Ferdinando Villa

Modeling the dynamics of the integrated earth system and the value of global ecosystem services using the GUMBO model

A global unified metamodel of the biosphere (GUMBO) was developed to simulate the integrated earth system and assess the dynamics and values of ecosystem services. It is a ‘metamodel’ in that it represents a synthesis and a simplification of several existing dynamic global models in both the natural and social sciences at an intermediate level of complexity. The current version of the model contains 234 state variables, 930 variables total, and 1715 parameters. GUMBO is the first global model to include the dynamic feedbacks among human technology, economic production and welfare, and ecosystem goods and services within the dynamic earth system. GUMBO includes modules to simulate carbon, water, and nutrient fluxes through the Atmosphere, Lithosphere, Hydrosphere ,a ndBiosphere of the global system. Social and economic dynamics are simulated within the Anthroposphere. GUMBO links these five spheres across eleven biomes, which together encompass the entire surface of the planet. The dynamics of eleven major ecosystem goods and services for each of the biomes are simulated and evaluated. Historical calibrations from 1900 to 2000 for 14 key variables for which quantitative time-series data was available produced an average R 2 of 0.922. A range of future scenarios representing different assumptions about future technological change, investment strategies and other factors have been simulated. The relative value of ecosystem services in terms of their contribution to supporting both conventional economic production and human well-being more broadly defined were estimated under each scenario, and preliminary conclusions drawn. The value of global ecosystem services was estimated to be about 4.5 times the value of Gross World Product (GWP) in the year 2000 using this approach. The model can be downloaded and run on the average PC to allow users to explore for themselves the complex dynamics of the system and the full range of policy assumptions and scenarios.

An ontology for describing and synthesizing ecological observation data

Abstract Research in ecology increasingly relies on the integration of small, focused studies, to produce larger datasets that allow for more powerful, synthetic analyses. The results of these synthetic analyses are critical in guiding decisions about how to sustainably manage our natural environment, so it is important for researchers to effectively discover relevant data, and appropriately integrate these within their analyses. However, ecological data encompasses an extremely broad range of data types, structures, and semantic concepts. Moreover, ecological data is widely distributed, with few well-established repositories or standard protocols for their archiving and retrieval. These factors make the discovery and integration of ecological data sets a highly labor-intensive task. Metadata standards such as the Ecological Metadata Language and Darwin Core are important steps for improving our ability to discover and access ecological data, but are limited to describing only a few, relatively specific aspects of data content ( e.g. , data owner and contact information, variable “names”, keyword descriptions, etc. ). A more flexible and powerful way to capture the semantic subtleties of complex ecological data, its structure and contents, and the inter-relationships among data variables is needed. We present a formal ontology for capturing the semantics of generic scientific observation and measurement. The ontology provides a convenient basis for adding detailed semantic annotations to scientific data, which crystallize the inherent “meaning” of observational data. The ontology can be used to characterize the context of an observation ( e.g. , space and time), and clarify inter-observational relationships such as dependency hierarchies ( e.g. , nested experimental observations) and meaningful dimensions within the data ( e.g. , axes for cross-classified categorical summarization). It also enables the robust description of measurement units ( e.g. , grams of carbon per liter of seawater), and can facilitate automatic unit conversions ( e.g. , pounds to kilograms). The ontology can be easily extended with specialized domain vocabularies, making it both broadly applicable and highly customizable. Finally, we describe the utility of the ontology for enriching the capabilities of data discovery and integration processes.

Patuxent landscape model: integrated ecological economic modeling of a watershed

The Patuxent Landscape Model (PLM) is designed to simulate fundamental ecological processes on the watershed scale, in interaction with an economic component that predicts the land use patterns. The paper focuses on the ecological component of the PLM and describes how the spatial and structural rescaling can be instrumental for calibration of complex spatially distributed models. The PLM is based on a modified General Ecosystem Model (GEM) that is replicated across a grid of cells that compose the rasterized landscape. Different habitats and land use types translate into different parameter sets to be fed into GEM. Cells are linked by horizontal fluxes of material and information, driven mostly by the hydrologic flows. This approach provides additional flexibility in scaling up and down over a range of spatial resolutions and is essential to track the land use change patterns generated by the economic component. Structural modularity is another important feature that is implemented in the general purpose software packages (Spatial Modeling Environment and Collaborative Modeling Environment), that the PLM employs.

A Methodology for Adaptable and Robust Ecosystem Services Assessment

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant “one model fits all” paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES - both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

Integrated ecological economic modeling of the patuxent river watershed, Maryland

Understanding the way regional landscapes operate, evolve, and change is a key area of research for ecosystem science. It is also essential to support the “place-based” management approach being advocated by the U.S. Environmental Protection Agency and other management agencies. We developed a spatially explicit, process-based model of the 2352 km2 Patuxent River watershed in Maryland to integrate data and knowledge over several spatial, temporal, and complexity scales, and to serve as an aid to regional management. In particular, the model addresses the effects of both the magnitude and spatial patterns of human settlements and agricultural practices on hydrology, plant productivity, and nutrient cycling in the landscape. The spatial resolution is variable, with a maximum of 200 × 200 m to allow adequate depiction of the pattern of ecosystems and human settlement on the landscape. The temporal resolution is different for various components of the model, ranging from hourly time steps in the hydrologic sector to yearly time steps in the economic land-use transition module. We used a modular, multiscale approach to calibrate and test the model. Model results show good agreement with data for several components of the model at several scales. A range of scenarios with the calibrated model shows the implications of past and alternative future land-use patterns and policies. We analyzed 18 scenarios including: (1) historical land-use in 1650, 1850, 1950, 1972, 1990, and 1997; (2) a “buildout” scenario based on fully developing all the land currently zoned for development; (3) four future development patterns based on an empirical economic land-use conversion model; (4) agricultural “best management practices” that lower fertilizer application; (5) four “replacement” scenarios of land-use change to analyze the relative contributions of agriculture and urban land uses; and (6) two “clustering” scenarios with significantly more and less clustered residential development than the current pattern. Results indicate the complex nature of the landscape response and the need for spatially explicit modeling.

Semantic links in integrated modelling frameworks

It is commonly accepted that modelling frameworks offer a powerful tool for modellers, researchers and decision makers, since they allow the management, re-use and integration of mathematical models from various disciplines and at different spatial and temporal scales. However, the actual re-usability of models depends on a number of factors such as the accessibility of the source code, the compatibility of different binary platforms, and often it is left to the modellers own discipline and responsibility to structure a complex model in such a way that it is decomposed in smaller re-usable sub-components. What reusable and interchangeable means is also somewhat vague; although several approaches to build modelling frameworks have been developed, little attention has been dedicated to the intrinsic re-usability of components, in particular between different modelling frameworks. In this paper, we focus on how models can be linked together to build complex integrated models. We stress that even if a model component interface is clear and reusable from a software standpoint, this is not a sufficient condition for reusing a component across different integrated modelling frameworks. This reveals the need for adding rich semantics in model interfaces.

GrOWL: A tool for visualization and editing of OWL ontologies

In an effort to optimize visualization and editing of OWL ontologies we have developed GrOWL-a browser and visual editor for OWL that accurately visualizes the underlying DL semantics of OWL ontologies while avoiding the difficulties of the verbose OWL syntax. In this paper, we discuss GrOWL visualization model and the essential visualization techniques implemented in GrOWL.

Design of multi-paradigm integrating modelling tools for ecological research

Abstract Integrating modelling tools allow different modelling paradigms to coexist and cooperate in the same simulation model. The need for such tools in ecological modelling is due to the high level of complexity of ecological and environmental decision-making problems, their multiple scales of description, the diversity of the available approaches, and the size and heterogeneity of the available datasets. This article discusses problems and perspectives in developing integrating modelling tools and introduces the Simulation Network Interface (SNI), a software package for easy coordination of different existing simulation models. The interface allows the coordination of independent simulation models residing on different machines into higher-level, multi-paradigm, distributed simulation, with minimal recoding efforts of existing models. The interface can also be used to easily provide a remote interface to simulation or data retrieval services running on different architectures. As examples of its application, we describe three ongoing projects using the SNI: (1) the integration of Swarm, an agent-based simulation toolkit, with the Spatial Modelling Environment (SME), a process-based spatial simulation toolkit; (2) the straightforward implementation of a GIS-based spatial data repository for network-based data retrieval and manipulation; and (3) a network-based calibration service for complex simulation models.

Food security in a perfect storm: using the ecosystem services framework to increase understanding

Achieving food security in a ‘perfect storm’ scenario is a grand challenge for society. Climate change and an expanding global population act in concert to make global food security even more complex and demanding. As achieving food security and the millennium development goal (MDG) to eradicate hunger influences the attainment of other MDGs, it is imperative that we offer solutions which are complementary and do not oppose one another. Sustainable intensification of agriculture has been proposed as a way to address hunger while also minimizing further environmental impact. However, the desire to raise productivity and yields has historically led to a degraded environment, reduced biodiversity and a reduction in ecosystem services (ES), with the greatest impacts affecting the poor. This paper proposes that the ES framework coupled with a policy response framework, for example Driver-Pressure-State-Impact-Response (DPSIR), can allow food security to be delivered alongside healthy ecosystems, which provide many other valuable services to humankind. Too often, agro-ecosystems have been considered as separate from other natural ecosystems and insufficient attention has been paid to the way in which services can flow to and from the agro-ecosystem to surrounding ecosystems. Highlighting recent research in a large multi-disciplinary project (ASSETS), we illustrate the ES approach to food security using a case study from the Zomba district of Malawi.

Integrating modelling architecture: a declarative framework for multi-paradigm, multi-scale ecological modelling

Multiple modelling paradigms are necessary to formulate crucial modelling problems in modern environmental science. Modelling paradigms help researchers to conceive, formulate and solve problems by providing semantic structures to organise their view of a system or process. An unusually large array of different paradigms is used in Ecology, reflecting the complexity and variety of the natural world. As a result of this, multi-disciplinary problems in particular suffer of representational difficulties that prevent them to be approached efficiently with available software toolkits. In this paper I outline the theoretical aspects of model compatibility in the operational aspects of representation, scale and domain, and I describe the Integrating Modelling Architecture (IMA), a declarative framework and an open-source software toolkit to allow integrated meta-modelling. The IMA allows to specify generic model components using a common markup language, and loads paradigm-specific grammars that can be extended to support multiple paradigms. Among the projects goals are: (1) allow web-based integration of models and state-of-the-art resources distributed across a wide area network; (2) integrate and reuse existing simulation programs and toolkits; (3) allow integration between independently developed models adopting different modelling paradigms, scales, and domains; and (4) provide extendible, efficient and clear abstractions to conceptualise and solve complex, multiple-paradigm modelling problems in environmental science. At the end of the paper I argue that an integrative meta-modelling paradigm allows us to formulate and solve new important problems, and illustrate some of the new modelling scenarios enabled by the availability of these new concepts and tools.