# Leafless Vanilla species from Madagascar as potential models to study bioclimatic adaptation

Rinah H. Ravelonanosy, Cathucia F. Andriamihaja, Michel Grisoni, Aro

Vonjy Ramarosandratana, Verohanitra Rafidison, Nicola Fuzzati, Carine

Charron, Pascale Besse

► To cite this version:

Rinah H. Ravelonanosy, Cathucia F. Andriamihaja, Michel Grisoni, Aro Vonjy Ramarosandratana, Verohanitra Rafidison, et al. Leafless Vanilla species from Madagascar as potential models to study bioclimatic adaptation. Dakal Tikam (ed.). Advances in genetic diversity - Recent advances, challenges, and prospects, InTechOpen, 20 p., 2025, 10.5772/intechopen.1012448. hal-05482924

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# Leafless *Vanilla* Species from Madagascar as Potential Models to Study Bioclimatic Adaptation

Rinah H. Ravelonanosy, Cathucia F. Andriamihaja,

Michel Grisoni, Aro V. Ramarosandratana,

Verohanitra M. Rafidison, Nicola Fuzzati, Carine Charron

and Pascale Besse

## Abstract

Within the *Vanilla* genus, leaflessness has evolved as an adaptation to heat and drought conditions. Bioclimatic factors have been shown to be responsible for the ecologically driven speciation of seven leafless *Vanilla* species indigenous to Madagascar. In the present study, we further assessed whether some of these species could be good models for genome-environment associations (GEA) studies at the intraspecific level, in order to later identify genomic signatures of adaptation, which may be useful in vanilla crop breeding for abiotic stress tolerance. For this, we selected two target species in Madagascar, *V. perrieri* and *V. bosseri*, as they are the most widely distributed, spanning over different bioclimatic regions. Principal component analysis (PCA) of 19 environmental variables revealed a bioclimatic structure based on sample collection sites. We also assessed intra-specific genetic variation using previously generated data from seven microsatellite loci. For each species, redundancy analyses demonstrated that environmental variations (temperature, precipitation, and vegetation index) contributed to the genetic structure of the populations through isolation-by-environment (IBE). These results indicate local adaptation of the populations of these two species and suggest that they represent appropriate models for further studying the climatic adaptation of vanilla plants.

**Keywords:** microsatellite, genome-environment associations, local adaptation, heat and drought adaptation, leafless vanilla

## 1. Introduction

### 1.1 Climate change and its effects on agriculture in Madagascar

Climate change has an adverse effect on agriculture. It enhances the outbreak of diseases, increases the frequency and severity of droughts and floods, and increases the likelihood of poor yields and crop failures [1, 2]. Drought alone will reduce global agricultural production by more than 10% in 2050 [3].
The effects of climate change on biodiversity and human activities are more pronounced in tropical, subtropical, and island environments [4, 5]. In Madagascar, for example, temperatures change more rapidly during the dry season than during the rainy season. An increase of more than 2°C is predicted over the next decade [6]. From 1990 to 2015, the degree of increased heat and multiple floods caused agricultural production to drop by more than 50%. The dry season has been extended in recent years in humid climates, and humidity levels have decreased by 10% [6]. Farmers are frequently exposed to pest disease outbreaks and extreme weather events, which lead to significant crop and income losses and worsen food insecurity. Unfortunately, the livelihood of the majority of the rural Malagasy population depends on agriculture [7, 8]. The identification and characterization of new genetic sources of tolerance to abiotic stresses are therefore part of the requirements for dealing with the effects of climate change on agriculture [9–11].

## 1.2 Vanilla cultivation and climate change

The commercial vanilla species, *Vanilla planifolia* G. Jackson, known as “Mexican” or “Bourbon” vanilla, is native to the rainforests of southeastern Mesoamerica [12]. In the late nineteenth century, *V. planifolia* was introduced into other tropical countries in Asia and Africa from vanilla plants cultivated in Mexico [13]. Madagascar has been one of the world’s leading producers of vanilla since 1960 [14]. Vanilla is among the most important agricultural products for the island’s economy. However, the vanilla crop is considered particularly sensitive to climatic variations [15]. The species has a limited response capacity to disease proliferation, prolonged drought episodes, and heat stress [16]. *V. planifolia* is a hygrosciaphilous species. Indeed, the production of a vanilla plantation depends on the local ecology, particularly light and shade. The tissues of the vanilla plant are very sensitive to sunlight: a lot of shade promotes vegetative growth, to the detriment of flowering. Conversely, an excess of sun and light can cause chlorosis and does not promote growth, and the plant becomes exhausted trying to flower [12]. Immature fruit falling and yellowing occur at high temperatures exceeding 32°C and low relative humidity (below 80%) [17]. Fruit drop can also occur 2 months after pollination after heavy rain. It varies from 15 to 90% depending on the cultivation system [18]. Therefore, it is important to identify strategies to reduce these harmful impacts of climate change.

Various methods are used in agriculture to reduce the effects of climate change on vanilla plants. In Mexico and Latin America, vanilla plants are cultivated in association with coffee, orange, and leguminous trees to provide shade and support [18]. However, this method is not applicable to all regions [19]. Other farmers use artificial shade houses. Irrigation systems can also be installed during the dry season when flowering and pollination occur. However, these strategies are not widely applicable to the majority of small farmers, who have very small plots and unfavorable economic situations. Their application may reduce the production diversification and increase the water usage, which can reduce the resilience to climate variation [7]. A sustainable solution would therefore be to select *V. planifolia* varieties adapted to climate change. To begin with, such an objective requires knowledge of the regions of the genome involved in this trait.

## 1.3 Leafless vanillas, a model of drought adaptation

The species of the genus *Vanilla* Plumier ex Miller are divided into two large morphological groups: species with leaves (Foliosae section), which include cultivated
vanilla (*V. planifolia*), and species without perennial leaves (Aphyllae section) [12].
Leaflessness was shown to represent an evolutionary convergence, as it appeared at
least three times independently in the evolution of the *Vanilla* genus, in America,
Asia, and Africa, as an adaptive response to drought conditions, mainly on islands or
nearby continental coastal regions [20, 21].

A recent integrative study, based on phylogenetic, genotypic, and morphologi-
cal data, has allowed the resolution of the taxonomic puzzle [20] represented by
the leafless species in the South-West Indian Ocean (SWIO) region [22]. This SWIO
monophyletic group, of African descent, has diversified in Madagascar and shows
two main lineages corresponding to white versus yellow flower species, regardless
of their geographical origin (Figure 1). Three independent colonization events
from Madagascar have further allowed species diversification in the Seychelles, the
Comoros archipelago, and the southeastern coast of Africa (Figure 1) [22]. Nine spe-
cies were discriminated, including seven species present in Madagascar [22].

In Madagascar, five of the seven species (V. bosseri L. Allorge, V. decaryana H. Perrier, V. madagascariensis Rolfe, V. humblotii Rchb.f., and V. perrieri Schltr) have been observed in the hot dry forests along the west coast (from north to south) and the arid zones in the south, in relation to their adaptation to drought and heat [23]. Two species have been newly described and observed in more humid bioclimatic regions, such as in the east (V. atsinananensis Andriamihaja & Pailler) and in a high central elevation area (V. allorgeae Andriamihaja & Pailler) (Figure 1) [22]. In addition, some leafless species were shown to be resistant to diseases such as Fusarium wilt [25, 26]. They are also used in traditional medicine in Madagascar [20].

Although only the species *V. planifolia* is of major economic interest on a global
scale, leafless vanilla plants represent a reservoir of important genetic diversity for
future varietal selection programs due to their agronomic and pharmacological
properties, especially in the context of current climate change.

<figure><img src="image_2.png" /><figcaption>**Figure 1.**
The nine leafless Vanilla species from the SWIO region: schematic representation of the ITS phylogeny (adapted from Andriamihaja et al., [22]) and their geographical distribution (adapted from Andriamihaja, [23] and Andriamihaja et al. [24]).</figcaption></figure>
## 1.4 *V. perrieri* and *V. bosseri* as model species for local bioclimatic adaptation studies

Andriamihaja et al. [24] detected the combined effects of geographical distance and environmental distance on the genetic differentiation of the seven Malagasy leafless species. A predominant contribution of abiotic factors (precipitation, temperature, soil pH, and altitude) to their ecological speciation process was demonstrated [23]. These species, therefore, constitute a potential model for studying climate adaptation [20].

Local adaptation occurs when a population of organisms adapts to local conditions and is commonly observed in spatially heterogeneous environments [27]. In the present study, we aimed to verify whether local adaptation was present at the intraspecific level for those leafless species. For this, we selected two species, *V. perrieri* and *V. bosseri*, because they are the two most widely distributed in Madagascar (**Figure 1**), spanning various bioclimatic regions [20]. Local bioclimatic adaptation studies can be conducted using a landscape genetics approach with microsatellite markers combined with the geographical coordinates of individuals and environmental data [28, 29]. This makes it possible to correlate geographical information and the structuring of genetic diversity, for example, to detect the existence of isolation-by-distance (IBD). Genome-environment association analyses [30] also make it possible to determine the existence of phenomena such as isolation-by-environment (IBE) [31, 32]. Such analyses were conducted in the present study as an essential prerequisite for assessing the potential of these two species as models for the future detection of genomic signatures of climate adaptation, to be used in the varietal selection of cultivated vanilla.

# 2. Material and methods

## 2.1 Plant material

Seventy-five accessions of *V. perrieri* from nine sites (with 1 to 34 accessions per site) and 192 accessions of *V. bosseri* from nine sites (with 9 to 38 accessions per site) were studied (**Table 1**). The accessions of *V. perrieri* and *V. bosseri* were distributed across three and two geographical zones, respectively, and each spanned three different bioclimatic regions in Madagascar. The species identity of these accessions was verified in previous studies [24].

## 2.2 Genetic, geographic, and environmental data

The genotyping data of the 267 accessions for seven microsatellite markers previously generated by Andriamihaja et al. [24] were used in our study.

The geographic coordinates and elevation of each sample collection site were collected during sampling. Elevation data was used as an environmental variable. Global climate layers in raster format with a resolution of 1 km², downloadable from the WORLDCLIM online database (http://www.worldclim.org), were used to extract 19 bioclimatic variables (mean of 30 years) for each sample using QGIS (v 3.36.2). Eleven variables were related to temperature, and eight were associated with precipitation. The annual mean value of the Normalized Difference Vegetation Index (NDVI) for each sample was extracted from a raster dataset with a resolution of 250 m provided by the GIMMS Global Agricultural Monitoring System [33].
<table><thead><tr><th rowspan="2">Population</th><th colspan="2">Number of individuals</th><th rowspan="2">Geographical zone</th><th rowspan="2">Bioclimatic region</th></tr><tr><th>V. perrieri</th><th>V. bosseri</th></tr></thead><tbody><tr><td>AKF</td><td>1</td><td>20<br>14</td><td>W</td><td>DF</td></tr><tr><td>AKLL</td><td>12</td><td>15</td><td>SW</td><td>ST</td></tr><tr><td>ATD</td><td>1</td><td></td><td>SW</td><td>ST</td></tr><tr><td>ATS</td><td></td><td>23</td><td>W</td><td>DF</td></tr><tr><td>B66</td><td>1</td><td></td><td>SE</td><td>LF</td></tr><tr><td>BBL</td><td>3</td><td>19</td><td>W</td><td>DF</td></tr><tr><td>BFD</td><td>21</td><td></td><td>W</td><td>DF</td></tr><tr><td>BTM</td><td>34</td><td></td><td>W</td><td>DF</td></tr><tr><td>CMK</td><td></td><td>9</td><td>W</td><td>DF</td></tr><tr><td>KRD</td><td></td><td>29</td><td>W</td><td>SWo</td></tr><tr><td>MLT</td><td>1</td><td></td><td>SW</td><td>ST</td></tr><tr><td>MMD</td><td>1</td><td></td><td>W</td><td>DF</td></tr><tr><td>MSL</td><td></td><td>38</td><td>W</td><td>DF</td></tr><tr><td>NMK</td><td></td><td>23</td><td>W</td><td>DF</td></tr><tr><td>ZVB</td><td></td><td>16</td><td>SW</td><td>SWo</td></tr><tr><td>Total</td><td>75</td><td>192</td><td></td><td></td></tr></tbody></table>

SE south-east, SW south-west, W west; DF Dry Deciduous Forest, LF Lowland Forest, ST Spiny Thickets, SWo Succulent Woodland.

<caption>**Table 1.**
Geographical zone, bioclimatic region, and the number of accessions of the populations studied.</caption>

## 2.3 Intraspecific genetic structuring

To examine the genetic structure of the populations for both *V. perrieri* and *V. bosseri*, a Bayesian clustering analysis was conducted using the STRUCTURE software using the genetic data from seven microsatellites [34]. The number of K tested in this study varied between 1 and 12 for each of the two species. No prior assumptions were made about the geographical origin of the samples. For each value of K, 100,000 burn-in periods followed by 1,000,000 MCMC iterations were performed with 10 repetitions. As recommended by Wang [35] and used by Andriamihaja et al. [24] on leafless vanilla, a model with admixture and independent allelic frequencies with a low value of the ancestral prior alternative Alpha (reduced to 1/K = 1/12 in our study) was chosen to correct the sampling imbalance and accurately assign individuals to clusters. The most probable value of K was determined by the use of the maximum value of DeltaK [36] and of the maximum value of the mean of the log posterior probability of K, Ln P(X|K) [34] with POPHELPER (pophelper.com). The geographical distribution of the K genetic groups across the populations of the two species was made on QGIS (v 3.36.2). An accession was considered to belong to a genetic group (cluster) when at least 80% of its genome was assigned to that genetic group.

## 2.4 Bioclimatic population structuring

For each species, principal component analysis (PCA) was performed on the environmental variables collected by using the “dudi.pca” function from the R package
"ADE4" [37]. This was done in order to verify the bioclimatic structuration and to identify the variables that most determine the distribution area of each species.

To minimize the multicollinearity of these variables, their variance inflation factor (VIF) values were calculated with the functions "vif" and "vifstep" of the R package "usdm" [38]. Only the variables with a VIF <10 were selected. Subsequently, a Pearson correlation test was conducted between the selected variables using the "cor" function from the R package "stats." When the Pearson correlation coefficient between two selected variables was greater than |r| > 0.7, one of the variables was excluded [38–40]. Only the variables retained after these two steps were used for the PCA.

## 2.5 Isolation by geographic distance and by environment

To check the effects of geographical and climatic factors on the genetic structure, modeling using the distance-based redundancy analysis (dbRDA) with the random-ization method was conducted for each species. The presence of IBD (isolation by geographical distance) and IBE (isolation by environmental distance) was tested using the "dbrda" function from the R package "vegan" [41]. To conduct the analysis, three datasets were necessary: (i) a genetic distance matrix between individuals, (ii) a geographical distance matrix between each individual reflecting their geographical position, and (iii) the environmental data of each individual.

Genetic distances were obtained by calculating Bruvo distances [42] between individuals with their microsatellite data using the "bruvo.dist" function from the R package "poppr" [43]. The geographical coordinates of each accession were used to obtain a geographical distance matrix between them using the "spDists" function from the R package "sp" [44]. This geographical distance matrix was then transformed into a continuous rectangular vector through PCA using the "pcnm" function in the R package "vegan" [41]. For the environmental data, the polar coordinates of the individuals derived from the PCA of the noncollinear variables analyzed for bioclimatic structuring were used.

Three types of dbRDA analyses were done: (i) a global analysis using the two variables (geographical and environmental), (ii) marginal analyses independently evaluating the relationship between the genetic distance matrix and each of the two explanatory variables (geographical or environmental factors), and (iii) a partial dbRDA estimating the relative contribution of one explanatory variable (geographical distance or environmental factors) to genetic differentiation, while controlling for the effect of the other variable. The "varpart" function from the R package "vegan" was used to partition the variance and to obtain the proportion of variance explained by the intersection of IBD and IBE. The adjusted coefficient of determination (R²<sub>adj</sub>) was calculated to evaluate the quality of the model. The higher the R² of a model, the more robust the model is. The contribution of a variable to a model was considered significant if the p-value was less than 0.05.

# 3. Results

## 3.1 Genetic structuring

With the microsatellite data from the 75 accessions of *V. perrieri*, the distribution of DeltaK and Ln P(X|K) values revealed a maximum value at K = 3, indicating that the accessions analyzed were distributed in three genetic clusters (Figure 2A, C).
The genetic structuration of the nine populations showed a geographical pattern: the populations assigned to the first cluster were located in the southern part, while most of the populations assigned to the second and third genetic clusters were located in the northwestern part of Madagascar (**Figure 2A, C**).

The analysis of the 192 V. bosseri accessions also showed maximum DeltaK and Ln P(X|K) values for K = 3 (**Figure 2B, D**). The distribution of the three genetic groups among the nine populations of this species revealed that the three genetic groups were present in the western populations, while the southern populations were mainly composed of two groups (**Figure 2B, D**).

**3.2 Bioclimatic structuring**

After checking for collinearity between the initial variables, six specific variables were retained for each species to study the bioclimatic structuration with PCA.

For *V. perrieri*, the first three dimensions of the PCA explained 74.8% of the total variance. PC1 (29.7%) was strongly determined by the variables BIO12 (Annual Precipitation) and BIO18 (Precipitation of Warmest Quarter), suggesting it represents a trend related to precipitation. PC2 (24.1%) was associated with the variables NDVI (Normalized Difference Vegetation Index) and BIO10 (Mean Temperature of Warmest Quarter). PC3 (21%) was dominated by two temperature variables: BIO2 (Mean Diurnal Range) and BIO8 (Mean Temperature of Wettest Quarter).

The distribution of the 75 accessions along the first two axes of the PCA showed a bioclimatic structuration of the nine populations analyzed (**Figure 3A**). PC1 separated the western sites (BFD, BTM, MMD, and AKF) from the south-west sites (AKLL, ATD, and MLT), revealing that the collection sites in the south-west region have lower precipitation than the others. The site B66 in the south-east was characterized by a low vegetation index (NDVI) and a high temperature on PC2 (**Figure 3A**).

<figure><img src="image_3.png" /><figcaption>**Figure 2.**
Geographical distribution of the three genetic groups in the nine populations of V. perrieri (A) and V. bosseri (B): the pie charts indicate the average values of the probability of a population belonging to the three genetic groups. Genetic assignment graph of individuals to the three genetic groups of V. perrieri (C) and V. bosseri (D): each individual is represented by a vertical bar divided into colored segments representing the probability of an individual belonging to the genetic group of that color.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3.**
Distribution of the samples of V. perrieri (A) and V. bosseri (B) on the first two axes (PC1 and PC2) of the PCA of six noncollinear environmental variables. Variables are described in the text.</figcaption></figure>

For *V. bosseri*, the first three dimensions of PCA explained 83.6% of the total variance of the data. PC1 explained 37.1% of the variation and was determined by BIO5 (Maximum Temperature of Warmest Month), BIO18 (Precipitation of Warmest Quarter), NDVI (Normalized Difference Vegetation Index) and BIO3 (Isothermality).
The variables Elevation and BIO14 (Precipitation of Driest Month) formed PC2 (29.2%). PC3 (17.2%) was associated with BIO18 (Precipitation of Warmest Quarter) and NDVI.

Regarding the western populations, the first axis of the PCA allowed for individu-
alizing the sites MSL, AKF, and KRD, located inland, and corresponding to hot and
humid areas with vegetation (BIO5, BIO18, and NDVI) (Figure 3B). On the other
hand, the sites BBL, ATS, and CMK, located closer to the sea, were characterized by
a pronounced Isothermality (BIO3). In the south-west zone, AKLL and ZVB were
influenced by Precipitation of the Driest Month (BIO14), with AKLL characterized by
Isothermality (BIO3) and ZVB distinguished by a higher Elevation (Figure 3B).

**3.3 Isolation by distance and isolation by environment**

The dbRDA analysis allowed the estimation of the amount of the genetic diversity (based on microsatellite data) that can be explained by uncorrelated climatic and geographic factors. The global model revealed an R²adj of 13.3% for the 75 V. *perrieri* accessions, compared to 7.6% for the 192 accessions of V. *bosseri*. In V. *perrieri*, environmental distance explained slightly more of the genetic structure than geographic distance (IBE: R²adj = 11.5%; IBD: R²adj = 10.2%), whereas in V. *bosseri*, the contributions of IBE and IBD were approximately the same (IBE: R²adj = 5.2%; IBD: R²adj = 5.4%) (Table 2).

Marginal tests indicated significant effects of both IBE and IBD on the genetic structure of the two species. In *V. perrieri*, variance partitioning showed a significant strict contribution of IBE (R²adj = 3.1%) and a shared effect of IBE∩IBD accounting for 8.4% of the genetic differentiation (Figure 4A). However, the strict contribution of IBD (R²adj = 1.8%) was not statistically significant (p = 0.054). For *V. bosseri*, after removing the shared IBE∩IBD effects (3.1%), the strict contributions of IBE (R²adj = 2.2%) and IBD (R²adj = 2.3%) remained significant in explaining the genetic differentiation of the 192 accessions (Figure 4B).

A variance analysis (ANOVA) was performed on the partial redundancy analysis
of IBE to determine which environmental factor was mainly involved in the genetic
structuration of the species. As a result, five bioclimatic variables (NDVI Normalized
Difference Vegetation Index, BIO8 Mean Temperature of Wettest Quarter, BIO12
Annual Precipitation, BIO15 Precipitation Seasonality, and BIO18 Precipitation of
Warmest Quarter) were significantly associated with the genetic variation of the

<table>
  <thead>
    <tr>
      <th rowspan="2">Models</th>
      <th colspan="3">V. perrieri</th>
      <th colspan="3">V. bosseri</th>
    </tr>
    <tr>
      <th>F-statistic</th>
      <th>P-value</th>
      <th>R<sup>2</sup> adj</th>
      <th>F-statistic</th>
      <th>P-value</th>
      <th>R<sup>2</sup> adj</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>BD ~ GD + ED</td>
      <td>2.42</td>
      <td>0.001***</td>
      <td>0.133</td>
      <td>2.31</td>
      <td>0.001***</td>
      <td>0.076</td>
    </tr>
    <tr>
      <td>BD ~ GD</td>
      <td>3.80</td>
      <td>0.001***</td>
      <td>0.102</td>
      <td>2.83</td>
      <td>0.001***</td>
      <td>0.054</td>
    </tr>
    <tr>
      <td>BD ~ ED</td>
      <td>2.92</td>
      <td>0.001***</td>
      <td>0.115</td>
      <td>2.77</td>
      <td>0.001***</td>
      <td>0.052</td>
    </tr>
    <tr>
      <td>BD ~ GD/ED</td>
      <td>1.48</td>
      <td>0.054</td>
      <td>0.018</td>
      <td>1.78</td>
      <td>0.001***</td>
      <td>0.023</td>
    </tr>
    <tr>
      <td>BD ~ ED/GD</td>
      <td>1.50</td>
      <td>0.029*</td>
      <td>0.031</td>
      <td>1.72</td>
      <td>0.001***</td>
      <td>0.022</td>
    </tr>
  </tbody>
</table>

<caption>**Table 2.**

Results of modeling and variance partitioning with dbRDA of the effects of environmental distance (ED) and geographical distance (GD) on the Bruvo genetic distance (BD) of the 75 accessions of V. perrieri and 192 accessions of V. bosseri.</caption>

<footnote>P-value: * p < 0.05, ** p ≤ 0.01, *** p ≤ 0.001.</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 4.**
Variance partitioning of the effects of environmental distance (IBE) and geographic distance (IBD) on the genetic differentiation of the accessions of V. perrieri (A) and V. bosseri (B).</figcaption></figure>

<table><thead><tr><th colspan="3">V. perrieri</th><th colspan="3">V. bosseri</th></tr><tr><th>Variables</th><th>F-statistic</th><th>P-value</th><th>Variables</th><th>F-statistic</th><th>P-value</th></tr></thead><tbody><tr><td>NDVI</td><td>1.96</td><td>0.025 *</td><td>Elevation</td><td>3.16</td><td>0.001 ***</td></tr><tr><td>BIO2</td><td>1.48</td><td>0.156</td><td>NDVI</td><td>1.75</td><td>0.023 *</td></tr><tr><td>BIO8</td><td>3.73</td><td>0.001 ***</td><td>BIO3</td><td>1.14</td><td>0.287</td></tr><tr><td>BIO12</td><td>5.48</td><td>0.001 ***</td><td>BIO5</td><td>2.29</td><td>0.002 **</td></tr><tr><td>BIO15</td><td>2.19</td><td>0.014 *</td><td>BIO14</td><td>0.81</td><td>0.686</td></tr><tr><td>BIO18</td><td>1.81</td><td>0.035 *</td><td>BIO18</td><td>1.18</td><td>0.275</td></tr></tbody></table>

P-value: * p < 0.05, ** p ≤ 0.01, *** p ≤ 0.001. Variables are described in the text.

<caption>**Table 3.**
Results of the ANOVA on the dbRDA model with the relative contributions of environmental variables to the genetic structuration of the two species.</caption>

accessions of *V. perrieri* and three bioclimatic variables (Elevation, NDVI Normalized Difference Vegetation Index, and BIO5 Max Temperature of Warmest Month) for *V. bosseri* (Table 3).

# 4. Discussion

## 4.1 Genetic structuring and IBD

The intraspecific genetic analysis of the *V. perrieri* and *V. bosseri* accessions showed that they were each represented by three genetic groups, revealing a genetic structuring based on geographical location (western versus southern populations).

The populations of *V. perrieri* from the north-west were more admixed, suggesting the existence of a higher genetic diversity in that region than in populations in the south. Andriamihaja et al. [22] suggested that *V. perrieri* had recently differentiated from *V. humblotii*, and the two species remain very similar morphologically. In Madagascar, *V. humblotii* is a species that is found only in the northern part of the island (Figure 1). *V. perrieri* spreads along the west coast, from north to south, toward much drier ecological niches [45, 46].

Similarly, *V. bosseri* populations showed more admixture in the north-west than in the south-west. *V. bosseri* is part of a phylogenetic group that includes *V. madagascaniensis* [22], a species that is restricted to the north of the island (Figure 1). These two species are very similar morphologically, but *V. bosseri* is distinguished by the total
absence of hairs on the labellum. *V. bosseri* has a wider distribution, along the west coast to the south, and may have differentiated very recently from *V. madagascariensis*.

These results are concordant with the reported diversity decrease observed with successive stages of colonization, with populations at the limits of their range distribution area often characterized by a loss of diversity [47–52].

In addition to the genetic structuration of the two species matching their geographical location, the dbRDA analyses showed the existence of IBD with a global contribution of 10.2% for *V. perrieri* and 5.4% for *V. bosseri*. However, in the case of *V. perrieri*, this contribution was only significant in its combined component with IBE (IBE∩IBD) for 8.4%; the strict contribution of IBD was not significant. For *V. bosseri*, the strict contribution of IBD (2.3%) and IBE∩IBD (3.1%) was significant. The importance of the IBE∩IBD factor in both analyses was expected, given the longitudinal geographical pattern of the bioclimatic regions along the west coast of Madagascar, where accessions were sampled.

## 4.2 Bioclimatic structuring and IBE

The climatic data of the *V. perrieri* and *V. bosseri* accessions analyzed clearly showed a bioclimatic structuring of the collection sites for the two species, in accordance with their localization in various bioclimatic regions in Madagascar (**Figure 2**). The results indicated that both species exhibit a high tolerance to environmental variability (heat and humidity for the two species and thermal stability and altitude specifically for *V. bosseri*). Their occurrence across diverse microclimates, even within the same biogeographic region, highlights their ecological adaptability.

A difference in precipitation was revealed between sites from the northwestern and southern regions of *V. perrieri*. Indeed, the western coast of Madagascar is made up of contrasting ecoregions with an average annual rainfall of between 1000 and 1500 mm in the north and a maximum annual rainfall of 500 mm in the arid zones of the south [20, 53]. In addition, the dbRDA analysis showed the influence of bioclimatic factors (IBE) on the genetic structuring of *V. perrieri* with an overall contribution of 11.5%.

Bioclimatic structuring was observed in the nine *V. bosseri* collection sites based on their proximity to the sea. Among the western populations, inland sites (AKF, KRD, MSL) have a humid and warm climate during the hot season, as well as dense vegetation. Western coastal sites (BBL, CMK, ATS) are marked by less extreme temperature variations (high isothermality) due to the influence of the ocean, which acts as a natural temperature regulator [54, 55]. Conversely, inland areas experience higher temperature variations influenced by the geographical relief [56]. The two southwestern sites are characterized by higher precipitation during the driest month of the year. The gradient of isothermality from the inland to the coastal zones was also observed in the south-west areas. The coastal site AKLL has a more stable temperature, as expected [57, 58], compared to the inland site ZVB, located at a higher elevation. Isothermality may influence the water cycles and contribute to ecological divergences across landscapes [59]. Finally, the dbRDA analysis showed that bioclimatic factors contributed globally to 5.2% of the genetic variation in *V. bosseri*.

The variance partitioning allowed us to demonstrate that, even by controlling the effect of IBD, IBE still remained a significant factor in the genetic structuring of both *V. perrieri* and *V. bosseri* (3.1 and 2.2%, respectively). This strict and significant part of IBE therefore shows that the effect of the environment on the genetic structure of each species is important, even for individuals that are geographically close.
4.3 Prospects

This study showed that *V. perrieri* and *V. bosseri* were suitable model species for a future approach based on genomic scans and genome-environment association (GEA) to identify genomic regions involved in climate adaptation.

Genomic scans are methods applied to the entire genome to detect the genetic structuring expected following local divergent adaptation processes [60, 61]. By genotyping different populations, adapted to contrasting environments, using several thousand single-nucleotide polymorphism (SNP) molecular markers, it becomes possible to identify genomic regions that present selection signatures (such as differences in allele frequencies between environments or reduction in genetic diversity within the same environment). These regions, which potentially control traits under selection [62], correspond to so-called “outlier” loci that deviate from the neutrality hypothesis (detected using F<sub>ST</sub> or PCA tests) [63, 64]. It is also possible to search for alleles specifically correlated with particular putative selective characteristics of the environment, i.e., genome-environment associations (GEA) with linear modeling [64–67].

A difficulty with this type of approach is that the effect of geography on environmental and genetic variation can confound the interpretation of tests [65], as local adaptation can be hampered by gene flow [68] and can be masked by the effects of genetic drift and demographic history [69]. Indeed, without corrections for the effect of population structure or isolation-by-distance (IBD), tests of associations between loci and environmental variables will be subject to high rates of false positives [61, 70, 71]. However, linear mixed models allow for the potentially confounding effects of demographic history to be controlled by including population structure as a covariate [66, 72]. In view of our results, we will therefore have to use these specific methods to control the effects of genetic structuring and IBD detected in our two species, particularly for *V. bosseri*.

Furthermore, to be suitable for a genome scan/GEA study, the populations studied must be located in environments characterized by contrasting bioclimatic conditions. We demonstrated that this was the case for the two species studied here.

Finally, the plant material studied should show IBE, i.e., a correlation between genetic and bioclimatic distances. The contribution levels of strict IBE revealed in the present intraspecific study (3.1 and 2.2%) are substantially the same as in the previous interspecific study (3.4%) of Andriamihaja et al. [24], suggesting that the sampling of *V. perrieri* and *V. bosseri* encompasses the majority of the genetic variation explained by the environment detected in leafless vanilla plants of Madagascar. Thus, the study of the genomic regions involved in bioclimatic adaptation at the intraspecific level in *V. perrieri* and *V. bosseri* appears entirely feasible. The significant values of R<sup>2</sup><sub>adj</sub> found for the strict contribution of bioclimatic data to genetic structuring (IBE), although relatively low, appear high enough to develop future research on the genomic signatures of climate adaptation. Indeed, Du et al. [40], in a study of climate adaptation of *Quercus aquifolioides*, used microsatellite data of 587 individuals from 60 sites and found two ancestral populations with STRUCTURE and a strict IBE of 2.45% with dbRDA. With 381 SNPs, they were able to locate 12 candidate genes with the successful use of outlier F<sub>ST</sub>-based and GEA analyses. Similarly, Pluess et al. [73] revealed a nonsignificant IBD and a marginally significant IBE of 4% on 79 genetically unstructured populations of *Fagus sylvatica*. With GEA analyses, they were able to locate 16 SNPs in 10 genes associated with 1 to 20 environmental variables (temperature and precipitation).
**5. Conclusions**

For each species, redundancy analyses demonstrated that environmental varia-
tions (temperature, precipitation, and vegetation index) contributed to the genetic
structure of the populations. Such isolation-by-environment (IBE) indicates local
adaptation in the populations of *V. perrieri* and *V. bosseri* and suggests that they repre-
sent appropriate model species for further identifying genomic signatures of climatic
adaptation of vanilla plants, provided special care will be taken to use appropriate
methods to correct for the genetic structuring and IBD confounding effects.

Therefore, SNP markers are now being developed using genotyping-by-sequenc-
ing (GBS) [74, 75] to perform genomic scans and GEA analyses in populations of
these two species. Ultimately, the candidate loci identified will be useful for the
selection of heat- and drought-tolerant vanilla (*V. planifolia*) cultivars.

**Acknowledgements**

This work was funded by Chanel Ltd. (Toarova project). The research was co-
funded by the Centre de Coopération Internationale en Recherche Agronomique
pour le Développement (CIRAD) and the Université de la Réunion (UR). Rinah H.
Ravelonanosy is the recipient of a PhD grant funded by the ERDF and the Conseil
Régional de la Réunion. We are grateful to the Plant Protection Platform (3P, IBISA)
for lab facilities and plant resources access (BRC Vatel).

**Conflict of interest**

The authors declare no conflict of interest.
## Author details

Rinah H. Ravelonanosy¹, Cathucia F. Andriamihaja¹, Michel Grisoni²,
Aro V. Ramarosandratana³, Verohanitra M. Rafidison³, Nicola Fuzzati⁴,
Carine Charron⁵ and Pascale Besse¹*

1 University of La Réunion, UMR PVBMT, St. Pierre, La Réunion, France

2 CIRAD, UMR PVBMT, Toamasina, Madagascar

3 Department of Plant Biology and Ecology, University of Antananarivo,
Antananarivo, Madagascar

4 Ingredients Innovation and Development Department, CHANEL Parfums Beauté,
Pantin, France

5 CIRAD, UMR PVBMT, St. Pierre, La Réunion, France

*Address all correspondence to: pascale.besse@univ-reunion.fr

## IntechOpen

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