Graph Theory Appliations on Agro-Ecosystem

Applied Ecology and Environmental Research, 2006

An agro-ecosystem is directed by the interactions among the populations living in it and depending on many abiotic factors. It is needed to investigate as many factors as possible. People are also interested in the effects of predicted climate change, experienced climate variability and frequently present extremal weather conditions nowadays. Elements of the system have direct and indirect influence on each other. Indirect or hidden types of interactions can not be expressed as different kinds of material flow. It would be also nice to combine the optimalisation of the proficiency and environmental protection together with the forecast risk, damages and profit. When extending the already existing models, they become more complex and immense, so simulation and monitoring are not enough for examining and describing the whole interaction process. Application of graph theory-using well known graph theory theorems and having the help of computers-is especially powerful for controlling huge systems that are difficult to survey by other existing methods. Using informatics and electronics agricultural production can be controlled through a complex system, which integrates biological, technological and economical factors.

Ecological Networks in an Agricultural World, 2013

Introduction 438 2. Which Network Model for Which Ecosystem Service Question? 440 2.1 Food web models for pest regulation services 444 2.2 Spatial network models for describing spatial and spatio-temporal agroecosystem dynamics 453 2.3 Decision interaction models for the design of management strategies 461 3. Toward a Comprehensive Approach That Links Networks and Services 469 4. Conclusions and Future Directions 470 Acknowledgements 472 References 473

19th International Scientific Conference "Economic Science for Rural Development 2018". Rural Development and Entrepreneurship Production and Co-operation in Agriculture, 2018

Nowadays the decision making process within changing condition is a crucial issue in success of the company. A practical tool for decision support is the graphical model reflecting the decision problem of its structure, to formulate and to obtain the exact mathematical solution in the form of precise assessment. The aim of the paper is to present characteristics of the role of this theory in agribusiness enterprises and its impact on the agricultural sector. In the research paper, the descriptive and comparative methods were used. The paper presents general overview of the importance of the graph theory and nets. The authors underline that it is a crucial issue for many areas of science. Scientific interest in networks has a long tradition and is associated with the emergence of graph theory in mathematics. Another area of research for graphs and networks is social network analysis. The transfer of network theory to the field of logistics is related to the companies striving to develop supply, production and distribution, and to increase the effects of cooperation between companies. Regardless of organizational and legal conditions, enterprises become participants in so-called logistics networks. In order to explain the concept of logistic network, the authors have used-graph theory. The authors' research has shown the application of the theory in agribusiness.

Zhang WJ. Computational Ecology: Graphs, Networks and Agent-based Modeling. World Scientific, Singapore, 2012

Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences. From this integrated and self-contained book, researchers, university teachers and students will be provided with an in-depth and complete insight on knowledge, methodology and recent advances of graphs, networks and agent-based-modeling in ecology and environmental sciences. Java codes and a standalone software package will be presented in the book for easy use for those not familiar with mathematical details. Contents: Graphs: Definitions and Concepts Fundamentals of Topology Matrix Representations and Computer Storage of Graphs Trees and Planar Graphs Algorithms of Graphs Directed Graphs Networks: Networks Complex Networks and Network Analysis Ecological Network Analysis: Research Advances Ecological Network Analysis: Innovations Agent-based Modeling: Agent-based Modeling Cell Automata Modeling of Pest Percolation ABM frame for Biological Community Succession and Assembly Agent-based Modeling of Ecological Problems Readership: Research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, computational science and applied mathematics.

HAL (Le Centre pour la Communication Scientifique Directe), 2014

Ecological Modelling, 2011

Methods to predict ecosystem responses to changing environmental conditions, including options for mitigation and management, are vital for scientists and policy makers. We propose that modelling ecosystems by grouping species into trophic-functional types has great utility, because it is potentially generic and applicable to any ecosystem.

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis (ENA) techniques. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ENA. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this

Applied Network Science, 2017

Complex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management.

2000

A mathematical basis for describing and analyzing holistic properties of ecosystems based on a fundamental theory of systems ecology, environ theory, is presented. The analytical methodology, network environ analysis (NEA), is introduced as a quantitative approach for ecological network analysis using a conceptual five-compartment steady-state model simulating the flow of a conserved tracer in an ecosystem. Throughflow, total system throughflow,