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Spatial Distribution Pattern of the Populations of Camellia japonica in Busan

부산 사하구 동백나무 집단의 공간적 분포 양상

  • Kang, Man Ki (Department of Data Information Science, College of Natural Sciences & Human Ecology, Dongeui University) ;
  • Huh, Man Kyu (Department of Molecular Biology, College of Natural Sciences & Human Ecology, Dongeui University)
  • 강만기 (동의대학교 자연.생활과학대학 데이터정보학과) ;
  • 허만규 (동의대학교 자연.생활과학대학 분자생물학과)
  • Received : 2014.05.09
  • Accepted : 2014.07.13
  • Published : 2014.08.30

Abstract

The spatial distribution of geographical distances at five natural populations of Camellia japonica in Busan, Korea was studied. The four plots (Mollundae, Gadeok-do, Du-do, and Jwiseum) of C. japonica were uniformly distributed in the forest community and only one plot (Amnam-dong) was aggregately distributed in the forest community. Morisita index is related to the patchiness index showed that the plot $20m{\times}50m$ had an overly steep slope when the area was larger than $20m{\times}20m$, which indicated that the degree of aggregation increased significantly with increasing quadrat sizes, while the patchiness indices did not change from the plot $5m{\times}10m$ to $10m{\times}10m$. The spatial structure was quantified by Moran's I, a coefficient of spatial autocorrelation. Ten of the significant values (76.9%) were positive, indicating similarity among individuals in the first 4 distance classes (80 m), i.e., pairs of individuals with dissimilarity characteristics can separate by more than 100 m.

부산광역시 사하구 동백나무 네 집단과 강서구 가덕도의 동백나무 한 집단 분포지에 대한 지리적 거리에 의한 공간적 분포 양상을 연구하였다. 네 프롯(몰운대, 두도, 쥐섬, 가덕도)은 군집에서 균질한 분포 양상을 나타내었으나 한 플롯(암남동)은 응진 형태를 나타내었다. 모리시타 지수는 패치 지수와 유관하며 $20m{\times}20m$ 프롯보다 큰 $20m{\times}50m$ 프롯으로 값이 급격한 증가를 나타내었는데 이는 방형구가 커지면 응집의 정도가 유의하게 증가한다는 것을 의미한다. 반면 패치지수는 $5m{\times}10m$에서 $10m{\times}10m$까지는 큰 변화가 없었다. 공간적 상관관계 계수인 Moran's I에 의해 유의한 공간 구조를 정량화하였다. 유의한 개체간 유사도(76.9%)는 처음 4거리 등급(80 m)에서 유사성을 보였으며 100 m거리를 초과하면 비유사성 특성을 지닌 개체들의 쌍은 분리될 수 있다.

Keywords

Introduction

In recent decades, there was much increase in the statistical tools used in spatial ecology [13]. Botanists, ecologists, geographers and plant evolutionary biologists have long recognized that plants are not distributed at random within communities but are rather clustered in distinct patches [8, 20]. Environmental heterogeneity is usually cited as playing a critical role in determining the spatial structure, but colonization patterns and stochastic events affecting establishment and mortality are also important [18]. More recently, plant evolutionary biologists have demonstrated that genetic variations in plant populations are also nonrandomly distributed [6]. This nonrandom distribution of genetic variation is often referred to as the genetic structure of a population [9]. The genetic structure is an integral part of the process of population genetics [5]. Population structure interacts with a number of factors: microenvironmental heterogeneity, mortality due to stochastic events [19], and mating systems that feature limited dispersal of seed or pollen [5].

Of the several methods of describing the spatial distribution of plant community the simplest way is percentage distribution of individuals over the geographical areas. Another methodology usually adopted is to list the geographical areas of a given class into rank order which enables comparison of ranking from individual to individual. In this report, the several statistical tools of percentage distribution and population structure of the geographical areas are used to study the spatial distribution of Camellia japonica in Busan.

In theory, genetic differentiation over short distances may occur either as a result of spatially variable selection or localized genetic drift, provided that gene flow is sufficiently restricted [14]. Indirect evidence for genetic correlations between neighboring plants has been obtained from data on mating systems [5]. Localized seed and pollen dispersals produce family clusters within these populations [7]. Several studies have revealed decreased seed set and seed survivorship from mating between genetically similar near-neighbors, which has been interpreted as inbreeding depression [10].

In the wild, C. japonica is found in mainland China (Shandong, east Zhejiang), Taiwan, southern Korea and southern Japan. C. japonica is sometimes called the rose of winter, it belongs to the Theaceae family. An edible oil is obtained from the seeds of this species. The leaves are a tea substitute. The dried flowers used as a vegetable or mixed with gelatinous-rice to make a Japanese food called ‘mochi’.

Saha-gu and Gangseo-gu locate in south of the Korean, and at the western part of the North Pacific Ocean. A sample of a large (more than 300 individuals) natural population of wild species C. japonica collected at Saha-gu and Gangseogu in Korea was used in this study.

The purpose of this paper was to describe a statistical analysis for detecting a species association, which is valid even when the assumption of within- species spatial randomness is violated. The purpose of this study is to find if there a spatial structure within four populations of C. japonica and 2) if so, what is the spatial pattern and if it is the same for all populations?

 

Materials and Methods

Study area

We conducted the spatial analysis in the communities of Camellia japonica at Saha-gu and Gangseo-gu in Busan-si (Fig. 1). This area is on the southern margin of Busan. It has a temperate climate with a little hot and long summer. In this region the mean annual temperature is 14.7℃ with the maximum temperature being 29.4℃ in August and the minimum −0.6℃ in January. Mean annual precipitation is about 1519.1 mm with most rain falling period between June and August.

Fig. 1.The five studied populations of Camellia japonica in Busan, Korea. ANM: Amnam-dong, DUD: Du=do, JWI: Jwiseum, GAD: Gadeok-do

Sampling procedure

We established nine plots with an area of 20 m × 160 m each around four populations at Saha-gu and one population at Gangseo-gu in Busan, 2014. We randomly located quadrates in each plot which we established populations. The quadrat sizes were 5 m × 5 m, 5 m × 10 m, 10 m × 10 m, 10 m × 20 m, 20 m × 20 m, and 20 m × 50 m. We mapped all plants to estimate population density.

Index calculation and data analysis

The spatial pattern of C. japonica was analyzed according to the Neatest Neighbor Rule [3, 11] with Microsoft Excel 2010.

Average viewing distance (rA) was calculated as follows:

Where ri is the distance from the individual to its nearest neighbor. N is the total number of individuals within the quadrat.

The expectation value of mean distance of individuals within a quadrat (rB) was calculated as follows:

Where D is population density and D is the number of individuals per plot size. R = rA / rB

When R>1, it is a uniform distribution, R=1, it is a random distribution, R<1, it is an aggregated distribution.

The significance index of the deviation of R that departs from the number of “1” is calculated from the following formula [11].

When CR >1.96, the level of the significance index of the deviation of R is 5%, and when CR >2.58, the level is 1%.

We calculated the degree of population aggregation under different sizes of plots by dispersion indices: index of clumping or the index of dispersion (C), aggregation index (CI), mean crowding (M*), patchiness index (PAI), negative binominal distribution index K, Ca indicators (Ca is the name of one index) [12] and Morisita index (IM) were calculated with Microsoft Excel 2010. The formulae are as follows:

Index of dispersion: C = S2/m Aggregation index Mean crowding = m+CI=m+C−1−1 Patchiness index Aggregation intensity Ca indicators Ca = 1/k IM =

Where S2 is variance and m is mean density of C. japonica.

When C, M*, PAI >1, it means aggregately distributed, when C, M*, PAI <1, it means uniformly distributed, when CI, PA, Ca >0, it means aggregately distributed, and when CI, PA, Ca <0 it means uniformly distributed.

We used the mean aggregation number to find the reason for the aggregation of C. japonica [1]. δ = mr/2k

Where r is the value of chi-square when the degree of freedom is 2k and k is the aggregation intensity.

Spatial structure

Numerical simulations of previous analyses were performed to investigate the significant differences at various distance scales, i.e., 10.0, 20.0 m, and so on. However, no significant population structure was found within the 20.0 m distance classes by means of Moran's I, and a significant population structure was revealed beyond 20.0-m. Thus, the distance classes are 0-20.0 m (class I), 20.0-40.0 m (class II), 40.0-60.0 m (class III), 60.0-80.0 m (class IV), 80.0-100.0 m (class V), 100.0-120.0 m (class VI), 120.0-140.0 m (class VII), and 140.0-160.0 m (class VIII). The codes of classes are the same as in the distance classes and are listed Table 1.

Table 1.R and CR were shown in text.

The spatial structure was quantified by Moran's I, a coefficient of spatial autocorrelation (SA) [15, 16]. As applied in this study, Moran's I quantifies the similarity of pairs of spatially adjacent individuals relative to the population sample as a whole. The value of I ranges between +1(completely positive autocorrelation, i.e., paired individuals have identical values) and −1(completely negative autocorrelation). Each plant was assigned a value depending on the presence or absence of a specific individual. If the ith plant was a homozygote for the individual of interest, the assigned pi value was 1, while if the individual was absent, the value 0 was assigned.

Pairs of sampled individuals were classified according to the Euclidian distance, dij, so that class k included dij satisfying k − 1 < dij < k + 1, where k ranges from 1 to 7. The interval for each distance class was 20 m. Moran's I statistic for class k was calculated as follows:

where Zi is pi − p (p is the average of pi); Wij is 1 if the distance between the ith and jth plants is classified into class k; otherwise, Wij is 0; n is the number of all samples and S is the sum of in class k. Under the randomization hypothesis, I(k) has the expected value u1 = −1/(n − 1) for all k. Its variance, u2, has been given, for example, in Sokal and Oden (1978a). Thus, if an individual is randomly distributed for class k, the normalized I(k) for the standard normal deviation (SND) for the plant genotype, g(k) = {I(k) − u1}/u21/2, asymptotically has a standard normal distribution [3]. Hence, SND g(k) values exceeding 1.96, 2.58, and 3.27 are significant at the probability levels of 0.05, 0.01, and 0.001, respectively.

 

Results

The spatial pattern of individuals

Population densities (D) varied from 0.097 to 0.190, with a mean of 0.130 (Table 1). The values of spatial distance (the rete of observed distance-to-expected distance) among the nearest individuals were higher than 1 but Amnam-dong was lower than 1. The four plots (Mollundae, Gadeok-do, Du-do, and Jwiseum) of C. japonica were uniformly distributed in the forest community and only one plot (Amnam-dong) was aggregately distributed in the forest community (Table 1).

The degree of population aggregation

Dispersion index (C) were higher than 1 except for three quadrats (5 m × 5 m, 5 m × 10 m, and 10 m × 10 m) of Amnam-dong (Table 2). As the sizes of quadrat were greater, the values of C. japonica were high. Thus aggregation indices were positive except for plots which indicate a clumped distribution. The values of PI and Ca were shown greater than zero. The values of PAI except three quadrats of Anman-dong were greater than 1 (Table 2). Thus, the most individuals of C. japonica were clustered and the distribution pattern of the C. japonica was quadrat-sampling dependent. When the sampling quadrat in Amnam-dong was smaller than 10 m × 10 m, C. japonica were aggregately distributed, and when the sampling quadrat was greater than 10 m x 10 m, the aggregation index showed the trend of being uniformly distributed for C. japonica. The mean crowding (M*) and aggregation intensity (Ca) indicator were higher with a big quadrat.

Table 2Aggregation indices were shown in text.

Morisita index (IM) related to the patchiness index (PAI) showed that the plot 20 m × 50 m had an overly steep slope when the area was larger than 20 m × 20 m, which indicated that the degree of aggregation increased significantly with increasing quadrat sizes, while the patchiness indices did not change from the plot 5 m × 10 m to 10 m × 10 m (Fig. 2).

Fig. 2.The curves of patchiness in four populations of Camellia japonica using values of Green index.

The mean aggregation number analysis showed that the reasons for aggregation of C. japonica differed in quadrats with different plot sizes (Table 3). The cluster at 5 m × 5 m quadrat was determined by environmental factors. When the sizes were greater than 10 m × 10 m quadrat, the clusters were determined by both species characteristics and environmental factors.

Table 3.* p<0.05, ** p<0.01, *** p<0.001.

Fig. 3.The changes in the mean aggregation numbers for five populations.

Analysis of spatial autocorrelation

The spatial autocoefficient, Moran's I is presented in Table 3. Separate counts for each type of joined individuals and for each distance class of separation were tested for significant deviation from random expectations by calculating the SND. Moran's I of C. japonica significantly differed from the expected value in only 13 of 35 cases (37.1%). Thirteen of these values (37.1%) were negative, indicating a partial dissimilarity among pairs of individuals in the seven distance classes. Ten of the significant values (76.9%) were positive, indicating similarity among individuals in the first four distance classes, i.e., pairs of individuals can separate by more than 100 m. Namely, significant aggregations were partially observed within IV classes. As a matter of course, the negative SND values at classes IV, V, and VI. Thus, dissimilarity among pairs of individuals could be found by more than 100 m.

The comparison of Moran’s I values to a logistic regression indicated that a highly significant percentage of individual dispersion in C. cammelia populations of the Saha-gu and Gangseo-gu could be explained by isolation by distance.

 

Discussion

When the value of δ is less than 2, the aggregation is mainly caused by the environmental factors [11]. When δ is higher than 2, the aggregation is mainly caused by both species characteristics and environmental factors [11]. We recognized that the important environmental factors might be considered competition, growth rate, little decomposition, light, and below-ground resources. The characteristics of the C. japonica included primarily their life history, artificial disturbance, and population density. Life history theory seeks to understand the variation in traits such as growth rate, number and size of offsprings and life span observed in nature, and to explain them as evolutionary adaptations to environmental conditions [17]. The cluster was determined by environmental factors when the sampling quadrat was smaller than 10 m × 10 m (<2). Artificial disturbance such as constitutional roads is an important environmental factor affecting C. japonica in Amnam-dong. At the plots which had fewer C. japonica, the cluster was mainly determined by C. japonica themselves; the mean value of the aggregation index changed irregularly with the variation in plot sizes.

A significant positive value of Moran's I indicated that pairs of individuals separated by distances that fell within distance class IV had similar individuals, whereas a significant negative value indicated that they had dissimilar individuals. The overall significance of individual correlograms was tested using Bonferroni's criteria. The results revealed that patchiness similarity was shared among individuals within up to a scale of an 80 m distance. Thus it was looked for the presence of dispersion correlations between neighbors at this scale.

The results from this study are consistent with the supposition that a plant population is subdivided into local demes, or neighborhoods of related individuals [4, 7]. Previous reports on the local distribution of genetic variability suggested that microenvironmental selection and limited gene flow are the main factors causing substructuring of alleles within a population [5].

The community of C. japonica at Gadeok-do is the Busan Natural Monument No. 38. There are some communities of the Korean Natural Monuments of C. japonica: No. 66 (Daecheon-do, Ungjin-gun, Gyeonggi-do). No. 161 (Baekryeonsa, Gangjin-gun, Jeon-nam), No. 169 (Marhang-ri, Seocheon-gun, Chung-nam), No. 184 (Seonsa, Gochang-gun, Jeon-buk), No. 489 (Okrhongsa, Gwangyang-ci, Jeon-nam). C. japonica is one of very important resource of East Asia. The camellia in Europe was brought details of over 30 varieties back from Asia. Camellias were introduced into Europe during the 18th century and had already been cultivated in the Orient for thousands of years.

In conclusion, C. japonica populations within the Saha-gu and Gangseo-gu were observed a strong spatial structure. Neighboring patches of C. japonica were predominantly 80 to 100 m apart on average. The present study demonstrates that a spatial structure of C. japonica in the Saha-gu and Gangseo-gu populations could be explained by isolation by distance, limited gene flow, and topography. The results of this study were used as systematic conservation planning which is an effective way to seek and identify efficient and effective types of reserve design to capture or sustain the highest priority biodiversity values and to work with communities in support of local ecosystems.

References

  1. Arbous, A. G. and Kerrich, J. E. 1951. Accident statistics and the concept of accident proneness. Biometrics 7, 340-342. https://doi.org/10.2307/3001656
  2. Clark, P. J. and Evans, F. C. 1954. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445-453. https://doi.org/10.2307/1931034
  3. Cliff, A. D. and Ord, J. K. 1971. Spatial autocorrelation. Pion, London.
  4. Ehrlich, P. R. and Raven, P. H. 1969. Differentiation of populations. Science 165, 1228-1232. https://doi.org/10.1126/science.165.3899.1228
  5. Epperson, B. K., Chung M. G. and Telewski, F. W. 2003. Spatial pattern variation in a contact zone of Pinus ponderosa and P. arizonica (Pinaceae). Am J Bot 90, 25-31. https://doi.org/10.3732/ajb.90.1.25
  6. Evans, K. L., Duncan, R. P., Blackburn, T. M. and Crick, H. Q. P. 2005. Investigating geographic variation in clutch size using a natural experiment. Functional Ecol 19, 616-624. https://doi.org/10.1111/j.1365-2435.2005.01016.x
  7. Garnier, L. K. M., Durand, J. and Dajoz, I. 2002. Limited seed dispersal and microspatial population structure of an agamospermous grass of west African savannahs, Hyparrhenia diplandra (Poaceae). Am J Bot 89, 1785-1791. https://doi.org/10.3732/ajb.89.11.1785
  8. Hamrick, J. L., Godt, M. J. W. and Sherman-Broyles, S. L. 1992. Factors influencing levels of genetic diversity in woody plant species. New Forests 6, 95-124. https://doi.org/10.1007/BF00120641
  9. Hayakawa, T., Tomaru, N. and Yamamoto, S. 2004. Stem distribution and clonal structure of Chamaecyparis pisifera growing in an old-growth beech-conifer forest. Ecol Res 19, 411-420. https://doi.org/10.1111/j.1440-1703.2004.00652.x
  10. Ishida, T. A. and Kimura, M. T. 2003. Assessment of within-population genetic structure in Quercus crispula and Q. dentate by amplified fragment length polymorphism analysis. Ecol Res 18, 619-623. https://doi.org/10.1046/j.1440-1703.2003.00583.x
  11. Lian, X., Jiang, Z., Ping, X., Tang, S., Bi, J. and Li, C. 2012. Spatial distribution pattern of the steppe toad-headed lizard (Phrynocephalus frontalis) and its influencing factors. Asian Herpet Res 3, 46-51. https://doi.org/10.3724/SP.J.1245.2012.00046
  12. Lloyd, M. 1967. Mean crowding. J Anim Ecol 36, 1-30. https://doi.org/10.2307/3012
  13. Reiczigel, J., Lang, Z. Rozsa, L. and Tothmeresz, B. 2005. Properties of crowding indices and statistical tools to analyze parasite crowding data. J Parasitol 91, 245-252 https://doi.org/10.1645/GE-281R1
  14. Schoen, D. J. and Latta, R. G. 1989. Spatial autocorrelation of genotypes in populations of Impatiens pallida and Impatiens capensis. Heredity 63, 181-189. https://doi.org/10.1038/hdy.1989.90
  15. Sokal, R. R. and Oden, N. L. 1978a. Spatial autocorrelation in biology 1. methodology. Biol J Linn Soc 10, 199-228. https://doi.org/10.1111/j.1095-8312.1978.tb00013.x
  16. Sokal, R. R. and Oden, N. L. 1978b. Spatial autocorrelation in biology 2. some biological implications and four applications of evolutionary and ecological interest. Biol Linn Soc 10, 229-249. https://doi.org/10.1111/j.1095-8312.1978.tb00014.x
  17. Souza, A. F. and Martins, F. R. 2004. Microsite specialzation and spatial distribution of Geonoma brevispatha, a clonal palm in south-eastern Brazil. Ecol Res 19, 521-532. https://doi.org/10.1111/j.1440-1703.2004.00670.x
  18. Stearns, S. 1992. The Evolution of Life Histories. Oxford Univ. Press, UK.
  19. Walter, R. and Epperson, B. K. 2004. Microsatatellite analysis of structure among seedling in populations of Pinus strobus (Pinaceae). Am J Bot 91, 549-557. https://doi.org/10.3732/ajb.91.4.549
  20. Woodward, F. I. 1987. Climate and plant distribution. Cambridge Univ. Press, Cambridge, UK.
  21. Xia, B. and Abbott, D. 1987. Edible seaweeds of China and their place in the Chinese diet. Econ Bot 41, 341-353. https://doi.org/10.1007/BF02859049

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