Since the beginning of the industrial era, anthropogenic influences on the climate have steadily increased. Covering more than two thirds of the Earth’s surface, the area for exchange between the atmosphere and sea surface is large. Apart from that, the ocean is subject to several effects triggered by climate change.
Climate Change in the Ocean
The two most prominent changes to the ocean triggered by climate change are ocean warming and acidification. Both aspects affect the ocean globally. Increasing anthropogenic carbon dioxide emissions have increased partial pressure of carbon dioxide, both, in the atmosphere and the ocean. The ocean acts as sink for anthropogenic carbon dioxide and is, by increasingly taking up carbon dioxide, gradually acidified. It is estimated that surface water pH decreased by 0.1 since the beginning of the industrial era. With increasing acidification, ocean surface water becomes gradually corrosive to calcium carbonate minerals, of which many seashells are composed (Fig. 4) (Ciais et al. 2013; Rhein et al. 2013).
The ocean has a high heat capacity and absorbs solar radiation more readily than ice. It is virtually certain that the upper ocean has warmed. This warming dominates the global energy change inventory and accounts for more than 90% of the total energy change inventory, while melting ice, warming of continents, and the warming of the atmosphere play only a minor role. Warming of the upper ocean is an important factor that has led to an average sea level rise of 0.19 m between 1901 and 2010 and it is likely that the sea level rise will accelerate (Fig. 4) (Rhein et al. 2013).
Furthermore, there are plenty of regional changes connected to climate change such as patterns of salinity trends. The IPCC report defines a region as a territory characterized by specific geographical and climatological features, whose climate is affected by scale features (e.g., topography, land use characteristics, and lakes) and remote influences from other regions (IPCC 2013). Local changes in salinity are expected (Fig. 4). In general, a higher contrast between fresh and salty regions is expected with salty regions becoming saltier and vice versa. Sea level rise in combination with wind stress is expected to result in high waves in some regions. Intermediate and deep water changes are yet difficult to assess, since long-term data are lacking. Generally, changes in salinity, density, and temperature appear to occur regionally. Anthropogenic influences on coastal runoff and atmospheric deposition of nutrients are another important regional factor. Changing nutrients, such as the input of nitrogen fertilizers, can influence the biological carbon pump and ultimately lead to an increasing eutrophication of waters (Fig. 4) (Ciais et al. 2013; Rhein et al. 2013).
Seasonality and Future Changes in Phytoplankton Communities
Phytoplankton communities undergo seasonal changes. Depending on regional properties like climatic or biogeographic conditions, the changes can differ greatly. While regions near the equator undergo relatively small changes in temperature during the year, the poles are influenced by large changes caused by severe differences in sunshine intensity and daylight duration. As the environmental factors are already highly influenced by these changes, phytoplankton communities need to adapt to these different conditions as well. Specifically useful study sites are the poles, since seasonal climate variability is very distinct. Other sites that are under continuous and alternating changes are shelf and coastal systems, which are for example influenced by freshwater inflow from the mainland as well as tides and wave actions. The following examples of different regional seasonal changes over the globe and the corresponding phytoplankton community successions shall give a small overview about the vast influence of climate conditions on phytoplankton communities.
In the Arctic summer, glacial ice melt water adds iron and other nutrients into the Labrador Sea (Fig. 2). Apart from the coastal summer blooms resulting from that input, glacial meltwater nutrients travel distances of up to 300 km on the ocean’s surface (Arrigo et al. 2017). In the western Arctic, even at closely located sites, different stages of seasonal development could be observed for local phytoplankton communities. The considerable variability in quantitative abundances and biomass values of local phytoplankton species is highly dependent on the irregularity of seasonal processes in the physical environment, ice melting, heating, and the dynamics of stratification (Sukhanova et al. 2009). Furthermore, massive and widespread phytoplankton blooms could occur under the Arctic sea ice, given regional nitrogen concentrations higher than 10 μmol L−1 in 50% of the ice covered continental shelf. Those under-ice blooms are also an important factor to be taken into account when estimating changes in the arctic environment (Arrigo et al. 2012).
In the Antarctic, species abundance and composition are largely influenced by seasons in the distinct regions subjected to differences in environmental factors and processes (Deppeler and Davidson 2017). Tréguer and Jacques (1992) divided the Southern Ocean into four different zones, without considering the Permanent Ice Zone, with regard to their different nutrient regimes, physical parameters, and extents of primary production. While diatom-dominated blooms and severe nutrient decreases can be observed in the Coastal and Continental Shelf Zone, the Seasonal Ice Zone is characterized by a very variable hydrological system depending on the ice cover retreat and growth. The Permanently Open Ocean Zone is a nutrient rich region, while the northern part is characterized by a silicate limitation and the Polar Front Zone can harbor high amounts of phytoplankton. There are, however, vast regions, which are suffering under iron limitations. In general, nanoplankton dominates within sea ice and open water unless diatom blooms occur, which happens in May, November and December at the bottom of the ice as well as in January and February in open water (e.g., Perrin et al. 1987; Swart et al. 2015).
In the Cooperation Sea, which is located in the Seasonal Ice Zone as defined by Tréguer and Jacques (1992), nutrient concentrations increase throughout the year until December, where nitrate and silicate drop notably followed by a phosphate decrease. Here, the largest species diversity occurs during summer, while plenty of dead diatoms are observable in winter (Perrin et al. 1987).
On the Weddell Sea ice edge, huge differences between spring and autumn can be observed. During spring, long chains of vegetative cells form and diatoms as well as haptophytes build up gelatinous colonies in the open water, while only short chains and few single cells occur under the ice. The same conditions hold true for autumn, where short chains and single cells dominate. Furthermore, diatom spores with storage products and enlarged cells are produced then, as results of their sexual reproduction cycle. Thus, the ice edge serves as boundary of different life stages (Fryxell 1989).
One of the most productive regions in the Southern Ocean is the Western Antarctic Peninsula, where phytoplankton blooms occur around November to December, after the sea ice retreats in October. In 2012, the bloom was dominated by diatoms and Phaeocystis sp., with diatoms being the most dominant group at the peak of the bloom. Mixing events can cause a crash in the phytoplankton population, which happened in mid-December in this region. Afterwards, the population consisted of large groups of cryptophytes and Phaeocystis sp. In March, a second, smaller bloom, dominated by diatoms and Phaeocystis sp., could be observed (Goldman et al. 2015).
Within the Eastern English Channel, diatoms, Chrysophyceae, Raphidophyceae and Prymnesiophyceae contribute most to carbon biomass. 40 species of diatoms, and two species for Chrysophyceae and Raphidophyceae can be found, respectively. A yearly occurring Phaeocystis sp. spring bloom represents the group Prymnesiophyceae. During summer, mostly large diatoms (>100 μm) dominated the community, whereas during the rest of the year mostly small cells could be found. Furthermore, Cryptophyceae (seven genera) could be found in early spring and autumn, Dinophyceae (26 genera or species) were found with the highest abundance in summer as well as Chlorophyceae and Prasinophyceae (Breton et al. 2000).
Not et al. (2004) found that in the eukaryotic picoplankton the Prasinophyceae Micromonas pusilla was the dominating species in the Western English Channel. In contrast to bigger size classes, picoplankton shows a high abundance throughout the year. The microphytoplankton bloom was dominated by a few diatom species like Guinardia delicatula, Chaetoceros socialis, Pseudo-nitzschia spp. and Thalassiosira spp. during late spring and had maximum abundances during late summer (Ward et al. 2011).
Long-term data from Helgoland in the German Bight suggested interactions of different environmental conditions with phytoplankton seasonality. Increase in sunshine hours correlates with increasing Secchi depths (measure of water transparency) and water temperature. Less turbulence in the water body leads to increasing Secchi depths. Higher temperatures improve growth rates of phytoplankton, but cause lower abundances in early spring. Increased river discharge causes a decrease in salinity in spring, which negatively correlates with Secchi depth. Increasing Secchi depth and thus a bigger euphotic zone benefits the growth of phytoplankton. Concentrations of nutrients such as nitrate, phosphate, and silicate decline rapidly during spring, when the phytoplankton bloom starts and stay at low levels until autumn, when another phytoplankton bloom occurs. Depletion of nutrients causes inhibition of phytoplankton growth. In autumn and winter, new nutrients are released, causing concentrations to increase again. High zooplankton abundances cause belated phytoplankton blooms during spring. Higher grazing pressure during winter decreases phytoplankton abundances, which then need a longer recovery time (Wiltshire et al. 2015). The phytoplankton community is dominated by diatoms in spring and early summer according to daily counts (Wiltshire et al. 2008). Dinoflagellate abundance rose from spring and reached maximum values during summer, where Noctiluca scintillans, Gyrodinium spp., and Protoperidinium spp. dominated. Mixotrophic dinoflagellates occurred in lower abundances than heterotrophs, which correlate with phytoplankton availability. However, during summer 2007, a bloom could be observed, in which several dinoflagellates such as Lepidodinium chlorophorum, Scrippsiella/Pentapharsodinium spp., and Akashiwo sanguinea occurred (Löder et al. 2012). Cryptophytes could be found throughout the year with decline during diatom dominated times (Metfies et al. 2010).
As a sub-tropical region, the estuaries of the Gulf of Mexico are representing a warmer temperate region with long periods of warm temperatures as well as tropical storms (Georgiou et al. 2005; D’sa et al. 2011; Turner et al. 2017). In a study in the Pensacola Bay (Florida, USA) from 1999 to 2001, Murrell and Lores (2004) investigated the role of cyanobacteria on the seasonal dynamics. The three most abundant taxa were belonging to diatoms (Thalassiosira sp., Pennales, and Cyclotella sp.), and diatoms represented over 50% of total abundance of phytoplankton counts. During December and January, dinoflagellates had high abundances (Prorocentrum minimum, Gymnodinium sp.), whereas high abundances of chlorophytes and cryptophytes were found during the spring and summer months. Cyanobacteria showed a strong correlation with high water temperatures and had highest abundances in summer. Further characterization indicated that the cyanobacteria belonged to the Synechococcus genus. In total, cyanobacteria made up of a large percentage of total chlorophyll (on average 43%) and dominated the chlorophyll biomass during their summer peak (Murrell and Lores 2004).
Another study showing similar results was conducted by Dorado et al. (2015) in Galveston Bay (Texas, USA) from February 2008 to December 2009. North of Galveston Bay high phytoplankton biomass could be observed, with diatoms being the dominating phytoplankton group, followed by dinoflagellates, cryptophytes, and green algae. In comparison, the phytoplankton biomass was lower in the southern part of the bay and dominated by cyanobacteria. Cyanobacteria and green algae correlated inter alia to temperature and chlorophyll a. Results of a multivariate analysis also showed that dinoflagellates and cyanobacteria are more abundant in areas where vertical mixing is limited (mid- and lower region of the bay). Seasonal patterns showed that diatoms, dinoflagellates, and cryptophyte abundances were highest during winter and spring, whereas cyanobacteria were most abundant in summer. It was found that high freshwater discharge correlated with diatom growth, indicating that a decrease of freshwater is accompanied with lower nutrient concentrations. These conditions coupled with the temperature changes are then more favorable for cyanobacteria growth.
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 extensive changes in the net atmosphere-ocean balance of carbon dioxide (Gruber et al. 2009). The authors also noted the possibly important role of other oceanic regions that could be large contributors to feedbacks in the climate system.
Primary production in the ocean has declined in the last decades and corresponds with increasing sea surface temperature and decreasing iron input. Since especially in high latitudes the ocean acts as important carbon sink, a climate change related further decline in primary production suggests major implications for the carbon cycle (Gregg et al. 2003). The same trend was predicted for many regions using a global model due to increasing stratification and nutrient limiting conditions in the ocean, with exception of the poles (Henson et al. 2018). Reduced sea ice and longer bloom periods in the Arctic have already lead to an increase in net primary production (Arrigo and van Dijken 2015). In contrast, net primary production decreases were also predicted with simulations from nine Earth system models within the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (Fu et al. 2016).
Useful tools are one-dimensional biogeochemical models such as MEDUSA (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification) that can globally simulate multi-decadal plankton ecosystem scenarios (Yool et al. 2011). In a global approach, the model was used to investigate spring bloom timing related to climate change in a high resolution. The change in bloom initiation timing was substantial, which could lead to food shortages for predators. Additionally, increasing ocean stratification and nutrient limiting conditions will likely result in less total primary production (Henson et al. 2018). Detailed future predictions using this one-dimensional biogeochemical model exist for the Ross Sea in the Antarctic. Primary production for the twenty-first century was estimated and presumably increases 5% in the early and 14% in the late twenty-first century. Melting ice, increased radiance, and decreasing mixed layer depths influence primary production during the first half, diatom mass likely stays constant while Phaeocystis antarctica multiplies, which then switches for the second half. Shallower mixed layer depths will change phytoplankton composition and carbon export (Kaufman et al. 2017).
On the Patagonian coast, average primary production will likely increase and phytoplankton communities sequester significant carbon amounts important for secondary production. However, these predictions cannot be made for open ocean areas without restrictions (Villafañe et al. 2015). Furthermore, changes will vastly differ regionally, showing increasing primary production in some areas and decreasing primary production in others. Another critical value influencing phytoplankton variability and competition is the increase of stratified conditions within the water column (Yoshiyama et al. 2009).
Many studies have been conducted to gather more information about phytoplankton community changes and their effects on the food web (e.g., Edwards and Richardson 2004; Schlüter et al. 2012; Harding Jr. et al. 2015). Fu et al. (2016) used a model to simulate climate change impacts on net primary production and export production. Using an intense warming scenario, the net primary production was critically dependent on the phytoplankton community structure. This model gives a good insight in the importance of community-based studies in order to monitor changes in this sensitive system. Changes in phytoplankton communities have, for example, already been observed under changing environmental conditions in the Arctic regions. Shifts in certain protist abundances indicate an enhanced presence of potentially toxic Alexandrium dinoflagellate species (Elferink et al. 2017).
Changes in the phytoplankton composition also cause the whole food web to change since predators might have to adapt to new food sources. Alternating environmental factors can facilitate the invasion of new species, which can migrate naturally inside the water masses or might be introduced via ballast water. These atypical range expansions cause structural changes in the food web, especially if the invasive species can adapt well or even better than indigenous species and may even become dominating (Walther et al. 2002; Olenina et al. 2010).
Other effects include the shifts of bloom events, mainly due to temporal and long-term climatic changes, or the timing of phyto- and zooplankton growth. These changes in timing could result in drastic consequences of ecosystem functionality. Existing studies on trophic mismatching in the plankton community are mostly focused on interactions between spring blooms (e.g., Edwards and Richardson 2004; Wiltshire et al. 2008). Therefore, not much is known about the ecological impacts and the functioning of the marine ecosystem (Thackeray 2012).
The floral composition of Chesapeake Bay at the US coast of the Atlantic Ocean revealed a shift in phytoplankton community. With nitrogen being the limiting nutrient but diatoms requiring relatively large amounts, the local community will likely shift to a smaller diatom proportion. Anthropogenic nutrient input might trigger changes as well as climate-related shifts in phytoplankton composition (Harding Jr. et al. 2015). Seasonal variability studies can provide useful insights into future climate changes, as they can give an impression about the mechanisms leading to changes. Studies at the coast of Patagonia in Argentina exposed seasonally different phytoplankton communities to possible future conditions, like enhanced temperatures and solar radiance, nutrient enrichment, and ocean acidification. Increasing ocean temperature has little effect on pre-bloom communities. However, ultraviolet radiance during blooms leads to photochemical inhibition of phytoplankton. Increasing temperatures might lead to a decreasing mixed layer depth, which would expose the community to higher radiations (Villafañe et al. 2013). Shallower mixed layers combined with stronger solar radiance as future condition might also result in cellular stress. Chlorophyll a can decline in phytoplankton cells as response to light stress. The cells can contract and move their chloroplasts, which leads to a temporary photoinhibition of photosynthesis (Kiefer 1973). Diatoms are more prone to ozone-related negative solar UV-B radiation, which can affect aquatic systems, thus generally likely dominating future communities (Häder et al. 2007).
Ocean acidification might lead to a shift in nutrient requirements and C:N:P stoichiometry, thus influencing biogeochemical cycles (Bellerby et al. 2008). In addition, decreasing pH influences micronutrient bioavailability such as leading to decreased concentrations of iron bioavailability and increase phytoplankton iron stress (Shi et al. 2010).
Besides phytoplankton being influenced by nutrients and other environmental factors, they themselves influence the climate and environment they are living in. One example is the role of phytoplankton in the formation of former ice ages. Iron-rich dust was transported to the Southern Ocean, where water masses were rich of nutrients such as nitrate and phosphate but lacked iron. This natural iron fertilization of phytoplankton in the Subantarctic could partly explain atmospheric carbon dioxide changes over the last 1.1 million years. Measurements of foraminifera-bound nitrogen isotopes from sediment cores taken in the Subantarctic Atlantic indicated dust flux, productivity and the degree of nitrate consumption as characterizing factors for peak glacial times and millennial cold events. Triggering blooms and changes in the Southern Ocean’s food web and biological pump can be seen as the cause of the full emergence of ice age conditions. However, the main drivers for the initial carbon dioxide decrease were most likely physical processes, such as surface water stratification, wind changes and changes in sea ice extent (Martínez-Garcia et al. 2009, 2014; Jaccard et al. 2013).
Another example for phytoplankton impacts on the climate is dimethylsulfide (DMS). DMS is the degradation product of dimethylsulfoniopropionate (DMSP), which is produced by phytoplankton as an osmoprotectant and degraded by marine bacteria (Yoch 2002). The main DMSP producing phytoplankton belong to the groups of dinoflagellates and prymnesiophytes, but also include some diatoms and Chrysophyceae species (Keller et al. 1989). Important phytoplankton include Phaeocystis sp., Emiliania huxleyi, Prorocentrum sp. and Gymnodinium sp. (Yoch 2002). Since atmospheric DMS is an important sulfur source for the global environment and its oxidation causes reflection of solar radiation, it can have a cooling effect on the Earth’s temperature (for further reading, see Yoch 2002; Stefels et al. 2007; Lana et al. 2012).
Effects like these make phytoplankton blooms interesting candidates to actively help reversing the effects of climate change, for example by trying to trigger carbon sequestering blooms (Bakker et al. 2005). However, large scale blooms may have unforeseen ecological effects, such as becoming toxic (Silver et al. 2010).
Harmful Algal Blooms
Harmful algal blooms (HABs) refer to blooms of diatoms, dinoflagellates, raphidophytes, haptophytes, cyanobacteria, and certain macroalgae perceived as harmful due a negative impact on the environment or public health. Some have the capability to express toxins under certain circumstances. Other blooms are harmful not due to toxins but because the build-up of high biomass leads to disruption of food webs and development of anoxic zones (Kudela et al. 2017).
Apart from ecological effects, HABs can affect human health upon exposure to poisoned seawater, food or marine aerosols and can have severe socio-economic impacts (Pierce et al. 2003; Fleming et al. 2007). Most frequent HAB related illness worldwide is Ciguatera Fish Poisoning (CFP), which occurs manly in the tropics and subtropics. A variety of dinoflagellate species can produce toxins, such as the Gambierdiscus toxicus species complex, which can produce the toxins maitotoxin and ciguatoxin (Murata et al. 1992).
Other illnesses are Paralytic Shellfish Poisoning (PSP) and Diarrhetic Shellfish Poisoning (DSP), which can occur worldwide (Berdalet et al. 2016). Prorocentrum lima can produce a variety of toxins, i.a. DSP causative okadaic acid (Murakami et al. 1982). HABs can have extensive ecological effects, such as mass mortality of whales suffering from PSP by feeding on mackerels poisoned with saxitoxins from dinoflagellates or enhanced fish kills (Geraci et al. 1989; Glibert et al. 2001; Nash et al. 2017).
Furthermore, some diatoms can also express toxins. Several species of the genus Pseudo-nitzschia are, for example, capable of producing domoic acid (Rao et al. 1988).
HABs can have widespread occurrences. They can occur at coastlines all over the world and have been reported throughout history from Canada, Japan, Scotland, Australia, and many other places (e.g., White 1977; Murakami et al. 1982; Bruno et al. 1989; Nash et al. 2017). Although toxic blooms are a natural phenomenon, they can also be a reaction to environmental shifts and the production of toxins can be connected to environmental conditions (Etheridge and Roesler 2005). Experiments with toxin producing Alexandrium sp. showed that increasing radiance and temperature significantly enhanced toxin production (Lim et al. 2006). Nutrient changes in particular can have distinct effects in triggering toxin production. Iron fertilization can lead to formation of a toxic Pseudo-nitzschia spp. bloom and natural iron fertilization might have the same effect (Silver et al. 2010). Also low ammonium concentrations and low salinities that can be found in estuaries can lead to enhanced toxin production in Alexandrium sp. (Hamasaki et al. 2001).
Because of the damages HABs may cause, their detection and prediction is an on-going scientific challenge, which is approached with different techniques, such as molecular methods, chromatographic pigment analysis, optical spectroscopy, and remote sensing (Millie et al. 1997; John et al. 2005; Trainer et al. 2009). Automated monitoring showed promising predictions (Campbell et al. 2010). Besides establishing a monitoring network, Wells et al. (2015) suggested parameters for routine measurements, including physical parameters, nutrient concentrations, phytoplankton identification, and toxin concentrations.
Linking the effects of climate change with changes in global HAB occurrence and developing monitoring strategies has been the subject of many studies up to date (e.g., Edwards et al. 2006; Moore et al. 2008; Hallegraeff 2010; Hinder et al. 2012; Kudela et al. 2017).