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Title: Individual-based modeling of fish: Linking to physical models and water quality.

Abstract

The individual-based modeling approach for the simulating fish population and community dynamics is gaining popularity. Individual-based modeling has been used in many other fields, such as forest succession and astronomy. The popularity of the individual-based approach is partly a result of the lack of success of the more aggregate modeling approaches traditionally used for simulating fish population and community dynamics. Also, recent recognition that it is often the atypical individual that survives has fostered interest in the individual-based approach. Two general types of individual-based models are distribution and configuration. Distribution models follow the probability distributions of individual characteristics, such as length and age. Configuration models explicitly simulate each individual; the sum over individuals being the population. DeAngelis et al (1992) showed that, when distribution and configuration models were formulated from the same common pool of information, both approaches generated similar predictions. The distribution approach was more compact and general, while the configuration approach was more flexible. Simple biological changes, such as making growth rate dependent on previous days growth rates, were easy to implement in the configuration version but prevented simple analytical solution of the distribution version.

Authors:
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Electric Power Research Inst., Palo Alto, CA (United States)
OSTI Identifier:
634025
Report Number(s):
CONF-9707113-1; ESD-4692
ON: DE97009379; CNN: Contract RP2932-2; TRN: AHC2DT01%%35
DOE Contract Number:  
AC05-96OR22464
Resource Type:
Technical Report
Resource Relation:
Conference: Conference on ecosystem modeling at Army Corp of Engineers sites, Morrilton, AR (United States), 1-3 Jul 1997; Other Information: PBD: Aug 1997
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 56 BIOLOGY AND MEDICINE, APPLIED STUDIES; MATHEMATICAL MODELS; POPULATION DYNAMICS; WATER POLLUTION; BIOLOGICAL INDICATORS; FISHES; EXPERIMENTAL DATA

Citation Formats

Rose, K A. Individual-based modeling of fish: Linking to physical models and water quality.. United States: N. p., 1997. Web. doi:10.2172/634025.
Rose, K A. Individual-based modeling of fish: Linking to physical models and water quality.. United States. https://doi.org/10.2172/634025
Rose, K A. 1997. "Individual-based modeling of fish: Linking to physical models and water quality.". United States. https://doi.org/10.2172/634025. https://www.osti.gov/servlets/purl/634025.
@article{osti_634025,
title = {Individual-based modeling of fish: Linking to physical models and water quality.},
author = {Rose, K A},
abstractNote = {The individual-based modeling approach for the simulating fish population and community dynamics is gaining popularity. Individual-based modeling has been used in many other fields, such as forest succession and astronomy. The popularity of the individual-based approach is partly a result of the lack of success of the more aggregate modeling approaches traditionally used for simulating fish population and community dynamics. Also, recent recognition that it is often the atypical individual that survives has fostered interest in the individual-based approach. Two general types of individual-based models are distribution and configuration. Distribution models follow the probability distributions of individual characteristics, such as length and age. Configuration models explicitly simulate each individual; the sum over individuals being the population. DeAngelis et al (1992) showed that, when distribution and configuration models were formulated from the same common pool of information, both approaches generated similar predictions. The distribution approach was more compact and general, while the configuration approach was more flexible. Simple biological changes, such as making growth rate dependent on previous days growth rates, were easy to implement in the configuration version but prevented simple analytical solution of the distribution version.},
doi = {10.2172/634025},
url = {https://www.osti.gov/biblio/634025}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Aug 01 00:00:00 EDT 1997},
month = {Fri Aug 01 00:00:00 EDT 1997}
}