PJZ_57_6_2885-2900

Introduction

The conservation and maintenance of biodiversity is imperative for the regular functioning and stability of natural ecosystems (Arlinghaus et al., 2015). Genetic diversity plays a crucial role in ensuring the fitness of a species by offering the potential to efficiently adapt to environmental challenges and pressures from natural selection in a dynamic and evolving context. The biological diversity of a species is dependent upon the phenotypic plasticity and adaptability exhibited by its populations in response to both natural environmental factors and manmade interventions (Nogués-Bravo et al., 2018). During the past few decades, the survival of fish species has been significantly impacted by a range of environmental hazards. These hazards include floods, climatic change, and human interventions such as hydrological alterations, pollution, the introduction of exotic species, the impoundment of rivers, habitat degradation, overexploitation, and overfishing (Prakash, 2021).

The Labeo species, Labeo rohita (Rohu), Labeo calbasu (Orange fin fish), and Labeo gonious (Kuria Labeo) are widely recognized members of the Cyprinidae family. These species hold significant commercial value in the field of aquaculture, making them highly desirable globally (Mosha, 2018). These Labeo species are native to Pakistan and widespread in Punjab riverine systems. Like many other aquaculture species in Pakistan, L. rohita, L. calbasu, and L. gonious face seed quality issues due to poor genetic management of brood stock (Javed and Abbas, 2018). Pakistan has minimal genetic data on these species (Díaz et al., 2019). In recent years, hydrological changes and human activities have destroyed natural water body spawning, breeding, and nursing sites. Dam construction disrupts several fish species migratory, which affects their life cycles (Duponchelle et al., 2021). Furthermore, the implications of conventional breeding operations in rivers and reductions in natural population size have been found to be negative, resulting in the loss of growth capacity, disease resistance, and stress tolerance. Since 1998, there has been a continuous annual fall of 2% in fish production from natural sources in Pakistan (Khan et al., 2016; Qadeer and Abbas, 2017).

Genetic diversity is essential for population fitness and adaptability to the environmental fluctuation (Hoffmann et al., 2017). Population geneticists have given multiple decades to establishing the significance of genetic variation within natural populations (Allendorf, 2017). Regarding the conservation of genetic resources, it is anticipated that there would be advantages associated with the preservation of a species’s utmost level of genetic diversity. The reduction in genetic diversity, resulting from several factors like prolonged selective pressures, inbreeding, and isolation, has resulted in a deterioration the population’s natural potential for adaptation (Bernatchez, 2016). Hence, a comprehensive understanding of the genetic composition of populations is essential in order to mitigate detrimental impacts and ensure the sustainable and efficient management of exploited stocks.

The development of molecular markers has facilitated the genetic analysis of economically significant fish species, so enabling their conservation and genetic management. Microsatellite markers have proven to be highly applicable in various research endeavors within the fields of fisheries and aquaculture, particularly in cases with modest genetic heterogeneity within and across populations (Davis et al., 2018). In last few decades, microsatellite markers are extensively utilized owing to their distinctive characteristics, such as a high degree of polymorphism, a rapid detection process, and a relatively compact size. These markers are being used in fisheries to aid in the identification of individuals, identify their ancestry, monitor brood stock, and assist in marker-assisted breeding operations, to conduct investigations into fluctuations in population dynamics (Wenne, 2018). These advantages made microsatellites valuable markers for examining population structure of different species.

Uncovering the fine-scale genetic structure has been long recognized as a key component in policymaking for the management of freshwater fisheries. In the present investigation, polymorphic microsatellite DNA markers already characterized for L. rohita by (Tripathy, 2018) were used to investigate the patterns of genetic diversity of Labeo species in riverine system of Punjab, Pakistan. We hypothesized that there exists substantial genetic diversity within the Labeo genus populations inhabiting these waters. Through the utilization of microsatellite markers, we aimed to uncover the extent of genetic differentiation, gene flow patterns, and population structure among Labeo species, providing valuable insights into their evolutionary history and potential adaptive responses to environmental pressures. Access to up-to-date genetic data regarding the genetic structure of Labeo species is crucial for determining policy decisions, particularly in relation to the conservation of genetic resources and the efficient management of natural populations.

Materials and Methods

Sampling and DNA extraction

Total 360 specimens, 30 of each population of each species of Labeo (L. rohita (Lr), L. calbasu (Lc) and L. gonious (Lg) were collected from the four riverine sources Indus River (Head Tounsa), Chenab River (Head Trimmu), Jhelum River (Head Rasool) and Ravi River (Head Balloki) from the Province of Punjab, Pakistan (Table I, Fig. 1). The muscle tissue samples were collected from the dorsal side of the each fish. Approximately 20mg of the muscle tissue was cut and preserved in 70% ethanol and stored at -20 oC until further laboratory analysis. Total genomic DNA was isolated from the muscle tissue using the chloroform-isoamyl-alcohol method that was used following (Renshaw et al., 2015). We detected DNA integrity by performing 1% agarose gel electrophoresis. Additionally, we quantified the DNA concentration and

Table I. Sampling details of Labeo species.

Species

Site

Populations

Code

Geographical location

Date of capture

No. of samples

L. rohita

Indus River

Tounsa Barrage

Lr-IR

24° 18' 43.41"N

67° 45' 49.22" E

7/10/2021

Chenab River

Trimmu Head works

Lr-CR

29°20′57″N

71°1′41″E

21-10-2021

Jhelum River

Rassol Barrage

Lr-JR

32° 56′ 33″ N,

73° 43′ 32″ E

17-12-2021

Ravi River

Baloki Head Works

Lr-RR

30° 34' 59.99" N

71° 48' 59.99" E

22-01-2022

L. gonious

Indus River

Tounsa Barrage

Lg-IR

24° 18' 43.41"N

67° 45' 49.22" E

12/10/2021

Chenab River

Trimmu Head works

Lg-CR

29°20′57″N

71°1′41″E

24-10-2021

Jhelum River

Rassol Barrage

Lg-JR

32° 56′ 33″ N,

73° 43′ 32″ E

28-12-2021

Ravi River

Baloki Head Works

Lg-RR

30° 34' 59.99" N

71° 48' 59.99" E

20-01-2022

L. calbasu

Indus River

Tounsa Barrage

Lc-IR

24° 18' 43.41"N

67° 45' 49.22" E

22/10/2021

Chenab River

Trimmu Head works

Lc-CR

29°20′57″N

71°1′41″E

27-10-2021

Jhelum River

Rassol Barrage

Lc-JR

32° 56′ 33″ N,

73° 43′ 32″ E

13-12-2021

Ravi River

Baloki Head Works

Lc-RR

30° 34' 59.99" N

71° 48' 59.99" E

23-01-2022

purity using the NanoDrop 2000c spectrophotometer (Thermo Scientific, US). The isolated DNA was diluted to a final concentration of approximately 50 ng/μL and then stored at -20°C for amplification with PCR.

Microsatellite genotyping

A total of 10 microsatellite loci were amplified with polymorphic microsatellite DNA markers already characterized for L. rohita by (Tripathy, 2018). The microsatellites used for the present study consisted are Lr-1, Lr-3, Lr-12, Lr-10, Lr-21, Lr-24, Lr-26, Lr-32, Lr-38, and Lr-46. The PCR amplification reactions consisted of 0.8 µl of dNTPs (10mM), 2.0 µl of 10x PCR buffer (20mM), 1.5 µl MgCl2 (20mM), 0.4 µl (2U/ µl) Taq polymerase (Sure Bio Diagnostic and Pharmaceutical),1µl forward and 1µl of reverse primer, 50ng (10mM) preserved DNA and14.1µl of ddH2Oup to final volume of 20µl.Thermal cycling (Cycler-25 CA. 95070, USA) conditions for each locus were DNA denaturation at 94°C for 2 min, followed by 32 cycles of 94°C for 2 min, annealing temperature for 2 min, and extension at 72°C for 1 min, and then a final extension of 72°C for 1 min (Table II). Genotyping data was analyzed by using different software’s.

Genetic data analysis

Genetic diversity

The assessment of genetic variation was conducted by employing fundamental summary statistics, such as expected heterozygosity (He), observed heterozygosity (Ho),

Table II. Information on microsatellite primers used in PCR.

S. No.	Locus No	Motif	Primer sequence	Gene bank accession No	Fragment size	Ta	NA
1	Lr-46	(CA)18	F-TGACGTATTGTCAACTATGGTG R- TCCACCTTCAATACCATGACTG	AM269536	190-240	58°C	4
2	Lr-32	--	F: GGC TCT CAG AAG ACC AGC G R: TCC CCT GCC GTT CTC TGA	AM231181	295 bp	60.05°C	4
3	Lr-38	(GT)12	F: ATAGCATCACCATCTGTTGGTG R: TCTGCTTCAGTCACTCAGCAC	AM269528	240-250bp	59°C	2
4	Lr-10	(CA)13	F-GATCTTCAGCGCCAGCGTG R-GAGGACCTGCCCAGCATG	AJ507523	240-250bp	60°C	4
5	Lr-21	(CA)11	F-GATCAGAGGGTCAATGTGG R-CAGCAGAGTACTATGGAAGA	AJ831436	176bp	58°C	6
6	Lr-24	(TG)17	F- CAA GGC GAA AAG TGT CCA T R-AGGAAATTGGTAAAGTGT TTC	AJ831438	170bp	56°C	5
7	Lr-26	(TG)8	F- CCAGGGAGCTGCTAAGAAT R- AGCGCTTCATGCAGTCTAC	AJ831439	151bp	56°C	5
8	Lr-1	(TG)14	F-GACCCTTAACCCTTGACCTT R-TGGGATAATGCAGGGAAAAC	AJ507518	171	58°C	2
9	Lr-3	(TG)19	F-ATCTGGCTGCCTATTCACC R-CATCGGCGACTGCACTGGA	AJ507520	152	58°C	5
10	Lr-12	(CA)13	F-CACCGCTGCTGTCCATCA R-AGGTCGGCCAGATACACG	AJ507524	161	58°C	4

F, forward; R, reverse; N, no. of alleles; Ta, primer specific annealing temperature.

number of alleles (MNA), allelic richness (Ar) and inbreeding coefficient (FIS). These statistics were estimated for each population across loci, and their respective 95% confidence intervals were calculated using POPGENE (ver. 1.31) and F-statistics (Cockerham and Weir, 1993) in FSTAT ver. 2.9.3.2 (Goudet, 2002).

Population structure

The genetic divergence among populations of Labeo species was assessed by calculating the FST values for all pairwise comparisons between sample locations, using the method proposed by Weir and Cockerham (1984). The statistical significance of the FST estimations was evaluated through the implementation of 10,000 permutations. The approach provided by GENEPOP ver. 4.2 (Rousset and Raymond, 1995) was used to test linkage disequilibrium (LD) for all pairs of populations of Labeo species. The evaluation of the divergence from Hardy-Weinberg equilibrium (HWE) at each locus was conducted using POPGENE software (version 1.31) (Yeh et al., 1999). The significance level of deviations from HWE and LD was adjusted using the sequential Bonferroni correction method in order to control the within-sample type-I error rate at α = 0.05 for each locus. The hierarchical partitioning of genetic diversity was estimated by doing an Analysis of Molecular Variance (AMOVA) using ARLEQUIN ver. 3.1. (Meirmans, 2012). POPGENE (ver. 1.31) was used to assess the UPGMA dendrogram based on Nei’s (1973) unbiased distance (Yeh et al., 1999). A total of one thousand random permutations were conducted in order to evaluate the statistical significance of each pairwise comparison. The species were categorized based on their respective geographic origins (Fig. 1).

The assessment of the pattern of genetic structures among species was conducted utilizing the Bayesian technique in Structure 2.3.4 (Pritchard et al., 2000). This analysis employed an admixture model and correlated allele frequency. A burn-in of 100,000 iterations was used, followed by 1,000,000 Markov chain-Monte Carlo (MCMC) repetitions. To ensure consistency and identify genetic clusters, 12 independent runs were performed for each K value (ranging from 1 to 12), where K represents the number of clusters. The determination of the maximum number of clusters was achieved by adding the number of species by a single one, hence enabling the identification of potential substructure (Liu et al., 2017). The evaluation of the optimal K genetic cluster was conducted by determining ΔK using the Evanno method (Evanno et al., 2005) in structure harvester (Earl and VonHoldt, 2012). Then, Clumpp 1.1.2 (Jakobsson and Rosenberg, 2007). The highest similarity coefficient was utilized to estimate and compare the clustering results over multiple runs for various values of K. The two-phased model (TPM) with 90% single step mutations with 1000 replications and the mode shift test were used to assess whether populations of the common carp had experienced recent bottlenecks by using Bottleneck v1.2.02 software (Yang et al., 2022). The bottleneck effects were estimated for each population of common carp species.

In order to analyze the gene flow patterns among populations, the relative migration rates were evaluated using the Gst statistic method proposed by (Nei, 1973). This analysis was conducted using the divMigrate-online software (Sundqvist et al., 2016), which can be accessed at https://popgen.shinyapps.io/divMigrate-online. Additionally, the estimation of asymmetric links between population pairs was performed with 1000 bootstrap iterations.

Results

Genetic diversity

Ten microsatellite loci (Lr-1, Lr-3, Lr-12, Lr-10, Lr-21, Lr-24, Lr-26, Lr-32, Lr-38, and Lr-46) analyzed in twelve populations (30 samples were taken from each population) of Labeo species (L. rohita, L. gonious and L. calbasu) were found to be polymorphic, inheriting with Mendelian mode. The average number of alleles in all populations of each species of Labeo ranged from 4.0 to 5.7 as given in the Table III. The maximum value of Ar (5.700, 5.500, and 4.900) was found in the IR population of L. rohita, L. gonious and L. calbasu, respectively.

Table III. Genetic diversity parameters of four populations at ten microsatellite loci of L. rohita, L. gonious and L. calbasu.

Species	Pop.	Para-meters	Lr-46	Lr-32	Lr-38	Lr-10	Lr-21	Lr-24	Lr-26	Lr-1	Lr-3	Lr-12	Average
L. rohita	IR	Na	7	4	5	7	5	7	6	5	6	5	5.7
		Ar	7	4	5	7	5	7	6	5	6	5	5.7
		Ho	0.5	0.367	0.4	0.533	0.367	0.467	0.4	0.4	0.367	0.433	0.423
		He	0.869	0.767	0.802	0.875	0.716	0.798	0.826	0.76	0.836	0.803	0.805
		FIS	0.465	0.565	0.505	0.465	0.492	0.419	0.559	0.514	0.565	0.464	0.501
		Ne	6.622	3.92	4.724	6.7	3.377	4.639	5.035	3.875	5.607	4.74	4.925
	CR	Na	5	6	3	6	5	5	4	4	5	5	4.8
		Ar	5	6	3	6	5	5	4	4	5	5	4.8
		Ho	0.433	0.367	0.267	0.367	0.3	0.367	0.4	0.333	0.4	0.367	0.36
		He	0.79	0.824	0.633	0.806	0.746	0.778	0.755	0.757	0.794	0.803	0.769
		FIS	0.495	0.559	0.583	0.549	0.602	0.533	0.475	0.564	0.501	0.585	0.545
		Ne	4.279	5.263	2.651	4.825	3.75	4.255	3.887	3.913	4.568	4.595	4.199
	JR	Na	7	5	4	6	5	5	4	5	5	5	5.1
		Ar	7	5	4	6	5	5	4	5	5	5	5.1
		Ho	0.333	0.3	0.233	0.4	0.267	0.333	0.333	0.367	0.333	0.333	0.323
		He	0.81	0.784	0.708	0.826	0.79	0.788	0.769	0.782	0.793	0.799	0.785
		FIS	0.63	0.701	0.674	0.558	0.666	0.581	0.609	0.574	0.584	0.587	0.616
		Ne	4.778	4.032	3.29	5.097	4.477	4.444	3.976	4.184	4.534	4.675	4.348
	RR	Na	5	3	5	6	4	4	4	4	5	5	4.5
		Ar	5	3	5	6	4	4	4	4	5	5	4.5
		Ho	0.4	0.3	0.367	0.367	0.267	0.333	0.3	0.367	0.367	0.433	0.35
		He	0.779	0.683	0.796	0.824	0.735	0.75	0.751	0.716	0.812	0.75	0.76
		FIS	0.528	0.608	0.544	0.559	0.68	0.56	0.643	0.492	0.628	0.468	0.571
		Ne	4.132	2.925	4.603	5.263	3.541	3.813	3.738	3.377	4.625	3.617	3.963
L. gonious	IR	Na	6	5	7	6	6	5	7	5	4	4	5.5
L. gonious		Ar	6	5	7	6	6	5	7	5	4	4	5.5
		Ho	0.414	0.414	0.367	0.4	0.414	0.414	0.433	0.379	0.5	0.4	0.413
									Table contibued on next page..............
Species	Pop.	Para-meters	Lr-46	Lr-32	Lr-38	Lr-10	Lr-21	Lr-24	Lr-26	Lr-1	Lr-3	Lr-12	Average
		He	0.829	0.748	0.824	0.814	0.782	0.769	0.823	0.779	0.714	0.758	0.784
		FIS	0.52	0.471	0.559	0.513	0.499	0.491	0.478	0.539	0.365	0.476	0.491
		Ne	5.391	3.779	5.278	5.013	4.312	4.092	5.247	4.258	3.35	3.921	4.464
	CR	Na	6	6	6	4	5	4	6	4	4	5	5
		Ar	6	6	6	4	5	4	6	4	4	5	5
		Ho	0.448	0.414	0.429	0.448	0.379	0.517	0.483	0.433	0.414	0.448	0.441
		He	0.825	0.796	0.803	0.711	0.728	0.673	0.809	0.737	0.755	0.733	0.757
		FIS	0.481	0.504	0.507	0.386	0.506	0.275	0.431	0.461	0.471	0.423	0.445
		Ne	5.272	4.595	4.722	3.317	3.511	2.956	4.875	3.636	3.875	3.571	4.033
	JR	Na	4	5	5	3	5	4	3	5	5	4	4.3
		Ar	4	5	5	3	5	4	3	5	5	4	4.3
		Ho	0.433	0.367	0.345	0.367	0.393	0.433	0.414	0.379	0.483	0.467	0.408
		He	0.732	0.807	0.8	0.677	0.763	0.752	0.652	0.804	0.794	0.731	0.751
		FIS	0.412	0.55	0.587	0.463	0.522	0.428	0.382	0.548	0.418	0.365	0.468
		Ne	3.564	4.838	4.685	2.995	3.989	3.838	2.78	4.764	4.558	3.55	3.956
	RR	Na	6	3	5	4	3	5	5	4	5	4	4.4
		Ar	6	3	5	4	3	5	5	4	5	4	4.4
		Ho	0.414	0.448	0.433	0.345	0.467	0.4	0.379	0.414	0.464	0.467	0.423
		He	0.829	0.668	0.786	0.759	0.64	0.785	0.789	0.728	0.747	0.761	0.749
		FIS	0.521	0.354	0.453	0.565	0.274	0.495	0.539	0.45	0.431	0.391	0.447
		Ne	5.391	2.915	4.401	3.939	2.698	4.379	4.449	3.511	3.76	3.973	3.941
L. calbasu	IR	Na	6	5	5	5	7	4	4	4	5	4	4.9
		Ar	6	5	5	5	7	4	4	4	5	4	4.9
		Ho	0.5	0.567	0.467	0.533	0.467	0.467	0.5	0.467	0.433	0.467	0.487
		He	0.787	0.799	0.798	0.79	0.843	0.764	0.74	0.684	0.787	0.75	0.774
		FIS	0.442	0.331	0.458	0.329	0.489	0.431	0.365	0.322	0.489	0.418	0.407
		Ne	4.17	4.485	4.438	4.488	5.532	3.875	3.548	3.056	3.346	3.696	4.163
	CR	Na	5	4	4	5	5	5	4	5	6	4	4.7
		Ar	5	4	4	5	5	5	4	5	6	4	4.7
		Ho	0.467	0.433	0.5	0.567	0.433	0.533	0.467	0.433	0.467	0.500	0.480
		He	0.75	0.758	0.738	0.8	0.788	0.798	0.755	0.797	0.838	0.758	0.778
		FIS	0.382	0.471	0.36	0.329	0.491	0.374	0.386	0.496	0.483	0.379	0.415
		Ne	3.805	3.771	3.571	4.583	4.301	4.438	3.887	4.521	5.496	3.84	4.221
	JR	Na	5	4	4	3	4	4	5	4	4	5	4.2
		Ar	5	4	4	3	4	4	5	4	4	5	4.2
		Ho	0.467	0.467	0.5	0.567	0.4	0.467	0.433	0.467	0.4	0.533	0.470
		He	0.758	0.762	0.742	0.674	0.759	0.759	0.788	0.709	0.707	0.772	0.743
		FIS	0.425	0.391	0.33	0.202	0.477	0.428	0.454	0.384	0.438	0.351	0.388
		Ne	3.796	3.982	3.696	2.836	3.938	3.771	4.444	3.191	3.278	3.976	3.691
	RR	Na	6	4	3	5	4	3	4	3	5	3	4
		Ar	6	4	3	5	4	3	4	3	5	3	4
		Ho	0.433	0.4	0.533	0.533	0.467	0.433	0.533	0.433	0.467	0.467	0.470
		He	0.805	0.743	0.66	0.727	0.681	0.666	0.773	0.666	0.819	0.673	0.721
		FIS	0.502	0.503	0.232	0.27	0.353	0.353	0.388	0.389	0.507	0.31	0.381
		Ne	4.659	3.617	2.757	3.425	2.966	2.898	3.91	2.826	4.839	2.955	3.485

Na, number of alleles per locus; Ar, Allelic Richness per locus and population; Ne, Effective number of alleles; Ho, Observed Heterozygosity; He, Expected Heterozygosity; FIS, Coefficient of Inbreeding. For details of populations see, Table I.

Table IV. Species deviation from Hardy-Weinberg Equilibrium at all loci.

Species	Populations	Locus
Species	Populations	Lr-46	Lr-32	Lr-38	Lr-10	Lr-21	Lr-24	Lr-26	Lr-1	Lr-3	Lr-12
L. rohita	IR	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	CR	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	JR	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	RR	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
L. gonious	IR	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.001	0.000
	CR	0.000	0.000	0.000	0.005	0.000	0.000	0.000	0.000	0.000	0.000
	JR	0.000	0.000	0.000	0.000	0.000	0.000	0.001	0.000	0.000	0.000
	RR	0.000	0.003	0.000	0.000	0.002	0.000	0.000	0.000	0.000	0.000
L. calbasu	IR	0.000	0.000	0.000	0.000	0.000	0.000	0.001	0.004	0.000	0.000
	CR	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	JR	0.000	0.000	0.000	0.526	0.000	0.000	0.000	0.002	0.000	0.000
	RR	0.000	0.000	0.104	0.000	0.017	0.012	0.000	0.415	0.000	0.099

For details of populations see, Table I.

The average observed values of heterozygosity Ho in L. rohita ranged from 0.323(JR) to 0.423(IR). While in L. gonious these values ranged from 0.408 (JR) to 0.423(RR). And in L. calbasu these values of Ho ranged from 0.470(RR) to 0.487(IR). Ho at all loci in overall populations of three species was lower than the corresponding He. The mean values of FIS in L. rohita varied from 0.501 to 0.616, in L. gonious from 0.445 to 0.491, and in L. calbasu from 0.381 to 0.415. The JR population had the highest FIS value in L. rohita, whereas the IR population had the highest FIS value in L. gonious, and the CR population had the highest FIS value in L. calbasu (Table III).

Testing was done to observe HWE (Table IV) and genotypic LD (Table V) deviations. The χ2 results indicate significant variations in most loci across all populations. The majority of tests showed considerable variance. All populations demonstrated non-significant departures from HWE at some loci. Half of the ten microsatellite loci pairs in all populations showed significant LD values (P<0.05) out of 45 comparisons using GENEPOP v.4.2 (Rousset, 2008) with Markov chain parameters (dememorization 10,000, batches 1000, iterations 10,000 per batch). A sequential Bonferroni correction was performed to HWE and genotypic LD tests to adjust for multiple comparisons. Any locus-pair combination had no significant LD after Bonferroni correction for multiple testing.

Population structure and differentiation

The population genetic differentiation was analyzed by pair-wise comparison of each population as given in the Table VI. The value of FST among all the populations of Labeo species ranged from 0.027 (between Lr-CR and Lc-IR) to 0.225 (between Lc-RR and Lg-JR). The highest value of FST was observed between Lc-RR and Lg-JR (FST=0.225). While the lowest value was observed between Lr-CR and LC-RR (FST=0.027). Nei’s standard unbiased genetic distances between pairs of populations of Labeo species varied from 0.089 to 0.456. The smallest distance was observed between Lc.IR and Lc.CR of L.calbasu and the largest was between populations Lg.JR and Lg.RR of L.gonious (Table VII).

The UPGMA dendrogram grouped the twelve populations into two major clusters as given in the Figure 2. The first major cluster includes the populations of L. rohita and L. Calbasu. While the second cluster include the populations of L. gonious. Cluster one is further subdivided into 2 sub clusters. 1st sub cluster include the four populations of L. rohita and the 2nd sub cluster include the populations of L. calbasu and also the JR population of Labeo gonious. While the cluster 2 includes the IR, CR and RR populations of L. gonious.

AMOVA results (Table VIII) show that 9.185% variation among the species, 6.097% variation among populations or within species and 84.718% variation was found within populations of Labeo species. The results proved that the majority of variations were from the intra-population diversity. The logarithm probabilities, denoted as Ln p (X/K), obtained from the initial structure run and associated with various genetic cluster numbers K, were computed using a structure analysis of 360 individuals. The results indicated that the highest value

Table VI. Pairwise measures of population differentiation (FST) between populations of Labeo species across all the ten loci.

Species/Pop.		L. rohita				L. gonious				L. calbasu
Species/Pop.		IR	CR	JR	RR	IR	CR	JR	RR	IR	CR	JR	RR
L. rohita	IR	0
	CR	0.027*
	JR	0.043*	0.039*	0
	RR	0.042*	0.047*	0.313	0
L. gonious	IR	0.180	0.190	0.189	0.199	0
	CR	0.181	0.192	0.192	0.200	0.044*	0
	JR	0.195	0.208	0.209	0.221	0.079	0.081	0
	RR	0.115	0.120	0.110	0.122	0.079	0.090	0.099	0
L. calbasu	IR	0.139	0.140	0.123	0.141	0.189	0.195	0.200	0.045*	0
	CR	0.131	0.132	0.111	0.126	0.184	0.185	0.198	0.055	0.016	0
	JR	0.148	0.144	0.134	0.141	0.193	0.178	0.217	0.088	0.091	0.089	0
	RR	0.146	0.148	0.148	0.153	0.203	0.193	0.225	0.085	0.080	0.081	0.083	0

*P < 0.05 and NS, Not Significant. For details of populations see, Table I.

was observed at K = 3 (Fig. 3A), followed by K = 11 (Fig. 3B). The observed dissimilarities among individuals from different species were found to be statistically significant, as indicated by the value of K = 11 (Fig. 3B).

Bottleneck effect and contemporary gene flow

The impacts of the bottleneck were evaluated for each population of Labeo species using the two-phased model (TPM) with 90% single step mutations and 1000 replications as well as the mode shift test (Table IX). In some Labeo species populations, a high Ho compared to He level

Table VII. Comparison of all the four populations of Labeo species, based on the genetic distance and identity.

Species compared	Populations	Biased		Unbiased
Species compared	Populations	Dist.	Ident.	Dist.	Ident.
L. rohita	IR vs. CR	0.165	0.847	0.136	0.872
	IR vs. JR	0.258	0.772	0.227	0.796
	IR vs. RR	0.229	0.795	0.200	0.818
	CR vs. JR	0.214	0.806	0.187	0.829
	CR vs. RR	0.226	0.797	0.201	0.817
	JR vs. RR	0.176	0.838	0.149	0.860
L. gonious	IR vs. CR	0.235	0.789	0.208	0.811
	IR vs. JR	0.403	0.668	0.376	0.686
	IR vs. RR	0.353	0.702	0.326	0.721
	CR vs. JR	0.383	0.681	0.358	0.698
	CR vs. RR	0.423	0.654	0.398	0.671
	JR vs. RR	0.480	0.618	0.456	0.633
L. calbasu	IR vs. CR	0.116	0.890	0.089	0.914
	IR vs. JR	0.439	0.644	0.414	0.660
	IR vs. RR	0.340	0.711	0.316	0.728
	CR vs. JR	0.422	0.655	0.397	0.672
	CR vs. RR	0.353	0.702	0.329	0.719
	JR vs. RR	0.330	0.718	0.309	0.734

For details of populations see Table I.

Table VIII. Hierarchal AMOVA analysis of populations of L. rohita, L. gonious and L. calbasu species.

Source of variation	Sum of squares	Variance component	Percentage variation
Among species	264.937	0.424	9.185
Among populations or within species	197.313	0.281	6.097
Within Populations	3003.517	3.911	84.718
Total	3465.767	4.616

raises the hypothesis that some populations may be experiencing a bottleneck. Under the two-phase mutation model, the presence of notable but non-significant heterozygosity excess (p<0.05) in the L. calbasu populations (Lc-JR, Lc-JR Lc-JR and Lc-RR) across all loci indicates that these populations have also undergone a bottleneck event. However, conversely, according to the bottleneck test, there was notable significant (p>0.05) excess of heterozygosity seen in the L. rohita and L. gonius populations indicated that no recent genetic bottleneck had occurred in any population. The observed ratio (10:0) exhibited a notable deviation from the anticipated ratio (1:1) just in the context of non-bottlenecked, equilibrium populations, as determined by the Wilcoxon test (Sign test: P = 0.133; Wilcoxon test: One tail for Hex, P= 0.042) in Lc-JR and (Sign test: P= 0.128; Wilcoxon test: one tail for Hex, P = 0.043) in Lc-RR population. In contrast, the datasets obtained from the CR and JR populations of L. gonious did not exhibit a significant excess of heterozygosity (Sign test: P = 0.280 and Wilcoxon test: one tail for Hex, P = 0.067 in Lg-CR; Sign test: P = 0.250 and Wilcoxon test: one tail for Hex, P = 0.063 in Lg-JR) deviated toward an excess of heterozygosity, as expected for bottlenecked populations (nearly significant). The data sets from the Lr-IR and Lr-CR populations of L. rohita have significant excess of heterozygosity (Hex/Hd=5/5).

Table IX. Heterozygosity excess under two-phase mutation model at ten microsatellite loci in each of the twelve populations of Labeo species.

Species/ Populations		Hex/Hd	Sign test P	Wilcoxon test P (One tail for Hex)
L. rohita	IR	5/5	0.420	0.942
	CR	5/5	0.400	0.938
	JR	6/4	0.340	0.921
	RR	7/3	0.330	0.900
L. gonious	IR	7/3	0.310	0.070
	CR	8/2	0.280	0.067
	JR	9/1	0.250	0.063
	RR	8/2	0.200	0.060
L. calbasu	IR	9/1	0.200	0.035
	CR	9/1	0.150	0.038
	JR	10/0	0.133	0.042
	RR	10/0	0.128	0.043

P; probability, Hex; heterozygosity excess, Hd; heterozygosity deficiency.

The directed relative migratory network was established by utilizing a dataset consisting of 12 populations from three distinct species of Labeo, as outlined in Table X. The analysis of the directed relative migration network for the Labeo species under investigation revealed that the Lr-RR population served as the central hub, exhibiting a substantial degree of genetic interchange with other populations, namely Lr-CR, Lr-JR, Lr-RR, and Lr-IR. This is evident from the presence of directional migration connections in the relative migration networks. There was no discernible presence of an asymmetric migration pattern, as indicated by Figure 5. The lowest observed relative migration was recorded between Lg-JR and Lg-RR, with a value of 0.001. Conversely, the highest relative migration value was seen between Lr-RR and Lr-CR, with a value of 1.00.

Table X. The directional relative migration of 12 populations of three species of Labeo.

Species population		L. rohita				L. gonious				L. calbasu
Species population		IR	CR	JR	RR	IR	CR	JR	RR	IR	CR	JR	RR
L. rohita	IR	0.000	0.137	0.082	0.023	0.005	0.018	0.004	0.003	0.008	0.01	0.008	0.013
	CR	0.256	0.000	0.103	0.090	0.011	0.016*	0.006	0.010	0.023	0.028	0.021	0.042
	JR	0.138	0.030	0.000	0.031	0.004	0.011	0.003	0.004	0.02	0.026	0.020	0.021
	RR	0.928*	1.000*	0.871*	0.000	0.004	0.015*	0.002	0.005	0.016	0.047	0.030	0.048
L. gonious	IR	0.004	0.003	0.003	0.003	0.000	0.059	0.02	0.109	0.003	0.003	0.002	0.004
	CR	0.005	0.004	0.004	0.003	0.051	0.000	0.012	0.02	0.002	0.002	0.002	0.003
	JR	0.003	0.003	0.001	0.001	0.051	0.049	0.000	0.043	0.007	0.003	0.003	0.003
	RR	0.007	0.006	0.005	0.007	0.189	0.019	0.016	0.000	0.002	0.004	0.002	0.007
L. calbasu	IR	0.010	0.010	0.011	0.010	0.004	0.004	0.012	0.003	0.000	0.054	0.022	0.025
	CR	0.013	0.012	0.021	0.013	0.003	0.005	0.003	0.002	0.260	0.000	0.020	0.020
	JR	0.012	0.012	0.012	0.012	0.004	0.004	0.004	0.003	0.028	0.031	0.000	0.110
	RR	0.019	0.019	0.016	0.019	0.007	0.009	0.005	0.007	0.068	0.075	0.115	0.000

Discussion

The fishing sector in Pakistan has experienced a sudden decline as a result of human interventions, such as habitat degradation, excessive exploitation of fish resources, hydrological modifications, and the detrimental impacts of hatchery practices on wild populations. The preservation of evolutionary potential necessitates and the maintenance of allelic differences in order to achieve its ultimate objective. This phenomenon not only facilitates the adaptation of Labeo species populations to potential environmental changes, but also serves as a assurance for the enhancement of selective breeding operations in hatchery populations. In the present study, 30 individuals for each L. rohita, L. calbasu and L. gonious species were collected from Indus River, Chenab River, Jhelum River and Ravi River of Punjab, Pakistan to determine their genetic diversity and population structure by using the 10 microsatellite markers.

Genetic diversity

In this study, a total of 10 different microsatellite DNA loci derived were utilized to conduct a genetic analysis of three fish species, namely L. rohita, L. gonious, and L. calbasu. Based on the criterion of a 0.95 percent allele frequency, it was observed that all of the microsatellite loci exhibited polymorphism. This is similar with the findings of Alam et al. (2009), who used four microsatellite loci (Lr3, Lr12, Lr14a, and Lr21) to analyze the genetic makeup of L. rohita, a species that was collected from three significant rivers in Bangladesh (River Halda, River Jamuna, and river Padma). It was revealed that all of the loci exhibited polymorphism. According to the study conducted by Wang et al. (2021) the use of microsatellite markers provide a significant and comprehensive insights in the context of fish populations management. Sahoo et al. (2022a) successfully isolated and characterized the microsatellite loci in L. rohita. These researchers also effectively amplify these loci across different species of carps, demonstrating the polymorphism characteristics of the microsatellite loci employed in this particular investigation. In another study, from a total of 25 individuals of L. rohita, Patel et al. (2009) produced 21 polymorphic microsatellite loci and documented the polymorphism nature of microsatellite loci in L. rohita.

In the present study, the number of alleles ranged from 3 to 7 for Indus River, Chenab River, Jhelum River and Ravi River populations of L. rohita, L. gonious and L. gonious species. The maximum average number of alleles per locus across all the populations were 5.7 in L. rohita, 5.5 in L. gonious and 4.9 in L. calbasu. Except for some populations, the range of potential allele sizes was comparable to the information acquired from the first established loci (Sahoo et al., 2022b). The variation in the size and number of alleles scored in the present study could be attributed to the difference in populations and the sampling. In the present study, the average Ho ranged between 0.323 - 0.423 in L. rohita, 0.408-0.423 in L. gonious and 0.470-0.487 in L. calbasu that is comparable with the Ho (0.4466–0.7761) reported by Singh et al. (2012). Over all, the values of the Ho in RR populations were found lower than those in the IR populations of L. rohita, L. gonious and L. calbasu species.

In present study, for 30% of the riverine the population, the departure from HWE was highly significant. Numerous species of freshwater fishes were found to deviate significantly from their HWE (Inoue and Berg, 2017). The lack of heterozygotes in natural populations may be the result of non-random sampling, inbreeding, intra-population structure, genetic drift, fishing pressure, or any combination of the aforementioned reasons (Qadeer and Abbas, 2017). In the current investigation, departures from HWE could mostly be attributable to deficits in the number of heterozygotes. Alam et al. (2009) evaluated the genetic variation in three riverine populations and one hatchery population of C. catla was assessed by employing eight microsatellite loci. The findings of this study revealed notable deviations from HWE, primarily attributed to heterozygote deficiency. In light of the extensive environmental dangers and anthropogenic activities in the riverine system of Pakistan, a significant decrease in fish biodiversity in the country’s rivers has been observed throughout the latter part of the previous century. The potential cause of heterozygote deficit in riverine populations could be attributed to migration-drift disequilibrium, which arises from environmental dangers and hydrological modifications in natural water bodies. The values of FIS indicated that all the populations were not homogenous. The highest rate of inbreeding was observed in JR population of L. rohita, IR population of L. gonious and CR population of L. calbasu and the lowest in IR population of L. rohita, CR population of L. gonious and RR population of L. calbasu.

Genetic differentiation and population structure

We found a low level of genetic differentiation among L. rohita populations (Fst < 0.05), which provided evidence of widespread gene flow across the sampling populations. While FST values for L. gonius L. calbasu are found to be non-significant which indicated lack of heterogeneity in the populations of these species. The low pairwise FST values (0.027) obtained for Lr-CR in this study were likely the concomitant result of high heterozygosity detected within populations. The findings of the current study correspond with the research conducted by Napora-Rutkowski et al. (2017), which investigated the extent of genetic variation in various species of common carp in Bangladesh through the utilization of microsatellite DNA markers. AMOVA analysis demonstrated significant genetic structure of L. rohita, L .gonious and L. calbasu in the sample populations. AMOVA demonstrated that the level of genetic diversity within populations exceeded that observed between populations in all of the riverine populations. This study also revealed a notable presence of genetic structure among the populations that were investigated, with the majority of the observed variance being attributed to differences between groups.

The UPGMA dendrogram based on genetic distance revealed genetic relation of the sample populations. As evident from the Figure 2, it produced two major clusters, categorizing the populations into two population groups. The populations IR, and CR appeared as one group while the JR and RR clustered into the second group in L. rohita and L. calbasu species, while in L. gonious IR, CR and RR clustered in one group and JR population appeared to be separate.

The genetic differentiation between the distant riverine populations is caused by the non-migratory behavior of the species and the presence of non-crossable barriers. The results of the structure-based analysis (Fig. 4A) revealed that the largest ∆K value was observed at K=3 indicating significant differentiation across Labeo species. Significant differences were also seen among individuals from different species, as determined by the value of K= 11 (Fig. 4B). It is widely recognized that the preservation of a stable polymorphism is contingent upon the presence of a heterozygous advantage, specifically an overweight effect. The obtained outcome can serve as empirical support for a comparatively substantial genetic diversification among typical Labeo species.

Genetic bottleneck and contemporary gene flow

Genetic bottlenecks are likely to occur from the loss of genetic variation, reduced adaptive potential, and probability of persisting in particular populations because of habitat fragmentation and increasing insularization. Once Ho exceeds anticipated heterozygosity, a population bottleneck may be observed. The examination of mutation-drift equilibrium as a means of identifying genetic bottlenecks indicated a population reduction among the species. Populations with a notable surplus of heterozygosity were predominantly those that have experienced the most severe and extensively documented bottlenecks.

The Ho decreases in L. calbasu populations are likely attributable to a combination of deterministic causes, including but not limited to excessive harvesting, contamination of water, loss of habitat, and alterations to water bodies through engineering activities. The presence of notable non-significant heterozygosity excess (p<0.05) in the L. calbasu populations (Lc-JR, Lc-JR Lc-JR and Lc-RR) across all loci indicates that these populations have also undergone a bottleneck event. However, conversely, according to the bottleneck test, there was notable significant (p<0.05) excess of heterozygosity seen in the L. rohita and L. gonius populations indicated that no recent genetic bottleneck had occurred in any population. The findings of the current study align with the study conducted by Saha et al. (2010) which investigated the bottleneck phenomenon in the endangered kalibaus, L. calbasu (cyprinidae: cypriniformes) populations in Bangladesh using microsatellite DNA markers. Fazzi-Gomes et al. (2021) also found a loss of genetic diversity and high inbreeding rates in farmed populations of the fish Arapaima gigas throughout the Amazon basin due to genetic bottlenecks caused by the domestication process and founding effects.

Gene flow increases the genetic variation within a population; however, it tends to make populations genetically similar to each other (Dudu et al., 2015) which is driven by individual movement. The directional relative migration network for the studied Labeo species indicated that Lr-RR is core population that had a high level of genetic exchange with other populations (Lr-JR and Lr-RR, Lr-IR) (i.e., migration in directional relative migration networks). No significant asymmetric migration pattern was detected. The lowest relative migration found between Lg-JRand Lg-RR (0.001) and the highest value between Lr-RR and Lr-CR (1.00). As other riverine cyprinid species, L. rohita migrates upstream in riverine system for spawning and downstream for feeding and nursing. Zhu et al. (2022) described the different relative directional migration rates (0.23–1.00) in bighead carps populations of the Yangtze River indicating the presence of both directional gene flows.

Regarding the increasing significance of aquaculture to the availability of aquatic products, ensuring genetic diversity of aquatic organisms has become a key concern. In the current study, the utilization of microsatellite markers demonstrated their efficacy and dependability as tools for assessing genetic diversity in L. rohita, L. gonius, and L. calbasu. The current research study’s findings would serve as foundational data for the genetic management of Labeo species in the populations of the Indus River, Chenab River, Jhelum River, and Ravi River. The necessity of maintaining a pollution-free environment and preventing inbreeding has been widely acknowledged as essential for ensuring the genetic sustainability of the riverine system in Punjab, Pakistan. The simultaneous advancement in both areas will offer essential maintaining of Labeo species’ population from genetic deterioration, so ensuring the conservation of genetic variability and attaining sustainable fisheries stocks.

Conclusions

The study suggests that Labeo species in Punjab, Pakistan’s riverine system had low to moderate genetic diversity, giving a baseline for conservation and exploitation. We successfully characterized genetic differences within and across Labeo species using microsatellite loci. The average number of alleles in all populations of each species of Labeo ranged from 4.0 to 5.7. In L. rohita, L. calbasu, and L. gonious, the observed heterozygosity ranged from 0.323 to 0.423, 0.408 to 0.423, and 0.470 to 0.487, respectively. Overall, Labeo species exhibit low to moderate level of genetic diversity. Structure clustered the Labeo species were into 11 categories using Bayesian analysis. Genetic evidence of mixing was absent in pure species. There is significant gene flow among L. rohita populations. The L. calbasu showed a bottleneck event due to non-significant heterozygosity excess (p<0.05) at all loci. The bottleneck test shows no recent genetic bottlenecks in L. rohita and L. gonius (p>0.05). We recommend using larvae from licensed native breeding farms and an effective number of parents for enhancement and restocking to reduce anthropogenic effects e.g. genetic shift and inbreeding) on natural resources. Due to the slight but significant genetic divergence between some population combinations, we propose cross-breeding for genetic enhancement and output, but oppose releasing offspring into native rivers. Working with more polymorphic markers and performing broad geographically diverse sample could help us better understand the genetic problems relating to L. rohita, L. calbasu, and L. gonious species. The application of molecular markers in this instance could act as a standard for the genetic maintenance of other aquaculture species.

DeclarationS

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

IRB approval

This experimental study was approved and reviewed by the Department of Animal Welfare and Ethical Committee of Department of Zoology, Government Sadiq College Women University, Bahawalpur 63100, Pakistan.

Statement of conflict of interest

The authors have declared no conflict of interest.

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