Process-based Dynamic Model Research Articles

Coral Symbiosis Carbon Flow: A Numerical Model Study Spanning Cellular to Ecosystem Levels

Corals rely on a symbiotic relationship with algae (zooxanthellae), which reside in the host tissue and play a critical role for host metabolism through photosynthesis, respiration, carbon translocation, and calcification. These processes affect coral reefs on different scales from cellular to organismal and ecosystem levels. A process-based dynamic model was developed and coupled with a one-dimensional (1-D) biogeochemical model to describe coral photosynthesis, respiration, and carbon translocation at the cellular level, calcification and ion transport in different coral polyp components (i.e., coelenteron, calcifying fluid) at the organismal level; and the exchange of material between corals and the ambient seawater at the ecosystem level. Major processes controlling the carbon budget in internal symbiosis were identified. For the symbiont, photosynthesis is the primary carbon source and translocation to the host is the major sink. For the host, most of the carbon translocated from the symbiont is lost through mucus leakage. In the host dissolved inorganic carbon (DIC) pool, most of the carbon is obtained from the surrounding seawater through uptake; photosynthesis and calcification are the major sinks of DIC. Based on a series of scenario studies, the model produced increase of photosynthesis rate with decline of calcification rate under higher air pCO2 and associated carbonate chemistry variabilities in different polyp components. The model results support the hypothesis that elevated pCO2 stimulates photosynthesis, resulting in a reduced supply of DIC to calcification. Such coupled models allow the exploration of process-based mechanisms, complementing laboratory and field studies.

Integration of Forest Growth Component in the FEST-WB Distributed Hydrological Model: The Bonis Catchment Case Study

In this paper, the FEST-FOREST model is presented. A FOREST module is written in the FORTRAN-90 programming language, and was included in the FEST-WB distributed hydrological model delivering the FEST-FOREST model. FEST-FOREST is a process-based dynamic model allowing the simulation at daily basis of gross primary production (GPP) and net primary production (NPP) together with the carbon allocation of a homogeneous population of trees (same age, same species). The model was implemented based on different equations from literature, commonly used in Eco-hydrological models. This model was developed within the framework of the INNOMED project co-funded under the ERA-NET WaterWorks2015 Call of the European Commission. The aim behind the implementation of the model was to simulate in a simplified mode the forest growth under different climate change and management scenarios, together with the impact on the water balance at the catchment. On a first application of the model, the results are considered very promising when compared to field measured data.

Host-pathogen immune feedbacks can explain widely divergent outcomes from similar infections.

A long-standing question in infection biology is why two very similar individuals, with very similar pathogen exposures, may have very different outcomes. Recent experiments have found that even isogenic Drosophila melanogaster hosts, given identical inoculations of some bacterial pathogens at suitable doses, can experience very similar initial bacteria proliferation but then diverge to either a lethal infection or a sustained chronic infection with much lower pathogen load. We hypothesized that divergent infection outcomes are a natural result of mutual negative feedbacks between pathogens and the host immune response. Here, we test this hypothesis in silico by constructing process-based dynamic models for bacterial population growth, host immune induction and the feedbacks between them, based on common mechanisms of immune system response. Mathematical analysis of a minimal conceptual model confirms our qualitative hypothesis that mutual negative feedbacks can magnify small differences among hosts into life-or-death differences in outcome. However, explaining observed features of chronic infections requires an extension of the model to include induced pathogen modifications that shield themselves from host immune responses at the cost of reduced proliferation rate. Our analysis thus generates new, testable predictions about the mechanisms underlying bimodal infection outcomes.

Suitable habitat mathematical model of common reed (Phragmites australis) in shallow lakes with coupling cellular automaton and modified logistic function

Simulating of suitable habitat for the common reed (Phragmites australis) can provide theoretical support for water-resources managing of shallow lakes. Earlier research has focused on statistical models of shallow lakes and process-based dynamic models for coastal wetlands. However, process-based dynamic modeling for shallow lakes remains relatively incomplete, and the loss of biomass in unsuitable situations is seldom considered. This study established an occupied habitat model by coupling a cellular automaton with a modified logistic function. The cellular automaton was used to simulate the spatial diffusion of the reed. The logistic function was used to simulate the accumulation of biomass over time, and modified with the loss of biomass in unsuitable habitats. Both reed distribution and dynamic variations in reed growth can be simulated by this model. Water level was considered the most important factor in reed growth. In Baiyangdian Lake, China, the growing season is from April to October. The suitable habitat area (SHA) and aboveground biomass (AGB) of reeds were simulated using data from actual hydrological processes and hypothetical scenarios. Element contents and water consumption by transpiration were estimated on the basis of the AGB data. The results are as follows. (1) Water-level changes affected the habitat of the common reed. The SHA increased slightly (from 6.0 m) and then decreased with rising water level, reaching its maximum value at 6.7 m of the 90 % guarantee rate water level. SHA declined rapidly when the water level was higher than 7.5 m. (2) When the water level was maintained at 6.3–7.3 m, the water consumption for transpiration per unit AGB was lower than that for other water levels. (3) To maintain a higher water level for a suitable reed habitat area and to reduce water consumption per unit biomass transpiration, a reference value of 6.7 m can be regarded as the lowest ecological water level in Baiyangdian Lake.

Bayesian parameter estimation for the SWIFT model of eye-movement control during reading

Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.

Comparison of regression techniques to predict response of oilseed rape yield to variation in climatic conditions in Denmark

Abstract Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult to identify the most relevant input variables that should be included in regression models due to the high number of candidate variables and to their correlations. This paper compares several regression techniques for modeling response of winter oilseed rape yield to a high number of correlated input variables. Several statistical regression methods were fitted to a dataset including 689 observations of winter oilseed rape yield from replicated field experiments conducted in 239 sites in Denmark, covering nearly all regions of the country from 1992 to 2013. Regression methods were compared by cross-validation. The regression methods leading to the most accurate yield predictions were Lasso and Elastic Net, and the least accurate methods were ordinary least squares and stepwise regression. Partial least squares and ridge regression methods gave intermediate results. The estimated relative yield change for a +1°C temperature increase during flowering was estimated to range between 0 and +6 %, depending on choice of regression method. Precipitation was found to have an adverse effect on yield during autumn and winter. It was estimated that an increase in precipitation of +1 mm/day would result in a relative yield change ranging from 0 to −4 %. Soil type was also important for crop yields with lower yields on sandy soils compared to loamy soils. Later sowing was found to result in increased crop yield. The estimated effect of climate on yield was highly sensitive to the chosen regression method. Regression models showing similar performance led in some cases to different conclusions with respect to effect of temperature and precipitation. Hence, it is recommended to apply an ensemble of regression models, in order to account for the sensitivity of the data driven models for projecting crop yield under climate change.

Comparison of different modeling approaches to better evaluate greenhouse gas emissions from whole wastewater treatment plants

New tools are being developed to estimate greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). There is a trend to move from empirical factors to simple comprehensive and more complex process-based models. Thus, the main objective of this study is to demonstrate the importance of using process-based dynamic models to better evaluate GHG emissions. This is tackled by defining a virtual case study based on the whole plant Benchmark Simulation Model Platform No. 2 (BSM2) and estimating GHG emissions using two approaches: (1) a combination of simple comprehensive models based on empirical assumptions and (2) a more sophisticated approach, which describes the mechanistic production of nitrous oxide (N(2) O) in the biological reactor (ASMN) and the generation of carbon dioxide (CO(2) ) and methane (CH(4) ) from the Anaerobic Digestion Model 1 (ADM1). Models already presented in literature are used, but modifications compared to the previously published ASMN model have been made. Also model interfaces between the ASMN and the ADM1 models have been developed. The results show that the use of the different approaches leads to significant differences in the N(2) O emissions (a factor of 3) but not in the CH(4) emissions (about 4%). Estimations of GHG emissions are also compared for steady-state and dynamic simulations. Averaged values for GHG emissions obtained with steady-state and dynamic simulations are rather similar. However, when looking at the dynamics of N(2) O emissions, large variability (3-6 ton CO(2) e day(-1) ) is observed due to changes in the influent wastewater C/N ratio and temperature which would not be captured by a steady-state analysis (4.4 ton CO(2) e day(-1) ). Finally, this study also shows the effect of changing the anaerobic digestion volume on the total GHG emissions. Decreasing the anaerobic digester volume resulted in a slight reduction in CH(4) emissions (about 5%), but significantly decreased N(2) O emissions in the water line (by 14%).

Using the INCA-Hg model of mercury cycling to simulate total and methyl mercury concentrations in forest streams and catchments

We present a new, catchment-scale, process-based dynamic model for simulating mercury (Hg) in soils and surface waters. The Integrated Catchments Model for Mercury (INCA-Hg) simulates transport of gaseous, dissolved and solid Hg and transformations between elemental (Hg0), ionic (Hg(II)) and methyl (MeHg) Hg in natural and semi-natural landscapes. The mathematical description represents the model as a series of linked, first-order differential equations describing chemical and hydrological processes in catchment soils and waters which we believe control surface water Hg dynamics. The model simulates daily time series between one and 100years long and can be applied to catchments ranging in size from <1 to ~10,000km2. Here we present applications of the model to two boreal forest headwater catchments in central Canada where we were able to reproduce observed patterns of stream water total mercury (THg) and MeHg fluxes and concentrations. Model performance was assessed using Monte Carlo techniques. Simulated in-stream THg and MeHg concentrations were sensitive to hydrologic controls and terrestrial and aquatic process rates.

Yield-SAFE: A parameter-sparse, process-based dynamic model for predicting resource capture, growth, and production in agroforestry systems

1. Silvoarable agroforestry (SAF) is the cultivation of trees and arable crops on the same parcel of land. SAF may contribute to modern diversified land use objectives in Europe, such as enhanced biodiversity and productivity, reduced leaching of nitrogen, protection against flooding and erosion, and attractiveness of the landscape. Long-term yield predictions are needed to assess long-term economic profitability of SAF. 2. A model for growth, resource sharing and productivity in agroforestry systems was developed to act as a tool in forecasts of yield, economic optimization of farming enterprises and exploration of policy options for land use in Europe. The model is called Yield-SAFE; from “YIeld Estimator for Long term Design of Silvoarable AgroForestry in Europe”. The model was developed with as few equations and parameters as possible to allow model parameterization under constrained availability of data from long-term experiments. 3. The model consists of seven state equations expressing the temporal dynamics of: (1) tree biomass; (2) tree leaf area; (3) number of shoots per tree; (4) crop biomass; (5) crop leaf area index; (6) heat sum; and (7) soil water content. The main outputs of the model are the growth dynamics and final yields of trees and crops. Daily inputs are temperature, radiation and precipitation. Planting densities, initial biomasses of tree and crop species, and soil parameters must be specified. 4. A parameterization of Yield-SAFE is generated, using published yield tables for tree growth and output from the comprehensive crop simulation model STICS. Analysis of tree and crop growth data from two poplar agroforestry stands in the United Kingdom demonstrates the validity of the modelling concept and calibration philosophy of Yield-SAFE. A sensitivity analysis is presented to elucidate which biological parameters most influence short and long-term productivity and land equivalent ratio. 5. The conceptual model, elaborated in Yield-SAFE, in combination with the outlined procedure for model calibration, offers a valid tool for exploratory land use studies.

A new general dynamic model predicting radionuclide concentrations and fluxes in coastal areas from readily accessible driving variables

This paper presents a general, process-based dynamic model for coastal areas for radionuclides (metals, organics and nutrients) from both single pulse fallout and continuous deposition. The model gives radionuclide concentrations in water (total, dissolved and particulate phases and concentrations in sediments and fish) for entire defined coastal areas. The model gives monthly variations. It accounts for inflow from tributaries, direct fallout to the coastal area, internal fluxes (sedimentation, resuspension, diffusion, burial, mixing and biouptake and retention in fish) and fluxes to and from the sea outside the defined coastal area and/or adjacent coastal areas. The fluxes of water and substances between the sea and the coastal area are differentiated into three categories of coast types: (i) areas where the water exchange is regulated by tidal effects; (ii) open coastal areas where the water exchange is regulated by coastal currents; and (iii) semi-enclosed archipelago coasts. The coastal model gives the fluxes to and from the following four abiotic compartments: surface water, deep water, ET areas (i.e., areas where fine sediment erosion and transport processes dominate the bottom dynamic conditions and resuspension appears) and A-areas (i.e., areas of continuous fine sediment accumulation). Criteria to define the boundaries for the given coastal area towards the sea, and to define whether a coastal area is open or closed are given in operational terms. The model is simple to apply since all driving variables may be readily accessed from maps and standard monitoring programs. The driving variables are: latitude, catchment area, mean annual precipitation, fallout and month of fallout and parameters expressing coastal size and form as determined from, e.g., digitized bathymetric maps using a GIS program. Selected results: the predictions of radionuclide concentrations in water and fish largely depend on two factors, the concentration in the sea outside the given coastal area and/or adjacent coastal areas and the ecological half-life of the radionuclide in the sea. Uncertainties in these factors generally dominate all other uncertainties, e.g., concerning the surface water retention time, the settling velocity of the particulate fraction, the distribution coefficient regulating the fluxes in dissolved and particulate phases, the catchment area influences and the factors regulating biouptake and excretion of the radionuclide in fish. This means that the conditions in the sea are of paramount importance for the conditions in the coastal area, even for relatively enclosed coastal areas. This coastal model may be regarded as a tool for testing working hypotheses on the relative roles of different processes in different coastal areas. Such information is essential for getting realistic expectations of various remedial measures, such as coastal dredging discussed in this work.

Break-through in predictive modelling opens new possibilities for aquatic ecology and management – a review

Due to the complex nature of ecosystems, it has long been argued that process-based dynamic models will never predict well, and numerous studies and critical model tests have also shown this and that simple regression models often predict better for less work. A new generation of dynamic models have, however, been presented that invalidate previous statements about the predictive power of more comprehensive process-oriented dynamic models. These new dynamic models predict important ecosystem variables very well from few and readily accessible driving variables. This paper gives a review of these new models (mass-balance modelling for lakes, rivers and coastal areas and foodweb modelling based on functional groups) and highlights some important reasons for this break-through in modelling in terms of predictive power, wide applicability and practical use. This open new possibilities in aquatic ecology and ecosystem management, e.g., (1) to predict ecosystem effects of pollutants, (2) to estimate changes in the structure of aquatic foodwebs related to future climate changes, (3) to predict consequences of fish kill catastrophes and biomanipulations and (4) to develop new approaches to set fish quota to complement the methods used today where fish quotas are set from fish catch statistics, and not from the amount of food available for fish and for the prey of the fish, i.e., from the presuppositions given by the aquatic foodweb.

Designing an integrated knowledge base to support ecosystem services valuation

Quantifying the value of ecosystem services is important for the social recognition and acceptance of ecosystem management across multiple geographic scales. Yet, the data required to perform such quantifications and the dynamic models that allow the projection of policy changes into the future are currently scattered, incomplete, and difficult to use. We describe the design of the Ecosystem Services Database (ESD), an integrated, web-accessible knowledge base that links a relational database for temporally and spatially explicit data to dynamic simulation models. The ESD architecture supports unit standardization, scale translation in space and time, and statistical analysis. Process-based dynamic models and valuation methods can be run by end users either through a web-based simulation engine or on their own computers by means of open-source software. The knowledge base will serve as: (1) a communication tool for use by researchers in several fields; (2) an analytical tool for meta-analysis, synthesis, and prediction; (3) an educational tool to disseminate knowledge on ecosystem services and their valuation; (4) a collaborative tool for institutions involved in different aspects of ecosystem service valuation; and (5) a prototype for linking databases and dynamic models.

Assessment of nitrogen leaching from arable land in large river basins: Part I. Simulation experiments using a process-based model

A two-step procedure for analysing nitrogen leaching from arable land in large river basins is suggested: (1) application of a process-based dynamic model for a set of representative conditions in a large river basin to simulate water and nitrogen fluxes and (2) development of a fuzzy-rule based metamodel using the simulated nitrogen fluxes in Step 1 as a training set. After that the metamodel can be used for rapid assessment of water quality inside the considered ranges of parameters, describing natural conditions and management practices. This paper describes Step 1 of the procedure. Step 2 is described in an accompanying paper (Haberlandt et al., Ecological Modelling 150 (3) (2002) 277–294). The advantage of this approach is that it combines the ‘process-based foundation’ with the resulting simplicity of the metamodel. Simulation experiments for analysing nitrogen (N) leaching from arable land were performed using the Soil and Water Integrated Model (SWIM) for a set of representative conditions in the Saale basin (23 687 km 2) in Central Europe. The Saale River is one of the main tributaries of the Elbe. In advance, hydrological validation of the model was done for the whole Saale basin and validation of nitrogen dynamics was fulfilled in two mesoscale sub-basins of the Elbe. For the simulation experiments the drainage basin area was sub-divided into five climate zones and nine representative soil classes were chosen. The basic rotation and fertilisation schemes were established using regional information obtained from literature. In addition, the effects of changing the basic rotation to more/less intensive ones and changing fertilisation rates by 50% increase/decrease were studied. The ranges of simulated nitrogen fluxes for the basic rotation and fertilisation schemes are comparable to available regional estimates and differences between sub-regions and soils are plausible. The relative importance of natural and anthropogenic factors affecting nitrogen leaching for the Saale River basin was as follows: (1) soil, (2) climate, (3) fertilisation rate and (4) crop rotation. The simulation experiments provide a basis for a fuzzy-rule based metamodel approach, which aims at rapid water quality assessment of large regions.