Genetic Diversity of Natural Populations

Aust. J. Bot., 1993,41,65-77

Genetic Diversity of Natural Populations of Acacia auriculiformis

R. Wickneswari and M. Norwati Forest Research Institute of Malaysia, Kepong, 52109 Kuala Lumpur, Malaysia.

Abstract Seeds from 18 populations of Acacia auriculiformis A. Cum. ex Benth. from natural riverine and coastal forests in Australia and Papua New Guinea were electrophoretically analysed at 22 isozyme loci representing 17 enzyme systems. Genetic variability measures were determined using 12 isozyme loci. On average, 39.8% of the loci were polymorphic (0.99 criterion). Average and effectivenumbers of alleles per locus were 1.5 and 1.1 respectively. Mean expected heterozygosity was 0.081 with values ranging from 0.002 (South Alligator River, Northern Temtory) to 0.180 (North Mibini, Papua New Guinea). The genetic differentiation between populations was high (Gs, = 0.270), indicating that about 73% of the isozyme variation was among progenies within populations. Hence, both intra- and interpopulation genetic variations are important in initial selections in A. auriculiformis improvement programs. Nei's unbiased genetic distance between populations ranged from 0.000 to 0.120, with populations from the Northern Temtory, Australia, generally being very closely related to each other. UPGMA cluster analysis using Nei's unbiased genetic distance revealed three distinct clusters of populations corresponding to the geographic distribution of the species in the Northern Territory and Queensland, Australia, and Papua New Guinea. Populations from Queensland were closely related to populations from Papua New Guinea rather than to populations from the Northern Temtory, which is in the same land mass.

Introduction Understanding the genetic structure of forest tree species is basic for an appropriate utilisation of forest genetic resources, either for genetic improvement of plantations or for management or conservation of natural communities. In a tree improvement program, knowledge of the apportionment of genetic variation within and among natural tree populations is important for initial selection. Genetic variation in forest trees has been investigated using morphological, physiological and biochemical traits. Isozymes have proved to be the best and cheapest currently available method for studying genetic variation in forest trees (Yeh 1989), compared to the use of physiological and morphological characters (Libby et al. 1969) and molecular markers (Brown 1990). Acacia auriculiformis A. Cunn. ex Benth. grows naturally in Australia, Papua New Guinea and Indonesia between the latitudes of 5 O and 17O S (Turnbull 1986). It has been found growing at altitudes of up to 400 m above sea level. The Torres Strait, which separates mainland Australia from New Guinea, was last formed less than 10 000 years ago (Walker 1972; Galloway and Kemp 1980). Torres Strait first came into existence some time in the Pleistocene (Doutch 1972), but the land bridge has come and gone several times subsequently as a result of periods of glaciation giving rise to retreating sea levels. Acacia auriculiformis has been widely planted outside its natural distribution in China, India and South-east Asia. Recently, it has also been considered for large-scale plantations in Zaire, Africa. It has mainly been utilised as fuelwood (NAS 1983). A. auriculiformis can

R. Wickneswari and M. Norwati

produce good quality timber (Keating and Bolza 1982), pulpwood (Phillips et al. 1979) and tannin (Abdul Razak et al. 1981). A. auriculiformis has been selected as one of the five priority species with multiple-use by the International Union of Forestry Research Organisations for intensive research and development in the humid tropical lowlands (IWRO 1984). Recently A. auriculiformis and A. mangium have been selected for hybrid breeding programs in Malaysia (Griffin 1988; Wickneswari 1989) and Taiwan (Hsu and Yang 1989; Kiang et al. 1989; Yang et al. 1989). In this study, we surveyed 22 isozyme loci in 18 natural populations of A. auriculiformis distributed between the latitudes of 5 O and 17O S in the Northern Territory and Queensland, Australia, and Papua New Guinea. Populations with mainly single tree seed collections and some bulked seedlots were used to estimate the intra- and interpopulation genetic variation within the species. The relatedness between populations of A. auriculiformis in Australia and Papua New Guinea separated because of glaciation was also investigated.

Materials and Methods Seed Collections Study was carried out on progenies of single trees and bulked populations of A. auriculiformis collected from 18 populations (16 were single tree collections and 2 were bulked seedlots); 6 from Papua New Guinea (PNG), 5 from Queensland (QLD) and 7 from Northern Temtory (NT), Australia (Table 1). The location of populations in the study is given in Fig. 1. Seed collections were made by the Australian Tree Seed Centre CSIRO, for each tree. The seeds analysed by electrophoresiswere chosen at random from a bulked sample representing many pods from each tree.

Table 1. Details of seed origin of the 18 populations of A. auriculiformis N, number of mother trees; QLD, Queensland; NT, the Northern Territory; PNG, Papua New Guinea; S, South, E, East; na, not available*, bulked seedlots No.

CSIRO Seedlot No.

N

Locality

Wenlock River South Coen, Cape York Morehead River Mount Molloy, Rifle Creek Kings Plains Manton River Reynolds River Douglas River Gerowie Creek South Alligator River East Alligator River Goomadeer River Bensbach Morehead R. Rouku North Mibini Mibini Swamp Old Tonda Village Mai Kussa River

Latitude (S) QLD QLD QLD QLD QLD NT NT NT NT NT NT NT PNG PNG PNG PNG PNG PNG

Longitude (E)

Altitude (m)

Genetic Diversity within A. auriculiformis

Western Aurrraa

1

Northern T e r r ~ t o r v

1

Queensand

rig. 1. Natural distribution of A. auriculiformis in Australia, Papua New Guinea and Indonesia. indicates location of populations in the study.

Electrophoresis The seeds were germinated for electrophoresis for about 4-5 days. Germination was facilitated by nicking the seedcoat to allow water absorption (Doran and Gunn 1987). For each population, 7-10 trees were analysed and 5 progenies were assayed per tree. In the case of bulked seedlots, 50 seeds per population were assayed. Starch gel electrophoresis was carried out according to standard procedures (Moran and Bell 1983). dithiothreitol and 20 mg mL-I Individual seedlings were crushed in distilled water containing 1 mg m~-' polyvinylpyrrolidone (mol. wt = 40 000). Each seedling was assayed for electrophoretic variants from 17 enzyme systems listed in Table 2. The genetic control of isozymes was inferred from banding patterns of single tree progeny arrays. For each enzyme system, the most anodally migrating isozyme was designated to locus 1, the next fastest was locus 2 and so on. Likewise, within each locus the fastest migrating band was designated allele 1 and each successively slower band was numbered 2,3, etc. Data Analysis Biosys-1 computer program (Swofford and Selander 1989) was used to analyse the isozyme data for allelic frequencies, percentage of polymorphic loci, mean number of alleles, estimates of observed and expected heterozygosities and futation indices and their variances. Only allelic frequencies were calculated for isozyme loci which did not have scorings for all populations analysed. Genetic variability measures were determined using 12 isozyme loci which had scorings for all populations. Effective number of alleles (A,), taking into account both the number of alleles and their frequencies, was calculated according to Crow and Kimura (1970). Chi-square test for deviation from Hardy-Weinberg equilibrium was performed for each locus.

R. Wickneswari and M. Nonvati

Table 2. Details of enzymes stained for, their abbreviations, enzyme commission numbers, the gel buffer systems on which they were scored and references for staining Gel buffer systems used were: H: gel, 0.005 M histidiie HCl, adjusted to pH 8.0 with 10 N NaOH, tray, 0.41 M tri-sodium citrate, adjusted to pH 8.0 with 0.41 M citric acid (Brewer and Sing, 1970); L:gel, 90% 0.065 M tris, 0.01 M citric acid, pH 8.2, and 10% tray buffer; tray, 0.05 M lithium hydroxide, 0.19 M boric acid, pH 8.5 (Mom and Hopper 1983); TC: gel, 0.1 M his, 0.0069 M citric acid, pH 8.6; tray 0.3 M boric acid, 0.1 M NaOH, pH 8.6 (Kahler and Allard 1970); MC: gel, 1 in 20 dilution of tray buffer, pH 6.1; tray, 0.04 M citric acid, adjusted to pH 6.1 with N-(3-aminopropy1)morpholine(Clayton and Tretiak 1972) Enzyme name and abbreviation

Aspartate aminotransferase Aconitate hydratase Alcohol dehydrogenase Diaphorase Esterase Glutamate dehydrogenase Glycerate dehydrogenase Glucosephosphate isomerase Isocitrate dehydrogenase Malate dehydrogenase Menadione reductase Peptidase Peroxidase Phosphoglucomutase Phosphogluconate dehydrogenase Shikimate dehydrogenase Uridine diphosphogluconate pyrophosphatase

EC number Aat ACO Adh Dia Est Gdh G~Y Gpi Idh Mdh Mdr pep Per P P Pgd Sdh

ug~

Gel buffer

Reference

Conkle et al. (1982) Yeh and O'Malley (1980) Tanksley (1979) Brewer et al. (1967) Wickneswari (unpubl.) Hartman et al. (1973) Moran (unpubl.) Delorenzo and Ruddle (1969) F i e and Costello (1963) Brown et al. (1978) Moran (unpubl.) Scandalios (1969) Brewbaker et al. (1968) Tanksley (1979) Moran and Hopper (1983) Tanksley and Rick (1980) Moran (unpubl.)

Genetic differentiation between populations was analysed by the G-statistic (Wright 1978). The Gstatistic of Nei (1973, 1975) is defined by the formula HT = HS + DST,where HS is the average gene diversity within populations. DST,which is the average gene dwersity among populations, is obtained as the difference HT-Hs. The genetic differentiation between populations is obtained as GST= DSTIHT. The genetic differentiation between populations withii and between the three geographic zones, i.e. the Northern Temtory and Queensland in Australia and Papua New Guinea, was evaluated by pooling all populations within the same geographic zones. This analysis led to the estimation of genetic differentiationbetween populations withii and between geographic zone using the formula GST= GZT + GsZ,where GZT is the genetic differentiation between populations between geographic zones and Gsz is the genetic differentiation between populations within geographic zones. The genetic distance (D) between populations was computed according to Nei (1978). These values were then used to generate a dendrogram using the unweighted pair-group method with arithmetic averaging (UPGMA) as described by Sneath and Sokal(1973).

Results For the 17 enzyme systems listed in Table 2, the progeny were scored for their genotypes at 22 loci. Allelic frequencies at these loci are given in Table 3. Loci for which no allelic frequencies are given were either not assayed or not scored in these populations. Mdh-2 and Pgd-I were invariant in all the 18 populations of A. auriculiformis. Twenty loci were polymorphic (0.99 criterion) in at least one population. The common allele was the same in every population with the exception of Acs-I, Per-1 and Sdh-1. Rare alleles were present at Aat-3, Aat-4 and Gdh-1 in three populations from PNG.


R.Wickneswari and M.Norwati


The genetic variability measures within populations are presented in Table 4. The mean number of alleles per locus within each population ranged from 1.1 to 2.0 (mean = IS), whereas the effective number of alleles ranged from 1.0 to 1.2 (mean = 1.1). The percentage of polymorphic loci (0.99 criterion) ranged from 8.3 to 75.0% (mean = 39.8%). The mean observed (H,) and expected (He) heterozygosities across populations were 0.071 and 0.081 respectively. He was highest in A. auriculiformis populations from PNG (mean = 0.133), and lowest in A. auriculiformis populations from the NT (mean = 0.040). Although generally He was greater than H,, the differences were not significant, indicating a small deficiency in heterozygotes. Chi-square analysis of genotype frequencies for each locus indicated no significant departure from the Hardy-Weinberg equilibrium. G-tests showed that the total gene diversity of A. auriculiformis was high at several loci with the largest value at Per-1 (HT=0.571 (Table 5). Table 4. Estimates of mean number of alleles per locus (A), effective number of alleles (A,), percentage of polymorphic loci (0.99 criterion) (P), observed (H,) and expected (He) heterozygosities for 18 populations of A. auriculifonnis N, number of seedlings assayed; values in parentheses indicate s.d. Population Wenlock River South Coen, Cape York Morehead River Mount Molloy, Rifle Creek Kings Plains Manton River Reynolds River Douglas River Gerowie Creek South Alligator River East Alligator River Goomadeer River .Bensbach Morehead R.Rouku North Mibini Mibini Swamp Old Tonda Village Mai Kussa River Mean The mean HT for A. auriculiformis was 0.134. A. auriculiformis exhibited large degrees of genetic differentiation between populations at a number of loci with the largest value of GST = 0.484 at Per-1 (Table 5). The overall degree of genetic differentiation between populations (GST) was 0.270 indicating that about 73.0% of the isozyme variation was among progenies within populations. G S analysis of geographic zones showed that the genetic differentiation between zones was 30.7%. This accounts for only about 6.3% of the genetic differentiation between populations within geographic zones, indicating that the populations in the different geographic zones, i.e. the NT, QLD and PNG are genetically quite distinct from each other.


Table 5 :Gene diversity parameters in A. auriculifonnis populations HT, total gene diversity; DST,average gene diversity among populations; H,, average gene diversity within populations; GST,genetic differentiation between populations;GZT,genetic differentiation between geographic zones; GsZ,genetic differentiation between populations within geographic zones LOCUS

HT

D~~

Hs

G~~

GZT

Gsz

Aat-1 Aat-2 Aat-3 Aat-4 Adh-2 Gdh-I Idh-I Mdh-I Per-I Pgd-2 Mean

Inspection of single tree progeny genotype arrays (data not presented) at the more variable loci strongly suggested that the species is predominantly outcrossing. The mean genetic distances D (Nei 1978) between populations are summarised in Table 6. The mean D value was 0.035 with a range of 0.000 (Reynolds River and South Alligator River, NT) to 0.120 (Wenlock River, QLD, and Goomadeer River, NT). The D values for A. auriculiformis populations between different geographic regions were high and variable. The D values for A. auriculiformis populations in the NT were near zero, with the exception of Goomadeer River, indicating that they are very closely related to each other. UPGMA cluster analysis using Nei's unbiased genetic distance (Fig. 2) revealed three distinct clusters of populations corresponding to the natural distribution of the species in the NT, QLD and PNG. Populations from QLD were more closely related to populations from PNG rather than to populations from the NT which is in the same land mass. Within QLD, the South Coen population was different from the rest in the area, whereas in the NT, Goomadeer River was distinctly different from the other populations in the area. In PNG, genetic dissimilarity between populations was not apparent. Discussion Generally, genetic diversity in the A. auriculiformis populations (He = 0.081) was similar to preliminary estimates available for neotropical rainforest trees (He = 0.1 11, Hamrick and Loveless 1986), but less than temperate wind-pollinated conifers (He = 0.207, Hamrick et al. 1981) or animal-pollinated eucalypts (He = 0.182, Moran and Hopper 1987). The mean genetic diversity was, however, higher than A. mangium (He = 0.017, Moran et al. 1989a) which has been considered a genetically depauperate species among the acacias. The present study demonstrated low levels of genetic diversity in all A. auriculiformis populations from the Northern Territory (mean He = 0.040) compared with either Queensland (mean He = 0.079) or Papua New Guinea (mean He = 0.133) populations, indicating probably the limited gene exchange within and between the populations in the isolated monsoon vine forests of this region. This could be due to only a few mature trees representing some of these populations in the region as noted by Boland et al. (1990).

1. Wenlock River 2. South Coen Cape York 3. Morehead River 4. Mount Molloy Rifle Creek 5. Kings Plains 6. Manton River 7. Reynolds River 8. Douglas River 9. GerowieCreek 10. South Alligator River 11. EastAlligator River 12. GoomadeerR. 13. Bensbach 14. MoreheadR. Rouku 15. NorthMibini 16. Mibini swamp 17. OldTonda Village 18. MaiKussaR.

Population

0.009

0.045 0.032 0.043 0.029 0.027

0.011

0.033 0.034 0.023

0.032 0.036 0.020

0.031 0.026 0.025 0.021 0.017 0.038 0.045 0.016 0.015 0.016 0.018 0.015 0.041 0.044 0.028 0.012 0.025 0.019 0.018 0.030 0.033 0.015 0.019

0.022 0.011 0.014

9

0.120 0.102 0.103 0.102 0.082 0.023 0.020 0.028 0.054 0.044 0.052 0.037 0.035 0.013 0.019 0.006 0.031 0.024 0.031 0.019 0.018 0.020 0.024 0.014

8

0.002

7

0.091 0.071 0.091 0.082 0.074 0.001 0.001 0.003

6

0.001

0.071 0.074 0.062 0.058 0.074

5

0.001 0.002 0.005 0.001 0.001 0.003 0.002 0.00 0.006

0.004 0.091 0.093 0.080 0.077 0.093

0.004 0.078 0.082 0.066 0.064 0.082

0.022 0.073 0.074 0.060 0.061 0.074

0.012 0.091 0.093 0.081 0.076 0.093

4

-

-

0.020

3

0.009 0.016 0.008 0.024 0.010

2

1

-

11

12

13

-

14

15

0.020 0.018

17

0.009 -

-

16

0.039 0.002 0.001 0.011 0.017 0.003

0.045 0.044 0.059 0.016 0.009 0.044 0.042 0.065 0.024 0.011 0.005 0.033 0.031 0.054 0.010 0.002 0.009

0.020 0.022 0.020 0.016 0.038 0.024 0.023 0.046 0.005

0.001

-

10

Table 6. Estimates of mean genetic distance (Nei 1978) between 18 populations of A.auriculiformis

-

18


G r n r t c Ditance

WENLOCK RIVER I11 MDREHEAD RIVER 131 MOUNT MOLLOY. RIFLE CREEK 141 KINGS PLAINS 151 SOUTH COEN CAPE YORK 121

1

/

4

MOREHEAD R ROUKU 1141 MA1 KUSSA RIVER l l B 1 O L D TDNDA VILLAGE 1171

1

MANTON RIVER 161 GEROWE CREEK 191 REYNOLDS RIVER 171 SOUTH ALLIGATOR 1101 EAST ALLIGATOR I l l 1 DOUGLAS RIVER I81

I GOOMADEER RIVER 1121

Fig. 2. A dendrogram based on UPGMA clustering of A. auriculiformis populations using the genetic distances of Nei (1978). Numbers in parentheses indicate population number as shown in Fig. 1.

Higher levels of isozyme polymorphism were detected in the Papua New Guinea populations than in the Northern Territory and Queensland populations. Furthermore, loci with appreciable variation in populations outside Papua New Guinea were also variable within Papua New Guinea. These fidings suggest that Papua New Guinea is likely to be the centre of diversity for A. auriculifomzis. The presence of a number of rare allelic variants (e.g. allele 4 at Aat-3 and allele 2 at Gdh-I) only in the Papua New Guinea populations further supports this interpretation. These rare allelic variants could be due to mutations arising in the populations in the pre- or post-glacial period. Alternatively, the rare alleles could be introduced into the gene pools of the population through hybridisation with other co-occuning acacias (Skelton 1987; Gum et al. 1989; Wickneswari 1989). The degree of between-population differentiation in A. auriculiformis (GST = 0.270) was higher than in both insect-pollinatedeucalypts (GST= 0.10-0.12, Moran and Hopper 1987) and wind-pollinated conifers (GST = 0.05, Matheson et al. 1989). The genetic differentiation between populations was largely due to differences between the three geographic zones. The average genetic variation within populations in A. auriculiformis was low (Hs = 0.098). Windpollinated conifers have large population sizes with much migration between populations and such populations do not become differentiated because of extensive migration. Furthermore, their seeds are also widely dispersed by wind. In animal-pollinated species, the levels of within- and between-population variability are determined by the effective flight distance of the pollinators. There could be a more restricted gene flow in the case of short-distance flight pollinators and a wider gene flow in the case of animals with long distance flight. Gene flow in animal-pollinated species is also dependent on the mechanism of seed dispersal. Seed dispersal in animal-pollinated species could be by wind, water, animal or explosive mechanism.


The main pollinators of A. auriculiformis are small bees belonging to the Apidae (Trigona spp.), Collitidae and beetles (Sedgley et al. 1992). A. auriculiformis pods dehisce when mature and the seeds drop to the forest floor. A small proportion of the seeds are also dispersed by animals, especially birds. Hence, the observed high genetic differentiation between the A. auriculiformis populations could be related to these restricted pollen flow and seed dispersal mechanisms. The lower genetic diversity and higher degrees of population differentiation than in conifers observed in this study suggest that A. auriculiformis could occur in small effective population sues. Cluster analysis of isozyme variation revealed the existence of three distinct groups of A. auriculiformis populations corresponding to the three major natural distribution areas of the species; Queensland and the Northern Territory in Australia and Papua New Guinea. The cluster analysis showed that the Queensland populations were more closely related to the Papua New Guinea populations rather than to the Northern Temtory populations which are in the same land mass. This suggests that small scattered refuges from Papua New Guinea which had survived during the interglacial periods of high sea levels may have recolonised the coastal lowlands of Queensland, leading to expansion of the species further inland. The cluster analysis also showed that Goomadeer is different from the rest of the populations in the Northern Temtory, suggesting that Goomadeer could have been one of the early refuge populations to have recolonised in the Northern Territory. Besides Goomadeer, the rest of the Northern Territory, populations were generally genetically depauperate. Reduced genetic variability is generally assumed to result in increased susceptibility to agents of stress (Ledig 1986). Such genetically depauperate populations are also the most likely candidates for extinction. Based on evidence presented in this paper and previous work by Moran et al. (1989b) on breeding systems and Pinyopusarerk e t al. (1991) on morphological variation, A. auriculiformis appears to have sufficient genetic variation to support a selective breeding program. Since the species is predominantly outcrossing and the genetic differentiation between populations is high, sampling a few trees from many populations would be most effective in capturing its natural genetic variation. Sampling large numbers of pods from individual trees will result in a genetically diverse sample. Selection of provenances for tree breeding and reforestation projects should be carried out carefully.

Acknowledgments We thank ACIAR for research support. We thank Mr K. Pinyopusarerk of the CSIRO Australian Tree Seed Centre for providing the seed samples. We are grateful to Dr Salleh Mohd. Nor and Dr Wan Razali Wan Mohd of the Forest Research Institute Malaysia and Dr A.R. Griffin of the Division of Forestry, CSIRO (now at Shell, London) for their support for this study. The technical assistance of Miss Juraidah Mohd. Dom is gratefully acknowledged. A previous draft of this paper benefited enormously from the comments and criticisms of Dr G. F. Moran and Mr J. C. Bell of the Division of Forestry, CSIRO. References Abdul Razak, M.A., Low, C.K.,and Abu Said, A. (1981). Determination of relative tannin contents of the barks of some Malaysian plants. Malaysian Forester 44,87-92. Boland, D.J., Pinyopusarerk, K., McDonald, M.W., Jovanoic, T., and Booth, T.H.(1990). The habitat of Acacia auriculiformis and probable factors associated with its distribution. Journal of Tropical Forest Science 3(2), 159-180. Brewbaker, J.L., Upadhya, M.D., Makinen, Y., and Macdonald, T. (1968). Isozyme polymorphism in flowering plants. ID.Gel elecmophoretic methods and applications. Physiologia Plantarum 21,930 - 940. Brewer, G.J., and Sing, C.F. (1970). 'An Introduction to Isozyme Techniques.' (Academic Press: New York.)


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