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Effect of time series length and resolution on abundance‐ and trait‐based early warning signals of population declines.

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Title: Effect of time series length and resolution on abundance‐ and trait‐based early warning signals of population declines.
Authors: Arkilanian, A. A.1 (AUTHOR), Clements, C. F.2,3 (AUTHOR), Ozgul, A.2 (AUTHOR), Baruah, G.2 (AUTHOR) gaurav.baruah@ieu.uzh.ch
Source: Ecology. Jul2020, Vol. 101 Issue 7, p1-12. 12p.
Subjects: Time series analysis, Ecosystems, Signal-to-noise ratio, Progressive collapse, Generation X, Forecasting
Abstract: Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern‐to‐process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal‐to‐noise ratio of ecological systems and the need for high quality time series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet, it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation‐ and experimental‐based approach to assess the impacts of relative time series length and resolution on the forecasting ability of EWS. We show that EWS' performance decreases with decreasing time‐series length. However, there was no evident decrease in EWS performance as resolution decreased. Our simulations suggest a relative time series length between 10 and five generations as a minimum requirement for accurate forecasting by abundance‐based EWS. However, when trait information is included alongside abundance‐based EWS, we find positive signals at lengths one‐half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability. [ABSTRACT FROM AUTHOR]
Copyright of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 144335106
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PubTypeId: academicJournal
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  Data: Effect of time series length and resolution on abundance‐ and trait‐based early warning signals of population declines.
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  Data: <searchLink fieldCode="JN" term="%22Ecology%22">Ecology</searchLink>. Jul2020, Vol. 101 Issue 7, p1-12. 12p.
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  Data: Natural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern‐to‐process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal‐to‐noise ratio of ecological systems and the need for high quality time series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet, it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation‐ and experimental‐based approach to assess the impacts of relative time series length and resolution on the forecasting ability of EWS. We show that EWS' performance decreases with decreasing time‐series length. However, there was no evident decrease in EWS performance as resolution decreased. Our simulations suggest a relative time series length between 10 and five generations as a minimum requirement for accurate forecasting by abundance‐based EWS. However, when trait information is included alongside abundance‐based EWS, we find positive signals at lengths one‐half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/ecy.3040
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        Text: English
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        PageCount: 12
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        Type: general
      – SubjectFull: Ecosystems
        Type: general
      – SubjectFull: Signal-to-noise ratio
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      – SubjectFull: Progressive collapse
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      – SubjectFull: Generation X
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      – SubjectFull: Forecasting
        Type: general
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      – TitleFull: Effect of time series length and resolution on abundance‐ and trait‐based early warning signals of population declines.
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            NameFull: Ozgul, A.
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              Text: Jul2020
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              Y: 2020
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