text new page (beta)
English (pdf)
Article in xml format
Automatic translation
Cited by SciELO 
Cited by Google 
Similars in
SciELO 
Similars in Google 
Permalink
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 (Sarukhán 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-governmen- tal) (Burger, 2006).
Mexico faces great responsibilities regarding the conservation and management of its aquatic ecosys- tems; it has signed international agreements and treatieson 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) (Comisión 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 & Hernández-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, ecosys- tems, 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 repre- sentative 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, meta- bolism, behavior, genetic damage, etc.) that respond to disturbances in their environment (Butterworth et al., 2001), and which due to their ecological characteristics (Páez-Osuna & Osuna-Martínez, 2011; Berthet, 2012; González-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 ecosys- tem 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).
Figure 1. Number of published articles on the use of biomarkers to assess pollution in aquatic ecosystems in Mexico (2001- 2017).
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-conven- tional 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 interpre- tations 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 (Páez- Osuna & Osuna-Martínez, 2011). Other organisms considered validated in Mexico, include Gambusia yucatana (Rendón-Von Osten, 2015), cladocerans (Mendoza-Cantú et al., 2013) and rotifers (Rico- Martínez 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).
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 González et al. (2014). *Cortez-Gómez et al. (2018) was reviewed in 2017 when it was still in press.
| Species | Biomonitor | Origin | Reference | ||
| Limnodrilus hoffmeisteri | Oligochaeta | Model | Flores-Tena & Martínez-Tabche, (2001) | [1] | |
| Limnodrilus hoffmeisteri | Oligochaeta | Model | Martínez-Tabche et al. (2001) | [2] | |
| Lecane quadridentata | Rotifers | Model | Rico-Martínez et al. (2001) | [3] | |
| Daphnia magna | Cladocerans | Model | |||
| Daphnia pulex | Cladocerans | Native | |||
| Simocephalus vetulus | Cladocerans | Native | |||
| Daphnia magna | Cladocerans | Native | Villegas-Navarro et al. (2001) | [4] | |
| No identificado | Fishes | Native | Favari et al. (2002) | [5] | |
| Stagnicola sp. | Gastropods | Native | Martínez-Tabche et al. (2002) | [6] | |
| Xiphophorus hellerii | Fishes | Exotic | Favari et al. (2003) | [7] | |
| Xiphophorus hellerii | Fishes | Exotic | López -López et al. (2003) | [8] | |
| Goodea atripinnis | Fishes | Native | |||
| Ameca splendens | Fishes | Native | |||
| Ariopsis assimilis | Fishes | Native | Noreña-Barroso et al. (2004) | [9] | |
| Danio rerio | Fishes | Model | Báez-Ramírez & García-Prieto (2005) | [10] | |
| Dendrocygna autumnalis | Birds | Native | Rendón-Von Osten et al. (2005) | [11] | |
| Oreochromis niloticus | Fishes | Exotic | Gold-Bouchot et al. (2006) | [12] | |
| Girardinichthys viviparus | Fishes | Native | López-López et al. (2006) | [13] | |
| Gambusia yucatana | Fishes | Netive | Rendón-Von Osten et al. (2006) | [14] | |
| Crassostrea virginica | Bivalves | Native | Gold-Bouchot et al. (2007) | [15] | |
| Sorghum bicolor | Plants | Model | López-Hernández et al. (2007) | [16] | |
| Daphnia pulex | Cladocerans | Native | Sánchez-Meza et al. (2007) | [17] | |
| Lactuca sativa | Plants | Model | |||
| Lecane quadridentata | Rotifers | Model | Santos-Medrano et al. (2007) | [18] | |
| Daphnia magna | Cladocerans | Model | |||
| Ameca splendens | Fishes | Native | Tejeda-Vera et al. (2007) | [19] | |
| Goodea atripinnis | Fishes | Native | |||
| Xenotoca melanosoma | Fishes | Native | Torres-Bugarin et al. (2007) | [20] | |
| 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 | Valdivia et al. (2007) | [21] | |
| Girardinichthys viviparus | Fishes | Native | Vega-López et al. (2007) | [22] | |
| Carcinogenic cells MCF-7 | Humans | N/A | |||
| Ariopsis felis | Fishes | Native | Zapata-Pérez et al. (2007) | [23] | |
| Haemulon plumieri | Fishes | Native | Alpuche-Gual & Gold-Bouchot (2008) | [24] | |
| Simocephalus mixtus | Cladocerans | Native | Martínez-Jerónimo et al. (2008) | [25] | |
| Daphnia magna | Cladocerans | Model | |||
| Girardinichthys viviparus | Fishes | Native | Vega-López et al. (2008) | [26] | |
| Megapitaria squalida | Bivalves | Native | Cantú-Medellín et al. (2009) | [27] | |
| Crassostrea virginica | Bivalves | Native | Guzmán- Garcia et al. (2009) | [28] | |
| Pocillopora capitata | Corals | Native | Liñán-Cabello et al. (2009) | [29] | |
| Goodea atripinnis | Fishes | Native | 1) | [30] | |
| Species | Biomonitor | Origin | Reference | ||
| Girardinichthys viviparus | Fishes | Native | Vega-López et al. (2009) | [31] | |
| Crassostrea corteziensis | Bivalves | Native | Bernal-Hernández et al. (2010) | [32] | |
| Megapitaria squalida | Bivalves | Native | Escobedo-Fregoso et al. (2010) | [33] | |
| Ariopsis felis | Fishes | Native | González-Mille et al. (2010) | [34] | |
| Centropous parallelus | Fishes | Native | |||
| Oreochromis sp. | Fishes | Exotic | |||
| Mugil cephalus | Fishes | Native | |||
| Cyprinus carpio | Fishes | Model | Galar-Martínez et al. (2010) | [35] | |
| Ankistrodesmus falcatus | Plankton | Model | López-López et al. (2010) | [36] | |
| Hyallela azteca | Amphipod | Native | |||
| Ambystoma mexicanum | Tritons | Native | |||
| Chelonia mydas | Turtles | Native | Richardson et al. (2010) | [37] | |
| Caretta caretta | Turtles | Native | |||
| Lepidochelys olivacea | Turtles | Native | |||
| Nassarius vibex | Gastropods | Native | Rodríguez-Romero (2010) | [38] | |
| Daphnia magna | Cladocerans | Model | Salazar-Coria et al. (2010) | [39] | |
| Panagrellus redivivus | Nematode | Model | |||
| Vibrio fischeri | Bacteria | Model | |||
| Salmo trutta | Fishes | Exotic | |||
| Mugil curema | Fishes | Native | Ríos-Sicarios et al. (2010) | [40] | |
| Lecane quadridentata | Rotifers | Model | Torres-Guzmán et al. (2010) | [41] | |
| Daphnia magna | Cladocerans | Model | |||
| Pandion haliaetus | Birds | Native | Rivera-Rodríguez & Rodriguez-Estrella (2011) | [42] | |
| Echinolittorina ziczac | Birds | Native | |||
| Cerithium lutosum | Birds | Native | |||
| Goodea atripinnis | Fishes | Native | Arévalo-Hernández et al. (2011) | [43] | |
| Chelonia mydas | Turtles | Native | Labrada-Martagón et al. (2011) | [44] | |
| Goodea atripinnis | Fishes | Native | López-López et al. (2011) | [45] | |
| Danio rerio | Fishes | Model | Rodríguez-Fuentes et al. (2011) | [46] | |
| Astyanax aeneus | Fishes | Native | Trujillo-Jimenez et al. (2011) | [47] | |
| Chirostoma riojai | Fishes | Native | Vega-López et al. (2011) | [48] | |
| Crassostrea virginica | Bivalves | Native | Aguilar et al. (2012) | [49] | |
| Prionace glauca | Sharks | Native | Barrera-Garcia et al. (2012) | [50] | |
| Cupleidae spp. (embryo) | Fishes | Native | Jaward et al. (2012) | [51] | |
| Goodea atripinnis | Fishes | Native | Ruiz-Picos & López-López (2012) | [52] | |
| Prionace glauca | Sharks | Native | Barrera-Garcia et al. (2013) | [53] | |
| Crassostrea corteziensis | Bivalves | Native | Girón-Pérez et al. (2013) | [54] | |
| Rhinella marina | Anurans | Exotic | González-Mille et al. (2013) | [55] | |
| Rhinella marina | Anurans | Exotic | Ilizaliturri-Hernández et al. (2013) | [56] | |
| Vicia faba | Plants | Model | Juárez-Santa Cruz et al. (2013) | [57] | |
| Goodea gracilis | Fishes | Native | Olivares-Rubio et al. (2013) | [58] | |
| Cyprinus carpio | Fishes | Model | San juan- Reyes et al. (2013) | [59] | |
| Astyanax aeneus | Fishes | Native | Trujillo-Jiménez et al. (2013) | [60] | |
| Not identified | Fitoplankton | N/E | Vega-López et al. (2013) | [61] | |
| Isurus oxyrinchus | Sharks | Native | Vélez-Alvez et al. (2013) | [62] | |
| Lactuca sativa | Plants | Model | Rodríguez-Romero et al. (2014) | [63] | |
| Chirostoma jordani | Fishes | Native | Dzul-Caamal et al. (2014) | [64] | |
| Cyprinus carpio | Fishes | Model | García-Nieto et al. (2014) | [65] | |
| Cyprinus carpio | Fishes | Model | González-González et al. (2014) | [66] | |
| Daphnia magna | Cladocerans | Model | Mejía-Saavedra et al. (2014) | [67] | |
| Crassostrea gigas | Bivalves | Exotic | Vázquez-Boucard et al. (2014) | [68] | |
| Ankistrodesmus falcatus | Fitoplankton | Model | Abeja-Pineda et al. (2015) | [69] | |
| Humans | Humans | N/A | Alvares-Moya & Reynoso-Silva (2015) | [70] | |
| Crocodylus moreletii | Crocodiles | Native | Buenfil-Rojas et al. (2015) | [71] | |
| Plicopurpura pansa | Gastropods | Native | Domínguez-Ojeda et al. (2015) | [72] | |
| Species | Biomonitor | Origin | Reference | ||
| Cyprinus carpio | Fishes | Model | Morachis-Valdez et al. (2015) | [73] | |
| Cyprinus carpio | Fishes | Model | Neri-Cruz et al. (2015) | [74] | |
| Girardnichthys viviparus | Fishes | Native | Olivares-Rubio et al. (2015) | [75] | |
| Goodea atripinnis | Fishes | Native | Ruiz-Picos et al. (2015) | [76] | |
| Cyprinus carpio | Fishes | Model | San juan- Reyes et al. (2015) | [77] | |
| Selenastrum capricornutum | Fitoplankton | Model | Sobrino-Figueroa et al. (2015) | [78] | |
| Daphnia magna | Cladocerans | Model | |||
| Crocodylus moreletii | Crocodiles | Native | Dzul-Caamal et al. (2016) | [79] | |
| Girardinichthys viviparus | Fishes | Native | Dzul-Caamal et al. (2016) | [80] | |
| Fulica americana | Birds | Native | López-Islas et al. (2016) | [81] | |
| Chirostoma jordani | Fishes | Native | López-López et al. (2016) | [82] | |
| Ambystoma mexicanum | Tritons | Native | |||
| Hyalella azteca | Amphipod | Native | Novoa-Luna et al. (2016) | [83] | |
| Cyprinus carpio | Fishes | Model | Olvera-Néstor et al. (2016) | [84] | |
| Ambystoma mexicanum | Tritons | Native | Ortiz-Ordoñez et al. (2016) | [85] | |
| Crassostrea corteziensis | Bivalves | Native | Toledo-Ibarra et al. (2016) | [86] | |
| Crassostrea sp. | Bivalves | N/E | Bautista-Covarrubias et al. (2017) | [87] | |
| Lepidochelys olivacea | Turtles | Native | Cortez-Gómez et al. (2018)* | [88] | |
| Rhincodon typus | Fishes | Nativo | Fossi et al. (2017) | [89] | |
| Haemulon aurolineatum | Fishes | Native | Gold-Bouchot et al. (2017) | [90] | |
| Ocyurus chrysurus | Fishes | Exotic | |||
| Daphnia magna | Cladocerans | Model | Guerrero-Jiménez et al. (2017) | [91] | |
| Lecane quadridentata | Rotifers | Model | |||
| Fulica americana | Birds | Native | López-Islas et al. (2017) | [92] | |
| Cyprinus carpio | Fishes | Model | Pérez-Coyotl et al. (2017) | [93] | |
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 environ- mental 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 Rendón- 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 (Roméo & Giambérini, 2012).
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.
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.
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 (Rendón-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 biomar- kers 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 δ-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, pharmaceu- ticals) 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 metabo- lomics) (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 (González & 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-Martagón 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:
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.
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.
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 pro- vides 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).
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 | Organisms used in the different studies |
| Acetylcholinesterase (AChE) | Agricultural and urban runoff, organochlorine and organophosphates pesticides, PAHs, PCBs, heavy metals | Fishes [5, 7, 8, 12, 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 aminotransferase (ALT) | Urban wastewater and agricultural runoff | Birds [92] |
| Alcohol dehydrogenase (ADH) | PCBs | Fishes [31] |
| Algal growth potential | Urban wastewater, agricultural runoff, heavy metals, PAHs | Phytoplankton [36, 61, 69] |
| Alkaline phosphatase (ALP) | Urban wastewater, agricultural runoff, heavy metals, PAHs | Fishes [8] |
| Apoptosis tunnel assay | Hospital and urban effluents, pharmaceutical products, heavy metals. | Fishes [77, 84, 93] |
| Bioluminescence inhibition | Refineries, aromatic hydrocarbon | Bacteria [39] |
| Carboxylesterases (CbE) | Benzo (a) pyrene and chlorpyrifos | Fishes [24] |
| Caspase-3 activity | Hospital and urban effluents, pharmaceutical products, heavy metals | Fishes [77, 84, 93] |
| Catalase activity (CAT) | Urban and industrial wastewater, agricultural runoff, heavy metals, PAHs, pharmaceutical products, organochlorine pesticides, halomethanes | Fishes [26, 35, 45, 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 | Urban and industrial wastewater, agricultural runoff, heavy metals, halomethanes | Fishes [39,58, 60, 82]; Bivalves [49]; Birds [81,92]; |
| Ethoxyresorufin-O-deethylase (EROD) | Urban and industrial wastewater, agricultural runoff, PCBs, PAHs, organochlorine pesticides | Fishes [12, 19, 31, 39, 47, 75] |
| Cytochrome P450-1A1(CYP1A1) (regard western-blotting and gene expression) | Urban and industrial wastewater, agricultural runoff, PCBs, PAHs, organochlorine pesticides, heavy metals, halomethanes | Fishes [12, 40, 46, 48, 58, 89, 90]; Turtles [37]; Crocodiles [79] |
| Embryo deformities | PAHs | Fishes [51] |
| Comet assay | Urban wastewater, organochlorine pesticides, pharmaceutical products, heavy metals, PAHs, PCBs | Fishes [10, 30, 34, 43, 65, 77, 93]; Anurans [55]; Bivalves [68]; Humans [70] |
| Epoxide hydrolase (EH1) | Urban wastewater, halomethanes | Fishes [75] |
| Esterases | Agricultural, urban and industrial runoff | Rotifers [3, 18] |
| Gamma-glutamyl transferase (GGT) | Urban and agricultural runoff, organochlorine and organophosphorus pesticides | Fishes [5, 8, 19]; Birds [92] |
| Glutathione S-transferase activity (GST) (regard gene expression) | Urban wastewater and agricultural runoff, heavy metals, PAHs, PCBs, Halomethanes, Organochlorine pesticides | Fishes [47, 48, 90]; Bivalves [27, 68, 86]; Sharks [62]; Turtles [21, 37, 88]; Phytoplankton [61]; Tritons [36]; Corals [29] |
| Glutathione peroxidase activity (GPx) (include gene expression) | Urban and industrial wastewater, agricultural runoff, heavy metals, PAHs, pharmaceutical products, organochlorine pesticides, halomethanes | Fishes [35, 45, 48, 52, 58, 59, 73, 82]; Bivalves [86]; Sharks [62]; Turtles [44]; Phytoplankton [61, 69]; Tritons [36, 82]; Corals [29]; Amphipods [83] |
| Glutathione reductase activity (GR) (regard gene expression) | Urban wastewater and agricultural runoff, heavy metals, PAHs | Sharks [50, 53, 62]; Turtles [88]; Fishes [80]; Crocodiles [79] |
| Heat shock protein (HSP70) | Urban and agricultural effluents | Fishes [40] |
| Histopathological lesions (HPL) | Urban and agricultural effluents, PAHS, PCBs, Heavy metals, organochlorine pesticides | Bivalves [15, 28]; Fishes [9, 40, 76]; Tritons [85]; Birds [82, 92] |
| Hydrogen peroxide (H2O2) content | Urban wastewater and agricultural runoff, PAHs, Heavy metals, halomethanes | Fishes [58, 80]; Bivalves [54]; Crocodiles [79] |
| Hydroperoxides (ROOH) content | Urban wastewater and agricultural runoff, PAHs, heavy metals, halomethanes, pharmaceutical products | Fishes [58, 59, 66, 73, 74]; Bivalves [54, 86]; Plankton [61]; Amphipods [83] |
| Biomarkers | Sources of contamination or pollutants associated with the study areas | Organisms used in the different studies |
| Imposex | Port activity and agricultural areas | Gastropods [38, 72] |
| Germination index | Agricultural areas | Plants [63] |
| Root elongation index | Urban and agricultural areas, industrial effluents | Plants [16, 17, 63] |
| Gonadosomatic index (GSI) | Urban wastewater and agricultural runoff, industrial discharges | Fishes [60, 82]; Birds [81, 92] |
| Hepatosomatic index (HSI) | Urban wastewater and agricultural runoff, industrial discharges | Fishes [39, 58, 60, 82]; Birds [81, 92] |
| Ingestion rate | Industrial wastewater | Cladocerans [18] |
| Lactate dehydrogenase (LDH) | Diffuse pollution, hospital effluent | Fishes [47, 84] |
| Lethality | Urban and industrial wastewater, PAHs, PCBs, hormones, | Fishes [22, 26]; Rotifers [41, 91]; Nematodes [39]; Cladocerans [3, 4, 17, 25, 41, 67, 78, 91] |
| Lipid peroxidation (TBARS) | Urban and industrial wastewater, agricultural runoff, heavy metals, PAHs, organochlorine pesticides, Organophosphorus pesticides, Heavy metals, Pharmaceutical products, Halomethanes | Sharks [50, 53, 62]; 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 (MT’s) | Urban wastewater and runoff, PAHs, PCBs, heavy metals, hormones | Fishes [75, 80]; Bivalves [15, 32, 33]; Crocodiles [71] |
| Mycosporine-like amino acids (MAAs) | Urban wastewater | Corals [29] |
| Na/K-ATPase | Urban and industrial wastewater | Fishes [45] |
| Neutral Red Retention Time (NRRT) | Organochlorine pesticides, PCBs, PAHs, heavy metals | Bivalves [15] |
| PAH bile metabolites | PAHs, PCBs, heavy metals | Fishes [12, 90] |
| Phospholipase A2 | Agricultural, urban, and industrial runoff | Rotifers [3, 18] |
| Carbonyl radical (RC=O) content | Urban wastewater and agricultural runoff, PAHs, heavy metals, halomethanes, pharmaceutical products | Fishes [58, 59, 66, 73, 74, 80]; Crocodiles [79]; Phytoplankton [61]; Amphipods [83]; Sharks [62] |
| Superoxide dismutase activity (SOD) | Urban wastewater and agricultural runoff, heavy metals, PAHs, pharmaceutical Urban wastewater and agricultural runoff, heavy metals, PAHs, pharmaceutical products, Halomethanes | Fishes [26, 35, 45, 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 (O2 •−) content | Urban wastewater and agricultural runoff, PAHs, heavy metals | Fishes [58, 80]; Bivalves [54]; Sharks [50, 53, 62]; Turtles [21]; Crocodiles [79]; Corals [29] |
| Vitellogenin (VTG) (include gene expression) | Urban wastewater and agricultural runoff, PAHs, PCBs, organochlorine pesticides, hormones | Fishes [22, 23,46, 75, 80, 90] |
| δ-aminolevulinic acid dehy-dratase (δ-ALAD) | Urban areas and petrochemical industry | Anurans [56] |
| Micronucleus (MN) | Urban wastewater, agricultural runoff, organo-chlorine pesticides, PCBs, pharmaceutical pro-ducts | Fishes [20, 34, 65, 77, 84, 93]; Plants [57] |
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 Camichín 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-López 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 biomar- kers), 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.
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.
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, preser- vation, 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.
