Throughflow analysis: A stochastic approach

Ecological network analysis (ENA), predicated on systems theory and Leontiev input–output analysis, is a method widely used in ecology to reveal ecosystem properties. An important ecosystem property computed in ENA is throughflows, the amount of matter/energy leaving each compartment of the ecosystem. Throughflows are analyzed via a matrix representing their relationships to the driving input at the boundary. Network particle tracking (NPT) builds on ENA to offer a Lagrangian particle method that describes the activity of the ecosystem at the microscopic level. This paper introduces a Lagrangian throughflow analysis methodology using NPT and shows that the NPT throughflow matrix, , agrees with the conventional ENA throughflow matrix, , for ecosystems at steady-state with donor-controlled flows. The matrix is computed solely from the pathways (particles’ histories) generated by NPT simulations and its average over multiple runs of the algorithm with longer simulation time agrees with the Eulerian matrix (Law of Large Numbers). While the traditional NEA throughflow analysis is mostly used with steady-state ecosystem models, the Lagrangian throughflow analysis that we propose can be used with non-steady-state models and paves the way for the development of dynamic throughflow analysis.     

Modelling assessment of PM10 exposure control policies in Northern Italy

To assess the impact of three different emission reduction scenarios on PM10 concentrations in Northern Italy, the TCAM multiphase model has been applied in the framework of the CityDelta III-CAFE EU project. The considered domain, that is characterized by high urban and industrial emissions and a dense road traffic, due to frequently stagnating meteorological conditions is often affected by severe PM10 levels, far from the European standard regulations. The impact evaluation has been performed in terms of both yearly mean values and 50 g/m³ exceedance days for the 2004 Base Case simulation. The results show that the three selected emission reduction scenarios up to 2020 improve air quality all over the domain, in particular, in the area with higher emission density.     

A One-Equation Turbulence Model for Geophysical Applications: Comparison with Data and the k−ε Model

A one-equation turbulence model is presented, in which the turbulent kinetic energy k is calculated with a transport equation whereas the turbulent length scale l is calculated with an algebraic expression. The value of l depends on the local stratification and reduces to the classical |z| scaling for unstratified flows near a wall, where |z| is the distance to the wall. The length scale decreases during stable stratification, and increases for unstable stratification compared to the neutral case. In the limit of strong stable stratification, the so-called buoyancy length scale proportional to k ^1/2 N ^–1 is obtained, where N is the buoyancy frequency. The length scale formulation introduces a single model parameter which is calibrated against experimental data. The model is verified extensively against laboratory measurements and oceanic data, and comparisons are made with the two-equation k- model. It is shown that the performance of the proposed k model is almost identical to that of the k- model. In addition, the stability functions of Launder are revisited and adjusted to obtain better agreement with recent data.     

Estimation methods for nonlinear state-space models in ecology

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.     

Allocation of45calcium to body components of starved and fed purple sea urchins (Strongylocentrotus purpuratus)

Several lines of evidence in the literature indicate that environmental stress such as starvation may initiate reallocation of sea urchin endoskeletal tissue. For example, Aristotle's lantern enlarges under conditions of starvation, and sea urchins tagged with tetracycline and then fed develop a distinct growth line, while starved individuals develop a diffuse pattern. We designed anin vivo system to examine stress-related changes in calcification in the purple sea urchinStrongylocentrotus purpuratus. SmallS. purpuratus (ca. 2 cm test diam) were collected from the Mission Bay jetty or Imperial Beach (San Diego, California, USA) in 1987.⁴⁵Ca was incorporated from seawater into all body fractions including the organic tissue/coelomic fluid. In an initial experiment, sea urchins were fed or starved for 4 wk and then post-incubated in isotope. Overall, starved individuals deposited new calcite more slowly than did fed individuals; however, allocation was very different and calcification of teeth of starved sea urchins was nearly as great as in fed individuals. In a second experiment,S. purpuratus were first pre-labeled with isotope and then treated by feeding or starving. More of the labeled calcium was mobilized from the soft tissues and coelomic fluid into calcite in fed than in starved individuals. Growth of the teeth in starved sea urchins was significantly greater than in those fed. We conclude that starvation changes the metabolism of calcium in order to preferentially build teeth. However, we also found no evidence that calcium was resorbed from old skeletal calcite in order to build new skeleton.     

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.     

Modeling Free-Surface Fluctuation Effects for Calculation of Turbulent Open-Channel Flows

A new method of introducing the free-surface effects in the calculation of turbulent open-channel flows using the amplitude of the free-surface fluctuation is proposed along with a modeling method of the equation for the free-surface fluctuation. It can be incorporated in two-equation models like k-or k-type models by introducing the damping factor to represent the interaction of the eddies with the fluctuating free-surface. Test calculations for fully developed flows and those over backward-facing step indicate good agreement with direct numerical simulation results as well as experimental results.     

Metabolic responses to changing environmental conditions in the brackish water polychaetes Marenzelleria viridis and Hediste diversicolor

The metabolic strategies of the polychaete Marenzelleria viridis (Verrill 1873), a successful immigrant into Baltic shallow eutrophic coastal waters with meso-to oligohaline salinities since the 1980's, were determined by simultaneous calorimetry and respirometry. Resistance to oxygen deficiency under varying ecological conditions was also examined. The results of the studies with this immigrant were compared to those with the common indigeneous polychaete Nereis (Hediste) diversicolor (O.F. Müller). At 10 and 20 °C and the average habitat salinity of 5 M. viridis gradually reduced its metabolic activity with declining oxygen partial preessures (pO₂), whereas H. diversicolor maintained its metabolic activity. The metabolism of both species remained fully aerobic down to a pO₂ of 2 kPa. An additional hyposmotic stress of 0.5 salinity at a temperature of 20 °C led to a decrease in the rate of oxygen consumption in H. diversicolor below a pO₂ of 10 kPa, whereas metabolic heat dissipation remained constant. M. viridis, however, further reduced both, metabolic heat dissipation and oxygen consumption. The metabolic rates of both species under anoxia were similar, amounting to ca. 20% of the normoxic rate. The resistance of the two species to oxygen deficiency was also similar, ranging between 21 and 290 h (median survival time LT₅₀), depending on temperature and salinity. Specimens used in the present study were collected from the Southern Baltic coastal inlet of Darß-Zingster-Boddenkette during 1992 and 1993.     

Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression

We discuss a method for analyzing data that are positively skewed and contain a substantial proportion of zeros. Such data commonly arise in ecological applications, when the focus is on the abundance of a species. The form of the distribution is then due to the patchy nature of the environment and/or the inherent heterogeneity of the species. The method can be used whenever we wish to model the data as a response variable in terms of one or more explanatory variables. The analysis consists of three stages. The first involves creating two sets of data from the original: one shows whether or not the species is present; the other indicates the logarithm of the abundance when it is present. These are referred to as the presence data and the log-abundance data, respectively. The second stage involves modelling the presence data using logistic regression, and separately modelling the log-abundance data using ordinary regression. Finally, the third stage involves combining the two models in order to estimate the expected abundance for a specific set of values of the explanatory variables. A common approach to analyzing this sort of data is to use a ln (y+c) transformation, where c is some constant (usually one). The method we use here avoids the need for an arbitrary choice of the value of c, and allows the modelling to be carried out in a natural and straightforward manner, using well-known regression techniques. The approach we put forward is not original, having been used in both conservation biology and fisheries. Our objectives in this paper are to (a) promote the application of this approach in a wide range of settings and (b) suggest that parametric bootstrapping be used to provide confidence limits for the estimate of expected abundance.