Nonlinear reaction–diffusion process models improve inference for population dynamics
Abstract Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ec...
Published in: | Environmetrics |
---|---|
Main Authors: | , , , , , |
Other Authors: | |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2019
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1002/env.2604 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2604 https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2604 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/env.2604 https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/env.2604 |
id |
crwiley:10.1002/env.2604 |
---|---|
record_format |
openpolar |
spelling |
crwiley:10.1002/env.2604 2024-06-23T07:52:59+00:00 Nonlinear reaction–diffusion process models improve inference for population dynamics Lu, Xinyi Williams, Perry J. Hooten, Mevin B. Powell, James A. Womble, Jamie N. Bower, Michael R. National Science Foundation 2019 http://dx.doi.org/10.1002/env.2604 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2604 https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2604 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/env.2604 https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/env.2604 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor Environmetrics volume 31, issue 3 ISSN 1180-4009 1099-095X journal-article 2019 crwiley https://doi.org/10.1002/env.2604 2024-05-31T08:15:58Z Abstract Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long‐term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density‐regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska. Article in Journal/Newspaper glacier Alaska Wiley Online Library Glacier Bay Environmetrics 31 3 |
institution |
Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
Abstract Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long‐term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density‐regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska. |
author2 |
National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Lu, Xinyi Williams, Perry J. Hooten, Mevin B. Powell, James A. Womble, Jamie N. Bower, Michael R. |
spellingShingle |
Lu, Xinyi Williams, Perry J. Hooten, Mevin B. Powell, James A. Womble, Jamie N. Bower, Michael R. Nonlinear reaction–diffusion process models improve inference for population dynamics |
author_facet |
Lu, Xinyi Williams, Perry J. Hooten, Mevin B. Powell, James A. Womble, Jamie N. Bower, Michael R. |
author_sort |
Lu, Xinyi |
title |
Nonlinear reaction–diffusion process models improve inference for population dynamics |
title_short |
Nonlinear reaction–diffusion process models improve inference for population dynamics |
title_full |
Nonlinear reaction–diffusion process models improve inference for population dynamics |
title_fullStr |
Nonlinear reaction–diffusion process models improve inference for population dynamics |
title_full_unstemmed |
Nonlinear reaction–diffusion process models improve inference for population dynamics |
title_sort |
nonlinear reaction–diffusion process models improve inference for population dynamics |
publisher |
Wiley |
publishDate |
2019 |
url |
http://dx.doi.org/10.1002/env.2604 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fenv.2604 https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2604 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/env.2604 https://onlinelibrary.wiley.com/doi/am-pdf/10.1002/env.2604 |
geographic |
Glacier Bay |
geographic_facet |
Glacier Bay |
genre |
glacier Alaska |
genre_facet |
glacier Alaska |
op_source |
Environmetrics volume 31, issue 3 ISSN 1180-4009 1099-095X |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/env.2604 |
container_title |
Environmetrics |
container_volume |
31 |
container_issue |
3 |
_version_ |
1802644453939216384 |