@article{Thomas-2017-The,
title = "The predictability of a lake phytoplankton community, from hours to years",
author = "Thomas, Mridul K. and
Fontana, Simone and
Reyes, Marta and
Kehoe, Michael and
Pomati, Francesco",
journal = "",
year = "2017",
publisher = "Copernicus GmbH",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G17-42003",
doi = "10.1101/230722",
abstract = "Abstract Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R 2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.",
}
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<abstract>Abstract Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R 2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.</abstract>
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%0 Journal Article
%T The predictability of a lake phytoplankton community, from hours to years
%A Thomas, Mridul K.
%A Fontana, Simone
%A Reyes, Marta
%A Kehoe, Michael
%A Pomati, Francesco
%D 2017
%I Copernicus GmbH
%F Thomas-2017-The
%X Abstract Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R 2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.
%R 10.1101/230722
%U https://gwf-uwaterloo.github.io/gwf-publications/G17-42003
%U https://doi.org/10.1101/230722
Markdown (Informal)
[The predictability of a lake phytoplankton community, from hours to years](https://gwf-uwaterloo.github.io/gwf-publications/G17-42003) (Thomas et al., GWF 2017)
ACL
- Mridul K. Thomas, Simone Fontana, Marta Reyes, Michael Kehoe, and Francesco Pomati. 2017. The predictability of a lake phytoplankton community, from hours to years.
