An accessible method for implementing hierarchical models with spatio-temporal abundance data.
A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynami...
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| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0049395&type=printable |
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| author | Beth E Ross Mevin B Hooten David N Koons |
| author_facet | Beth E Ross Mevin B Hooten David N Koons |
| author_sort | Beth E Ross |
| collection | DOAJ |
| description | A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, 'INLA'). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time. |
| format | Article |
| id | doaj-art-d893523a94954373820e7e2ffc29b6f1 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-d893523a94954373820e7e2ffc29b6f12025-08-20T02:33:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-01711e4939510.1371/journal.pone.0049395An accessible method for implementing hierarchical models with spatio-temporal abundance data.Beth E RossMevin B HootenDavid N KoonsA common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, 'INLA'). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0049395&type=printable |
| spellingShingle | Beth E Ross Mevin B Hooten David N Koons An accessible method for implementing hierarchical models with spatio-temporal abundance data. PLoS ONE |
| title | An accessible method for implementing hierarchical models with spatio-temporal abundance data. |
| title_full | An accessible method for implementing hierarchical models with spatio-temporal abundance data. |
| title_fullStr | An accessible method for implementing hierarchical models with spatio-temporal abundance data. |
| title_full_unstemmed | An accessible method for implementing hierarchical models with spatio-temporal abundance data. |
| title_short | An accessible method for implementing hierarchical models with spatio-temporal abundance data. |
| title_sort | accessible method for implementing hierarchical models with spatio temporal abundance data |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0049395&type=printable |
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