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Proposal of an integrated system for forecasting Harmful Algal Blooms (HAB) in Chile.

INTRODUCTION

Harmful Algal Blooms (HAB) are recurrent phenomena in aquatic ecosystems. HAB events correspond to "proliferation of microalgae in marine or brackish waters that can cause the massive death of fish, contaminate shellfish with toxins and alter ecosystems in ways that humans perceive as harmful, as it generates negative effects on public health, fishing activities, aquaculture and tourism" (Clement & Lembeye, 1994, HAB Program UNESCO/IOC, 2005). The proliferations of phytoplankton that give rise to HAB, are triggered by favorable environmental conditions in specific places, where a combination of events of different nature (biological, physical and/or chemical) determine the beginning, development and end of a bloom in a certain period. HABs can last for days and up to several months, and their spatial coverage can reach up to hundreds of kilometers (Avaria et al., 1999). Additionally, when environmental conditions are not favorable for vegetative growth, some of these microalgae can form resistance cysts that persist long time in marine sediments (Seguel et al., 2010). Thus the resuspension of sediments added to the recurrence in the triggering environmental conditions, make HABs a spatially recurrent process. The temporal and spatial scales where HABs occurs make the observation and prediction of such events a complex but inescapable task to reduce the risks in the coastal populations.

HABs have increased globally in intensity, length and geographic coverage (Villanueva, 2005). In general, the causes of this increase are not clear, but it is believed that HABs are caused by multiple factors: 1) in the case of Alexandrium, the expansion of cysts banks and their location would play a determining role in the magnitude of the events (F. Villanueva, pers. comm.), 2) the "fertilization" in closed systems such as channels (by anthropogenic action) would be contributing nitrogen and phosphorus mainly, which are the basis and substrate for many species (Smayda, 1990), 3) the climate change would favor dinoflagellates due to their extreme conditions (Lindahl, 1993) and summer reduction in rainfall, which would result in a decrease in the contributions of silicon forcing a change in functional groups from diatoms to dinoflagellates (Torres et al., 2014), and (4) the discharge of ballast water disperse contaminated water from affected regions to other areas of the country (Hallegraeff, 1993). In addition, it is possible that the higher effort of observation and detection of these events (detection by molecular tools), as well as the greater use of coastal waters (aquaculture), entails an increase in their records (Anderson, 1989).

In Chile, this increase has also been manifested. Since 1827, when the German naturalist Eduard Poeppig observed a particular coloration in Valdivia, to date more than 140 events of some harmful phytoplankton organism have been reported with more than 36 people dead (mostly in the nineties) and have been recorded around 500 poisoned persons for consumption of contaminated seafood (Silva et al., 2016). These events generated public health, economic (Martinez et al., 2008) and ecological problems (Van Dolah et al., 2001; Fire et al., 2010; Haussermann et al., 2017). As recent, a study showed that in 2015 at least 343 whales were found dead in the south of Chile, due to the consumption of the "prawn of the channels" (Munida gregaria) contaminated with HAB (Haussermann et al., 2017). Then, in early 2016, a bloom of Pseudochattonella spp. caused mortalities of salmonids of the order of 40,000 ton in Chiloe (41-43[degrees]S, Fig. 1b). Subsequently, in April of the same year, one of the largest blooms events of Alexandrium catenella occurred in the south of Chile. It extended about 400 km from the zone initially affected, interrupting the extractive activity and turning the Chiloe Archipelago into a zone of environmental, social, economic and health catastrophe without precedent in the region (Buschmann et al., 2016).

Historically, in Chile the main toxins have been found (Paralytic Shellfish Poisoning, PSP, Diarrhetic Shellfish Poisoning, DSP, and Amnesic Shellfish Poisoning, ASP) associated with HAB events. However, the only cases of severe poisoning and death have been generated in the south of the country by the dinoflagellate Alexandrium catenella (associated with the production of saxitoxins causing PSP). This species belongs to the complex Alexandrium tamarense/ catenella/fundyense (complex tamarensis) defined by its morphological attributes (Aguilera-Belmonte et al., 2011). Other dinoflagellate, Dinophysis acuta (associated with the DSP), has produced non-lethal intoxication cases. On the other hand, the species of Pseudo-nitzschia, associated with ASP, are diverse. In Chile, P. pseudodelicatissima (Hasle, 1965; Rivera, 1985), P. australis (Hasle, 1972; Rivera, 1985), P. fraudulenta (Hasle, 1972), P. delicatissima (Rivera, 1985), P. pungens (Hallegraeff, 1994), P. seriata (Cassis et al., 2002), P. calliantha and P. subfraudulenta (Alvarez et al., 2009) have been identified. In various parts of the world, these species have presented Domoic Acid (DA), a neurotoxin associated with ASP. Currently, it has been discussed that P. calliantha, P. australis, P. seriata and P. pseudodelicatissima show presence of DA in Chile (Ferrario et al., 2002; Alvarez et al., 2009; Seguel et al., 2010; Gil, 2014). However, none of these species has been related to human poisoning or mass mortalities of marine organisms, but they have severely impacted shellfish production during some periods and constitute a potential permanent threat to public health (Suarez et al., 2002; Lopez-Rivera et al., 2009).

The scientific literature on this subject is essentially descriptive for inland waters of southern Chile, with emphasis on the regions of Los Lagos, Aysen and Magallanes (Figs. 1b-1d), thanks to various studies and reports from the Instituto de Fomento Pesquero (IFOP) in the period 2006-2016 (Guzman et al, 2007, 2009, 2010, 2011, 2012, 2013, 2014, 2015), in the context of the project "Manejo y monitoreo de las mareas rojas en las regiones de Los Lagos, Aysen y Magallanes". The area south of ~45[degrees]S is where most of the studies have been carried out, because there these phenomena began to be observed in the country, being this area the best characterized in this matter (see IFOP reports). In Los Lagos Region and to the north of the Aysen Region (4045[degrees]S, Fig. 1a), the different natural phenomena that affect the frequency, intensity and geographical distribution of HABs are beginning to be understood, as well as their possible synergistic effects with anthropogenic factors that could be interacting (Buschmann, 2005).

However, there is currently no study that integrates all available information on the spatio-temporal distribution of HAB events in Chile, neither the discussion of study methodologies within the framework of ocean observing systems in the world, raising future challenges regarding HAB forecast in Chile. This study aims to perform a historical reconstruction of HAB events for the species Alexandrium catenella and Pseudo-nitzschia spp. and its spatial variability, to categorize the physical, chemical and biological factors that determine the bloom of both species. Finally, the state of the art for HAB study and a methodology for forecasting HABs events in the country are discussed.

MATERIALS AND METHODS

The information compiled from events or occurrences of these phenomena ranges from 18[degrees] to 45[degrees]S approximately (Fig. 1a). However, in the characterization of factors related to HAB in Chile and government programs for monitoring, mention is also made of the southernmost areas of the country (south of 45[degrees]S). It should be noted that, as will be seen below, the three regions most affected by these phenomena are the three to the south (Los Lagos, Aysen and Magallanes, Figs. 1b-1d).

Historical reconstruction and spatial variability of events

A historical reconstruction and study of the variability of HAB events was made based on the review and compilation of the information of occurrences or events and localities of all the reports (12) of the Programa de Vigilancia de la Marea Roja in Chile of the Instituto de Salud Publica (ISP), (2002, 2012) scientific articles and IFOP reports (8). The analyzed information consists of 34 documents during the period 1993-2012 and from 18[degrees] to 45[degrees]S approximately (Fig. 1a). These records identify the presence of the PSP and/or Alexandrium catenella and ASP and/or Pseudo-nitzschia spp. either in water samples or in seafood meat. It must be noted that the species of this last genus have similar characteristics, which makes identification difficult, so that all the species of the genus were grouped for their study (Bates et al., 1998; Anderson et al., 2002; Marchetti et al., 2004; Kudela et al., 2010).

From this information, a database was generated where the following variables were recorded; latitude, longitude, year, month and region of occurrence of these events. After this discretization of the variables in space and time, the information was presented in two ways, in 1) histograms by region, month and year to analyze the variability interannual and within a year and between regions; and 2) by a statistic for each species of the percentage of occurrence of HAB in each season of the year (summer, fall, winter, spring) for 16 groups of latitudes. This statistic is presented in pie charts that allow seeing the intraseasonal and spatial variability, and Hovmoller graphs where the seasonal and interannual variability for each one of the aforementioned groups is evaluated. Finally, all the references collected are summarized in tables by region for a better understanding of the references used.

HABs determinant factors and forecasting methodologies in ocean observing systems

An analysis of the information of the main HAB species associated with the most relevant toxin (PSP) and the one of potential danger (ASP) in the country was elaborated. This compilation was generated on the basis of historical records (articles, reports) and foreign scientific literature.

A synthesis and compilation of the above information was carried out, according to the following criteria; toxin producing species, factors and environmental conditions associated with the species, forecasting methodologies and systems in which they develop, both in Chile and overseas. In addition, the historical and current situation of Chile is described in the various programs dedicated to monitoring, vigilance and possible prediction of HABs.

RESULTS

Historical reconstruction and spatial variability of events

Below, the distribution of events or appearances of HAB by region, month, and year for each of the species considered is presented (Fig. 2).

The main affected regions (Fig. 2a) by Alexandrium catenella are Los Lagos (Fig. 1b) and Aysen (Fig. 1c), where it was present in the period 2002 and 2009 (Fig. 2b, Table 1). The presence of these species can occur throughout the year, but less in late fall and winter (Fig. 2c). The records in these last stations are also due to the fact that toxins can last several months in mollusks. In general, for A. catenella the available antecedents (Fig. 3, Guzman et al, 2002, 2007, 2009, 2010a, 2010b), show that the periods with higher probabilities of occurrence of A. catenella blooms and PSP outbreaks for Los Lagos Region (Fig. 1b), are in late spring or more likely in the summer, mainly in the south of Chiloe. While, in the Aysen Region (Fig. 1c) the blooms are most likely in summer, although there are precedents that indicate that they can also occur in fall and spring (2009, Fig. 3). In the case of Magallanes (not shown) three fields emerge: a) northern during spring-summer season and fall, b) central area during spring-summer and occasionally fall season, and c) southern during late spring and early summer (Guzman et al., 2002, 2007, 2009, 2010a, 2010b). Additionally, the information gathered supports the inter-annual variability of the distribution and levels reached for the toxic complex as well as for the relative abundance and microalgae density (Fernandez & Tocornal, 2000; Guzman et al, 2002, 2007, 2009). Thus, the years without blooming are 2007-2008, 2010-2011, 2011-2012, 2012-2013, 2013-2014 y 2014-2015 and the years with blooming are 1994, 2002, 2006-2007, 2008-2009 (Fig. 2b) and 2015-2016 (Table 1). Since the periods 2013-2014 and 2014-2015 it has been observed that blooms have been increasing in both extension and permanence in comparison to previous years, even covering winter season (June-August, Fig. 2c) (Guzman et al., 2015).

Potentially harmful species of Pseudo-nitzschia, P. cf. pseudodelicatissima and P. cf. australis are common in the channels and fjords of Chilean Patagonia, with important contributions in some sectors, being more important quantitatively P. cf. pseudodelicatissima than P. cf. australis, except in some stations of Aysen and Magallanes (Guzman et al., 2015). Pseudonitzschia has been presented mainly in the regions of Los Lagos, Antofagasta and Biobio (Fig. 2d), with the years 1994 and 2006 being the most important (Fig. 2e, Table 2), preferably in spring-summer (Fig. 2f). According to available antecedents (Guzman et al., 2002, 2007, 2009, 2010a, 2010b) that are presented in part in Fig. 3, in Los Lagos Region DA detections are manifested during the summer (January-February), and in Aysen during fall-winter (April-September), decreasing in spring (October-December, Fig. 3). Meanwhile, in the Magallanes Region (not shown, Guzman et al., 2009), DA detection is concentrated in the fall months (May and June).

Figure 4 presents the percentage of occurrence of HAB for each season (summer, fall, winter, spring) in the years analyzed for the different latitudes. As will be discussed later, contrasting spatial patterns of bloom are observed. Pseudo-nitzschia spp. a) has a more latitudinal distribution with apparitions in the north, center and south of the country that have been increasing in recent years. For its part, Alexandrium catenella b) began in the southernmost part of the country (Martinez et al., 2008) and has gradually extended to the northern sector of Chiloe (Los Lagos Region, Fig. 1b). It has remained more constant over the years and like Pseudo-nitzschia spp., has higher probabilities of occurrence in spring and summer.

The detection of subtoxic levels of PSP in northern Chile (28[degrees]-34[degrees]S, Fig. 4, Table 1), may not be caused by A. catenella, but rather by Alexandrium ostenfeldii, whose distribution north of 41[degrees]S (up to ~27[degrees]S) has been recently analyzed (Salgado et al., 2012b). However, A. ostenfeldii has been associated with PSP and/or saxitoxin (STX) in toxic episodes in temperate and subpolar waters in the oceans, but there have been no STX records in Chile associated with this species (Salgado et al., 2012b). Another source of income of PSP to the northern system comes from the transport/commercialization of marine resources from the southern part of the country (Puerto Montt, Fig. 1b; Aysen, Fig. 1c) or ballast water from maritime transport vessels (Avaria, 1999; Robles et al, 2003). It should be mentioned that the technique used for the detection of these toxins is highly vulnerable to false positives (Mouse Bioassay). In the southern zone of the country, the presence of A. catenella has presented sporadic occurrences since 1991 in Magallanes (Martinez et al., 2008) although the first poisoning by this in the area was in 1972 (Guzman et al., 1975). Since 1991 it has spread to regions further north, reaching up to Aysen in 1992 (Munoz et al., 1992) and later, to Los Lagos Region in 1998 (Avaria et al., 1999; Salgado et al., 2012). It is still present in the area with magnitude, intensity and duration not known until now (Silva et al., 2016). It should be noted that recent sediment studies indicate that the appearance of A. catenella in Los Lagos may corresponds to a process of recolonization, since there are records from the beginning of the 20th century (Salgado, 2011). For its part, the first appearance of Pseudo-nitzschia was recorded in 1995 in Magallanes (Uribe et al., 1995). However, the first detections of DA were recorded in 1999, the first in June in Tongoy Bay (Coquimbo Region) and the second in Chiloe (Los Lagos Region), affecting Chilean mussel cultures (Gil, 2014). It has been disscused in diverse localities of the country with stable toxin concentration values but increasing its frequency and spatial extension (Suarez et al., 2002; Lopez-Rivera et al., 2009). There are records of occurrence in Los Lagos, Biobio and Coquimbo regions (Fig. 1a) in mollusks, such as semelidae, oyster and clam (Gil, 2014). This has forced the Ministry of Health to initiate a program to monitor this phenomenon.

Factors and environmental conditions that determine HAB events

Information is presented on environmental factors, as well as their ranges, during HABs events that occurred mainly in Chile. Due to the small number of studies that integrate records of environmental factors in Chile (Table 3), information on foreign scientific articles is included (Foreign Literature, FL, and Table 3). It is observed that these two species in general have some contrasting characteristics (e.g., temperature, upwelling conditions, and salinity) and others in common (e.g., some nutrients, pC[O.sub.2], favorable seasons, Figs. 2-4). These characteristics are typical of the natural succession of phytoplankton, presenting differences between dinoflagellates and diatoms, the ability to mobilize and develop in different environments. For example, in turbulent environments (e.g., influenced by upwelling, kinetic energy), diatoms are developed because turbulence prevents sedimentation. In more stable environments (e.g., decrease in salinity, high temperatures in summer) dinoflagellates are favored because they have movement capacity that allows them to reduce sedimentation (Margalef et al., 1979).

There is a great difference between the three regions most affected (Figs. 1b-1d), where different phytoplankton species and environmental conditions can be found (Guzman et al., 2015). These environmental conditions are related to the latitudinal difference of each zone, with different geomorphologies characteristics and land-sea interactions (e.g., water run-off, river discharges and melting of glaciers), as well as exceptional irradiation conditions, calm and environmental temperature (Arriagada et al., 2003). Despite this, the study areas show a strong interaction between oceanographic, meteorological, and biological variables in relation to the behavior of phytoplankton abundance. Although the variables are treated separately for clarity, it is evident and it is demonstrated that there are interactions or have synergistic effects between them (Buschmann et al., 2016).

Temperature, salinity, and stratification

The positive relationship of temperature with the higher abundance of phytoplankton (Lembeye, 1998; Vila et al., 2001), the easy obtaining and recording from the first events (Guzman & Lembeye, 1975) has constituted the temperature as the factor of greatest knowledge and perhaps the most important in the study of HABs (Uribe et al., 1995; Vidal et al., 2012). For A. catenella the increase in populations mainly in springsummer occurs in optimal temperature windows (Itakura & Yamahuchi, 2001) or in relation to its increase (Lembeye, 1998; Vila et al., 2001); therefore, it is estimated that certain temperature thresholds must be reach for the A. catenella cysts to germinate. As for pseudodelicatissima distribution is favored by the wide tolerance to temperatures (2-28[degrees]C), which helps especially in the lower part of the range where other species do not seem to compete. In turn, a decrease in P. cf. australis with a slight increase in temperature in the Strait of Magellan (52[degrees]58'S, Fig. 1d) and Cape Horn (55[degrees]59'S, Fig. 1d) (Avaria et al., 2003) has been identified.

Like temperature, salinity is a variable recorded from the first events and reflects the different effects of rainfall, thaws, and contribution of continental waters in different regions (Uribe et al., 1995). Salinity is associated on the one hand with the characteristics of phytoplankton (number and/or type of species; Valenzuela & Avaria, 2009; Vidal et al., 2012) and, on the other, with the importance in density (stratification) (Cornejo et al., 2016) and the aggregation of cells, due to the formation of zones of convergence by saline oceanic waters and estuarine fresh waters (Valenzuela & Avaria, 2009; Espinoza et al., 2016). However, for A. catenella it is not clear whether salinity should be a positive anomaly (e.g., Guzman et al., 2002) or negative (Uribe et al., 1995; Diaz et al., 2013). For diatoms, a decrease in salinity is related to a decrease in P. cf. australis (Avaria et al., 2003).

Finally, stratification (caused by various mechanisms) seems to be a preponderant factor to trigger HAB events, especially in the ability to influence the decrease of the mixing (Valenzuela & Avaria, 2009) and generate possible particle retention zone (Guzman et al., 1975; Espinoza et al., 2016). In this way, the stratification can regulate the vertical and horizontal distribution of A. catenella blooms (Espinoza et al., 2016), which affect the distribution of their possible triggering factors (e.g., temperature, salinity) and drive (or inhibit, Braun et al., 1993; Guzman et al., 2002) the transport of harmful species to the photic layer (Guzman et al., 2015).

Climate-oceanographic interaction or disturbance

The evidence seems to point out that climatic disturbances and interactions lead to trigger events of blooms of A. catenella and Pseudo-nitzschia spp. They have been related from local factors like atmospheric disturbances in winter (Suarez & Clement, 2002) or after a strong storm (Arriagada et al., 2003) in Chiloe, to large scale factors (Uribe, 1988; Benavides et al., 1995; Salgado et al, 2012). In the latter case, Molinet et al. (2003), for example, hypothesizes that the A. catenella blooms would originate between mixed subantarctic surface waters and subantarctic surface waters. In addition, it states that the appropriate conditions for the massive germination of cysts come from oscillations (and possibly their interactions) of the ACC, ACW, El Nino Southern Oscillation (ENSO), interdecadal extratropical anomalies of the SST and the QBO (acronyms in the Table 3). With respect to these interactions of climate-hydrographic type of low frequency (Uribe, 1988; Guzman et al., 2002), the most cited are the teleconnections with the ENSO (see authors Table 3). This phenomenon shows a prolonged maintenance of the South Pacific anticyclone with clear positive anomalies of temperature and atmospheric pressure, high radiation, absence of precipitation, less runoff from rivers, high water temperatures, a weakening of force and wind direction, a strong anomaly of less cloudiness and probably melting of glaciers (Guzman et al., 2014, 2016). In this way, a triggering factor is suggested on a macro scale that regulates the distributions and abundances of harmful species (Salgado et al., 2012), but later local factors would regulate these biotic variables (Guzman et al., 2009, 2010a, 2014, 2016). In addition, climate change is likely to favor the occurrence of exceptional highintensity El Nino events, which would increase the probability of occurrence of HAB events (Collins et al., 2010; Vecchi & Wittenberg, 2010; Power et al, 2013; Cai et al, 2014, 2015; Sun et al, 2017).

Upwelling, retention, and transport

HABs in the world are frequently associated with upwelling fronts where passive accumulation or concentration of cells occurs in the sea (Suarez & Guzman, 1992). These fronts are generated by marked differences in density, by the presence of currents or by a combination of these factors. If the blooms are initiated near the coast, they can be advected along these lower density water plumes (Molinet et al., 2003). When they are trapped on the coast, physical aggregation and vertical migration of A. catenella are the mechanisms that seem to promote the formation of a HAB (Espinoza-Gonzalez & Bosain, 2016). On the other hand, the characteristics of the general circulation, the transport of water masses (Mardones et al., 2010) and the dispersion between 0 and 15 m depth (where it registers its greatest abundances) allow to increase the spatial distribution, which in turn, it is influenced by the drift caused by the winds (Molinet et al., 2003; Buschmann et al., 2016).

Biological and biogeochemical factors

As other agents that initiate or support the maintenance of a harmful bloom can be considered biological factors such as resistance cysts, changes in the structure of the phytoplankton community (phytoplankton taxocenosis), and chemical factors such as nutrients or ocean acidification.

Studies infer that, for a wide spatial scale, the periods where the highest values of PSP are recorded, are related to certain environmental conditions that allow differences in the composition, distribution and abundance of phytoplankton, going from a predominance of diatoms (in qualitative terms) to a predominance of dinoflagellates (Uribe et al., 1995; Arriagada et al., 2003; Vidal et al., 2012). These mechanisms are manifested due to the constant competition for resources, where in the presence of an environment without nutrient limitations and high availability of silicon, diatoms tend to predominate (M. Vergara, pers. comm.). They live and reproduce quickly but have low efficiency of resource use and when there are not enough, they begin to lose competitiveness and longerlived species such as dinoflagellates appear which tolerate lower concentrations of certain nutrients better (Margalef et al, 1979; Smayda & Reynolds, 2001; Glibert et al., 2016; D. Cassis, pers. comm.).

On the other hand, the dynamics of A. catenella blooms is highly dependent on the existence of cyst banks (Molinet et al., 2003). The formation of this state is favored by the increases of sedimentation in areas of channels with little circulation (<5 cm [s.sup.-1], Vidal et al., 2006), as well as conditions of low temperature ([10.sup.12][degrees]C), high salinity (30), depletion of N and/or P and low irradiation (20 [micro]E [m.sup.-2] s-1) (Mardones et al., 2015).

Finally, among the chemical variables, a decrease in dissolved oxygen concentrations was observed for the A. catenella bloom in Chiloe in 2002 (Arriagada et al., 2003), as well as functional adaptation responses to variations in pC[O.sub.2]/pH (Table 3) in the laboratory (Muller et al., 2016). With respect to the latter, it is proposed that the Chilean strains of A. catenella and other species of coastal phytoplankton are highly adapted to the spatio-temporal fluctuations of pC[O.sub.2]/pH in marine surface waters, becoming resilient winners in the expected effects of the climate change and ocean acidification (Muller et al., 2016). In addition to this, it can be expected that ocean acidification combined with nutrient limitation or temperature changes may increase the toxicity of HABs (Sun et al., 2011; Tatters et al., 2012).

HAB monitoring programs in Chile and forecasting methodologies

In Chile, there are different institutions that have funded research with various funds, as well as government projects and programs devoted to the study, monitoring, detecting, and control of HABs. Since 1995, the MINSAL, the ISP, and the SEREMIS (see acronyms in legend Fig. 5) formally implemented the Programa de Vigilancia de la Marea Roja for all country (Ord. 4B/6518 y Ord. 9B/3557). Subsequently, was reinforced to the Programa Nacional de Vigilancia y Control de las Intoxicaciones por Fenomenos Algales Nocivos (R. Ex. No24) in 2008, part of the PNIMR coordinated by ACHIPIA Since 2010 together with SERNAPESCA and the Undersecretary of Public Health in the context of the PNIA in 2009 (Fig. 5). The general objective is to prevent intoxications of the population derived from the consumption of marine resources contaminated with toxins. This program contemplates the monthly sampling of coast bivalves from the stations (227) and its subsequent sending for analysis (Villanueva, 2005). This has allowed monitoring these toxins in order to detect HABs in a timely manner; there are online reports from 2002 to 2013. In December 2005, REPLA was approved (Fig. 5) by Supreme Decree No345. The purpose of this regulation is to establish protection and control measures to prevent the introduction of species that they constitute hydrobiological plagues, isolate their presence, prevent the spread and move towards its eradication. In this context, through a monitoring and vigilance program of A. catenella (executed by IFOP), SUBPESCA prepares technical reports that contain the foundations for the declaration of HAB area of A. catenella, with the contribution of an advisory committee and national experts. The last Exempt Resolution No1770 of May 29, 2017, extended that of 2014 (R. Ex. No3575) until December 31, 2017, which declared as a HAB area the macrozone that extends from the south of Chiloe to the south end of the Magallanes Region, dividing it into 2 zones with certain restrictions; the sector between the Taitao Peninsula and the Gulf of Penas (Aysen Region, Fig. 1c), as well as south of the 55[degrees]S (Magallanes Region, Fig. 1d), will be considered undeclared areas as to the date does not exist information that confirms the presence of A. catenella. Additionally, a PVCAc (R. Ex. No529 of2009, Sernapesca), R. Ex. No2004 of 2017 was established, which implements a qualitative monitoring for A. catenella in Moraleda Channel and Gulf of Corcovado (Fig. 1b) for the control of wellboats. In addition, SERNAPESCA, in agreement with the United States, has been developing the PSMB since 1989 for exports to the European Union. Since 2002, the PSMB passed into the hands of private laboratories, with the ISP remaining as the reference laboratory for this program. The PSMB works on the classification and monitoring of the production areas of bivalve molluscs and other resources susceptible to being affected by marine toxins within the culture centers (~121). In this program, the environmental consulting company Plancton Andino is positioned as one of the most far-reaching, as well as in the POAS. The POAS was born in 1998 and consists of the execution of an active and systematic monitoring with different companies of the salmon industry, in order to inform opportunely in relation to the spatio-temporal distribution of the presence and concentration of harmful algae in the salmon farms in the southern Chile. Today, POAS is in the strengthening stage (POAS 2.0) and working on a HAB index. Finally, as mentioned above, the IFOP has been monitoring the XII Region since 1994 (Fig. 1d) first with FIP projects (Program 1, Fig. 5) and now between May 2006 and until 2018 with the Program 2 (Fig. 5) (Guzman et al, 2007, 2009, 2010a, 2011, 2012, 2013, 2014, 2015). In addition, since 2015, the Biobio Region has been included. This program is based on the collection of transvector shellfish for a toxicity analysis by the SEREMI of Health, along with registering oceanographic variables (temperature, salinity, density, transparency and oxygen of the water column). To date, it is the largest database of concentration of harmful plankton and toxins in these regions. It is executed with the support of the SUBPESCA, having a total of 251 stations distributed in representative areas of each region.

The effectiveness of these monitoring systems depends on the frequency of sent samples to laboratory by the regions involved and the financing available and obtained by the various public tenders; it must be kept in mind that samplings at sea (carried out by ISP or IFOP) have a limited budget, and therefore, there are a number of specific sampling stations at strategic locations.

The monitoring plan in Chile has been oriented towards the detection of HAB events from the frequent measurement of samples of potentially contaminated bivalves. However, the delivery of information is aimed at making decisions in an operational context and the management of HAB requires a study of the trigger variables, and the evolution and development of the events in an integral manner. In September 2017, the Centro de Estudios de Algas Nocivas (CREAN), located in Puerto Montt, Los Lagos Region, was inaugurated as an effort made by the IFOP with the financing of the Corporacion de Fomento de la Produccion (CORFO) to focus, go further and understand the processes of HAB that are happening with intensity in recent times on the coasts of Chile. In addition, starting in 2018 IFOP will begin the study "Programa de manejo y monitoreo de las floraciones de algas nocivas y toxinas marinas en el Oceano Pacifico desde Biobio a Aysen (I etapa) 2018". Therefore, studies on harmful algae under the IFOP will cover from 37[degrees] to 55[degrees]S, in order to have timely and reliable information to protect public health and minimize the impacts associated with HAB events.

In developed countries, there are ocean observation systems (OOS such as Ocean Observing System) that are oriented to make forecasts in the ocean conditions that allow predicting potential HAB events; namely: NOAA Harmful Algal Bloom Operational Forecast System (HAB-OFS), Southern California Coastal Ocean Observing System y Central and Northern California Ocean Observing System (CeNCOOSj (Harmful Algal Bloom Monitoring and Alert Program, HABMAP), Northwest Association of Networked Ocean Observing Systems (NANOOS), WHOI-New England Harmful Algal Bloom, Gulf of Mexico Coastal Ocean Observing System Regional Association (Harmful Algal Bloom Integrated Observing System, HABIOS), Alaska Harmful Algal Bloom Network (in initiation) y European Global Ocean Observing System (in initiation).

One of the most advanced systems in terms of monitoring and forecasting of HAB is the National Oceanic and Atmospheric Administration (NOAA). NOAA maintains an operational HAB observation and prediction system to assist federal, municipal, and industry organizations in managing the risks of harmful algal blooms that affect coastal regions. This is materialized through an Integrated Ocean Observing System (IOOS, www.ioos.noaa.gov). NOAA generates weekly HAB bulletins in three sectors monitored routinely; the Gulf of Mexico, the Gulf of Maine and California. Additionally, it offers a weekly experimental forecast for Lake Erie West. In addition, NOAA is in the development stage of new sensors for HAB detection, for the forecasting (seasonal and weekly) in other regions and the transition of these new systems in routine operations in observation systems.

Environmental data collected in real time by the IOOS have proven to be fundamental in assessing HAB threats. Hourly records of ocean temperature and information of ocean currents have helped to identify the conditions triggering the proliferation of toxic algae. This allows them to evaluate the risk associated with the consumption of marine resources during these periods [http://www.ioosassociation.org/]. IOOS works with partner research institutions to integrate ocean and coastal data, and make them compatible and easily available in one place and in the formats needed for each HAB forecast. This makes scientific work more efficient, so they invest more time in improving models and forecasts [www.ioos.noaa.gov].

The detection, monitoring, expansion, and intensification of events have been extensively addressed by several research groups (Anderson et al., 2015). However, only some of these studies are dedicated to the prediction of events (Tables 4-5). Tables 4 and 5 (based on Table 17.2 of Anderson et al., 2015) summarize some proposed models to predict different species in the two genus of this study. One observation to take into account with this information is that a single strain culture is not representative of the world population, due to the heterogeneity between same species of different localities (Muller et al., 2015).

The physical and biological coupled mechanistic models are widely used to simulate the population dynamics and the production of toxins by HABs. With these models, it is possible to make predictions of blooms by investigating the various factors that influence the germination and growth of cysts (McGillicuddy et al, 2003, 2005; Stock et al, 2005), initiation and development of blooms (He et al., 2008) and the mortality of the species that leads to the decay of bloom (McGillicuddy et al, 2003, 2005). Physical models may have more or less complexity (McGillicuddy et al., 2003) and biological models may be "on-line" (Stock et al., 2005) or "off-line" type based on the individual (McGillicuddy et al., 2003). Furthermore, in these species-specific physiological models, growth can be calibrated by applying different rates of nutrient supplement (phosphorus, Chapelle et al., 2010).

On the other hand, there are approximations for the prediction of HAB based on logic and reasoning based on rules, such as fuzzy logic. This approach constructs and quantifies a conceptual model based on a review of the literature and expert knowledge, representing the relationships between input variables, intermediate variables and the probability of a HAB event (Blauw et al., 2006). With temperature thresholds and wind mixing, it is possible to represent the gradual transitions between suitable (or not) for a bloom. It is worth noting the interest that concerns A. catenella due to the ability to become cyst and remain in the sediments while waiting for favorable conditions for their vegetative growth (Molinet et al, 2003; Seguel et al, 2011). That is why statistical models that determine the location and geographic extent of abundance of sediment cysts banks to be able to define future regions and magnitudes in events, are an important step for the prediction and prevention of these phenomena (Genovesi et al, 2009, 2013; Angeles et al, 2010, 2012; Ni-Rathaille & Raine, 2011; Anderson et al, 2014).

Data sets of several years of sampling, develop predictive logistic models (Lane et al., 2009) of Pseudo-nitzschia, as well as find thresholds of bloom values that trigger the production of toxins (Anderson et al., 2009, 2010). By achieving the statistical reconstruction of biogeochemical fields through the integration of satellite data and hydrodynamic models, the latter statistical model manages to predict HAB events of this species and its toxin (DA) up to three days in advance (Anderson et al., 2011, 2014). In addition, the predictive ability in toxic DA events can be improved by using a validated ecosystem model coupled to a ROMS model (Table 5) and with particle tracking of Pseudo-nitzschia spp. from the well-known "hot spots" of HAB training (Giddings et al., 2014).

As we said previously (Table 3), upwelling is a relevant factor for Pseudo-nitzschia species. By quantifying this phenomenon (Sacau-Cuadradro et al., 2003) by generating indexes (Palma et al., 2010) and relating them to the presence of these blooms, statistical models can be constructed to assess seasonal variation and predict conditions that are likely to promote HAB of this species (Palma et al., 2010). In this sense, the combination of SST and pigment information (remote sensing) allows the identification of key processes in blooms and their relationship with physical dynamics (Sacau-Cuadradro et al., 2003). In addition to the upwelling, another factor that controls the production of DA is the limitation in Si or P that Pseudo-nitzschia spp. may present. This is evaluated by a mechanistic model (Terseleer et al., 2013).

Finally, empirical or statistical models are used to characterize the concentration of DA in strains of Pseudo-nitzschia pungens (Blum et al., 2006). In this case, linear regression and logistic methods are employed. However, to predict parameters of a complex and dynamic environment in an autonomous way and in real time, more complex statistics such as Case Based Reasoning systems with artificial neural networks and a set of diffuse inference systems are tested (Fernandez-Riverola & Corchado, 2003). Models of artificial neural networks and genetic programming are widely used in the context of HAB problem (Recknagel et al., 2002; Muttil & Lee, 2005; Muttil & Chau, 2006; Velo-Suarez & Gutierrez-Estrada, 2007; Qian & Zhang, 2009; Gu et al., 2012). In addition, it should be noted that neural networks along with generalized and additives linear models have already been implemented in Chile (Silva et al., 2016).

According to the above, there are other prediction approaches for other species such as cyanobacteria, Dinophysis spp., Phaeocystis globosa, etc. (see Table 17.2 of Anderson et al., 2015) and other models that are not species-specific, but attack the problem as HAB in general (Wong et al, 2007, 2009; Bisset et al, 2008; Mao et al., 2009; Glibert et al., 2010; Park et al., 2013; Park & Lee, 2014; Jeong et al., 2015).

Detection, monitoring, and prediction efforts in Chile

Currently, different detection, monitoring and possible prediction projects of these events have been carried out in the country:

1) The Laboratorio de Toxinas Marinas of the Los Lagos Health Service and the Mariscope company, in conversation with the Proyecto Asociativo Regional Explora of CONICYT (Comision Nacional de Investigacion Cientifica y Tecnologica) Los Lagos. From 2003 until today they use data from 4 satellites to develop satellite maps of phytoplankton concentration and algal metabolic activity (Rodriguez-Benito et al., 2003, 2006, 2008; Grant et al, 2009).

2) There is a project of researchers of the Centro de Biotecnologia y Bioingenieria, of the Center for Mathematical Modeling (both in the Faculty of Physical and Mathematical Sciences of the University of Chile and with financing of the Programa de Investigacion Asociativa of CONICYT), scientists of the Center i-Mar from the University of Los Lagos and the IFOP in Puerto Montt. They form a multidisciplinary team that seeks to analyze the viability of a biological-mathematical model that allows understanding the dynamics of HAB episodes.

3) The project entitled "Modelo e implementacion de un sistema de seguimiento y vigilancia de marea roja al sistema de informacion geografica de la Subsecretaria de Pesca y Acuicultura" Silva et al., 2016), is a computational visualization system [http://mapas.subpesca.cl/visualizador/], with a geos-patial analysis model that relates the biological, oceanographic and meteorological variables collected by the IFOP Program 2 (see Fig. 5). This allows monitoring and vigilance of the occurrence of these events and estimating the area of influence. In 2016, FIPA No2016-13 ID No4728-43-LP16 was approved, which is the second stage that will continue with this project.

4) The Instituto Tecnologico del Salmon (INTESAL) has an online platform that seeks to follow the conditions of abundance of total phytoplankton, the presence of harmful algae and HABs, specifically of the species Alexandrium catenella, and the concentration of chlorophyll-a [http://mapas.intesal.cl/publico]. It is carried out thanks to the data collected by the Programa de Monitoreo de Fitoplancton (PROMOFI), which is owned by the companies associated with SalmonChile.

5) Taking synthetic biology tools, at the end of 2016, a group of students of the Molecular Biotechnology Engineering career at the University of Chile (UChileBiotec) develop BiMaTox, a biosensor specifically aimed at the detection of STX. The advance that this model would make with respect to the current method of detection of paralyzing toxins (mouse bioassay), would be in a lower cost and time of reaction (~3 h) as well as higher efficiency.

6) In June 2017, the Universidad de La Frontera (UFRO), together with the University of Kyoto, Okayama University, the Fisheries Research Agency of Japan, the University de Los Lagos, and the University of Antofagasta were awarded a project to monitor, predict, and detect HABs in southern Chile, especially in areas where salmon are grown for export. The aim is to build a monitoring and prediction kit (1 or 2 days) that allows identifying all the microorganisms that accompany the HAB or that can predict it before it happens.

7) During 2017, the Fondo de Fomento al Desarrollo Cientifico y Tecnologico (FONDEF) called a first thematic technological research contest on aquaculture fisheries systems against HABs (FANs-IDEA). Among those awarded, the following stand out: "Analisis de riesgo y sistema de alerta temprana de Floraciones Algales Nocivas para la acuicultura y areas de manejo en el norte de Chile" (Northern Catholic University), "Sistema masivo y de bajo costo para el monitoreo in situ de algas nocivas en toda la costa Chilena" (Pontifical Catholic University of Chile) and "Huella digital hiper-espectral de especies de marea roja mediante el acoplamiento de senales bio-opticas remotas e in situ en Chile Austral" (CSIRO-CHILE Research Foundation).

8) The Programa Nacional Estrategico, Fisheries and Aquaculture component, designed a mobile application with preventive information on HAB in Chile (i~FAN). The application, designed by the company Dialecto Sur, allows knowing the updated and detailed reports on HAB that are generated by the CREAN specialists of the IFOP.

At present, there are monitoring, detection, and/or forecasting efforts to find solutions to understand these events and thus delimit areas of risk. However, these efforts correspond to studies of particular and uncoordinated research groups. This means that at present there are no available HAB prediction models in constant operation in Chile, delivered as public forecasting tools. Given the tendency of the frequency of events (Figs. 2, 4) and the spatial extension (Fig. 3, Table 1-2) of these in the country, it is urgent to understand the processes and factors that trigger HABs to be able to have an integrated and associative system for predicting these events.

DISCUSSION

The future in the problematic of HAB: System of observation and forecast

Ocean observation systems are powerful tools for the constant and integrated monitoring of the oceans in the world and have been able to respond to various environmental problems in these systems. In developed countries that have an OOS, the policy making agencies and the central government: 1) identify and prioritize areas of basic and applied strategic research to address the HAB problem, 2) identify the most important vulnerabilities of the system, and 3) focus work on mitigation measures that have the greatest impact on society (Anderson et al., 2015). On the other hand, universities and research institutions work to strengthen scientific studies and joint collaboration to address the problems associated with HAB in a more efficient way from the point of view of research and operation (Wilson, 2011). The problem of the prediction of HAB events has been addressed from the perspective of in situ observations or remote sensing, to the use of numerical models that allow projections of the HAB (see Tables 4 and 5 of this article and 17.2 from Anderson et al., 2015). Anderson et al. (2009, 2011) represent an efficient alert system that includes multidisciplinary efforts and provides quantitative predictions of the probability of HAB events, their intensity and movement or influence along the coast. CeNCOOS, a regional association of the IOOS, with this methodology generates predictions of 1 to 3 days calculating in each pixel the probability of bloom of a toxic event (maps with color scales representative of the probability of a toxic event, http://www.cencoos. org/data/models/habs). The combination of an HAB in situ monitoring system and operational forecasting models with data assimilation, determines the HAB detection system in California, currently fully operational (Anderson et al., 2015).

HABs in Chile have been approached from the point of view of monitoring programs (PSMB, PNPCI (MR/FAN), Program 2, Fig. 5) and control through laboratory studies. However, these efforts are not enough when necessary to develop a forecasting strategy. Currently, in the Biobio Region, a research initiative has been launched called "Chilean Integrated Ocean Observation System-CHIOOS" (INNOVA 15.5 -IN.IIP, http://chioos.cl/en/). This developing system provides a technological platform that collects, integrates and delivers coastal ocean information to facilitate decision-making regarding security and resource management, the environment, maritime transport and predictions and mitigation of coastal threats in the Chilean territory. Although it is an expanding observation system, the basic components that a HAB prediction system and its requirements could have, due to their importance in the area (in particular Pseudo-nitzschia spp., A. catenella and A. ostenfeldii, Figs. 2-3), have been evaluated. These requirements are listed below: 1) Improve the current observation and continuous monitoring system by defining key observation sites ("hot spots") and incorporating the cysts distribution study, more environmental data (physical and biogeochemical), satellite information, and more and better technologies of timely detection, 2) it requires experts dedicated exclusively to the various areas that form the problem (multidisciplinary analysis). These experts must be able to integrate the exceptional combination of different conditions of these phenomena, analyze and synthesize the information, as well as conceive and validate hydrodynamic and biogeochemical models for the prediction of the oceanic environment, 3) incorporate the control and storage of data in charge of a Departamento de Almacenamiento, Manejo y Control de datos (DAMAC, Fig. 6), to generate and disseminate in a sustained manner data, information, models, products and services, 4) a comprehensive or ecosystemic understanding of these phenomena is required, understanding the physiological adaptations of these microalgae, the population growth and encystment rates (e.g., A. catenella). With this purpose, we will obtain an understanding of which are the variables that are directly related to the HAB at the national, regional and local levels. This will lead to a study of a set of variables that are basic measurements made by possible regional observation systems. The occurrence of HAB events may be conditioned for A. catenella, for example, by positive temperature anomalies, stability and stratification of the column, wind reduction, nutrients, chlorophyll-a, distribution of cysts, among others (Table 3). On the other hand, the occurrence of HAB events for Pseudo-nitzschia may be conditioned by indicators such as negative temperature anomalies, upwelling wind, presence of Equatorial Subsurface Water and turbulence indicators, chlorophylla, among others (Table 3), 5) Understanding the key biological aspects, a short-term forecasting model based on data and hydrodynamic and ecosystemic models should be developed (e.g., biophysical and/or statistical approach). This model must be validated both in laboratories and at sea in a permanent and retroactive way (T. Antezana, pers. comm.), 6) a system must be elaborated that delivers efficient operational products that are used by the community such as: mobile applications, information displayed on the Internet (online system), newsletters, among others. This must reach a communication system with information for users, public and technical government entities that are capable of disseminating this information and making it available to local governments, responsible for formulating policies and decision makers (Fig. 6) and finally, 7) implement a feedback system between oceanographic observations and models to better adjust and force predictive models. In addition, the community should be asked for feedback about the products provided and possible non-registered blooms/ intoxications (see Fig. 6).

In response to this idea and within the framework of an integrated ocean observing system for Chile, a model of HAB event information management should incorporate: a) government participation in the planning processes and delivery of updated information to the citizens and institutions involved, and b) coordinate information between participating institutions and incorporate channels to transmit information from authorities to users and vice versa. Only in this way, we will be able to have a forecast both in the causes that trigger an event and possible extension, and in the explanation of intensity, magnitude, toxicity and eventually its permanence or recurrence. The aforementioned, it would be an important step for the strengthening of the institutionality in Chile, delivering instruments that allow to face effectively the potential events of HAB. In the Biobio Region, in June 2016, the POSAR system (Plataforma de Observacion del Sistema Acoplado Oceano Atmosfera, http://dgf. uchile.cl/POSAR/) anchored in front of the Itata River mouth (36[degrees]S, 72[degrees]W, Fig. 1) was installed and is currently measuring. This buoy has hourly observations of meteorological variables and physical and chemical parameters of the surface ocean relevant to detect and forecast HAB phenomena (e.g., wind, solar radiation, salinity, pH and chlorophyll in the sea). Thanks to these efforts, the creation of the monitoring program for harmful species and marine toxins in selected bays of the Biobio Region by the IFOP (18 stations) and together with other mentioned platforms (satellites, numerical models, etc.), could enhance this integrated observation system to forecast these events in the region.

The ultimate social objective of all HAB model efforts should be to mitigate negative impacts. The costs of developing an operational forecasting system are balanced by socio-economic benefits and the protection of living marine resources, or at least they should provide significant added value (Anderson et al., 2015). An unique advantage of a prevention system for fishermen and shellfish cultivators is the spatial and temporal prediction of bloom or the presence and dispersion of the toxin, which would allow better definition of cultivation and resting areas, geographical changes in the fishermen's efforts, protection of health in people, the marine environment and reduction in monitoring costs and medical attention.

CONCLUSION

In Chile, the HAB events of Alexandrium catenella have been reported since 1992 in Aysen and 1998 in the Los Lagos Region to the present. These events have generated environmental, social, economic and health catastrophes mainly in the years 2002, 2006, 2009 and 2016. However, in the last time the various appearances of Pseudo-nitzschia spp. have had a serious impact during some periods on the production of shellfish mainly in the years 2006, 2007, and 2009. The latter represents a risk to public health, as A. catenella, due to the content of ASP. The spatial and temporal patterns of both species are different in the records however, what they do have in common is that there has been an increase in the coverage and frequency of these phenomena. In addition, although both are HAB species and are part of marine plankton, they have some contrasting or antagonistic characteristics in the differences between these two functional groups: dinoflagellates (A. catenella) and diatoms (Pseudonitzschia spp.).

Although the Chilean system based on monitoring has managed to prevent fatalities in the last bloom of 2016, it has not generated mitigation solutions and contingency plans based on prediction. This generates the consequent loss of effectiveness in the management actions before a HAB. HABs are a complex problem that may depend on the interaction of many biological, physical, chemical, climatological and anthropic factors. Adequate monitoring of the column of water and sediments (resistance cysts), long time series (>30 years), understanding of the life cycles of microalgae, satellite images together with numerical and statistical models should be included in the search to forecast these phenomena and improve our understanding of the structures and processes that give rise to HABs.

In the world there are various models of prediction of these phenomena. From the simplest based on empirical relationships between predictive variables, to the most complex systems of artificial neural networks that require much expert statistical knowledge. The fact that certain technology is used in other latitudes is not ruled out for the national situation, but it must be adapted to the current Chilean situation. It is clear that the lack of records and long-term historical environmental data, as well as in real time during the HAB in Chile, is a serious obstacle to identify and understand the mechanisms that are involved in the triggering, intensity, extent and toxicity of these events. This limits our understanding of the relationship with natural and/or anthropic factors. We must be able to group the individual efforts, in order to generate a model with integrative capacity of this problem. Although, the initial costs for this model, the trained personnel and the computational requirement would be high, they would be balanced by the socioeconomic benefits that they bring, such as higher protection of living marine resources, prevention for public health, and lower economic losses. The latter because it would not be necessary to paralyze the total fishing activity, a significant decrease in the purchase of seafood products (national and international) and an increase in monitoring costs for medical care.

DOI: 10.3856/vol46-issue2-fulltext-18

ACKNOWLEDGEMENTS

Funding for this article was thanks to the project FONDECYT 1140385. The elaboration of this work was carried out with the help of the Department of Geophysics (DGEO), University of Concepcion and the project "Chilean Integrated Ocean Observation SystemCHIOOS" (INNOVA 15.5-IN.IIP) materialized with the contribution of Innova Bio-Bio at the University of Concepcion.

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Received: 3 Jannuary 2017; Accepted: 14 March 2018

Marco Sandoval (1,2), Carolina Parada (1,2,3) & Rodrigo Torres (4)

(1) Department of Geophysics, University of Concepcion, Concepcion, Chile

(2) Chilean Integrated Ocean Observing System, University of Concepcion, Concepcion, Chile

(3) Millennium Institute of Oceanography, University of Concepcion, Concepcion, Chile

(4) Center for Patagonian Ecosystems Research (CIEP), Coyhaique, Chile

Corresponding author: Marco Sandoval (marcsandoval@udec.cl)

Corresponding editor: Sergio Palma

Captions: Figure 1. a) Regions of Chile. Dotted lines limit the study area for HAB records. Note that they are the simplified names of the regions of Chile and does not include the recent Nuble, Region between 36[degrees] and 37[degrees]S approximately. In b), c) and d) a zoom to the three regions most affected by HAB phenomena is shown; Los Lagos, Aysen and Magallanes respectively.

Captions: Figure 2. Histograms by a) region, c) years, and e) months, for Alexandrium catenella, and Pseudo-nitzschia spp. (b, d, and f). In the graphs e) and f) N.I. means No Information about the month in which the event occurred. The colors in the lower graphs (c and f) are matched with the colors shown in the percentage graphs in Fig. 2.

Captions: Figure 3. Spatial distribution by regions (in Roman numerals, see Fig. 1) of: a) Alexandrium catenella, and b) Pseudonitzschia spp. The groups (G) are ordered by appearances with common distance and are represented in graphs that indicate the percentage of occurrence of an event in the period 1993-2012. MR: Metropolitan Region of Santiago. NI: No information about the month in which the event occurred.

Captions: Figure 4. The "y" axis presents different groups (represented by mean latitude) for both species: a) Pseudo- nitzschia spp. and b) Alexandrium catenella. On the "x" axis, the different seasons are observed, indicating spring with a dotted line from 1993 to 2012. Finally, the colors indicate the percentage of probability of occurrence.

Captions: Figure 5. Diagram of the different monitoring programs in Chile. The length of the bar corresponds to the length of time in the timeline. PSMB: Programa de Sanidad de Moluscos Bivalvos, ISP: Instituto de Salud Publica, USA: United States of America, Program 1: Monitoreo de Marea Roja en la Region de Magallanes y Antartica Chilena, Program 2: Manejo y Monitoreo de las Mareas Rojas en las regiones de Los Lagos, Aysen y Magallanes, PNV(MR/FAN): Programa de Vigilancia de la Marea Roja/Floraciones Algales Nocivas, MINSAL: Ministerio de Salud, SEREMIS: Secretarias Ministeriales Regionales, PNPCI(MR/FAN): Programa Nacional de Prevencion y Control de las Intoxicaciones por Marea Roja/Floraciones Algales Nocivas, PNFAN: Plan Nacional sobre Floraciones Algales Nocivas, CONA: Comite Oceanografico Nacional, IOC: Intergovernmental Oceanographic Commission, SCOR: Scientific Committee on Oceanic Research, PNIA: Programa Nacional de Inocuidad de los Alimentos, PNIMR: Programa Nacional Integrado de Marea Roja, ACHIPA: Agencia Chilena de Inocuidad Alimentaria, SERNAPESCA: Servicio Nacional de Pesca y Acuicultura, Undersecretary of Public Health. POAS: Programa Oceanografico y Ambiental en Salmonidos PVCAc: Programa de Vigilancia, deteccion y control de plaga Alexandrium catenella, REPLA: Reglamento de Plagas Hidrobiologica.

Captions: Figure 6. Diagram of the integrated forecasting system for HABs events in Chile, based on Anderson et al. (2014). DAMAC: Departamento de Almacenamiento, Manejo y Control de Datos, VIIRS: Visible Infrared Imaging Radiometer Suite, OCM2: Ocean Color Monitor of Oceansat-2, AVHRR: Advanced Very High Resolution Radiometer, BGQ: Biogeochemical. Captions: Figure 6. Diagram of the integrated forecasting system for HABs events in Chile, based on Anderson et al. (2014). DAMAC: Departamento de Almacenamiento, Manejo y Control de Datos, VIIRS: Visible Infrared Imaging Radiometer Suite, OCM2: Ocean Color Monitor of Oceansat-2, AVHRR: Advanced Very High Resolution Radiometer, BGQ: Biogeochemical.
Table 1. Information on occurrences and locations in the reports in
the different sources of records for PSP and/or Alexandrium
catenella. The Santiago Metropolitan Region, the Araucania and Los
Rios regions (see Fig. 1a) did not present cases. The Magallanes
Region (see Fig. 1d) is outside the study range.

Region       Year          Reference

Atacama      2002          [10]
Coquimbo     2002 a 2003   [10, 11]
             2010          [18]
Valparaiso   2009          [17]
Biobio       2007          [15]
Los Lagos    1998 a 1999   [2, 23, 29]
             2002 a 2012   [1, 3, 6, 7, 8, 9, 11, 12, 13, 14, 15,
                             16, 19, 20, 21, 22, 23, 24, 26, 27,
                             29, 30, 31, 32, 34, 35]
             2015 a 2016   [4]
Aysen        1994 a 1998   [22, 23, 33]
             2000 a 2010   [6, 11, 13, 15, 18, 19, 22, 23, 29, 35]
             2013          [26]
             2015 a 2016   [4]

[1] Arriagada et al. (2003); [2] Avaria et al. (1999); [3] Buschmann
(2005); [4] Buschmann et al. (2016); [5] Clement & Lembeye (1994);
[6] Guzman et al. (2007); [7] Guzman et al. (2009); [8] Guzman et
al. (2010a); [9] Guzman et al. (2011); [10] Informe ISP 2002; [11]
Informe ISP 2003; [12] Informe ISP 2004; [13] Informe ISP 2005;
[14] Informe ISP 2006; [15] Informe ISP 2007; [16] Informe ISP
2008; [17] Informe ISP 2009; [18] Informe ISP 2010; [19] Informe
ISP 2011; [20] Informe ISP 2012; [21] Mardones et al. (2010);
[22] Martinez et al. (2008); [23] Molinet et al. (2003); [24]
Murillo et al. (2008); [25] Pizarro et al. 2011; [26] Rivera
(2013); [27] Robles et al. (2003); [28] Suarez et al. (2002);
[29] Salgado et al. (2012); [30] Seguel & Sfeir (2003); [31]
Seguel et al. (2005); [32] Suarez & Clement (2002); [33] Uribe
et al. (1995); [34] Valenzuela & Avaria (2009); [35] Vidal et al.
(2006).

Table 2. Information on occurrences and locations in the reports in the
different sources of records for ASP and/or Pseudo-nitzschia spp.
The Santiago Metropolitan Region, the Araucania and Los Rios
regions (see Fig. 1a) did not present cases. The Magallanes Region
(see Fig. 1d) is outside the study range.

Region               Year        Reference

Arica y Parinacota   2005        [13]
Tarapaca             2005-2006   [13, 14]
Antofagasta          2004-2006   [12, 13, 14]
                     2010-2011   [18, 19]
Atacama              1999        [28]
                     2004-2012   [12, 13, 14, 15, 16, 17, 18, 19,
                                   20, 26]
Coquimbo             1999        [28]
                     2003        [11]
                     2005-2012   [13, 14, 15, 16, 17, 18, 19, 20]
Valparaiso           2007        [15]
O'Higgins            2011        [19]
Maule                2004        [12]
                     2006        [14]
Biobio               2003-2006   [11, 12, 13, 14]
                     2008        [16]
Los Lagos            1993.       [5]
                     1997-2000   [28, 33]
                     2003        [11]
                     2005-2009   [13, 14, 15, 16, 17]
                     2012        [20]
                     2016        [4]
Aysen                1995        [33]
                     2007        [15]
                     2008        [25]
                     2010-2012   [18, 19, 26]
                     2016        [4]

[1] Arriagada et al. (2003); [2] Avaria et al. (1999); [3] Buschmann
(2005); [4] Buschmann et al. (2016); [5] Clement & Lembeye (1994);
[6] Guzman et al. (2007); [7] Guzman et al. (2009); [8] Guzman et
al. (2010a); [9] Guzman et al. (2011); [10] Informe ISP 2002; [11]
Informe ISP 2003; [12] Informe ISP 2004; [13] Informe ISP 2005; [14]
Informe ISP 2006; [15] Informe ISP 2007; [16] Informe ISP 2008;
[17] Informe ISP 2009; [18] Informe ISP 2010; [19] Informe ISP 2011;
[20] Informe ISP 2012; [21] Mardones et al. (2010); [22] Martinez
et al. (2008); [23] Molinet et al. (2003); [24] Murillo et al. (2008);
[25] Pizarro et al. 2011; [26] Rivera (2013); [27] Robles et al.
(2003); [28] Suarez et al. (2002); [29] Salgado et al. (2012); [30]
Seguel & Sfeir (2003); [31] Seguel et al. (2005); [32] Suarez
& Clement (2002); [33] Uribe et al. (1995); [34] Valenzuela & Avaria
(2009); [35] Vidal et al. (2006).

Table 3. Oceanographic, biogeochemical and meteorological
variables that are associated with the presence of HAB species
such as Pseudo-nitzschia spp. and Alexandrium spp. FL:
indicates that it was obtained from foreign literature. NH: North
Hemisphere, pC[O.sub.2]: C[O.sub.2] Partial Pressure RUV-B:
UV-B radiation (280-320 nm), DON: Dissolved Organic Nitrogen, DOC:
Dissolved Organic Carbon, ACC: Antarctic Circumpolar Current,
ACW: Antarctic Circumpolar Wave, SST: Sea Surface
Temperature, QBO: Quasi Biennial Oscillation.

Variable           Alexandrium spp.        Pseudo-nitzschia spp.

Favorable season   * End of spring and     * Spring to summer
                     summer [69, 84,         [69, 79, 34].
                     61, 34, 28, 29,       * ASP would be during
                     30, 31, 32].            the period when
                   * It can occur            densities are
                     throughout the          lowest, between
                     year but is more        mid-fall and
                     frequent in             mid-winter (end of
                     February and            May to end of
                     April [84].             August) [30].
Temperature        * Positive thermal      * FL: Negative thermal
                     anomalies [84,          anomalies [8, 70,
                     28, 79, 46, 47,         26, 4, 1]
                     65, 61, 34, 83        * T between 2 and
                     21, 19, 10, 35,         28.5[degrees]C [37,
                     63, 29, 30, 32,         12, 79].
                     27, 36]. T from       * Higher
                     low values of           concentrations
                     4.82- 4.91              between
                     [degrees]C [28],        9-14[degrees]C [82].
                     up to as high
                     as 10-15[degrees]
                     C [36, 12, 24,
                     5, 21, 31]. PSP
                     range 5-17
                     [degrees]C
                     [30, 31].
                   * Notorious surface
                     thermocline [36].
                   * Influences
                     concentration and
                     increases
                     toxicity
                     [79, 83].
                   * Related to the
                     temperature at
                     5 m [34].
Salinity           * FL: Negative          * FL: Positive
                     salt anomalies          saline anomalies
                     [38, 45].               [8, 4].
                   * S as low as 8.15      * 30 to 36 [12,
                     but high as 33          37, 82].
                     [36, 59, 84, 24,
                     28, 5, 21, 31,
                     36]. PSP range
                     15 to 32 [30,
                     31, 27].
                   * Marked halocline
                     at superficial
                     levels [36].
                   * It limits
                     distribution and
                     abundance, but
                     does not exclude
                     presence during
                     certain
                     periods [31].
Winds              * It is favored by      * FL: It is favored
                     the decrease in         by intense winds,
                     intensity and           mainly favorable to
                     variation of a          upwelling [70].
                     normal wind
                     condition [84,
                     61, 39, 10].
Large-scale        * Hydrographic-         * It has been seen
  climate            climate                 in an El Nino
  variability        interaction             event [60, 10].
                     every 10 years
                     [78, 28, 67].
                     Favored by El
                     Nino [9, 79, 28,
                     61, 33, 35, 19,
                     10, 39, 58, 14,
                     84, 31, 34].
                   * ACC, ACW,
                     interdecadal
                     extratropical
                     anomalies of SST
                     and QBO [61].
Turbulence         * It prefers less       * FL: It prefers
                     turbulence [84].        more turbulence [70].
                   * The mixing
                     implies lower
                     cell
                     concentrations
                     [81].
Precipitation      * It prefers a          * FL: Not relevant.
                     decrease in             Higher nutrient
                     precipitation           carryover by
                     [63, 10, 20].
                   * Relative              precipitation [8, 43].
                     abundance related
                     to cloudiness [34].
Water              * More stable water     * FL: Thermal
  stratification     columns [36, 84,        stratification [8].
                     27, 10, 63].
                   * Marked
                     thermohaline
                     stratification
                     [28, 79, 5, 82,
                     21].
                   * High stability
                     values, especially
                     between surface
                     and 10 m depth
                     [36].
Upwelling          * Relaxation of the     * FL: It favors
                     upwelling [10].         upwelling and
                                             potential cold eddy
                                             (anticyclonic, NH)
                                             [75, 8, 3, 70,
                                             44, 43].
                                           * FL: High luminosity
Radiation          * Abnormal period         [8, 26].
                     of insolation
                     [36, 27, 28, 5,
                     83, 84, 63, 10,
                     29, 30, 32].
                   * Tolerates
                     increases in
                     UV-B [57].
Climate change     * It may be             * It may be
                     favored [84, 62].       disfavored [74].
                   * It is favored by
                     the occurrence
                     of exceptional.
                     high-intensity
                     El Nino
                     events [63].
Depth              * 0-15 m depth          * FL: 0-36 m
                     [61, 49].               depth [75].
Nutrients          * It develops best      * FL: Important
                     in environments         contribution of
                     with few                macro [23] and
                     nutrients.              micro [3,43]
                     Micronutrients          nutrients [75],
                     from freshwater         especially of
                     inputs [32].            organic
                                             substances [26].
Phosphorus (P)     * FL: Phosphorus is     * FL: Phosphate is
                     a limiting factor       important [75, 4,
                     [25, 15, 16,            75, 43]. P/Si radio
                     17, 42].                is a limiting
                   * Phosphate is            factor [26].
                     relevant [10, 60].    * Regulates the
                                             production of DA [2].
                                           * ASP would be in
                                             limiting phosphate
                                             periods [30].
Ammonium           * FL: Ammonium          * FL: Ammonium
  ([NH4.sup.+])      supplement [15,         supplement [26,
                     16, 68, 18].            77, 68].
                   * Favorite
                     nutrient [60].
Nitrogen (N)       * FL: Inorganic and     * FL: Nitrate
                     organic nitrogen        supplement [40, 75,
                     supplement [13,         8, 75, 54, 43, 26],
                     17, 18], high           Low N:P radio [4].
                     nitrate [10, 73,        DON is a source of
                     7, 80, 11, 60]          N [51].
                     and urea [68].
                   * Limitation by         * Increase in nitrate
                     nitrogen                values decreases
                     increases               cell
                     toxicity [25].          concentration [6].
Silicon (Si)       * Dissolved             * FL: Silicate
                     Silicate Deficit        supplement [75, 76,
                     relative to             43, 3, 64, 70, 26]
                     Nitrate [10, 60,        and a low Si/N
                     73, 74].                radio [54].
                                           * Silicate limitation
                                             increases toxicity
                                             [72, 2].
                                           * ASP would be in
                                             limiting silicate
                                             period [30].
pH and pC[O.sub.2] * FL: Low pH;           * FL: High pC[O.sub.2]
                     7.5 [41].               increases growth and
                   * Increase in             toxicity [71].
                     pC[O.sub.2]           * High pH: 8.7-9.8
                     increases               [26, 52].
                     toxicity [72].
                   * Optimum growth
                     rates specific
                     to the species
                     at pH ~ 8.1 [62].
Others             * Correlated with       * FL: Iron is a
                     the air                 limiting agent [66,
                     temperature [83].       43, 26, 53].
                   * Changes in              Limitation by iron
                     phytoplankton           increases
                     structure [79,          toxicity [2].
                     5, 34, 83, 27].       * Importance of
                   * Advection by            Lithium [43].
                     circulation and       * DOC Supplement [4].
                     winds [61, 22,        * It was found with a

                     55, 10].                water transparency
                   * Cysts retaining         cloud cover of four
                     areas (cyst             eighths [82].
                     banks) [61, 82,       * "Excluded" when a
                     56, 34, 48].            bloom of A.
                   * More associated         catenella occurs in
                     with chlorophy          Aysen and almost
                     ll-a [29, 30, 32].      simultaneously in
                                             Magallanes [34].

[1] Almandoz et al., 2007; [2] Anderson et al., 2002; [3]
Anderson et al., 2006, [4] Anderson et al., 2009; [5]
Arriagada et al., 2003; [6] Avaria et al., 2003; [7] Avila
et al., 2015; [8] Bates et al., 1998; [9]  Braun et al.,
993; [10] Buschmann et al., 2016; [11] Carlsson & Graneli,
1998; [12] Cassis et al., 2002; [13] Chapelle et al., 2010;
[14] Clement et al., 2016; [15] Collos et al., 2004, [16]
Collos et al., 2007; [17] Collos et al., 2009; [18] Collos et al.,
2014; [19] Cornejo et al., 2016; [20] Diaz et al, 2013; [21]
Espinoza et al., 2016; [22] Espinoza-Gonzalez & Bosain,
2016; [23] Fehling et al, 2006; [24] Fuentes et al., 2006;
[25] Garrido et al., 2012; [26] Graneli & Flynn, 2006; [27]
Guzman et al., 1975; [28] Guzman et al., 2002; [29] Guzman et al.,
2007; [30] Guzman et al., 2009; [31] Guzman et al., 2010a; [32]
Guzman et al,. 2010b; [33] Guzman et al., 2014; [34] Guzman
et al., 2015; [35] Guzman et al., 2016;[36] Guzman & Lembeye,
1975; [37] Hasle, 1965; [38] Hamasaki et al, 200; [39]
Hernandez et al,. 2016; [40] Howard et al, 2007; [41] Hwang &
Lu, 2000; [42] Jauzein et al., 2010; [43] Kudela et al.,
2003; [44] Kudela et al, 2010; [45] Laabir et al., 2011;
[46] Lembeye, 1981a; [47] Lembeye, 1981b; [48] Lembeye,
2006; [49] Lembeye, 1998; [51] Loureiro et al, 200; [52]
Lundholm et al., 2004; [53] Maldonado et al., 2002; [54]
Marchetti et al, 2004; [55] Mardones et al., 2010; [56]
Mardones et al. (2015); [57] Martinez et al, 2000; [58]
Martinez et al., 2016; [59] Medina 1997; [60] Molina,
2016; [61] Molinet et al., 2003; [62] Muller et al., 2016;
[63] Nunez & Letelier, 2016; [64] Pan et al., 1996; [65]
Pizarro et al., 1997; [66] Rue & Brulan, 2001; [67] Salgado
et al., 2012; [68] Seeyave et al., 2009; [69] Seguel et al,
2010; [70] Shin, 1999; [71] Sun et al. (2011); [72] Tatters
et al. (2012), [73] Torres et al, 2011, [74] Torres et al,
2014, [75] Trainer et al, 2000, [76] Trainer et al, 2002,
[77] Trainer et al, 2007; [78] Uribe, 1988; [79] Uribe et
al., 1995; [80] Uribe et al., 201; [81] Valenzuela &
Avaria, 2009; [82] Vidal et al., 2006; [83] Vidal et
al., 2012; [84] Villanueva, 2005.

Table 4. Models for the prediction of Alexandrium
spp. in the world, together with a brief review of
the method and the variables that they use, in addition
to their advantages and disadvantages. T: Temperature,
S: Salinity, chl-a: Chlorophyll-a, ROMS: Regional
Ocean Modeling System, MODIS: Moderate-Resolution
Imaging Spectroradiometer.

Model           Method              Variable

Mechanisist     Growth and          Estuarine, Coastal and
                  germination         Model (ECOM) coupled
                  cyst model          with a biological model
                  (determinist).      of the A. fundyense
                                      cyst (germition and
                                      growth rates) based on
                                      environmental forcing.
                                    A fundyense population
                                      dynamic model coupled to
                                      ROMS model forced by
                                      momentum and density
                                      fluxes, tides, river
                                      runoff, nutrients and
                                      benthonic cyst abundance
                                      of the species
Empiric /       Fuzzy logic         Survey data (T, S), river
  Statistical                         discharge, wind stress,
                                      surface heat flux and
                                      insolation
                                    Water temperature, wind
                                      speed and nutrients
                                      fluxes.
Ecosystem /     Biogeochemical      Based on the P limitation
  Biogeo          specie-specific     for A. minutum and H.
  -chemical       model               triquetra cells.

                Advantages and
Model           Disadvantages            Reference

Mechanisist     Advantages: 1) Capture   Stock et al.
                  duration and             (2005)
                  magnitude of the
                  bloom, and 2)
                  reproduce the A.
                  fundyense dynamic.
                Disadvantages: 1)
                  Biological model
                  simplified restrict
                  variability scales
                  reproduced, and 2)
                  overestimate of the
                  cell abundance of
                  A. fundyense.
                Advantages: 1) The       He et al.
                  hydrodynamic model       (2008)
                  reproduce small
                  scale coastal
                  dynamic and 2)
                  reproduce spatial
                  distribution of
                  bloom.
                  Disadvantages: 1)
                  Underestimate
                  abundance of cells,
                  2) there are
                  problems with the
                  mortality rates,
                  and 3) no metal
                  trace for growth
                  are included.
Empiric /       Advantages: 1)           Mcgillicuddy
  Statistical     Provide a                et al.
                  quantitative method      (2003,
                  to assess                2005, 2011)
                  distribution,
                  timing and magnitude
                  of the bloom, and 2)
                  provide a base to
                  the configuration of
                  the conceptual
                  model for the
                  season dynamic of
                  A. fundyense.
                Disadvantages: 1)
                  Reduced data of
                  small scale to
                  contrast the model
                  output, and 2)
                  ecological pronostic
                  require real time
                  data fluxes and
                  assimilation
                  techniques to be
                  used operationally
                Advantage: 1)            Blauw et al.
                  Quantitative             (2006)
                  technique to model
                  development of
                  blooms based on a
                  number of variables
                  and its interactions
                  Disadvantage: 1)
                  Lack of understand
                  of the species
                  dynamics and the
                  ecosystem
                  functioning and, 2)

                  difficult to
                  implement feedback
                  mechanisms
Ecosystem /     Advantage: 1) Provide    Chapelle et
  Biogeo          information on           al. (2010)
  -chemical       impact of nutrient
                  on the blooms of the
                  study species.
                Disadvantage: 1)
                  Presents limitation
                  because mathematical
                  parametrization are
                  valid to specific
                  conditions and, 2)
                  it is limited to
                  assess nutrients,
                  physical control
                  and plankton
                  dynamic.

Table 5. Models for the prediction of Pseudo-nitzschia
spp. in the world, together with a brief review of the
method and the variables they use, in addition to their
advantages and advantages. T: Temperature, S: Salinity,
chl-a: Chlorophyll-a, ROMS: Regional Ocean Modeling
System, MODIS: Moderate-Resolution Imaging Spectroradiometer.

Model              Method              Variable

Mechanisist        Generalized         Phytoplankton abundance,
                     Lineal Model        water quality, fresh
                                         water discharge, chl-a,
                                         T, S, nitrate, nitrite,
                                         ammonium, orthophosphate,
                                         silicic acid, dissolved
                                         oxygen, dissolved
                                         organic carbon, Secchi
                                         disk depth.
                   ROMS model and      Ensamble of high resolution
                     satellite           ROMS configuration,
                     products            Modis-Aqua (1 km) and
                     (MODIS)             hydrodynamic, optic and
                                         chemical data linked in a
                                         statistical threshold
                                         model.
                   Generalized         Statistical model that use
                     lineal model        Pseudo-nitzshchia cells,
                     and multiple        chl-a, T, nutrients,
                     lineal              upwelling index and
                     regression          river discharge.

Empirical /        Neuronal networks   Model based on buoy,
  Statistical                            satellite and monitoring
                                         data.

Ecosystem /        Multiple linear     Predictor variables include
  Biogeochemical     regression          nutrients ratios and
                                         Pseudo-nitzschia
                                         multiseries cell
                                         abundance.
                   Ecosystem model     Model forced with realistic
                     including           atmospheric forcing,
                     particle            tides, river flow and
                     tracking and        boundary conditions.
                     wind indices

Physical indices   Index of            Upwelling indices SST and
  and monitoring     upwelling,          Pseudo-nitzschia
  with               SST and wind        concentration.
  Lagrangian
  particles

                                       SST and chl-a satellite
                                         images

                   Deterministic       Nutrients (Si, P, N),
                     production          light, T, chl-a and DA
                     model focused       concentration
                     on growth-
                     mortality and
                     toxicity

                   Advantages and
Model              Disadvantages                Reference

Mechanisist        Advantages: (1) Allow to     Anderson
                     estimate DA, and, (2),       et al.
                     identify environmental       (2009,
                     variables related to         2010)
                     Pseudo-nitzschia.
                   Disadvantages: (1) It
                     cannot identify
                     environment indicators
                     associated to the toxic
                     agent, DA.
                   Advantages: (1) Allow to     Anderson
                     monitor ocean                et al.
                     perturbations to track       (2011,
                     environmental variables      2014)
                     that trigger a bloom,
                     and (2) satellite data
                     provide a broad image
                     of the chl-a synoptic
                     variability.
                   Disadvantage: (1)
                     Requires validates
                     ROMS and biogeochemical
                     models, and (2) the
                     teledetection models
                     overestimated bloom
                     events driving to more
                     false positives
                     detections.
                   Advantage: (1) First         Lane et
                     statistical model used       al.
                     to predict                   (2009)
                     Pseudo-nitzshchia based
                     on long monitoring data.
                   Disadvantage: (1)
                     Difficulty to access
                     counting data and time
                     series of Pseudo-
                     nitzshchia cells.

Empirical /        Advantages: (1) Provides     Fernandez
  Statistical        a system allow               -Riverola
                     predicting HAB events.       & Corchado
                   Disadvantages: (1) The         (2003)
                     system is place
                     specific and cannot
                     apply to other cases,
                     and (2) require a
                     lot of data.

Ecosystem /        Advantages: (1) Model        Blum et
  Biogeochemical     cellular DA and              al. (2006)
                     nutrients in the labs
                     and field conditions,
                     and (2) model helps to
                     support DA monitoring.
                   Disadvantages: (1) Model
                     was validated with lab
                     transformations which
                     does not fit de
                     concentration values
                     in the field.
                   Advantages: (1) Coupled      Giddings
                     biogeochemical model         et
                     that improve HAB             al. (2014)
                     predictivity, and (2)
                     model reproduce well
                     physical process.
                   Disadvantage: (1) Several
                     false positives occur.

Physical indices   Advantages: (1) Allow to     Palma et
  and monitoring     explain physical and         al. (2010)
  with               biological interactions,
  Lagrangian         and (2) reproduces
  particles          annual cycles and
                     seasonality of upwelling
                   Disadvantages: (1) It
                     does not reproduce
                     different
                     Pseudo-nitzschia
                     species.
                   Advantages: (1)              Sacau-
                     Relationship between         Cuadrado
                     different sources            et
                     (wind, upwelling index,      al. (2003)
                     SST, chl-a) set the
                     favorable conditions
                     to develop an algae
                     bloom.
                   Disadvantages (1) It is
                     just an analysis of
                     the different satellite
                     images of chl-a and
                     SST in HAB events.

                   Advantages: (1)              Terseleer
                     Development of a model       et
                     that allow to link           al. (2013)
                     toxin production and
                     environmental variables.
                     Disadvantages (1) Lack
                     of studies oriented
                     on adaptation of
                     Pseudo-nitzschia to
                     different light regimes.
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Title Annotation:Research Article
Author:Sandoval, Marco; Parada, Carolina; Torres, Rodrigo
Publication:Latin American Journal of Aquatic Research
Date:May 1, 2018
Words:18950
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