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Food web dynamics in correlated and autocorrelated environments

Ripa, Jörgen LU and Ives, A R (2003) In Theoretical Population Biology 64(3). p.369-384
Abstract
The densities of populations in a community or food web vary as a consequence of both population interactions and environmental (e.g. weather) fluctuations. Populations often respond to the same kinds of environmental fluctuations, and therefore experience correlated environments. Furthermore, some environmental factors change slowly over time, thereby producing positive environmental autocorrelation. We show that the effects of environmental correlation and autocorrelation on the dynamics of the populations in a food web can be large and unintuitive, but can be understood by analyzing the eigenvectors of the community (system) matrix of interactions among populations. For example, environmental correlation and autocorrelation may either... (More)
The densities of populations in a community or food web vary as a consequence of both population interactions and environmental (e.g. weather) fluctuations. Populations often respond to the same kinds of environmental fluctuations, and therefore experience correlated environments. Furthermore, some environmental factors change slowly over time, thereby producing positive environmental autocorrelation. We show that the effects of environmental correlation and autocorrelation on the dynamics of the populations in a food web can be large and unintuitive, but can be understood by analyzing the eigenvectors of the community (system) matrix of interactions among populations. For example, environmental correlation and autocorrelation may either obscure or enhance the cyclic dynamics that generally characterize predator-prey interactions even when there is no direct effect of the environment on how species interact. Thus, understanding the population dynamics of species in a food web requires explicit attention to the correlation structure of environmental factors affecting all species. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Theoretical Population Biology
volume
64
issue
3
pages
369 - 384
publisher
Academic Press
external identifiers
  • wos:000185834300011
  • pmid:14522176
  • scopus:0348228999
ISSN
1096-0325
DOI
10.1016/S0040-5809(03)00089-3
language
English
LU publication?
yes
id
e4f553ce-62d9-47d3-a6ab-9ec0c681f043 (old id 135438)
date added to LUP
2007-07-03 15:42:32
date last changed
2018-01-07 08:59:04
@article{e4f553ce-62d9-47d3-a6ab-9ec0c681f043,
  abstract     = {The densities of populations in a community or food web vary as a consequence of both population interactions and environmental (e.g. weather) fluctuations. Populations often respond to the same kinds of environmental fluctuations, and therefore experience correlated environments. Furthermore, some environmental factors change slowly over time, thereby producing positive environmental autocorrelation. We show that the effects of environmental correlation and autocorrelation on the dynamics of the populations in a food web can be large and unintuitive, but can be understood by analyzing the eigenvectors of the community (system) matrix of interactions among populations. For example, environmental correlation and autocorrelation may either obscure or enhance the cyclic dynamics that generally characterize predator-prey interactions even when there is no direct effect of the environment on how species interact. Thus, understanding the population dynamics of species in a food web requires explicit attention to the correlation structure of environmental factors affecting all species.},
  author       = {Ripa, Jörgen and Ives, A R},
  issn         = {1096-0325},
  language     = {eng},
  number       = {3},
  pages        = {369--384},
  publisher    = {Academic Press},
  series       = {Theoretical Population Biology},
  title        = {Food web dynamics in correlated and autocorrelated environments},
  url          = {http://dx.doi.org/10.1016/S0040-5809(03)00089-3},
  volume       = {64},
  year         = {2003},
}