Analyzing complex water governance regimes: the Management and Transition Framework

This paper presents the Management and Transition Framework (MTF), an interdisciplinary conceptual and methodological framework supporting the analysis of water systems, management processes and multi-level governance regimes. It serves to improve the scientific understanding of system properties and to give practical guidance for the implementation of transition processes towards more adaptive systems. Doing so requires a systemic perspective to embrace complexity and the wealth of interactions characterizing resource governance regimes. The MTF supports and provides guidance for an interdisciplinary approach in the social sciences and across the social–natural science interface.The framework integrates a range of concepts to develop a more coherent understanding of the complexity of water management regimes. Specific emphasis is given to adaptive capacity and learning processes. However, the MTF is not linked to one specific theory but provides a language that can be tailored to specific research questions and different theoretical approaches. The structured yet flexible approach is a condition for developing shared databases that allow comparative analyses over a wider range of case studies. The MTF does not promote panaceas but a diagnostic approach supporting context sensitive analysis without being case specific and thus not directly transferable.     

Heterozygosity‐Fitness Correlations and Inbreeding Depression in Two Critically Endangered Mammals

The relation among inbreeding, heterozygosity, and fitness has been studied primarily among outbred populations, and little is known about these phenomena in endangered populations. Most researchers conclude that the relation between coefficient of inbreeding estimated from pedigrees and fitness traits (inbreeding‐fitness correlations) better reflects inbreeding depression than the relation between marker heterozygosity and fitness traits (heterozygosity‐fitness correlations). However, it has been suggested recently that heterozygosity‐fitness correlations should only be expected when inbreeding generates extensive identity disequilibrium (correlations in heterozygosity and homozygosity across loci throughout the genome). We tested this hypothesis in Mohor gazelle (Gazella dama mhorr) and Iberian lynx (Lynx pardinus). For Mohor gazelle, we calculated the inbreeding coefficient and measured heterozygosity at 17 microsatellite loci. For Iberian lynx, we measured heterozygosity at 36 microsatellite loci. In both species we estimated semen quality, a phenotypic trait directly related to fitness that is controlled by many loci and is affected by inbreeding depression. Both species showed evidence of extensive identity disequilibrium, and in both species heterozygosity was associated with semen quality. In the Iberian lynx the low proportion of normal sperm associated with low levels of heterozygosity was so extreme that it is likely to limit the fertility of males. In Mohor gazelle, although heterozygosity was associated with semen quality, inbreeding coefficient was not. This result suggests that when coefficient of inbreeding is calculated on the basis of a genealogy that begins after a long history of inbreeding, the coefficient of inbreeding fails to capture previous demographic information because it is a poor estimator of accumulated individual inbreeding. We conclude that among highly endangered species with extensive identity disequilibrium, examination of heterozygosity‐fitness correlations may be an effective way to detect inbreeding depression, whereas inbreeding‐fitness correlations may be poor indicators of inbreeding depression if the pedigree does not accurately reflect the history of inbreeding. Correlaciones Heterocigosidad‐ Adaptabilidad y Depresión Endogámica en Dos Especies de Mamíferos Críticamente en Peligro     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

The politics of employment liability

Employment liability is an important check and balance against employee harm at work. In practice, however, an inadvertent consequence of employment liability is a potential shift in power from organizations to employees that affects subsequent managerial decision making. In this Incubator, we discuss behavioral and attitudinal ramifications of employment liability at work. Copyright © 2014 John Wiley & Sons, Ltd.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.