Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability
Abstract Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a...
Published in: | Methods in Ecology and Evolution |
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Online Access: | http://dx.doi.org/10.1111/2041-210x.14423 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14423 |
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crwiley:10.1111/2041-210x.14423 2024-09-30T14:27:22+00:00 Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability Şen, Bilgecan Che‐Castaldo, Christian Lynch, Heather J. Ventura, Francesco LaRue, Michelle A. Jenouvrier, Stéphanie National Aeronautics and Space Administration National Science Foundation 2024 http://dx.doi.org/10.1111/2041-210x.14423 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14423 en eng Wiley http://creativecommons.org/licenses/by-nc/4.0/ Methods in Ecology and Evolution ISSN 2041-210X 2041-210X journal-article 2024 crwiley https://doi.org/10.1111/2041-210x.14423 2024-09-19T04:19:11Z Abstract Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of ... Article in Journal/Newspaper Aptenodytes forsteri Pygoscelis adeliae Wiley Online Library Methods in Ecology and Evolution |
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Wiley Online Library |
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English |
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Abstract Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of ... |
author2 |
National Aeronautics and Space Administration National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Şen, Bilgecan Che‐Castaldo, Christian Lynch, Heather J. Ventura, Francesco LaRue, Michelle A. Jenouvrier, Stéphanie |
spellingShingle |
Şen, Bilgecan Che‐Castaldo, Christian Lynch, Heather J. Ventura, Francesco LaRue, Michelle A. Jenouvrier, Stéphanie Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
author_facet |
Şen, Bilgecan Che‐Castaldo, Christian Lynch, Heather J. Ventura, Francesco LaRue, Michelle A. Jenouvrier, Stéphanie |
author_sort |
Şen, Bilgecan |
title |
Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
title_short |
Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
title_full |
Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
title_fullStr |
Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
title_full_unstemmed |
Detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
title_sort |
detecting stochasticity in population time series using a non‐parametric test of intrinsic predictability |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1111/2041-210x.14423 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14423 |
genre |
Aptenodytes forsteri Pygoscelis adeliae |
genre_facet |
Aptenodytes forsteri Pygoscelis adeliae |
op_source |
Methods in Ecology and Evolution ISSN 2041-210X 2041-210X |
op_rights |
http://creativecommons.org/licenses/by-nc/4.0/ |
op_doi |
https://doi.org/10.1111/2041-210x.14423 |
container_title |
Methods in Ecology and Evolution |
_version_ |
1811633473444642816 |