Printer Friendly

Validation of an index of biological integrity based on aquatic macroinvertebrates assemblages in two subtropical basins of central Mexico.


The increasing degradation of freshwater ecosystems has recently demanded the development of methods that allow us to know the significance of the alteration due to human activities and to differentiate it from natural effects (Mercado-Silva et al., 2006b). The Index of Biological Integrity (IBI) is a methodological approach that combines structural and functional elements of aquatic ecosystems to assess the ecological condition (Moya et al., 2007). Biological integrity is defined as the environment's capability to support and maintain a balanced and adapted community of organisms that have a specific composition, diversity and functional organization (Karr, 1981). The assessment of biological integrity in freshwater ecosystems allows a holistic estimation of the negative effects of the impact of human activities, and it is a methodology widely used to guide the management of aquatic resources in several parts of the world (Wente, 2000; Mercado-Silva et al., 2006a; Alexandrino et al., 2017). The quantification of biological integrity is obtained by adding the values of measurable ecological attributes, such as the structure, composition, and function of a biological community (Weigel et al., 2002).

The IBI is one strategy with right cost-benefit balance, and it is scientifically valid and oriented to i) facilitate the analysis of multiple study sites, ii) obtaining quick results, iii) providing scientific reports for easy access for the public, and iv) promoting environmentally healthy practices (Moya et al., 2007). Also, the IBI is used for biological monitoring for environmental risk assessments, because it measures the numerous biological conditions present and not only chemical ones; therefore, it becomes a significant source of information, describing expected environmental conditions in the absence of human impact (Alexandrino et al., 2017). Also, the IBIs are designed to assess regional conditions (mostly the unit is one basin), representing regional processes (Moya et al., 2007).

However, it is not enough to generate and to apply the IBI, it must be validated before proposing it for extended use (Lyons et al., 2000; Ramirez-Herrejon et al., 2012). The validation consists of the analysis of IBI data and its correlation with the water physicochemistry and habitat variables. The validation is supported under the premise that water physical and chemical conditions and the physical condition of the habitat are the primary influence factors on the assemblages of biological communities of rivers (Gonzalez-Zuarth et al., 2014). In this way, the aquatic macroinvertebrates assemblages and physical and chemical environmental conditions respond together to the natural and anthropic alterations of streams and rivers (Merritt et al., 2008).

In the development of these methodologies, the aquatic macroinvertebrates are used as a study model, due to the rich data that they provide (Bonada et al., 2006; Serrano-Balderas et al., 2016): a) aquatic macroinvertebrates are structured assemblages made up of taxa with broad ecological functions. Ranging from generalists to micro-specialists, they rapidly respond to anthropic and natural changes of freshwater systems, b) they are relatively sedentary and representative of the area where they are collected, c) they have relatively short life cycles, and they reflect the changes in their environment rapidly, and d) they live in or on the sediment allowing the accumulated organic matter to return to the trophic web.

Only two IBIs based on aquatic macroinvertebrates have been developed for freshwater ecosystems in Mexico. The first was developed by Weigel et al. (2002) in streams of the Sierra de Manantlan Biosphere Reserve. The second, the Index of Biological Integrity based on macroinvertebrates assemblages (IIBAMA) was developed by Perez-Munguia & Pineda-Lopez (2005) to estimate the environmental condition of rivers and streams in central Mexico, including the Mexican states of Guerrero, Jalisco, Hidalgo, State of Mexico, Queretaro, and Michoacan. The IBI of Weigel et al. (2002) in the west-central Mexico shows a methodological disadvantage because it is based on the taxonomic level of genera, increasing the difficulty of its application, contradicting the premise of the simple use of the index, i.e., to facilitate the analysis of multiple study sites and obtaining quick results. On another hand, the IIBAMA is based on the taxonomic level of family, and this taxonomic resolution represents a confident indicator of the degradation level in river ecosystems (Serrano-Balderas et al., 2016; Wright & Ryan, 2016). The IIBAMA has been validated with independent data; however, the validation was done only for two rivers and two streams located in the Lerma-Chapala River Basin (LRB) and Balsas River Basin, in the Michoacan State (Perez-Munguia et al., 2006; Pinon-Flores et al., 2014). IIBAMA represents a useful tool for the biological monitoring of the environmental quality in the Chiquito River in the Michoacan State (Pinon-Flores et al., 2014). However, it is not validated for its widespread use in other streams and rivers in wider regions of Mexico.

The Lerma River Basin is considered as the most degraded basin in Mexico (Cotler-Avalos & Garrido-Perez, 2010) and the Panuco River Basin (PRB) has been considered a priority zone for conservation (Wikramanayake et al., 2002; Gutierrez-Yurrita et al., 2013). For these reasons, both basins have streams and rivers located on an environmental condition gradient with different conservation status, which represent an appropriate model to validate the IIBAMA. Because of this, the present study focuses on estimating the biological integrity based on aquatic macroinvertebrate assemblages and validating the IIBAMA in the headwaters of 12 permanent rivers of Lerma-Chapala River Basin and Panuco River Basin located in five Mexican states (Aguascalientes, Jalisco, Guanajuato, Queretaro, and San Luis Potosi) in Central Mexico.


Study area

The study area is in the LRB and PRB, found in the east-central region of Mexico (Fig. 1). Central Mexico has the most degraded basins in the country (Mercado-Silva et al., 2006b). Lerma-Chapala River Basin shows a distinct problem of physical and chemical anthropogenic transformation and is considered the most degraded in Mexico (Cotler-Avalos et al., 2004). This river basin also suffers from excessive water extraction to cover the needs of Mexico City inhabitants (Rascon et al., 2001). The Lerma-Chapala River Basin has been profoundly impacted by the loss of vegetation cover (>30%), expansion of cultivated pastures for livestock, increased agricultural activities combined with expanded industrialization and urbanization (Cuevas et al., 2010).

Meanwhile, the PRB shows a severe problem with water pollution and the water exploitation activities mainly for irrigation and drainage control. However, this basin harbors one natural protected area with high biodiversity, the Sierra Gorda Biosphere Reserve (Ruiz-Corzo & Pedraza-Ruiz, 2007). This Biosphere Reserve is characterized by their biological importance and the conservation status of their natural and ecosystem elements and process (Carabias-Lillo et al., 1999).

Both river basins suffer serious problems of environmental degradation, such as pollution and structural modification in the high parts of the basin, caused mainly by industry, livestock activity, and farming, as well as the increase in urban sprawl (Alvarez et al., 2008). In addition, at present, the headwaters of both drainages are being considered for special protection status as Water Reserves in Mexico by the National Commission of Water (Comision Nacional del Agua, 2011).

The sampling sites are in permanent rivers from the headwater of San Pedro River and Calvillo River in Aguascalientes State (LRB), Grande River in Jalisco State (LRB), Laja and Apaseo rivers in Guanajuato State (LRB), rivers Extoraz, Huimilpan, Queretaro, San Juan, Jalpan, and Santa Maria in Queretaro State (PRB), and Verde River in San Luis Potosi State (PRB).

Data collection

A total of 33 study sites were selected from a habitat quality gradient (Fig. 1). The field work was done during the dry season (February-Abril 2014) for several reasons: i) dry season represents the more stable habitat conditions, ii) the low-flow phase of the river exposes aquatic macroinvertebrates for sampling, iii) human impacts are enhanced creating spatial variation along the length of the river system, and iv) for comparing to previous studies, because research on river ecology is commonly done during the dry season (Moncayo-Estrada et al., 2015).

Prior to the macroinvertebrates sampling, chemical and physical water characteristics were measured with a multimeter (HachHydromet Quanta, Loveland, Colorado, USA) including pH, dissolved oxygen (DO, mg [L.sup.-1]), total dissolved solids (TDS, g [L.sup.-1]), conductivity (C, mS [cm.sup.-1]) and temperature ([degrees]C). The condition of the habitat was assessed by a Visual-Based Habitat Assessment (VBHA) proposed by Barbour et al. (1999), that includes variables as sinuosity, materials of the substrate and the banks, sediment retention points, condition of riparian vegetation and riparian zone, and the status of the floodplain (Table 1).

The macroinvertebrate samples were collected using a D-net (300 mm of diameter and 300 [micro]m of mesh size) in all available habitats with a sample effort of 30 min per site, including all of the microhabitats in a section of the river (five times the width of the river, following the Official Mexican Standard NMX-AA159-SCFI-2012). The macroinvertebrate individuals were separated from detritus in the field and were preserved in a solution of ethanol 80%, and the samples were transported to the Biotic Integrity Lab at UAQ-Campus Aeropuerto. The taxonomic identification of macroinvertebrates was made to the level of family based on specific keys (e.g., Merritt et al., 2008).

Additionally, we estimated the Family-Level Biotic Index (FBI) proposed by Hilsenhoff (1987) as an auxiliary tool for the validation of the IIBAMA, because it is a rapid bioassessment procedure related to water quality which has been validated for the west central region of Mexico (Weigel et al., 2002). FBI is based mainly on the tolerance values for arthropods families and the number of individuals per family.

Index of Biological Integrity

The biological integrity was assessed using the IIBAMA proposed by Perez-Munguia & Pineda-Lopez (2005). The metrics and explanation of each variable of the index are described below:

1) Taxa Richness (TR).

This metric refers to the number of macroinvertebrates families founded in the sample. The taxa distribution is limited by the heterogeneity of ecological process (Hengeveld, 1996; Lambeck, 1997), for this reason, a high taxa richness can highlight a habitat heterogeneity (Williams, 1964; Currie, 1991; Tews et al., 2004). This habitat heterogeneity is related to the availability of fauna refuges, and it is associated with an increased speciation likelihood (Seto et al., 2004).

2) Ephemeroptera, Plecoptera and Trichoptera Richness (EPTR).

This metric must be calculated with the number of Families included in the Ephemeroptera (except the Baetidae family), Plecoptera and Trichoptera Orders (EPT) founded in the sample. These mentioned Orders are important biological groups because of their wide distribution, high abundance, and species richness, and are key elements for an ecological process such as the nutrients cycles in freshwater ecosystems (Righi-Cavallaro et al., 2010). These groups are associated with the transformation of organic matter into available nutrients for superior trophic levels (Graca et al., 2001; Boyero et al., 2012), and they represent the food of vertebrates and other macroinvertebrates (Ferro & Sites, 2007). The EPT Orders are sensitive indicator of right ecological conditions due to their low tolerance to environmental stress, which means the families composition and richness are negatively affected by degraded environmental conditions (Usseglio-Polatera et al., 2000; Callisto et al., 2001; Klemm et al., 2003; Ferreira et al., 2011). EPT is usually present in aquatic ecosystems with high water quality (Lemly, 1982; Buss et al., 2004; Bispo et al., 2006). For these reasons, EPT is considered a good indicator of water quality (Rosenberg & Resh, 1993).

3) The Richness of Sensitive Insects (RSI).

This metric refers to the number of Families of aquatic insects that are sensitive to environmental degradation. The insects are the most conspicuous group of macroinvertebrates of freshwater ecosystems (Macadam & Stockan, 2015). They can fly between freshwater bodies during adult stages as a survival strategy. However, the absence of sensitive insects is related with limiting conditions of temperature, dissolved oxygen, alkalinity, salinity, water flow rate, water level, aquatic vegetation cover and specific substrate (Ward, 1992). For these reasons, sensitive insects offer current and long-term information about environmental conditions.

4) The Richness of Sensitive Taxa (RST).

This metric combines the previous RSI with the rest of sensitive macroinvertebrate families. The sensitive taxa of aquatic macroinvertebrates (not insects), generally, spend no part of their lifecycle out of the water. For this reason, their presence can indicate an ecosystem where the habitat quality has been optimal for a long time.

5) Tolerance Value Average (TV A).

This metric refers to the average values of tolerance of the sample. The tolerance represents the capability of aquatic macroinvertebrate to survive under environmental degradation. The values of tolerance show a relationship among anthropic stress and the presence of aquatic organisms in a spatiotemporal way. For this reason, TVA indicates the condition of freshwater systems (Chutter, 1972; Winget & Mangum, 1979; Hilsenhoff, 1987; Lenat, 1993).

6) The number of Clingers Taxa (#CT).

This metric refers to the number of taxa that have life habits gripping to the substrate. These organisms are moderately sensitive to water pollution, and they depend on biotope diversity and heterogeneity of flow patterns (Posada-Garcia & Roldan-Perez, 2013). Accordingly, the #CT depletion can indicate the loss of aquatic habitat heterogeneity and availability, caused by the riverbank's degradation (Perez-Munguia & Pineda-Lopez, 2004). Also, the land use change in the catchment, that can increase in fine sediments depo-sition can reduce available habitat (Wood & Armitage, 1997) and food resource (cf. Yamada & Nakamura, 2002) for clinger organisms.

The index is calculated by the sum of the scores obtained from each variable (Table 2). The information about each variable of the index; tolerance value and life habit were obtained from Pineda-Lopez et al. (2014).

Statistical analysis

The IIBAMA was validated through the comparison among the values of IIBAMA, and the values of FBI, VBHA, and the chemical and physical water characteristics. The correlations among IIBAMA with FBI, VBHA and water characteristics (pH, DO, temperature) were made by the Spearman correlation analysis (Zar, 1999) using the software SPSS ver. 20 (IBM Corp., 2011). All variables were evaluated together to analyze and to elucidate patterns of all measured parameters in both river basins, a principal component analysis (PCA) ordination was conducted using PAST ver. 3.07 (Hammer et al., 2001). For this analysis, we normalize all variables using division by their standard deviations because the indices and variables were measured in different units. Additionally, to compare the differences between basins, we analyzed similarities (ANOSIM), which is a robust method to compare groups of multivariate sample units (Clarke, 1993; Anderson & Walsh, 2013).


We collected a total of 10,723 individuals, included in 86 families (Table 3), distributed in five classes: i) Insecta (eight families belong to Ephemeroptera, nine to Odonata, one to Plecoptera, 12 to Hemiptera, 10 to Trichoptera, one to Megaloptera, 14 to Coleoptera, 16 to Diptera, and one to Lepidoptera), ii) Maxillopoda (two families belong to order Decapoda, one to Amphipoda, and one to Isopoda); iii) Gastropoda (one family belong to Unionida order, one to Veneroida, two to Basommatophora, four to Neotaenioglossa; iv) Turbellaria (one family belonging to Tricladida order); and v) Acari (the order Hydrachnidia). From these groups, seven families were determined as very tolerant, 28 as tolerant, 29 such as intolerant, six as very intolerant, and 16 were not classified. Furthermore, we obtained 31 families with clinger's habits, 13 swimmers, ten climbers, five skaters, 11 burrowers, one hiker, and

15 were not determined (Table 3).

We obtained the following mean values, pH: 7.86 [+ or -] 0.41; TDS 363.54 [+ or -] 236.77 g [L.sup.-1]; DO 4.13 [+ or -] 2.4 mg [L.sup.-1]; and temperature: 21 [+ or -] 4.68[degrees]C, including both basins (Table 4). The habitat quality based on VBHA were estimated as Optimal for eight localities, Suboptimal in 16 localities, Marginal in four localities, Poor in four localities, and one site was not determinate. The FBI shows three localities with excellent conditions, two as very good, eight as good, 11 as fairly, five as fairly poor, three as poor, and one as very poor. Considering the IIBAMA, 87.88% of all sites shows a poor condition (IIBAMA<13), and 6.06 % moderate (13< IIBAMA<16) and 6.06% good (16<IIBAMA<21) (Table 4).

The rivers were classified in three of four biotic integrity categories, poor, regular and good (88%, 6%, 6% of the study sites respectively), we did not find study river locations with excellent biotic integrity. The associations of the IIBAMA scores showed significant correlations with measures of FBI, TDS, pH, DO (r = 0.38, P = 0.029; r = -0.39, P = 0.022; r = 0.559, P = 0.001 and r = 0.522, P = 0.002 respectively); however, there were no significant correlations with VBHA and temperature (r = 0.318, P = 0.076 and r = 0.208, P = 0.246 respectively) (Fig. 2).

In the relationship among the values of IIBAMA, the values of water pH and water DO were positive and significant; pH showed basic values (7.27-8.65) and DO values were >2 mg [L.sup.-1] in most of the study sites, which means an optimal condition for biological organisms. The total dissolved solids (TDS) and the FBI showed significant negative relationships as was expected. The water temperature showed a weak association with IIBAMA (r = 0.208, P = 0.246).

The PCA results showed that the majority of the variance was explained by VBHA, FBI, and IIBAMA, following by pH, TDS, DO, Temp (Table 5). In the ordination, a tendency gradient of segregation of data between basins (LRB and PRB) are showed (Fig. 3), and the ANOSIM demonstrates significate differences between basins considering all the measured variables (P = 0.03). The study sites of the LRB are located in the lower left quadrant of the ordination. They show a worse ecological condition compare with the PRB sites, including the water quality indicated by the FBI, the habitat quality indicated by the VBHA, the availability of dissolved oxygen, the acidification of the water (pH), the water temperature and the biotic integrity evidenced by the IIBAMA.


This study demonstrates a successful validation of the IIBAMA using an independent dataset in the Panuco and Lerma-Chapala river basins in central Mexico. It implies the availability of a new bioassessment tool for the ecological condition of streams and rivers on these two major basins. It was a first step to apply and perform the IIBAMA for the validation and application in a wide array of rivers in other basins in the country, even, in another region of the world. However, despite a successful validation of the index, the IIBAMA shows a moderate relationship among the environmental variables (r < 0.56), and our results differ from those of Pinon-Flores et al. (2014), because they demonstrated a strong positive relationship among the IIBAMA scores and the VBHA (r = 0.82) in rivers on the Chiquito River micro-watershed.

The majority of headwaters of both river basins (29 of 33 study sites) have lost the ecological processes that kept the energy flux and river ecosystems functions. The rivers that presented normal and proper conditions are located in the PRB, while the LRB is represented only by sampling locations with poor condition. These results are evidence of the environmental problem that faces LRB and PRB. Some authors argue that the agriculture, livestock and timber forestry, as well as mining, organic pollution, channeling and damning of rivers, led to a continued deterioration due to the constant use of the soil, which promoted erosion, loss of vegetation cover and habitat disturbance for wildlife species (Cotler-Avalos & Garrido-Perez, 2010). The cumulative effects of these practices can affect the physical hydrology, the riparian function, the water quality and channel morphology, which impinges on the aquatic invertebrates' communities (Reiter & Beschta, 1995).

The poor water quality of most of the study sites including both basins can be a consequence of agriculture, industry and drainage discharge, the main human activities (Cotler-Avalos et al., 2004; Alvarez et al., 2008). The agriculture practices significantly affect the water quality by contributing an excess of nutrients including sediments, through a process is known as leaching (Rai et al., 2012). It is evident by the dominance of highly tolerant taxa, and the loss of sensitive taxa; patter showed by the FBI analyses.

The optimal and suboptimal conditions of habitat mean that natural elements such as substrate at the bottom and habitat heterogeneity are stable and sustainable. However, some habitat elements such as riparian vegetation, vegetal bank (on the right riverbank), channel sinuosity, riffles frequency, and pool variability show a marginal category. The mechanisms of flux energy and dissipation remain, and the present infrastructure is not common. The VBHA do not represent a short-term response to habitat degradation, it represents the long-term visual degradation process, such as was proposed by Allan (2004).

The significant correlation of the IIBAMA with environmental quality in most of the study sites means that poor biotic integrity was related to poor environmental quality. However, the found several sites in both basins that showed suboptimal habitat condition associated with poor biotic integrity could occur when the water properties were altered by local pollution. There are several ways and forms of water pollution, but this is one of the major causes of freshwater degradation worldwide and reflects the past, present, and future of human activities (Scholz & McIntyre, 2016). In this case, the wastewater discharge can attenuate the recuperation and maintenance of the composition and structure of macroinvertebrate communities and the dominance of tolerant taxa is reflected in this pattern. It has been found that wastewater treatment discharges are related with an increase in tolerance metrics (Poulton et al., 2015).

The Ayutla and Santa Maria rivers located in PRB have a medium size large, where the macroinvertebrates communities have a diversity of functional feeding groups, such as collectors, grazers, predators and shredders. These functional feeding groups will be influenced by river width, the solar radiation, the allochthonous organic matter input, sediments size and substrate size (Vannote et al., 1980). Moreover, in these kinds of sites with a low slope (<3%), allochthonous organic matter input from riparian vegetation (deciduous forest) and small sediment sizes; it is expected to find macroinvertebrates families with tolerance values from medium to high. Both study sites suffer from local anthropic negative effects of recreational activities at regional scales (people from other states) mainly in the dry season (March-April). These sites have regular biotic integrity and harbor degraded macroinvertebrates communities where the most sensitive taxa have lost. Trophic interactions have decreased, and the mechanisms of energy transfer from terrestrial systems to the aquatic system are negatively affected (Cotler-Avalos & Garrido-Perez, 2010). However, the excellent water quality (indicated by FBI) and the suboptimal habitat quality (VBHA), despite local organic contamination from tourism activities and mismanagement of wastewater, are evidence that the watershed area degradation is moderate, where the anthropic changes have not been enough to decrease their function and resilience, habitat structure and essential environmental services to people. Both sites showed regular biotic integrity, which means that the functional processes are present even with the loss of some sensitive taxa.

The good category of IIBAMA (two study sites, Tancuilin and Chuveje) shows that macroinvertebrates communities are negatively affected which is evident by the loss of sensitive taxa. However, the communities still maintain the energy flux mechanisms, because the functional organization is preserved, evidenced by the presence of tolerant clingers taxa, taxa richness, EPTR richness. Good biotic integrity was associated with suboptimal habitat condition, and with very good water quality condition based on FBI. Good water quality conditions were present when the anthropic impacts had not embedded the substrates available for macroinvertebrates. The natural habitat structure and macroinvertebrates diversity are preserved, which proves the conservation of ecological integrity. This pattern of a suitable biological condition related to good habitat condition refers to ecological integrity, which can be associated with preserved ecosystem services and good condition of watershed area (Weigel & Dimick, 2011).

The site Chuveje is in high altitude of the state of Queretaro (1,277 m over sea level, m.o.s.l.) and shows anthropic channel modification and high variations of the physical and chemical characteristics of waterrelated with its importance as a tourist destination. However, this site has an optimal habitat condition, which indicates that the dynamics of the river support alterations present in this place not directly influence the ecological processes and these impacts.

The positive relationships among the values of the IIBAMA with water quality (FBI), pH, DO, and the negative relationships with TDS and FBI (water quality depletion) demonstrate that the physical and chemical processes in the river are determinant factors for ecological integrity evidenced by the aquatic macroinvertebrates assemblages. In another hand, the weak and no significant association between the IIBAMA, water temperature, and the VBHA indicates that in the headwaters of the two studied basins the water temperature, the habitat structure and stability by themselves, do not represent a determinant variable for biotic integrity. The water temperature is not a likely factor that determines the structure and composition in the aquatic macroinvertebrate's assemblages, especially in broad scale such as the basin (Friberg et al., 2009; Buendia et al., 2014). Moreover, the good habitat condition provides refuges for the fauna but, the current chemical condition affected by organic pollution can limit the aquatic macroinvertebrate assemblage's establishment. This process can occur when the watershed condition is stable, but the river has an additive pointsource impact affecting the aquatic biota and ecosystems processes.

Our study demonstrates that the LRB and PRB are significantly degraded which coincides with Cuevas et al. (2010). However, the LRB is more degraded, and it has been affected by its physical, chemical and biological processes. While the PRB is mainly located into a Biosphere Reserve and its rivers harbor more stable and adapted biological communities and most of the sites the ecological process is close to the natural condition.

The IIBAMA, is a good estimator of the biological integrity in streams and rivers in the central basins Lerma-Chapala and Panuco, it reflects patterns related with the physical and chemical processes. We validated and recommended the using of the IIBAMA with independent data to assess the biotic integrity in these two basins. However, we suggest using IIBAMA together with indexes to estimate the habitat and water quality, such as VBHA and FBI to assess the environmental quality of streams and rivers accurately, even in other regions with similar conditions. The IIBAMA responded to a variety of stressors affecting the streams and rivers in the region, and it allows to differentiate in conditions status among the two basins assessed. With their implementation, the legislation efficacy or programs aimed at river ecosystem protection and restoration can be evaluated. This study is the first to validate an index of biological integrity based on aquatic macroinvertebrates in a broad scale in Mexico and provide a framework for their widespread use, and to approach the validation and implementation of other IBIs in other regions with similar ecosystems.

DOI: 10.3856/vol46-issue5-fulltext-8


We would like to thank all those who collaborated on the project "Temporal Variation of the Biotic Integrity on Rivers of the Lerma-Chapala and Panuco Basins", financed by the postgraduate program Maestria en Gestion Integrada de Cuencas (MGIC) and Fondo para el Fortalecimiento de la Investigacion de la Universidad Autonoma de Queretaro (FOFI-UAQ-2013). Thanks to Dra. Miriam Guadalupe Bojorge Garcia for technical support and GAM. Israel Ugalde Villanueva for designing Fig. 1. Caleb Ulliman, science teacher and friend, for his English support during editing. Thanks to CONACYT, the direction of the ANP biosphere reserve Sierra Gorda by the National Commission of Protected Natural Areas (CONANP) and to the Centro de Educacion e Investigacion para el Bienestar Ambiental y Social (CEIBAS) for the facilities provided for the development of this investigation.


Alexandrino, E.R., E.R. Buechley, J.R. Karr, K.M.P.M. de B. Ferraz, S.F. de B. Ferraz, H.T.Z. do Couto & C.H. [section]ekercioglu. 2017. Bird-based index of biotic integrity: assessing the ecological condition of Atlantic Forest patches in the human-modified landscape. Ecol. Indic., 73: 662-675.

Allan, J.D. 2004. Landscapes and riverscapes: the Influence of Land Use on Stream Ecosystems. Annu. Rev. Ecol. Evol. Syst., 35: 257-284.

Alvarez, J.P.A., J.E.R. Panta, C.R. Ayala & E.H. Acosta. 2008. Calidad integral del agua superficial en la cuenca hidrologica del Rio Amajac. Inf. Tecnologica, 19: 21-32.

Anderson, M.J. & D.C.I Walsh. 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol. Monogr., 83: 557-574.

Barbour, M., J. Gerritsen, B.D. Zinder & J.B. Stribling. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates, and fish. Environmental Protection Agency Office of Water, Washington D.C., 339 pp.

Bispo, P.C., L.G. Oliveira, L.M. Bini & K.G. Sousa. 2006. Ephemeroptera, Plecoptera and Trichoptera assemblages from riffles in mountain streams of Central Brazil: environmental factors influencing the distribution and abundance of immature. Braz. J. Biol., 66: 611-622.

Bonada, N., N. Prat, V.H. Resh & B. Statzner. 2006. Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. Annu. Rev. Entomol., 51: 495-523.

Boyero, L., R.G. Pearson, D. Dudgeon, V. Ferreira, M.A.S. Graca, M.O. Gessner, A.J. Boulton, et al. 2012. Global patterns of stream detritivore distribution: implications for biodiversity loss in changing climates. Global Ecol. Biogeogr., 21: 134-141.

Buendia, C., C.N. Gibbins, D. Vericat & R.J. Batalla. 2014. Effects of flow and fine sediment dynamics on the turnover of stream invertebrate assemblages. Ecohydrology, 7: 1105-1123.

Buss, D.F., D.F. Baptista, J.L. Nessimian & M. Egler. 2004. Substrate specificity, environmental degradation and disturbance structuring macroinvertebrate assemblages in neotropical streams. Hydrobiologia, 518: 179-188.

Callisto, M., M. Goulart & M. Moretti. 2001. Macroinvertebrados bentonicos como ferramenta para avaliar a saude de riachos. Rev. Bras. Rec. Hidricos, 6: 71-82.

Carabias-Lillo, J., E. Provencio, J. De la Maza Elvira & M.I. Ruiz-Corso. 1999. Programa de manejo reserva de la biosfera Sierra Gorda. Instituto Nacional de Ecologia, Mexico, 172 pp.

Chutter, F.M. 1972. An empirical biotic index of the quality of water in South African streams and rivers. Water Res., 6: 19-30.

Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol., 18: 117-143.

Comision Nacional del Agua. 2011. Identificacion de reservas potenciales de agua para el medio ambiente en Mexico. Secretaria de Medio Ambiente y Recursos Naturales, Mexico D.F., 87 pp.

Cotler-Avalos, H. & A. Garrido-Perez. 2010. Las cuencas hidrograficas de Mexico: diagnostico y priorizacion. Pluralia Ediciones e Impresiones, Mexico D.F., 231 pp.

Cotler-Avalos, H., A. Priego-Santander, C. Rodriguez, C. Enriquez-Guadarrama & J.C. Fernandez. 2004. Determinacion de zonas prioritarias para la ecorehabilitacion de la cuenca Lerma-Chapala. Gaceta Ecologica, 71: 79-92.

Cuevas, M.L., A. Garrido, D.J.L. Perez & I.D. Gonzalez. 2010. Procesos de cambio de uso de suelo y degradacion de la vegetacion natural. In: H. Cotler (ed.). Las cuencas hidrograficas de Mexico. Diagnostico y priorizacion. Instituto Nacional de Ecologia/Fundacion Gonzalo Rio Arronte I.A.P., Mexico D.F., pp. 96-103.

Currie, D.J. 1991. Energy and large-scale patterns of animal--and plant-species richness. Am. Nat., 137: 27-49.

Ferreira, W.R., L.T. Paiva & M. Callisto. 2011. Development of a benthic multimetric index for biomonitoring of a neotropical watershed. Braz. J. Biol., 71: 15-25.

Ferro, M.L. & R.W. Sites. 2007. The Ephemeroptera, Plecoptera, and Trichoptera of Missouri State Parks, with notes on biomonitoring, mesohabitat associations, and distribution. J. Kans. Entomol. Soc., 80: 105-129.

Friberg, N., J.B. Dybkjar, J.S. Olafsson, G.M. Gislason, S.E. Larsen & T.L. Lauridsen. 2009. Relationships between structure and function in streams contrasting in temperature. Freshwater Biol., 54: 2051-2068.

Gonzalez-Zuarth, C.A., A. Vallarino, J.C. Perez-Jimenez & A.M. Low-Pfeng. 2014. Bioindicadores: guardianes de nuestro futuro ambiental. ECOSUR, Mexico, 779 pp.

Graca, M.A.S., R.C.F. Ferreira & C.N. Coimbra. 2001. Litter processing along a stream gradient: the role of invertebrates and decomposers. J. North Am. Benthol. Soc., 20: 408-420.

Gutierrez-Yurrita, P.J., J.A. Morales-Ortiz & L. Marin-Garcia. 2013. Diversidad biologica, distribucion y estrategias de conservacion de la ictiofauna de la cuenca del rio Moctezuma, Centro de Mexico. Limnetica, (32)2: 215-228.

Hammer, O., D.A.T. Harper & P.D. Ryan. 2001. PAST: Paleontological statistics software package for education and data analysis. Paleontol. Electron., 4: 9 pp.

Hengeveld, R. 1996. Measuring ecological biodiversity. Biodivers. Lett., 3: 58-65.

Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution. Great Lakes Entomol., 20(1): 31-40.

Karr, J.R. 1981. Assessment of biotic integrity using fish communities. Fisheries, 6: 21-27.

Klemm, D.J., K.A. Blocksom, F.A. Fulk, A.T. Herlihy, R.M. Hughes, P.R. Kaufmann, D.V. Peck, J.L. Stoddard, W.T. Thoeny, M.B. Griffith & W.S. Davis. 2003. Development and evaluation of a Macroinvertebrate Biotic Integrity Index (MBII) for regionally assessing Mid-Atlantic highlands streams. Environ. Manage., 31: 656-669.

Lambeck, R.J. 1997. Focal species: a multi-species umbrella for nature conservation. Conserv. Biol., 11: 849-856.

Lemly, A.D. 1982. Modification of benthic insect communities in polluted streams: combined effects of sedimentation and nutrient enrichment. Hydrobiologia, 87: 229-245.

Lenat, D.R. 1993. A biotic index for the southeastern United States: derivation and list of tolerance values, with criteria for assigning water-quality ratings. J. North Am. Benthol. Soc., 12: 279-290.

Lyons, J., A. Gutierrez-Hernandez, E. Diaz-Pardo, E. Soto-Galera, M. Medina-Nava & R. Pineda-Lopez. 2000. Development of a preliminary index of biotic integrity (IBI) based on fish assemblages to assess ecosystem condition in the lakes of central Mexico. Hydrobiologia, 418: 57-72.

Macadam, C.R. & J.A. Stockan. 2015. More than just fish food: ecosystem services provided by freshwater insects. Ecol. Entomol., 40: 113-123.

Mercado-Silva, N., J. Lyons & S. Contreras-Balderas. 2006a. Mexican fish-based indices of biotic integrity, their use in the conservation of freshwater resources. In: M.L. Lozano-Vilano & S. Contreras-Balderas (eds.). Studies of North American Desert Fishes in Honor of E.P. (Phil) Pister, conservationist. Universidad Autonoma de Nuevo Leon, Nuevo Leon, pp. 138-150.

Mercado-Silva, N., J. Lyons, E. Diaz-Pardo, A. Gutierrez-Hernandez, C.P. Ornelas-Garcia, C. Pedraza-Lara & M.J. Vander Zanden. 2006b. Long-term changes in the fish assemblage of the Laja River, Guanajuato, central Mexico. Aquat. Conserv. Mar. Freshw. Ecosyst., 16(5): 533-546.

Merritt, R.W., K.W. Cummins & M.B. Berg (eds.). 2008. An introduction to aquatic insects of North America. Kendall/Hunt Publishing, Dubuque, 1,158 pp.

Moncayo-Estrada, R., J. Lyons, J.P. Ramirez-Herrejon, C. Escalera-Gallardo & O. Campos-Campos. 2015. Status and trends in biotic integrity in sub-tropical river drainage: analysis of the fish assemblage over a three-decade period. River Res. Appl., 31: 808-824.

Moya, N., S. Tomanova & T. Oberdorff. 2007. Initial development of a multi-metric index based on aquatic macroinvertebrates to assess streams condition in the Upper Isiboro-Secure Basin, Bolivian Amazon. Hydrobiologia, 589: 107-116.

Perez-Munguia, R.M. & R. Pineda-Lopez. 2004. Estructura trofica de las asociaciones de coleopteros acuaticos de manantiales carsticos en la Huasteca Mexicana. Entomol. Mex., 3: 218-223.

Perez-Munguia, R.M. & R. Pineda-Lopez. 2005. Diseno de un indice de integridad biotica para rios y arroyos del Centro de Mexico usando las asociaciones de macroinvertebrados. Entomol. Mex., 4: 241-245.

Perez-Munguia, R.M., M. Madrigal-Pedraza, R.M. Ortiz-Munoz, V.M. Ramirez-Melchor, U. Torres-Garcia & M.A. Pinon-Flores. 2006. Analisis comparativo del indice de integridad biotica con base en las asociaciones de macroinvertebrados acuaticos (IIBAMA) con el indice biologico global normalizado (IBGN) en arroyos y rios del estado de Michoacan. Entomol. Mex., (5)1: 375-380.

Pineda-Lopez, R., R.M. Perez-Munguia, C. Mathuriau, J.L. Villalobos-Hiriart, R. Barba-Alvarez, T. Bernal & E. Barba-Macias. 2014. Protocolo de muestreo de macroinvertebrados en aguas continentales para la aplicacion de la Norma de Caudal Ecologico (NMX-AA-159-SCFI-2012). Programa Nacional de Reservas de Agua, Mexico, 29 pp.

Pinon-Flores, M.A., R.M. Perez-Munguia, U. Torres-Garcia & R. Pineda-Lopez. 2014. Integridad biotica de la microcuenca del Rio Chiquito, Morelia, Michoacan, Mexico, basada en la comunidad de macroinvertebrados acuaticos. Rev. Biol. Trop., 62(2): 221-231.

Posada-Garcia, J.A. & G. Roldan-Perez. 2013. Clave ilustrada y diversidad de las larvas de Trichoptera en el noroccidente de Colombia. Caldasia, 25: 169-192.

Poulton, B.C., J.L. Graham, T.J. Rasmussen & M.L. Stone. 2015. Responses of macroinvertebrate community metrics to a wastewater discharge in the upper blue river of Kansas and Missouri. J. Water Resour. Prot., 7: 1195-1220.

Rai, R.K., A. Upadhyay, C.S.P. Ojha & V.P. Singh. 2012. The Yamuna River Basin. Water resources and environment. Springer Netherlands, Dordrecht, 478 pp.

Ramirez-Herrejon, J.P., N. Mercado-Silva, M. Medina-Nava & O. Dominguez-Dominguez. 2012. Validation of two indices of biological integrity (IBI) for the Angulo River subbasin in Central Mexico. Rev. Biol. Trop., 60: 1669-1685.

Rascon, M., L. Elena & A. Jimenez-Roman. 2001. Alteracion del ciclo hidrologico en la parte baja de la cuenca alta del rio Lerma por la transferencia de agua a la Ciudad de Mexico. Invest. Geogr., 24-38.

Reiter, L.M. & R.L. Beschta. 1995. Effects of forest practices on water. In: R.L. Beschta, J.R. Boyle, C.C. Chambers, W.P. Gibson, S.V. Gregory, J. Grizzel, J.C. Hagar, J.L. Li, W.C. McComb, T.W. Parzybok, M.L. Reiter, G.H. Taylor & J.E. Warila (compilers). Cumulative effects of forest practices. Oregon Department of Forestry, Salem, Oregon, pp. 14-23.

Righi-Cavallaro, K.O., M.R. Spies & A.E. Siegloch. 2010. Ephemeroptera, Plecoptera e Trichoptera assemblages in Miranda River basin, Mato Grosso do Sul State, Brazil. Biota Neotropica, 10: 253-260.

Rosenberg, D.M. & V.H. Resh (eds.). 1993. Freshwater biomonitoring and benthic macroinvertebrates. Springer, New York, 488 pp.

Ruiz-Corzo, M.I. & R. Pedraza-Ruiz. 2007. Servicios ambientales en la reserva de la biosfera Sierra Gorda: Pago e integracion de productos ecosistemicos. In: G. Halffter, S. Guevara & A. Melic (eds.). Hacia una cultura de la conservacion de la diversidad biologica. SEA, CONABIO, CONANP, CONACYT, INECOL, UNESCO-MAB, Ministerio de Medio AmbienteGobierno de Espana, Zaragoza, pp. 109-113.

Scholz, N.L. & J.K. McIntyre. 2016. Chemical pollution. In: G.P. Closs, M. Krkosek & J.D. Olden. (eds.). Conservation of freshwater fishes. Cambridge University Press, Cambridge, pp. 149-177.

Serrano-Balderas, E.C., C. Grac, L. Berti-Equille & M.A. Armienta-Hernandez. 2016. Potential application of macroinvertebrates indices in bioassessment of Mexican streams. Ecol. Indic., 61(2): 558-567.

Seto, K.C., E. Fleishman, J.P. Fay & C.J. Betrus. 2004. Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI. Int. J. Remote Sens., 25: 4309-4324.

Tews, J., U. Brose, V. Grimm, K. Tielborger, M.C. Wichmann, M. Schwager & F. Jeltsch. 2004. Animal species diversity driven by habitat heterogeneity/ diversity: the importance of keystone structures. J. Biogeogr., 31: 79-92.

Usseglio-Polatera, P., M. Bournaud, P. Richoux & H. Tachet. 2000. Biomonitoring through biological traits of benthic macroinvertebrates: how to use species trait databases? Hydrobiologia, 422: 153-162.

Vannote, R.L., G.W. Minshall, K.W. Cummins, J.R. Sedell & C.E. Cushing. 1980. The river continuum concept. Can. J. Fish. Aquat. Sci., 37: 130-137.

Ward, J.V. 1992. Aquatic insect ecology: biology and habitat. 1. Biology and habitat. Wiley, New York, 438 pp.

Weigel, B.M. & J.J. Dimick. 2011. Development, validation, and application of a macroinvertebratebased Index of Biotic Integrity for nonwadeable rivers of Wisconsin. J. North Am. Benthol. Soc., 30: 665-679.

Weigel, B.M., L.J. Henne & L.M. Martinez-Rivera. 2002. Macroinvertebrate-based index of biotic integrity for protection of streams in west-central Mexico. J. North Am. Benthol. Soc., 21: 686-700.

Wente, S.P. 2000. Proximity-based measure of land use impacts to aquatic ecosystem integrity. Environ. Toxicol. Chem., 19: 1148-1152.

Wikramanayake, E., E. Dinerstein, C. Loucks, D. Olson, J. Morrison, J. Lamoreux, M. McKnight & P. Hedao. 2002. Ecoregions in ascendance: reply to Jepson and Whittaker. Conserv. Biol., 16: 238-243.

Williams, C.B. 1964. Patterns in the balance of nature and related problems in quantitative ecology. Academic Press, New York, 324 pp.

Winget, R.N. & F.A. Mangum. 1979. Biotic condition index: integrated biological, physical, and chemical stream parameters for management. U.S. Forest Service Intermountain Region, Ogden, 57 pp.

Wood, P.J. & P.D. Armitage. 1997. Biological effects of fine sediment in the lotic environment. Environ. Manage., 21: 203-217.

Wright, I.A. & M.M. Ryan. 2016. Impact of mining and industrial pollution on stream macroinvertebrates: importance of taxonomic resolution, water geochemistry and EPT indices for impact detection. Hydrobiologia, 772: 103-115.

Yamada, H. & F. Nakamura. 2002. Effect of fine sediment deposition and channel works on periphyton biomass in the Makomanai River, northern Japan. River Res. Appl., 18: 481-493.

Zar, J.H. 1999. Biostatistical analysis. Pearson Education, New Jersey, 663 pp.

Received: 25 October 2017; Accepted: 7 May 2018

Martin J. Torres-Olvera (1), Omar Y. Duran-Rodriguez (1), Ulises Torres-Garcia (2) Raul Pineda-Lopez (3) & Juan P. Ramirez-Herrejon (4)

(1) Laboratorio de Integridad Biotica, Facultad de Ciencias Naturales Universidad Autonoma de Queretaro, Ejido Bolanos, Queretaro, Queretaro, Mexico

(2) Coordinacion de Monitoreo para la Biodiversidad de la Comision Nacional de Areas Naturales Protegidas (CONANP), Reserva de la Biosfera Sierra Gorda, Jalpan de Serra, Queretaro, Mexico

(3) Direccion de Planeacion, Universidad Autonoma de Queretaro, Queretaro, Mexico

(4) CONACYT-Universidad Autonoma de Queretaro, Campus Aeropuerto, Queretaro, Mexico

Corresponding Author: Omar Y. Duran-Rodriguez (

Corresponding editor: Claudia Bremec

Caption: Figure 1. Sampling locations. 1) Fraccion Sanchez, 2) La Planta-La Hacienda, 3) Puente La Plazuela, 4) Pinihuan, 5) Canoas, 6) Quinta Matilde, 7) El Realito, 8) Quiotillos, 9) El Salto, 10) Presa del Carmen, 11) Presa de Rayas, 12) Comonfort, 13) La Quemada, 14) Los Galvanes, 15) El Xote, 16) El Oasis, 17) Chuveje, 18) Carpintero, 19) Rascon, 20) Tamasopo, 21) Jalpan, 22) Ayutla, 23) Santa Maria, 24) El Carrizal, 25) Rio Grande, 26) Calvillo, 27) El Salto de los salados, 28) Tancuilin, 29) Santa Maria (Tancoyol), 30) Rio Moctezuma, 31) Conca, 32) Extoraz, 33) Rio Blanco.

Caption: Figure 2. Correlations of the Index of biotic integrity based on macroinvertebrates assemblages (IIBAMA). All graphics shows the IIBAMA scores in the "Y" axes. The letters represent the environmental variable of correlation: a) Family biotic index (FBI), b) Visual based habitat assessment (VBHA), c) pH, d) Total dissolved solids (TDS), e) Dissolved oxygen (DO), f) Temperature.

Caption: Figure 3. Principal Components Analysis based on parameters measured and indices calculated in the sampling sites in Lerma-Chapala River Basin and Panuco River Basin. FBI: Family biotic index, VBHA: Visual based habitat assessment, TDS: Total dissolved solids (ppm), DO: Dissolved oxygen (mg [L.sup.-1]); Temp: Temperature ([degrees]C), ES: El Salto, PC: Presa del Carmen, PR: Presa de Rayas, Com: Comonfort, LQ: La Quemada, LG: Los Galvanes, Xo: El Xote, RG: Rio Grande, Cal: Calvillo, SS: El salto de los salados, FS: Fraccion Sanchez, PH: La planta-La Hacienda, PP: Puente la Plazuela, Pin: Pinihuan, Can: Canoas, QM: Quinta Matilde, ER: El Realito, Qui: Quiotillos, EO: El Oasis, Chu: Chuveje, Car: Carpintero, Ras: Rascon, Tam: Tamasopo, Jal: Jalpan, Ayu: Ayutla, SM: Santa Maria, EC: El Carrizal, Tan: Tancuilin, SMT: Santa Maria (Tancoyol), RM: Rio Moctezuma, Con: Conca, Ex: Extoraz, RB: Rio Blanco. Triangles represent the sampling sites in Lerma-Chapala River Basin, circles represent the sampling sites in Panuco River Basin.
Table 1. Characterization of the study sites in Lerma-Chapala River
basin and Panuco River Basin based on the Visual-Based Habitat
Assessment (VBHA). EB: Embeddedness, SD: Sediment deposition, CA:
Channel alteration, FR: Frequency of riffles, BS(L&R): Bank
stability (left and right bank), BVP(L&R): Bank vegetative
protection (left and right bank), RVZW(L&R): Riparian vegetative
zone width (left and right bank), C: Category, D: Description, O:
Optimal, SO: Suboptimal, MG: Marginal, P: Poor. * Because of the
large size of the table, we do not include the following variables:
Epifaunal substrate/Available cover; Velocity/Depth combinations;
Channel flows status, and Frequency of riffles. ES: El Salto, PC:
Presa del Carmen, PR: Presa de Rayas, Com: Comonfort, LQ: La
Quemada, LG: Los Galvanes, Xo: El Xote, RG: Rio Grande, Cal:
Calvillo, SS: El Salto de Los Salados, FS: Fraccion Sanchez, PH: La
Planta-La Hacienda, PP: Puente la Plazuela, Pin: Pinihuan, Can:
Canoas, QM: Quinta Matilde, ER: El Realito, Qui: Quiotillos, EO: El
Oasis, Chu: Chuveje, Car: Carpintero, Ras: Rascon, Tam: Tamasopo,
Jal: Jalpan, Ayu: Ayutla, SM: Santa Maria, EC: El Carrizal, Tan:
Tancuilin, SMT: Santa Maria (Tancoyol), RM: Rio Moctezuma, Con:
Conca, Ex: Extoraz, RB: Rio Blanco.

Basin       Sites
                    C                 D

 Lerma-      ES     O    Gravel, cobble, and
 Chapala                 boulder particles are 0-
                         25% surrounded by fine
                         sediment. (1)

             PC     --                --

             PR     MG   Gravel, cobble, and
                         boulder particles are 50-
                         75% surrounded by fine
                         sediment (3)

             Com    MG                3

             LQ     SO   Gravel, cobble, and
                         boulder particles are 25-
                         50% surrounded by fine
                         sediment (2)

             LG     P    Gravel, cobble, and
                         boulder particles are
                         more than 75%
                         surrounded by fine
                         sediment (4)

             Xo     P                 4

             RG     MG                3

             Cal    --                --

             SS     O                 1

 Panuco      FS     P                 4

   --        PII    SO                2

   --        PP     SO                1

   --        Pin    O                 1

   --        Can    O                 1

   --        QM     O                 1

   --        ER     SO                2

   --        Qui    O                 1

   --        EO     O                 1

   --        Chu    O                 1

   --        Car    O                 1

   --        Ras    O                 1

   --        Tam    O                 1

   --        Jal    SO                2

   --        Ayu    SO                2

   --        SM     SO                2

   --        EC     SO                2

   --        Tan    O                 1

   --        SMT    SO                2

   --        RM     O                 1

   --        Con    O                 1

   --        Ex     MG                3

   --        RB     O                 1

Basin       Sites
                    C                D

 Lerma-      ES     O    Less than 5% of the
 Chapala                 bottom affected by
                         sediment deposition (1)

             PC     --               --

             PR     P    More than 50% of the
                         bottom changing
                         frequently (4)

             Com    P                4

             LQ     P                4

             LG     P                4

             Xo     P                4

             RG     P                4

             Cal    --               --

             SS     O                1

 Panuco      FS     P                4

   --        PII    SO   5-30% of the bottom
                         affected. (2)

   --        PP     SO               2

   --        Pin    O                1

   --        Can    O                1

   --        QM     O                1

   --        ER     p                4

   --        Qui    O                1

   --        EO     SO               2

   --        Chu    O                1

   --        Car    O                1

   --        Ras    SO               2

   --        Tam    O                1

   --        Jal    O                1

   --        Ayu    O                1

   --        SM     O                1

   --        EC     SO               2

   --        Tan    O                1

   --        SMT    MG   30-50% of the
                         bottom affected. (3)

   --        RM     O                1

   --        Con    O                1

   --        Ex     MG               3

   --        RB     O                1

Basin       Sites
                    C              D

 Lerma-      ES     O    Stream with normal
 Chapala                 pattern (1)

             PC     --            --

             PR     P    Over 80% of the
                         stream reach
                         channelized and
                         disrupted (4)

             Com    MG   40 to 80% of stream
                         reach channelized
                         and disrupted (3)

             LQ     SO   Some channelization
                         present, usually in
                         areas of bridge
                         abutments. (2)

             LG     SO             2

             Xo     SO             2

             RG     MG             3

             Cal    --            --

             SS     O              1

 Panuco      FS     MG             3

   --        PII    O              1

   --        PP     O              1

   --        Pin    O              1

   --        Can    O              1

   --        QM     O              1

   --        ER     O              1

   --        Qui    O              1

   --        EO     MG             3

   --        Chu    P              4

   --        Car    O              1

   --        Ras    O              1

   --        Tam    O              1

   --        Jal    P              4

   --        Ayu    P              4

   --        SM     SO             2

   --        EC     MG             3

   --        Tan    O              1

   --        SMT    O              1

   --        RM     SO             2

   --        Con    p              4

   --        Ex     SO             2

   --        RB     MG             3

Basin       Sites
                    C          D

 Lerma-      ES     O    <5% of bank
 Chapala                 affected (1)

             PC     --         --

             PR     P    60-100% of
                         bank has
                         scars (4)

             Com    P          4

             LQ     SO   5-30% of the
                         bank in reach
                         has areas of
                         erosion (2)

             LG     MG   30-60% of the
                         bank in reach
                         has areas of
                         erosion (3)

             Xo     SO         2

             RG     SO         2

             Cal    --         --

             SS     O          1

 Panuco      FS     P          4

   --        PII    MG         3

   --        PP     O          1

   --        Pin    O          1

   --        Can    O          1

   --        QM     O          1

   --        ER     O          1

   --        Qui    O          1

   --        EO     SO         2

   --        Chu    SO         2

   --        Car    O          1

   --        Ras    O          1

   --        Tam    O          1

   --        Jal    MG         3

   --        Ayu    SO         2

   --        SM     SO         2

   --        EC     SO         2

   --        Tan    O          1

   --        SMT    SO         2

   --        RM     O          1

   --        Con    O          1

   --        Ex     SO         2

   --        RB     SO         2

Basin       Sites
                    C             D

 Lerma-      ES     O    More than 90% of
 Chapala                 the riparian zones
                         covered by native
                         vegetation (1)

             PC     --            --

             PR     P    less than 50% of
                         the streambank
                         surfaces covered
                         by vegetation (4)

             Com    MG   50-70% of the
                         surfaces covered
                         by vegetation (3)

             LQ     P             4

             LG     MG            3

             Xo     SO   70-90% of the
                         surfaces covered
                         by native
                         vegetation. (2)

             RG     SO            2

             Cal    --            --

             SS     O             1

 Panuco      FS     P             4

   --        PII    MG            3

   --        PP     O             1

   --        Pin    O             1

   --        Can    O             1

   --        QM     O             1

   --        ER     O             1

   --        Qui    O             1

   --        EO     SO            2

   --        Chu    MG            3

   --        Car    O             1

   --        Ras    O             1

   --        Tam    O             1

   --        Jal    MG            3

   --        Ayu    MG            3

   --        SM     MG            3

   --        EC     MG            3

   --        Tan    O             1

   --        SMT    SO            2

   --        RM     SO            2

   --        Con    P             4

   --        Ex     P             4

   --        RB     SO            2

Basin       Sites
                    C          D

 Lerma-      ES     P    Width of
 Chapala                 riparian zone
                         <6 m (4)

             PC     --         --

             PR     P          4

             Com    MG   The width of
                         the riparian
                         zone between
                         6-12 m (3)

             LQ     P          4

             LG     P          4

             Xo     P          4

             RG     SO   Width of
                         riparian zone
                         12-18 m (2)

             Cal    --         --

             SS     P          4

 Panuco      FS     P          4

   --        PII    P          4

   --        PP     SO         2

   --        Pin    O    Width of
                         riparian zone
                         >18 m (1)

   --        Can    O          1

   --        QM     O          1

   --        ER     O          1

   --        Qui    MG         3

   --        EO     SO         2

   --        Chu    MG         3

   --        Car    O          1

   --        Ras    O          1

   --        Tam    O          1

   --        Jal    MG         3

   --        Ayu    MG         3

   --        SM     P          4

   --        EC     MG         3

   --        Tan    O          1

   --        SMT    SO         2

   --        RM     SO         2

   --        Con    P          4

   --        Ex     MG         3

   --        RB     P          4

Table 2. Criteria to the assignation of the scores of each
parameter of the Index of Biological Integrity based on aquatic
macroinvertebrates assemblages (IIBAMA). TR: Taxa richness, EPTR:
Ephemeroptera, Plecoptera, and Trichoptera Richness, RSI: Richness
of sensitive insects, RST: Richness of sensitive taxa, TVA:
Tolerance value average, #CT: Number of clinger taxa. "Y"
represents the value obtained for each variable.


Variable                        1

TR            Y<23
EPTR          Y<9
RSI           Y<9
RST           Y<10
TVA           Y [greater than or equal to] 5.33
#CT           Y<9


Variable                         2

TR                23 [less than or equal to] Y<27
EPTR                            Y=9
RSI               9 [less than or equal to] Y<12
RST               10 [less than or equal to] Y<12
TVA             5.13 [less than or equal to] Y<5.33
#CT               9 [less than or equal to] Y<11


Variable                         3

TR                27 [less than or equal to] Y<30
EPTR                           Y=10
RSI               12 [less than or equal to] Y<14
RST               12 [less than or equal to] Y<14
TVA             4.65 [less than or equal to] Y<5.13
#CT                            Y=11

                                                    Response to
Variable                       4                    degradation

TR            Y [greater than or equal to] 30         Decrease
EPTR          Y [greater than or equal to] 11         Decrease
RSI           Y [greater than or equal to] 14         Decrease
RST           Y [greater than or equal to] 14         Decrease
TVA           Y<4.65                                  Increase
#CT           Y [greater than or equal to] 12         Decrease

Table 3. Families collected in rivers of Lerma-Chapala River Basin
and Panuco River Basin and attributes used for the Index of
Biological Integrity based on aquatic macroinvertebrates
assemblages (IIBAMA). UNK: Unknown, I: Intolerant, T; Tolerant, VT:
Very tolerant, VI: Very intolerant, Clg: Clinger, Sw: Swimmer, Br:
Burrower, Clb: Climber, Sk: Skater, Hk: Hiker.

Family                  Tolerance        Tolerance    Life habit

Baetidae                     5               I            Clg
Ephemerellidae               3               I            Clg
Polymitarcyidae             UNK             UNK            --
Caenidae                     6               T            Clg
Leptophlebiidae              3               I             Sw
Leptohyphidae                6               T            Clg
Heptageniidae                3               I            Clg
Ephemeridae                 UNK             UNK            --
Gomphidae                    3               I             Br
Coenagrionidae               8               T            Clb
Lestidae                     9               VT           Clb
Platystictidae              UNK             UNK            Sw
Macromiidae                 UNK             UNK            --
Libellulidae                 9               VT            Sw
Aeshnidae                    3               I            Clg
Calopterygidae               6               T            Clb
Protoneuridae               UNK             UNK            --
Perlidae                     1               VI           Clg
Corixidae                    9               VT            Sw
Hebridae                    UNK             UNK           Clg
Veliidae                     6               T             Sk
Mesovellidae                UNK             UNK            Sk
Gerridae                     5               I             Sk
Belostomatidae              10               VT           Clb
Naucoridae                   5               I             Sw
Notonectidae                 4               I             Sw
Saldidae                    10               VT           Clb
Pleidae                     UNK             UNK            Sw
Macroveliidae               UNK             UNK            Sk
Nepidae                     UNK             UNK            --
Corydalidae                  0               VI           Clg
Hydroptilidae                4               I            Clg
Polycentropodidae            5               I            Clg
Philopotamidae               3               I            Clg
Odontoceridae                0               VI           Clb
Hydrobiosidae               UNK              VI           Clg
Limnephilidae                3               I            Clg
Calamoceratidae              3               I            Clg
Lepidostomatidae             1               VI           Clg
Leptoceridae                 4               I            Clg
Hydropsychidae               4               I            Clg
Gyrinidae                    4               I             Sk
Dytiscidae                   6               T             Sw
Hydrophilidae                5               I            Clg
Helophoridae                 5               I             Br
Staphylinidae                8               T            Clg
Hydraenidae                  5               I            Clg
Psephenidae                  4               I            Clg
Scirtidae                    7               T            Clb
Dryopidae                    5               I             Br
Elmidae                      4               I            Clg
Limnichidae                  3               I            Clg
Lutrochidae                  3               I            Clg
Ptiliidae                   UNK             UNK            --
Haliplidae                   7               T            Clb
Tipulidae                    3               I             --
Ceratopogonidae              6               I             Br
Chironomidae                 6               I             Br
Simuliidae                   6               I            Clg
Syrphidae                   10               VT            --
Dixidae                      1               VI            Sw
Culicidae                    8               T             Sw
Thaumaleidae                UNK             UNK            --
Tabanidae                    6               T             --
Stratiomyidae                7               T             Br
Muscidae                     6               T             --
Ephydridae                   6               T             Br
Psychodidae                  8               T             Br
Chaoboridae                  7               T             Br
Athericidae                  4               I             Br
Empididae                    8               T             Br
Crambidae                    5               I            Clb
Cambaridae                   6               T             Sw
Palaemonidae                 6               T             Hk
Hyalellidae                  8               T             Sw
Asellidae                    8               T             Sw
Unionidae                   UNK             UNK            --
Corbiculidae                UNK             UNK            --
Planorbidae                  7               T            Clg
Pachychilidae               UNK             UNK            --
Hydrobiidae                  7               T            Clg
Physidae                     8               T            Clb
Thiaridae                   UNK             UNK            --
Pleuroceridae                6               T            Clg
Dugesiidae                   1               VI           Clg
Undetermined                 5               I            Clg

Family                  Class             Order

Baetidae               Insecta        Ephemeroptera
Ephemerellidae            --               --
Polymitarcyidae           --               --
Caenidae                  --               --
Leptophlebiidae           --               --
Leptohyphidae             --               --
Heptageniidae             --               --
Ephemeridae               --               --
Gomphidae                 --             Odonata
Coenagrionidae            --               --
Lestidae                  --               --
Platystictidae            --               --
Macromiidae               --               --
Libellulidae              --               --
Aeshnidae                 --               --
Calopterygidae            --               --
Protoneuridae             --               --
Perlidae                  --           Plecoptera
Corixidae                 --            Hemiptera
Hebridae                  --               --
Veliidae                  --               --
Mesovellidae              --               --
Gerridae                  --               --
Belostomatidae            --               --
Naucoridae                --               --
Notonectidae              --               --
Saldidae                  --               --
Pleidae                   --               --
Macroveliidae             --               --
Nepidae                   --               --
Corydalidae               --           Megaloptera
Hydroptilidae             --           Trichoptera
Polycentropodidae         --               --
Philopotamidae            --               --
Odontoceridae             --               --
Hydrobiosidae             --               --
Limnephilidae             --               --
Calamoceratidae           --               --
Lepidostomatidae          --               --
Leptoceridae              --               --
Hydropsychidae            --               --
Gyrinidae                 --           Coleoptera
Dytiscidae                --               --
Hydrophilidae             --               --
Helophoridae              --               --
Staphylinidae             --               --
Hydraenidae               --               --
Psephenidae               --               --
Scirtidae                 --               --
Dryopidae                 --               --
Elmidae                   --               --
Limnichidae               --               --
Lutrochidae               --               --
Ptiliidae                 --               --
Haliplidae                --               --
Tipulidae                 --             Diptera
Ceratopogonidae           --               --
Chironomidae              --               --
Simuliidae                --               --
Syrphidae                 --               --
Dixidae                   --               --
Culicidae                 --               --
Thaumaleidae              --               --
Tabanidae                 --               --
Stratiomyidae             --               --
Muscidae                  --               --
Ephydridae                --               --
Psychodidae               --               --
Chaoboridae               --               --
Athericidae               --               --
Empididae                 --               --
Crambidae                 --           Lepidoptera
Cambaridae           Maxillopoda        Decapoda
Palaemonidae              --               --
Hyalellidae               --            Amphipoda
Asellidae                 --             Isopoda
Unionidae             Gastropoda        Unionoida
Corbiculidae              --            Veneroida
Planorbidae               --         Basommatophora
Pachychilidae             --         Neotaenioglossa
Hydrobiidae               --         Neotaenioglossa
Physidae                  --         Basommatophora
Thiaridae                 --         Neotaenioglossa
Pleuroceridae             --         Neotaenioglossa
Dugesiidae           Turbellaria       Tricladida
Undetermined            Acari         Hydrachnidia

Table 4. Values of the parameters measured and indices calculated
in the sampling sites in Lerma-Chapala River Basin and Panuco River
Basin. FBI: Family biotic index, VBHA: Visual based habitat
assessment, TDS: Total dissolved solids (ppm), DO: Dissolved oxygen
(mg L-1), Temp: Temperature ([degrees]C), P: Poor, G: Good, Mt:
Moderate, F: Fair, FP: Fairly poor, E: Excellent, VG: Very good,
SO: Suboptimal, O: Optimal, Mg: Marginal. ES: El Salto, PC: Presa
del Carmen, PR: Presa de Rayas, Com: Comonfort, LQ: La Quemada, LG:
Los Galvanes, Xo: El Xote, RG: Rio Grande, Cal: Calvillo, SS: El
Salto de los Salados, FS: Fraccion Sanchez, PH: La Planta-La
Hacienda, PP: Puente la Plazuela, Pin: Pinihuan, Can: Canoas, QM:
Quinta Matilde, ER: El Realito, Qui: Quiotillos, EO: El Oasis, Chu:
Chuveje, Car: Carpintero, Ras: Rascon, Tam: Tamasopo, Jal: Jalpan,
Ayu: Ayutla, SM: Santa Maria, EC: El Carrizal, Tan: Tancuilin, SMT:
Santa Maria (Tancoyol), RM: Rio Moctezuma, Con: Conca, Ex: Extoraz,
RB: Rio Blanco.

Basin            Sites

Lerma-Chapala     ES      20[degrees]23'21.1"
      --          PC      20[degrees]48'33.3"
      --          PR      20[degrees]47'59.6"
      --          Com      20[degrees]45'03"
      --          LQ      20[degrees]57'06.0"
      --          LG      21[degrees]03'40.4"
      --          Xo      20[degrees]57'08.5"
      --          RG      21[degrees]28'53.5"
      --          Cal     21[degrees]50'53.5"
      --          SS      21[degrees]45'18.2"
    Panuco        FS      21[degrees]47'20.3"
      --          PH      21[degrees]55'25.3"
      --          PP      21[degrees]47'27.3"
      --          Pin      21[degrees]42'43"
      --          Can     21[degrees]56'36.7"
      --          QM      21[degrees]55'27.5"
      --          ER      21[degrees]36'24.9"
      --          Qui     20[degrees]18'06.5"
      --          EO      20[degrees]59'54.5"
      --          Chu     21[degrees]10'17.9"
      --          Car      21[degrees]53'45"
      --          Ras     21[degrees]59'12.8"
      --          Tam     21[degrees]57'18.5"
      --          Jal     21[degrees]12'44.8"
      --          Ayu      21[degrees]23'18"
      --          SM      21[degrees]23'50.9"
      --          EC      21[degrees]23'53.7"
      --          Tan     21[degrees]16'04.3"
      --          SMT     21[degrees]30'09.3"
      --          RM      21[degrees]09'22.5"
      --          Con     21[degrees]26'51.4"
      --          Ex      20[degrees]59'59.1"
      --          RB      21[degrees]12'37.1"

Basin            Sites                            IIBAMA    FBI

Lerma-Chapala     ES      100[degrees]16'48.9"    6    P    6.0
      --          PC      100[degrees]18'33.9"    6    P    6.3
      --          PR      100[degrees]13'22.1"    6    P    6.9
      --          Com     100[degrees]46'25.6"    6    P    8.4
      --          LQ      100[degrees]47'40.8"    7    P    5.9
      --          LG      100[degrees]48'12.1"    6    P    6.9
      --          Xo      100[degrees]47'42.7"    6    P    6.3
      --          RG      100[degrees]48'05.9"    6    P    5.8
      --          Cal     102[degrees]42'51.6"    6    P    5.0
      --          SS      102[degrees]21'31.2"    6    P    10.0
    Panuco        FS      100[degrees]42'04.1"    6    P    6.0
      --          PH      99[degrees]57'54.2"     6    P    6.7
      --          PP      99[degrees]55'29.5"     6    P    5.6
      --          Pin     99[degrees]34'28.3"     6    P    3.4
      --          Can     99[degrees]30'35.4"     10   P    5.7
      --          QM      99[degrees]30'35.9"     8    P    7.8
      --          ER      100[degrees]13'46.1"    6    P    6.2
      --          Qui     100[degrees]09'03.7"    8    P    8.1
      --          EO      99[degrees]42'11.3"     7    P    5.8
      --          Chu     99[degrees]33'26.1"     19   G    5.4
      --          Car     99[degrees]14'44.8"     6    P    2.3
      --          Ras     99[degrees]15'16.8"     6    P    3.5
      --          Tam     99[degrees]23'15.4"     8    P    5.0
      --          Jal      99[degrees]28'5.4"     12   P    5.9
      --          Ayu     99[degrees]35'11.7"     13   Mt   5.1
      --          SM      99[degrees]35'04.7"     13   Mt   3.9
      --          EC      99[degrees]35'04.7"     11   P    4.8
      --          Tan     99[degrees]03'59.9"     19   G    3.9
      --          SMT     99[degrees]22'27.9"     7    P    5.3
      --          RM       99[degrees]06'39"      6    P    6.7
      --          Con     99[degrees]38'01.1"     6    P    6.8
      --          Ex       99[degrees]42'13"      11   P    5.5
      --          RB      99[degrees]44'19.7"     11   P    5.1

Basin            Sites  FBI     VBHA      pH    TDS    DO

Lerma-Chapala     ES     F    117   SO   7.56   443   5.3
      --          PC     F    121   SO   7.46   190   2.85
      --          PR     FP   31    P    7.64   112   1.24
      --          Com    P    37    P    7.85   544   0.05
      --          LQ     F    88    Mg   7.62   97    6.89
      --          LG     FP   32    P    7.28   225   6.23
      --          Xo     F    61    Mg   7.25   248   3.88
      --          RG     F    82    Mg   7.48   162    0
      --          Cal    G     -    -    8.26   294   0.61
      --          SS     VP   54    Mg   7.27   672   0.93
    Panuco        FS     F    32    P    7.32   607   2.59
      --          PH     FP   121   SO   7.55   874   1.86
      --          PP     F    131   SO   7.63   754   4.15
      --          Pin    E    200   O    7.68   838   1.98
      --          Can    F    196   O    8.24   205   4.03
      --          QM     P    189   O    7.96   229   3.72
      --          ER     F    122   SO   8.61   130   1.52
      --          Qui    P    112   SO   7.74   87    1.53
      --          EO     F    133   SO   8.12   254   5.7
      --          Chu    G    122   SO   7.97   202   4.8
      --          Car    E    200   O    7.83   592   6.18
      --          Ras    E    190   O    7.85   391   4.78
      --          Tam    G    199   O    7.93   806   4.3
      --          Jal    F    117   SO   7.83   175   4.6
      --          Ayu    G    126   SO   8.49   198   8.06
      --          SM     VG   126   SO   8.18   297   6.25
      --          EC     G    140   SO   8.19   297   6.11
      --          Tan    VG   176   O    8.34   152   6.44
      --          SMT    G    150   SO   8.15   313   6.99
      --          RM     FP   171   O    8.65   647   8.1
      --          Con    FP   115   SO   7.12   501   1.54
      --          Ex     G    121   SO   8.37   282   7.74
      --          RB     G    149   SO   8.22   179   5.54

Basin            Sites   Temp

Lerma-Chapala     ES     13.86
      --          PC      18
      --          PR     16.76
      --          Com    14.47
      --          LQ     15.09
      --          LG     18.18
      --          Xo     28.4
      --          RG     15.03
      --          Cal    24.37
      --          SS     13.05
    Panuco        FS     20.56
      --          PH     22.63
      --          PP     19.11
      --          Pin    19.35
      --          Can    14.94
      --          QM     15.88
      --          ER     29.31
      --          Qui    20.69
      --          EO     24.91
      --          Chu    20.83
      --          Car    23.3
      --          Ras    21.48
      --          Tam    24.58
      --          Jal    21.99
      --          Ayu    21.58
      --          SM     28.82
      --          EC     28.17
      --          Tan    21.83
      --          SMT    25.76
      --          RM     21.75
      --          Con    28.16
      --          Ex     23.46
      --          RB     18.8

Table 5. Principal Component Analysis based on parameters measured
and indices calculated in the sampling sites in Lerma-Chapala River
Basin and Panuco River Basin. IIBAMA: Index of biological
integrity, FBI: Family biotic index, VBHA: Visual based habitat
assessment, TDS: Total dissolved solids (g [L.sup.-1]), DO:
Dissolved oxygen (mg [L.sup.-1]), Temp: Temperature ([degrees]C).

  Physical and chemical variables

                PC1     PC2     PC3

Eigenvalue      2.88    1.39    0.84
% variance     41.17   19.82   12.04

IIBAMA          0.38   -0.42    0.38
FBI            -0.44   -0.29   -0.44
VBHA            0.41    0.39    0.41
pH              0.45   -0.13    0.45
TDS            -0.12    0.73   -0.12
DO              0.43   -0.10    0.43
Temp            0.30    0.20    0.30
COPYRIGHT 2018 Pontificia Universidad Catolica de Valparaiso, Escuela de Ciencias del Mar
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2018 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Torres-Olvera, Martin J.; Duran-Rodriguez, Omar Y.; Torres-Garcia, Ulises; Pineda-Lopez, Raul; Ramir
Publication:Latin American Journal of Aquatic Research
Date:Nov 1, 2018
Previous Article:Thermal tolerance and aerobic scope of tetra-hybrid tilapia Pargo-UNAM.
Next Article:An emerging infection caused by Gyrodactylus cichlidarum Paperna, 1968 (Monogenea: Gyrodactylidae) associated with massive mortality on farmed...

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters