63
Time Series Monitoring of Phytoplankton
Diversity
Due to the importance of phytoplankton for the environment
and their large seasonal variability, long-term studies dealing
with phytoplankton diversity are a very important feature to
monitor changes and to yield predictions for the future
(Zingone et al. 2015).
Examples for European time series are Plymouth Station
L4 (Harris 2010) in the western English Channel, Helgoland
Roads in the south-eastern North Sea (Wiltshire et al. 2010)
or the HELCOM surveys in the Baltic Sea (Wasmund et al.
2011). One example for an automated system is the
Continuous Plankton Recorder survey, which collects information about plankton communities in the North Atlantic
basin (Reid et al. 2003; McQuatters-Gollop et al. 2015).
In general, time series use different time scales and sampling intervals, depending on the methods chosen, the
amount and variety of parameters and sampling area.
Therefore, sampling can range from daily sampling (e.g.,
Helgoland Roads) over monthly sampling to sampling during certain periods like phytoplankton spring blooms. A distinction can be made between manual sampling and
automatic systems like ferry boxes, floats, gliders, and moorings for measurements in the open ocean or other places that
are difficult to access. The latter are being implemented more
and more, especially during the last decades (Wiltshire et al.
2010; Church et al. 2013).
The responses monitored depend on the focus of the
respective phytoplankton studies. Short-term responses
caused by nutrient changes can be tested in lab experiments
as well as in situ during short time cruises. The observation of
responses to habitat changes, regime shifts, climate change
and other permanent adaptions require studies that cover one
or more stations over a longer time period, which for climate
change related studies is at least 30 years (e.g., Walther et al.
2002). Therefore, plankton time series are an important component in the study of long-term changes in marine biodiversity and the obtained data serve as a first indicator for changes
in the ecosystem. They can help understanding changes in
species distributions and, if explicit enough, provide working
hypotheses, which can be tested in the laboratory. Applied
benefits in using time series are a better understanding and
prediction of the occurrence of possible toxic as well as invasive organisms. For these reasons, time series serve as important tool in marine ecological research (Boero et al. 2015).
A good example for using time series for predictions is
the Continuous Plankton Recorder (CPR) survey of 50 years
of monitoring dinoflagellate and diatom compositions in the
northeast Atlantic Ocean and the North Sea, which could
help to predict the following compositional changes. In this
area, the ratio is shifting towards a larger diatom proportion.
Increasing winds and resulting turbulences yielding better
conditions for diatoms compared to dinoflagellates reinforce
this assumption (Hinder et al. 2012). These composition
shifts of the past and trends in combination with modelling
approaches can therefore be used as a forecasting system.
However, the complexity of phytoplankton communities
and a high analogy in their morphology make it difficult to
identify in particular small sized nano- and picoplankton and
to distinguish potentially toxic from non-toxic species. Most
conventional time series still use traditional microscopy
techniques. Due to their size, small protists are usually
underreported or cannot be resolved to species level in these
time series. During the last years scientists tried to implement new methods into these long-term studies to include
yet underreported organisms. Whereas pigment analyses
using HPLC or chlorophyll analyses are already part of many
long-term studies (e.g., Karl et al. 2001; Harding Jr. et al.
2015), molecular methods such as DNA microarrays and
next-generation sequencing have only been implemented in
short-termed studies so far (e.g., Gescher et al. 2008;
Medinger et al. 2010; Charvet et al. 2012).
Predictions of Phytoplankton Community
Changes in Response to Climate Change
Phytoplankton can serve as indicator for climate or environmental change-induced shifts in the plankton community.
Early studies showed that climate change does have an
observable influence on the ocean (Madden and Ramanathan
1980; Manabe and Wetherald 1980; Cess and Goldenberg
1981; Hansen et al. 1981; Ramanathan 1981; Etkins and
Epstein 1982). Enhanced carbon dioxide levels result in a
climatic change all over the globe, influencing precipitation
and temperature. Higher global temperatures ultimately lead
to higher ocean temperatures and thus a reduction of sea ice
in both coverage and thickness (Manabe and Stouffer 1980;
Rhein et al. 2013). This results in a local desalination of the
ocean, to which phytoplankton cells have to respond. Higher
carbon dioxide saturation in the atmosphere will furthermore
lead to a shift in equilibrium between air and water and result
in elevated carbon dioxide concentrations in the ocean. As a
result, the marine environment will become more acidic,
potentially influencing sensitive molecular interactions
(Kuma et al. 1996).
Physical and biological changes concerning the oceanic
carbon sink have been predicted by Sarmiento et al. (1998).
They predicted a possible reduction of carbon downward
flux in the Southern Ocean due to increasing rainfall and
stratification. Their simulations hinted at already occurring
physical and biological changes due to climate change and
atmosphere-ocean interactions. More recent studies and
models show that already small changes in the Southern
Ocean can induce feedbacks in the climate system due to
Phytoplankton Responses to Marine Climate Change – An Introduction
Time Series Monitoring of Phytoplankton
Diversity
Due to the importance of phytoplankton for the environment
and their large seasonal variability, long-term studies dealing
with phytoplankton diversity are a very important feature to
monitor changes and to yield predictions for the future
(Zingone et al. 2015).
Examples for European time series are Plymouth Station
L4 (Harris 2010) in the western English Channel, Helgoland
Roads in the south-eastern North Sea (Wiltshire et al. 2010)
or the HELCOM surveys in the Baltic Sea (Wasmund et al.
2011). One example for an automated system is the
Continuous Plankton Recorder survey, which collects information about plankton communities in the North Atlantic
basin (Reid et al. 2003; McQuatters-Gollop et al. 2015).
In general, time series use different time scales and sampling intervals, depending on the methods chosen, the
amount and variety of parameters and sampling area.
Therefore, sampling can range from daily sampling (e.g.,
Helgoland Roads) over monthly sampling to sampling during certain periods like phytoplankton spring blooms. A distinction can be made between manual sampling and
automatic systems like ferry boxes, floats, gliders, and moorings for measurements in the open ocean or other places that
are difficult to access. The latter are being implemented more
and more, especially during the last decades (Wiltshire et al.
2010; Church et al. 2013).
The responses monitored depend on the focus of the
respective phytoplankton studies. Short-term responses
caused by nutrient changes can be tested in lab experiments
as well as in situ during short time cruises. The observation of
responses to habitat changes, regime shifts, climate change
and other permanent adaptions require studies that cover one
or more stations over a longer time period, which for climate
change related studies is at least 30 years (e.g., Walther et al.
2002). Therefore, plankton time series are an important component in the study of long-term changes in marine biodiversity and the obtained data serve as a first indicator for changes
in the ecosystem. They can help understanding changes in
species distributions and, if explicit enough, provide working
hypotheses, which can be tested in the laboratory. Applied
benefits in using time series are a better understanding and
prediction of the occurrence of possible toxic as well as invasive organisms. For these reasons, time series serve as important tool in marine ecological research (Boero et al. 2015).
A good example for using time series for predictions is
the Continuous Plankton Recorder (CPR) survey of 50 years
of monitoring dinoflagellate and diatom compositions in the
northeast Atlantic Ocean and the North Sea, which could
help to predict the following compositional changes. In this
area, the ratio is shifting towards a larger diatom proportion.
Increasing winds and resulting turbulences yielding better
conditions for diatoms compared to dinoflagellates reinforce
this assumption (Hinder et al. 2012). These composition
shifts of the past and trends in combination with modelling
approaches can therefore be used as a forecasting system.
However, the complexity of phytoplankton communities
and a high analogy in their morphology make it difficult to
identify in particular small sized nano- and picoplankton and
to distinguish potentially toxic from non-toxic species. Most
conventional time series still use traditional microscopy
techniques. Due to their size, small protists are usually
underreported or cannot be resolved to species level in these
time series. During the last years scientists tried to implement new methods into these long-term studies to include
yet underreported organisms. Whereas pigment analyses
using HPLC or chlorophyll analyses are already part of many
long-term studies (e.g., Karl et al. 2001; Harding Jr. et al.
2015), molecular methods such as DNA microarrays and
next-generation sequencing have only been implemented in
short-termed studies so far (e.g., Gescher et al. 2008;
Medinger et al. 2010; Charvet et al. 2012).
Predictions of Phytoplankton Community
Changes in Response to Climate Change
Phytoplankton can serve as indicator for climate or environmental change-induced shifts in the plankton community.
Early studies showed that climate change does have an
observable influence on the ocean (Madden and Ramanathan
1980; Manabe and Wetherald 1980; Cess and Goldenberg
1981; Hansen et al. 1981; Ramanathan 1981; Etkins and
Epstein 1982). Enhanced carbon dioxide levels result in a
climatic change all over the globe, influencing precipitation
and temperature. Higher global temperatures ultimately lead
to higher ocean temperatures and thus a reduction of sea ice
in both coverage and thickness (Manabe and Stouffer 1980;
Rhein et al. 2013). This results in a local desalination of the
ocean, to which phytoplankton cells have to respond. Higher
carbon dioxide saturation in the atmosphere will furthermore
lead to a shift in equilibrium between air and water and result
in elevated carbon dioxide concentrations in the ocean. As a
result, the marine environment will become more acidic,
potentially influencing sensitive molecular interactions
(Kuma et al. 1996).
Physical and biological changes concerning the oceanic
carbon sink have been predicted by Sarmiento et al. (1998).
They predicted a possible reduction of carbon downward
flux in the Southern Ocean due to increasing rainfall and
stratification. Their simulations hinted at already occurring
physical and biological changes due to climate change and
atmosphere-ocean interactions. More recent studies and
models show that already small changes in the Southern
Ocean can induce feedbacks in the climate system due to
Phytoplankton Responses to Marine Climate Change – An Introduction