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Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics
Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3862810/ https://www.ncbi.nlm.nih.gov/pubmed/24349567 http://dx.doi.org/10.1371/journal.pone.0084211 |
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author | Lacy, Robert C. Miller, Philip S. Nyhus, Philip J. Pollak, J. P. Raboy, Becky E. Zeigler, Sara L. |
author_facet | Lacy, Robert C. Miller, Philip S. Nyhus, Philip J. Pollak, J. P. Raboy, Becky E. Zeigler, Sara L. |
author_sort | Lacy, Robert C. |
collection | PubMed |
description | Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation. |
format | Online Article Text |
id | pubmed-3862810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38628102013-12-17 Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics Lacy, Robert C. Miller, Philip S. Nyhus, Philip J. Pollak, J. P. Raboy, Becky E. Zeigler, Sara L. PLoS One Research Article Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation. Public Library of Science 2013-12-13 /pmc/articles/PMC3862810/ /pubmed/24349567 http://dx.doi.org/10.1371/journal.pone.0084211 Text en © 2013 Lacy et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lacy, Robert C. Miller, Philip S. Nyhus, Philip J. Pollak, J. P. Raboy, Becky E. Zeigler, Sara L. Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics |
title | Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics |
title_full | Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics |
title_fullStr | Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics |
title_full_unstemmed | Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics |
title_short | Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics |
title_sort | metamodels for transdisciplinary analysis of wildlife population dynamics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3862810/ https://www.ncbi.nlm.nih.gov/pubmed/24349567 http://dx.doi.org/10.1371/journal.pone.0084211 |
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