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1 January 2020 Massive Extraction of the Orchid Laelia speciosa (HBK) Schltr. for Trading in Local Markets Affect Its Population Genetic Structure in a Fragmented Landscape in Central Mexico
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Laelia speciosa is an orchid species listed as threatened of extinction in the Mexican standard NOM-059. Wild populations of L. speciosa have been declining due to fragmentation of its habitat and massive extraction for trading in local markets in Mexico. In this study, we aimed to evaluate the evolutionary potential of L. speciosa within a fragmented landscape of ca. 4000 km2 in the Cuitzeo basin, State of Michoacán. We sampled 15 populations throughout the Cuitzeo basin and amplified eight nuclear microsatellite markers to assess genetic diversity, structure, and connectivity and to test for evidence of recent bottlenecks. Surprisingly, L. speciosa populations showed high genetic diversity, with values ranging from moderate to high compared with those reported for other orchid species. Also, the analysis of molecular variance and RST results indicated the existence of low genetic differentiation, favored by its cross pollination habit which facilitates the maintenance of gene flow and that have been observed in other orchid species. Wright's within-population inbreeding (FIS) was positive in all cases, denoting a heterozygosity deficit, with moderate-to-high values. Fragmentation may also lead to inbreeding due to either increased self-fertilization or mating between related individuals within remnant fragments. The L. speciosa populations examined showed evidence suggesting that some populations had recently gone through a bottleneck. We also observed that all the L. speciosa populations had a moderate effective population size. The history of L. speciosa in the Cuitzeo basin suggests that both fragmented and non-fragmented populations may have been recently subject to moderate reductions in effective population size, large enough to affect their allelic diversity, FIS, but not their HE. Such reductions may have been caused by episodic environmental fluctuations or resulted from the recent founding of some of the populations. The effective population size can be used as an indicator of habitat quality, and this was confirmed for the L. speciosa populations, which have undergone a drastic decline due to environmental changes, habitat destruction, and illegal collection. The ultimate goals of conservation are to ensure the continuous survival of populations and maintain their evolutionary potential by preserving natural levels of genetic diversity. Great efforts should be made to preserve this species' extant populations and their habitats to prevent further population reductions and preserve its overall genetic basis. Collection of this orchid should be banned and robust legal protection measures should be enforced through local authorities.


The Orchidaceae is one of the most species-rich plant families in the world, with an estimation of over 27,000 species (Chase, 2005; Dressler, 1990). However, orchids also include the highest proportion of threatened or extinct species of all plant families (Swarts & Dixon, 2009). In general, orchids are habitat specialists with distinctive requirements for biotic interactions and environmental quality. Most orchid species depend on highly specific biotic interactions with both mycorrhizal fungi (Ávila-Díaz, Garibay-Orijel, Magaña-Lemus, & Oyama, 2013; Rasmussen & Rasmussen, 2009; Smith & Read, 2008) and pollinators (Chung, Chong-Wook, Myers, & Chung, 2007; Van der Pijl & Dodson, 1996) to complete their life cycle. Even minor environmental changes can affect such interactions with significant negative impacts on plant performance that might lead to population decline and eventually local extinction (Honnay, Jacquemyn, Bossuyt, & Hermy, 2005; Rasmussen & Rasmussen, 2009). It is therefore important, from the conservation standpoint, to understand the genetic consequences of the decline in population size of orchids (Izawa, Kawahara, & Takahashi, 2007; Swarts & Dixon, 2009). Habitat fragmentation may induce genetic changes in remnant plant populations, including erosion of their genetic variability and acceleration of the genetic divergence between them through reduced gene flow or increasing genetic drift (Jacquemyn, de Meester, Jongejans, & Honnay, 2012; Lowe, Boshier, Wars, Bacles, & Navarro, 2005; Schaberg, DeHayes, Hawley, & Nijensohn, 2008; Young & Pickup, 2010). Genetic erosion could reduce the adaptive flexibility of populations to respond to environmental changes (Farwig, Braun, & Bohning-Gaese, 2008; Griffiths, Wessler, Lewontin, & Carroll, 2008; Jump & Peñuelas, 2006).

Some 1,106 species and subspecies of orchids, distributed in 159 genera, occur in Mexico. One outstanding feature is the high level of endemism: about 444 of the known orchids species occurring in Mexico are endemic to this country (Soto-Arenas, 1996). This makes the Mexican flora one of the most endemic-rich among tropical countries (Dressler, 1990). Today, approximately 180 orchid species are listed in the official Mexican standard NOM-059-SEMARNAT (Semarnat, 2010) under some category of risk, including the species Laelia speciosa (HBK) Schltr. Among epiphytic orchid species, L. speciosa is considered one of the most beautiful flowers, and its wild populations are intensively collected in Mexico (Halbinger, 1993; Soto-Arenas, 1996). In Mexico, it inhabits the oak forest of the Sierra Madre Occidental and Sierra Madre Oriental, the southern part of the Central Mexican plateau, and the Trans-Mexican Volcanic Belt, at elevations ranging from 1440 to 2500 m asl (Halbinger & Soto, 1997). The inflorescence displays one to three large colorful flowers, 10 to 16 cm in diameter, widely open with the petals and sepals almost on the same plane, which may be pale pink to lilac pink often with purple stripes along the margins (Van der Pijl & Dodson, 1996). L. speciosa is pollinated by Bombus pennsylvanicus, B. sonorous, and B. ephippiatus (Neiland & Wilcock, 1998). Due to the massive extraction and habitat fragmentation, wild populations of L. speciosa have been declining to the extent that this species is now listed as threatened in the Mexican standard NOM-059-SEMARNAT (Ávila-Díaz & Oyama, 2007; Semarnat, 2010; Soto-Arenas, 1996), see Figure 1. A rough estimation states that some 1,500 flowers and 6,000 plants or segments of plants are extracted every day during the blooming season to be traded in local markets in the State of Michoacán in central Mexico (Ávila-Díaz & Oyama, 2007). The use of this orchid is closely related to commercial and cultural activities. Artisans in several towns in the state of Michoacán extract mucilage from the pseudobulbs to make handcrafts called “cane paste figurines” (Miranda, 1997). Large amounts of flowers are also used in religious ceremonies, a practice that has severely reduced its natural populations (Ávila-Díaz & Oyama, 2007). Other factors contributing to the decline of L. speciosa populations include the plants' slow growth and poor seed formation as well as the fragmentation of their habitat in the basin caused by human activities such as agriculture, grazing, wildfires, and deforestation (Ávila-Díaz & Oyama, 2007).

Figure 1.

Map showing the delimitation of 15 populations of L. speciosa sampled in the Cuitzeo Basin, Michoacán, Mexico. We include, information about the forest fragment of oak forest remained in the area to illustrate the pattern of fragmentation on the host species L. speciosa within the Cuitzeo basin in green color. The miniature map of Mexico highlighted the Michoacán state and the Cuitzeo Basin.


With that jeopardizing scenario, we could expect a negative effect of intensive extraction along with the destruction of the pine-oak forests which have possibly been triggering the extensive fragmentation in their populations. The majority of population genetic studies have been focused on both epiphytic and terrestrial orchids and was mainly based on isozymes (Ávila-Díaz & Oyama, 2007; Borba, Felix, Solferini, & Semir, 2001; Murren, 2003; Trapnell, Hamrick, & Nason, 2004) and microsatellites (Da Cruz, Selbach-Schnadelbach, Lambert, Ribeiro, & Borba, 2011; Muñoz, Warner, & Albertazzi, 2010; Pinheiro et al., 2012; Stone, Crystal, Devlin, Downer, & Cameron, 2012; Swarts, Sinclair, Krauss, & Dixon, 2009; Vargas, Parra-Tabla, Feinsinger, & Leirana-Alcocer, 2006). Despite the molecular marker used, the few studies in orchids indicated that epiphytes enjoy some of the dispersal advantages of trees (e.g., greater potential for gene flow derived from longer-distance dispersal of pollen and seeds; Borba et al., 2001; Flores-Palacios & García-Franco, 2003; Trapnell & Hamrick, 2004), which may attenuate the genetic impacts of habitat fragmentation.

In this study, we aimed to assess the genetic diversity and population structure of the threatened orchid L. speciosa using microsatellite markers. In particular, we are interested to know the levels of inbreeding and population bottlenecks, gene flow, and connectivity, as a consequence of massive extraction for trading in a fragmented landscape.


Collecting Sites and DNA Amplification

Samples of L. speciosa were collected in 15 forest fragments in the Cuitzeo basin, Michoacán (Table 1, Figure 1). We collected 12 to 20 individual plants from each population; the plants sampled were growing on Quercus deserticola trees separated from each other by at least 30 m. Samples were frozen until DNA extraction. Genomic DNA was extracted from 100 mg of fresh leaf material using the protocol proposed by Lefort and Douglas (1999). Eight nuclear microsatellite loci were selected and amplified by means of multiplex polymerase chain reaction (PCR; Cortés-Palomec, McCauley, & Oyama, 2008). Based on allele size, annealing temperature, and fluorescent labels, the PCR amplification products were sorted into three groups of primers. The first group included the primer pairs for Lspe8 and Lspe12; the second group those for Lspe1, Lspe4, and Lspe7; and the primer pairs for Lsp6 and Lspe10 were included in the third group (Cortés-Palomec et al., 2008). The PCR was performed using the QIAGEN Multiplex PCR kit (QIAGEN) in a 5-µl volume containing 1X Multiplex PCR Master Mix, 2 µM of each primer, dH2O, and 20 ng of template DNA (Cortés-Palomec et al., 2008). The thermal cycling conditions consisted of 35 cycles of 94℃ for 1 min, annealing for 1 min, extension at 72℃ for 2 min, and final extension at 72℃ for 10 min. Multiplex PCR products were combined with a GeneScan-500 LIZ size standard and the analyses performed on an ABI-PRISM 3100 Avant sequencer (Applied Biosystems). Fragments were analyzed and recorded using the Peak Scanner program 1.0 (Applied Biosystems).

Table 1.

Locality Name, Sample Size, Geographical Coordinates (Degrees), Mean Number of Effective Alleles per Locus (Ne), Mean Observed Heterozygosity (HO), Mean Expected Heterozygosity (HE), and the Inbreeding Index (FIS) for 15 Populations of L. speciosa From Cuitzeo Basin, Michoacán.


Genetic diversity

We tested for the presence of null alleles, large-allele dropout, and errors due to stuttering in the microsatellite data using the MICRO-CHECKER 2.2.3 program (Van Oosterhout, Weetman, & Hutchinson, 2006) with 102 bootstrap simulations and a 95% confidence interval (CI). Deviations from the Hardy-Weinberg equilibrium, such as excess heterozygosity, or FIS deficit, were tested by means of a Markov-chain approach (with 103 dememorization steps, 102 batches, 103 iterations per batch) using the GENEPOP 4.1 software (Raymond & Rousset, 1995). For each of the 15 populations of L. speciosa, we estimated the following genetic diversity parameters: number of alleles per locus (A), observed heterozygosity (HO), and expected heterozygosity (HE), using the GENETIX 4.02 software (Belkhir, Borsa, Chikhi, Raufaste, & Bonhomme, 2004).

Genetic Structure and Bayesian Admixture Analysis

To have a clear scenario about the genetic assignment, we run two Bayesian approaches, Geneland and Structure, to test if the genetic assignments are the same or differ in the number of genetic cluster detected. The first approach, Structure, was tested to infer the genetic ancestry of each individual plant (Falush, Stephens, & Pritchard, 2003; Hubisz, Falush, Stephens, & Pritchard, 2009; Pritchard, Stephens, & Donnelly, 2000). STRUCTURE 2.3.4 software (Falush et al., 2003; Hubisz et al., 2009; Pritchard et al., 2000) uses a Bayesian clustering model to determine the proportion of each individuals' ancestry originating from different populations (Evanno, Regnaut, & Goudet, 2005). The optimal number (K) of groups was determined by varying K from 1 to 10 and running the analysis 10 times for each K value to find the maximum posterior likelihood (LnP (D)). Each run was performed using 504 burn-in periods and 106 Markov Chain Monte Carlo (MCMC) repetitions after burn-in. We used an admixture model that allows correlated allele frequencies without any prior information. Following Evanno et al. (2005), we determined the most likely value of K based on the maximum value of ΔK as implemented in the Structure Harvester 0.6.9 software (Earl & von Holdt, 2012). In the second approach, we run the spatial cluster model implemented in the GENELAND package (Guillot, Mortier, & Estoup, 2005) of the R 3.2.3 program (R Development Core Team, 2016). Different sets of parameters (MCMC, thinning and burn-in) were used in different test runs, in order to find the optimal parameters. Following the recommendation of the user's manual, the first step was replicated 10 times, allowing K to vary from 1to 10 clusters and using the Markov chain Monte Carlo (MCMC) repetitions were set at 100,000, thinning was set at 100, and the burn-in period was set at 200, to know the effective number of genetic clusters in the data set. Then, we set ten long runs MCMC iterations were performed, with K fixed to the maximum value of the logarithm of posterior probability of the data (PPD), using 5 × 106 MCMC iterations, and the other parameters unchanged. We calculated the mean logarithm of posterior probability of the data (PPD) for each of the 10 runs and selected one with the highest PPD (Guillot et al., 2005; Guillot, Santos, & Estoup, 2008). To ensure that the run was long enough, we obtained 10 different runs and compared the parameter estimates (K, individual population membership, maps). The pairwise population genetic differentiation RST was estimated using the infinite allele model (IAM), performing 104 permutations in the ARLEQUIN software (Excoffier & Lischer, 2010). A hierarchical test of population structure was conducted using the stepwise mutation model (SMM) with analysis of molecular variance (AMOVA) in ARLEQUIN 3.5 (Excoffier & Lischer, 2010). We conducted a hierarchical analysis to estimate genetic variance components between groups, between populations within groups, and within populations, using the assignation of groups obtained by Structure and Geneland. Statistical significance was tested using 104 permutations. The genetic exchange between L. speciosa populations was assessed by estimating the Bayesian-scaled long-term effective population size (Ne) and migration rate (m) with the MIGRATE 3.5.1 software (Beerli & Felsenstein, 2001). For all the analyses, the starting value of the chain was set to 206 visited and 16 recorded genealogies, following a burn-in period of 503 iterations.

To identify geographic and genetic breaks between L. speciosa populations, we used the Monmonier's maximum difference algorithm with the BARRIER version 2.2 software (Manni, Guerard, & Heyer, 2004). BARRIER creates a map of the sampling locations from their geographical coordinates. Barriers are then represented on the map by identifying the maximum values within the population-pairwise genetic distance matrix. We used a pairwise matrix of average square distances (Goldstein, Ruiz-Linares, Cavalli-Sforza, & Feldman, 1995; Slatkin, 1995) estimated for the 15 populations of L. speciosa. Resampling random subsets of individuals within populations with the MSA program (Dieringer & Schotterer, 2003) provided 100 bootstrap replicate distances that were used to achieve statistical significance for the predicted barriers.

Changes in population size

We used the BOTTLENECK 1.2 software (Piry, Luikart, & Cornuet, 1999) to detect population bottlenecks, using the three genetic groups previously identified with Structure. Recent bottlenecks can be identified as a population where rare alleles are the first to be lost, then the mean number of alleles per locus is reduced accordingly. In contrast, heterozygosity is less affected, producing a transient excess in heterozygosity relative to that expected based on the resulting number of alleles (Cornuet & Luikart, 1996; Luikart & Cornuet, 1998). For this test, we used 90% stepwise and 10% multistep mutations and performed 104 iterations of the Wilcoxon's signed-rank test with the SMM, the IAM, and the two-phase mutation (TPM) model.

Additionally, we estimated the effective size of 15 populations of L. speciosa using the LDNe software (Waples & Do, 2008). This program implements the bias-correction method developed by Waples (2006) to obtain Ne from a sample of S individuals. We set Pcrit = 0.02 (i.e., alleles with a frequency <0.02 are excluded), which generally provides a good balance between accuracy and bias (Waples & Do, 2008). CIs for Ne were calculated with the chi-square approximation implemented in LDNe (Waples & Do, 2008).


Genetic Diversity

No evidence of null alleles was found in any of the sample-loci combinations. The tests for errors due to stuttering and large-allele dropout yielded negative results in all cases. Values of the genetic diversity parameters estimated in 15 populations of L. speciosa ranged from moderated to high in Zacazo, Indapa, and Caurio (Ne = 6.87–9.62, HO = 0.635–0.714); Laguna, Laguni, Potrer2, Felipe, Potrer1, and Huiram (Ne = 6.87–7.62, HO = 0.602–0.682); and in Olvido, Robles, Aguila, Coapas, Coapas2, and Correo (Ne = 6.25–7.0, HO = 0.534–0.589), see Table 1. Wright's inbreeding coefficient within populations (FIS) showed positive values in all cases, denoting a heterozygote deficiency (Table 1); FIS values were low in Zacazo, Indapa, and Caurio (FIS = 0.009–0.279); slightly high in Laguna, Laguni, Potrer2, Felipe, Potrer 1, and Huiram (FIS = 0.124–0.215); and high in Olvido, Robles, Aguila, Coapas, Coapas2, and Correo (FIS = 0.154–0.285). The gene exchange levels detected were moderate to high in all the populations examined (Table 2).

Table 2.

Levels of Genetic Exchange Estimated With the Program MIGRATE.


Genetic Structure and Bayesian Admixture Analysis

We applied two complementary Bayesian clustering algorithms, namely Structure (Falush et al., 2003; Pritchard et al., 2000) and Geneland (Guillot et al., 2005), to infer population structure (i.e., a number of clusters, K) and to assign individuals probabilistically to populations based on individual multilocus. Both approaches shown similar results over 10 replicated runs tested varying K from 1 to 10 to get the highest probability. In terms of the number of genetic clusters, both approaches, according to the maximum posterior likelihood (LnP (D)) and the maximum ΔK value, showed that K = 3 is the optimum number of genetic groups, see Figure 2. Also, Structure and Geneland approaches confirm in the majority of the assignment of each individual to each genetic cluster (Figures 3, 4, and 5). For instance, in the bar plot and the pie charts in Figures 3, 4, and 5 show the distribution of ancestry proportions in each collection site. Cluster 1 (e.g., red in Structure and cluster 1 Geneland) is widespread across the Cuitzeo basin and includes the Zacazo, Indapa, Laguna, Potrer2, Olvido, and Caurio populations, except for Laguna that shift for Laguni in Geneland. Cluster 2 (e.g., green in Structure and cluster 3 Geneland) includes the populations of Robles, Felipe, Aguila, and Potrer1, which are consistently structured across the landscape. Cluster 3 (e.g., blue in Structure and cluster 2 in Geneland) has a wider distribution and includes the populations of Correo, Coapas, Coapas2, Laguni, and Huiram (Figures 3, 4, and 5) except for Laguni that was changed for Laguna in Geneland. Geneland and Structure also differs in the presence of admixture (not noticeable by Geneland), as an evidence of gene exchange, particularly in the populations of Indapa, Caurio, Laguna, Potrer1, and Potrer2, which have ancestry coefficient (Q) values ranging between 0.732 and 0.848 (Figures 3 and 4).

Figure 2.

Estimated number of populations from Structure and Geneland analyses. Structure values of K plotted against K, the peak indicates the most probable number of genetic groups given the data using Structure Harvester program. Geneland plot of the number of populations simulated from the posterior distribution obtained with GENELAND.


Figure 3.

Genetic assignment of individuals and populations according to the Bayesian method implemented in the program STRUCTURE. Each thin horizontal line represents an individual and the proportion of each color is the proportion of ancestry derived from each of the three main genetic groups (K 3) inferred. Green, red, and blue genotypes are representing the genetic ancestry groups corresponding to L. speciosa populations. Populations are separated by black lines.


Figure 4.

Each pie chart represents the proportions in each population of the three genetic groups as assigned by the program STRUCTURE. Green, red, and blue genotypes are representing the genetic ancestry groups corresponding to L. speciosa populations. Genetic discontinuities (bold lines B-1 and B2) obtained with Monmonier's maximum difference algorithm on genetic distances derived from microsatellite allele frequencies.


Figure 5.

Maps of Geneland individual assignments to clusters for K = 3 (scale units in latitude, longitude). The three plots represent the assignment of pixels to each cluster: cluster 1, cluster 2, and cluster 3. The highest membership values are in light yellow and the level curves illustrate the spatial changes in assignment values. The plot is based on the highest probability run at that value of K.


The AMOVA was performed using both genetic assignation from Structure and Geneland. Results showed that most of the variation (95.7 and 95.6%) occurred within L. speciosa populations; the variation between groups accounted for 3.29% and 3.51%, and the variation between populations within groups accounted for 1.02% and 0.87% (Table 3). Similarly, there was a moderate pairwise genetic differentiation between some populations, as indicated by the RST values observed (Table 4): Aguila/Olvido (RST = 0.149), Coapas/Aguila (RST = 0.143), Caurio/Coapas2 (RST = 0.082), Laguna/Olvido (RST = 0.107), and Caurio/Correo (RST = 0.082). Finally, a low differentiation was found between the following population pairs: Indapa/Caurio (RST = 0.006), Zacazo/Coapas (RST = 0.014), and Laguni/Felipe (RST = 0.028), see Table 4.

Table 3.

Analysis of Molecular Variance (AMOVA) Performed on the nSSR Data and Using RST for the Three Group Genetic Clusters Obtained by Means of STRUCTURE and by GENELAND for Populations of L. speciosa.


Table 4.

Pairwise Population Genetic Differentiations Using the Matrix of Slatkin Linearized RST (SMM) Among Localities of L. speciosa in Cuitzeo Basin, Michoacán, Over 104 Replicates in the Arlequin 3.5 Software (Schneider et al., 2005).


The analysis performed using 100 bootstrap replicates of the average square distance matrices revealed two barriers (with over 50% bootstrap support) between the 15 populations of L. speciosa studied (Figure 4). The most significant barrier, with 95% bootstrap support, is a complex break separating the southwestern part from the central part of the Cuitzeo basin. This indicates that, despite the admixture revealed by the Structure analysis, some populations have become isolated such as the Potrer1, Potrer2, Olvido, Coapas, Coapas 2, Laguni, Laguna, and Aguila (Figure 4). The second barrier, with 89% bootstrap support, sets apart the Caurio population (located in the northwest part of the basin) from the rest of the L. speciosa populations.

Changes in population size

Results from the analyses to test for evidence of recent bottlenecks (excess heterozygosity) using the IAM, the TPM, and the SMM are shown in Table 5. No significant results were found with the IAM and TPM. However, the SMM showed significant (p = .001, .003, .001) evidence of mild bottlenecks in populations of the three genetic groups, particularly in loci Lspe 4, 6, and 14, and also significant evidence of excess heterozygosity in the L. speciosa populations suggesting that they have recently gone through a bottleneck. The estimates of effective population size (Ne) obtained using LDNe for the L. speciosa populations showed that the third genetic group had the highest value (Ne = 441 individuals), followed by the second (Ne = 349) and the first (Ne = 122) groups. All Ne estimates had a high Jackknife support and a good CI (Table 5).

Table 5.

Bottleneck Analysis for L. speciosa Populations in the Cuitzeo Basin Using Wilcoxon Rank Test Under Infinite Allele, Stepwise Mutation, and Two-Phase Model.



Genetic Diversity

Habitat fragmentation, coupled with the massive extraction of individuals from natural populations, has reduced the genetic diversity and population size of these plants, with the ensuing reduction in population connectivity (Honnay et al., 2005; Jump & Peñuelas, 2006). In this study, we examined the genetic structure of populations of L. speciosa, an orchid species that is threatened by the indiscriminate extraction of specimens for trading in local markets. Surprisingly, the L. speciosa populations examined showed moderate to high levels of genetic diversity, as determined from nuclear microsatellites. Recent studies based on microsatellite variation have also found high genetic diversity (HO: 0.0–0.728) in the rare orchid Isotria medeoloides (Stone et al., 2012), the critically endangered spider orchid Caladenia huegelii (HO: 0.587–0.766; Swarts et al., 2009), and in two Ophrys species (HO: 0.76–0.91 to 0.77–0.92; Mant, Peakall, & Schiestl, 2005; Soliva & Widmer, 2003). Most of these species share similar characteristics with L. speciosa, such as being epiphytic, long-lived, cross-pollinated, and long-distance seed dispersal. Furthermore, widespread species such as L. speciosa tend to exhibit a higher genetic diversity compared with threatened or endemic species (Li & Jin, 2007; Mathiasen, Rovere, & Premoll, 2007; Qian, Wang, & Tian, 2013; Yu, Yang, Sun, & Liu, 2011). This may be the result of historical patterns of genetic variation in L. speciosa, for example, from a formerly wide-ranging distribution to becoming rare only recently. These patterns suggest that the massive extraction of individuals of this species has not yet had a noticeable effect on gene frequencies and heterozygosity or in the alleles that cause genetic drift in populations (Aguilar, Quesada, Ashworth, Herrerías-Diego, & Lobo, 2008; Schaberg et al., 2008). Similarly, Wright's inbreeding coefficients within populations (FIS) were positive in all cases, denoting heterozygote deficiency (see Table 1). Fragmentation may also lead to inbreeding due to either increased self-fertilization or mating between related individuals within remnant fragments (Chung et al., 2014; Honnay et al., 2005; Mathiasen et al., 2007; Swarts & Dixon, 2009). In this case, inbreeding seems to have been due to the activity of pollinators, as they were reported visiting nearby forest fragments more often than distant ones (Bacles, Lowe, & Ennos, 2004; Smith-Ramírez & Armesto, 2003). The ecological features of L. speciosa could also contribute to an increased gene flow via pollinators or long-distance dispersal between populations and suggest that isolated trees might serve as stepping-stones for gene flow between populations (Barrett & Kohn, 1991; Herrera-Arroyo et al., 2013; Soliva & Widmer, 2003; Tremblay & Ackerman, 2001).

Some of the populations of L. speciosa exhibited evidence of having recently gone through a mild-bottleneck, but only with the SMM model. Based on the well-documented history of L. speciosa in Michoacán (Miranda, 1997), we hypothesized that these populations have been subject to massive extraction and habitat fragmentation for at least 100 years. Studies on conservation genetics have put forward a rule to define the effective population size necessary to prevent genetic damage (Barrett & Kohn, 1991). An effective population size of 50 is considered the minimum necessary to maintain sufficient genetic variability, while 500 individuals are required to offset effective drift (Barrett & Kohn, 1991; Brzosko, Wróblewska, & Talalaj, 2004; Honnay, et al., 2005; Jacquemyn et al., 2012; Tremblay & Ackerman, 2001). Despite the massive decline in abundance caused by drastic environmental change, habitat destruction, and illegal collection, the populations of L. speciosa still have moderate to high values of effective population size. The wide distribution range and the cross-pollinated syndrome of L. speciosa seem to have been partly responsible for its high genetic diversity and moderate-to-high effective population size.

Genetic Structure and Bayesian Admixture Analysis

The AMOVA showed that most of the variation in L. speciosa occurs within populations. This is consistent with the results obtained by Da Cruz et al. (2011); based on the genetic structure of plants, Li and Ge (2006) suggested that cross-pollinated plants tend to have a greater diversity within populations and very little between them.

The distribution of ancestry proportions clearly showed a substantial connectivity between populations across the Cuitzeo basin. Nevertheless, we identified breaks in the continuity of gene flow such as the first barrier, which separates populations located in the southwestern from those in the central part of the Cuitzeo basin. We also observed some populations that have become isolated by a complex barrier; this was also evident in the map of oak forests remaining in this region (Figure 4). Geographical restrictions and a low gene flow between populations were also indicated by the second barrier that separate populations located in the northwestern from those in the central part of the basin. This pattern reflects the effect of habitat fragmentation on the patchy distribution of populations across the Cuitzeo basin. Pine and oak trees have almost disappeared from some localities of this basin due to their value as timber for construction or as a source of charcoal, an important economic activity in the region (Aguilar, Guilardi, Vega, Skutsch, & Oyama, 2013).

Small forest fragments that might still remain between larger populations would possibly function as vegetation corridors where isolated trees could function as stepping stones for gene flow between populations, as is the case in Quercus castanea in the same basin (Herrera-Arroyo et al., 2013). The history of L. speciosa in the Cuitzeo basin points to the fact that both fragmented and continuous populations have been recently subject to moderate reductions in effective population size, which have been large enough to affect allelic diversity (Barrett & Kohn, 1991; Lowe et al., 2005). Such reductions may have been caused by episodic environmental fluctuations (e.g., drought or geographical restrictions) or resulted from the recent founding of some of the populations (Chung et al., 2014; Forrest, Hollingsworth, Hollingsworth, Sydes, & Bateman, 2004; Murren, 2003). One likely explanation could be the effect of historic long-distance dispersal, which can maintain diversity through colonization. Thus, diversity is maintained at the regional rather than at the population level, with a moderate differentiation between populations (Bialozyt, Ziegenhagen, & Petit, 2006; Trapnell & Hamrick, 2004). Exponential population growth in newly colonized sites can also preserve genetic diversity near the leading edge, as rare alleles are less likely to be lost to drift (Excoffier, Foll, & Petit, 2009). Loveless and Hamrick (1984) suggest that genetic differentiation is the result of the ability of the species to disperse pollen and seeds; this holds true in orchids as they are wind dispersed (Chung et al., 2007; Gustafsson & Sjögren-Gulve, 2002; Li & Ge, 2006) and are capable of long-distance dispersal (Ackerman & Ward, 1999; Arditti & Ghani, 2000; Neiland & Wilcock, 1998). Therefore, it is quite possible for orchids to disperse across considerable distances of up to several kilometers (Flores-Palacios & García-Franco, 2003; Sharma, Clements, & Jones, 2000; Trapnell & Hamrick, 2004). Furthermore, Bombus pennsylvanicus, B. sonorus, and B. ephippiatus have been documented as potential pollinators of L. speciosa (Van der Pijl & Dodson, 1996). Other studies have concluded that, in orchids, population genetic structure is strongly influenced by the behavior of pollinators (Chung et al., 2014; Cozzolino & Widmer, 2005; Neiland & Wilcock, 1998; Qian et al., 2013; Sharma, Jones, & French, 2003; Tremblay, Ackerman, Zimmerman, & Calvo, 2005).

Implications for Conservation

The life-history characteristics and growth form of L. speciosa, such as its epiphytic habit, allow it to occupy a three-dimensional space on the branches, trunks, and canopy of trees, whereas terrestrial herbs can only occupy a two-dimensional setting. As a consequence, epiphytes enjoy some of the dispersal advantages of trees (e.g., greater potential for gene flow derived from longer-distance dispersal of pollen and seeds; Borba et al., 2001; Flores-Palacios & García-Franco, 2003; Trapnell & Hamrick, 2004), which may attenuate the genetic impacts of habitat fragmentation. Because of this, the effects of the extensive extraction that L. speciosa populations are currently facing will become evident in subsequent generations. We found evidence of a moderate effect on the inbreeding levels, the recent bottlenecks detected in the three groups, and the effective population size of isolated populations. The ultimate goal of conservation is to ensure the continued survival of populations and the maintenance of their evolutionary potential by preserving natural levels of genetic diversity (Godt & Hamrick, 1995; Izawa et al., 2007). Great efforts should be made to preserve extant populations of L. speciosa and its habitats to prevent further population reductions and preserve its overall genetic basis (Barrett & Kohn, 1991; Chung et al., 2014; Jacquemyn et al., 2012). Collection of this orchid should be banned and robust legal protection measures should be enforced through local authorities. It might be necessary to transfer mature plants between populations as an effective way to increase genetic diversity (Qian et al., 2013; Swarts et al., 2009). We also recommend the maintenance of a germplasm bank for ex-situ conservation of L. speciosa (Izawa et al., 2007; Li & Ge, 2006) and its extensive cultivation for future cultural and religious uses by people in local communities.


The authors thank A. L. Albarrán-Lara and N. Pérez-Nasser for technical support.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from CONABIO to MMR.



Ackerman, J. D., Ward, S., (1999) Genetic variation in a widespread, epiphytic orchid: Where is the evolutionary potential?. Systematic Botany 24: 282–291. Google Scholar


Aguilar, R., Quesada, M., Ashworth, L., Herrerías-Diego, Y., Lobo, J. A., (2008) Genetic consequences of habitat fragmentation in plant populations: Susceptible signals in plant traits and methodological approaches. Molecular Ecology 17: 5177–5188. . Google Scholar


Aguilar, R., Guilardi, A., Vega, E., Skutsch, M., Oyama, K., (2013) Sprouting productivity and allometric relationship of two oak species managed for traditional charcoal making in Central Mexico. Biomass & Bioenergy 36: 192–207. Google Scholar


Arditti, J., Ghani, A. K., (2000) Numerical and physical properties of orchid seeds and their biological implications. New Phytologist 145: 367–421. Google Scholar


Ávila-Díaz, I., Oyama, K., (2007) Conservation genetics of an endemic and endangered epiphytic Laelia speciosa (Orchidaceae). American Journal of Botany 94: 184–193. Google Scholar


Ávila-Díaz, I., Garibay-Orijel, R., Magaña-Lemus, R. E., Oyama, K., (2013) Molecular evidence reveals fungi associated within the epiphytic orchid Laelia speciosa (HBK) Schltr. Botanical Sciences 91: 1–8. Google Scholar


Bacles, C. F. E., Lowe, A. J., Ennos, R. A., (2004) Genetic effects of chronic habitat fragmentation on tree species: The case of Sorbus aucuparia in a deforested Scottish landscape. Molecular Ecology 13: 573–584. Google Scholar


Barrett, S. C. H., Kohn, J. R., (1991) Genetic and evolutionary consequences of small population size in plants: Implication for conservation. In: Falkand, D. A., Holsinger, K. E., (eds) Genetics and conservation of rare plants, Oxford, NY: Oxford University Press, pp. 3–30. Google Scholar


Beerli, P., Felsenstein, J., (2001) Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proceedings of the National Academy of Sciences of the United States of America 98: 4563–4568. Google Scholar


Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N., Bonhomme, F., (2004) Genetix 4.05, France: Université de Montpellier II. Google Scholar


Bialozyt, R., Ziegenhagen, B., Petit, R. J., (2006) Contrasting effects of long distance seed dispersal on genetic diversity during range expansion. Journal of Evolutionary Biology 19: 12–20. Google Scholar


Borba, E. L., Felix, J. M., Solferini, V. N., Semir, J., (2001) Fly pollinated Pleurothallis (Orchidaceae) species have high genetic variability: Evidence from isozyme markers. American Journal of Botany 88: 419–428. Google Scholar


Brzosko, E., Wróblewska, A., Talalaj, I., (2004) Genetic variation and genotypic diversity in Epipactis helleborine populations from NE Poland. Plant Systematics and Evolution 248: 57–69. Google Scholar


Chase, M. W., (2005) Classification of Orchidaceae in the age of DNA data. Curtis's Botanical Magazine 22: 2–7. Google Scholar


Chung, M. Y., Chong-Wook, P., Myers, E. R., Chung, M. G., (2007) Contrasting levels of genetic diversity between the common, self-compatible Liparis kumokiri and rare, self-incompatible Liparis makinoana (Orchidaceae) in South Korea. Botanical Journal of the Linnean Society 153: 41–48. Google Scholar


Chung, M. Y., Nason, J. D., Lopez-Pujol, J., Yamashiro, T., Yang, B.-Y., Luo, Y.-B., Chung, M. G., (2014) Genetic consequences of fragmentation on populations of the terrestrial orchid. Cymbidium goeringii. Biological Conservation 170: 222–231. Google Scholar


Cornuet, J. M., Luikart, G., (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144: 2001–2014. Google Scholar


Cortés-Palomec, A. C., McCauley, R. A., Oyama, K., (2008) Isolation, characterization and cross-amplification of polymorphic microsatellite loci in Laelia speciosa (Orchidaceae). Molecular Ecology Resources 8: 135–138. Google Scholar


Cozzolino, S., Widmer, A., (2005) Orchid diversity: An evolutionary consequence of deception?. Trends in Ecology & Evolution 20: 487–494. Google Scholar


Da Cruz, D. T., Selbach-Schnadelbach, A., Lambert, S. M., Ribeiro, P. L., Borba, E. L., (2011) Genetic and morphological variability in Cattleya elongata Barb. Rodr. (Orchidaceae), endemic to the campo rupestre vegetation in northeastern Brazil. Plant Systematics and Evolution 294: 87–98. Google Scholar


Dieringer, D., Schotterer, C., (2003) Microsatellite analyzer (MSA): A platform independent analysis tool for large microsatellite data sets. Molecular Ecology Notes 3: 167–169. Google Scholar


Dressler, R. L., (1990) The orchids: Natural history and classification, Cambridge, MA: Harvard University Press. Google Scholar


Earl, D. A., von Holdt, B. M., (2012) STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4: 359–361. Google Scholar


Evanno, G., Regnaut, S., Goudet, J., (2005) Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology 14: 2611–2620. Google Scholar


Excoffier, L., Foll, M., Petit, R. J., (2009) Genetic consequences of range expansions. Annual Review of Ecology, Evolution, and Systematics 40: 481–501. Google Scholar


Excoffier, L., Lischer, H. E. L., (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10: 564–567. Google Scholar


Falush, D., Stephens, M., Pritchard, J. K., (2003) Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164: 1567–1587. Google Scholar


Farwig, N., Braun, C., Bohning-Gaese, K., (2008) Human disturbance reduces genetic diversity of an endangered tropical tree, Prunus africana (Rosaceae). Conservation Genetics 9: 317–326. Google Scholar


Flores-Palacios, A., García-Franco, J. G., (2003) Effects of floral display and plant abundance on fruit production of Rhyncholaelia glaunca (Orchidaceae). Revista de Biologia Tropical 51: 71–78. Google Scholar


Forrest, A. D., Hollingsworth, M. L., Hollingsworth, P. M., Sydes, C., Bateman, R. M., (2004) Population genetic structure in European populations of Spiranthes romanzoffiana set in the context of other genetic studies on orchids. Heredity 92: 218–227. Google Scholar


Godt, M. J. W., Hamrick, J. L., (1995) Allozyme variation in two Great Smoky Mountain endemics: Cacalia regalia and Glyceria nubigena. The Journal of Heredity 86: 194–198. Google Scholar


Goldstein, D. B., Ruiz-Linares, A., Cavalli-Sforza, L. L., Feldman, M. W., (1995) An evaluation of genetic distances for use with microsatellite loci. Genetics 139: 463–471. Google Scholar


Griffiths, A. J. F., Wessler, S. R., Lewontin, R. C., Carroll, S. B., (2008) Introduction to genetic analysis, New York, NY: W.H. Freeman and Co. Google Scholar


Guillot, G., Mortier, F., Estoup, A., (2005) GENELAND: A computer package for landscape genetics. Molecular Ecology Notes 5: 712–715. Google Scholar


Guillot, G., Santos, F., Estoup, A., (2008) Analysing georeferenced population genetics data with Geneland: A new algorithm to deal with null alleles and a friendly graphical user interface. Bioinformatics 24: 1406–1407. Google Scholar


Gustafsson, S., Sjögren-Gulve, P., (2002) Genetic diversity in the rare orchid, Gymnadenia odoratissima and a comparison with the more common congener. G. conopsea. Conservation Genetics 3: 225–234. Google Scholar


Halbinger, F., (1993) Laelias de México, México, DF: Asociación Mexicana de Orquideología, A.C, pp. 71, . Google Scholar


Halbinger, F., & Soto, M. (1997). Laelias of México. Orquídea (Méx.) 15, 1–160. Google Scholar


Herrera-Arroyo, M. L., Sork, V. L., González-Rodríguez, A., Rocha-Ramírez, V., Vega, E., Oyama, K., (2013) Seed-mediated connectivity among fragmented populations of Quercus castanea (Fagaceae) in a Mexican landscape. American Journal of Botany 100: 1663–1671. Google Scholar


Honnay, O., Jacquemyn, H., Bossuyt, B., Hermy, M., (2005) Forest fragmentation effects on patch occupancy and population viability of herbaceous plant species. New Phytologist 166: 723–736. Google Scholar


Hubisz, M., Falush, D., Stephens, M., Pritchard, J., (2009) Inferring weak population structure with the assistance of sample group information. Molecular Ecology Resources 9: 1322–1332. Google Scholar


Izawa, T., Kawahara, T., Takahashi, H., (2007) Genetic diversity of an endangered plant, Cypripedium macranthos var. rebunense (Orchidaceae): Background genetic research for future conservation. Conservation Genetics 8: 1369–1376. Google Scholar


Lefort, F., Douglas, G. C., (1999) An efficient micro-method of DNA isolation from mature leaves of four hardwood tree species Acer, Fraxinus, Prunus and Quercus. Annals of Forest Science 56: 259–263. Google Scholar


Li, A., Ge, S., (2006) Genetic variation and conservation of Changnienia amoena, an endangered orchid endemic to China. Plant Systematics and Evolution 258: 251–260. Google Scholar


Li, J. M., Jin, Z. X., (2007) Genetic variation and differentiation in Torreya jackii Chun, an endangered plant endemic to China. Plant Science 172: 1048–1053. Google Scholar


Loveless, M. D., Hamrick, J. L., (1984) Ecological determinants of genetic structure in plant populations. Annual Review of Ecology and Systematics 15: 65–95. Google Scholar


Lowe, A., Boshier, D., Wars, M., Bacles, C., Navarro, C., (2005) Genetic resource impacts of habitat loss and degradation; reconciling empirical evidence and predicted theory for Neotropical trees. Heredity 95: 255–273. Google Scholar


Luikart, G., Cornuet, J. M., (1998) Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conservation Biology 12: 228–237. Google Scholar


Jacquemyn, H., de Meester, L., Jongejans, E., Honnay, O., (2012) Evolutionary changes in plant reproductive traits following habitat fragmentation and their consequences for population fitness. Journal of Ecology 100: 76–87. Google Scholar


Jump, A. S., Peñuelas, J., (2006) Genetic effects of chronic habitat fragmentation in a wind-pollinated tree. Proceedings of the National Academy of Sciences 103: 8096–8100. Google Scholar


Manni, F., Guerard, E., Heyer, E., (2004) Geographic patterns of (genetic, morphologic, linguistic) variation: How barriers can be detected by “Monmonier's algorithm”. Human Biology 76: 173–190. Google Scholar


Mant, J., Peakall, R., Schiestl, F. P., (2005) Does selection on floral odour promote differentiation among populations and species of the sexually deceptive orchid genus Ophrys?. Evolution 59: 1449–1463. Google Scholar


Mathiasen, P., Rovere, A. E., Premoll, A. C., (2007) Genetic structure and early effects of inbreeding in fragmented temperate forests of a self-incompatible tree. Embothrium coccineum. Conservatio Biology 21: 232–240. Google Scholar


Miranda, F. (1997). Surviving of prehispanic handcrafts. In: V. Oikión S. (Ed.), Manos Michoacanas (pp. 35–48). Michoacán, México: El Colegio de Michoacán, Gobierno del Estado de Michoacán y Universidad Michoacana de San Nicolás de Hidalgo. Google Scholar


Muñoz, M., Warner, J., Albertazzi, F. J., (2010) Genetic diversity analysis of the endangered slipper orchid Phragmipedium longifolium in Costa Rica. Plant Systematics and Evolution 290: 217–223. Google Scholar


Murren, C. J., (2003) Spatial and demographic population genetic structure in Catasetum viridiflavum across a human-disturbed habitat. Journal of Evolutionary Biology 16: 333–342. Google Scholar


Neiland, M. R., Wilcock, C. C., (1998) Fruit set, nectar reward and rarity in the Orchidaceae. American Journal of Botany 85: 1657–1671. Google Scholar


Pinheiro, L. R., Rabbani, A. R. C., da Silva, A. V. C., da Silva Lédo, A., Pereira, K. L. G., Diniz, L. E. C., (2012) Genetic diversity and population structure in the Brazilian Cattleya labiata (Orchidaceae) using RAPD and ISSR markers. Plant Systematics and Evolution 298: 1815–1825. Google Scholar


Piry, S., Luikart, G., Cornuet, J. M., (1999) BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. The Journal of Heredity 90: 502–503. Google Scholar


Pritchard, J. K., Stephens, M., Donnelly, P., (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959. Google Scholar


Qian, X., Wang, C.-X., Tian, M., (2013) Genetic diversity and population differentiation of Calanthe tsoongiana, a rare and endemic orchid in China. International Journal of Molecular Science 14: 20399–20413. Google Scholar


R Development Core Team (2016) R: A language and environment for statistical computing, Vienna, Austria: R Foundation for Statistical ComputingRetrieved from Scholar


Rasmussen, H. N., Rasmussen, F. N., (2009) Orchid mycorrhiza: Implications of a mycophagous life style. Oikos 118: 334–345. Google Scholar


Raymond, M., Rousset, F., (1995) An exact test for population differentiation. Evolution 49: 1280–1283. Google Scholar


Schaberg, P. G., DeHayes, D. H., Hawley, G. J., Nijensohn, S. E., (2008) Anthropogenic alterations of genetic diversity within tree populations: Implications for forest ecosystem resilience. Forest Ecology and Management 256: 855–862. Google Scholar


Semarnat, Environmental and Natural Resources Secretary. (2010). Norma Oficial Mexicana NOM-059-SEMARNAT-2010. Diario Oficial de la Federación (DOF), jueves 30 de diciembre de 2010. Google Scholar


Sharma, I. K., Clements, M. A., Jones, D. L., (2000) Observation of high genetic variability in the endangered Australian terrestrial orchid Pterostylis gibbosa R. Br. (Orchidaceae). Biochemical Systematics and Ecology 28: 651–663. Google Scholar


Sharma, I. K., Jones, D. L., French, C. J., (2003) Unusually high genetic variability revealed through allozymic polymorphism of an endemic and endangered Australian orchid, Pterostylis aff. picta (Orchidaceae). Biochemical Systematics and Ecology 31: 513–526. Google Scholar


Slatkin, M., (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics 139: 457–462. Google Scholar


Soliva, M., Widmer, A., (2003) Gene flow across species boundaries in sympatric, sexually deceptive Ophrys (Orchidaceae) species. Evolution 57: 2252–2261. Google Scholar


Smith-Ramírez, C., Armesto, J. J., (2003) Foraging behaviour of bird pollinators on Embothrium coccineum (Proteaceae) trees in forest fragments and pastures in southern Chile. Austral Ecology 28: 53–60. Google Scholar


Smith, S. E., Read, D. J., (2008) Mycorrhizal Symbioses, 2nd ed. London, England: Academic Press. Google Scholar


Soto-Arenas, M. A. (1996). México (regional account). In: E. Hágsater, & V. Dumont (Eds.), Orchids – Status survey and conservation action plan IUCN/SSC orchids specialist group (pp. 53–58). Cambridge, UK: UCN, Gland, Suiza. Google Scholar


Stone, J. L., Crystal, P. A., Devlin, E. E., Downer, R. H. L., Cameron, D. S., (2012) Highest genetic diversity at the northern range limit of the rare orchid. Isotria medeoloides. Heredity 109: 215–221. Google Scholar


Swarts, N. D., Dixon, K. W., (2009) Terrestrial orchid conservation in the age of extinction. Annals of Botany 104: 543–556. Google Scholar


Swarts, N. D., Sinclair, E. A., Krauss, S. L., Dixon, K. W., (2009) Genetic diversity in fragmented populations of the critically endangered spider orchid Caladenia huegelii: Implications for conservation. Conservation Genetics 10: 1199–1208. Google Scholar


Trapnell, D. W., Hamrick, J. L., (2004) Partitioning nuclear and chloroplast variation at multiple spatial scales in the Neotropical epiphytic orchid. Laelia rubescens. Molecular Ecology 13: 2655–2666. Google Scholar


Trapnell, W. D., Hamrick, J. L., Nason, J. D., (2004) Three-dimensional finescale genetic structure of the neotropical epiphytic orchid. Laelia rubescens. Molecular Ecology 13: 1111–1118. Google Scholar


Tremblay, R. L., Ackerman, J. D., (2001) Gene flow and effective population size in Lepanthes (Orchidaceae): A case for genetic drift. Biological Journal of Linnean Society 72: 47–62. Google Scholar


Tremblay, R. L., Ackerman, J. D., Zimmerman, J. K., Calvo, R. N., (2005) Variation in sexual reproduction in orchids and its evolutionary consequences: A spasmodic journey to diversification. Biological Journal of Linnean Society 84: 1–54. Google Scholar


Van der Pijl, L., Dodson, C. H., (1996) Orchid flowers, their pollination and evolution, Miami, FL: University of Miami Press. Google Scholar


Van Oosterhout, C., Weetman, D., Hutchinson, W. F., (2006) Estimation and adjustment of microsatellite null alleles in non-equilibrium populations. Molecular Ecology Notes 6: 255–256. Google Scholar


Vargas, C. F., Parra-Tabla, V., Feinsinger, P., Leirana-Alcocer, J., (2006) Genetic diversity and structure in fragmented populations of the tropical orchid Myrmecophila christinae var christinae. BIOTROPICA 38: 754–763. Google Scholar


Waples, R. S., (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conservation Genetics 7: 167–184. Google Scholar


Waples, R. S., Do, C., (2008) LdNe: A program for estimating effective population size from data on linkage disequilibrium. Molecular Ecology Resources 8: 753–756. Google Scholar


Young, A. G., Pickup, M., (2010) Low S allele numbers limit mate availability, reduce seed set and skew fitness in small populations of a self-incompatible plant. Journal of Applied Ecology 47: 541–548. Google Scholar


Yu, H. H., Yang, Z. L., Sun, B., Liu, R. N., (2011) Genetic diversity and relationship of endangered plant Magnolia officinalis (Magnoliaceae) assessed with ISSR polymorphisms. Biochemical Systematics and Ecology 39: 71–78. Google Scholar
© The Author(s) 2017 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License ( which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (
Karla Joanna Rojas-Méndez, Juan Manuel Peñaloza-Ramírez, Víctor Rocha-Ramírez, Aurea Cortés-Palomec, Ross A. McCauley, and Ken Oyama "Massive Extraction of the Orchid Laelia speciosa (HBK) Schltr. for Trading in Local Markets Affect Its Population Genetic Structure in a Fragmented Landscape in Central Mexico," Tropical Conservation Science 10(1), (1 January 2020).
Received: 19 January 2017; Accepted: 20 January 2017; Published: 1 January 2020

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