Bayesian multivariate process modeling for prediction of forest attributes

This article investigates multivariate spatial process models suitable for predicting multiple forest attributes using a multisource forest inventory approach. Such data settings involve several spatially dependent response variables arising in each location. Not only does each variable vary across space, they are likely to be correlated among themselves. Traditional approaches have attempted to model such data using simplifying assumptions, such as a common rate of decay in the spatial correlation or simplified cross-covariance structures among the response variables. Our current focus is to produce spatially explicit, tree species specific, prediction of forest biomass per hectare over a region of interest. Modeling such associations presents challenges in terms of validity of probability distributions as well as issues concerning identifiability and estimability of parameters. Our template encompasses several models with different correlation structures. These models represent different hypotheses whose tenability are assessed using formal model comparisons. We adopt a Bayesian hierarchical approach offering a sampling-based inferential framework using efficient Markov chain Monte Carlo methods for estimating model parameters.     

The modular modeling system (MMS) — The physical process modeling component of a database-centered decision support system for water and power management

The Modular Modeling System (MMS) is an integrated system of computer software that is being developed to provide the research and operational framework needed to support development, testing, and evaluation of physical-process algorithms, and to facilitate integration of user-selected sets of algorithms into operational physical-process models. MMS uses a module library that contains compatible modules for simulating a variety of water, energy, and biogeochemical processes. A model is created by selectively linking modules from the library using MMS model-building tools. A geographic information system (GIS) interface also is being developed for MMS to support a variety of GIS tools for use in characterizing and parameterizing topographic, hydrologic, and ecosystem features, visualizing spatially and temporally distributed model parameters and variables, and analyzing and validating model results. MMS is being coupled with the Power Reservoir System Model (PRSYM) to provide a database-centered decision support system for making complex operational decisions on multipurpose reservoir systems and watersheds. The U.S. Geological Survey and the Bureau of Reclamation are working collaboratively on a project titled the Watershed Modeling Systems Initiative to develop and apply the coupled MMS — PRSYM models to the San Juan River basin in Colorado, New Mexico, Arizona, and Utah.     

Effects of varying field geometry and machine configurations on spatial field traffic intensity: A case study for winter wheat harvest

Increasing traffic intensities in agricultural work processes increase the risk of subsoil compaction. The objective of this study is to investigate whether the geometry of the field and size of the machine influence the traffic intensity. This study focuses on analysing the influence of the machinery and field geometry on the traffic intensities in the field based on wheat harvest operations performed with one combined harvester. For this purpose, 177 routes on 59 different fields are simulated with a route planner, and subsequently, the tracks and traffic intensities are modelled. The structure of the field was specified using eight different shape indices. The analyses showed that the configuration of the machines mainly influences the total passed area and the wheel load distribution on the field. Independent of the machine, the geometry of the field has an influence on the area proportions in the headland with wheel passes above 5 and 10. The indices that highly correlate with traffic intensity are the mean interior edge ratio, the interior area ratio and the mean fractal dimension. It means that the larger the field area in proportion to the perimeter and the infield area, the larger the area fraction with more than 5 or 10 passes. Moreover, this area share increases with the complexity of the field geometry, expressed by the mean fractal dimension. Consequently, the size of the field mainly influences the traffic in the headland because of the removal of the crop.     

Leaf litter-colonizing microbiota: supplementary food source or indicator of food quality for Porcellio scaber (Isopoda: Oniscidea)?

Different explanations have been proposed for why terrestrial isopods prefer, and gain from, feeding on microbially inoculated food materials. In the present study, no-choice feeding experiments are used to test three contrasting, but not mutually exclusive, hypotheses. (1) The digestion and nutritional utilization of microbial cells enhance the nutritive value of leaf litter; (2) extracellular digestive enzymes of microbial origin promote digestion of leaf litter; (3) leaf litter-colonizing microbiota serve as indicators for easily digestible leaf litter of high nutritive value. Predictions derived from these hypotheses are compared with the results of a study with experimentally manipulated leaf litter, serving as food sources for the common woodlouse Porcellio scaber. Leaf litter-colonizing microbiota increased consumption of leaf litter by isopods and contributed somewhat to biomass gain of isopods, but not to the assimilation of ingested food. Thus, the present results coincide with predictions derived from hypothesis (3) suggesting that leaf litter-colonizing microbiota stimulate consumption by indicating high food quality. Their positive effects on assimilation and growth, however, are context-specific, being stronger in case of high-quality food than in case of low-quality food.