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Intraspecific variability of leaf traits and functional strategy of Himantoglossum adriaticum H. Baumann
expand article infoMattia Baltieri, Edy Fantinato, Silvia Del Vecchio, Gabriella Buffa
‡ University Ca’ Foscari of Venice, Venice, Italy
Open Access

Abstract

Trait-based studies have become extremely common in plant ecology. In this work we analysed intraspecific trait variation of Himantoglossum adriaticum, a European endemic orchid species of Community interest, to investigate whether different populations growing on managed and abandoned semi-natural dry grasslands show differences in the CSR strategy. In seven populations occurring in Veneto Region (NE Italy), we measured H. adriaticum maximum vegetative height, leaf traits (LA, LDMC, SLA) and calculated the CSR strategy. Through CCA we investigated the relationship between plant traits and both plant community attributes (cover and height of herbs and shrubs), and geomorphologic features (aspects and slope). PERMANOVA test was used to investigate if the CSR strategy of H. adriaticum varied according to the management regime. Results showed that individuals of H. adriaticum develop different strategies when growing in different habitats. Specifically, individuals growing in managed fully sunny dry grasslands reached higher vegetative height (MH), had lower values of SLA and a higher relative contribution of the C parameter than individuals growing in abandoned dry grasslands, which, on the contrary, were shorter, had higher values of SLA (and correspondingly lower values of LDMC) and a higher relative contribution of the R parameter. Further data on reproductive traits (e.g. fruit and seed-set) may corroborate our results. Although the number of individuals addressed in this study is rather low, and our conclusions may not be considered of general validity for the species, our study demonstrated the applicability of the CSR strategy scheme in detecting functional strategies at intraspecific level.

Keywords

CSR plant strategy theory, dry grasslands, Himantoglossum adriaticum, intraspecific trait variation, land-use change, plant traits

Introduction

Trait-based studies have become extremely common in plant ecology (Chelli et al. 2019). This conceptual framework implies describing species through their biological characteristics by measuring suites of traits, i.e. features measurable at the individual level, that are assumed to impact plant fitness and survival (Pierce et al. 2017), either directly or indirectly (Violle et al. 2007).

Although species identity provides major information for ecological studies, the traditional taxonomic description becomes less effective when we are to understand plant reactions to environmental changes, e.g. climate or natural/anthropogenic disturbance, and the processes behind them (Westoby et al. 2002; Garnier et al. 2004). As measurable properties of individuals, functional traits allow understanding the interactions between organisms and their environment (both abiotic and biotic) and bring a functional perspective to the study on biodiversity and how it affects processes at higher levels of organization. Indeed, plant traits have been proved to vary with e.g. environmental conditions (Tautenhahn et al. 2008; Garnier and Navas 2012; Ivanova et al. 2019), land-use (Purschke et al. 2013; Duflot et al. 2014), and species distributions (Violle et al. 2014) suggesting that traits reflect the strategies plants use to cope with the environment and directly influence species interactions and the properties of communities and ecosystems (Grime 2006; Albert et al. 2011; Fantinato and Buffa 2019; Fantinato et al. 2019a, 2019b).

Several studies suggest the existence of sets of plant traits that co-vary, thereby allowing the identification of syndromes that affect survival (e.g. Díaz et al. 2004; Cerabolini et al. 2010; Funk et al. 2016); however, there is no consensus yet on any particular ecological scheme (Pierce et al. 2017). One of the first suggested frameworks is the so-called “leaf economic spectrum” (LES), proposed by Wright et al. (2004); it distinguishes species according to their resource uptake dynamics from quick (acquisitive) to slow return on investment of resources (conservative). Another notable and popular strategy scheme is the CSR (competitor, stress tolerator, ruderal) theory by Grime (1974, 1977), recently reviewed by Grime and Pierce (2012). According to this theory, plant species are assigned to three main strategies which represent trait combinations arising under conditions of competition, abiotic limitation to growth or periodic biomass destruction, respectively (Pierce et al. 2017). These primary strategies represent the extremes, and CSR classification reflects the full spectrum of possible intermediate types. The scheme has been so far adopted at different levels of organization, worldwide (e.g. Yildirim et al. 2012; Astuti et al. 2019; Giupponi and Giorgi 2019; Nyakunga et al. 2018), and values on CSR strategy are currently available for several species (Cerabolini et al. 2010; Pierce et al. 2017). In most trait databases, species are described by mean trait values, regardless of environmental or genetic context (Albert et al. 2010), mostly due to the search for general patterns (Albert et al. 2011). However, plants change their traits not only from species to species (interspecific trait variability) but also among populations of the same species (intraspecific trait variability) when growing in different environment. Recent examples (Lecerf and Chauvet 2008; Messier et al. 2010) showed that intraspecific trait variability plays a crucial role, especially at local scale (Albert et al. 2011). Volis et al. (2004) found that plants of wild barley (Hordeum spontaneum Koch) from favourable environments were better competitors than those from stressful environments. Similarly, Pierce et al. (2013) classified individual plants of the genus Poa, showing within populations trait variation. Intraspecific trait variability thus might help reveal a species’ ability to respond to environmental variations and has been proven to have direct effects on e.g. community assembly (Jung et al. 2010) and nutrient cycles (Lecerf and Chauvet 2008).

In light of the above, this study aims at investigating whether populations of the Adriatic Lizard Orchid (Himantoglossum adriaticum H. Baumann), growing on managed and abandoned semi-natural dry grasslands show differences in the CSR strategy. The evaluation of intraspecific trait variability might be the key to understanding the interactions between H. adriaticum and the extant community and reveal its ability to respond to environmental variations.

Materials and method

Study Area

The study took place on two hilly massifs of the Veneto Region (NE Italy): the Berici Hills (N 45.443.909; E 11.516.229) and the Euganei Hills (N 45.315.955; E 11.702.056), originated from volcanic (basaltic and rhyolitic) bedrock (Macera et al. 2003), and located southward of the Pre-Alps. Average annual rainfall is 800-1000 mm, distributed according to an equinoctial pattern with two maxima in April and September and two minima in December and July. Annual mean temperature is about 13.0-14.0 °C; the highest average temperature values can be found in July, while the lowest in December (Fantinato et al. 2019c).

Sampling sites were represented by small- to medium-sized (0.2–2 ha) dry grasslands, dispersed in an agricultural landscape among forests and arable fields. Created by low-intensity agricultural land use, semi-natural dry grasslands represent unique and species-rich plant assemblages. Temperate dry grasslands are maintained by traditional management practices as mowing, pasturing and haymaking (Habel et al. 2013), which regulate species composition and richness (Valkó et al. 2012). When low-intensity agricultural regimes are maintained, dry grasslands host important orchid populations (Fantinato et al. 2016a, 2016b, 2017; Slaviero et al. 2016). Abandonment results in structural changes such as the development of thermophilous fringe vegetation and scrubland facies (Biondi et al. 2006; Slaviero et al. 2016), with possible impacts on orchid populations (Slaviero et al. 2016).

Target species

Himantoglossum adriaticum is a European endemic species of priority interest (Directive 92/43/ EEC, Annex II), suffering population decline in many European countries (Dostalova et al. 2013). Its range comprises Austria, Bosnia-Herzegovina, Croatia, Czech Republic, Hungary, Italy, Slovakia, and Slovenia (Dostalova et al. 2013). It is described in the global IUCN Red List (Dostalova et al. 2013) and in the Italian Red List (Rossi et al. 2013) as LC (Least Concern). At regional level (Buffa et al. 2016) the species is classified as NT (Near Threatened), but its status worsens in lowland areas. In Northern Italy, it is mostly found in dry grasslands and nearby ecotonal meso-xeric scrubland/woodland patches (Slaviero et al. 2016; Del Vecchio et al. 2019).

H. adriaticum is a robust, perennial, tuberous, orchid with an over-wintering rosette composed of lanceolate, pale green basal leaves. The generative shoots are on average 40–80 cm tall, but they can reach 120 cm. The inflorescence is elongate and lax, composed of several (15–40) flowers and typically 14–24 cm in length (Bódis et al. 2019). The labellum is the main characteristic that differentiates Himantoglossum species (Perazza and Lorenz 2013). In H. adriaticum, the violet trilobal labellum can reach 7 cm in length, the central lob has some tufts of purple hairs while the laterals are very thin and shorter. The flowering season usually starts in May or June. Its pollination system is rather generalised (Fantinato et al. 2017). H. adriaticum is normally found in light to semi-shaded habitats (Delforge 2006), where it takes advantage of shadows produced by shrubs or slopes to avoid direct sunlight and drought conditions. However, if shrubs become dominant thereby increasing shadow, H. adriaticum can suffer limited growth or even local population extinction (Tornadore et al. 2006).

Data collection

We selected seven populations of H. adriaticum; three at the Euganei Hills and four at the Berici Hills. Between the 1st and the 10th of June, we placed a plot of 2 m × 2 m at the core of each population. In each plot, the plant community attributes were described by recording the composition of vascular plant species and visually estimating the percentage cover and average height of the herb and shrub layer (Ch and Cs; Hh and Hs, respectively). To account for geomorphic variability, in each plot topographic data were collected: aspect (AS), expressed in degrees clockwise from the North and slope (SL), measured in percentage steepness with respect to the horizontal plane (steepness = 0%).

Given the threatened status of the species, we restricted the sampling to a low number of individuals per population (less than 15% of the overall individuals’ abundance) in order to avoid negative impacts on the populations’ persistence (Astuti et al., 2019). We collected individuals by cutting the stem at 1 cm from the ground and we rehydrated them for 24 hours to reach full turgidity. For each individual, we measured the maximum vegetative height, i.e. the height of the whole foliage (MH) and we chose at least two undamaged and well-developed leaves for the subsequent measurements (Pérez-Harguindeguy et al. 2013). Laboratory measurements followed the standardized methodologies detailed by Pérez-Harguindeguy et al. (2013). We determined leaf fresh weight (LFW) and leaf area (LA, i.e. a single-sided leaf area with petiole). LA was determined using the Leaf Area Measurement Software (Askew 2003). Leaf dry weight (LDW) was then determined following drying for 72 h at 60 °C, and the specific leaf area (SLA; the ratio of leaf area to leaf dry mass) and the leaf dry matter content (LDMC; the ratio of leaf dry mass to fresh mass) were calculated.

LA, LDMC and SLA provide strong indications about vascular plant adaptations under different environmental conditions and are at the basis of the calculation of the CSR strategies (Pierce et al. 2013). They can be defined as follows.

- Leaf area (LA): it is the size of the photosynthetic organ. It measures how much a plant invests in the photosynthesis. The leaf area is directly linked to the plant capacity to intercept light, and hence to the plant productivity. Leaf area can vary with changes in climate, altitude, and stress, such as soil aridity. LA decreases under increasing stressful conditions (Pérez-Harguindeguy et al. 2013).

- Leaf dry matter content (LDMC): it quantifies leaf tissue density and nutrient retention capacity. High values of LDMC indicate a preference in conserving nutrients. Species with high level of LDMC show tough leaves, highly resistant to hazards (Freschet et al. 2010; Lienin and Kleyer 2012).

- Specific leaf area (SLA): it is defined as the ratio of total leaf area to total leaf dry mass. In other words, it describes the amount of leaf area for light capture per unit of biomass invested. High SLA values are generally recorded in resource-rich environments, while low values in resource-poor environments (Pérez-Harguindeguy et al. 2013; Freschet et al. 2010).

Data analysis

We used the canonical correspondence analysis (CCA) to study the relationship between plant traits, plant community attributes and plot geomorphological features. In the CCA ordination, the sample units were the individuals of H. adriaticum collected in each population. We used values of LDMC, SLA and the maximum height of the whole foliage (MH) as dependent variables, while the percentage cover and average height of the herb and shrub layer (Ch and Cs; Hh and Hs), plot aspect (AS) and slope (SL) as predictor variables. We used 1,000 Monte Carlo permutations to assess statistical significance of the association between plant traits, plant community attributes and plot geomorphological features. To define the groups in the CCA diagram we performed a cluster analysis on the plots × plant community attributes (using average-linkage method and Bray–Curtis distance).

The relative contribution (%) of C, S, and R parameters to the tertiary CSR strategy was automatically calculated and represented by a ternary plot using the spreadsheet provided by Pierce et al. (2017). The CSR classification investigates the trade-off between the traits LA, LDMC and SLA (high values of these traits are mutually-exclusive and represent extremes of leaf economic and size) to calculate the relative proportion of C-, S- and R-selection exhibited by the species. We quantified the relative contribution (%) of C, S, and R parameters to the tertiary CSR strategy for each sampled leaf and results were averaged per individual. In order to evaluate intraspecific trait variability, in terms of percentage of C, S and R parameters, among individuals of H. adriaticum growing in the different groups revealed by the cluster analysis, we performed a one-way PERMANOVA applying the Bray–Curtis similarity index with 9,999 (Anderson and Ter Braak 2003; Past 3.0 Software). Then, the Tukey HSD test was used to determine which parameters differed between the two groups.

Results

We sampled 29 individuals of H. adriaticum. The number of sampled individuals varied from 1 to 15, with an average number of 4.5 ± 5.2 (mean ± SD) individuals per population (Table 1).

Table 1.

Plant community attributes and geomorphological features of sampled populations.

Population ID Sampling site Total number of individuals Number of sampled individuals Aspect (degrees) Slope (%) Herb layer cover (%) Shrub layer cover (%) Herb height (cm) Shrub height (cm) Management regime
1 Euganei 15 2 315 5 70 - 20 0 Managed
2 Euganei 115 13 45 5 40 - 25 0 Managed
3 Euganei 96 3 0 0 90 30 20 300 Abandoned
4 Berici 15 2 90 35 100 28 70 200 Abandoned
5 Berici 23 1 135 20 65 50 40 250 Abandoned
6 Berici 62 3 135 15 80 65 40 180 Abandoned
7 Berici 101 3 135 10 75 50 30 400 Abandoned

The two CCA axes explained 88.81% (p = 0.018) and 11.19% (p = 0.002) of the variance, respectively (Trace = 0.021; P < 0.001). The first CCA axis was strongly positively related to the percentage cover of the herb (Ch; 0.71) and shrub layer (Cs; 0.63), to the average high of the shrub layer (Hs; 0.62) and to the plot slope (SL; 0.53). The largest loadings on the second axis were from plot aspect (AS; -0.27) and slope (SL; 0.25). The CCA revealed a strong positive relationship between SLA, community attributes (i.e., Ch, Cs, Hh and Hs) and plot geomorphological features (i.e., AS and SL; Fig. 1), and a slightly negative relationship between LDMC, community attributes and plot geomorphological features (Fig. 1). The cluster analysis highlighted that sampled plots belonged to two distinct groups; the first corresponding to flat, managed grasslands without shrubs, while the second to steep, abandoned grasslands, highly encroached by shrubs (Table 1).

Figure 1. 

Scatterplot of the canonical correspondence analysis (CCA) utilized to investigate the relationship between plant traits (blue triangles), plant community attributes and plot geomorphological features (red lines). Different symbols indicate individuals of H. adriaticum growing in managed (black dots) or in abandoned grasslands (black stars), as revealed by the cluster analysis.

The overall strategy of H. adriaticum obtained by averaging single individual strategy irrespectively of the habitat type was CR (C:S:R; 60:0:40 %). Significant differences in the CSR strategy of H. adriaticum growing in managed vs. abandoned dry grasslands were revealed by PERMANOVA (F= 28.4; d.f. = 40.71; P < 0.0001). Tukey test revealed that individuals of H. adriaticum growing in managed dry grasslands showed a significantly higher percentage of the C parameter (P < 0.0001; Fig. 2), and a significantly lower percentage of the R parameter (P < 0.0001; Fig. 2) than those growing in abandoned dry grasslands (Table 2).

In other words, our results suggest that individuals of H. adriaticum show different strategies when growing in different habitats (Fig. 3). In our study, individuals growing in managed dry grasslands were more competitive than individuals growing in abandoned dry grasslands, which, on the contrary, were more ruderal.

Figure 2. 

Differences in the relative contribution (%) of C, S and R parameters, among individuals of H. adriaticum growing in managed vs. abandoned grasslands. Different letters indicate significant differences according to Tukey's test.

Figure 3. 

CSR triangular scheme for the individuals of H. adriaticum growing in managed (red dots) and abandoned grasslands (blue dots).

Table 2.

Trait values and CSR strategy of sampled individuals of H. adriaticum.

Managed grasslands
ID Maximum vegetative height (cm) LA (mm2) LDMC (%) SLA (mm2 mg-1) %C %S %R Tertiary strategy
1 22.7 2.347.033 9.937 17.685 77.43 0 22.57 C/CR
2 27.4 1.935.357 11.290 20.531 67.94 0 32.06 C/CR
6 38.7 1.869.641 11.597 21.933 64.49 0 35.51 C/CR
7 45.2 1.766.543 10.141 23.411 61.14 0 38.86 C/CR
17 40.6 2.936.839 12.720 12.847 95.71 4.29 0 C
18 31.1 1.346.199 10.101 24.798 56.52 0 43.48 CR
19 33.9 1.804.074 11.796 16.346 81.46 0 18.54 C
20 33.7 2.301.610 12.784 14.276 89.15 4.69 6.159 C
21 35.2 1.091.375 12.789 15.440 79.25 5.38 15.37 C
22 24.2 1.315.645 13.901 17.281 67.44 10.9 21.7 C/CR
23 45.6 1.672.339 13.610 14.696 80.97 10.1 8.909 C
24 34.0 1.755.570 12.237 18.075 73.72 1 25.28 C/CR
25 35.8 1.650.154 9.290 20.705 66.4 0 33.6 C/CR
26 36.0 2.073.885 11.139 16.157 82.95 0 17.05 C
27 49.1 2.709.226 12.363 14.069 93.29 1.94 4.776 C
28 32.7 1.809.762 11.325 19.574 70 0 30 C/CR
29 28.5 1.531.902 10.782 18.836 71.08 0 28.92 C/CR
Abandoned grasslands
ID Maximum vegetative height (cm) LA (mm2) LDMC (%) SLA (mm2 mg-1) %C %S %R Tertiary strategy
3 29.4 2.381.676 9.724 21.063 68.02 0 31.98 C/CR
4 29.4 2.067.571 9.769 26.650 57.07 0 42.93 CR
5 27.0 1.934.965 9.010 22.896 62.78 0 37.22 C/CR
8 38.7 1.547.654 9.624 30.906 49.52 0 50.48 CR
9 45.2 854.140 7.471 40.698 35.96 0 64.04 R/CR
10 41.2 1.018.072 10.568 40.301 37.84 0 62.16 R/CR
11 32.3 2.131.742 9.468 23.533 62.28 0 37.72 C/CR
12 39.5 868.181 8.752 35.873 39.62 0 60.38 R/CR
13 33.4 1.184.001 9.111 23.704 57.33 0 42.67 CR
14 37.4 1.404.920 11.609 32.653 46.9 0 53.1 CR
15 37.2 1.273.419 13.051 28.582 48.44 4.23 47.33 CR
16 27.2 2.379.992 15.152 22.667 56.45 12.7 30.87 C/CR

Discussion

Plant traits are increasingly used to detect plant responses to changes in environmental conditions, since they allow to determine species ecological roles in the environment (Violle et al. 2007) and to detect their ecological strategies (Grime and Pierce 2012; Pierce et al. 2017). Based on the results of this study, leaf traits and Grime’s (1974; 1977) theoretical triangular scheme of competitor, stress tolerator and ruderal plant strategies (CSR theory) proved to be applicable for assessing the functional strategies at intraspecific level.

According to Delforge (2006), H. adriaticum is a species of light or semi-shaded habitats on shallow rocky soils with neutral or basic reaction. It does not show any strong phytocoenological preference. Its primary habitat includes dry grasslands (Festuco-Brometea Br.-Bl. & Tüxen ex Br.-Bl. 1949 class) and open shrubby communities (Rhamno-Prunetea Rivas Goday & Borja ex Tüxen 1962 class) with a mosaic of fully sunny and shaded patches (Bódis et al. 2019), however it can persist in a wide range of habitats including mesic grasslands (Molinio-Arrhenatheretea Tüxen 1937 class), and can also colonise secondary habitats such as roadsides or abandoned vineyards (Fekete et al. 2017).

Overall, H. adriaticum exhibited a Competitive-Ruderal (CR) secondary strategy, incorporating substantial competitive and, to a lesser extent, ruderal features. Interestingly, neither the species nor the two groups of individuals showed the stress-tolerant component. This low inclination for the S- strategy may be explained by the particular phenology of H. adriaticum. Contrary to other dry grassland orchid species, its growing period starts in late spring, and the time of flowering is usually around late May or June. The capsules mature in 4–6 weeks, and seeds are shed rather quickly, in July or August (Bódis et al. 2019). This period corresponds with the rainfall season in the study area, with the average peak of rain in April; at the same time, temperatures, although increasing, do not have reached their peak, which is in July (Fantinato et al. 2017). Thus, H. adriaticum can take advantage of climatic conditions that allow it to complete its life cycle without incurring in stress conditions. Conversely, its characteristic perennial features and persistent rootstock account for the species’ ability to compete and allow a rapid growth and development of a large standing biomass.

Despite these general characteristics, the CCA ordination showed that specific leaf area (SLA) and leaf dry matter content (LDMC) of H. adriaticum varied along with community attributes and plot geomorphological features, reaching, respectively, the highest and the lowest values in partially shaded, sloping grasslands encroached by shrubs. These traits are known to reflect crucial life history trade-offs between the investment of resources in further resource acquisition vs. conservation (Pierce et al. 2017). High values of SLA ensure for high rates of resource acquisition in productive habitats, while high values of LDMC indicate a conservative leaf economics, implying the acquisition of traits responsible for retention of resources in unproductive conditions. Thus, strategies arise as an adaptive response of plants to site ecological condition (Cerabolini et al. 2010), and our results suggest that individuals of H. adriaticum develop different strategies when growing in different habitats. Specifically, individuals growing in managed fully sunny dry grasslands had lower values of SLA and a higher relative contribution of the C parameter than individuals growing in abandoned dry grasslands, which, on the contrary, had higher values of SLA (and correspondingly lower values of LDMC) and a higher relative contribution of the R parameter. The relatively higher ruderal component of individuals of H. adriaticum growing in abandoned dry grasslands, may be explained considering the local geomorphological characteristics. Dry grassland abandonment started from steep slopes, where management practices were more difficult. Steep slopes are generally characterized by high levels of natural disturbance, such as accelerated erosion processes from stormwater runoff and soil loss, with vegetation destruction and fragmentation. Under these unstable environmental conditions, individuals of H. adriaticum may have been selected according to their strategy, with the ruderal component being favoured on the competitive one.

We are aware that the number of individuals addressed in this study is rather low, and that our conclusions may not be considered of general validity for the species. However, as reported by Astuti et al. (2019), in the case of rare and threatened species, the use of small samples can be justified for conservation purposes. Although further research is needed to disentangle to what extent the morphological and ecological variability of the species is due to phenotypic plasticity or genetic variability of the populations, these results are stimulating as they show that plant species present adaptations to changing environmental conditions and extant community attributes that can be detected through the CSR scheme, at a very small spatial scale. Moreover, and consistently to other previous studies (e.g. May et al. 2017; Giupponi and Giorgi 2019), this research confirms that all components (Competitive, Stress tolerant and Ruderal component) of the CSR functional strategy can vary significantly among individuals of the same species. In this regard, a further step towards the comprehension of processes underlying patterns of trait variation should account for regeneration traits of H. adriaticum. Trait-based ecology has so far mainly focused on adult plants despite early stages of a plant life history being very sensitive to environmental factors (Fantinato et al. 2018; Del Vecchio et al. 2020a, 2020b). Specifically, beside differing in leaf traits, the two components (competitive and ruderal) also vary in trade-offs of resource allocation to seed production, with competitors primarily composed of species with relatively low seed production and ruderals allocating resources mainly to seeds. Thus, reproductive traits and precisely fruit and seed-set may corroborate our results.

Bibliography

  • Albert CH, Thuiller W, Yoccoz NG, Soudant A, Boucher F, Saccone P, Lavorel S (2010) Intraspecific functional variability: extent, structure and sources of variation. Journal of Ecology 98: 604–613. https://doi.org/10.1111/j.1365-2745.2010.01651.x
  • Albert CH, Grassein F, Schurr FM, Vieilledent G, Violle C (2011) When and how should intraspecific variability be considered in trait-based plant ecology? Perspectives in Plant Ecology, Evolution and Systematics 13: 217–225. https://doi.org/10.1016/j.ppees.2011.04.003
  • Anderson MJ, Ter Braak C (2003) Permutation tests for multifactorial analysis of variance. Journal of Statistical Computation and Simulation 73: 85–113. https://doi.org/10.1080/00949650215733
  • Askew AP (2003) Leaf Area Measurement. Downloadable from the Nucleo DiverSus toolbox
  • Astuti G, Ciccarelli D, Roma-Marzio F, Trinco A, Peruzzi L (2019) Narrow endemic species Bellevalia webbiana shows significant intraspecific variation in tertiary CSR strategy. Plant Biosystems 153: 12–18. https://doi.org/10.1080/11263504.2018.1435576
  • Biondi E, Casavecchia S, Pesaresi S (2006) Spontaneous renaturalization processes of the vegetation in the abandoned fields (Central Italy). Annali Botanica 6: 65–93.
  • Bódis J, Biróa E, Nagya T, Takács A, Sramkó G, Batemane RM et al. (2019) Biological flora of Central Europe Himantoglossum adriaticum H. Baumann. Perspectives in Plant Ecology, Evolution and Systematics 40: 125461. https://doi.org/10.1016/j.ppees.2019.125461
  • Buffa G, Carpenè B, Casarotto N, Da Pozzo M, Filesi L, Lasen C, Marcucci R, Masin R, Prosser F, Tasinazzo S, Villani M, Zanatta K (2016) Lista rossa regionale delle piante vascolari. Regione del Veneto.
  • Carey PD, Farrell L, Stewart NF (2002) The sudden increase in the abundance of Himantoglossum hircinum in England in the past decade and what has caused it. In: Kindlmann, P., Willems, J., Whigham, D.F. (Eds.) Trends and Fluctuations and Underlying Mechanisms in Terrestrial Orchid Populations. Backhuys Publishers, Leiden, 187–208.
  • Chelli S, Marignani M, Barni E, Petraglia A, Puglielli G, Wellstein C, et al. (2019) Plant–environment interactions through a functional traits perspective: a review of Italian studies, Plant Biosystems 153(6): 853-869. https://doi.org/10.1080/11263504.2018.1559250
  • Delforge P (2006) Orchids of Europe, North Africa and Middle East. A&C Black, London.
  • Del Vecchio S, Pierce S, Fantinato E, Buffa G (2019) Increasing the germination percentage of a declining native orchid (Himantoglossum adriaticum) by pollen transfer and outbreeding between populations. Plant Biology 21(5): 935–941. https://doi.org/10.1111/plb.12986
  • Del Vecchio S, Fantinato E, Roscini M, Acosta ATR, Bacchetta G, Buffa G (2020a) The germination niche of coastal dune species as related to their occurrence along a sea–inland gradient. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12899
  • Del Vecchio S, Mattana E, Ulian T, Buffa G (2020b) Functional seed traits and germination patterns predict species coexistence in NE Mediterranean foredune communities. Annals of Botany mcaa186, https://doi.org/10.1093/aob/mcaa186
  • Dostalova A, Montagnani C, Hodálová I, Jogan N, Király G, Ferakova V, Bernhardt KG (2013) Himantoglossum adriaticum. In: IUCN 2013. The IUCN red list of threatened species. Version 2013.2. Available from www.iucnredlist.org. (accessed on 22 October 2020).
  • Fantinato E, Del Vecchio S, Slaviero A, Conti L, Acosta ATR, Buffa G (2016a) Does flowering synchrony contribute to the sustainment of dry grassland biodiversity? Flora 222: 96–103. https://doi.org/10.1016/j.flora.2016.04.003
  • Fantinato E, Giovanetti M, Del Vecchio S, Buffa G (2016b) Altitudinal patterns of floral morphologies in dry calcareous grasslands. Plant Sociology 53: 83–90.
  • Fantinato E, Del Vecchio S, Baltieri M, Fabris B, Buffa G (2017) Are food-deceptive orchid species really functionally specialized for pollinators? Ecological Research 32, 951–959. https://doi.org/10.1007/s11284-017-1501-0
  • Fantinato E, Del Vecchio S., Giovanetti M., Acosta A.T.R., Buffa G. (2018) New insights into plants coexistence in species-rich communities: the pollination interaction perspective. Journal of Vegetation Science 29: 6–14.
  • Fantinato E, Sonkoly J, Silan G, Valkó O, Tóthmérész B, et al. (2019a) Pollination and dispersal trait spectra recover faster than growth form spectrum during the spontaneous succession in sandy old-fields. Applied Vegetation Science 22: 435–443. https://doi.org/10.1111/avsc.12439
  • Fantinato E, Del Vecchio S, Gaetan C, Buffa G (2019b) The resilience of pollination interactions: importance of temporal phases. Journal of Plant Ecology 12: 157–162.
  • Fekete R, Nagy T, Bódis J, Biró É, Löki V, Süveges K et al. (2017) Roadside verges as habitats for endangered lizard-orchids (Himantoglossum spp.): Ecological traps or refuges? Science of The Total Environment 607-608: 1001-1008. http://dx.doi.org/10.1016/j.scitotenv.2017.07.037
  • Funk JL, Larson JE, Ames GM, Butterfield BJ, Cavender-Bares J, Firn J et al. (2016) Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biological Reviews 92: 1156–1173. https://doi.org/10.1111/brv.12275
  • Garnier E, Cortez J, Billès G, Navas M-L, Roumet C, Debussche M, et al. (2004) Plant functional markers capture ecosystem properties during secondary succession. Ecology 85(9): 2630–2637. https://doi.org/10.1890/03-0799
  • Garnier E, Navas M‐L (2012) A trait‐based approach to comparative functional plant ecology: concepts, methods and applications for agroecology. A review. Agronomy for Sustainable Development 32: 365–399. http://10.1007/s13593-0.11-0036
  • Giupponi L, Giorgi A (2019) Effectiveness of modern leaf analysis tools for the morpho-ecological study of plants: the case of Primula albenensis. Nordic J. Bot. 37, e02386. https://doi.org/10.1111/njb.02386
  • Grime JP (1977) Evidence for the Existence of Three Primary Strategies in Plants and Its Relevance to Ecological and Evolutionary Theory. The American Naturalist 111(982): 1169–1194. http://dx.doi.org/10.1086/283244
  • Grime J (2006) Plant strategies, vegetation processes, and ecosystem properties. Wiley, Chichester, UK.
  • Grime JP, Pierce S (2012) The Evolutionary Strategies that Shape Ecosystems. Wiley-Blackwell, Chichester, UK.
  • Habel JC, Dengler J, Janišová M, Török P, Wellstein C, Wiezik M (2013) European grassland ecosystems: threatened hotspots of biodiversity. Biodiversity and Conservation 22: 2131–2138. https://doi.org/10.1007/s10531-013-0537-x
  • Ivanova LA, Ivanov LA, Ronzhina DA, Yudina PK, Migalina SV, Shinehuu T et al. (2019) Leaf traits of C3- and C4-plants indicating climatic adaptation along a latitudinal gradient in Southern Siberia and Mongolia. this issue. Flora 254: 122–134 https://doi.org/10.1016/j.flora.2018.10.008
  • Macera P, Gasperini D, Piromallo C, Blichert-Toft J, Bosch D, Del Moro A, Martin S (2003) Geodynamic implications of deep mantle upwelling in the source of Tertiary volcanics from the Veneto region (South-Eastern Alps). Journal of Geodynamics 36(5): 563–590. http://dx.doi.org/10.1016/j.jog.2003.08.004
  • May RL, Warner S, Wingler A (2017) Classification of intra-specific variation in plant functional strategies reveals adaptation to climate. Annals of Botany 119: 1343–1352. https://doi.org/10.1093/aob/mcx031
  • Nyakunga OC, Del Vecchio S, Buffa G (2018) Effects of management regimes on structure, composition and diversity of seasonally inundated herbaceous communities in the Mkomazi National Park, Tanzania. African Journal of Ecology 56: 949–956. https://doi.org/10.1111/aje.12524
  • Perazza G, Lorenz R (2013) Le orchidee dell'Italia nordorientale. Osiride, Rovereto (TN).
  • Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P et al. (2013) New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany 61(3): 167–234. http://dx.doi.org/10.1071/bt12225
  • Pierce S, Brusa G, Vagge I, Cerabolini BEL (2013) Allocating CSR plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. Functional Ecology 27: 1002–1010. http://dx.doi.org/10.1111/1365-2435.12095
  • Pierce S, Negreiros D, Cerabolini BEL, Kattge J, Díaz S, Kleyer M et al. (2017) A global method for calculating plant CSR ecological strategies applied across biomes world‐wide. Functional Ecology 31: 444–457. https://doi.org/10.1111/1365-2435.12722
  • Purschke O, Schmid B, Sykes MT, Poschlod P, Michalski SG, Durka W et al. (2013) Contrasting changes in taxonomic, phylogenetic and functional diversity during a long‐term succession: insights into assembly processes. Journal of Ecology 101: 857–866. https://doi.org/10.1111/1365-2745.12098
  • Rossi G, Montagnani C, Gargano D, Peruzzi L, Abeli T, Ravera S et al. (Eds.) (2013) Lista Rossa della Flora Italiana. 1. Policy Species e altre specie minacciate. Comitato Italiano IUCN e Ministero dell’Ambiente e della Tutela del Territorio e del Mare
  • Slaviero A, Del Vecchio S, Pierce S, Fantinato E, Buffa G (2016) Plant community attributes affect dry grassland orchid establishment. Plant Ecology 217: 1533–1543. https://doi.org/10.1007/s11258-016-0666-x
  • Tautenhahn S, Heilmeier H, Götzenberger L, Klotz S, Wirth C, Kühn I (2008) On the biogeography of seed mass in Germany – distribution patterns and environmental correlates. Ecography 31: 457–468. https://doi.org/10.1111/j.2008.0906-7590.05439.x
  • Tornadore N, Villani M, Marcucci R, Colussi S (2006) Demographic research of rare and threatened wild orchids of the Euganean Hills (North- eastern Po Plain, Padua, Italy). Proceeding of International Congress “Ophrys 2005”, 13-16 April 2005, Chios, Greece. Jour. Eur. Orch. 38(2): 300.
  • Valkó O, Török P, Matus G, Tóthmérész B (2012) Is regular mowing the most appropriate and cost-effective management maintaining diversity and biomass of target forbs in mountain hay meadows? Flora 207: 303–309. https://doi.org/10.1016/j.flora.2012.02.003
  • Violle C, Navas M, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E (2007) Let the concept of trait be functional! Oikos 116(5): 882-892. http://dx.doi.org/10.1111/j.0030- 1299.2007.15559.x
  • Volis S, Mendlinger S, Ward D (2004) Differentiation in populations of Hordeum spontaneum Koch along a gradient of environmental productivity and predictability: intra- and interspecific competitive responses. Israel Journal of Plant Sciences 52: 223–234. https://doi.org/10.1560/N76Q-2W94-Y4L1-79C5
  • Wright I, Reich P, Westoby M, Ackerly D, Baruch Z, Bongers F et al. (2004) The worldwide leaf economics spectrum. Nature 428(6985): 821–827. https://doi.org/10.1038/nature02403
  • Yildirim C, Karavin N, Cansaran A (2012) Classification and evaluation of some endemic plants from Turkey using Grime’s CSR strategies. Eurasian J Bioscience 6: 97–104. https://doi.org/10.5053/ejobios.2012.6.0.12