Deriving state-and-transition models from an image series of grassland pattern dynamics

We present how state-and-transition models (STMs) may be derived from image data, providing a graphical means of understanding how ecological dynamics are driven by complex interactions among ecosystem events. A temporal sequence of imagery of fine scale vegetation patterning was acquired from close range photogrammetry (CRP) of 1 m quadrats, in a long term monitoring project of Themeda triandra (Forsskal) grasslands in north western Australia. A principal components scaling of image metrics calculated on the imagery defined the state space of the STM, and thereby characterised the different patterns found in the imagery. Using the state space, we were able to relate key events (i.e. fire and rainfall) to both the image data and aboveground biomass, and identified distinct ecological ‘phases’ and ‘transitions’ of the system. The methodology objectively constructs a STM from imagery and, in principle, may be applied to any temporal sequence of imagery captured in any event-driven system. Our approach, by integrating image data, addresses the labour constraint limiting the extensive use of STMs in managing vegetation change in arid and semiarid rangelands.     

Different proxies for the reactivity of aquatic sediments towards oxygen: A model assessment

Information on benthic carbon mineralization rates is often derived from the analysis of oxygen microprofiles in sediments. To enable a direct comparison of different sediment environments, it is often desirable to characterize sediments by a single proxy that expresses their “reactivity” towards oxygen. For this, there are three commonly used proxies: the oxygen penetration depth (OPD), the oxygen flux at the sediment-water interface (DOU), and the maximum volumetric oxygen consumption rate (R_max). The OPD can be directly determined from the oxygen depth profile, while the DOU is usually obtained by a linear fit to the oxygen gradient either in diffusive boundary layer. The oxygen consumption rate R_max requires the fitting of a reactive-transport model to the data profile. This article shows that the OPD alone is a suboptimal proxy, because it shows a strong dependence on the half-saturation constant K_s, and secondly, because it is sensitive to the particular re-oxidation conditions right above the oxic-anoxic interface. Similarly, the volumetric oxygen consumption rate R_max is rather strongly dependent on the kinetic model formulation employed. To show this we fitted three different (Bouldin, Blackman and Monod) kinetics to the same oxygen data profiles. When fitting these models, the R_max values obtained differed by 20% for exactly the same oxygen profile. Accordingly, if one reports R_max values, it is crucial to specify the kinetic model alongside. Overall, DOU emerges as sediment reactivity proxy which is the least model dependent.     

Carbon neutral Biggar: calculating the community carbon footprint and renewable energy options for footprint reduction

The objective of this research was to develop a community carbon footprint model that could be used to assess the size and major components of a community’s carbon dioxide (CO₂) emissions. The town of Biggar aims to become Scotland’s first carbon neutral town. As expected for this rural community, car transport accounted for nearly half of the CO₂ emissions, with natural gas and electricity consumption resulting in a further 24% and 12% of total emissions, respectively, and air travel being the last major component at 10% of emissions. An assessment was also made of the wind and solar resources of the town. One large wind turbine would provide the town’s electricity, while three to four turbines would be needed to offset all CO₂ emissions. In contrast, offsetting by tree planting would require in the region of 2,000 ha of trees.

Simulation studies of ammonia removal from water in a membrane contactor under liquid–liquid extraction mode

Simulation studies were carried out, in an unsteady state, for the removal of ammonia from water via a membrane contactor. The contactor had an aqueous solution of NH₃ in the lumen and sulphuric acid in the shell side. The model equations were developed considering radial and axial diffusion and convection in the lumen. The partial differential equations were converted by the finite difference technique into a series of stiff ordinary differential equations w.r.t. time and solved using MATLAB. Excellent agreement was observed between the simulation results and experimental data (from the literature) for a contactor of 75 fibres. Excellent agreement was also observed between the simulation results and laboratory-generated data from a contactor containing 10,200 fibres. Our model is more suitable than the plug-flow model for designing the operation of the membrane contactor. The plug-flow model over-predicts the fractional removal of ammonia and was observed to be limited when designing longer contactors.     

Modelling N mineralization from green manure and farmyard manure from a laboratory incubation study

Predicting N mineralization from organic manures like farmyard manure (FYM) is more difficult than from fresh organic materials like crop residues, as the manures vary greatly in composition. A laboratory incubation experiment was carried out for 98 days at 30 °C under aerobic conditions to study the effects on N dynamics of Gliricidia (Gliricidia sepium, Jacquin) and FYM application to soil at 5 and 10 g kg⁻¹. Application of Gliricidia induced N mineralization from the start of incubation period, with the amount of N mineralized increasing with rate of application. In contrast, application of FYM resulted in immobilization of mineral N in soil, irrespective of the rate of application. The initial net immobilization from FYM was limited by availability of N in the soil for the higher rate of application.We used the APSIM SoilN module to simulate these contrasting patterns of mineralization of N from Gliricidia and from FYM. The prediction of N mineralized from Gliricidia was better than FYM. The default model parameters specify that the fresh organic matter pools (FPOOL1, FPOOL2 and FPOOL3) have the same C:N ratio and this assumption was ineffective in predicting N mineralized from FYM. The predictive ability of the model improved when this default assumption was modified based on the size of the individual pools (FPOOL1, FPOOL2 and FPOOL3), and the pool's C:N ratios. The modelling efficiency, a measure of goodness of fit between the simulated and observed data, improved markedly for the modified model. The discrepancy between the modelled and observed data was a tendency for the model to underestimate the rate of re-mineralization at the lower rate of application of FYM in the later part of incubation. Unfortunately the appropriate modification to the size and C:N ratios of the FPOOLs could not be determined on the basis of chemical analysis alone. Thus, a true predictive application of the model to a new FYM material is not yet possible.     

What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods

Species distribution model is the term most frequently used in ecological modelling, but other authors used instead predictive habitat distribution model or species-habitat models. A consensual ecological modelling terminology that avoids misunderstandings and takes into account the ecological niche theory does not exist at present. Moreover, different studies differ in the type of niche that is represented by similar distribution models. I propose to use as standard ecological modelling terminology the terms “ecological niche”, “potential niche”, “realized niche” models (for modelling their respective niches), and “habitat suitability map” (for the output of the niche models). Therefore, the user can understand more easily that models always forecast species’ niche and relate more closely the different types of niche models.     

Modelling the temperature and the phytoplankton distributions at the Aveiro near coastal zone,Portugal

The main objective of this paper is to implement a coupled three-dimensional physical and ecological model for the Aveiro coast, and to apply it to study the temperature and the phytoplankton biomass spatial distributions along the coastal ecosystem. The Aveiro coast is located at Portugal within the upwelling system of the Atlantic Iberian coast, characterized by nutrients availability and phytoplankton biomass accumulation, from April to October. In order to implement the ecological model, its validation was assessed by comparing simulations to data relative to the horizontal and vertical distributions of the temperature, nutrients and phytoplankton biomass, obtained during the CICLOS I survey off the Portuguese coast [Moita, M.T., 2001. Estrutura, Variabilidade e Dinâmica do Fitoplâncton na Costa de Portugal Continental. PhD Thesis. Faculdade de Ciências da Universidade de Lisboa, 272 pp.]. A sensitivity analysis of the model has been performed in order to assess the influence of the main ecological model variables. The simulation results show that the model is capable of predicting realistic the temperature, the nutrients and the chlorophyll-a distributions for the study area. The scenarios evidence the setup of a thermal stratification pattern resulting from the upwelling of deep and rich in nutrients water to the surface layer and a chlorophyll-a maxima extending offshore, along the picnocline and the nutricline. The results confirm the crucial role played by the physical processes in the phytoplankton bloom along the Aveiro coast. They also evidence the close link between the surface phytoplankton distribution and the surface temperature distribution.     

Modelling the abundance of three key plant species in New Zealand hill-pasture using a decision tree approach

Decision tree models were developed to investigate and predict the relative abundance of three key pasture plants [ryegrass (Lolium perenne), browntop (Agrostis capillaris), and white clover (Trifolium repens)] with integration of a geographical information system (GIS) in a naturalised hill-pasture in the North Island, New Zealand, and were compared with regression models with respect to model fit and predictive accuracy. The results indicated that the decision tree models had a better model fit in terms of average squared error (ASE) and a higher percentage of adequately predicted cases in model validation than the corresponding regression models. These decision tree models clearly revealed the relative importance of environmental and management variables in influencing the abundance of these three species. Hill slope was the most significant environmental factor influencing the abundance of ryegrass while soil Olsen P and annual P fertilizer input were the most significant factors influencing the abundance of browntop, and white clover, respectively. Soil Olsen P of approximately 10 μg/g, or a slope of about 10.5° was critical points where the competition between ryegrass and browntop tended to come to an equilibrium. Integrating the decision tree models with a GIS in this study not only facilitated the model development and analyses, but also provided a useful decision support tool in pasture management such as in assisting precision fertilizer placement. The insights obtained from the decision tree models also have important implications for pasture management, for example, it is important to maintain a soil Olsen P higher than 10 μg/g in order to keep the dominance of ryegrass in the hill-pasture.     

Spatio-temporal dissolved oxygen dynamics in the Orbetello lagoon by fuzzy pattern recognition

This paper models the dissolved oxygen (DO) dynamics in the Orbetello lagoon as a function of the physico-chemical and ecological system variables, including the submerged vegetation, nutrients, and hydrodynamics. It should be viewed as the concluding sequel to a previous paper describing the dynamics of the lagoon ecosystem [Giusti, E., Marsili-Libelli, S., 2006. An integrated model for the Orbetello lagoon ecosystem, Ecol. Model. 196, 379–394] by introducing the missing DO dynamics. The model considers the oxygen demand originating from the decay of carbonaceous and nitrogenous compounds, as well as photosynthesis and natural reaeration by winds and currents as the oxygen producing processes. With a fixed-parameter set the model could accurately reproduce each single circadian DO cycle, but in the long run it failed to extend this fit and could not accommodate the large DO fluctuations induced by the seasonal variability. In order to enhance the model flexibility, a fuzzy pattern recognition algorithm was designed to classify the circadian DO patterns into four typical behaviours, related to the season, and estimate the corresponding parameters, with the overall model output being a fuzzy combination of these sets. The paper discusses several methods to patch the parameter sets and compares their performance in tracking long-term DO variations. A final assessment of the model validity is obtained by incorporating the whole DO dynamics (model, fuzzy pattern recognition and parameter combination) into the general lagoon model and producing a consistently correct series of DO daily distributions over a yearly cycle. Thus the paper contains both a practical and a methodological aspect. The practical one is the linking of all the lagoon dynamics to the dissolved oxygen kinetics in order to clarify to what extent macroalgae and macrophytes influence the oxygen balance. The methodological aspect consists of extending the validity of short-term models to long time-horizons through a patching technique supported by fuzzy pattern recognition.