Ivana F. da Rosa1 , Daniela J. de Oliveira1, Vanessa P. da Cruz1, Fernando Y. Ashikaga1, Gabriela Omura Costa1, Lucas B. Doretto1, José A. Senhorini2, Rita de C. Rocha2, Fabio P. Foresti3, Claudio Oliveira1 and Fausto Foresti1
Freshwater ecosystems are currently imperiled by anthropogenic activities worldwide (Jeremias et al., 2018; Strungaru et al., 2019; Iordache et al.,2020). In general, rivers undergo serious degradation due to uncontrolled wastewater disposals, affecting several species and causing the depletion of their natural populations by mortality (Martins et al., 2012; Warr, Greenfield 2012; Paschoal et al., 2020). Species that exhibit migratory behavior are more susceptible to the effect of anthropogenic and environmental changes since environmental fragmentation and mortality generally hinders their reproductive process (Izzo et al., 2016; Nieminen et al., 2017).
Despite the remarkable abundance and wide distribution, previous studies have reported that variation in water quality has also caused severe reductions in many migratory species including Prochilodus lineatus (Valenciennes, 1837)(Agostinho et al., 2007; Garcez et al., 2011; Machado, Foresti, 2012; Rueda et al., 2013; Perini et al., 2021). Among the main migratory species of Neotropical fish, the curimbata P. lineatus is an ordinary species that dwells in the Paraná River basin and is found mainly in the Grande, Pardo and Mogi-Guaçu rivers (Godoy, 1975). Although it is more frequently detected during its reproductive migration, individuals of this species can be captured during the whole year along these rivers and in the Cachoeira de Emas region in the city of Pirassununga, São Paulo (Godoy, 1975; Agostinho, Gomes, 2018). During the reproductive period, P. lineatus forms large fish aggregations, which are usually found downstream of waterfalls or dams (Godoy, 1975). Juveniles grow fast; males may attain 20 cm in the first year and may already be mature after the first year of life (Gomes, Agostinho, 1997). Moreover, P. lineatus is considered an important species, playing a very important role in preventing sediment accumulation and promoting the transfer of energy in the food chain. Nevertheless, P. lineatus could be more exposed to toxicants, since many pollutants concentrate in the sediments (Weber et al., 2013).
Anthropogenic activities can affect genetic variation over both long- and short-time scales (Blaber et al., 2000). Population structuring and the accumulation of genetic differences between different groups can lead to reproductive isolation, gene flow disruption, decrease in the effective population size, increased inbreeding, making these groups more susceptible to the process of extinction (Frankham et al., 2014; Krishan et al., 2015). Given that, it is increasingly important to examine factors that influence the loss of biodiversity of migratory species (Strayer, Dudgeon, 2010; Van Leeuwen et al.,2018; Morita et al., 2019).
Despite many environmental processes occurring too slowly to be appreciated in short to medium-term surveys, there is an increasing interest in temporal studies analyzing the effects of environmental changes on molecular genetics (Barcia et al., 2005; Fullerton et al., 2011; Prunier et al., 2018). In this sense, the use of molecular markers such as microsatellites (SSRs) and the sequencing of the mitochondrial DNA control region (D-loop) has proven to be an excellent tool for estimating recent and ancient genetic variation in populations (Avise, 1994; Guo et al., 2019). These molecular markers allow quantifying the genetic variability between/among shoals, providing the understanding of the mechanisms involved in the genetic structuring of populations more accurately, and such information made it possible to propose better conservation strategies for the species (Dowling et al., 2015; Ferreira et al., 2017; Castellanos-Galindo et al., 2019; Guzman et al., 2020).
Long-term temporal studies of genetic variability are needed to understand the dynamics of populations and the effects of natural and human-induced forces on populations. In this context, the present study aimed to identify possible structural changes in the stock of Prochilodus lineatus from the Mogi-Guaçu River based on the analysis of specimens collected at Cachoeira de Emas over the period between 2003 and 2015, using nuclear and mitochondrial molecular markers in a temporal genetic analysis. The information presented here could provide a first insight into the genetic structure of the species in this ecosystem, and the consequences of anthropic actions on wild stocks of the species. This information could be further used in the proposition of adequate management and conservation programs for the curimbata and other species in the ecosystem.
Material and methods
Study site and sampling collection. The study area comprises the locality of Cachoeira de Emas in the Mogi-Guaçu River, Pirassununga, São Paulo State, Brazil (Fig. 1). Prochilodus lineatus is a plentiful fish species from the Paraná River basin, especially within the waters of the Grande, Pardo and Mogi-Guaçu rivers (Godoy, 1975). A total of 209 adult specimens were obtained at one study site (Cachoeira de Emas) in the Mogi-Guaçu River from seven temporal collections (Sep03, Jan05, Aug05, Jan06, Jan09, Sep10 and Feb15) (Tab. S1). All individuals were adults, possibly of reproductive age (1–2 years), showing body lengths above 25 cm (Vazzoler, 1996). Tissue fragments of each individual were collected and preserved in 95% alcohol before being deposited in the collection of the Laboratório de Biologia e Genética de Peixes (LBP) at UNESP, in Botucatu, São Paulo, Brazil. The total genomic DNA was obtained from fin tissue fragments of the individuals using the protocol proposed by Ivanova et al. (2006). The genetic analysis was performed using microsatellites and mitochondrial DNA (D-loop) sequences as genomic markers.
FIGURE 1| Map of the State of Sao Paulo showing the main components of the hydrographic system in the Southeast of Brazil. In detail square, the collection site of samples located in Cachoeira de Emas, Pirassununga-SP. The Mogi-Guaçu River is a component of the Upper Paraná River basin.
Microsatellites amplification. The 209 individuals sampled from 2003 to 2015 were analyzed using seven microsatellite loci, of which six were previously described for P. lineatus (PL3, PL9, PL14, PL119, PL139 and PL216;Rueda et al., 2013) and one (AG72) was obtained through heterologous amplification from Megaleporinus macrocephalus (Garavello & Britski, 1988) (Morelli et al.,2007). Both primers were labeled with FAM and HEX fluorescence. PCR reactions were performed in five separate runs for each multiplex in a thermocycler (ABI 3130, Applied Biosystems), using specific primers in each multiplex. The following reagents were used for each sample: 3.25 μl H2O; 2.4 μl dntps; 1.5 μl buffer; 0.2 μl primers f; 0.2 μl primers r; 0.75 μl MgCl2; 0.3 μl taq; 1 μl DNA; Formamide; and size standard Rox. The products were genotyped in ABI 3130 sequencer (Applied Biosystems) according to the methodology proposed by Schuelke (2000). Microsatellite profiles were manually analyzed using the Gene mapper 4.3 program (Applied Biosystems).
Mitochondrial DNA amplification. Polymerase chain reaction (PCR) was performed using a set of forward primers (F-TTF: GCCTAAGAGCATCGGTCTTGTAA) and reverse primers (F-12R: GTCAGGACCATGCCTTTGTG) described by Sivasundar et al. (2001) following the protocol suggested by Platinum TaqDna Polymerase (Invitrogen). PCR reaction (total volume of 25 µL) contained 0.5 µM of each primer, 0.2 mM of dNTPs, 1.5 mM of MgCl2, 0.02 µl of Platinum TaqDna Polymerase (Invitrogen), 1 × amplification buffer and 2 ng/µL of DNA template. Thermal conditions were as follows: initial denaturation at 95 °C for 5 min, 35 cycles of denaturation (94 °C 40 s), annealing (50 °C 40 s) and elongation 72 °C 40 s, with final elongation at 72 °C for 10 min. Samples from 209 individuals were sequenced over seven temporal collections (Sep03, Jan05, Aug05, Jan06, Jan09, Sep10, and Feb10). Fragments of approximately 600bp were purified and sequenced in the ABI 3130 Genetic Analyzer (Applied Biosystems) with the BigDyeTM Thermator v 3.1 Cycle Sequencing Ready Reaction (Applied Biosystems) kit. Reverse and forward fragments were sequenced, and the consensus sequences were aligned using the Geneious 4.8.5 software (Kearse et al., 2012).
Population structure analysis. The temporal genetic structure of P. lineatus was investigated using the Bayesian clustering STRUCTURE v.2.3.3 software (Pritchard et al., 2000; Falush et al., 2003). The Markov chain Monte Carlo (MCMC) was run for 1 million generations, with an initial burn-in of 10% steps discarded, and 20 iterations of each K and the admixture model. The true number of populations is expected to be the value of K that maximizes the estimated model log‐likelihood, log (P(X|K)) (Falush et al., 2003), and the K values were selected using the delta K (Evanno et al., 2005) method described by (Earl, VonHoldt, 2012) in Structure Harvester (https://taylor0.biolo gy.ucla.edu/structureHarvester). The AMOVA (Excoffier et al., 1992) examined the temporal genetic heterogeneity between groups using the Arlequin v.188.8.131.52 program (Excoffier, Lischer, 2010). Genetic population structure was inferred using FST values estimated with the Arlequin v.184.108.40.206 program (Excoffier, Lischer, 2010) and the genetic differentiation index Djost proposed by Jost (2008).
Genetic diversity. Genetic diversity for the mitochondrial (D-loop) marker estimating the number of haplotypes, haplotype diversity rather (h), nucleotide diversity (π), and polymorphic sites were evaluated using the DnaSP v.5.10.01 program (Librado, Rozas, 2009). The Fstat v.2.9.3 program (Goudet, 2002) was used in the analysis of genetic diversity with the SSR markers to estimate the total number of alleles per locus (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He) and the inbreeding rate (FIS). The Microcheker 2.2.1 program (van Oosterhout et al., 2004) was used for the detection of possible genotyping errors such as the presence of stutter, dropout and null alleles. Possible deviations of Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium were calculated using the Genepop v4.2 program (Raymond, Rousset, 1995; Rousset, 2008). P values for HWE were corrected for multiple tests (P = 0.05/number of combinations) by applying a sequential Bonferroni correction (Rice, 1989).
Demographic analysis and effective population size. The neutrality test based on the model of Tajima D (Tajima, 1989) and Fu FS (Fu, 1997) were used for demographic analyzes. The analysis of a mismatch distribution was performed with the programs Arlequin 220.127.116.11 (Excoffier, Lischer, 2010) and Bottleneck v.1.2.02 (Piry et al., 1999). These tests are used to verify the occurrence of recent population expansion or reduction (bottleneck) (Fu, Li, 1993). Past changes in effective population size were also explored with the coalescent-based Bayesian skyline plot (BSP) created in BEAST v1.6.1 software (Drummond, Rambaut, 2007). The Markov chain Monte Carlo (MCMC) sampling for intervals along the genealogy was determined from coalescent events (Drummond et al., 2005) and the mutation rate was estimated considering1.935×10-2 mutations per site per million years (Mondin et al., 2018). All BEAST input.xml files were created in Beauti v1.6.1 software available in the package. The runs used a strict molecular clock set to one and therefore time was in mutational units (substitution/site). The results from multiple runs were combined using the LogCombiner v1.6.1 program available in the BEAST package and examined in Tracer v1.6 (Drummond, Rambaut, 2007) to check adequate mixing and convergence of the MCMC. The effective sample size for all parameters was > 200.
Population structure. The structure analysis conducted under the admixture model and K = 1–7 populations indicated the highest likelihood (ln(P)D) in a population structure of K = 3 (4790.34 ± 0.079), a result corroborated by the estimation of ∆K (Fig. 2). Likewise, the results from AMOVA for microsatellites revealed moderate genetic structure among all the groups (Fst = 0.14; p ≤ 0.007; Tab. 1). In addition to the three temporal clusters identified with STRUCTURE, pairwise Fst, Rst and D ‘Jost analyses also suggested moderate significant differentiation in genetic structure over the years (Tab. S2). However, the genetic differentiation for mitochondrial DNA was found either among populations within groups, with low genetic structure (Fst = 0.01; p ≤ 0.08; Tab. S3). Nevertheless, a significant genetic differentiation between pairwise groups was found for mitochondrial DNA between Jan05xJan09 (Fst = 0.11), Sep10xJan05 (Fst = 0.09), Jan09xJan06 (Fst = 0.05), as well as, between Jan05xSep10 (Fst = 0.03) (p ≤ 0.07; Tab. 2; Tab. S4).
FIGURE 2| Graph of the Bayesian analysis of population structure of microsatellites for Prochilodus lineatus. A. Delta(k) showing the highest value in a population structure of K = 3; B. The estimated mean log-likelihoods [ln(PrK)]; C. Structure bar plot. Black lines separate the different sampled populations based on temporal collection.
TABLE 1 | Analysis of Molecular Variance (AMOVA) of different groups of Prochilodus lineatus using microsatellite markers. *Statistically significant values p ≤ 0.007.
Source of variation
Components of variance
Percentage of variation
Among the populations
Among individuals within populations
TABLE 2 | Analysis of FST coupled with Prochilodus lineatus collected along the period of 2003 to 2015 obtained with the D-loop marker. *Statistically significant values at the level of 5%.
Genetic diversity. A total of 209 consensus D-loop sequences of 600 base pairs in length were obtained for P. lineatus.The analysis of mitochondrial data of P. lineatus collected in a sampling period of six years revealed an extensive genetic variability expressed by 51 polymorphic sites and 71 haplotypes. The haplotype diversity (h) ranged from 0.94 (Sep10) to 0.98 (Sep03/Aug05), whereas the nucleotide diversity (π) ranged from 0.08 (Jan09/Sep10) to 0.12 (Feb15) (Tab. 3). The greater genetic diversity was also characterized by the number of haplotypes in each group, and the groups with a large index of variability were those characterized by the high number of haplotypes. The haplotype H5 was the most frequent in the network and appears internally, while the other haplotypes present peripheral distribution and lower frequencies (Fig. 3). The genetic variability of the 7 microsatellite loci for each collection sampled is described in Tab. 3. The highest mean number of alleles (Na = 12.00) and mean number of effective alleles (Ne = 8.05) were found in Sep03, while Jan05 had the lowest average for alleles (Na = 6.71), showing the smallest mean number of effective alleles (Ne = 3.40). Interestingly, the lowest number of average alleles and effective alleles was observed over the same sample period (Jan05). The average observed and expected heterozygosities (Ho and He), ranged from 0.41 (Sep10) to 0.58 (Jan09) and from 0.59 (Jan05) to 0.77 (Jan09), respectively. The inbreeding coefficient values (FIS) were significant in 35 of 49 comparisons in the seven groups analyzed, suggesting the existence of heterozygote deﬁciency. Likewise, the departure of HWE was detected in 27 out of 49 loci, which significantly deviated from expectations after Bonferroni correction (Tab. S2; p ≤ 0.007). Analysis in Micro-Checker did not show any presence of null alleles, stuttering, or large allele dropout. The greatest source of variability for microsatellites was found within the individuals among collections (63%, FIT 0.36), rather than within collections (22%, FIS = 0.26) or among collections (14%, FST = 0.14) (Tab. 1).
TABLE 3 | Genetic variability data and results of Tajima’s D and Fu’s Fs neutrality tests in different groups of Prochilodus lineatus through microsatellite and D-loop markers. N: number of samples; Na: number of average alleles; Ne: number of effective alleles; Ho: observed heterozygosity; He: expected heterozygosity; FIS: intrapopulation index. h: haplotype diversity, π: nucleotide diversity. *Statistically significant values at the level of 5%.
FIGURE 3| Median-joining network of Prochilodus lineatus, based on haplotypes of D-loop marker. Sizes of the circled are proportional to the frequencies of the haplotypes at issue. The colors indicate the groups according to the collections: red circles: Sep_03; yellow circles: Jan_05; purple circles: Aug_05; blue circles: Jan_06; pink circles: Jan_09; green circles: Sep_10; pastel pink circles: Feb_15. Hatch marks represent the number of mutations by which haplotypes differ.
Demographic analysis and population expansion. The Tajima D (Tajima, 1989) and Fu FS (Fu, 1997) tests showed more extensive values of differentiation in the populations of Sep03 and Jan09 (Tab. 3), but all indices presented negative values. The Bayesian skyline plots to track historical fluctuation of effective population size in P. lineatus samples set by D-loop sequences showed a demographic balance in 6 out of the 7 groups analyzed (Fig. 4; Fig. S5), except for the group of Feb15 that presented a sharp curve of population expansion.
FIGURE 4| Bayesian skyline plot (BSP) showing change in effective population size of Prochilodus lineatus in Feb_15 group from Cachoeira de Emas in the Mogi-Guaçu River based on Dloop marker. The y-axis, population size × generation time*; x-axis, time (indicated in thousands of years ago). *Generation time measured in million years. Solid lines represent median estimates, and shaded areas represent the 95% HPD limits.
Genetic diversity plays an important role in the origin, survival, and adaptability of species (Dachapak et al., 2017). The results of the present study provide interesting insights concerning the temporal genetic diversity of P. lineatus occurring in individuals captured in the Mogi-Guaçu River at the region of Cachoeira de Emas. The analysis of the mitochondrial DNA (D-loop) region revealed a high diversity of haplotypes (h > 0.5) and lower nucleotide diversity (π < 0.005), which is similar to the results of other studies with P. lineatus in the Paraná River basin (Yazbeck, Kalapothakis, 2007; Ferreira et al., 2017; Perini et al., 2021). Our finding was not in contrast to patterns observed in many other Chraraciform fish species, such as Brycon opalinus of the Paraíba do Sul basin (Hilsdorf et al., 2002), in which nucleotide diversity (π) ranged from 0.00 to 1.35%, Leporinus elongatus (Martins et al., 2003) from 1.78 to 7.70% including another study with Prochilodus lineatus (Sivasundar et al., 2001) which varied from 0.3 to 3.6%.
The results of lower nucleotide diversity observed in the Feb15 group might be the result of the accumulation of mutations since in a short time, haplotype diversity is easier to accumulate than nucleotide polymorphisms (Zhang et al.,2020). Although the recovery of the variability in the populations over the years is probably due to the large effective population size, as expected for populations with high migration rates (Santos et al., 2007). The migratory movements of these species enable large populations to be maintained (high effective population sizes), minimizing the loss of genetic diversity through genetic drift (Santos et al., 2007). Therefore, P. lineatus possesses one of the largest fish populations in the Grande River basin, with no signal of bottlenecks (Revaldaves et al.,1997; Sivasundar et al., 2001; Aguirre-Pabón et al., 2013; Perini et al., 2021). In addition, large effective population sizes also increase the rate of allelic recombination (Agostinho et al.,2003; Ferreira et al., 2015). Thus, over time, the frequency of common alleles may increase and thus promote their reestablishment in the population (Charlesworth, Willis, 2009). Another possibility for recovering the previous condition of variability in fish stocks would be the contribution of complementary stocks found in the Pardo River, the secondary component of the river basin, which could lead to the re-establishment of the populations over the years. These results could agree with the analysis of Bayesian skyline plots. Even if not observed in the other groups, Feb15 presented a pronounced population expansion between 2014,9 and 2014,95 TBP, providing independent evidence for range expansion of Prochilodus lineatus. In addition, patterns containing high h (> 0.5) values combined with low π (< 0.5%) values observed by this group often demonstrate the occurrence of accumulation of mutations (Grant, Bowen, 1998).
On the contrary, and despite their high genetic diversity, the P. lineatus population has undergone significant heterozygosity deficiency. Our findings detected a large decline in the observed heterozygosity values (Ho) relative to expected heterozygosity (He) and the groups of Aug05 and Jan09 showed the larger differences. Similar levels were obtained by Yazbeck, Kalapothakis (2007), with values of Ho = 0.00-1.00 in the intermediate section of the Paraná River, as well in components of the Paraná River basin (Ho = 0.634-0.82; He = 0.803-0.874) (Ferreira et al., 2017).
The Intrapopulation Index FIS used to estimate the rate of inbreeding within the groups (Chistiakov et al., 2006), and divergences between He and Ho generate a positive value for the FIS. The values found were higher than those reported by Ferreira et al. (2017) for the same species in a spatial study developed in the Paraná River basin (FIS = -0.04 to 0.09). Therefore, the high homozygous indices are common in impacted populations, and even large populations are subject to the restriction of the genetic variability due to anthropic actions (Mastrochirico et al., 2018). In this sense, events of population change caused by environmental damage are usually followed by a consequent reduction of genetic variability and an increase in the rate of inbreeding in the remaining biological stocks (Faulks et al.,2017).
In addition, another fact that may have influenced the decline of heterozygosity values of these populations may involve environmental disturbances that occurred in the Mogi-Guaçu, Pardo, and Grande hydrographic complex (IBAMA, 2003). Several freshwater fish including P. lineatus experienced severely mass mortality due to exposure to pesticides in the tributaries (IBAMA, 2003; Campagna et al., 2006), which should lead to a direct loss of genetic diversity and a further indirect reduction of genetic diversity via population bottlenecks and associated elevated genetic drift effects. Second, a general modification of the rainfall regime occurred after 2009 for some time in the region, interfering with a low water level of rivers in the ecosystem, and causing disturbances affecting the reproductive behavior of species with possible negative impacts on the ecosystem in the following years. In addition, the evidence of a heterozygosity deficiency with different allelic frequencies observed here could be also explained by subpopulation structure, suggesting a possible Wahlund effect, selection of specific alleles and inbreeding (Hartl, Clark, 2007; Ribolli et al., 2017).
It should be pointed out, that the ecosystem of the Upper Parana basin has a few tributaries with a free course sufficiently large for the trophic and reproductive migrations of fish species, with stretches favorable to the life of the rheophilic fish species. Thus, in this hydrographic complex, the Mogi-Guaçu and Pardo Rivers present themselves as fundamental for the maintenance of the variability of the stock of P. lineatus and, consequently, for other species with the same behavior. Furthermore, although this process can recover a level of variability that allows the stability of the species in the ecosystem, it does not guarantee the recovery of the previously existing genetic variability, since the lost rare alleles could hardly be recovered.
Our results also identified a temporal genetic structuring of P. lineatus sampled in the same site over 12 years for the first time. The application of the AMOVA test revealed moderate genetic structure (Fst = 0.14) for microsatellites, while mitochondrial DNA shows low genetic structure levels (Fst = 0.01). Although, the pairwise Fst values indicate significant mitochondrial DNA differentiation between some groups (Fst = 0.11). These results suggest that even tenuous, there is a temporal structuring in the stock over the years, being more evident in the periods of 2003/2005 and 2009/2010 in which a reduction in the number of alleles was observed.
Some divergences involving the markers used were observed. In summary, the results on microsatellite markers suggest that there is a genetic structure in P. lineatus populations and undergone significant heterozygosity deficiency, but it is not uniformly supported by the D-loop. Although, the pairwise Fst values indicate significant mitochondrial DNA differentiation between some groups. The effective population size of mitochondrial DNA is one‐fourth that of a nuclear‐autosomal gene. Whereas mitochondrial markers reflect evolutionary processes operating through the maternal germline, microsatellites reflect evolutionary processes of both sexes that have occurred more recently, e.g., over a few thousand years (Schlötterer, 2000). Therefore, evolutionary relationships may be oversimplified and historical events within and between populations may not be correctly detected with mitochondrial DNA data (Zhang, Hewitt, 2003). In this way, microsatellite markers may better reflect the genetic changes in this study and could be considered more accurate to test temporal structure.
The Bayesian analysis using Ln(P)D and delta de Evanno for the microsatellites corroborated the pattern observed, in which three populations were identified and the observed reduction of heterozygosity. As previously mentioned, the departure from HWE and the positive FIS values obtained would be explained by the existence of a population structure in our study area. Furthermore, the low deviation of Hardy-Weinberg equilibrium indicates that P. lineatus present a genetic structure probably a consequence of changing genetic flow followed by the genetic drift of their subpopulations.
Studies have shown that modification of ecological environments by human activities can lead to the genetic structure of freshwater fish (Meldgaard et al., 2007; Perkin et al., 2012; Nanninga et al., 2014; Pereira et al., 2016). The occurrence of genetic population structuring in P. lineatus was related before by Rueda et al. (2013) using spatial analysis and attributed to the presence of different shoals occurring during autumn-winter (Fst = 0.14) and autumn-spring (Fst = 0.12) in the lower section of the Uruguay River. Additionality, Perini et al. (2021) also showed a significant spatial genetic structure (Fst = 0.009 to 0.022) and three genetic clusters inhabiting the Grande-Pardo-Mogi Guaçu River system. However, temporal analysis with P. lineatus has not developed in the Paraná Basin until the present study. Furthermore, temporal structure values reported in this study are similar to those reported for Salminus brasiliensis in the Paraná River (Ribolli et al., 2017). The temporal structuring of a population could be associated with a possible decrease in the competition for resources such as food, space, sexual partners, and due to a different use of the stream corridor through time and across life stages (Braga-Silva, Galetti, 2016; Ribolli et al., 2017). Furthermore, such genetic structure detected at the Emas sample site could be due to distinct seasonal stocks (Rueda et al., 2013) or spawning waves consisting of different genetic populations (Braga-Silva, Galetti, 2016; Perini et al.,2021), obtained during the rainy season and/or to population dynamics of sink/source due to fish shoal migrations to feed during the dry season. Moreover, it is possible that environmental actions could contribute to the genetic differentiation of these species. Many cases of massive fish mortality in the Mogi-Guaçu River basin, São Paulo state, have been attributed to the presence of toxicants, high loads of organic matter, and toxins from algal bloom (IBAMA, 2003; Campagna et al., 2006; Meschiatti, Arcifa, 2009). The massive mortality of 30 tons of freshwater fish including P. lineatus (October of 2002), was related to exposure to pesticides in Paraná tributaries. Thus, a possible explanation for the population structure reported here could be associated with drastically reduced populations occurring in the Mogi-Guaçu River, Upper Paraná River basin (IBAMA, 2003). Added to the fact that the main fishing area of the region is organized around its breeding ground, there are many pollution sources along the basin in which the fish performs its migration (Esteves, Pinto Lôbo, 2001).
Furthermore, it must be considered that populations are dynamic entities in the ecosystem, governed by biological and environmental factors and subordinated to natural selection laws. As has been postulated, the rainfall regime is one of the main environmental factors underlying the reproductive process (Melo et al., 2013) of rheophilic fish species and for the synchronization of the biological mechanisms of gonadal development. The perturbation of these factors can result in the alteration of the biological cycle of the species and, ultimately, affect the population structure and dynamics of the species in the ecosystem (Lopes et al., 2018). Therefore, the genetic structuring described here might have to be considered in future conservation actions, considering that the biota of the Mogi-Guaçu River is highly impacted by human activities.
Overall, our findings show that populations of P. lineatus are capable of undergoing temporal changes in population genetic structure and diversity over short time periods. The evidence of temporal population structuring in the main channel of the Mogi-Guaçu River emphasizes the importance of unaltered tributaries in this area to conserve migratory fish populations. Finally, our findings could provide a basis for future management and conservation initiatives to protect migratory fish and their breeding environments.
We would like to thank the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio/CEPTA) who kindly provided material for the study and assisted in its collection, and to Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq process number 03913/2015–7) for financial support provided.
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Ivana F. da Rosa1 , Daniela J. de Oliveira1, Vanessa P. da Cruz1, Fernando Y. Ashikaga1, Gabriela Omura Costa1, Lucas B. Doretto1, José A. Senhorini2, Rita de C. Rocha2, Fabio P. Foresti3, Claudio Oliveira1 and Fausto Foresti1
 Departamento de Biologia Estrutural e Funcional, Instituto de Biociências, Universidade Estadual Paulista “Júlio de MesquitaFilho”, Distrito de Rubião Júnior, 18618-970 Botucatu, SP, Brazil. (IFR) firstname.lastname@example.org (corresponding author), (DJO)email@example.com, (VPC) firstname.lastname@example.org, (FYA) email@example.com, (GO) firstname.lastname@example.org, (LBD) email@example.com, (CO) firstname.lastname@example.org, (FF) email@example.com.
 Centro Nacional de Pesquisa e Conservação da Biota Aquática Continental, Instituto Chico Mendes de Conservação daBiodiversidade, Rod. Prefeito Euberto Nemésio Pereira Godói, km 6.5, Cachoeira das Emas, 13630-970 Pirassununga, SP, Brazil.(JAS) firstname.lastname@example.org, (RCR) email@example.com.
Ivana F. da Rosa: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Validation, Writing-original draft, Writing-review and editing.
Daniela J. de Oliveira: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization.
Vanessa P. da Cruz: Conceptualization, Formal analysis, Software, Visualization, Writing-review and editing.
Fernando Y. Ashikaga: Conceptualization, Data curation, Investigation.
Gabriela Omura Costa: Conceptualization, Methodology, Writing-review and editing.
Lucas B. Doretto: Methodology, Writing-review and editing.
José A. Senhorini: Conceptualization, Project administration, Resources.
Rita de C. Rocha: Conceptualization, Resources.
Fabio P. Foresti: Writing-review and editing.
Claudio Oliveira: Conceptualization, Project administration, Supervision, Writing-review and editing.
Fausto Foresti: Conceptualization, Resources, Supervision, Visualization, Writing-review and editing.
All samples were collected in strict accordance with the regulations of the Brazilian Federal Animal Ethics Committee (SISBIO 15163/1) and authorized by the Ethics Committee on the Use of Animals (CEUA) of the Biosciences Institute at UNESP through its (protocol 971).
The authors declare no competing interests.
How to cite this article
Rosa IF, Oliveira DJ, Cruz VP, Ashikaga FY, Costa GO, Doretto LB, Senhorini JA, Rocha RC, Foresti FP, Oliveira C, Foresti F. Temporal genetic structure of a stock of Prochilodus lineatus (Characiformes: Prochilodontidae) in the Mogi-Guaçu River ecosystem, São Paulo, Brazil. Neotrop Ichthyol. 2022; 20(2):e210156. https://doi.org/10.1590/1982-0224-2021-0156
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Diversity and Distributions Published by SBI
Accepted June 10, 2022 by Alexandre Hilsdorf
Submitted November 12, 2021