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A brief review of the use of biomarkers in Mexico's aquatic ecosystems pollution assessment: 2001-2017.

INTRODUCTION

The chemical contamination of aquatic ecosystems is a growing problem since these ecosystems are the destination of most of the pollutants derived from industrial, agricultural and domestic activities (Sarukhan et al., 2012; Amiard-Triquet, 2015; WWAP, 2015). This problem is expected to worsen as a direct consequence of population growth, industrialization and the expansion of urban sprawl (Halder & Islam, 2015), as well as the deficiencies of current public sanitation programs and wastewater treatment systems (mainly in developing countries) (Schwarzenbach et al., 2010).

The contamination of aquatic ecosystems is a complex, evolving and wide-ranging problem, with direct and indirect ecological, economic and social repercussions (Fleeger et al., 2003; Persson et al., 2010). This problem has created the need to develop and implement strategies that guarantee the protection and sustainable use of aquatic resources (MEA, 2005). At the global level, this concern is reflected in the creation of national and international agreements and organizations (both governmental and non-governmental) (Burger, 2006).

Mexico faces great responsibilities regarding the conservation and management of its aquatic ecosystems; it has signed international agreements and treaties on the matter and has implemented laws, norms and national policies that promote the conservation of its aquatic ecosystems. It has also instituted monitoring programs such as the National Network of Water Quality Monitoring (RNM) (Comision Nacional del Agua, 2015).

Environmental monitoring programs are a crucial tool to meet the commitments acquired in international treaties and conventions (such as the Minamata agreement, recently ratified by several countries of the European Union and by Mexico in 2015), and to guarantee the success of natural resource management programs. For several decades, aquatic monitoring programs and networks have worked in other countries, keeping track of changes in water quality (physical, chemical and microbiological), the presence and concentration of persistent toxic substances (metals, pesticides, etc.) in various environmental matrices (water, sediments, biota), and applying biotic indices based on diverse species (plants, mollusks and macroinvertebrates, among others) to determine the health status of the monitored ecosystems (Dixon & Chiswell, 1996; Markert et al, 2003; Li et al., 2010; Borja et al., 2015).

The use of indicator species (bioindicators) made it possible to evaluate the biological response to the presence of contaminants (known and unknown) in different ecosystems. The implementation of tools such as Indices of Biotic Integrity (IBI), for example, provides information on the medium and long-term effect of contaminants at the higher levels of biological organization (populations and/or communities) (De la Lanza-Espino & Hernandez-Pulido, 2014; Schmitter-Soto, 2014).

The need to identify earlier the effect of pollutants stimulated the use of early-response biomarkers. Even though they have been widely used by researchers for several decades to assess pollution in aquatic ecosystems, the use of biomarkers in monitoring programs is relatively new (Hook et al., 2014; Trapp et al., 2014). In recent decades, the U.S.A. and some countries of the European Union have incorporated the use of biomarkers into national monitoring programs (Collier et al., 2012; Wernersson et al., 2015). However, in other countries biomarkers are still little used in national monitoring programs (Trapp et al., 2014). In Mexico, biomarkers have been little used in large-scale monitoring programs (temporal and spatial), but Mexican researchers have been using them to assess the contamination of aquatic ecosystems. The objective of this work was to conduct a review of the different biomarkers and organisms that have been used to evaluate the pollution of aquatic ecosystems in Mexico in the last 16 years (2001-2017).

Selection of bibliographic material

Different academic databases and search engines (Elsevier-Scopus, SCIELO, CONRICyT, and Google Scholar) were queried for scientific publications on the use of biomarkers to assess the pollution of aquatic ecosystems in Mexico (including studies that evaluated environmental samples). The search queries included combinations of keywords such as Mexico, ecosystems, aquatic, lagoon, estuary, bay, lake, river, wetland, pollution, pollutant, COPs, heavy metals, PCBs, PAHs, biomonitor, biomarker, genotoxic, histopathological, oxidative stress, cytotoxic, aquatic organisms, fish, bivalves, clams, crustaceans, aquatic birds, rotifers, and cladocerans, among others, both in Spanish and in English. The search range was from 2001 to 2017. The following criteria were used for selecting articles, and book chapters: 1) studies conducted in Mexican aquatic ecosystems, 2) field or laboratory studies (in the case of laboratory studies, those using water and/or sediment as exposure matrix, excluding air), and 3) studies on the use of biomarkers of effect and/or exposure, at the individual and/or sub-individual level.

Selected studies

Ninety-three publications were selected based on the above criteria. The chosen studies provide a representative picture of the field even though the total number of related studies conducted in Mexico during the studied period is higher (Dalzochio et al., 2016). The number of associated publications per year in the period 2001-2017 had an upward trend, with an average of 5.5 papers per year (Fig. 1). The growing number of publications may be related to the increase in spending on science and technology by Mexico, which went from 0.31% of Gross Domestic Product (GDP) in 2000 to 0.55% of GDP in 2015. This type of research is financed mostly by federal and state funds through the National Council of Science and Technology (CONACyT). Although it has made some progress, Mexico is still one of the of OECD countries that invest less in science, given that the average spending on science of the member countries of the OECD was about 2.5% of GDP in 2015 (UNESCO, 2015; World Bank, 2018).

Use of reference organisms as biomarkers

In general, bioindicator organisms can be defined as whole organism systems with one or more easily detectable endpoints (for example, viability, metabolism, behavior, genetic damage, etc.) that respond to disturbances in their environment (Butterworth et al., 2001), and which due to their ecological characteristics (Paez-Osuna & Osuna-Martinez, 2011; Berthet, 2012; Gonzalez-Zuarth & Villarino, 2014) can be used as indicators of the ecological status of the ecosystems in in which they inhabit. The use of bioindicators for the assessment of pollution allows for a more realistic analysis of the effect of various pollutants in different ecosystems, at the population, community and ecosystem level. Moreover, the use of the biomarker approach based on reference species provides information on the molecular, cellular, physiological and individual levels of an ecosystem (Van-der Oost et al., 2003). In this regard, ecotoxicology can serve to evaluate the early effect of various pollutants through the use of a series of tools (molecular tests, bioassays, transplantation of organisms, etc.) that include not only ecologically relevant species, but also model organisms, transgenic organisms and even cell lines (human and animal) (Zhang et al., 2013; Lee et al., 2015). Currently, a large number of organisms (both wild and model) are used as bioindicators in pollution assessment studies in aquatic ecosystems; the most commonly used are fish (marine and freshwater), bivalves (marine), cladocerans, rotifers, macro-crustaceans, plants, and birds, among others (Zhou et al., 2008; Li et al., 2010; Minier et al., 2015; Colin et al., 2016).

Table 1 shows the variety of species that have been used in Mexico in biomarker studies to assess the pollution of aquatic ecosystems. The review of the data indicates that, in the last 16 years, close to 70 species, belonging to 19 taxa, have been used in this manner, fish being the most commonly used. Other groups of interest are cladocerans and bivalves. Non-conventional organisms such as turtles, sharks, crocodiles, and corals have also been used (Fig. 2).

Regarding the frequency with which the different species have been used, 62% of the species identified in the present study had only a single record between 2001-2017; 25% of the species had two records, and the remaining 13% were used in three or more studies. It is worth noting that of the nearly 70 species used, 50 are native to Mexico and were used in 62% of the studies; 8 can be considered as model species that are widely used in laboratories in different countries and were used in 25% of the total number of studies reviewed here. Another seven species can be considered an exotic species that have been introduced to Mexico (ornamental or aquaculture species) and were used in 8% of the studies. The remaining species were not identified (5%). The species that were most frequently used during the period under review are shown (Fig. 3).

The above data shows the great variety of native species that have been used to assess the pollution of aquatic ecosystems in Mexico. It is an indication of the efforts made to diversify the organisms used as bioindicators. Thus, improving the accuracy of the biological response by using the regions' native organism, or by comparing the response of different organisms at different trophic levels, is especially important for countries such as Mexico, which has a great variety of bioclimates. Moreover, the data shows that most native species were used only in one or two studies during the entire period under review, which makes it difficult to compare different experiences with the same species under different environmental conditions.

In contrast, model species such as Daphnia magna and Cyprinus carpio have been frequently used (Fig. 2). These organisms have been validated in many countries; however, since they are not representative of the ecosystems studied, their response cannot be considered entirely realistic. To make valid interpretations of the effects of pollution in a given ecosystem, researchers should use local species as bioindicators. In Mexico, species such as Goodea atripinnis and Girardinichthys viviparus have been frequently used to assess pollution in aquatic ecosystems, mainly in the center of the country. Other species such as Crassostrea virginica and C. corteziensis have been used in the Mexican northwestern Pacific coast; in addition to bivalves (for example Crassostrea and Megapitaria), they have proven to be very useful in this environment due to their excellent qualities as bioindicators (PaezOsuna & Osuna-Martinez, 2011). Other organisms considered validated in Mexico, include Gambusia yucatana (Rendon-Von Osten, 2015), cladocerans (Mendoza-Cantu et al., 2013) and rotifers (RicoMartinez et al., 2013). However, these organisms have been little used to evaluate the effect of pollution on environmental samples (in situ or ex-situ). Probably be because these and other species used as bioindicators are endemic to specific regions of the country, as is the case of Goodea atripinnis and Girardinichthys viviparus, a consequence of the climatic and orographic diversity of Mexico (Dzul-Caamal et al., 2012).

Use of biomarkers

Biomarkers provide information on the early effects of exposure to environmental pollutants at organism or sub-organism levels, allowing researchers to detect and quantify these effects during their first manifestations, facilitating the implementation of a rapid preventive and/or restorative response in impacted ecosystems (Amiard-Triquet & Berthet, 2015). Biomarkers can be defined as biochemical, cellular, physiological or behavioral variations that can be measured in fluid or tissue samples at the whole organism level and which provide evidence of exposure to and/or effect of one or more pollutants (Van der Oost et al., 2003).

For several decades, biomarkers have been widely used by researchers to assess the effect of environmental pollution and have recently been integrated into monitoring programs in some countries (Collier et al., 2012; Wernersson et al., 2015). Biomarkers can be classified into effect biomarkers, exposure biomarkers and susceptibility biomarkers (Van der Oost et al., 2003), or into defense biomarkers, damage biomarkers, energy biomarkers and behavioral biomarkers (Amiard-Triquet & Berthet, 2015). It is well known that biomarkers can be influenced by variation factors (Forbes et al., 2006) that are both intrinsic and extrinsic to the test organism (Amiard-Triquet & Berthet, 2015). However, several biomarkers have been validated, both in laboratory and field tests. Thus, they can be used successfully, with due precautions (Forbes et al., 2006), and thanks to their specificity (see Rendon-Von Osten, 2005 and Hook et al., 2014 for an in-depth discussion of the biomarkers specificity most commonly used to evaluate aquatic contamination), to determine the presence and effects of various pollutants (metals, PAHs, pesticides and estrogenic compounds, mainly). Which is why several of these biomarkers are well recommended for regulatory applications and monitoring programs (Romeo & Giamberini, 2012).

The selection of suitable biomarkers for use in ecotoxicological studies depends on several factors, including the type of pollutant to be evaluated, the reference species, or even technical and budgetary factors (Rendon-Von Osten, 2005). The present work found that a great variety of biomarkers have been used in Mexico to evaluate the effect of pollution in the aquatic ecosystems. The biomarkers that have been used, the associated organisms, and the different pollution scenarios in which they have been used are shown (Table 2). In this Table we shown the complex pollution conditions that can be found in aquatic ecosystems in Mexico; it also shows that the most commonly used biomarkers, which were associated with a wide variety of organisms, are non-specific and of the rapid response type, such as biomarkers of oxidative stress, which include CAT, SOD, and TBARS, among others. This strategy is recommended for complex pollution scenarios because these biomarkers respond to a wide variety of pollutants and mixtures of contaminants. These characteristics make this type of biomarker a versatile and relatively cheap tool that can be suitable for a first assessment of the effect of pollution on aquatic ecosystems in complex pollution scenarios.

Specific biomarkers such as [delta]-ALAD, which is specific for lead (Wernersson et al., 2015), and semi-specific biomarkers such as Vitellogenin, AChE, EROD and MT's, among others, were also used during the period under review. These biomarkers can be used as evidence of the presence of a group of pollutants (heavy metals, PAHs, organophosphates, pharmaceuticals) when no previous evidence has been found, or to correlate the level of response to a given concentration of the pollutant when its presence is already known. In Mexico, specific and non-specific biomarkers have been used simultaneously in different scenarios.

Some researchers recommend the use of biomarkers in association with the so-called omic sciences (mainly genomics, transcriptomics, proteomics, and metabolomics) (Martyniuk & Simmons, 2016). These new approaches offer a number of advantages that allow an in-depth analysis of the effect of pollutants on biological systems, which can be used to find new and better biomarkers, and to shorten the time to implement preventive and/or restorative actions in the affected areas. However, these tools also have certain drawbacks that may limit their application in monitoring programs; for example, they are relatively expensive and require an in-depth knowledge of bioinformatics, as well as access to omic databases, which currently have information only about a limited number of wild aquatic species (Gonzalez & Pierron, 2015). In Mexico, omic approaches have been used (although very little) in association with some biomarkers, such as VTG and CYP1A1, and oxidative stress biomarkers such as SOD, GST, and HSP70 (Table 2).

One of the advantages of the use of omic tools in ecotoxicological studies is the fact that they allow to extract biological material in a non-destructive way (through the extraction of biopsies and/or fluid samples), which can reduce the pressure on the populations studied (e.g., protected species) and improve the bioethical standards of ex-situ tests. In most of the studies reviewed here, the biological material was obtained using destructive techniques; however, non-destructive techniques have been used to obtain skin biopsies from fish (Fossi et al., 2017) and crocodiles (Dzul-Caamal et al., 2016), blood samples from turtles (Labrada-Martagon et al., 2011) and skin mucus from fish (Dzul-Caamal et al., 2016), among others.

The present work also showed that the reviewed studies used biomarkers under three main approaches, which can be classified as follows:

a) Baseline studies: This type of studies aims to evaluate a biomarker's behavior within a reference species under certain environmental conditions (scenarios with known or unknown contamination). In general, these studies compare the behavior of a biomarker between sexes, sizes, reproductive stages or organs; when several species are used, the behavior of the biomarker is compared between them. Thus, this type of studies can be used to study how the behavior of different biomarkers vary in species that had not been previously considered as bioindicators and can help identify species with potential to be used as such. These studies constituted 20% of the total number of studies reviewed here.

b) Studies of the association between pollutants and effects. This type of research aims to correlate the response of a biomarker with the concentration of one or several contaminants of interest. For example, these studies can be used to identify which biomarkers are more sensitive to certain pollution conditions, and this can serve to validate their use in pollution assessment studies. This type of approach was used in 30% of the studies reviewed here.

c) Characterization studies. This type of studies aims to characterize the study areas based on the response of the biomarkers used and can be used to find sites of interest or pollution hot spots. In general, a single reference species is used in large or multiple study areas, for two or more sampling campaigns; frequently an already known species and validated biomarkers are used. This type of approach was used in 50% of the studies reviewed here. Most studies with this approach evaluated only a single area and carried out only one sampling campaign in a single annual cycle. However, some of these studies involved two or more sampling campaigns during an annual cycle (such as spring, summer or rainy and dry seasons); these cases are already considered monitoring studies.

Use of biomarkers to monitor aquatic pollution in Mexico

It is a fact that monitoring programs are one of the most important tools for the protection of aquatic ecosystems and for ensuring rational use of the resources of these ecosystems, as well as for complying with the commitments acquired in international treaties and agreements. In past decades, aquatic pollution monitoring programs focused on measuring physical and chemical variables, while biological variables were only occasionally taken into account (Lam, 2009). Currently, this approach is used by many countries. In Mexico, the RNM monitors water quality from a physical, chemical and bacteriological point of view (CNA, 2015); however, this type of approach only provides information about the nature of the pollutants and their concentrations in the environment, but cannot predict their possible effects on the organisms that inhabit the affected ecosystems (Lam, 2009). It is currently accepted that the careful use of biomarkers may be the best tool to assess the early effects of aquatic pollution (Hook et al., 2014); however, biomarkers are not widely used in national monitoring programs. The European Union has incorporated the use of biomarkers into monitoring programs; for example, under the framework of the Marine Strategy Framework Directive (MSFD), a series of biomarkers (EROD, AChE, Vtg, MT's, PAH bile metabolites, ALAD, among others) have been used to monitor the effect of pollution on European coasts (Wernersson et al., 2015). Moreover, decades ago the United States implemented the use of biomarkers (histopathology, PAH bile metabolites and CYPA1 in benthic fish) to evaluate the effects of pollution (PAHs mainly) in lakes and rivers (Collier et al, 2012).

In Mexico as in other countries, biomarkers are not used in national monitoring programs; however, as already mentioned, researchers have used biomarkers in monitoring studies. Close to 34% of the studies reviewed here evaluated the behavior of different biomarkers in two or more sampling campaigns (Fig. 4). Biomarkers can be affected by a series of sources of variation called confounding factors, which may be intrinsic (size, weight, age, sex, reproductive stage, etc.) or extrinsic (temperature, salinity, dissolved oxygen, pH and time of year) to the test organism (Amiard-Trique & Berthet, 2015).

Monitoring studies allow us to understand the patterns of variation within an annual cycle; for example, changes between spring, summer, autumn, and winter, or, in tropical zones, between the rainy season and the dry season. Moreover, because pollutants flow into aquatic ecosystems constantly, carrying out more than one sampling campaign allows understanding the relationship between variations in the effect of pollutants and these natural cycles, making it possible to identify the seasons in which organisms are more or less affected by pollutants.

For example, Toledo-Ibarra et al. (2016) and Bautista-Covarrubias et al. (2017) studied an estuary (Boca de Camichin Estuary) in northeastern Mexico, that is under the strong influence of agricultural areas during the dry and rainy seasons. Both studies used ACEh in bivalve gills (Crassostrea), and both studies found that ACEh decreases considerably from the dry to the rainy season because of the increase of pollutants during the rainy season, which inhibits the activity of ACEh in bivalve gills. As was corroborated by Toledo-Ibarra et al. (2016) who evaluated eight aquatic bodies under the influence of agricultural zones (Nayarit State, Mexico) and found the same pattern in all of them.

Monitoring studies not only evaluate the variation patterns within a single annual cycle but also seek to understand how the effects of pollution evolve; thus, it is recommended to extend the studies to more than one annual cycle (monitoring programs). As mentioned earlier, in Mexico, biomarker studies that evaluate more than one annual cycle are still scarce (Fig. 4). Although this review found that some study areas were assessed in more than one occasion during the review period, in most of these occasions different types of organisms and different biomarkers were used, and this makes it difficult to understand the evolution of the effects of pollution in those study areas. Only in very rare cases, one area was evaluated using the same group of organisms and biomarkers (e.g., Vega-Lopez et al., 2007, 2008, 2009, 2011; Olivares-Rubio et al, 2013, Dzul-Caamal et al., 2014). Those studies analyzed a lake in the Valley of Mexico (Lake Zumpango) using fish (see Table 1 for species and Table 2 for biomarkers), showing how that ecosystem evolved.

Some of the reviewed studies evaluated an area for more than one annual cycle, sometimes for a period equivalent to two annual cycles, although most of those studies limited their evaluation to one and half cycles, allowing for a broader understanding of the behavior (evolution) of the effects of pollutants on living organisms. The main studies that evaluated the impact of pollution for more than one annual cycle are shown (Table 3).

The results of this review show that the main strategy used by biomarker studies that assessed pollution in aquatic ecosystems during more than seasonal cycle has been to carry out in situ studies using native fish. This strategy has the advantage of being cheaper and more practical than laboratory studies using environmental matrices. The results also show that most studies used multiple biomarkers, which can validate each other or detect anomalies. Although in a limited way, these studies allow observing the trend followed by biomarkers from one cycle to another. The combination of all these elements allows reaching much more robust conclusions about the status of the study areas.

It is clear that, in recent decades, biomarkers have gone from being a good alternative tool for assessing pollution in aquatic ecosystems to be a necessary instrument for guaranteeing the protection, preservation, and management of these ecosystems. It has become evident that no pollution monitoring programs should be carried out without them. Mexico has a great responsibility because it has a great wealth of aquatic ecosystems; for example, it has 142 wetlands considered RAMSAR sites, making it the signatory country with the second largest number of sites registered under this agreement (RAMSAR Convention, 2018). This fact obliges the country to develop and implement strategies that guarantee the protection of its aquatic ecosystems. The present review shows that, in Mexico, the use of biomarkers in national monitoring programs to assess aquatic pollution has not been fully imple-mented yet. However, as in other countries, researchers have been using these tools in the last decades. The present review also shows which biomarkers and species have been used to assess pollution in aquatic ecosystems in Mexico.

In Mexico, researchers have used both classical biomarkers and omic biomarkers, although the latter approach has been used only rarely. The alternative, non-lethal strategies have been used to obtain samples, such as biopsies or fluids, in accordance with the need to develop new biomarkers and strategies for using them in large-scale monitoring programs. Although omic sciences have allowed the development of what has been proposed, as the next generation of biomarkers for ecotoxicological evaluations, in Mexico, as in other countries, these tools are just beginning to be used by researchers. Due to the technical and financial needs involved in their application, it is unlikely that their use will become widespread in the coming years. There is still the need to find effective strategies that can be applied to the national context, which could be done more easily if the country's research centers worked in a coordinated way to find and standardize the largest number of common biomarkers that offer a good cost-benefit ratio. It is also necessary to increase efforts regarding the study of reference species; although researchers in Mexico have experimented with a large number of native species, the results of this review show that only a small number of species were used repeatedly during the study period.

Nevertheless, the review also shows that some native species have already been validated, even though their use is limited to specific regions due to the high endemism rates in the country. Thus, the task remains to continue to the study species and biomarkers that can be used in each region of the country to implement a country-wide monitoring network. In general, it is possible to conclude that, in Mexico, the use of biomarkers in the assessment of the effects of aquatic pollution is a practice well known by researchers; however, there are still important challenges to face, which make it difficult to spread their use in the country. We must not forget the great responsibility that falls on Mexico as the owner of a great wealth of aquatic ecosystems, which requires the commitment not only of research centers but also of the government and the society in general.

DOI: 10.3856/vol46-issue5-fulltext-1

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Received: 15 November 2017; Accepted: 25 June 2018

Eduardo Ramirez-Ayala (1), Miguel Angel Arguello-Perez (1), Cesar Arturo Ilizaliturri-Hernandez (2) Adrian Tintos-Gomez (1,3), Jesus Mejia-Saavedra (2) & Imelda Borja-Gomez (3)

(1) Programa de Doctorado en Ciencias en Biosistematica, Ecologia y Manejo de Recursos Naturales y Agricolas (BEMARENA), Departamento de Estudios para el Desarrollo de la Zona Costera Universidad de Guadalajara, Jalisco, Mexico

(2) Programas Multidisciplinarios de Posgrado en Ciencias Ambientales, Agenda Ambiental, Centro de Investigacion Aplicada en Ambiente y Salud (CIAAS), CIACyT, Facultad de Medicina Universidad Autonoma de San Luis Potosi, San Luis Potosi, Mexico

(3) Facultad de Ciencias Marinas, Universidad de Colima, Colima, Mexico

Corresponding author: Adrian Tintos-Gomez (tintos_adrian@ucol.mx)

Corresponding editor: Sergio Contreras

Caption: Figure 1. Number of published articles on the use of biomarkers to assess pollution in aquatic ecosystems in Mexico (2001-2017).

Caption: Figure 2. The number of species of each group of organisms used as bioindicators, and the number of studies in which they have been used. The total number of studies differs from the total number of articles because some studies used more than one species.

Caption: Figure 3. Most used species in biomarker studies in Mexico between 2001 and 2017. The total number of studies differs from the total number of articles because some studies used more than one species.

Caption: Figure 4. Temporal range of biomarker studies carried out in Mexico between 2001 and 2017.
Table 1. Basic information of the reviewed studies on the use of
biomarkers to assess pollution in aquatic ecosystems in Mexico:
2001-2017. The column titled Origin refers to the origin of the
organisms used, classifying them into the following categories.
Model, refers to model organisms or organisms widely used in
toxicological or ecotoxicological studies around the world; Native,
refers to organisms that are native to Mexico; N/A: not applicable;
N/E: not specified; Exotic, refers to organisms that are exotic to
Mexico according to Gonzalez et al. (2014). * Cortez-Gomez et al.
(2018) was reviewed in 2017 when it was still in press.

Species                        Biomonitor      Origin

Limnodrilus hoffmeisteri       Oligochaeta     Model
Limnodrilus hoffmeisteri       Oligochaeta     Model
Lecane quadridentata           Rotifers        Model
Daphnia magna                  Cladocerans     Model
Daphnia pulex                  Cladocerans     Native
Simocephalus vetulus           Cladocerans     Native
Daphnia magna                  Cladocerans     Native
No identificado                Fishes          Native
Stagnicola sp.                 Gastropods      Native
Xiphophorus hellerii           Fishes          Exotic
Xiphophorus hellerii           Fishes          Exotic
Goodea atripinnis              Fishes          Native
Ameca splendens                Fishes          Native
Ariopsis assimilis             Fishes          Native
Danio rerio                    Fishes          Model
Dendrocygna autumnalis         Birds           Native
Oreochromis niloticus          Fishes          Exotic
Girardinichthys viviparus      Fishes          Native
Gambusia yucatana              Fishes          Netive
Crassostrea virginica          Bivalves        Native
Sorghum bicolor                Plants          Model
Daphnia pulex                  Cladocerans     Native
Lactuca sativa                 Plants          Model
Lecane quadridentata           Rotifers        Model
Daphnia magna                  Cladocerans     Model
Ameca splendens                Fishes          Native
Goodea atripinnis              Fishes          Native
Xenotoca melanosoma            Fishes          Native
Oreochromis aureus             Fishes          Exotic
Chirostoma consocium           Fishes          Native
Chirostoma lucius              Fishes          Native
Lepomis macrochirus            Fishes          Native
Alloophorus robustus           Fishes          Native
Zoogoneticus quitzeoensis      Fishes          Native
Chapalychthys encaustus        Fishes          Native
Poeciliopsis infans            Fishes          Native
Goodea atripinnis              Fishes          Native
Chelonia mydas agassizii       Turtles         Native
Girardinichthys viviparus      Fishes          Native
Carcinogenic cells MCF-7       Humans          N/A
Ariopsis felis                 Fishes          Native
Haemulon plumieri              Fishes          Native
Simocephalus mixtus            Cladocerans     Native
Daphnia magna                  Cladocerans     Model
Girardinichthys viviparus      Fishes          Native
Megapitaria squalida           Bivalves        Native
Crassostrea virginica          Bivalves        Native
Pocillopora capitata           Corals          Native
Goodea atripinnis              Fishes          Native
Girardinichthys viviparus      Fishes          Native
Crassostrea corteziensis       Bivalves        Native
Megapitaria squalida           Bivalves        Native
Ariopsis felis                 Fishes          Native
Centropous parallelus          Fishes          Native
Oreochromis sp.                Fishes          Exotic
Mugil cephalus                 Fishes          Native
Cyprinus carpio                Fishes          Model
Ankistrodesmus falcatus        Plankton        Model
Hyallela azteca                Amphipod        Native
Ambystoma mexicanum            Tritons         Native
Chelonia mydas                 Turtles         Native
Caretta caretta                Turtles         Native
Lepidochelys olivacea          Turtles         Native
Nassarius vibex                Gastropods      Native
Daphnia magna                  Cladocerans     Model
Panagrellus redivivus          Nematode        Model
Vibrio fischeri                Bacteria        Model
Salmo trutta                   Fishes          Exotic
Mugil curema                   Fishes          Native
Lecane quadridentata           Rotifers        Model
Daphnia magna                  Cladocerans     Model
Pandion haliaetus              Birds           Native
Echinolittorina ziczac         Birds           Native
Cerithium lutosum              Birds           Native
Goodea atripinnis              Fishes          Native
Chelonia mydas                 Turtles         Native
Goodea atripinnis              Fishes          Native
Danio rerio                    Fishes          Model
Astyanax aeneus                Fishes          Native
Chirostoma riojai              Fishes          Native
Crassostrea virginica          Bivalves        Native
Prionace glauca                Sharks          Native
Cupleidae spp. (embryo)        Fishes          Native
Goodea atripinnis              Fishes          Native
Prionace glauca                Sharks          Native
Crassostrea corteziensis       Bivalves        Native
Rhinella marina                Anurans         Exotic
Rhinella marina                Anurans         Exotic
Vicia faba                     Plants          Model
Goodea gracilis                Fishes          Native
Cyprinus carpio                Fishes          Model
Astyanax aeneus                Fishes          Native
Not identified                 Fitoplankton    N/E
Isurus oxyrinchus              Sharks          Native
Lactuca sativa                 Plants          Model
Chirostoma jordani             Fishes          Native
Cyprinus carpio                Fishes          Model
Cyprinus carpio                Fishes          Model
Daphnia magna                  Cladocerans     Model
Crassostrea gigas              Bivalves        Exotic
Ankistrodesmus falcatus        Fitoplankton    Model
Humans                         Humans          N/A
Crocodylus moreletii           Crocodiles      Native
Plicopurpura pansa             Gastropods      Native
Cyprinus carpio                Fishes          Model
Cyprinus carpio                Fishes          Model
Girardnichthys viviparus       Fishes          Native
Goodea atripinnis              Fishes          Native
Cyprinus carpio                Fishes          Model
Selenastrum capricornutum      Fitoplankton    Model
Daphnia magna                  Cladocerans     Model
Crocodylus moreletii           Crocodiles      Native
Girardinichthys viviparus      Fishes          Native
Fulica americana               Birds           Native
Chirostoma jordani             Fishes          Native
Ambystoma mexicanum            Tritons         Native
Hyalella azteca                Amphipod        Native
Cyprinus carpio                Fishes          Model
Ambystoma mexicanum            Tritons         Native
Crassostrea corteziensis       Bivalves        Native
Crassostrea sp.                Bivalves        N/E
Lepidochelys olivacea          Turtles         Native
Rhincodon typus                Fishes          Nativo
Haemulon aurolineatum          Fishes          Native
Ocyurus chrysurus              Fishes          Exotic
Daphnia magna                  Cladocerans     Model
Lecane quadridentata           Rotifers        Model
Fulica americana               Birds           Native
Cyprinus carpio                Fishes          Model

Species                        Reference

Limnodrilus hoffmeisteri       Flores-Tena & Martinez-
                               Tabche, (2001)
Limnodrilus hoffmeisteri       Martinez-Tabche et al. (2001)
Lecane quadridentata           Rico-Martinez et al. (2001)
Daphnia magna
Daphnia pulex
Simocephalus vetulus
Daphnia magna                  Villegas-Navarro et al. (2001)
No identificado                Favari et al. (2002)
Stagnicola sp.                 Martinez-Tabche et al. (2002)
Xiphophorus hellerii           Favari et al. (2003)
Xiphophorus hellerii           Lopez -Lopez et al. (2003)
Goodea atripinnis
Ameca splendens
Ariopsis assimilis             Norena-Barroso et al. (2004)
Danio rerio                    Baez-Ramirez & Garcia-Prieto (2005)
Dendrocygna autumnalis         Rendon-Von Osten et al. (2005)
Oreochromis niloticus          Gold-Bouchot et al. (2006)
Girardinichthys viviparus      Lopez-Lopez et al. (2006)
Gambusia yucatana              Rendon-Von Osten et al. (2006)
Crassostrea virginica          Gold-Bouchot et al. (2007)
Sorghum bicolor                Lopez-Hernandez et al. (2007)
Daphnia pulex                  Sanchez-Meza et al. (2007)
Lactuca sativa
Lecane quadridentata           Santos-Medrano et al. (2007)
Daphnia magna
Ameca splendens                Tejeda-Vera et al. (2007)
Goodea atripinnis
Xenotoca melanosoma            Torres-Bugarin et al. (2007)
Oreochromis aureus
Chirostoma consocium
Chirostoma lucius
Lepomis macrochirus
Alloophorus robustus
Zoogoneticus quitzeoensis
Chapalychthys encaustus
Poeciliopsis infans
Goodea atripinnis
Chelonia mydas agassizii       Valdivia et al. (2007)
Girardinichthys viviparus      Vega-Lopez et al. (2007)
Carcinogenic cells MCF-7
Ariopsis felis                 Zapata-Perez et al. (2007)
Haemulon plumieri              Alpuche-Gual & Gold-Bouchot (2008)
Simocephalus mixtus            Martinez-Jeronimo et al. (2008)
Daphnia magna
Girardinichthys viviparus      Vega-Lopez et al. (2008)
Megapitaria squalida           Cantu-Medellin et al. (2009)
Crassostrea virginica          Guzman- Garcia et al. (2009)
Pocillopora capitata           Linan-Cabello et al. (2009)
Goodea atripinnis              Reinoso-Silva et al. (2014)
Girardinichthys viviparus      Vega-Lopez et al. (2009)
Crassostrea corteziensis       Bernal-Hernandez et al. (2010)
Megapitaria squalida           Escobedo-Fregoso et al. (2010)
Ariopsis felis                 Gonzalez-Mille et al. (2010)
Centropous parallelus
Oreochromis sp.
Mugil cephalus
Cyprinus carpio                Galar-Martinez et al. (2010)
Ankistrodesmus falcatus        Lopez-Lopez et al. (2010)
Hyallela azteca
Ambystoma mexicanum
Chelonia mydas                 Richardson et al. (2010)
Caretta caretta
Lepidochelys olivacea
Nassarius vibex                Rodriguez-Romero (2010)
Daphnia magna                  Salazar-Coria et al. (2010)
Panagrellus redivivus
Vibrio fischeri
Salmo trutta
Mugil curema                   Rios-Sicarios et al. (2010)
Lecane quadridentata           Torres-Guzman et al. (2010)
Daphnia magna
Pandion haliaetus              Rivera-Rodriguez & Rodriguez-
                               Estrella (2011)
Echinolittorina ziczac
Cerithium lutosum
Goodea atripinnis              Arevalo-Hernandez et al. (2011)
Chelonia mydas                 Labrada-Martagon et al. (2011)
Goodea atripinnis              Lopez-Lopez et al. (2011)
Danio rerio                    Rodriguez-Fuentes et al. (2011)
Astyanax aeneus                Trujillo-Jimenez et al. (2011)
Chirostoma riojai              Vega-Lopez et al. (2011)
Crassostrea virginica          Aguilar et al. (2012)
Prionace glauca                Barrera-Garcia et al. (2012)
Cupleidae spp. (embryo)        Jaward et al. (2012)
Goodea atripinnis              Ruiz-Picos & Lopez-Lopez (2012)
Prionace glauca                Barrera-Garcia et al. (2013)
Crassostrea corteziensis       Giron-Perez et al. (2013)
Rhinella marina                Gonzalez-Mille et al. (2013)
Rhinella marina                Ilizaliturri-Hernandez
                               et al. (2013)
Vicia faba                     Juarez-Santa Cruz et al. (2013)
Goodea gracilis                Olivares-Rubio et al. (2013)
Cyprinus carpio                San juan- Reyes et al. (2013)
Astyanax aeneus                Trujillo-Jimenez et al. (2013)
Not identified                 Vega-Lopez et al. (2013)
Isurus oxyrinchus              Velez-Alvez et al. (2013)
Lactuca sativa                 Rodriguez-Romero et al. (2014)
Chirostoma jordani             Dzul-Caamal et al. (2014)
Cyprinus carpio                Garcia-Nieto et al. (2014)
Cyprinus carpio                Gonzalez-Gonzalez et al. (2014)
Daphnia magna                  Mejia-Saavedra et al. (2014)
Crassostrea gigas              Vazquez-Boucard et al. (2014)
Ankistrodesmus falcatus        Abeja-Pineda et al. (2015)
Humans                         Alvares-Moya & Reynoso-Silva (2015)
Crocodylus moreletii           Buenfil-Rojas et al. (2015)
Plicopurpura pansa             Dominguez-Ojeda et al. (2015)
Cyprinus carpio                Morachis-Valdez et al. (2015)
Cyprinus carpio                Neri-Cruz et al. (2015)
Girardnichthys viviparus       Olivares-Rubio et al. (2015)
Goodea atripinnis              Ruiz-Picos et al. (2015)
Cyprinus carpio                San juan- Reyes et al. (2015)
Selenastrum capricornutum      Sobrino-Figueroa et al. (2015)
Daphnia magna
Crocodylus moreletii           Dzul-Caamal et al. (2016)
Girardinichthys viviparus      Dzul-Caamal et al. (2016)
Fulica americana               Lopez-Islas et al. (2016)
Chirostoma jordani             Lopez-Lopez et al. (2016)
Ambystoma mexicanum
Hyalella azteca                Novoa-Luna et al. (2016)
Cyprinus carpio                Olvera-Nestor et al. (2016)
Ambystoma mexicanum            Ortiz-Ordonez et al. (2016)
Crassostrea corteziensis       Toledo-Ibarra et al. (2016)
Crassostrea sp.                Bautista-Covarrubias et al. (2017)
Lepidochelys olivacea          Cortez-Gomez et al. (2018) *
Rhincodon typus                Fossi et al. (2017)
Haemulon aurolineatum          Gold-Bouchot et al. (2017)
Ocyurus chrysurus
Daphnia magna                  Guerrero-Jimenez et al. (2017)
Lecane quadridentata
Fulica americana               Lopez-Islas et al. (2017)
Cyprinus carpio                Perez-Coyotl et al. (2017)

Species

Limnodrilus hoffmeisteri       [1]
Limnodrilus hoffmeisteri       [2]
Lecane quadridentata           [3]
Daphnia magna
Daphnia pulex
Simocephalus vetulus
Daphnia magna                  [4]
No identificado                [5]
Stagnicola sp.                 [6]
Xiphophorus hellerii           [7]
Xiphophorus hellerii           [8]
Goodea atripinnis
Ameca splendens
Ariopsis assimilis             [9]
Danio rerio                    [10]
Dendrocygna autumnalis         [11]
Oreochromis niloticus          [12]
Girardinichthys viviparus      [13]
Gambusia yucatana              [14]
Crassostrea virginica          [15]
Sorghum bicolor                [16]
Daphnia pulex                  [17]
Lactuca sativa
Lecane quadridentata           [18]
Daphnia magna
Ameca splendens                [19]
Goodea atripinnis
Xenotoca melanosoma            [20]
Oreochromis aureus
Chirostoma consocium
Chirostoma lucius
Lepomis macrochirus
Alloophorus robustus
Zoogoneticus quitzeoensis
Chapalychthys encaustus
Poeciliopsis infans
Goodea atripinnis
Chelonia mydas agassizii       [21]
Girardinichthys viviparus      [22]
Carcinogenic cells MCF-7
Ariopsis felis                 [23]
Haemulon plumieri              [24]
Simocephalus mixtus            [25]
Daphnia magna
Girardinichthys viviparus      [26]
Megapitaria squalida           [27]
Crassostrea virginica          [28]
Pocillopora capitata           [29]
Goodea atripinnis              [30]
Girardinichthys viviparus      [31]
Crassostrea corteziensis       [32]
Megapitaria squalida           [33]
Ariopsis felis                 [34]
Centropous parallelus
Oreochromis sp.
Mugil cephalus
Cyprinus carpio                [35]
Ankistrodesmus falcatus        [36]
Hyallela azteca
Ambystoma mexicanum
Chelonia mydas                 [37]
Caretta caretta
Lepidochelys olivacea
Nassarius vibex                [38]
Daphnia magna                  [39]
Panagrellus redivivus
Vibrio fischeri
Salmo trutta
Mugil curema                   [40]
Lecane quadridentata           [41]
Daphnia magna
Pandion haliaetus              [42]
Echinolittorina ziczac
Cerithium lutosum
Goodea atripinnis              [43]
Chelonia mydas                 [44]
Goodea atripinnis              [45]
Danio rerio                    [46]
Astyanax aeneus                [47]
Chirostoma riojai              [48]
Crassostrea virginica          [49]
Prionace glauca                [50]
Cupleidae spp. (embryo)        [51]
Goodea atripinnis              [52]
Prionace glauca                [53]
Crassostrea corteziensis       [54]
Rhinella marina                [55]
Rhinella marina                [56]
Vicia faba                     [57]
Goodea gracilis                [58]
Cyprinus carpio                [59]
Astyanax aeneus                [60]
Not identified                 [61]
Isurus oxyrinchus              [62]
Lactuca sativa                 [63]
Chirostoma jordani             [64]
Cyprinus carpio                [65]
Cyprinus carpio                [66]
Daphnia magna                  [67]
Crassostrea gigas              [68]
Ankistrodesmus falcatus        [69]
Humans                         [70]
Crocodylus moreletii           [71]
Plicopurpura pansa             [72]
Cyprinus carpio                [73]
Cyprinus carpio                [74]
Girardnichthys viviparus       [75]
Goodea atripinnis              [76]
Cyprinus carpio                [77]
Selenastrum capricornutum      [78]
Daphnia magna
Crocodylus moreletii           [79]
Girardinichthys viviparus      [80]
Fulica americana               [81]
Chirostoma jordani             [82]
Ambystoma mexicanum
Hyalella azteca                [83]
Cyprinus carpio                [84]
Ambystoma mexicanum            [85]
Crassostrea corteziensis       [86]
Crassostrea sp.                [87]
Lepidochelys olivacea          [88]
Rhincodon typus                [89]
Haemulon aurolineatum          [90]
Ocyurus chrysurus
Daphnia magna                  [91]
Lecane quadridentata
Fulica americana               [92]
Cyprinus carpio                [93]

Table 2. Biomarkers used to evaluate aquatic contamination in
Mexico: 2001-2017. The numbers in square brackets refer to the
numerical assignment made in Table 1.

Biomarkers                Sources of
                          contamination or
                          pollutants
                          associated with the
                          study areas

Acetylcholinesterase      Agricultural and
(AChE)                    urban runoff,
                          organochlorine and
                          organophosphates
                          pesticides, PAHs,
                          PCBs, heavy metals

Alanine                   Urban wastewater and
aminotransferase          agricultural runoff
(ALT)

Alcohol                   PCBs
dehydrogenase (ADH)

Algal growth              Urban wastewater,
potential                 agricultural runoff,
                          heavy metals, PAHs

Alkaline phosphatase      Urban wastewater,
(ALP)                     agricultural runoff,
                          heavy metals, PAHs

Apoptosis tunnel          Hospital and urban
assay                     effluents,
                          pharmaceutical
                          products, heavy
                          metals.

Bioluminescence           Refineries, aromatic
inhibition                hydrocarbon

Carboxylesterases         Benzo (a) pyrene and
(CbE)                     chlorpyrifos

Caspase-3 activity        Hospital and urban
                          effluents,
                          pharmaceutical
                          products, heavy
                          metals

Catalase activity         Urban and industrial
(CAT)                     wastewater,
                          agricultural runoff,
                          heavy metals, PAHs,
                          pharmaceutical
                          products,
                          organochlorine
                          pesticides,
                          halomethanes

Condition index           Urban and industrial
                          wastewater,
                          agricultural runoff,
                          heavy metals,
                          halomethanes

Ethoxyresorufin-O-        Urban and industrial
deethylase (EROD)         wastewater,
                          agricultural runoff,
                          PCBs, PAHs,
                          organochlorine
                          pesticides

Cytochrome P450-          Urban and industrial
1A1(CYP1A1) (regard       wastewater,
western-blotting and      agricultural runoff,
gene expression)          PCBs, PAHs,
                          organochlorine
                          pesticides, heavy
                          metals, halomethanes

Embryo deformities        PAHs

Comet assay               Urban wastewater,
                          organochlorine
                          pesticides,
                          pharmaceutical
                          products, heavy
                          metals, PAHs, PCBs

Epoxide hydrolase         Urban wastewater,
(EH1)                     halomethanes

Esterases                 Agricultural, urban
                          and industrial
                          runoff

Gamma-glutamyl            Urban and
transferase (GGT)         agricultural runoff,
                          organochlorine and
                          organophosphorus
                          pesticides

Glutathione S-            Urban wastewater and
transferase activity      agricultural runoff,
(GST) (regard gene        heavy metals, PAHs,
expression)               PCBs, Halomethanes,
                          Organochlorine
                          pesticides

Glutathione               Urban and industrial
peroxidase activity       wastewater,
(GPx) (include gene       agricultural runoff,
expression)               heavy metals, PAHs,
                          pharmaceutical
                          products,
                          organochlorine
                          pesticides,
                          halomethanes

Glutathione               Urban wastewater and
reductase activity        agricultural runoff,
(GR) (regard gene         heavy metals, PAHs
expression)

Heat shock protein        Urban and
(HSP70)                   agricultural
                          effluents

Histopathological         Urban and
lesions (HPL)             agricultural
                          effluents, PAHS,
                          PCBs, Heavy metals,
                          organochlorine
                          pesticides

Hydrogen peroxide         Urban wastewater and
([H.sub.2][O.sub.2])      agricultural runoff,
content                   PAHs, Heavy metals,
                          halomethanes

Hydroperoxides            Urban wastewater and
(ROOH) content            agricultural runoff,
                          PAHs, heavy metals,
                          halomethanes,
                          pharmaceutical
                          products

Imposex                   Port activity and
                          agricultural areas

Germination index         Agricultural areas

Root elongation           Urban and
index                     agricultural areas,
                          industrial effluents

Gonadosomatic index       Urban wastewater and
(GSI)                     agricultural runoff,
                          industrial
                          discharges

Hepatosomatic index       Urban wastewater and
(HSI)                     agricultural runoff,
                          industrial
                          discharges

Ingestion rate            Industrial
                          wastewater

Lactate dehydrogenase     Diffuse pollution,
(LDH)                     hospital effluent

Lethality                 Urban and industrial
                          wastewater, PAHs,
                          PCBs, hormones,

Lipid peroxidation        Urban and industrial
(TBARS)                   wastewater,
                          agricultural runoff,
                          heavy metals, PAHs,
                          organochlorine
                          pesticides,
                          Organophosphorus
                          pesticides, Heavy
                          metals,
                          Pharmaceutical
                          products,
                          Halomethanes

Metallothionein           Urban wastewater and
(MT's)                    runoff, PAHs, PCBs,
                          heavy metals,
                          hormones

Mycosporine-like          Urban wastewater
amino acids (MAAs)

Na/K-ATPase               Urban and industrial
                          wastewater

Neutral Red               Organochlorine
Retention Time            pesticides, PCBs,
(NRRT)                    PAHs, heavy metals

PAH bile metabolites      PAHs, PCBs, heavy
                          metals

Phospholipase A2          Agricultural, urban,
                          and industrial
                          runoff

Carbonyl radical          Urban wastewater and
(RC=O) content            agricultural runoff,
                          PAHs, heavy metals,
                          halomethanes,
                          pharmaceutical
                          products

Superoxide dismutase      Urban wastewater and
activity (SOD)            agricultural runoff,
                          heavy metals, PAHs,
                          pharmaceutical

                          Urban wastewater and
                          agricultural runoff,
                          heavy metals, PAHs,
                          pharmaceutical
                          products,
                          Halomethanes

Superoxide radical        Urban wastewater and
([O.sub.2] *-)            agricultural runoff,
content                   PAHs, heavy metals

Vitellogenin (VTG)        Urban wastewater and
(include gene             agricultural runoff,
expression)               PAHs, PCBs,
                          organochlorine
                          pesticides, hormones

[delta]-                  Urban areas and
aminolevulinic acid       petrochemical
dehydratase               industry
([delta]-ALAD)

Micronucleus (MN)         Urban wastewater,
                          agricultural runoff,
                          organochlorine
                          pesticides, PCBs,
                          pharmaceutical
                          products

Biomarkers                Organisms used in
                          the different
                          studies

Acetylcholinesterase      Fishes [5, 7, 8, 12,
(AChE)                    13, 14, 19, 24, 64];
                          Bivalves [15, 32,
                          68, 86, 87];
                          Amphipods [36];
                          Birds [11];
                          Oligochaetes [2];
                          Gastropods [6];
                          Tritons [36]; Birds
                          [11]; Phytoplankton
                          [36]

Alanine                   Birds [92]
aminotransferase
(ALT)

Alcohol                   Fishes [31]
dehydrogenase (ADH)

Algal growth              Phytoplankton [36,
potential                 61, 69]

Alkaline phosphatase      Fishes [8]
(ALP)

Apoptosis tunnel          Fishes [77, 84, 93]
assay

Bioluminescence           Bacteria [39]
inhibition

Carboxylesterases         Fishes [24]
(CbE)

Caspase-3 activity        Fishes [77, 84, 93]

Catalase activity         Fishes [26, 35, 45,
(CAT)                     48, 52, 58, 59, 66,
                          73, 74, 80, 82];
                          Bivalves [27, 54,
                          86]; Sharks [50, 53,
                          62]; Turtles [21,
                          44, 88];
                          Phytoplankton [61,
                          69]; Crocodiles
                          [79]; Tritons [36,
                          82]; Corals [29];
                          Amphipods [83]

Condition index           Fishes [39,58, 60,
                          82]; Bivalves [49];
                          Birds [81,92];

Ethoxyresorufin-O-        Fishes [12, 19, 31,
deethylase (EROD)         39, 47, 75]

Cytochrome P450-          Fishes [12, 40, 46,
1A1(CYP1A1) (regard       48, 58, 89, 90];
western-blotting and      Turtles [37];
gene expression)          Crocodiles [79]

Embryo deformities        Fishes [51]

Comet assay               Fishes [10, 30, 34,
                          43, 65, 77, 93];
                          Anurans [55];
                          Bivalves [68];
                          Humans [70]

Epoxide hydrolase         Fishes [75]
(EH1)

Esterases                 Rotifers [3, 18]

Gamma-glutamyl            Fishes [5, 8, 19];
transferase (GGT)         Birds [92]

Glutathione S-            Fishes [47, 48, 90];
transferase activity      Bivalves [27, 68,
(GST) (regard gene        86]; Sharks [62];
expression)               Turtles [21, 37,
                          88]; Phytoplankton
                          [61]; Tritons [36];
                          Corals [29]

Glutathione               Fishes [35, 45, 48,
peroxidase activity       52, 58, 59, 73, 82];
(GPx) (include gene       Bivalves [86];
expression)               Sharks [62]; Turtles
                          [44]; Phytoplankton
                          [61, 69]; Tritons
                          [36, 82]; Corals
                          [29]; Amphipods [83]

Glutathione               Sharks [50, 53, 62];
reductase activity        Turtles [88]; Fishes
(GR) (regard gene         [80]; Crocodiles
expression)               [79]

Heat shock protein        Fishes [40]
(HSP70)

Histopathological         Bivalves [15, 28];
lesions (HPL)             Fishes [9, 40, 76];
                          Tritons [85]; Birds
                          [82, 92]

Hydrogen peroxide         Fishes [58, 80];
([H.sub.2][O.sub.2])      Bivalves [54];
content                   Crocodiles [79]

Hydroperoxides            Fishes [58, 59, 66,
(ROOH) content            73, 74]; Bivalves
                          [54, 86]; Plankton
                          [61]; Amphipods [83]

Imposex                   Gastropods [38, 72]

Germination index         Plants [63]

Root elongation           Plants [16, 17, 63]
index

Gonadosomatic index       Fishes [60, 82];
(GSI)                     Birds [81, 92]

Hepatosomatic index       Fishes [39, 58, 60,
(HSI)                     82]; Birds [81, 92]

Ingestion rate            Cladocerans [18]

Lactate dehydrogenase     Fishes [47, 84]
(LDH)

Lethality                 Fishes [22, 26];
                          Rotifers [41, 91];
                          Nematodes [39];
                          Cladocerans [3, 4,
                          17, 25, 41, 67, 78,
                          91]

Lipid peroxidation        Sharks [50, 53, 62];
(TBARS)                   Bivalves [27, 54,
                          86]; Crocodiles
                          [79]; Fishes [5, 7,
                          8, 13, 19, 26, 28,
                          32, 35, 44, 45, 47,
                          48, 52, 58, 59, 60,
                          66, 73, 74, 80, 82];
                          Corals [29]; Turtles
                          [21, 44]; Amphipods
                          [36, 83]; Gastropods
                          [6]; Tritons [36,
                          82, 85];
                          Phytoplankton [61,
                          69]; Birds [81, 92]

Metallothionein           Fishes [75, 80];
(MT's)                    Bivalves [15, 32,
                          33]; Crocodiles [71]

Mycosporine-like          Corals [29]
amino acids (MAAs)

Na/K-ATPase               Fishes [45]

Neutral Red               Bivalves [15]
Retention Time
(NRRT)

PAH bile metabolites      Fishes [12, 90]

Phospholipase A2          Rotifers [3, 18]

Carbonyl radical          Fishes [58, 59, 66,
(RC=O) content            73, 74, 80];
                          Crocodiles [79];
                          Phytoplankton [61];
                          Amphipods [83];
                          Sharks [62]

Superoxide dismutase      Fishes [26, 35, 45,
activity (SOD)            48 , 52, 58, 59, 66,
                          73, 74, 80, 82];
                          Phytoplankton [61,
                          69]; Sharks [50, 53,
                          62]; Crocodiles
                          [79]; Turtles [21,
                          44]; Corals [29];
                          Tritons [36, 82];
                          Bivalves [86];
                          Amphipods [83]

Superoxide radical        Fishes [58, 80];
([O.sub.2] *-)            Bivalves [54];
content                   Sharks [50, 53, 62];
                          Turtles [21];
                          Crocodiles [79];
                          Corals [29]

Vitellogenin (VTG)        Fishes [22, 23,46,
(include gene             75, 80, 90]
expression)

[delta]-                  Anurans [56]
aminolevulinic acid
dehydratase
([delta]-ALAD)

Micronucleus (MN)         Fishes [20, 34, 65,
                          77, 84, 93]; Plants
                          [57]

Table 3. Monitoring studies covering more than one seasonal cycle
carried out in Mexico: 2001-2017. In the seasonal cycle [season]
column, reference is first made to the seasonal cycles to which the
sampling campaigns extend, including complete or incomplete cycles,
and the second refers to the stations in which the sampling was
conducted. * R: refers to the numerical assignment made in Table 1
to the reviewed articles.

 * R     Study         Study zone        Seasonal cycles
                                            [season]

[14]    In situ     Marentes stream,     2 [Dry; Rains;
                    Las Pinas stream       Dry; Rains]

[19]    In situ      De La Vega dam      2 [Rains; Dry;
                                             Rains]

                                         2 [Rains; Dry;
                                             Rains]

[28]    In situ     Mandinga lagoon      2 [Dry; Rains;
                                           Dry; Rains]

[47]    In situ     Chanpoton river      2 [Dry; Rains;
                                              Dry]

[40]    In situ      Urias lagoon,       2 [Rains; Dry;
                    Teacapan lagoon          Rains]

[60]    In situ     Chanpoton river      2 [Dry- Rains-
                                               Dry

[75]    In situ      Mayor lake and      2 [Rains- Dry-
                       Menor lake          Rains- Dry]

[82]    In situ      Yuriria lagoon      2 [Rains; Dry;
                                             Rains]

 * R        Reference species        Biomarke
                                         rs

[14]         Fishes (Gambusia           AChE
                yucatana)

[19]          Fishes (Ameca             GGTP
                splendens)              AChE
                                        EROD
                                       TBARS

              Fishes (Goodea            GGTP
               atripinnis)              AChE
                                        EROD
                                       TBARS

[28]      Bivalves (Crassostrea      Condition
                virginica)             index
                                        (K)

[47]         Fishes (Astyanax          TBARS
                 aeneus)                GST
                                        EROD
                                        LDH

[40]      Fishes (Mugil curema)        CYP1A
                                       HSP70

[60]         Fishes (Astyanax          TBARS
                 aeneus)                 GC
                                        IGS
                                        IHS

[75]     Fishes (Girardinichthys        VTG
                viviparus)              MT's
                                        EROD
                                        EH1

[82]        Fishes (Chirostoma          SOD
                 jordani)               GPx
                                        CAT
                                       TBARS
                                        IHS
                                        IGS

 * R         General trend between seasonal cycles

[14]    AChE it shows a decrease from dry to dry
        in Las Pinas Stream and does not present
        an important variation in Marentes
        Stream.

[19]    TBARS hepatica presents a very high
        decrease of rains to rains. GGTP hepatic
        and AChE in muscle do not show
        significant variation from rains to
        showers. EROD in the liver presents a
        large reduction in rainfall to rainfall.

        TBARS hepatica presents a large
        decrease in rainfall to rainfall. GGTP
        hepatic presents an increase, while AChE
        in muscle does not show significant
        variation from rains to rains. EROD in
        the liver presents a large increase in
        rainfall to rainfall.

[28]    K shows an increase from dry to dry and
        decreases from rains to rains

[47]    EROD in the liver does not show a
        significant variation from dry to dry. GST
        presents a large increase from dry to dry.
        EROD presents a decrease from dry to
        dry. LDH presents a very high rise from
        dry to dry.

[40]    Hepatic CYP1A and HSP70 tend to
        decrease in Urias lagoon from rains to
        rains, while in Teacapan lagoon.
        CYP1A and HSP70 tend to increase from
        rains to rains.

[60]    TBARS Hepatic does not show a
        significant variation from dry to dry. GC
        presents a slight increase from dry to dry.
        IGS and IHS do not show considerable
        variation from dry to dry.

[75]    VTG in the liver of males from Menor
        lake there is a great increase in rains.
        Hepatic EROD showed a large decrease
        from dry to dry in Lake Mayor for both
        sexes. EH1 in the liver showed a rise
        from dry to drain in Mayor lake for both
        genders. MTs hepatica presents a great
        increase of the first cycle to second for
        both sexes of Menor lake.

[82]    CAT, GPx, SOD they decrease slightly
        from rains to rains. TBARS decreases
        from rains to rains. IGS tends to increase
        rainfall to rain while IHS does not vary
        significantly during cycles.
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Author:Ramirez-Ayala, Eduardo; Arguello-Perez, Miguel Angel; Ilizaliturri-Hernandez, Cesar Arturo; Tintos-G
Publication:Latin American Journal of Aquatic Research
Date:Nov 1, 2018
Words:13034
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