Using dynamic factor analysis to show how sampling resolution and data gaps affect the recognition of patterns in limnological time series

Aguilera, Rosana; Livingstone, David M.; Marcé, Rafael; Jennings, Eleanor; Piera, Jaume; Adrian, Rita

doi:10.1080/iw-6.3.948

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…Analyses of existing time series data can provide information on how data gaps (i.e., lower sampling frequency) impact pattern detection (Aguilera et al, 2016). Quantitative treatment of seasonal dynamics, such as continuous wavelet transforms to assess periodicity (Carey, Hanson, Lathrop, & St. Amand, 2016), hysteresis (Lloyd, Freer, Johnes, & Collins, 2016) and multitable multivariate analyses (Anneville et al, 2002) calculate deviations of observations from average seasonal trajectories to objectively assess if observed phytoplankton dynamics are a result of a storm, part of seasonal trajectories that happen to overlap with a storm, or perhaps a result of other factors (e.g., seasonality in top–down processes; Sommer et al, 2012).…”

Section: Research Directionsmentioning

confidence: 99%

Storm impacts on phytoplankton community dynamics in lakes

Stockwell

Doubek

Adrian

et al. 2020

Global Change Biology

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In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments.Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short-and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.

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Section: Research Directionsmentioning

confidence: 99%

Storm impacts on phytoplankton community dynamics in lakes

Stockwell

Doubek

Adrian

et al. 2020

Global Change Biology

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177

122

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“…Thermal energy input is a key driver of variability in lake emissions during the ice‐free season (Wik et al, 2014; Yvon‐Durocher et al, 2014), in part due to the exponential temperature dependency of microbial CH 4 production (Zeikus & Winfrey, 1976). Resolving the flux variability in order to understand the ecological response to climate warming requires measuring consistently across a wide temperature range (Aguilera et al, 2016; Hampton et al, 2018). However, out of 733 northern lakes and ponds where ice‐free season CH 4 fluxes have been obtained (Wik, Varner, et al, 2016), a majority (60%) were sampled ≤3 months of the year and more than half (53%) were sampled exclusively in the warm summer months (June, July, August) when emissions tend to peak (Huttunen et al, 2003; Natchimuthu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Temperature Proxies as a Solution to Biased Sampling of Lake Methane Emissions

Jansen

Thornton

Wik

et al. 2020

Geophysical Research Letters

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Lake emissions of the climate forcing trace gas methane (CH4) are spatiotemporally variable, but biases in flux measurements arising from undersampling are poorly quantified. We use a multiyear data set (2009–2017) of ice‐free CH4 emissions from three subarctic lakes obtained with bubble traps (n = 14,677), floating chambers (n = 1,306), and surface concentrations plus a gas transfer model (n = 535) to quantify these biases and evaluate corrections. Sampling primarily in warmer summer months, as is common, overestimates the ice‐free season flux by a factor 1.4–1.8. Temperature proxies based on Arrhenius functions that closely fit measured fluxes (R2 ≥ 0.93) enable gap filling the colder months of the ice‐free season and reduce sampling bias. Ebullition (activation energy 1.36 eV) expressed greater temperature sensitivity than diffusion (1.00 eV). Resolving seasonal and interannual variability in fluxes with proxies requires ∼135 sampling days for ebullition, and 22 and 14 days for diffusion via models and chambers, respectively.

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“…This has been well documented for non-mining catchments [e.g. 2,35] and for ecosystems in general [e.g. 1].…”

Section: Introductionmentioning

confidence: 80%

Application of spectral analysis and wavelet transforms to full-scale dynamic drainages at minesites

Morin¹

2019

SN Appl. Sci.

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This paper focusses on the application of spectral analysis and continuous wavelet transforms to water drainages from full-scale minesite components, such as open pits, waste-rock piles, and tailings impoundments. Three minesite-drainage databases included high-frequency monitoring (as frequently as every 15 min) and/or long-term monitoring (up to 31 years) of both flows and aqueous chemistries. These databases were cleaned only by deleting very obvious outliers and ignoring statistical significance, so that extreme events and fractal patterns could be detected. In all three full-scale minesite-drainage databases, 1-over-f fractal slopes were common in the spectral analyses, but other slopes mostly between zero and 2.0 were also found. Spectral analyses also produced anomalous spectral slopes. Simple simulations showed these could be explained by major unseen seasonal changes in water retention by upstream buried ponds or subsurface aquitards. Wavelet transforms for the three minesite-drainage databases provided important observations such as (1) the varying strengths of periodicity with time, (2) the differing periodicities between physical drainage flows and their aqueous chemistries, and (3) the effect of placing a fine-grained soil/till cover over a waste-rock pile. Based on all three minesite-drainage databases, the most common wavelengths for strong, persistent periodicities were 1 year and 1 week. Other wavelengths of strong periodicity for at least two minesites were 10 years, approximately 4 months, and half-monthly to monthly. The minesite with data as frequent as every 15 min also showed strong periodicities over 1 day and less.

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Using dynamic factor analysis to show how sampling resolution and data gaps affect the recognition of patterns in limnological time series

Cited by 9 publications

References 3 publications

Storm impacts on phytoplankton community dynamics in lakes

Storm impacts on phytoplankton community dynamics in lakes

Temperature Proxies as a Solution to Biased Sampling of Lake Methane Emissions

Application of spectral analysis and wavelet transforms to full-scale dynamic drainages at minesites

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