Genetica (2014) 142:11–21 DOI 10.1007/s10709-013-9750-5
Genetic diversity of four populations of Qualea grandiflora Mart. in fragments of the Brazilian Cerrado Lia Maris Orth Ritter Antiqueira Paulo Yoshio Kageyama
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Received: 25 June 2013 / Accepted: 11 December 2013 / Published online: 19 December 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract We analyzed the genetic structure and diversity of Qualea grandiflora Mart., the most abundant woody species in the Brazilian Cerrado. Eight microsatellite loci were used to analyze samples from four populations subjected to different types of anthropic pressure, distributed throughout the state of Sa˜o Paulo in the regions of Assis, Brotas, Itirapina and Pedregulho. Results indicated a mean number of 12 alleles per locus, but only six effective alleles. Alleles private to particular populations and rare alleles were also detected. An excess of homozygotes and moderate levels of inbreeding were observed. No clones were identified. All populations departed from Hardy– Weinberg equilibrium (p \ 0.05). Spatial structure was observed in the distribution of specimens in distance classes ranging from 30 to 40 km and three genetic clusters were identified, with genotypes in the Pedregulho population differing from the others by up to 90 %. The influence of the Wahlund effect on the studied populations lies between 8.5 and 53.3 %. Estimates of effective population size were low (\10), and the minimum viable area for conservation in the short-, medium- and long-term was estimated to be between 4 and 184 ha. Gene flow was high enough to counter the effects of genetic drift. The genetic
L. M. O. R. Antiqueira (&) Departamento de Gene´tica, Escola Superior de Agricultura Luiz de Queiroz (ESALQ), Universidade de Sa˜o Paulo (USP), Av. Pa´dua Dias, 11, Piracicaba, Sa˜o Paulo 13418-900, Brazil e-mail: [email protected] P. Y. Kageyama Departamento de Cieˆncias Florestais, Escola Superior de Agricultura Luiz de Queiroz (ESALQ), Universidade de Sa˜o Paulo (USP), Av. Pa´dua Dias, 11, Piracicaba, Sa˜o Paulo 13418-900, Brazil
diversity and divergence between the studied populations indicated that the Pedregulho population should be considered an Evolutionary Significant Unit and a Management Unit. Keywords Conservation genetics Evolutionary Significant Unit Landscape genetics Management Unit Microsatellites Vochysiaceae
Introduction The Brazilian Cerrado contains a wide diversity of landscapes and vegetation physiognomies associated with specific physical and physiographic characteristics. Its vascular flora is estimated to be 44 % endemic (Silva and Bates 2002; Felfili et al. 2005). The Cerrado is also one of the most threatened biodiversity hotspots on Earth, and a primary target for conservation efforts (Myers et al. 2000; Mittermeier et al. 2005). Although they cover only 2.3 % of the Earth’s surface, biodiversity hotspots are home to over 60 % of life on the planet (Conservation International 2005). Recent studies suggest that approximately 80 % of the Brazilian Cerrado is under some degree of anthropic pressure, and only 432,814 km2 of the Cerrado remain intact (Conservation International 2005; Klink and Machado 2005). Less than 4 % of the intact area is protected by Conservation Units (CUs). Conservation Units consist of parks, forests, biological reserves and wildlife sanctuaries instituted by the Brazilian government. They are classified as integral protection and sustainable use areas, regulated by pertinent legislation. The natural resources and environmental characteristics of these regions play an important role in ensuring the
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conservation of significant and ecologically viable samples of different populations, habitats and biomes in the national territory or territorial waters, preserving the biological diversity in Brazil (Brasil 2006, 2010). Many CUs can be considered Evolutionary Significant Units (ESUs) or Management Units (MUs). The concept of ESUs was introduced by Ryder (1986) in the context of conservation efforts and captive breeding programs. Although ESUs are traditionally defined using molecular genetic data, they may also be identified using ecological or behavioral information. An efficient way to identify ESUs is by assessing population genetic structure (Stockwell et al. 1998). Recently developed molecular genetics techniques (Avise 1994) have also contributed to the identification of ESUs, especially in allopatric populations of poorly studied species. The MUs are populations of conspecifics with low connectivity which must be monitored and managed separately (Taylor and Dizon 1999). According to Pasboll et al. (2007), the identification of MUs is key for conservation efforts, as it allows for the identification of risk groups for monitoring purposes, and helps regulate the effects of human activity on species and populations. However, certain measures must be taken to ensure units are adequately identified. Assessments based on a single parameter (such as restricted gene flow between populations) may lead to biased estimates, which do not account for geographically close populations. To avoid bias, factors such as divergence rates among populations, allele frequencies and landscape features that influence fragment connectivity must also be studied. In this way, knowledge of the genetic structure and diversity in populations plays an important role in the assessment of variation in natural populations, and helps identify significant units for conservation (Crozier et al. 1997; Hitchings and Beebee 1997; Frankham 2003; Brede and Beebee 2004; Frankham 2005). It is also important to consider the genetic consequences of habitat fragmentation on different species, as these factors have led the scientific community to question the genetic vulnerability of populations and the strategies involved in the conservation of endangered species (Antiqueira 2013). Conservation and landscape genetics studies often focus on rare and endangered species, in the hope that the genetic information collected may help ensure the continued survival of the species. However, according to Diniz Filho and Telles (2006), the study of abundant and widely distributed species may also provide relevant information regarding the influence of habitat fragmentation and intensive anthropic actions on population genetic structure. Qualea grandiflora Mart. (Vochysiacae) is the most widely distributed woody species in the Cerrado (Ratter et al. 2003). It grows in areas ranging from open spaces to forests (Felfili and Silva Jr 1993), with seeds and seedlings
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sprouting in a variety of light and soil conditions (Felippe 1990). Plants are pollinated by Sphingidae moths (Silberbauer-Gottsberger and Gottsberger 1975) and seeds can travel long distances through anemochorous dispersal. Q. grandiflora also has a number of pharmaceutical applications, and is popularly used as an antiseptic and antimicrobial agent. It is also used in the textile industry for dye production, and in the furniture industry (Almeida et al. 1998; Ayres et al. 2008). Using Q. grandiflora as a model, the present study characterized the intra and interpopulational genetic variability in four populations subjected to different types of anthropic pressure. The present study aimed to answer the following questions: (1) Does geographic distance influence genetic differentiation of populations? (2) Should areas be considered ESUs or MUs?
Materials and methods Study area and sampling The present study was conducted in four Cerrado areas in the state of Sa˜o Paulo (Table 1). Two of these areas (Assis and Itirapina) were located in CUs managed by the Sa˜o Paulo State Forest Institute. The region of Assis, located near the Horto Florestal dam, is vulnerable to invasion by Urochloa spp (Pinheiro and Durigan 2009) and surrounded by sugarcane plantations, grasslands, and plantations of Pinus and Eucalyptus. The Itirapina region and the private property of Brotas (Marimbondo Farm) have similar characteristics. However, the private property is under the most intense anthropic pressure, where the fragment area is often used as grazing land. Of the four regions studied, only one—the Pedregulho site (Furnas do Bom Jesus State Park, Decree number 30.591, October 12th, 1989)—is a fully protected Conservation Unit. However, it is constantly exposed to large-scale fires, the last of which (in 2011, unknown cause) affected 500 ha of native vegetation. All regions can be classified as Cerrado sensu strictu and Cerrada˜o, according to Ribeiro and Walter (1998), and are located in ‘‘latossolo’’ soil (deep, acidic and well drained). A total of 97–113 trees with breast height diameter (BHD) over 5 cm were sampled in each region studied. Samples of each population were identified and integrated into the herbarium of the Universidade Estadual de Ponta Grossa (HUPG), Parana´ State, Brazil (Table 1). DNA extraction Total genomic DNA extraction was conducted according to the protocol of Doyle and Doyle (1990) with modifications described by Ferreira and Grattapaglia (1998). After DNA
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Table 1 Characteristics of the sites studied in Assis, Brotas, Itirapina and Pedregulho Local
Coordinates S/W
Assis
22°360 0000 /50°240 2800
Altitude
Climate
Area
N
Voucher
3,000
113
17892a
540
Cfa/Cwa
Brotas
00
22°22 18 /48°01 24
650
Cfa
400
97
17891a
Itirapina
22°130 0300 /47°500 1500
750
Cfa
170
110
17893a
Pedregulho
20°130 4000 /47°260 1800
1,000
Aw
2,000
100
17894a
0
00
0
Table columns include geographic coordinates, mean altitude in meters, climate classification according to Ko¨eppen (Rolim et al. 2007), total fragment area in hectares, number of individuals sampled (N) and voucher Cfa = humid subtropical climate; no dry season; Cwa = tropical climate with elevated rainfall and dry winters; Aw = tropical climate with dry winters a Ritter LMO, HUPG; herbarium of the Universidade Estadual de Ponta Grossa, Parana´, Brazil
quantification, samples were dissolved in MilliQ water at 3 ng/ll. Eight pairs of SSR primers developed specifically for Q. grandiflora by Ritter et al. (2012) were used for amplification. DNA fragments were amplified and separated by denaturing polyacrylamide gel electrophoresis in 1X TBE buffer for approximately 1 h and 30 min Fragments were then visualized using silver nitrate stain (Creste et al. 2001). Allele size was estimated using a standard molecular weight marker (10 bp ladder—InvitrogenÒ). Fragments of different sizes were considered different alleles. Data analysis The SPAGeDI software (Hardy and Vekemans 2002) was used to estimate genetic diversity based on the following values: number of alleles per locus, effective number of alleles per locus (Nei 1987), presence of exclusive alleles, mean observed and expected heterozygosity, and fixation indices. Results after 10,000 allele permutations were considered significant at 5 % with Bonferroni corrections. Means for each locus were compared between populations using 95 % confidence intervals estimated with the Jackknife method over standard error. Departures from Hardy– Weinberg equilibrium (HWE) were assessed using Fisher’s exact test calculated by the CERVUS 3.0 software (Kalinowski et al. 2007). The same software was used to identify clones and analyze the spatial genetic structure within populations by estimating coancestry coefficients for each allele (k) for each pair of specimens (x and y) in each distance class, according to Loiselle et al. (1995). Cluster (K) structures were analyzed using the STRUCTURE software (Pritchard et al. 2000). Its algorithm identifies genetic clusters using multilocus genotypes and estimates the percentage of genes in each genome belonging to each cluster. Unlike methods based on genetic distance, this algorithm does not require prior information regarding phenotypes, sampling locations and individual origins (Rosenberg et al. 2002). The K values were calculated by DK values, as per Evanno et al. (2005). As a
measure of genetic distance between populations, we used the distance of (Nei 1987). The dendrogram was based on the Unweighted Pair Group Method Arithmetic Average (UPGMA) method and, using the Isolation by Distance version 3.23 (Jensen et al. 2005), the correlation analysis between genetic and geographic distances was performed. The BOTTLENECK software (Piry et al. 1999) was used to identify recent reductions in effective population size considering stepwise (SMM) (Ohta and Kimura 1973) and two-phase (TPM) (Di Rienzo et al. 1994) mutation models. Wilcoxon tests of significance were conducted after 1,000 iterations for SMM and TPM, as recommended by Luikart and Cornuet (1998) for analyses with less than 20 SSR loci (Piry et al. 1999). The software uses both models independently, as they represent two extremes of a continuum of possible mutation models (Chakraborty and Jin 1992). Effective population size was calculated according to Cockerham (1969), and the minimum viable area (MVA) for in situ genetic conservation was estimated as a function of the effective population sizes postulated by Lynch (1996), as recommended by Whittaker and Ferna´ndezPalacios (2007). Genetic divergence was calculated using F-statistics proposed by Weir and Cockerham (1984) and Nei (1987) for subdivided populations, using the FSTAT software (Goudet 2001). Apparent gene flow between populations was estimated according to the model proposed by Crow and Aoki (1984).
Results The mean number of alleles per locus in the four populations studied ranged from 11 to 14, while the number of effective alleles was between 4.5 and 6.8. Except for the Qgr1 locus in the Brotas and Pedregulho population and the Qgr11 locus in Pedregulho, all observed heterozygosity (Ho) values were lower than the expected heterozygosity (He), indicating an excess of homozygotes in these populations (Table 2).
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Table 2 Description of SSR loci in populations of Assis, Brotas, Itirapina and Pedregulho ˆ A
Ae
Ho
He
HW
f (CI 95 %)
ta
Qgr1
15
4.1
0.626
0.757
0*
0.173
0.705
Qgr3
6
3
0.355
0.662
0*
0.463
0.367
Qgr4
17
6.5
0.389
0.846
0*
0.54
0.299
Qgr7
16
5.9
0.655
0.83
0.64
0.21
0.653
Qgr11
16
7.2
0.569
0.861
0*
0.339
0.494
Qgr12
14
10.5
0.44
0.905
0*
0.513
0.322
Qgr13
9
5.8
0.393
0.828
0*
0.525
0.311
Qgr20
17
5.6
0.559
0.822
0*
0.319
0.516
Average
0.387 (0.367–0.402)
0.458
Assis
13.7
6.1
0.498
0.814
Brotas Qgr1
9
3.1
0.734
0.68
0.66
-0.08
1.174
Qgr3
7
4.3
0.27
0.77
0*
0.649
0.213
Qgr4
15
7.6
0.552
0.869
0*
0.364
0.466
Qgr7
19
8.2
0.439
0.878
0*
0.5
0.333
Qgr11
15
9.3
0.794
0.893
0.21
0.111
0.800
Qgr12
11
8.4
0.688
0.881
0.04
0.219
0.641
Qgr13
12
5.6
0.53
0.821
0*
0.354
0.477
Qgr20
12
6.4
0.538
0.843
0*
0.361
0.470
Average
12.5
6.6
0.568
0.829
0.315 (0.256–0.336)
0.572
Itirapina Qgr1
12
2
0.382
0.509
0*
0.248
0.603
Qgr3
6
3.2
0.266
0.686
0*
0.611
0.241
Qgr4
10
3.2
0.212
0.69
0*
0.692
0.182
Qgr7
14
4.5
0.558
0.777
0.11
0.281
0.561
Qgr11
11
5.4
0.545
0.814
0.03
0.33
0.504
Qgr12 Qgr13
15 9
5.7 4.5
0.506 0.21
0.826 0.778
0* 0*
0.387 0.73
0.442 0.156
Qgr20
16
7.7
0.449
0.87
0.04
Average
11.6
4.5
0.391
0.743
Qgr1
17
5.6
0.978
0.821
Qgr3
8
4
0.081
0.75
Qgr4
15
3.9
0.454
Qgr7
19
10.1
Qgr11
12
Qgr12
10
Qgr13
0.483
0.349
0.474 (0.447–0.492)
0.380
0*
-0.192
1.475
0*
0.892
0.057
0.741
0*
0.387
0.442
0.595
0.901
0.05
0.339
0.494
7.5
0.924
0.866
1
-0.067
1.144
3.9
0.352
0.742
0*
0.525
0.311
16
11.4
0.671
0.912
0.67
0.264
0.582
Qgr20
15
8
0.681
0.875
0.05
0.221
0.638
Average
14
6.8
0.592
0.826
0.296 (0.255–0.336)
0.643
Pedregulho
ˆ = number alleles, Ae = effective number of alleles; Ho = observed heterozygosity, He = expected heterozygosity; HW = Hardy–WeinA berg; f = fixation index; CI 95 % = confidence interval at 95 % probability using 10,000 resampling bootstrap over loci; ta = apparent outcrossing rate * p \ 0.5 the mean deviation from equilibrium
Save for the Qgr1 locus in Brotas and Pedregulho and the Qgr11 locus in Pedregulho, all fixation indices were positive and significantly different from zero. The mean fixation index per locus ranged from 0.296 in Pedregulho to
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0.474 in Itirapina. All populations deviated from HWE (p \ 0.05). Seven loci departed from HWE in Assis and Itirapina, as did six loci in Brotas and four in Pedregulho (Table 2). The apparent outcrossing rate varied between
Genetica (2014) 142:11–21
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0.380 and 0.643 in Itirapina and Pedregulho. Some loci had values higher than 1. This result is due to the negative f of these loci. Twenty six exclusive alleles were found in the populations studied. The Pedregulho population showed the highest number of exclusive alleles (9), while Assis and Brotas contained the lowest number (5). Seven exclusive alleles were found in Itirapina. Six of the exclusive alleles could be considered rare (p B 0.05). Four of these alleles were found in Pedregulho, while the remaining two were found in Assis and Brotas, respectively (Table 3). No clones were found in the populations studied All populations showed spatial genetic structure in the first distance classes. These results indicate formation of pedigrees in smaller distance classes, although this tendency decreases as the distance between specimens increases; that is, clusters of trees separated by less than 25 m in Assis, Table 3 Frequency of private alleles per locus in four populations of Q. grandiflora Mart. studied in the state of Sa˜o Paulo, Brazil Loco
Allele
Assis
Brotas
Itirapina
Pedregulho
Qgr1
156
–
–
–
0.005a
Qgr1
182
–
–
–
0.021
Qgr1
170
–
–
–
0.129
Qgr1
200
–
–
0.018
– –
a
Qgr1
192
0.005
–
–
Qgr3
208
–
0.006
–
–
Qgr4
208
–
–
–
0.010
Qgr4
240
–
–
0.006
–
Qgr4
222
–
0.005a
–
–
Qgr4
206
0.011
–
–
–
Qgr7
266
–
–
–
0.005a
Qgr7
258
–
–
–
0.005a
Qgr7
228
–
–
0.006
–
Qgr7
246
–
0.006
–
–
Qgr7 Qgr11
224 212
– –
0.006 0.037
– –
– –
Qgr11
230
0.006
–
–
–
Qgr11
228
0.013
–
–
–
Qgr12
204
–
–
0.006
–
Qgr12
240
0.012
–
–
–
Qgr13
186
–
–
–
0.018
Qgr13
218
–
–
–
0.018
Qgr20
224
–
–
–
0.005a
Qgr20
260
–
–
0.025
–
Qgr20
216
–
–
0.006
–
Qgr20
212
Total a
Rare allele
–
–
0.006
–
5
5
7
9
30 m in Brotas, and 40 m in Itirapina and Pedregulho (Fig. 1) contain genetically similar individuals who are probably related. The influence of the Wahlund effect was assessed by correcting the fixation index (f) for the spatial genetic structure of each population. Results were 8.5 % in the Assis population, 15.1 % in Itirapina, and 19 % in Brotas. Particularly significant results were found in Pedregulho, where approximately 53.3 % of the f value was attributable to the Wahlund effect. An analysis of genetic similarity was used to assess the probable number of clusters (K) in the populations studied. The clusters identified in this analysis corresponded to the four locations in which specimens of Q. grandiflora Mart. were sampled, except for specimens from Assis and Itirapina, which were similar enough to be considered part of the same cluster. Therefore, the four populations studied were divided into three clusters (red, blue and green) (Fig. 2). The genetic distance between populations (Nei 1987) was assessed using the Unweighted Pair Group Method Arithmetic Average method. The analysis revealed that specimens from Assis and Itirapina were significantly close to each other (0.096), and more similar to Brotas (0.249) than to Pedregulho, which was the most genetically distant from the other areas studied (0.293) (Fig. 3, Table 4). The correlation analysis between genetic and geographic distance is shown in Fig. 4. Correlation strength was not significant (r = -0.33 and p = 0.68). The estimated effective population size (Ne) for Q. grandiflora Mart. was less than 10 specimens; that is, the individuals sampled are genetic representatives of approximately 9.6 plants from an ideal panmictic population. The lowest and highest values of the MVA for in situ genetic conservation were obtained in Brotas and Assis, respectively. MVA was estimated to be between 4.1 and 9.2 ha based on a short-term effective population size of 50. Medium-term estimates (MVA500) ranged between 41.3 and 92.1 ha, and long-term estimates (MVA1000), between 91 and 184.2 ha (Table 5). All study sites have sufficient size to be retained in the short-, medium- and long-term, because they are located in areas ranging from 170 to 3,000 ha. Wilcoxon tests for the Stepwise mutation model (Kimura and Ota 1975) indicated population expansions for all samples analyzed, occurring as recently as the last 12 generations, according to Van Rossum and Prentice (2004). Wilcoxon tests for the infinite allele mutation model (Cornuet and Luikart 1997; Luikart and Cornuet 1998; Piry et al. 1999) produced evidence for a recent population bottleneck in the Brotas fragment (Table 6). The mean coancestry coefficient for the populations studied was 0.43, indicating that most genetic variation
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Fig. 1 Correlogram of coancestry coefficients (hxy) in nine distance classes among Q. grandiflora Mart. trees in Assis, Brotas, Itirapina and Pedregulho. Traced lines indicate the lower and upper limits of
Genetica (2014) 142:11–21
the 95 % confidence interval and the solid line indicates coancestry coefficients estimated according to Loiselle et al. (1995)
Fig. 2 Red, blue and green (K = 3) groups, defined by genetic similarity for populations of Q. grandiflora Mart. in the four regions studied, classified as follows: 1 Assis, 2 Brotas, 3 Itirapina, 4 Pedregulho
(95.7 %) was within populations. Values for the hp index used to estimate population divergence are small (Table 7), indicating low genetic divergence among populations and corroborating the hypothesis that there is more genetic divergence within populations than among them. The number of migrants per generation (also known as apparent or historical gene flow) ranged between 1.7 and 4.1 (Table 7). According to the system proposed by Govindajaru (1989), gene flow can be classified as high (over
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1), intermediate (between 0.25 and 0.99) or low (below 0.25).
Discussion Overall, genetic divergence analyses highlighted over ten alleles per locus, although the number of effective alleles was low. Effective alleles contribute significantly to the
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17 Table 5 Estimates of effective population size and minimum variable area (MVA) for in situ genetic conservation of Q. grandiflora Mart., and Wilcoxon test for genetic bottlenecks in the populations of Assis, Brotas, Itirapina and Pedregulho Population
N
Neˆ
MVA(1,000)
MVA(500)
MVA(50)
Assis
113
9.6
184.2
92.1
9.2
Brotas
Fig. 3 Dendrogram of genetic distance between populations of Q. grandiflora Mart. in the four regions studied, classified as follows: 1 Assis, 2 Brotas, 3 Itirapina, 4 Pedregulho
Brotas
Itirapina
9.4
82.6
41.3
4.1
110
9.6
110
54.9
5.4
Pedregulho
100
9.8
91
45.5
4.5
N = sample size; Ne = effective population size; MVA(1,000) = area required to support Ne(ref) = 1,000; MVA(500) = area required to support Ne(ref) = 500; MVA(50) = area required to support Ne(ref) = 50
Pedregulho
Table 6 Wilcoxon tests for the Stepwise mutation model and infinite Allele Mutation Model where: D/E = loci with heterozygosity deficiency/excess; P = probability of bottlenecks in each population (95 % confidence interval) Population
Table 4 Genetic and geographic (in kilometers) distances in study sites Assis
97
Itirapina
Assis
0
0.168
0.096
0.212
Brotas
275 km
0
0.249
0.293
Stepwise mutation model
Infinite allele mutation model
Itirapina
350 km
24 km
0
0.265
D
D
Pedregulho
460 km
280 km
280 km
0
E
p (Wilcoxon)
E
p (Wilcoxon)
Assis
6
2
0.0195
4
4
1.0000
Brotas
7
1
0.0117
2
6
0.0195
Itirapina
7
1
0.0117
3
5
0.8437
Pedregulho
7
1
0.0078
2
6
0.4069
Table 7 Genetic divergence and gene flow between natural populations of Q. grandiflora in Assis, Brotas, Itirapina and Pedregulho, in the state of Sa˜o Paulo Population
Gene flow
hp
CI (95 %)
Assis 9 Brotas
3.47
0.031
(0.017–0.044)
Assis 9 Itirapina
4.16
0.026
(0.013–0.041)
Assis 9 Pedregulho
2.73
0.039
(0.031–0.047)
Itirapina 9 Pedregulho
1.71
0.061
(0.031–0.095)
Itirapina 9 Brotas
1.80
0.058
(0.034–0.082)
Brotas 9 Pedregulho
2.15
0.049
(0.030–0.071)
hp = index of genetic divergence (95 %) = 95 % confidence interval
Fig. 4 Scatter-plot for correlation analysis
formation of new generations, and in the present sample, less than half of total alleles could be considered effective. The low number of effective alleles may be attributable to the presence of exclusive and rare alleles, which may be indicative of the beginning of population differentiation. The highest number of exclusive and rare alleles was found in the Pedregulho population.
between
populations;
CI
Observed heterozygosity values were lower than expected in all populations, indicating an excess of homozygotes. None of the populations were at HWE. Deviations from HWE can be caused by a number of factors, such as mutation, genetic drift, selection, migration or mixed reproductive strategies (Ritter 2012). The excess of homozygotes indicates deviation from HWE as well as inbreeding. The inbreeding hypothesis is corroborated by the high fixation indices in the sample, mainly in Itirapina (0.474). The apparent outcrossing rate indicates whether the population reproduces by selfing or outcrossing. Values
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close to 1 indicate reproduction by outcrossing. Our result is consistent with those expected for a species of mixed mating system. It is also consistent with Antiqueira and Kageyama (2013, submitted) who analyzed progenies of Q. grandiflora and obtained the value of 0.913 for multiloci crossing rate. Those results demonstrate that selfing is found for Q. grandiflora Mart., and that it reproduces by outcrossing and is not self-incompatible. The spatial genetic structure of Q. grandiflora suggested that clusters of trees separated by less than 25 m in Assis, 30 m in Brotas, and 40 m in Itirapina and Pedregulho were constituted by genetically similar individuals who were also probably related. The moderate levels of genetic structure observed in short distance classes could be attributable to limited seed dispersal, vegetative reproduction and spatially variable selection (Loiselle et al. 1995; Shapcott 1995). These results can be justified by the anemochoric species dispersion that reaches longer distances in areas of higher altitudes (Itirapina and Pedregulho). It is also possible that dispersion is occurring more narrowly in Assis and Brotas, where Cerrado is denser (Cerrada˜o). The corrected fixation index values were able to assess the influence of the Wahlund effect. This phenomenon subdivides populations into genetically isolated reproductive units which, according to Wright (1951), may aggravate inbreeding risk, thus increasing coancestry and endogamy. Populations in the present study showed low spatial genetic structure but high inbreeding indices, particularly in Assis, Brotas and Itirapina. However, the corrected fixation index for Pedregulho indicated a low rate of inbreeding (0.103). The other populations were less influenced by the Wahlund effect. Cluster (K) analyses showed that the first cluster (red) was composed mostly of samples from Pedregulho, the green cluster was composed by the populations of Assis and Itirapina, whereas the blue cluster was concentrated in the population of Brotas. The population of Pedregulho is noticeably different from the others: it has formed an isolated group whose genome differs by approximately 90 % from the other populations. It is important to note that any value over 80 % suggests membership to a particular group. The analysis also grouped the populations of Assis and Itirapina into the same cluster, suggesting that these populations are similar and share the same genome. One possible explanation for this finding is the similar environmental conditions (such as climate, soil and altitude) among those two areas, which may have led populations to become genetically similar (Graudal et al. 1997). These two populations are also similar, exposed to the same anthropic pressure and have the same edaphic characteristics. The physical distance between the areas studied did
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not appear to influence cluster formation, as Assis and Itirapina are separated by 350 km. Conversely, the populations in Brotas and Itirapina, although separated by only 24 km, were considered distinct from each other. The analysis of the correlation between genetic and geographic distance was not significant, so there must be another pattern that explains results. Therefore we suggest sampling larger neighboring populations to elucidate the patterns of genetic flow and barriers to this flow in Cerrado areas of the Sa˜o Paulo State. The genetic distance analyses showed that Pedregulho was the most distinct population, Assis and Itirapina were the most similar, while Brotas represented an intermediate group. In the long-term, regardless of the environmental factors contributing to differentiation between populations (geographical or climate barriers, for instance), genetic differentiation may have negative effects in the form of a tendency toward genetic drift (Young et al. 1996; Ritter 2012). In some situations, genetic drift from the original population can lead to elimination and fixation of alleles, regardless of their adaptive significance (Frankham et al. 2002). Reduced variation as a result of the elimination of alleles in small populations can reduce adaptability to environmental changes, leading to the decline of a species due to fixation of non-adaptive alleles. These are key factors in determining whether a population should be considered an Evolutionarily Significant Unit (ESU). Results regarding effective population sizes show the low genetic representativeness of populations. Although this theoretical population measurement is an important concept for genetic conservation (Sugg and Chesser 1994), it can be significantly different from the census population size; in fact, the effective size is often lower than the actual population size (Wright 1938). Furthermore, the high density of Q. grandiflora in the areas studied suggests that an effective population size of 500 is likely to be reached. The same reasoning applies to estimates of the MVA for conservation. While the fragments studied were large enough to ensure in situ conservation in the short-, medium- and long-term, there is also the possibility of ex situ conservation (seed collection) and environmental recovery. The population bottlenecks identified in the Brotas fragment indicated that a number of loci showed heterozygosity deficits. That is, the expected heterozygosity (He) under HWE is less than the expected heterozygosity under mutation-drift equilibrium (Heq). Populations who have not yet adapted to problems associated with reduced size after a recent bottleneck may be at high risk of extinction (Lee et al. 2002). Therefore, as soon as a bottleneck is detected, mitigative management measures such as habitat enrichment or introduction of immigrants must be put in place to minimize or avoid potentially deleterious consequences (Luikart et al. 1998). Since the Brotas population is
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exposed to higher anthropic pressure than the other areas studied, the present results underscore the need for conservation efforts in this habitat fragment. The level of gene flow observed in the populations studied is high enough to counter the effect of genetic drift. Interestingly, most gene flow occurred between Assis and Itirapina, the most genetically similar populations, which were also part of the same cluster. Some studies associate gene flow to low population divergence and the geographical distance between populations. However, the role of geographical distance was not evident in the present study, since less gene flow was observed between the closest areas (1.8 for Brotas/Itirapina, 24 km apart) than between the farthest ones (4.1 for Assis/Itirapina, 350 km apart). The hp estimate confirmed the high levels of genetic divergence within populations. The least divergent populations were those from Assis and Itirapina. These results were congruent with others from the present study, indicating high genetic similarity between these fragments. Similarly, the high levels of divergence observed in the Pedregulho population show that it is clearly distinct from the others. Therefore, this population can be considered an ESU or MU. The reported results demonstrate that anthropic interference in the form of grazing, sugarcane plantations and Pinus and Eucalyptus forests have led to habitat fragmentation in the Cerrado biome, and influenced the genetic structure and diversity of populations in Assis, Brotas and Itirapina. The population of Pedregulho, located within an Integral Protection Conservation Unit, presented the highest genetic diversity, in spite of frequent exposure to fires. Moreover, Pedregulho is located in the Minas Gerais State border, where Cerrado is the predominant biome and their areas are continuous. According to Durigan (2003) the areas of Cerrado from the center to the north-northeast of Sa˜o Paulo are differentiated from the rest of the state due to particular environmental conditions. The population of Itirapina is also included in this pattern. The population of Brotas, although close, is under strong anthropogenic pressure, which decreased its levels of genetic diversity and reinforces the importance of conservation of this fragment. According to Brasil (2006, 2010), the Cerrado areas in the northern region of the state of Sa˜o Paulo should have the highest priority for conservation because of their ecological characteristics, size and location of native fragments, besides being one of the most heavily devastated area in the last 30 years because of agricultural expansion (Kronka et al. 2005). Considering these recommendations along with our results, we propose that the population of Pedregulho should be considered a MU and an ESU, as well as a major conservation target in the Cerrado.
19 Acknowledgments The authors would like to thank FAPESP (Fundac¸a˜o de Amparo a` Pesquisa do Estado de Sa˜o Paulo) for financial support (Grants 2007/06648-1 and 2008/06834-2), the research group LARGEA (Laborato´rio de Reproduc¸a˜o e Gene´tica de Espe´cies Arbo´reas—USP/ESALQ) for technical assistance, Lucas Antiqueira for revising this manuscript, and the reviewers for their valuable comments.
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Genetic diversity of four populations of Qualea grandiflora Mart. in fragments of the Brazilian Cerrado Lia Maris Orth Ritter Antiqueira Paulo Yoshio Kageyama
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Received: 25 June 2013 / Accepted: 11 December 2013 / Published online: 19 December 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract We analyzed the genetic structure and diversity of Qualea grandiflora Mart., the most abundant woody species in the Brazilian Cerrado. Eight microsatellite loci were used to analyze samples from four populations subjected to different types of anthropic pressure, distributed throughout the state of Sa˜o Paulo in the regions of Assis, Brotas, Itirapina and Pedregulho. Results indicated a mean number of 12 alleles per locus, but only six effective alleles. Alleles private to particular populations and rare alleles were also detected. An excess of homozygotes and moderate levels of inbreeding were observed. No clones were identified. All populations departed from Hardy– Weinberg equilibrium (p \ 0.05). Spatial structure was observed in the distribution of specimens in distance classes ranging from 30 to 40 km and three genetic clusters were identified, with genotypes in the Pedregulho population differing from the others by up to 90 %. The influence of the Wahlund effect on the studied populations lies between 8.5 and 53.3 %. Estimates of effective population size were low (\10), and the minimum viable area for conservation in the short-, medium- and long-term was estimated to be between 4 and 184 ha. Gene flow was high enough to counter the effects of genetic drift. The genetic
L. M. O. R. Antiqueira (&) Departamento de Gene´tica, Escola Superior de Agricultura Luiz de Queiroz (ESALQ), Universidade de Sa˜o Paulo (USP), Av. Pa´dua Dias, 11, Piracicaba, Sa˜o Paulo 13418-900, Brazil e-mail: [email protected] P. Y. Kageyama Departamento de Cieˆncias Florestais, Escola Superior de Agricultura Luiz de Queiroz (ESALQ), Universidade de Sa˜o Paulo (USP), Av. Pa´dua Dias, 11, Piracicaba, Sa˜o Paulo 13418-900, Brazil
diversity and divergence between the studied populations indicated that the Pedregulho population should be considered an Evolutionary Significant Unit and a Management Unit. Keywords Conservation genetics Evolutionary Significant Unit Landscape genetics Management Unit Microsatellites Vochysiaceae
Introduction The Brazilian Cerrado contains a wide diversity of landscapes and vegetation physiognomies associated with specific physical and physiographic characteristics. Its vascular flora is estimated to be 44 % endemic (Silva and Bates 2002; Felfili et al. 2005). The Cerrado is also one of the most threatened biodiversity hotspots on Earth, and a primary target for conservation efforts (Myers et al. 2000; Mittermeier et al. 2005). Although they cover only 2.3 % of the Earth’s surface, biodiversity hotspots are home to over 60 % of life on the planet (Conservation International 2005). Recent studies suggest that approximately 80 % of the Brazilian Cerrado is under some degree of anthropic pressure, and only 432,814 km2 of the Cerrado remain intact (Conservation International 2005; Klink and Machado 2005). Less than 4 % of the intact area is protected by Conservation Units (CUs). Conservation Units consist of parks, forests, biological reserves and wildlife sanctuaries instituted by the Brazilian government. They are classified as integral protection and sustainable use areas, regulated by pertinent legislation. The natural resources and environmental characteristics of these regions play an important role in ensuring the
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conservation of significant and ecologically viable samples of different populations, habitats and biomes in the national territory or territorial waters, preserving the biological diversity in Brazil (Brasil 2006, 2010). Many CUs can be considered Evolutionary Significant Units (ESUs) or Management Units (MUs). The concept of ESUs was introduced by Ryder (1986) in the context of conservation efforts and captive breeding programs. Although ESUs are traditionally defined using molecular genetic data, they may also be identified using ecological or behavioral information. An efficient way to identify ESUs is by assessing population genetic structure (Stockwell et al. 1998). Recently developed molecular genetics techniques (Avise 1994) have also contributed to the identification of ESUs, especially in allopatric populations of poorly studied species. The MUs are populations of conspecifics with low connectivity which must be monitored and managed separately (Taylor and Dizon 1999). According to Pasboll et al. (2007), the identification of MUs is key for conservation efforts, as it allows for the identification of risk groups for monitoring purposes, and helps regulate the effects of human activity on species and populations. However, certain measures must be taken to ensure units are adequately identified. Assessments based on a single parameter (such as restricted gene flow between populations) may lead to biased estimates, which do not account for geographically close populations. To avoid bias, factors such as divergence rates among populations, allele frequencies and landscape features that influence fragment connectivity must also be studied. In this way, knowledge of the genetic structure and diversity in populations plays an important role in the assessment of variation in natural populations, and helps identify significant units for conservation (Crozier et al. 1997; Hitchings and Beebee 1997; Frankham 2003; Brede and Beebee 2004; Frankham 2005). It is also important to consider the genetic consequences of habitat fragmentation on different species, as these factors have led the scientific community to question the genetic vulnerability of populations and the strategies involved in the conservation of endangered species (Antiqueira 2013). Conservation and landscape genetics studies often focus on rare and endangered species, in the hope that the genetic information collected may help ensure the continued survival of the species. However, according to Diniz Filho and Telles (2006), the study of abundant and widely distributed species may also provide relevant information regarding the influence of habitat fragmentation and intensive anthropic actions on population genetic structure. Qualea grandiflora Mart. (Vochysiacae) is the most widely distributed woody species in the Cerrado (Ratter et al. 2003). It grows in areas ranging from open spaces to forests (Felfili and Silva Jr 1993), with seeds and seedlings
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sprouting in a variety of light and soil conditions (Felippe 1990). Plants are pollinated by Sphingidae moths (Silberbauer-Gottsberger and Gottsberger 1975) and seeds can travel long distances through anemochorous dispersal. Q. grandiflora also has a number of pharmaceutical applications, and is popularly used as an antiseptic and antimicrobial agent. It is also used in the textile industry for dye production, and in the furniture industry (Almeida et al. 1998; Ayres et al. 2008). Using Q. grandiflora as a model, the present study characterized the intra and interpopulational genetic variability in four populations subjected to different types of anthropic pressure. The present study aimed to answer the following questions: (1) Does geographic distance influence genetic differentiation of populations? (2) Should areas be considered ESUs or MUs?
Materials and methods Study area and sampling The present study was conducted in four Cerrado areas in the state of Sa˜o Paulo (Table 1). Two of these areas (Assis and Itirapina) were located in CUs managed by the Sa˜o Paulo State Forest Institute. The region of Assis, located near the Horto Florestal dam, is vulnerable to invasion by Urochloa spp (Pinheiro and Durigan 2009) and surrounded by sugarcane plantations, grasslands, and plantations of Pinus and Eucalyptus. The Itirapina region and the private property of Brotas (Marimbondo Farm) have similar characteristics. However, the private property is under the most intense anthropic pressure, where the fragment area is often used as grazing land. Of the four regions studied, only one—the Pedregulho site (Furnas do Bom Jesus State Park, Decree number 30.591, October 12th, 1989)—is a fully protected Conservation Unit. However, it is constantly exposed to large-scale fires, the last of which (in 2011, unknown cause) affected 500 ha of native vegetation. All regions can be classified as Cerrado sensu strictu and Cerrada˜o, according to Ribeiro and Walter (1998), and are located in ‘‘latossolo’’ soil (deep, acidic and well drained). A total of 97–113 trees with breast height diameter (BHD) over 5 cm were sampled in each region studied. Samples of each population were identified and integrated into the herbarium of the Universidade Estadual de Ponta Grossa (HUPG), Parana´ State, Brazil (Table 1). DNA extraction Total genomic DNA extraction was conducted according to the protocol of Doyle and Doyle (1990) with modifications described by Ferreira and Grattapaglia (1998). After DNA
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Table 1 Characteristics of the sites studied in Assis, Brotas, Itirapina and Pedregulho Local
Coordinates S/W
Assis
22°360 0000 /50°240 2800
Altitude
Climate
Area
N
Voucher
3,000
113
17892a
540
Cfa/Cwa
Brotas
00
22°22 18 /48°01 24
650
Cfa
400
97
17891a
Itirapina
22°130 0300 /47°500 1500
750
Cfa
170
110
17893a
Pedregulho
20°130 4000 /47°260 1800
1,000
Aw
2,000
100
17894a
0
00
0
Table columns include geographic coordinates, mean altitude in meters, climate classification according to Ko¨eppen (Rolim et al. 2007), total fragment area in hectares, number of individuals sampled (N) and voucher Cfa = humid subtropical climate; no dry season; Cwa = tropical climate with elevated rainfall and dry winters; Aw = tropical climate with dry winters a Ritter LMO, HUPG; herbarium of the Universidade Estadual de Ponta Grossa, Parana´, Brazil
quantification, samples were dissolved in MilliQ water at 3 ng/ll. Eight pairs of SSR primers developed specifically for Q. grandiflora by Ritter et al. (2012) were used for amplification. DNA fragments were amplified and separated by denaturing polyacrylamide gel electrophoresis in 1X TBE buffer for approximately 1 h and 30 min Fragments were then visualized using silver nitrate stain (Creste et al. 2001). Allele size was estimated using a standard molecular weight marker (10 bp ladder—InvitrogenÒ). Fragments of different sizes were considered different alleles. Data analysis The SPAGeDI software (Hardy and Vekemans 2002) was used to estimate genetic diversity based on the following values: number of alleles per locus, effective number of alleles per locus (Nei 1987), presence of exclusive alleles, mean observed and expected heterozygosity, and fixation indices. Results after 10,000 allele permutations were considered significant at 5 % with Bonferroni corrections. Means for each locus were compared between populations using 95 % confidence intervals estimated with the Jackknife method over standard error. Departures from Hardy– Weinberg equilibrium (HWE) were assessed using Fisher’s exact test calculated by the CERVUS 3.0 software (Kalinowski et al. 2007). The same software was used to identify clones and analyze the spatial genetic structure within populations by estimating coancestry coefficients for each allele (k) for each pair of specimens (x and y) in each distance class, according to Loiselle et al. (1995). Cluster (K) structures were analyzed using the STRUCTURE software (Pritchard et al. 2000). Its algorithm identifies genetic clusters using multilocus genotypes and estimates the percentage of genes in each genome belonging to each cluster. Unlike methods based on genetic distance, this algorithm does not require prior information regarding phenotypes, sampling locations and individual origins (Rosenberg et al. 2002). The K values were calculated by DK values, as per Evanno et al. (2005). As a
measure of genetic distance between populations, we used the distance of (Nei 1987). The dendrogram was based on the Unweighted Pair Group Method Arithmetic Average (UPGMA) method and, using the Isolation by Distance version 3.23 (Jensen et al. 2005), the correlation analysis between genetic and geographic distances was performed. The BOTTLENECK software (Piry et al. 1999) was used to identify recent reductions in effective population size considering stepwise (SMM) (Ohta and Kimura 1973) and two-phase (TPM) (Di Rienzo et al. 1994) mutation models. Wilcoxon tests of significance were conducted after 1,000 iterations for SMM and TPM, as recommended by Luikart and Cornuet (1998) for analyses with less than 20 SSR loci (Piry et al. 1999). The software uses both models independently, as they represent two extremes of a continuum of possible mutation models (Chakraborty and Jin 1992). Effective population size was calculated according to Cockerham (1969), and the minimum viable area (MVA) for in situ genetic conservation was estimated as a function of the effective population sizes postulated by Lynch (1996), as recommended by Whittaker and Ferna´ndezPalacios (2007). Genetic divergence was calculated using F-statistics proposed by Weir and Cockerham (1984) and Nei (1987) for subdivided populations, using the FSTAT software (Goudet 2001). Apparent gene flow between populations was estimated according to the model proposed by Crow and Aoki (1984).
Results The mean number of alleles per locus in the four populations studied ranged from 11 to 14, while the number of effective alleles was between 4.5 and 6.8. Except for the Qgr1 locus in the Brotas and Pedregulho population and the Qgr11 locus in Pedregulho, all observed heterozygosity (Ho) values were lower than the expected heterozygosity (He), indicating an excess of homozygotes in these populations (Table 2).
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Table 2 Description of SSR loci in populations of Assis, Brotas, Itirapina and Pedregulho ˆ A
Ae
Ho
He
HW
f (CI 95 %)
ta
Qgr1
15
4.1
0.626
0.757
0*
0.173
0.705
Qgr3
6
3
0.355
0.662
0*
0.463
0.367
Qgr4
17
6.5
0.389
0.846
0*
0.54
0.299
Qgr7
16
5.9
0.655
0.83
0.64
0.21
0.653
Qgr11
16
7.2
0.569
0.861
0*
0.339
0.494
Qgr12
14
10.5
0.44
0.905
0*
0.513
0.322
Qgr13
9
5.8
0.393
0.828
0*
0.525
0.311
Qgr20
17
5.6
0.559
0.822
0*
0.319
0.516
Average
0.387 (0.367–0.402)
0.458
Assis
13.7
6.1
0.498
0.814
Brotas Qgr1
9
3.1
0.734
0.68
0.66
-0.08
1.174
Qgr3
7
4.3
0.27
0.77
0*
0.649
0.213
Qgr4
15
7.6
0.552
0.869
0*
0.364
0.466
Qgr7
19
8.2
0.439
0.878
0*
0.5
0.333
Qgr11
15
9.3
0.794
0.893
0.21
0.111
0.800
Qgr12
11
8.4
0.688
0.881
0.04
0.219
0.641
Qgr13
12
5.6
0.53
0.821
0*
0.354
0.477
Qgr20
12
6.4
0.538
0.843
0*
0.361
0.470
Average
12.5
6.6
0.568
0.829
0.315 (0.256–0.336)
0.572
Itirapina Qgr1
12
2
0.382
0.509
0*
0.248
0.603
Qgr3
6
3.2
0.266
0.686
0*
0.611
0.241
Qgr4
10
3.2
0.212
0.69
0*
0.692
0.182
Qgr7
14
4.5
0.558
0.777
0.11
0.281
0.561
Qgr11
11
5.4
0.545
0.814
0.03
0.33
0.504
Qgr12 Qgr13
15 9
5.7 4.5
0.506 0.21
0.826 0.778
0* 0*
0.387 0.73
0.442 0.156
Qgr20
16
7.7
0.449
0.87
0.04
Average
11.6
4.5
0.391
0.743
Qgr1
17
5.6
0.978
0.821
Qgr3
8
4
0.081
0.75
Qgr4
15
3.9
0.454
Qgr7
19
10.1
Qgr11
12
Qgr12
10
Qgr13
0.483
0.349
0.474 (0.447–0.492)
0.380
0*
-0.192
1.475
0*
0.892
0.057
0.741
0*
0.387
0.442
0.595
0.901
0.05
0.339
0.494
7.5
0.924
0.866
1
-0.067
1.144
3.9
0.352
0.742
0*
0.525
0.311
16
11.4
0.671
0.912
0.67
0.264
0.582
Qgr20
15
8
0.681
0.875
0.05
0.221
0.638
Average
14
6.8
0.592
0.826
0.296 (0.255–0.336)
0.643
Pedregulho
ˆ = number alleles, Ae = effective number of alleles; Ho = observed heterozygosity, He = expected heterozygosity; HW = Hardy–WeinA berg; f = fixation index; CI 95 % = confidence interval at 95 % probability using 10,000 resampling bootstrap over loci; ta = apparent outcrossing rate * p \ 0.5 the mean deviation from equilibrium
Save for the Qgr1 locus in Brotas and Pedregulho and the Qgr11 locus in Pedregulho, all fixation indices were positive and significantly different from zero. The mean fixation index per locus ranged from 0.296 in Pedregulho to
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0.474 in Itirapina. All populations deviated from HWE (p \ 0.05). Seven loci departed from HWE in Assis and Itirapina, as did six loci in Brotas and four in Pedregulho (Table 2). The apparent outcrossing rate varied between
Genetica (2014) 142:11–21
15
0.380 and 0.643 in Itirapina and Pedregulho. Some loci had values higher than 1. This result is due to the negative f of these loci. Twenty six exclusive alleles were found in the populations studied. The Pedregulho population showed the highest number of exclusive alleles (9), while Assis and Brotas contained the lowest number (5). Seven exclusive alleles were found in Itirapina. Six of the exclusive alleles could be considered rare (p B 0.05). Four of these alleles were found in Pedregulho, while the remaining two were found in Assis and Brotas, respectively (Table 3). No clones were found in the populations studied All populations showed spatial genetic structure in the first distance classes. These results indicate formation of pedigrees in smaller distance classes, although this tendency decreases as the distance between specimens increases; that is, clusters of trees separated by less than 25 m in Assis, Table 3 Frequency of private alleles per locus in four populations of Q. grandiflora Mart. studied in the state of Sa˜o Paulo, Brazil Loco
Allele
Assis
Brotas
Itirapina
Pedregulho
Qgr1
156
–
–
–
0.005a
Qgr1
182
–
–
–
0.021
Qgr1
170
–
–
–
0.129
Qgr1
200
–
–
0.018
– –
a
Qgr1
192
0.005
–
–
Qgr3
208
–
0.006
–
–
Qgr4
208
–
–
–
0.010
Qgr4
240
–
–
0.006
–
Qgr4
222
–
0.005a
–
–
Qgr4
206
0.011
–
–
–
Qgr7
266
–
–
–
0.005a
Qgr7
258
–
–
–
0.005a
Qgr7
228
–
–
0.006
–
Qgr7
246
–
0.006
–
–
Qgr7 Qgr11
224 212
– –
0.006 0.037
– –
– –
Qgr11
230
0.006
–
–
–
Qgr11
228
0.013
–
–
–
Qgr12
204
–
–
0.006
–
Qgr12
240
0.012
–
–
–
Qgr13
186
–
–
–
0.018
Qgr13
218
–
–
–
0.018
Qgr20
224
–
–
–
0.005a
Qgr20
260
–
–
0.025
–
Qgr20
216
–
–
0.006
–
Qgr20
212
Total a
Rare allele
–
–
0.006
–
5
5
7
9
30 m in Brotas, and 40 m in Itirapina and Pedregulho (Fig. 1) contain genetically similar individuals who are probably related. The influence of the Wahlund effect was assessed by correcting the fixation index (f) for the spatial genetic structure of each population. Results were 8.5 % in the Assis population, 15.1 % in Itirapina, and 19 % in Brotas. Particularly significant results were found in Pedregulho, where approximately 53.3 % of the f value was attributable to the Wahlund effect. An analysis of genetic similarity was used to assess the probable number of clusters (K) in the populations studied. The clusters identified in this analysis corresponded to the four locations in which specimens of Q. grandiflora Mart. were sampled, except for specimens from Assis and Itirapina, which were similar enough to be considered part of the same cluster. Therefore, the four populations studied were divided into three clusters (red, blue and green) (Fig. 2). The genetic distance between populations (Nei 1987) was assessed using the Unweighted Pair Group Method Arithmetic Average method. The analysis revealed that specimens from Assis and Itirapina were significantly close to each other (0.096), and more similar to Brotas (0.249) than to Pedregulho, which was the most genetically distant from the other areas studied (0.293) (Fig. 3, Table 4). The correlation analysis between genetic and geographic distance is shown in Fig. 4. Correlation strength was not significant (r = -0.33 and p = 0.68). The estimated effective population size (Ne) for Q. grandiflora Mart. was less than 10 specimens; that is, the individuals sampled are genetic representatives of approximately 9.6 plants from an ideal panmictic population. The lowest and highest values of the MVA for in situ genetic conservation were obtained in Brotas and Assis, respectively. MVA was estimated to be between 4.1 and 9.2 ha based on a short-term effective population size of 50. Medium-term estimates (MVA500) ranged between 41.3 and 92.1 ha, and long-term estimates (MVA1000), between 91 and 184.2 ha (Table 5). All study sites have sufficient size to be retained in the short-, medium- and long-term, because they are located in areas ranging from 170 to 3,000 ha. Wilcoxon tests for the Stepwise mutation model (Kimura and Ota 1975) indicated population expansions for all samples analyzed, occurring as recently as the last 12 generations, according to Van Rossum and Prentice (2004). Wilcoxon tests for the infinite allele mutation model (Cornuet and Luikart 1997; Luikart and Cornuet 1998; Piry et al. 1999) produced evidence for a recent population bottleneck in the Brotas fragment (Table 6). The mean coancestry coefficient for the populations studied was 0.43, indicating that most genetic variation
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16
Fig. 1 Correlogram of coancestry coefficients (hxy) in nine distance classes among Q. grandiflora Mart. trees in Assis, Brotas, Itirapina and Pedregulho. Traced lines indicate the lower and upper limits of
Genetica (2014) 142:11–21
the 95 % confidence interval and the solid line indicates coancestry coefficients estimated according to Loiselle et al. (1995)
Fig. 2 Red, blue and green (K = 3) groups, defined by genetic similarity for populations of Q. grandiflora Mart. in the four regions studied, classified as follows: 1 Assis, 2 Brotas, 3 Itirapina, 4 Pedregulho
(95.7 %) was within populations. Values for the hp index used to estimate population divergence are small (Table 7), indicating low genetic divergence among populations and corroborating the hypothesis that there is more genetic divergence within populations than among them. The number of migrants per generation (also known as apparent or historical gene flow) ranged between 1.7 and 4.1 (Table 7). According to the system proposed by Govindajaru (1989), gene flow can be classified as high (over
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1), intermediate (between 0.25 and 0.99) or low (below 0.25).
Discussion Overall, genetic divergence analyses highlighted over ten alleles per locus, although the number of effective alleles was low. Effective alleles contribute significantly to the
Genetica (2014) 142:11–21
17 Table 5 Estimates of effective population size and minimum variable area (MVA) for in situ genetic conservation of Q. grandiflora Mart., and Wilcoxon test for genetic bottlenecks in the populations of Assis, Brotas, Itirapina and Pedregulho Population
N
Neˆ
MVA(1,000)
MVA(500)
MVA(50)
Assis
113
9.6
184.2
92.1
9.2
Brotas
Fig. 3 Dendrogram of genetic distance between populations of Q. grandiflora Mart. in the four regions studied, classified as follows: 1 Assis, 2 Brotas, 3 Itirapina, 4 Pedregulho
Brotas
Itirapina
9.4
82.6
41.3
4.1
110
9.6
110
54.9
5.4
Pedregulho
100
9.8
91
45.5
4.5
N = sample size; Ne = effective population size; MVA(1,000) = area required to support Ne(ref) = 1,000; MVA(500) = area required to support Ne(ref) = 500; MVA(50) = area required to support Ne(ref) = 50
Pedregulho
Table 6 Wilcoxon tests for the Stepwise mutation model and infinite Allele Mutation Model where: D/E = loci with heterozygosity deficiency/excess; P = probability of bottlenecks in each population (95 % confidence interval) Population
Table 4 Genetic and geographic (in kilometers) distances in study sites Assis
97
Itirapina
Assis
0
0.168
0.096
0.212
Brotas
275 km
0
0.249
0.293
Stepwise mutation model
Infinite allele mutation model
Itirapina
350 km
24 km
0
0.265
D
D
Pedregulho
460 km
280 km
280 km
0
E
p (Wilcoxon)
E
p (Wilcoxon)
Assis
6
2
0.0195
4
4
1.0000
Brotas
7
1
0.0117
2
6
0.0195
Itirapina
7
1
0.0117
3
5
0.8437
Pedregulho
7
1
0.0078
2
6
0.4069
Table 7 Genetic divergence and gene flow between natural populations of Q. grandiflora in Assis, Brotas, Itirapina and Pedregulho, in the state of Sa˜o Paulo Population
Gene flow
hp
CI (95 %)
Assis 9 Brotas
3.47
0.031
(0.017–0.044)
Assis 9 Itirapina
4.16
0.026
(0.013–0.041)
Assis 9 Pedregulho
2.73
0.039
(0.031–0.047)
Itirapina 9 Pedregulho
1.71
0.061
(0.031–0.095)
Itirapina 9 Brotas
1.80
0.058
(0.034–0.082)
Brotas 9 Pedregulho
2.15
0.049
(0.030–0.071)
hp = index of genetic divergence (95 %) = 95 % confidence interval
Fig. 4 Scatter-plot for correlation analysis
formation of new generations, and in the present sample, less than half of total alleles could be considered effective. The low number of effective alleles may be attributable to the presence of exclusive and rare alleles, which may be indicative of the beginning of population differentiation. The highest number of exclusive and rare alleles was found in the Pedregulho population.
between
populations;
CI
Observed heterozygosity values were lower than expected in all populations, indicating an excess of homozygotes. None of the populations were at HWE. Deviations from HWE can be caused by a number of factors, such as mutation, genetic drift, selection, migration or mixed reproductive strategies (Ritter 2012). The excess of homozygotes indicates deviation from HWE as well as inbreeding. The inbreeding hypothesis is corroborated by the high fixation indices in the sample, mainly in Itirapina (0.474). The apparent outcrossing rate indicates whether the population reproduces by selfing or outcrossing. Values
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18
close to 1 indicate reproduction by outcrossing. Our result is consistent with those expected for a species of mixed mating system. It is also consistent with Antiqueira and Kageyama (2013, submitted) who analyzed progenies of Q. grandiflora and obtained the value of 0.913 for multiloci crossing rate. Those results demonstrate that selfing is found for Q. grandiflora Mart., and that it reproduces by outcrossing and is not self-incompatible. The spatial genetic structure of Q. grandiflora suggested that clusters of trees separated by less than 25 m in Assis, 30 m in Brotas, and 40 m in Itirapina and Pedregulho were constituted by genetically similar individuals who were also probably related. The moderate levels of genetic structure observed in short distance classes could be attributable to limited seed dispersal, vegetative reproduction and spatially variable selection (Loiselle et al. 1995; Shapcott 1995). These results can be justified by the anemochoric species dispersion that reaches longer distances in areas of higher altitudes (Itirapina and Pedregulho). It is also possible that dispersion is occurring more narrowly in Assis and Brotas, where Cerrado is denser (Cerrada˜o). The corrected fixation index values were able to assess the influence of the Wahlund effect. This phenomenon subdivides populations into genetically isolated reproductive units which, according to Wright (1951), may aggravate inbreeding risk, thus increasing coancestry and endogamy. Populations in the present study showed low spatial genetic structure but high inbreeding indices, particularly in Assis, Brotas and Itirapina. However, the corrected fixation index for Pedregulho indicated a low rate of inbreeding (0.103). The other populations were less influenced by the Wahlund effect. Cluster (K) analyses showed that the first cluster (red) was composed mostly of samples from Pedregulho, the green cluster was composed by the populations of Assis and Itirapina, whereas the blue cluster was concentrated in the population of Brotas. The population of Pedregulho is noticeably different from the others: it has formed an isolated group whose genome differs by approximately 90 % from the other populations. It is important to note that any value over 80 % suggests membership to a particular group. The analysis also grouped the populations of Assis and Itirapina into the same cluster, suggesting that these populations are similar and share the same genome. One possible explanation for this finding is the similar environmental conditions (such as climate, soil and altitude) among those two areas, which may have led populations to become genetically similar (Graudal et al. 1997). These two populations are also similar, exposed to the same anthropic pressure and have the same edaphic characteristics. The physical distance between the areas studied did
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Genetica (2014) 142:11–21
not appear to influence cluster formation, as Assis and Itirapina are separated by 350 km. Conversely, the populations in Brotas and Itirapina, although separated by only 24 km, were considered distinct from each other. The analysis of the correlation between genetic and geographic distance was not significant, so there must be another pattern that explains results. Therefore we suggest sampling larger neighboring populations to elucidate the patterns of genetic flow and barriers to this flow in Cerrado areas of the Sa˜o Paulo State. The genetic distance analyses showed that Pedregulho was the most distinct population, Assis and Itirapina were the most similar, while Brotas represented an intermediate group. In the long-term, regardless of the environmental factors contributing to differentiation between populations (geographical or climate barriers, for instance), genetic differentiation may have negative effects in the form of a tendency toward genetic drift (Young et al. 1996; Ritter 2012). In some situations, genetic drift from the original population can lead to elimination and fixation of alleles, regardless of their adaptive significance (Frankham et al. 2002). Reduced variation as a result of the elimination of alleles in small populations can reduce adaptability to environmental changes, leading to the decline of a species due to fixation of non-adaptive alleles. These are key factors in determining whether a population should be considered an Evolutionarily Significant Unit (ESU). Results regarding effective population sizes show the low genetic representativeness of populations. Although this theoretical population measurement is an important concept for genetic conservation (Sugg and Chesser 1994), it can be significantly different from the census population size; in fact, the effective size is often lower than the actual population size (Wright 1938). Furthermore, the high density of Q. grandiflora in the areas studied suggests that an effective population size of 500 is likely to be reached. The same reasoning applies to estimates of the MVA for conservation. While the fragments studied were large enough to ensure in situ conservation in the short-, medium- and long-term, there is also the possibility of ex situ conservation (seed collection) and environmental recovery. The population bottlenecks identified in the Brotas fragment indicated that a number of loci showed heterozygosity deficits. That is, the expected heterozygosity (He) under HWE is less than the expected heterozygosity under mutation-drift equilibrium (Heq). Populations who have not yet adapted to problems associated with reduced size after a recent bottleneck may be at high risk of extinction (Lee et al. 2002). Therefore, as soon as a bottleneck is detected, mitigative management measures such as habitat enrichment or introduction of immigrants must be put in place to minimize or avoid potentially deleterious consequences (Luikart et al. 1998). Since the Brotas population is
Genetica (2014) 142:11–21
exposed to higher anthropic pressure than the other areas studied, the present results underscore the need for conservation efforts in this habitat fragment. The level of gene flow observed in the populations studied is high enough to counter the effect of genetic drift. Interestingly, most gene flow occurred between Assis and Itirapina, the most genetically similar populations, which were also part of the same cluster. Some studies associate gene flow to low population divergence and the geographical distance between populations. However, the role of geographical distance was not evident in the present study, since less gene flow was observed between the closest areas (1.8 for Brotas/Itirapina, 24 km apart) than between the farthest ones (4.1 for Assis/Itirapina, 350 km apart). The hp estimate confirmed the high levels of genetic divergence within populations. The least divergent populations were those from Assis and Itirapina. These results were congruent with others from the present study, indicating high genetic similarity between these fragments. Similarly, the high levels of divergence observed in the Pedregulho population show that it is clearly distinct from the others. Therefore, this population can be considered an ESU or MU. The reported results demonstrate that anthropic interference in the form of grazing, sugarcane plantations and Pinus and Eucalyptus forests have led to habitat fragmentation in the Cerrado biome, and influenced the genetic structure and diversity of populations in Assis, Brotas and Itirapina. The population of Pedregulho, located within an Integral Protection Conservation Unit, presented the highest genetic diversity, in spite of frequent exposure to fires. Moreover, Pedregulho is located in the Minas Gerais State border, where Cerrado is the predominant biome and their areas are continuous. According to Durigan (2003) the areas of Cerrado from the center to the north-northeast of Sa˜o Paulo are differentiated from the rest of the state due to particular environmental conditions. The population of Itirapina is also included in this pattern. The population of Brotas, although close, is under strong anthropogenic pressure, which decreased its levels of genetic diversity and reinforces the importance of conservation of this fragment. According to Brasil (2006, 2010), the Cerrado areas in the northern region of the state of Sa˜o Paulo should have the highest priority for conservation because of their ecological characteristics, size and location of native fragments, besides being one of the most heavily devastated area in the last 30 years because of agricultural expansion (Kronka et al. 2005). Considering these recommendations along with our results, we propose that the population of Pedregulho should be considered a MU and an ESU, as well as a major conservation target in the Cerrado.
19 Acknowledgments The authors would like to thank FAPESP (Fundac¸a˜o de Amparo a` Pesquisa do Estado de Sa˜o Paulo) for financial support (Grants 2007/06648-1 and 2008/06834-2), the research group LARGEA (Laborato´rio de Reproduc¸a˜o e Gene´tica de Espe´cies Arbo´reas—USP/ESALQ) for technical assistance, Lucas Antiqueira for revising this manuscript, and the reviewers for their valuable comments.
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