Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Understanding evolutionary processes during past Quaternary climatic cycles: Can it be applied to the future?

Climate change affected ecological community make-up during the Quaternary which was probably both the cause of, and was caused by, evolutionary processes such as species evolution, adaptation and extinction of species and populations

A Review of the Enviro-Net Project

Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers' comments v1: 38 pages, 8 figure

Evolutionary biology for the 21st century

New theoretical and conceptual frameworks are required for evolutionary biology to capitalize on the wealth of data now becoming available from the study of genomes, phenotypes, and organisms - including humans - in their natural environments.Molecular and Cellular BiologyOrganismic and Evolutionary Biolog

Development of a new GIS-based method to detect high natural value farmlands. A case study in central Italy

An original method for the identification of High Natural Value farmlands is presented. Gathering information about land use (CORINE Land Cover), geomorphology (elevation and Terrain Ruggedness Index) and remote sensing data in a GIS environment we were able to develop a new detection process; its application to a wide sector of central Italy, in areas characterized by high biodiversity and relevant agronomic and cultural value, is presented. Thus, a new tool for diminishing sampling efforts and economic and time wastes in territorial studies is provided

An Introduction to Software Ecosystems

This chapter defines and presents different kinds of software ecosystems. The focus is on the development, tooling and analytics aspects of software ecosystems, i.e., communities of software developers and the interconnected software components (e.g., projects, libraries, packages, repositories, plug-ins, apps) they are developing and maintaining. The technical and social dependencies between these developers and software components form a socio-technical dependency network, and the dynamics of this network change over time. We classify and provide several examples of such ecosystems. The chapter also introduces and clarifies the relevant terms needed to understand and analyse these ecosystems, as well as the techniques and research methods that can be used to analyse different aspects of these ecosystems.Comment: Preprint of chapter "An Introduction to Software Ecosystems" by Tom Mens and Coen De Roover, published in the book "Software Ecosystems: Tooling and Analytics" (eds. T. Mens, C. De Roover, A. Cleve), 2023, ISBN 978-3-031-36059-6, reproduced with permission of Springer. The final authenticated version of the book and this chapter is available online at: https://doi.org/10.1007/978-3-031-36060-

Applying trait-based models to achieve functional targets for theory-driven ecological restoration

Manipulating community assemblages to achieve functional targets is a key component of restoring degraded ecosystems. The response-and-effect trait framework provides a conceptual foundation for translating restoration goals into functional trait targets, but a quantitative framework has been lacking for translating trait targets into assemblages of species that practitioners can actually manipulate. This study describes new trait-based models that can be used to generate ranges of species abundances to test theories about which traits, which trait values and which species assemblages are most effective for achieving functional outcomes. These models are generalisable, flexible tools that can be widely applied across many terrestrial ecosystems. Examples illustrate how the framework generates assemblages of indigenous species to (1) achieve desired community responses by applying the theories of environmental filtering, limiting similarity and competitive hierarchies, or (2) achieve desired effects on ecosystem functions by applying the theories of mass ratios and niche complementarity. Experimental applications of this framework will advance our understanding of how to set functional trait targets to achieve the desired restoration goals. A trait-based framework provides restoration ecology with a robust scaffold on which to apply fundamental ecological theory to maintain resilient and functioning ecosystems in a rapidly changing world

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

The onset of grasses in the Amazon drainage basin, evidence from the fossil record

Poaceae (the grass family) originated in the Cretaceous, but first dominate the palynological records of the Amazon drainage basin (ADB) in the Neogene (23 to 2.5 million years ago (Ma)). However, the ecological role of grasses in the landscape during this time remains to be resolved. In this paper, we summarise the global significance of grasses and the relevance of the fossil record, and evaluate the history of the grasses in the ADB. We present a 3-stage model of the changing role of grasses, which we based on a revision of Neogene depositional environments, the palynological record, and modern grass distribution in the Neotropics. Our model comprises the following hypotheses: (H1) assumes that from c. 23 to 9 Ma western Amazonia was dominated by a megawetland (the ‘Pebas system’) that harboured large amounts of (aquatic?) grasses. In (H2) we propose that from c. 9 Ma Andean uplift prompted megafans (extremely large alluvial fans) that extended from the Andes into the lowlands. Meanwhile, the ‘Pebas’ megawetland gradually transformed into a fluvial system. In this scenario, grasses would have had a competitive advantage and were able to colonise the newly formed megafan and fluvial landscapes. Finally, in (H3) we suggest that landscape dynamics and climatic change intensified from c. 3.5 Ma, allowing for a renewed expansion of the grasses. In addition, both the fossil and molecular records suggest that from c. 5 Ma grasses were firmly established in the tropical alpine vegetation (páramo), the tropical lowland floodplains (várzeas), and savannas (cerrado). Although further study will have to confirm the precise nature of the ADB grass history, we anticipate that abiotic processes during the Neogene and Quaternary left a strong imprint in the grass phytogeography of northern South America