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Network Particle Tracking (Npt) For Ecosystem Thermodynamics And Risk Analysis

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Conference

2009 Annual Conference & Exposition

Location

Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009

ISSN

2153-5965

Conference Session

Biological and Agricultural Tech Session I

Tagged Division

Biological & Agricultural

Page Count

24

Page Numbers

14.902.1 - 14.902.24

Permanent URL

https://peer.asee.org/5660

Download Count

54

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Paper Authors

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Ernest Tollner University of Georgia, Athens

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John Schramski University of Georgia

biography

Caner Kazanci University of Georgia

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Dr. Caner Kazanci is a native of Izmir, Turkey and received his MS and PhD degrees in Mathematical Sciences Department from Carnegie Mellon University, Pittsburgh, PA. His graduate work was on mathematical biology, and was concerned with modeling and analysis of large
biochemical pathways. He is currently an assistant professor at the University of Georgia, in a joint appointment in Department of
Mathematics and Faculty of Engineering. He is the developer of EcoNet, a web based software for ecosystem modeling, simulation and analysis. Dr. Kazanci and Dr. Tollner developed Network Particle Tracking (NPT), a new agent-based simulation technique that provides detailed analysis of ecological networks, which is compatible with the conventional differential equation representation of ecosystem models.

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Network Particle Tracking (NPT) for Ecosystem Thermodynamics and Risk Analysis Abstract

Network Particle tracking (NPT), building on the network environ analysis (NEA) foundation, represents a new development in the soft realist epistemological trajectory defined by numerous studies that have defined existential subsystems and coherence relations among the systems. Three ecosystem models are evaluated using conventional NEA approaches and with NPT. Compartments in a model with high indirect effects and Finn’s cycling index showed a lack of correlation among compartments between NEA storage/throughflow versus particle repeat visits numbers/particles in compartments at steady state, while with two models having lower indirect effect/Finn’s cycling, the correlation between NEA and NPT outputs was high. In an analysis of ecological orientors associated with NEA, it became apparent that NPT fully supports the conventional NEA analysis when the common assumptions of donor control and steady state flows are satisfied. Being able to track particle history enables views of multiple scales and the possibility of making pathway-dependent modeling decisions. NPT enables researchers and students alike to have a more realistic view of compartment dynamics in ecological and, by extension, other similar compartmental models found in bioprocessing and environmental domains.

Key Words: stochastic differential equation, network environ analysis, input-output models, compartment modeling, network particle tracking, ecological network.

Background

Definitions of an ecosystem that form distinct subsections of the biosphere are a common feature of many ecosystem models (Barkmann et al. 1998). These authors acknowledge delineated ecosystems have a subjective quality but never-the-less exist as units, which in principle may be described empirically as open systems. These systems exhibit developmental trends abstracted from observational data (e.g., Fath et. al 2004). Following classical enlightenment era approaches, Newtonian determinism prevailed in ecological modeling efforts beginning with linear trophic models, thoroughly western and Newtonian in orientation (e.g., Matis et. al 1979).

Patten and colleagues developed network environ analysis (NEA) (Patten 1978, Barber et al. 1979, Fath and Patten 1999, Fath and Borrett 2006, Schramski 2006), a form of Ecological Network Analysis (ENA), to model the networks of complex ecological systems. Affording particular mathematical and ecological interpretive advantages, NEA uniquely represents objects as simultaneously participating in the dual environments of both their incoming and outgoing networks. ENA and NEA in

Tollner, E., & Schramski, J., & Kazanci, C. (2009, June), Network Particle Tracking (Npt) For Ecosystem Thermodynamics And Risk Analysis Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. https://peer.asee.org/5660

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