaluate the spatial and local uncertainty in predicting depletion rates of soil nutrients (P, K, Ca, and Mg) by soil erosion from green and burnt sugarcane harvesting scenarios, using sequential Gaussian simulation (SGS). A regular grid with equidistant intervals of 50 m (626 points) was established in the 200-ha study area, in Tabapuã, São Paulo, Brazil. The rate of soil depletion (SD) was calculated from the relation between the nutrient concentration in the sediments and the chemical properties in the original soil for all grid points. The data were subjected to descriptive statistical and geostatistical analysis. The mean SD rate for all nutrients was higher in the slash-and-burn than the green cane harvest scenario (Student’s t-test, p<0.05). In both scenarios, nutrient loss followed the order: Ca>Mg>K>P. The SD rate was highest in areas with greater slope. Lower uncertainties were associated to the areas with higher SD and steeper slopes. Spatial uncertainties were highest for areas of transition between concave and convex landforms.

Confusió i borsa

En aquest article es conceptualitza la confusió en termes d'incertesa, considerant posteriorment com intervé en el procés de formació de creences i en la presa de decisions d'inversió i distingint tres tipus d'estratègies inversores, la diversificació, la concentració en empreses confiant en el pla empresarial i en la capacitat de gestió i, finalment, el seguidisme, referent a l'estratègia basada en confiar en tercers (rumors, notícies, experts, gurus ...). D'acord amb aquesta anàlisi, s'estableix la influència de la informació i la confusió en formació de les bombolles financeres i s'il·lustra amb l'exemple de la bombolla immobiliària i el crac borsari de 2008 a Espanya.

Testing the bivariate distribution of daily equity returns using copulas: an application to the Spanish stock market

In this paper we deal with the identification of dependencies between time series of equity returns. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several families of copulas are fitted and compared with Spanish stock market data. The results show that the t-copula generally outperforms other dependence structures, and highlight the difficulty in adjusting a significant number of bivariate data series

New, low estimate for carbon stock in global forest vegetation based on inventory data

Abstract

Quantifying Uncertainty in Real Time Performance Measurement for Highway Winter Maintenance Operations – Phase 2, RB03-013, 2014

Winter weather in Iowa is often unpredictable and can have an adverse impact on traffic flow. The Iowa Department of Transportation (Iowa DOT) attempts to lessen the impact of winter weather events on traffic speeds with various proactive maintenance operations. In order to assess the performance of these maintenance operations, it would be beneficial to develop a model for expected speed reduction based on weather variables and normal maintenance schedules. Such a model would allow the Iowa DOT to identify situations in which speed reductions were much greater than or less than would be expected for a given set of storm conditions, and make modifications to improve efficiency and effectiveness. The objective of this work was to predict speed changes relative to baseline speed under normal conditions, based on nominal maintenance schedules and winter weather covariates (snow type, temperature, and wind speed), as measured by roadside weather stations. This allows for an assessment of the impact of winter weather covariates on traffic speed changes, and estimation of the effect of regular maintenance passes. The researchers chose events from Adair County, Iowa and fit a linear model incorporating the covariates mentioned previously. A Bayesian analysis was conducted to estimate the values of the parameters of this model. Specifically, the analysis produces a distribution for the parameter value that represents the impact of maintenance on traffic speeds. The effect of maintenance is not a constant, but rather a value that the researchers have some uncertainty about and this distribution represents what they know about the effects of maintenance. Similarly, examinations of the distributions for the effects of winter weather covariates are possible. Plots of observed and expected traffic speed changes allow a visual assessment of the model fit. Future work involves expanding this model to incorporate many events at multiple locations. This would allow for assessment of the impact of winter weather maintenance across various situations, and eventually identify locations and times in which maintenance could be improved.