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Population Biology |
Departamento de Biología Vegetal, Universidad Politécnica de Madrid, Ciudad Universitaria, E-28040, Madrid, Spain
Received for publication December 3, 2002. Accepted for publication August 5, 2003.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: Antirrhinum microphyllum • constrained ordination • directional correlogram • genetic vicinities • Mantel correlogram • Mantel test • Moran's I • RAPD • Scrophulariaceae • spatial autocorrelation
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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; Williams and Hamrick, 1996
). The distribution of genetic variation in space is also a prime factor to consider in the conservation and management of natural populations (McCue et al., 1996
). For example, sampling methods for seed stocks and decisions on the size of an area for population preservation may greatly benefit from this information (Maki and Yahara, 1997
; Chung et al., 1998
). However, most genetic studies on endangered plants either lack any spatial considerations or analyze them only at scales where biological interpretations at the population level are not straightforward.
In a previous work, we studied the genetic diversity of the four known populations of endangered Antirrhinum microphyllum Rothm. (Scrophulariaceae). Allozyme analysis detected higher homozygosity than expected from the Hardy-Weinberg equilibrium and suggested that the deficit of heterozygotes might result from biparental inbreeding (Torres et al., 2003
). As inbreeding can derive from spatial structuring of genotypes in a population (Ennos and Clegg, 1982
; Turner et al., 1982
), we were led to explore the possible existence of nonrandom spatial genetic structures in the populations, associated to certain ecological traits.
Genetic diversity was assessed using random amplified polymorphic DNA (RAPD) analysis (Williams et al., 1990
). This technique has proved to be a useful tool for the genetic characterization of endangered species, as it requires no prior sequence-specific information and only small quantities of tissue are necessary (see Fritsch and Rieseberg, 1996
). The amplified fragments provide a large number of potentially polymorphic loci, which make RAPDs appropriate for distinguishing between related genotypes (e.g., Russell et al., 1993
; Heun et al., 1994
; Ayres and Ryan, 1997
). Nevertheless, RAPD markers have been criticized owing to reproducibility problems and high sensitivity to small alterations in polymerase chain reaction (PCR) conditions. Such nonsystematic bias may mask spatial genetic structure, but it is highly unlikely that such variation could generate spatial structure as an artefact (Degen et al., 2001
).
For the study of spatial genetic structure, we employed three complementary techniques. Firstly, the relationships between spatial and genetic data were tested by means of a Mantel test (Legendre and Fortin, 1989
). Secondly, the weight of the spatial structure on genetic diversity was estimated and the critical spatial dimensions were outlined. This was conducted using a constrained ordination technique for hypothesis testing in which each spatial component conforms the constraining matrix (see ter Braak and Prentice, 1988
; Legendre and Anderson, 1999
). Finally, we used both Moran and Mantel correlograms to describe the shape of the relationship.
We aimed to link the resulting knowledge of the spatial distribution of genetic diversity to existing information on ecological traits of the species to obtain a better understanding of the biological processes that govern the dynamics of the populations and to develop sound criteria for the conservation and management of this species.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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) and mainly pollinated by a solitary bee, Rhodanthidium sticticum (Megachilidae) (Torres et al., 2001
). Each plant produces on average 9000 seeds (Torres et al., 2002
). Primary seed dispersal from capsules takes place by gravity (E. Torres, personal observation). The four known populations of this species (Entrepeñas, Anguix, Bolarque, and Buendía) are located in the northern half of Sierra de Altomira (Guadalajara). The plants grow under similar Mediterranean climate and edaphic conditions, and they are only found in small crevices on vertical dolomite cliffs.
This species is classified as vulnerable according to the International Union for Conservation of Nature (IUCN) categories due to its narrow geographical range (30 km2) (Gómez-Campo, 1987
; VVAA., 2000
). At present, it is protected by the Castilla-La Mancha Autonomous Government (Anonymous, 1998
).
Plant material and RAPD amplification
Plant material was collected from Entrepeñas and Bolarque, the two most geographically distant populations (15 km). We chose these populations because allozyme studies had shown that the mean fixation index across loci of Entrepeñas was positive and significantly different from zero, indicating an excess of homozygotes, whereas in Bolarque this parameter was not significantly different from zero (Torres et. al., 2003
). Thus, we wanted to cover two possibly different scenarios of within-population spatial genetic structure in Antirrhinum microphyllum. Young leaves of A. microphyllum were collected from 46 plants chosen at random throughout the area of distribution of each population. This represented a sampling effort of about 10% in each population. The location of sampled plants is shown in Figs. 1a and 2a. DNA extraction, primer screening, and amplification were performed as described in Torres et al. (2003)
. The 12 primers used for amplification (eight from the University of British Columbia Biotechnology Laboratory, Vancouver, Canada, and four from Operon Technologies, Alameda, California, USA) generated a total of 67 bands.
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). The receiver was a 12-channel Trimble Pro XRS of L1 frequency and C/A code (Trimble, Sunnyvale, California, USA). Corrections were made by Omnistar (Fugro N.V., Leidschendam, Netherlands). All the observations were carried out with a position dilution of precision (PDOP) <4 and a minimum number of six satellites. The receptor was set in each position for at least 2 min. The accuracy of the position using this method and these conditions is below 1 m. A matrix of euclidean geographic distances between all possible pairs of plants was calculated from the x, y, and z coordinates. Amplification products were treated as phenotypes, where each band represented a character with present/absent states (1 or 0, respectively). Thus, a 46 x 67 matrix of RAPD phenotypes was formed for each population (hereafter called the basic genetic matrix), where individuals were represented by vectors of ones and zeroes across all primers. A euclidean genetic distance matrix was then calculated from the basic genetic matrix. In addition, the multidimensional genetic information of every individual was summarized in a new consensus and continuous variable generated by the first extracted axis of a principal component analysis (PCA).
The first question we wanted to answer was whether there was a relationship between the genetic composition and the geographic distribution of the individuals. This was first approached using the Mantel test in each population. The normalized Mantel statistic (rM) was calculated between the genetic and the geographic distance matrices. The significance of rM was tested by a permutation test (10 000 iterations).
The evaluation of the effect of geographic location on genetic structure can also be viewed as a problem of spatial covariation. This type of covariation can be efficiently approached by means of constrained ordinations considering the spatial location of each individual as a variable upon which statistical analyses are performed. This approach can also provide answers to two further questions: What fraction of genetic variation is explained by the spatial data set? Is there any directionality in the spatial genetic structure? As suggested by McCune (1997)
, these techniques can be used as tools for hypothesis testing (ter Braak and Prentice, 1988
; Palmer, 1993
; Legendre and Anderson, 1999
). Our null hypothesis was that the influence of spatial variables on genetic data set was not significantly different from random. In order to select the appropriate ordination constraining technique, the basic genetic matrix was submitted to a detrended correspondence analysis (DCA) with detrending by segments and nonlinear rescaling of the axes, which has the effect of changing the extracted axes such that they are scaled in units of mean standard deviation (Gauch, 1982
). Values above 3 SD (standard deviation) units suggest the use of techniques assuming unimodal responses such as canonical correspondence analysis (CCA) or other related techniques (ter Braak, 1986
; Legendre and Anderson, 1999
). Linear techniques such as those related to redundancy analysis (RDA) should be conducted when the extracted gradient is lower. Due to the relatively short gradient obtained (<2 SD), we performed an RDA, which has been previously tested in the framework of hypothesis testing (Verdonschot and ter Braak, 1994
). The total variation explained (TVE) in the basic genetic matrix by the spatial data set, which allowed us to quantify the effect of spatial variables on genetic structure, was calculated as the sum of all canonical extracted axes using the spatial coordinate matrix as the constraining matrix (Borcard et al., 1992
). A Monte Carlo permutation test was performed to determine the accuracy of the relationship (1000 randomizations). The sum of all canonical eigenvalues was used to build the F ratio statistic because it is more efficient for hypothesis testing than the F ratio built from the first eigenvalue (Verdonschot and ter Braak, 1994
; Legendre and Anderson, 1999
). A forward stepwise procedure was carried out to select a reduced model including only the relevant variables. Thus, anisotropic trends in genetic variability can be detected when only some of the spatial coordinates are selected. We incorporated explanatory variables (spatial coordinates) one at a time and step by step in the order of decreasing eigenvalues after the inclusion of the significant variables in a reduced model. The process stopped when the new variable was not significant (P < 0.05). Improvement of the reduced model with each new selected variable was determined by a Monte Carlo permutation test with 1000 randomizations. These analyses were conducted with CANOCO for Windows version 4.0 (ter Braak and Smilauer, 1997
).
The shape of the spatial genetic structure at each population and the testing for the presence of spatial autocorrelation were approached using Moran's I correlogram and Mantel correlogram. Moran's I statistic was calculated for each distance class using PASSAGE program (Rosenberg, 2001
). The values on the first extracted axis of the PCA were considered the variable of interest. In Bolarque, distance classes were defined at 15-m intervals, and each one of them included at least 30 pairs of points (Waser and Mitchell, 1990
). We followed a classical rule of thumb and only considered pairs of points separated by less than half the maximum distance observed (Le Corre et al., 1998
). Correlogram was therefore calculated for 17 distance classes ranging from 0–15 m (class 1) to 240–255 m (class 17). Following the same criteria, plants of Entrepeñas were grouped in nine distance classes: 0–15 m, 15–30 m, 30–45 m, 45–60 m, 60–90 m, 90–120 m, 120–150 m, 150–165 m, and 165–180 m. Each value of Moran's I was tested for significant deviations from the expected value under the null hypothesis of random spatial distribution (Cliff and Ord, 1981
). Global significance of both spatial correlograms was tested using Bonferroni's criterion, i.e., at least one of the I values corresponding to each class must be significant at 0.05/k, where k represents the number of distance classes considered (Oden, 1984
). As similarly shaped correlograms may correspond to different spatial patterns (Legendre, 1993
), we built contour maps of the consensus genetic variable by interpolation to help with their interpretation.
The multivariate Mantel correlogram is a modification of the Mantel test (Oden and Sokal, 1986
). This technique basically divides distance into classes and calculates the normalized Mantel statistic value (rM) for each distance class. Plotting the successive rM values for each distance class yields a Mantel correlogram. The degree of autocorrelation exhibited by pairwise values within each class is then tested to see whether it is greater than or less than the overall mean autocorrelation between sites. Calculations were made using the matrices of euclidean genetic distance and geographical distance, and distance classes were the same as defined for Moran's I analyses. Each rM value was tested for significance by a permutation test. Correlograms were tested for global significance using Bonferroni's criterion.
As a second and more detailed approach to detect anisotropic spatial patterns, i.e., directionality in the spatial genetic structure, directional correlograms (Oden and Sokal, 1986
) were applied. In directional correlograms, pairs of points are put into separate classes (sectors) based on both distance and direction. Autocorrelation coefficients are then calculated for each sector. Geographic and angular distance matrices were calculated from x and y coordinates, and four distance intervals (annuli) were considered in each population. Each annulus was divided into sectors that represented a set of angles. The number of sectors in the ith annulus was determined by ni = 4i – 2. Each annulus was divided evenly, so the width of each segment (in degrees) was 360/ni. The number of annuli, distance intervals, and resulting classes were chosen so that there would be a minimum of 20 pairs within each sector. In Bolarque the annuli were 0–50 m, 50–200 m, 200–450 m and 450–800 m, and in Entrepeñas 0–20 m, 20–80 m, 80–180 m, and 180–320 m. Moran I statistic was calculated for each sector using the values on the first extracted axis of PCA. Each value of Moran's I was tested for significant deviations from the expected value under the null hypothesis of random spatial distribution (Cliff and Ord, 1981
). Global significance of both directional correlograms was tested using Bonferroni's criterion (Oden, 1984
). Directional correlograms were performed with PASSAGE program (Rosenberg, 2001
).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Contour maps of the consensus genetic variables showed a patchy pattern for Bolarque (Fig. 1b), and an east–west gradient for Entrepeñas (Fig. 2b). Moran's I correlograms were globally significant, indicating that the overall spatial pattern of genetic diversity is not random. In Bolarque a significant positive value of I (P < 0.001) was observed for the first distance class followed by significant negative and positive values for distance classes greater than 15 m, suggesting a certain degree of spatial periodicity (Fig. 1c). In Entrepeñas a significant positive value of I was also observed for the first distance class followed by a negative value for 135–150 m (Fig. 2c). This pattern can be interpreted as a gradient.
Mantel correlograms were globally significant in both populations and also showed significant and positive autocorrelation values in the first distance class. Again, a sinusoidal pattern arose in Bolarque with autocorrelation values changing from significantly positive to significantly negative in different distance classes (Fig. 1d). In Entrepeñas, the resulting pattern shows a decline in genetic similarity with geographic distance (Fig. 2d).
Directional correlograms (Fig. 3) were globally significant (P < 0.05) and showed anisotropic spatial patterns. For Bolarque, positive autocorrelation was observed both in north–south and east–west directions. In Entrepeñas, positive autocorrelation was only detected in the east–west direction.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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The significant positive autocorrelation detected at the first distance class (0–15 m) in both populations indicates vegetative reproduction or family clusters (Sokal and Oden, 1978
). Because vegetative reproduction is not observed in Antirrhinum microphyllum, this autocorrelation at short distance classes probably reflects the occurrence of patches of genetically similar individuals. This is consistent with the concept of plant populations being subdivided into local demes or "neighborhoods" of interbreeding, related individuals (Levin and Kerster, 1974
). According to Sokal (1979)
, patch size can be estimated by the distance at which the Moran correlogram first intercepts the abcissa, as this corresponds to the shortest dimension of an irregularly shaped patch. In the present work, the patch sizes were about 25 and 37 m for Entrepeñas and Bolarque, respectively.
The profile of Mantel correlogram corresponding to Entrepeñas showed a decline of genetic similarity with increasing geographical distance. This pattern is similar to those obtained by simulation of an isolation-by-distance model (Sokal and Wartenberg, 1983
; Sokal and Jacquez, 1991
), which is based on the expectation that the probabillity of mating depends on the distance between individuals or the variance in the dispersion of their propagules. However, the pattern found in Bolarque cannot be simply intrepreted under this model. The sinusoidal variation observed in these correlograms may be explained by the distribution of the individuals in a reduced number of patches (Radeloff et al., 2000
). This sinusoidal variation might essentially reflect differences in diversity between local means and variance (Rossi et al., 1992
). Positive autocorrelation values at high distance classes may also result when there is a circular gradient (Sokal and Oden, 1978
) or when patch distribution is regular (Epperson and Clegg, 1986
; Legendre and Fortin, 1989
). The latter is found in the distribution of individuals in Bolarque (Fig. 1a).
Linking spatial genetic structure with ecological traits
The short-distance structuring of genetic variability in A. microphyllum could be a combined effect of local pollen and seed dispersal. We have observed that males of Rhodanthidium sticticum, the main pollinator of A. microphyllum, have a territorial behavior. Like Anthidium septemspinosum (Sugiura, 1994
) and Anthidium septemdentatum (Nachtigall, 1997
), they occupy and patrol areas of a few square meters to keep them free from other males and insects while females collect pollen. Furthermore, the movements of the females are mainly between plants less than 10 m apart (Torres et al., 2001
; E. Torres et al., personal observation). According to simulation studies, this type of pollination in an outcrossing species leads to the development of local genetic structures and increased homozygosity (Turner et al., 1982
), as we have detected in A. microphyllum (Torres et al., 2003
). We have also observed that most A. microphyllum seeds fall by gravity less than 10 cm away from the mother plant, which contributes to the establishment of clusters of related individuals. Another factor responsible for generating the observed pattern is the distribution of favorable sites for germination and seedling establishment. Pioneer chasmophytes, such as A. microphyllum, need cliff microsites with finely divided material, especially crevices, where the root system can find anchorage (Matthes-Sears and Larson, 1995
). Therefore, a patchy spatial distribution of these available safe sites would contribute to the development of local spatial genetic structure. Some authors have also proposed microenvironmental selection as a factor that generates spatial genetic structure (Sokal et al., 1989
). However, assuming that RAPD markers are neutral with regard to natural selection (Avise, 1994
), this explanation is not applicable to our results.
Other ecological factors, such as plant density, may affect the spatial genetic structure in A. microphyllum populations (Loveless and Hamrick, 1984
) and be responsible for the differences observed in the correlogram profiles of Entrepeñas and Bolarque. In dense patchy populations, as pollinators mainly move between nearby plants, mean flight distance decreases and, thus, pollen flow among patches also diminishes, favoring population subdivision in small patches. Low density has opposite effects, facilitating pollen dispersal events at longer distances and decreasing the probability of geographical differentiation (Levin et al., 1971
). In A. microphyllum, plant density is lower in Bolarque than in Entrepeñas (Figs. 1a and 2a) and results are consistent with the abovementioned considerations. Patch size was greater in Bolarque than in Entrepeñas, and significant positive Moran's I and normalized Mantel statistic values were found in Bolarque at long distances (Fig. 1c and 1d), suggesting gene flow among patches. An alternative hypothesis that would explain the positive autocorrelation values obtained at long distances in Bolarque concerns seed dispersal. As A. microphyllum plants grow on the walls of vertical cliffs, their seeds may occasionally be dispersed over long distances by wind and form new groups of individuals with similar genotypes. The incidence of strong air currents is well known in this type of cliff system. Such long-distance dispersal events would allow gene flow between neighborhoods and prevent populations from becoming genetically subdivided.
The operation of a self-incompatibility system in a species tends to eliminate existing spatial genetic structures because it impedes self-fertilization and cross-fertilization with close individuals that share the same incompatibility alleles (Doligez et al., 1998
). Antirrhinum microphyllum is a strict self-incompatible species (Torres et al., 2002
) but, contrary to what is expected, a clear spatial genetic structure persists in both populations studied. This shows that spatial genetic structures do not originate as a result of a single factor, but, on the contrary, several factors (pollinator behavior, limited seed dispersal, habitat heterogeinity, self-incompatibility, etc.) operate simultaneously in favor or against. Antirrhinum microphyllum is an interesting study case where other ecological factors override the tendency of self-incompatibility systems to eliminate local structures. The persistence of a local genetic structure may pose a problem when the species is self-incompatible because many related plants may share incompatibility alleles that prevent fertilization (Nettancourt, 1977
). Several examples of reduced fitness of progeny with decreasing interparental distance have been reported in self-incompatible plants (Heywood, 1991
; Waser and Price, 1994
). In contrast, other studies have shown no effect of parental distance on the fitness of self-incompatible plants (Newport, 1989
; Morán-Palma and Snow, 1997
). Although no specific experiments have been carried out to test the fitness of A. microphyllum plants in relation to interparental distance, the co-existence of a strict self-incompatibility system and a local genetic structure shows no evident deleterious effects on reproductive success. All monitored plants showed a very high fruit set and a large number of viable seeds per fruit (Torres et al., 2002
). This evidence supports the idea that between-patch gene flow occasionally takes place enabling the availability of different self-incompatibility alleles.
Directionality of the spatial genetic structure
In both populations, the easting component was selected in RDA, and significant positive autocorrelation was observed in directional correlograms in the east–west direction (Fig. 3). These results are consistent with the orientation of the cliffs where A. microphyllum plants grow. A plausible explanation is that prevailing air currents in this direction may favor seed dispersal or pollinator movements.
Differences in spatial genetic structure between both populations may be a consequence of the differential geomorphological structure of the localities. Bolarque appears with a bidimensional structure because plants there grow on two parallel cliffs that form the edges of the Tajo River, whereas Entrepeñas plants only grow on an impressive east–west oriented cliff. According to Wright's models of isolation by distance and given equal migration rates, linear patterns lead to more extensive subdivision than two-dimensional patterns (Rohlf and Schnell, 1971
; Loveless and Hamrick, 1984
). It is interesting to note that in Bolarque some patches are more closely related to individuals located on the opposite cliff across the river than to individuals in contiguous vicinities along the same cliff (Fig. 1b). This pattern was also detected by RDA and the directional correlogram (Fig. 3a), where the northing component and the north–south direction were selected, respectively. Specific pollination flows or seed dispersal events are likely to be responsible for the formation of this particular pattern. Nevertheless, further research is needed to obtain precise answers.
Concluding remarks
Different analytical methods were applied in this study because they supplied complementary information. As each analytical method has both particular advantages and limitations in detecting and characterizing spatial variation, an integrated analysis of data allows for a better and more complete knowledge of the real spatial pattern. In all cases we used multi-locus analysis, instead of the more common analysis of single-locus diallelic systems, because they are more sensitive and efficient in detecting spatial genetic structure (Smouse and Peakall, 1999
; Degen et al., 2001
). Both procedures used to analyze multiple loci, genetic distance matrix and the use of the first extracted axis of a PCA, yielded comparable results. This is important because many spatial analysis methods, such as Moran's I, are meant to be used with a single variable, and the use of a synthetic genetic variable obtained from PCA provides a reasonable way of applying them to multilocus genetic data.
When applying spatial analysis to genetic data, certain authors have suggested that factors such as genetic drift and mutation blur the observed patterns and make it difficult to infer biological processes from spatial autocorrelation analysis (Heywood, 1991
; Slatkin and Arter, 1991
). However, other authors have shown that spatial autocorrelation analysis may be used to estimate gene dispersal parameters (Hardy and Vekemans, 1999
) and to detect spatial structures at very small scales as a consequence of environmental constraints (see Peakall and Beattie, 1995
). Our study did not try to infer biological processes from spatial analyses but simply showed that the spatial genetic structure found in the populations was consistent with particular ecological traits already known about the species. This is helpful for identifying the ecological factors that are significantly affecting population genetic structure and dynamics. Spatial analysis greatly improves the applicability of genetic studies in management and conservation strategies, and constitutes an important step towards the integration of genetic, demographic, and ecological perspectives in conservation (McCue et al., 1996
; Ueno et al., 2000
; Manel et al., 2003
).
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FOOTNOTES |
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2 E-mail: iriondo{at}ccupm.upm.es
3 Current address: Area de Biodiversidad y Conservación. Escuela Superior de Ciencias Experimentales y Tecnología. Universidad Rey Juan Carlos. C/ Tulipán s/n, E-28933 Móstoles, Spain
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