Detecting Periodicity in Quantitative versus Semi-Quantitative Time Series

Stockwell, Jason D.; Burkey, Tormod V.; Cazelles, Bernard; Ménard, Frédéric; Ascioti, Fortunato A.; Himschoot, Patricia; Wu, Jieying

doi:10.1007/978-1-4615-1769-6_8

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Time series consisting of 50 observations are frequently necessary for ARIMA models, and extremely long time series may be required to elucidate complex processes operating at seasonal, annual, and decadal scales. Spectral analysis, contingency periodograms, and principal components analysis may also be used to characterize cyclical variability in time series (Chatfield 1984;Jassby & Powell 1990;Cloern & Jassby 1995;Stockwell et al 1995). The three techniques are primarily used to identify or quantify the magnitude of periodicities (e.g., diurnal, seasonal, annual, decadal) in a time series.…”

Section: Restoration Ecology December 1997mentioning

confidence: 99%

Quantitatively Evaluating Restoration Experiments: Research Design, Statistical Analysis, and Data Management Considerations

Michener

1997

Restoration Ecology

156

118

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Conceptual and logistical challenges associated with the design and analysis of ecological restoration experiments are often viewed as being insurmountable, thereby limiting the potential value of restoration experiments as tests of ecological theory. Such research constraints are, however, not unique within the environmental sciences. Numerous natural and anthropogenic disturbances represent unplanned, uncontrollable events that cannot be replicated or studied using traditional experimental approaches and statistical analyses. A broad mix of appropriate research approaches (e.g., long‐term studies, large‐scale comparative studies, space‐for‐time substitution, modeling, and focused experimentation) and analytical tools (e.g., observational, spatial, and temporal statistics) are available and required to advance restoration ecology as a scientific discipline. In this article, research design and analytical options are described and assessed in relation to their applicability to restoration ecology. Significant research benefits may be derived from explicitly defining conceptual models and presuppositions, developing multiple working hypotheses, and developing and archiving high‐quality data and metadata. Flexibility in research approaches and statistical analyses, high‐quality databases, and new sampling approaches that support research at broader spatial and temporal scales are critical for enhancing ecological understanding and supporting further development of restoration ecology as a scientific discipline.

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Section: Restoration Ecology December 1997mentioning

confidence: 99%

Quantitatively Evaluating Restoration Experiments: Research Design, Statistical Analysis, and Data Management Considerations

Michener

1997

Restoration Ecology

156

118

View full text Add to dashboard Cite

show abstract

Symbolic dynamics for identifying similarity between rhythms of ecological time series

Cazelles

2004

Ecology Letters

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Explaining the associations between animal populations or between population and environmental signals is an important challenge. The time series that quantify animal populations are often complex, nonlinear, noisy and non-stationary. These characteristics may make it inappropriate to use traditional techniques when analysing these time series and their mutual dependencies. Here I propose to use symbolic dynamics and techniques from Information Theory to evaluate the degree of dynamic cohesion between time series fluctuations. The main idea is to check whether two (or more) signals tend to oscillate simultaneously, rising and falling together with the same rhythm. Based on synthetic and real time series, I demonstrate that this method is robust to the presence of noise and to the short length of the analysed time series and gives relevant information about the weak relationships between different series. Furthermore, this method appears as simple as classical cross-correlation and outperforms it in the analysed examples.

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Detecting Periodicity in Quantitative versus Semi-Quantitative Time Series

Cited by 2 publications

References 14 publications

Quantitatively Evaluating Restoration Experiments: Research Design, Statistical Analysis, and Data Management Considerations

Quantitatively Evaluating Restoration Experiments: Research Design, Statistical Analysis, and Data Management Considerations

Symbolic dynamics for identifying similarity between rhythms of ecological time series

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