Estimation of phytoplanktonic production and system respiration from data collected by a real-time monitoring network in the Lagoon of Venice

Ciavatta, Stefano; Pastres, Roberto; Badetti, C.; Ferrari, Giorgio; Beck, M.B.

doi:10.1016/j.ecolmodel.2007.10.025

Cited by 30 publications

(18 citation statements)

References 32 publications

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“…Automated, in situ, optical and fluorescent sensors that detect chlorophyll-a (a pigment common to all phytoplankton) are relatively inexpensive and reliable enough that they are deployed in many non-turbid environments in both lakes and the ocean (Johnsen and Sakshaug 2000;Chan and Un 2001). In some regions, networks of automated "smart-buoys" relay real-time pigment data to researchers on shore (Ciavatta et al 2008;Porter et al 2005). In addition to monitoring total algal pigments, both spectral filtering approaches and specific pigment sensors allow for realtime measurement of key algal groups (Aberle et al 2006).…”

Section: Leading Indicators In the Field?mentioning

confidence: 99%

Leading indicators of phytoplankton transitions caused by resource competition

Carpenter¹,

Brock²,

Cole

et al. 2009

Theor Ecol

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Many important transitions in phytoplankton composition of lakes and oceans are related to shifts in nutrient supply ratios. Some phytoplankton transitions, such as cyanobacteria blooms in freshwater supplies and red tides in coastal oceans, are important for aquatic resource management. Therefore, it would be useful to have leading indicators which precede phytoplankton shifts and could be readily monitored in the field. We investigated potential indicators using a well-understood model of phytoplankton dynamics parameterized to mimic the transition toward cyanobacteria blooms in freshwater lakes. In stationary distributions, performance of the indicators depends on whether the species are capable of stable coexistence over a certain range of nutrient inputs. In transient simulations, however, indicators show consistent responses regardless of the possibility of stable coexistence. Leading indicators occurring 10 to 40 days prior to species shift include shift of lag-1 autoregression coefficient toward 0, low standard deviation, fluctuating skewness, and high kurtosis. These responses are different from those reported for critical transitions such as fold bifurcations. Thus, the indicators reveal clues to the mechanisms of important ecosystem transitions. In practice, indicators should be measured for multiple ecosystem variables, and interpretation of the indicators should be guided by experiments and mechanistic site-specific models to help resolve potential ambiguities.

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Section: Leading Indicators In the Field?mentioning

confidence: 99%

Leading indicators of phytoplankton transitions caused by resource competition

Carpenter¹,

Brock²,

Cole

et al. 2009

Theor Ecol

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show abstract

“…To better constrain atmospheric oxygen exchange, one can include the reaeration rate as part of the inverse modeling problem, rather than fixing its parameter value a priori. This approach was employed by Ciavatta et al () to estimate GPP in the lagoon of Venice from a 3‐yr time series of

{normalO}_{2}

concentrations measured at 30 min intervals. Respiration and reaeration rates were estimated from

{normalO}_{2}

data collected during night‐time, while GPP data was subsequently estimated from

{normalO}_{2}

data during the following day‐time.…”

mentioning

confidence: 99%

Estimating primary production from oxygen time series: A novel approach in the frequency domain

Cox

Maris

Soetaert

et al. 2015

Limnol. Oceanogr. Methods

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Based on an analysis in the frequency domain of the governing equation of oxygen dynamics in aquatic systems, we derive a new method for estimating gross primary production (GPP) from oxygen time series. The central result of this article is a relation between time averaged GPP and the amplitude of the diel harmonic in an oxygen time series. We call this relation the Fourier method for estimating GPP. To assess the performance and accuracy of the method, we generate synthetic oxygen time series with a series of gradually more complex models, and compare the result with simulated GPP. We demonstrate that the method is applicable in systems with a range of rates of mixing, air–water exchange and primary production. We also apply the new method to oxygen time series from the Scheldt estuary (Belgium) and compare it with 14C‐based GPP measurements. We demonstrate the Fourier method is particularly suited for estimating GPP in estuarine and coastal systems where tidal advection has a large imprint in observed oxygen concentrations. As such it enlarges the number of systems where GPP can be estimated from in situ oxygen concentrations. By shifting the focus to the frequency domain, we also gain some useful insights on the effect of observational error and of stochastic drivers of oxygen dynamics on metabolic estimates derived from oxygen time series.

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“…Existing models used to estimate metabolism from sonde data either omit a term for error (Cole et al 2000), or only include process error to represent lack of model fit (Hanson et al 2008;Ciavatta et al 2008). These types of models may be appropriate in some situations, but the nature of the noise present in metalimnetic data may require a different approach.…”

mentioning

confidence: 99%

Free‐water lake metabolism: addressing noisy time series with a Kalman filter

Batt

Carpenter

2012

Limnology & Ocean Methods

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Whole-ecosystem metabolism is often estimated in lakes using high frequency free-water measurements of dissolved oxygen (DO) taken in the upper mixed layer. DO dynamics in the metalimnion are not adequately captured by measurements made in the upper mixed layer, which could reduce the accuracy of whole-lake metabolism estimates made from such data. However, estimating metabolism from metalimnetic DO time series can be challenging because of high variability (noise). This study used simulated and field data to determine if metabolism estimates from metalimnetic data containing noise can be improved by accounting for both process and observation error in models. When DO time series exhibited high variability, free-water metabolism estimates obtained using a Kalman filter (which accounts for both process and observation error) were substantially more accurate than estimates obtained from models that did not account for error or accounted for process error only.

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Estimation of phytoplanktonic production and system respiration from data collected by a real-time monitoring network in the Lagoon of Venice

Cited by 30 publications

References 32 publications

Leading indicators of phytoplankton transitions caused by resource competition

Leading indicators of phytoplankton transitions caused by resource competition

Estimating primary production from oxygen time series: A novel approach in the frequency domain

Free‐water lake metabolism: addressing noisy time series with a Kalman filter

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