# Inter-annual variability in flowering of orchids: lessons learned from 8 years of monitoring in a Mediterranean region of France

Hélène Vogt-Schilb, Philippe Geniez, Roger Pradel, Franck Richard, Bertrand Schatz

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Hélène Vogt-Schilb, Philippe Geniez, Roger Pradel, Franck Richard, Bertrand Schatz. Inter-annual variability in flowering of orchids: lessons learned from 8 years of monitoring in a Mediterranean region of France. European Journal of Environmental Sciences, 2013, 3 (2), pp.129-137. 10.14712/23361964.2015.16 . hal-01911608

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# INTER-ANNUAL VARIABILITY IN FLOWERING OF ORCHIDS: LESSONS LEARNED FROM 8 YEARS OF MONITORING IN A MEDITERRANEAN REGION OF FRANCE

HÉLÈNE VOGT-SCHILB<fnref n="1" />,<fnref n="2" />, PHILIPPE GENIEZ<fnref n="3" />, ROGER PRADEL<fnref n="1" />, FRANCK RICHARD<fnref n="1" />, and BERTRAND SCHATZ<fnref n="1" />,*

## ABSTRACT

It is important to evaluate the loss of biodiversity caused by global changes. In the case of orchids, it is still unclear how long the monitoring duration should be chosen in order to achieve a good compromise between the reliability of the orchid dynamics recorded and sampling duration (e.g. years of monitoring). This study aims to propose a method of monitoring orchids. Using a large database, we investigated the inter-annual variability in flowering of orchids in a French Mediterranean region. The database includes an 8-year-long study (2006–2013) of 47 species at 26 locations in three different types of habitats. The number of individual plants that flowered per species varied significantly between years, but not the number of species. Depending on habitat, two to four years were needed to observe the total number of species per location. Therefore, in Mediterranean regions a one-year-study seems to be insufficient to produce reliable results.

**Keywords:** orchid community, diachronic studies, orchid monitoring, similarity index, conservation

## Introduction

In the era of global change that deeply affects organisms on our planet (Parmesan 2006), there is an increasing urgency to evaluate the loss in biodiversity (Barnosky et al. 2011; Thuiller et al. 2011). In order to propose appropriate conservation strategies, it is essential for conservation biologists to precisely evaluate shifts in communities. Limiting our topic in orchids, it has been well acknowledged that orchids are ideal models to evaluate the impact of global change on biodiversity. First of all, they show clear and fast responses to environmental changes, including a current and well documented decline in occurrence or population size (Whigham and Willems 2003; Schatz et al. 2013). Then, many orchids are emblematic species (Cribb et al. 2003) and there is an increasing need to study them with the aim of conservation. Thirdly, they show well-documented patterns of endemism and rarity (Bournérias and Prat 2005; Dusak and Prat 2010).

The availability of long-run data or diachronic studies on the distribution of orchids provides opportunities to document temporal variation in communities (Jacquemyn et al. 2005; Kull and Hutchings 2006). These studies enable us to predict response of organisms to future environmental changes. However, there is a lack of standardized methods of recording and analyzing the data (Kati et al. 2004; Archaux et al. 2009). As a result, it is necessary to document the natural variability in the life-history traits of species (e.g. frequency of flowering) and to develop of sampling methods suitable to monitor orchids.

At the species scale, several studies have shown that the number of flowering individuals varied from year to year in response to climatic fluctuations (Tamm 1991; Sieg and King 1995; Wells et al. 1998; Tali 2002; Oien and Moen 2002; Brzosko 2003; Kindlmann 2003; Hrivnak et al. 2006; Jacquemyn et al. 2007). This inter-annual variability is recorded in several different species of orchids, such as *Anacamptis morio* (Wells et al. 1998), *Dactylorhiza majalis* (Hrivnak et al. 2006), *D. lapponica* (Oien and Moen 2002), *D. sambucina* (Tamm 1991), *D. incarnata* (Tamm 1991), *Gymnadenia conopsea* (Oien and Moen 2002), *Neotinea ustulata* (Tali 2002), *Neottia ovata* (Tamm 1991), *Orchis mascula* (Tamm 1991), *Platanthera praeclara* (Sieg and King 1995), *P. bifolia* (Brzosko 2003) and *Spiranthes spiralis* (Jacquemyn et al. 2007). A common characteristic of these studies is that they are all located in northern Europe. Yet, little is known about such variation in Mediterranean species (but see Sirami et al. 2010; Schatz and Geniez 2011). In Mediterranean regions, a high variability in the incidence of flowering of orchids is expected due to the high intra- and inter-annual variation in climate. In the future, longer and more severe drought periods (IPCC 2007; Giorgi and Lionello 2008) may disturb the phenology of orchids. Moreover, the region hosts different types of habitats, e.g. grassland, shrubland and woodland, which may affect flowering patterns of orchids differently.

We investigated the temporal dynamics of orchid assemblages, at both the species and community level (47 species of orchids) in a Mediterranean region of France (Languedoc-Roussillon). This study aims to address the following questions:

<footnote n="1">¹ Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), UMR 5175 CNRS, 1919 Route de Mende, 34293 Montpellier, France</footnote>

<footnote n="2">² Biotope, 22 boulevard Maréchal Foch, 34140 Mèze, France</footnote>

<footnote n="3">³ Ecole Pratique des Hautes Etudes, Biogéographie et Ecologie des Vertébrés, Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), UMR 5175 EPHE, 1919 Route de Mende, 34293 Montpellier, France</footnote>

<footnote>* Corresponding author: bertrand.schatz@cefe.cnrs.fr</footnote>
(1) How does the fluctuating Mediterranean climate affect flowering patterns of orchids?

(2) Are there differences in inter-annual flowering of orchids between habitat types?

We documented the inter-annual variations in flow-
ering patterns (e.g. number in flowering individuals and
probability of presence) of orchids in order to provide
a framework for future monitoring in this region.

**Materials and Methods**

Study sites

Study sites are situated in the North of the Languedoc-Roussillon region in southern France (43°17′31″N-44°17′31″N, 3°05′27″E-3°50′41″E). At these sites, the climate is of Mediterranean type with annual precipitation ranging from 950 mm to 1350 mm (Debussche and Escarré 1983). Air temperature varies from 0 °C in winter to 28 °C in summer (Sirami et al. 2010). We sampled at 26 locations, where many species with patchy distributions co-occurred. Three types of habitat were distinguished, corresponding to three stages of succession, hereafter called “grassland” (n = 10), “shrubland” (n = 8) and “woodland” (n = 8). We excluded locations that experienced high levels of disturbance, e.g. intensive tree cutting and grazing. The sampled locations differed in area, ranging from 500 m² to 2000 m². In each location, yearly orchid inventory was always carried out in the same area so that outcomes between inventories can be compared.

The study was conducted for eight consecutive years,
from 2006 to 2013. In each year, all the flowering indi-
viduals were recorded at each location during the same
period (between March and July). In total, 47 species of
orchids were included in this study. A full list of the spe-
cies studied is given in Table S1.

Species traits

Species traits were not measured in the field, but based
on the information available in the literature (Bournérias
and Prat 2005). The traits included:

1. Number of species of pollinators and mycorrhizal symbionts. They were classified as either a specialist (1 species), an intermediate (2–5 species) or a generalist (more than 5 species).

2. Flower morphology. We considered the number of flowers, the size of the inflorescence (mean in cm) and plant height (mean in cm).

3. Phenology. We considered the duration of flowering (mean in months) and the flowering period (mean in terms of particular months).

Data analysis

We used Wilcoxon or Kruskal-Wallis tests, i.e. two
non-parametric tests, to investigate the effect of year
sampled on the variability in the presence and number

of flowering individuals. For each median, we calculated
the confidence interval (IC), as [1.57 × (Q₃ − Q₁)] / n<sup>0.5</sup>,
where, Q₁ and Q₃ are the 25th and 75th percentile of the
data, respectively; and n is the number of observations
(Chambers et al. 1983).

We averaged the number of species per location and the number of flowering individuals per species and location. Concerning the inter-annual variability between species, we calculated the probability of presence for each species, as the ratio of the number of years when the species was recorded divided by the total number of years monitored, i.e. eight years. We considered that an absence of a record in one or several years did not mean death of an individual, but flowering dormancy. We used Jaccard's similarity index (Jaccard 1901) to determine how similar the species composition was between pairs of consecutive years. The similarity index was calculated as c/(a + b - c), where a is the total number of species in one year; b is the total number of species recorded in the following year; and c is the number of species in common found in both of years (Jaccard 1901). In regard to the number of records, we applied the same similarity index. In this case, the similarity index was equal to the minimum recorded number of flowering individuals divided to the maximum recorded number of flowering individuals between two consecutive years. To compare the similarity index between species, we only considered species that occurred at least at five locations in order to augment the spatial representativeness of the study site.

We used a principal component analysis in order to
investigate the relationship between inter-annual vari-
bility of flowering (index of similarity and probability of
presence) and traits of species. We investigated the spe-
cies-time relationships using an accumulation curve of
species recorded at the site level. We calculated the ratio
of the cumulated number of species recorded and the
maximum number of species recorded at the same loca-
tion between 2006 and 2013. This process was repeated
for each pair of successive years (e.g. 2007 and 2008, i.e.
seven possibilities) and each location.

All analyses were performed using R software (R de-
velopment core team, version 2.15.0). For the principal
component analyses, we used the package FactoMineR
(Husson et al. 2007).

Results

Inter-annual variability in flowering at community level

The number of species recorded per location did not
differ significantly between years (Kruskal-Wallis test,
K = 3.64, *p* > 0.05), with the median varying slightly, from
5 ± 1.23 (Median ± IC, idem for all the following cases)
in 2011 to 6.5 ± 1.85 in 2010 (Wilcoxon test, W = 417,
*p* > 0.05, Fig. 1A). The median of Jaccard-similarity
index between consecutive years was 0.94 ± 0.02 for all
locations, meaning that the species composition differed
<table><caption>**Table 1** Median ± confidence interval of Jaccard-similarity index for pairs of consecutive years for all locations and three habitats. Based on Wilcoxon non-parametric test, different letters indicate a significant difference in the Jaccard similarity index between different pairs of consecutive years for a particular habitat. N is the number of locations.</caption><thead><tr><th></th><th></th><th>All locations</th><th>Grassland</th><th>Shrubland</th><th>Woodland</th></tr></thead><tbody><tr><td>N</td><td></td><td>26</td><td>10</td><td>8</td><td>8</td></tr><tr><td>2006 vs 2007</td><td>0.77</td><td>± 0.11</td><td>a</td><td>± 0.10</td><td>a</td></tr><tr><td>2007 vs 2008</td><td>0.97</td><td>± 0.05</td><td>bd</td><td>± 0.03</td><td>bc</td></tr><tr><td>2008 vs 2009</td><td>1.00</td><td>± 0.05</td><td>bc</td><td>± 0.00</td><td>b</td></tr><tr><td>2009 vs 2010</td><td>1.00</td><td>± 0.00</td><td>c</td><td>± 0.00</td><td>bc</td></tr><tr><td>2010 vs 2011</td><td>0.77</td><td>± 0.07</td><td>ad</td><td>± 0.10</td><td>a</td></tr><tr><td>2011 vs 2012</td><td>0.88</td><td>± 0.07</td><td>b</td><td>± 0.11</td><td>ac</td></tr><tr><td>2012 vs 2013</td><td>0.97</td><td>± 0.05</td><td>b</td><td>± 0.00</td><td>b</td></tr><tr><td>Total</td><td>0.94</td><td>± 0.02</td><td>1.00</td><td>± 0.03</td><td>0.87</td></tr></tbody></table>

by 6% between consecutive years (Table 1). The Jaccard-similarity index differed significantly between habitats ($p < 0.05^*$) and the medians were $1 \pm 0.03$, $0.87 \pm 0.06$ and $0.88 \pm 0.06$ for grassland, shrubland and woodland, respectively (Table 1).

For the 47 species of orchids studied the average number of flowering individuals per species and location differed between years (Kruskal-Wallis test, $K = 24.75$, $p < 0.001^{***}$), with the median varying significantly from $5.2 \pm 3.4$ individuals in 2011 to $28 \pm 10.7$ individuals in 2010 (Wilcoxon test, $W = 1619.5$, $p < 0.001^{***}$, Fig. 1B). The similarity index of the number of flowering individuals between two consecutive years did not differ between habitats ($p > 0.05$).

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Boxplots of (A) the average number of species per location and (B) the average number of flowering individuals per species and location recorded per year. Black points correspond to (A) the mean of the number of species per location or (B) the number of flowering individuals per species. Different letters indicate a significant difference based on Wilcoxon non-parametric test. Significant codes: \* = $p < 0.05$, \*\* = $p < 0.01$, \*\*\* = $p < 0.001$.</figcaption></figure>

## Inter-annual variability in flowering at species level

In the eight years of inventory, probability of being present differed significantly between species (Kruskal-Wallis test, $K = 69.7$, $p < 0.05^*$, Fig. 2A, Table S2). The median of the average probability of presence was $0.82 \pm 0.06$ and $0.87 \pm 0.05$ for the 17 species and all the species studied, respectively (Fig. 2B). The probability of presence varied from 0.52 (for *Platanthera chlorantha*) to 0.98 (for *Anacamptis pyramidalis*) (Fig. 2A).

<figure><img src="image_3.png" /><figcaption>**Fig. 2** Plot of the probability of presence and the similarity index of the number of flowering individuals between two consecutive years per year (A). Each point represents the mean probability of presence and similarity index of the orchid species studied that occurred in ≥ 5 locations (dark-grey points) and < 5 locations (hollow points). Boxplots of (B) the probability of presence and (C) inter-annual similarity index are depicted for all the species (in light-grey) and for the 17 species that occurred in ≥ 5 locations (in dark-grey). Abbreviations of species: Amo = *Anacamptis morio*, Apy = *Anacamptis pyramidalis*, Cda = *Cephalanthera damasonium*, Clo = *Cephalanthera longifolia*, Dfu = *Dactylorhiza fuchsii*, Ehe = *Epipactis helleborine*, Hhi = *Himantoglossum hircinum*, Lab = *Limodorum abortivum*, Nov = *Neottia ovata*, Oap = *Ophrys apifera*, Osc = *Ophrys scolopax*, Oan = *Orchis anthropophora*, Oma = *Orchis mascula*, Opu = *Orchis purpurea*, Osi = *Orchis simia*, Pbi = *Platanthera bifolia*, Pch = *Platanthera chlorantha*.</figcaption></figure>
In regard to inter-annual similarity of flowering in-
dividuals, there were significant differences between six
pairs of species: *P. chlorantha* versus *A. pyramidalis* (Wil-
coxon test, *W* = 74; *p* < 0.05*), *P. chlorantha* versus *H. hir-
cinum* (Wilcoxon test, *W* = 35, *p* < 0.05*), *P. chlorantha*
versus *O. purpurea* (Wilcoxon test, *W* = 26, *p* < 0.05*),
*O. scolopax* versus *H. hircinum* (Wilcoxon test, *W* = 49.5,
*p* < 0.05*), *O. purpurea* versus *O. anthropophora* (Wilcox-
on test, *W* = 59, *p* < 0.05*) and *O. purpurea* versus *O. scol-
opax* (Wilcoxon test, *W* = 37, *p* < 0.05*) (Fig. 2, Table S2).
Medians of the averaged similarity index were 0.61 ± 0.02
and 0.62 ± 0.02 for the 17 species and all of the species
studied, respectively (Fig. 2C). The similarity index varied
from 0.48 (for *Platanthera chlorantha*) to 0.70 (for *Orchis
purpurea*) (Fig. 2A).

According to the principal component analysis, the first two factorial axes accounted for 34.89% (axis 1) and 20.93% (axis 2) of the total variability, respectively (Fig. S1). The similarity index was negatively related with the duration of flowering. The other traits, e.g. height and number in pollinator species, were neither related to the similarity index, nor to the probability of presence (Fig. S1).

Accumulation in the number of species recorded over time

The increased percentage in the number of the record-
ed species that accumulated with time differed between
habitats (Fig. 3). One year of sampling captured 87.5%
of the species in grassland, 88.9% of those in shrubland
and 80% of those in woodland. Significant difference was
found between shrubland and woodland (Wilcoxon test,
W = 1620.5, p < 0.05*), but was absent between grass-
land and shrubland or between grassland and woodland
(p > 0.05). The total number of species (median-value of
100%) was observed after the 2nd year of sampling for
shrubland, the 3rd year for woodland and the 4th year
for grassland (Fig. 3).

Discussion

Inter-annual variability in flowering at community level

We showed that the number of orchid flowering indi-
viduals varied conspicuously between years in the Medi-
terranean region (Fig. 1B). These results are in accor-
dance with the previous findings on the variability in the
inter-annual records of orchid numbers in northern Eu-
rope (Tamm 1991; Kindlmann and Balounova 1999; Oien
and Moen 2002; Tali 2002; Brzosko 2003). Inter-annual
fluctuations in orchid abundance are mainly induced by
variations in climate, in particular temperature and rain-
fall (Wells et al. 1998; Pfeifer et al. 2006, 2011). 2006 and
2011 were the driest during the sampling period and the
lowest number of individuals per species was recorded in
2011 at most of the study sites. This result indicates that
orchids may respond to drought by reducing the num-
ber of flowering individuals, as observed by Hutch-ings
(2010) in the case of *Ophrys sphegodes*. We suppose that

<figure><img src="image_4.png" /><figcaption>**Fig. 3** Boxplot of the cumulative percentage of species recorded in successive yearly inventory, for (A) grassland, (B) shrubland and (C) woodland. A non-overlapping of the notches of two plots denotes a significant difference between the two medians as proposed by Chambers *et al*. (1983).</figcaption></figure>

the high inter-annual variability in flowering of orchids
is due to dormancy induced by unfavourable weather
conditions. Summer drought can cause premature senes-
cence and death of leaves, which results in insufficient
reserves of nutrients for the orchids to flower the subse-
quent year (Wells et al. 1998).

At community scale, no evidence was detected re-
garding the significance of the number of species be-
tween years (Fig. 1A). In addition, species composition
differed only by 6% between consecutive years (Table 1).
We suppose that rare species vary less in terms of the in-
cidence of flowering than abundant species, and thus can
be maintained between years. As a result, we can distin-
guish two different strategies, i.e. high numbers of indi-
viduals combined with a high incidence of inter-annual
fluctuations (abundant species) and few individuals com-
bined with a low incidence of inter-annual fluctuations
(rare species). Similarly, Lavergne et al. (2004) evidenced
that narrow endemic plants were locally persistent, despite the fact that they produced fewer flowers than their widespread congeners. The persistence of rare species could be related to a better success in reproduction or establishment (Simon and Hay 2003; Byers and Meagher 1996).

## Inter-annual variability in flowering at species level

We evidenced that species responded differently in terms of orchid presence and similarity index (Fig. 2). We attributed this to the disparity of traits between species. The results indicated that the similarity index seems to be negatively related to the duration of flowering. Increasing the flowering duration can lead to an increase in the probability of pollination, and thus to an increase of the reproductive success (fruit production). However, according to Primack and Stacy (1998), a cost of fruit production leads to a reduction in flowering probability in subsequent years (in case of *Cypripedium acaule*). Further analyses need to be carried out, in order to relate reproductive success to traits, as suggested for the nectar presence (Neiland and Wilcock 1998). In particular, our principal component analysis was based on the mean of traits. Traits should be measured locally in order to consider the variability in a trait in individuals and between habitats.

Orchid species can be detected more or less easily. This is probably due to their particular life-state (dormant or flowering state) and species biological traits, e.g. color of the flower and plant height. Our study evidenced a weak probability of presence for *Platanthera chlorantha*, in contrast to *Anacamptis pyramidalis*. This can be due to different levels of difficulty in terms of species detection. *Platanthera chlorantha* has white-greenish flowers, which is more difficult to be detected compared to *Anacamptis pyramidalis* that has purple flowers. In order to take into account the imperfect detection, we suggest using the capture-recapture model proposed by Kery and Gregg (2004) in future orchid inventories. Such model can be used to calculate the extent to which orchid records are underestimated due to imperfect detection of orchids. However, inventories of at least three years are needed in order to estimate and correct for imperfect detection.

## Inter-annual variability in flowering in different habitats

Our study revealed that, in Mediterranean regions, a one-year-study is insufficient for monitoring orchids. This may be due to the fact that most of orchid species can stay in dormancy during in which flowering does not occur for one to three years (Shefferson et al. 2001; Brzosko 2003; Kery and Gregg 2004; Coates et al. 2006). Regarding the number of years needed to observe 100% of the species (Fig. 3), we found: shrubland (2 years) < woodland (3 years) < grassland (4 years). We attribute this result to the disparity of microclimate and light availability between habitats. Higher light availability can increase probability of flowering (Diez et al. 2007; Jac-

quemyn et al. 2010). Stable microclimatic conditions, e.g. a warm winter or fresh summer, can favour population performance and avoid dormancy of flowering (Pfeifer et al. 2006). The two factors antagonistically act in the case of grassland and woodland toward probability in flowering. In grassland, there is high light availability, but the microclimatic condition tends to be unstable. On the contrary, in woodland, light availability is low, but the microclimate is more regulated than in open habitat. For example, because of the tree canopy closure, the seasonal variation of air and soil temperature tend to be less contrasted under tree clusters than in open areas (Morecroft et al. 1998; Mao et al. 2013). Therefore, a trade-off effect between microclimate and light availability may exist in both grassland and woodland, thus resulting in higher numbers of years needed to observe 100% of the species (≥ 3 years). Compared to grassland and woodland, an intermediate condition of light availability and microclimate exists in shrubland, resulting in a lower number of years needed to observe all of the species.

## Conclusion and perspectives

This study showed that in Mediterranean context, climate affects the inter-annual variability of orchid flowering. We evidenced a species-based response in regard to orchid presence. The number of years needed to observe 100% of species diverged between habitats and last from two to four years. As a result, we suggest that further studies use data collected over periods of a minimum of two years. Species and habitat should be considered equally important when interpreting results.

In the future, relationships between species-specific traits of orchids (e.g. strategies of rare species versus abundant species) and inter-annual variability of flowering need to be better understood in order to enhance the conservation of orchids. Capture-recapture models of orchids, which can take into account imperfect detection, will be a promising tool when characterizing the temporal dynamics of orchid communities.

## Acknowledgements

This study was funded by the Languedoc-Roussillon region, France and a CIFRE convention (No. 187/2011) between the French National Association for Research and Technology (ANRT) and Biotope. We are grateful to the people who helped during fieldwork, including F. Corbion, P. Dabin, F. Dabonneville, G. Delvare, B. Delprat, J. Demarque, T. Disca, P. Escudié, P. Feldmann, F. Geniez, M. Geniez, D. Gruffat, J.-Y. Guillosson, L. Guillosson, T. Guillosson, J.-P. Hervy, M. Imbert, T. Lafranchis, P. Martin, F. Melki, T. Menut, M. Nicole, J. Pommet, S. Roux-Viljoen, G. Soupart, J.-C. Stahl, G. Toreilles and M. Zimmermann.
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## SUPPLEMENTARY MATERIALS

<table><caption>**Table S1** List of the orchid species studied with the abbreviated names (Abb) and the number of locations where the species was recorded for each habitat (Grassland, Shrubland, Woodland).</caption><thead><tr><td>Species</td><td>Abb</td><td>Grassland</td><td>Shrubland</td><td>Woodland</td><td>Total</td></tr></thead><tbody><tr><td>Anacamptis fragrans</td><td>Afr</td><td>2</td><td>0</td><td>1</td><td>3</td></tr><tr><td>Anacamptis laxiflora</td><td>Ala</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Anacamptis morio</td><td>Amo</td><td>5</td><td>0</td><td>0</td><td>5</td></tr><tr><td>Anacamptis pyramidalis</td><td>Apy</td><td>10</td><td>7</td><td>5</td><td>22</td></tr><tr><td>Cephalanthera damasonium</td><td>Cda</td><td>1</td><td>2</td><td>3</td><td>6</td></tr><tr><td>Cephalanthera longifolia</td><td>Clo</td><td>1</td><td>2</td><td>3</td><td>6</td></tr><tr><td>Cephalanthera rubra</td><td>Cru</td><td>0</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Coeloglossum viride</td><td>Cvi</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Cypripedium calceolus</td><td>Cca</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Dactylorhiza elata</td><td>Del</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Dactylorhiza fuchsii</td><td>Dfu</td><td>1</td><td>3</td><td>4</td><td>8</td></tr><tr><td>Dactylorhiza occitanica</td><td>Doc</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Dactylorhiza sambucina</td><td>Dsa</td><td>1</td><td>0</td><td>1</td><td>2</td></tr><tr><td>Epipactis atrorubens</td><td>Eat</td><td>0</td><td>0</td><td>2</td><td>2</td></tr><tr><td>Epipactis helleborine</td><td>Ehe</td><td>0</td><td>1</td><td>4</td><td>5</td></tr><tr><td>Epipactis microphylla</td><td>Emi</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Goodyera repens</td><td>Gre</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Gymnadenia conopsea</td><td>Gco</td><td>3</td><td>0</td><td>1</td><td>4</td></tr><tr><td>Himantoglossum hircinum</td><td>Hhi</td><td>4</td><td>4</td><td>2</td><td>10</td></tr><tr><td>Himantoglossum robertianum</td><td>Hro</td><td>1</td><td>1</td><td>1</td><td>3</td></tr><tr><td>Limodorum abortivum</td><td>Lab</td><td>1</td><td>3</td><td>3</td><td>7</td></tr><tr><td>Neotinea ustulata</td><td>Nus</td><td>3</td><td>0</td><td>1</td><td>4</td></tr><tr><td>Neottia nidus-avis</td><td>Nni</td><td>0</td><td>0</td><td>2</td><td>2</td></tr><tr><td>Neottia ovata</td><td>Nov</td><td>2</td><td>1</td><td>4</td><td>7</td></tr><tr><td>Ophrys apifera</td><td>Oap</td><td>4</td><td>1</td><td>3</td><td>8</td></tr><tr><td>Ophrys araneola</td><td>Oar</td><td>1</td><td>0</td><td>1</td><td>2</td></tr><tr><td>Ophrys aveyronensis</td><td>Oav</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Ophrys aymonini</td><td>Oay</td><td>1</td><td>0</td><td>1</td><td>2</td></tr><tr><td>Ophrys bilunulata</td><td>Obi</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><td>Ophrys insectifera</td><td>Oin</td><td>1</td><td>1</td><td>1</td><td>3</td></tr><tr><td>Ophrys lupercalis</td><td>Olu</td><td>0</td><td>2</td><td>0</td><td>2</td></tr></tbody></table>
<table><thead><tr><td>Species</td><td>Abb</td><td>Grassland</td><td>Shrubland</td><td>Woodland</td><td>Total</td></tr></thead><tbody><tr><td>Ophrys lutea</td><td>Olut</td><td>2</td><td>1</td><td>0</td><td>3</td></tr><tr><td>Ophrys massiliensis</td><td>Omas</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><td>Ophrys occidentalis</td><td>Ooc</td><td>1</td><td>1</td><td>1</td><td>3</td></tr><tr><td>Ophrys passionis</td><td>Opa</td><td>3</td><td>0</td><td>0</td><td>3</td></tr><tr><td>Ophrys scolopax</td><td>Osc</td><td>2</td><td>4</td><td>1</td><td>7</td></tr><tr><td>Ophrys sulcata</td><td>Osu</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Ophrys virescens</td><td>Ovi</td><td>1</td><td>0</td><td>1</td><td>2</td></tr><tr><td>Orchis anthropophora</td><td>Oan</td><td>3</td><td>3</td><td>4</td><td>10</td></tr><tr><td>Orchis mascula</td><td>Oma</td><td>4</td><td>3</td><td>3</td><td>10</td></tr><tr><td>Orchis militaris</td><td>Omi</td><td>2</td><td>0</td><td>2</td><td>4</td></tr><tr><td>Orchis provincialis</td><td>Opr</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Orchis purpurea</td><td>Opu</td><td>4</td><td>2</td><td>1</td><td>7</td></tr><tr><td>Orchis simia</td><td>Osi</td><td>2</td><td>4</td><td>2</td><td>8</td></tr><tr><td>Platanthera bifolia</td><td>Pbi</td><td>4</td><td>3</td><td>2</td><td>9</td></tr><tr><td>Platanthera chlorantha</td><td>Pch</td><td>1</td><td>2</td><td>3</td><td>6</td></tr><tr><td>Spiranthes spiralis</td><td>Ssp</td><td>1</td><td>1</td><td>1</td><td>3</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. S1** Principal component analysis of the similarity index, probability of presence and species traits. Similarity = Similarity index, Presence = probability of presence, Polli.Int = number of species of pollinators, Myc.Int = number of mycorrhizal symbionts, Height = plant height, Inflo.Size = inflorescence size, Fl.Number = number of flowers, Fl Duration = duration of the flowering period, Fl.Period = flowering duration.</figcaption></figure>
<table><caption>**Table S2** Results of Wilcoxon test of the comparison of the similarity-index of the recorded numbers (upper right) and probability of presence (bottom left) of species (a *p* < 0.05 is considered as significant). Abbreviations of species: Amo = *Anacamptis morio*, Apy = *Anacamptis pyramidalis*, Cda = *Cephalanthera damasonium*, Clo = *Cephalanthera longifolia*, Dfu = *Dactylorhiza fuchsi*, Ehe = *Epipactis helleborine*, Hhi = *Himantoglossum hircinum*, Lab = *Limodorum abortivum*, Nov = *Neottia ovata*, Oan = *Orchis anthropophora*, Oap = *Ophrys apifera*, Oma = *Orchis mascula*, Opu = *Orchis purpurea*, Osc = *Ophrys scolopax*, Osi = *Orchis simia*, Pbi = *Platanthera bifolia*, Pch = *Platanthera chlorantha*.</caption><thead><tr><th></th><th>Amo</th><th>Apy</th><th>Cda</th><th>Clo</th><th>Dfu</th><th>Ehe</th><th>Hhi</th><th>Lab</th><th>Nov</th><th>Oan</th><th>Oap</th><th>Oma</th><th>Opu</th><th>Osc</th><th>Osi</th><th>Pbi</th><th>Pch</th></tr></thead><tbody><tr><td>Amo</td><td></td><td>0.7065</td><td>0.7619</td><td>0.91</td><td>1</td><td>0.73</td><td>0.84</td><td>0.79</td><td>0.79</td><td>0.63</td><td>0.93</td><td>0.63</td><td>0.31</td><td>0.48</td><td>0.93</td><td>0.93</td><td>0.34</td></tr><tr><td>Apy</td><td>0.24</td><td></td><td>1</td><td>0.26</td><td>0.56</td><td>0.61</td><td>0.80</td><td>0.67</td><td>0.78</td><td>0.11</td><td>0.67</td><td>0.19</td><td>0.47</td><td>0.05</td><td>0.32</td><td>0.53</td><td><strong>0.03*</strong></td></tr><tr><td>Cda</td><td>0.21</td><td><strong>5.85e-05***</strong></td><td></td><td>0.40</td><td>0.95</td><td>0.66</td><td>1</td><td>0.89</td><td>0.94</td><td>0.28</td><td>0.72</td><td>0.49</td><td>0.83</td><td>0.26</td><td>0.44</td><td>0.56</td><td>0.11</td></tr><tr><td>Clo</td><td>0.91</td><td>0.05</td><td>0.13</td><td></td><td>0.95</td><td>0.54</td><td>0.43</td><td>0.57</td><td>0.44</td><td>0.74</td><td>0.62</td><td>0.87</td><td>0.07</td><td>0.39</td><td>0.75</td><td>0.75</td><td>0.35</td></tr><tr><td>Dfu</td><td>0.73</td><td>0.48</td><td><strong>0.02*</strong></td><td>0.42</td><td></td><td>0.62</td><td>0.57</td><td>0.96</td><td>0.69</td><td>0.51</td><td>1</td><td>0.70</td><td>0.15</td><td>0.18</td><td>0.88</td><td>0.64</td><td>0.15</td></tr><tr><td>Ehe</td><td>0.48</td><td><strong>0.002**</strong></td><td>0.78</td><td>0.37</td><td>0.09</td><td></td><td>0.71</td><td>0.57</td><td>0.87</td><td>0.22</td><td>0.93</td><td>0.44</td><td>0.29</td><td>0.33</td><td>0.62</td><td>1</td><td>0.14</td></tr><tr><td>Hhi</td><td>0.53</td><td><strong>0.003**</strong></td><td>0.19</td><td>0.63</td><td>0.14</td><td>0.52</td><td></td><td>0.81</td><td>1</td><td>0.17</td><td>0.66</td><td>0.14</td><td>0.20</td><td><strong>0.04*</strong></td><td>0.46</td><td>0.37</td><td><strong>0.04*</strong></td></tr><tr><td>Lab</td><td>0.70</td><td><strong>0.01*</strong></td><td>0.55</td><td>0.57</td><td>0.18</td><td>0.86</td><td>0.79</td><td></td><td>0.80</td><td>0.30</td><td>1</td><td>0.36</td><td>0.08</td><td>0.15</td><td>0.46</td><td>0.64</td><td>0.07</td></tr><tr><td>Nov</td><td>0.70</td><td><strong>0.02*</strong></td><td>0.09</td><td>0.93</td><td>0.33</td><td>0.38</td><td>0.56</td><td>0.57</td><td></td><td>0.31</td><td>0.80</td><td>0.42</td><td>0.32</td><td>0.14</td><td>0.61</td><td>0.54</td><td>0.07</td></tr><tr><td>Oan</td><td>0.94</td><td><strong>0.04*</strong></td><td>0.14</td><td>1</td><td>0.33</td><td>0.33</td><td>0.76</td><td>0.53</td><td>1</td><td></td><td>0.49</td><td>0.85</td><td><strong>0.02*</strong></td><td>0.45</td><td>0.76</td><td>0.72</td><td>0.26</td></tr><tr><td>Oap</td><td>0.07</td><td><strong>1.56e-06***</strong></td><td>0.51</td><td><strong>0.02*</strong></td><td><strong>0.003**</strong></td><td>0.34</td><td><strong>0.04*</strong></td><td>0.24</td><td><strong>0.008**</strong></td><td><strong>0.03*</strong></td><td></td><td>0.74</td><td>0.34</td><td>0.39</td><td>0.87</td><td>0.77</td><td>0.16</td></tr><tr><td>Oma</td><td>0.93</td><td>0.21</td><td><strong>0.01*</strong></td><td>0.57</td><td>0.84</td><td>0.11</td><td>0.14</td><td>0.20</td><td>0.40</td><td>0.45</td><td><strong>9.15e-05***</strong></td><td></td><td>0.09</td><td>0.31</td><td>0.70</td><td>0.97</td><td>0.19</td></tr><tr><td>Opu</td><td>0.47</td><td><strong>0.002**</strong></td><td>0.46</td><td>0.43</td><td>0.10</td><td>0.80</td><td>0.68</td><td>1</td><td>0.37</td><td>0.50</td><td>0.14</td><td>0.10</td><td></td><td><strong>0.02*</strong></td><td>0.15</td><td>0.28</td><td><strong>0.02*</strong></td></tr><tr><td>Osc</td><td>0.63</td><td><strong>0.01*</strong></td><td>0.46</td><td>0.62</td><td>0.21</td><td>0.86</td><td>0.96</td><td>1</td><td>0.78</td><td>0.53</td><td>0.14</td><td>0.27</td><td>0.94</td><td></td><td>0.23</td><td>0.30</td><td>0.48</td></tr><tr><td>Osi</td><td>0.79</td><td><strong>0.03*</strong></td><td>0.07</td><td>1</td><td>0.37</td><td>0.30</td><td>0.49</td><td>0.51</td><td>0.95</td><td>0.91</td><td><strong>0.007**</strong></td><td>0.46</td><td>0.31</td><td>0.65</td><td></td><td>0.96</td><td>0.11</td></tr><tr><td>Pbi</td><td>0.42</td><td><strong>0.002**</strong></td><td>0.59</td><td>0.37</td><td>0.10</td><td>1</td><td>0.70</td><td>0.82</td><td>0.36</td><td>0.39</td><td>0.17</td><td>0.09</td><td>0.96</td><td>0.73</td><td>0.32</td><td></td><td>0.21</td></tr><tr><td>Pch</td><td>0.27</td><td><strong>0.00043***</strong></td><td>0.52</td><td>0.14</td><td><strong>0.03*</strong></td><td>0.40</td><td>0.15</td><td>0.23</td><td>0.11</td><td>0.10</td><td>0.80</td><td><strong>0.03*</strong></td><td>0.27</td><td>0.33</td><td>0.10</td><td>0.33</td><td></td></tr></tbody></table>