Using vegetation dynamics to face the challenge of the conservation status assessment in semi-natural habitats

  • Emanuela Carli
  • Eleonora Giarrizzo
  • Sabina Burrascano
  • Marta Alós
  • Eva Del Vico
  • Piera Di Marzio
  • Laura Facioni
  • Carmen Giancola
  • Barbara Mollo
  • Bruno Paura
  • Giovanni Salerno
  • Laura Zavattero
  • Carlo Blasi
Vegetation Science and Habitats Directive


The conservation of semi-natural habitats represents a primary challenge for European nature conservation due to their great species diversity and their vulnerability to ongoing massive land-use changes. As these changes rapidly transform and phase out semi-natural habitats, conservation measures should be prompt and specifically focused on a sound assessment of the degree of conservation. Here we develop a methodological strategy for the assessment of the degree of conservation of semi-natural grasslands based on well-defined criteria rather than on expert opinion. Through mixed effect models, we tested ten potential indicators, encompassing proxies of species composition, habitat structure, and landscape patterns, against a measure of compositional change from habitat favourable condition, i.e., an inverse proxy of conservation status. This measure derives from the re-visitation of 132 sampling units historically sampled between 1966 and 1992 along the Apennines. The compositional change was quantified as the dissimilarity between historical habitat species pools and the composition of current communities. The compositional change was significantly related to the number of habitat diagnostic species and the relative cover of woody species with opposite sign (positive and negative, respectively). We classified and combined the classes of these two indicators in each sampling unit to assess the habitat degree of conservation at the plot and at the Natura 2000 site level. At the plot level, our assessment was in good agreement with the occurrence of species of conservation concern. On the other hand, at the site level, our assessment was not always harmonic with the habitat conservation assessment officially reported for the site investigated.


Calcareous grasslands Habitat 6210 Habitats Directive Condition indicators Re-survey Habitat monitoring 

1 Introduction

Semi-natural habitats depend on the interaction between human activities and natural vegetation dynamics. In Europe, these ecosystems are mainly represented by grasslands that develop at sites where forests represent the main natural potential vegetation and require traditional agriculture activities, such as shepherding, for their maintenance (Halada et al. 2011). Semi-natural grasslands provide a wealth of ecosystem services (Hönigová et al. 2012) and are among the most species-rich ecosystems in the world (Wilson et al. 2012; Chytry et al. 2015), with flora and fauna of great conservation interest (Pykälä et al. 2005; Hobohm et al. 2014). After being supported by centuries or millennia of low-intensity agricultural activities, semi-natural grasslands are currently threatened by massive changes in land-use, which consist of either abandonment in unprofitable areas, or management intensification in the more accessible and productive areas (Falcucci et al. 2007; Gibson 2009; Maes 2013). These modifications cause habitat loss and fragmentation, with strong consequences on species diversity (Plieninger et al. 2014). Almost 80% of European grassland habitats of conservation concern are under unfavourable conservation status (23.1% inadequate and 54.5% bad); similarly, more than half of European grassland plant species relevant for conservation are under unfavourable conservation status (EEA 2015). Grasslands are also among the most threatened habitats, as highlighted in the recent European Red List of Habitats ( For these reasons, grasslands conservation is recognised as urgent by the scientific community (Janisova et al. 2011) and is addressed by several European Union (EU) environmental and agricultural policies and measures (Burrascano et al. 2016). Nevertheless, still no standardised measures exist to preserve and correctly manage European semi-natural grasslands (EEA 2015).

The cornerstone of biodiversity conservation within the EU is the Habitats Directive (92/43/EEC) that aims to maintain at, or restore to, favourable conservation status, natural and semi-natural habitats, and species of wild fauna and flora. To achieve this goal, the largest network of protected sites in the world was established (Natura 2000 Network) that covers 117 million hectares, corresponding to more than 18% of the EU terrestrial area (

The first step towards the effective conservation of habitats and species is the evaluation of the state of conservation. Such assessment should drive conservation priorities (Blasi et al. 2017) and give concrete information on the habitat potential to support biodiversity and to supply regulating and cultural ecosystem services (Maes et al. 2012).

Notwithstanding the relevance of this assessment and the ongoing efforts towards its improvement (, the EU still lacks standard methodologies to be applied at the local scale (EEA 2015). So far, the methodologies adopted by different EU Member States are designed for the biogeographical scale (Yera Posa and Ascaso Martorell 2009; BfN 2011; Gigante et al. 2016b), as fine-grain habitat quality data at large spatial extents can be prohibitively expensive to obtain (Mairota et al. 2014). This is particularly worrying for those habitats that likely go through rapid changes and decline, such as semi-natural grasslands, since for these habitats managers need prompt information on the occurring changes and on their consequences on multiple components of biodiversity. Only UK and France adopted national standard methodology referring to the site level (JNCC 2004; Maciejewski et al. 2015), and we followed their methodological approach in the selection of the potential indicators.

Habitat conservation status should be assessed repeatedly over time, in order to identify changes in composition, structure and functions that are crucial to the understanding of current ecosystem dynamics and for developing future projections (Chytry et al. 2014). In this view, the re-visitation of historical datasets provides valuable insights into species distributional shifts and community dynamics in the face of land use change (Kopecký and Macek 2015; Chiarucci et al. 2017). Data deriving from re-visitation studies allow both to relate vegetation changes to specific indicators (Van den Berg et al. 2011; Stroh et al. 2017) and to identify the ‘original’ species pool of habitats (Helm et al. 2015).

Here we stress the advantages of historical vegetation data and of the ‘habitat species pool’ as essential tools to support habitat conservation initiatives. We used a multi-temporal dataset from a re-visitation study to develop a methodology for the assessment of the conservation status of one of the most widespread semi-natural habitat in Europe, which is listed in the Annex I of the Habitats Directive (92/43/EEC). Particularly, we focused on the habitat 6210—semi-natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) (*important orchid sites). Despite the large inherent variability of this habitat, it is evident that it is extremely threatened by land-use changes caused by both abandonment and overexploitation (Halada et al. 2011; Maes 2013), and it has recently resulted in an unfavourable conservation status in Europe ( and in all the biogeographical regions of Italy (Genovesi et al. 2014). As for many other semi-natural habitats, the definition of efficient indicators of conservation status for the habitat 6210 and of their ranges is strongly recommended (Calaciura and Spinelli 2008; EEA 2015; Angelini et al. 2016; Gigante et al. 2016a, b).

Our aims are to: (1) define a set of effective indicators and a feasible methodology to combine them to assess the degree of conservation of the habitat 6210; (2) test the consistency of the degree of conservation here defined with the occurrence of species of conservation concern; and (3) compare our assessment at the site level to the one reported on the standard data forms.

We selected ten potential indicators related to species composition, structure and function, and landscape features. These indicators were tested against a measure of compositional change from the habitat species pool calculated from a re-visitation study in semi-natural grassland communities (Giarrizzo et al. 2017). The grasslands were mainly subjected to changes towards successional dynamics, highlighted by the increase of fringe, shrub and woody species. However, there were also few local situations that experienced an increase in disturbance-tolerant species (Giarrizzo et al. 2017). We assumed that the smaller the occurred compositional change, the higher the degree of conservation of the current community. We used those indicators that significantly explained the compositional change to assign a conservation category to each sampling unit. We assumed that grasslands showing a greater stability in species composition not only have a higher conservation degree, but are also more likely to host species of high conservation concern, which would be phased out by compositional shifts deriving from land use change (Hobohm et al. 2014). For these reasons, we hypothesised that the degree of conservation categories that we calculated at the plot level would comply with the occurrence of species of conservation concern. Moreover, since our approach is based on field data, we expected our site-level assessment will differ from the conservation assessment reported for each site in the official standard data form, which is instead solely based on the expert opinion.

2 Methods

2.1 The habitat 6210 and the study area

The semi-natural grasslands included in the habitat 6210 develop on calcareous to neutral substrates, usually on thin, well-drained, infertile lime-rich soils (Rodwell et al. 2007; Biondi et al. 2009). Species composition may be highly variable depending on the environmental factors, such as topography, soil conditions (Mazzoleni et al. 1992; Blasi et al. 2012a, b; Burrascano et al. 2013) and type of management (Calaciura and Spinelli 2008; Blasi et al. 2009).

The study area includes seven areas along the Apennines (Fig. 1), all of which are in mountain areas. Even if sampling units encompass a wide altitudinal range (from 579 to 1836 m a.s.l.), half of them occurs between 1100 and 1300 m (Table 1). Climatic variation across study areas is related to different amounts of annual rainfall rather than to differences in mean annual temperature (Table 1). The study areas are mainly characterised by Jurassic and Cretaceous limestones, with some incursions of marly limestone. All the areas are managed mainly as pasture for livestock, but during the past decades, management changed according to the general trend occurred in European mountain areas, i.e. a general decrease in grazing intensity combined with a shift in the type of animals (Catorci et al. 2012a; Giarrizzo et al. 2015; Lasanta et al. 2015; Tóth et al. 2016). All the study areas are mostly included in Natura 2000 sites.
Fig. 1

Distribution of the seven study areas along the Italian peninsula and their overlap with Natura 2000 sites. From North to South, the acronyms stand for: MC Mt. Catria, VA Valsorda, MP Mt. Pennino, MS Mt. Subasio, GS Gran Sasso, MV Mt. Velino, MA Mt. Alpi

Table 1

Description of the physic and climatic characteristics of the seven study areas. We reported the mean altitude (metres a.s.l.), mean annual temperature (°C), and annual precipitation (mm) calculated for each study area. Climate data were derived from the meteorological stations closest to the sampling units, altitude was logged during fieldwork by GPS device, and lithotype was extracted from the Geological Map of Italy (scale 1:250000—IGM)

Study area





Gran Sasso




Limestones and dolomitic limestones; glacial deposits

Mt. Alpi




Limestones and dolomitic limestones

Mt. Catria




Limestones and marly limestones

Mt. Pennino




Limestones and marly limestones

Mt. Subasio




Limestones and marly limestones

Mt. Velino




Limestones and dolomitic limestones; organogenic limestones





Limestones and dolomitic limestones

2.2 Multi-temporal approach

We based our analyses on a multi-temporal dataset that derives from the re-visitation of 132 sampling units (mean size 87 m2) historically sampled between 1966 and 1992 and associated with detailed vegetation maps (Giarrizzo et al. 2017). These historical sampling units are included in the tables that were used to originally describe part of the vegetation types belonging to the habitat 6210 in central Italy (Bruno and Covarelli 1968; Avena and Blasi 1979; Biondi and Ballelli 1982). Since the Habitats Directive used the vegetation types for the identification of the habitats included in its Annex I, they represent a reference for the habitat 6210 (Biondi et al. 2009). Through this link, we assumed them not only to be our reference for the measurement of the occurred change in species composition, but also to represent the habitat in a high degree of conservation.

The re-visitation was performed in 2013–2014 in the same period of the year and with the same method adopted by the authors of the historical surveys (Braun-Blanquet 1964; Dengler et al. 2008). Since the historical sampling units did not have an exact geographic reference, we used locality and map legend to identify a site for each historical plot. Then we applied a stratified random approach using altitude, slope, and aspect of each historical sampling unit, to identify the points where the new sampling should be performed (Giarrizzo et al. 2015). Our approach ensured the new sampling units to be surveyed in areas originally occupied by the target community type and in the same topographic conditions. For more details on the selection of the historical plots and on the location of the new ones, we refer the reader to Giarrizzo et al. (2015).

Based on the study area (Fig. 1) and the original plant community type (phytosociological association) the dataset was divided into 17 groups. For each group a quantitative historical species pool was obtained by averaging the species cover values of the historical sampling units belonging to it. As a measure of the occurred changes in species composition (hereafter compositional change), we used the Bray–Curtis dissimilarity between each 2013–2014 sampling unit and the quantitative historical species pool of the corresponding group, see Giarrizzo et al. (2017) for further details. These dissimilarities represent the degree of change occurred between the historical communities and those sampled in 2013–2014, and they were positively correlated with successional dynamics and, in some case, with the increase of species adapted to high levels of grazing disturbance (Giarrizzo et al. 2017). Therefore, the compositional change was considered as an inverse proxy for the degree of conservation of the current communities. We assumed that the smaller the compositional change the higher the degree of conservation of the current community, and vice versa.

For the identification of plant species we referred to Pignatti (1982), and specific literature (Viscosi et al. 2009).

2.3 Potential indicators

We defined a list of potential indicators of the degree of conservation based on those already applied in other EU Member States for semi-natural grasslands (Table 2) and on the specific habitat features (Carli et al. 2013). All the indicators were calculated for each sampling unit. For the selection of the indicators we started from the criteria of the Article 17 of the Habitats Directive at the biogeographical scale (Evans and Arvela 2011), and we adapted them to the local scale in order to maintain the greatest possible consistency between the two spatial scales. The list represents different habitat features: species composition, structure and function, landscape pattern. Differently from the Article 17, we kept as separated the indicators of species composition from those related to structure and function, because the former is the core of our methodological approach.
Table 2

List of the ten potential indicators of the degree of conservation. For each indicator we reported calculation method, methodological reference, and data source




Methodological reference

Data source


Habitat diagnostic species

Number of habitat diagnostic species

JNCC (2004), Yera Posa and Ascaso Martorell (2009), BfN (2011)

Biondi et al. (2009) and European Commission (2013)

Floristic consistency

Relative cover of the diagnostic, frequent, and abundant species of the three suballiances included in the habitat

JNCC (2004), BfN (2011), Maciejewski (2015)

Biondi et al. (2005), Di Pietro (2011) and Biondi and Galdenzi (2012)

Steppic species

Relative cover of steppic species

JNCC (2004), BfN (2011)

Meusel et al. (1978, 1965), Walter and Straka (1970) and Meusel and Jäger (1992)


Relative cover of Cichoriaee species

Florenzano et al. (2015)

Pignatti (1982) and The Angiosperm Phylogeny Group (2016); authors evaluation


Spiny species

Relative cover of spiny species

JNCC (2004), BfN (2011)

Pignatti (1982); authors evaluation

Toxic species

Ratio between toxic and non-toxic species

JNCC (2004), BfN (2011)

Roggero et al. (2002) and Banfi et al. (2012); authors evaluation


Relative cover of graminoids

JNCC (2004)

Pignatti (1982) and Kühn et al. (2004)

Woody species

Relative cover of the phanerophytes

JNCC (2004), BfN (2006), Gigante et al. (2016b)

Pignatti (1982)


Polygon Shape Index

Edge complexity of the polygon

Giarrizzo et al. (2017)

FRAGSTAT analyses McGarigal et al. (2012)

Contacts with woody communities

Percentage of perimeter shared with forest or shrublands polygon(s)

Smiraglia et al. (2007)

FRAGSTAT analyses McGarigal et al. (2012)

Even if we know that a more inclusive species approach would be preferable, we focused only on vascular plants indicators because they are officially recognised as the main taxa for the identification of the Annex I habitats (European Commission 2013), and they often represent the great majority of the typical species selected for the habitat conservation status assessment (Evans and Arvela 2011, DG Environment 2017). Moreover, plants are easier to observe and collect during monitoring activities than other taxonomic groups.

Among the composition indicators, we considered the number of the habitat diagnostic species integrating those characteristic species listed in the European habitat interpretation manual (European Commission 2013) with those listed in the Italian interpretation manual for the Apennine region (Biondi et al. 2009).

The floristic consistency (Carli et al. 2016, 2018) refers to the congruence with the vegetation type (suballiance) to which the historical sampling units were referred to (Biondi et al. 2005; Di Pietro 2011; Biondi and Galdenzi 2012). We derived this indicator from the abundance of frequent and diagnostic species of the following suballiances: Phleo ambigui-Bromenion erecti Biondi, Allegrezza and Zuccarello ex Di Pietro 2011, Sideritidenion italicae Biondi et al. 1995 corr. Biondi, Allegrezza & Zuccarello 2005, Brachypodenion genuensis Biondi, Ballelli, Allegrezza & Zuccarello ex Biondi and Galdenzi 2012.

Steppic and Cichorieae species were taken into account since they strongly characterise grassland habitats. We considered as steppic species those that occur or that are dominant and/or common in areas naturally dominated by grassland vegetation, according to their native range (Steppic, Pontic, Pannonic, Central Siberian species) (Wesche et al. 2016). These species were selected following Meusel et al. (1965, 1978), Walter and Straka (1970), and Meusel and Jäger (1992). Cichorieae were included in our list of potential indicators since they were demonstrated to indicate the continuous management of the grasslands in Florenzano et al. (2015); these were selected according to The Angiosperm Phylogeny Group (2016).

Structural and functional indicators were based on groups of species that change their frequency or cover when undesirable shifts in management are taking place (Gigante et al. 2016b). We selected both species that could indicate an increase in grazing pressure and species that can indicate abandonment. Among the first we included graminoids, spiny species, and toxic species (Díaz et al. 2007; Catorci et al. 2012b), whereas woody species were accounted as potential indicators of transition towards woody communities not referable to the habitat (JNCC 2004; BfN 2011). Species were identified as graminoids either by using the BiolFlor online database (Kühn et al. 2004), or by using the information included in Pignatti (1982). Species were considered spiny if they are endowed with hard spines, thorns and/or prickles; whereas, information on toxic species were drawn from specific literature (Roggero et al. 2002; Banfi et al. 2012;; or expert knowledge. Woody species (phanerophytes and nanophanerophytes) were identified using Pignatti (1982).

The landscape matrix in which semi-natural grasslands develop is known to have a key role in influencing their habitat quality and species composition (Haynes et al. 2007; Plieninger et al. 2014; Wilson and Meurk 2011). The compositional change we quantified was expected to derive partially from the landscape pattern occurring when the historical data were sampled since this may have determined different seed sources and patterns of species colonization. For this reason, we included two landscape features in our indicators’ list, derived directly from the vegetation maps associated with the historical data. Especially, we considered the polygon Shape Index that quantifies patch complexity, which can be important for different ecological processes (Rutledge 2003; Comber et al. 2003; McGarigal et al. 2012), and the percentage of perimeter shared with shrub and forest polygon(s) (Smiraglia et al. 2007; Capotorti et al. 2012).

2.4 Selection of the condition indicators

To select those indicators that could be effectively used in the assessment of the degree of conservation, all the potential indicators were related to the compositional change by means of linear mixed effect models. Due to modelling issues, we eliminated from the regression analysis those sampling units that had zero values for both the indicators relative to woody and steppic species (29 sampling units in total).

All the potential indicators were used as explanatory variables of the compositional change in a mixed effect model that included in the random part the 17 groups identified from study area and the original plant community type (see the paragraph Sect. 2.2). We chose the appropriate random part through Restricted Maximum Likelihood and AIC criterion, and we selected the best-fixed part through a backward elimination of the least relevant terms. We run the models following the indications by Zuur et al. (2009). Regression analyses were performed in R (R Core Team 2017) using the function lmer, from the lme4 R package (Bates et al. 2015).

2.5 Degree of conservation assessment

For each significant indicator retained by the model, we first defined four classes based on three natural breaks across its values in the dataset (Jenks 1967). Then, based on the combination of these classes for each indicator we associated each sampling unit with a status category among those defined in the framework of the Habitats Directive, i.e. unfavourable bad (hereafter ‘bad’), unfavourable inadequate (hereafter ‘inadequate’), favourable. The combination criterion was coarsely based on what was recommended in Sipkova et al. (2010): if one indicator was in the lowest class, the sampling unit was categorised as bad, whereas if all the indicators were in one of the two highest classes the assessment was favourable, everything in between resulted as inadequate. Therefore, by the combination of the classes defined for each indicator we could associate each sampling unit to one of the three categories of the assessment. By using, at the local scale, the same categories adopted for the biogeographical and the national scale we maintained the greatest possible consistency across different spatial scales.

To test the meaning of our assessment in a species conservation perspective we calculated the average number of species of conservation concern in the groups of sampling units assigned to different status categories. We focused on different groups of species:
  • Orchid species, which are linked to the priority status of the habitat;

  • Policy species from International Conventions and Directives (Habitats Directive, Bern Convention, CITES);

  • Threatened species listed in global, national and regional Red lists (Conti et al. 1992, 1997; IUCN 2016; Rossi et al. 2016);

  • Endemic species, from national to local (Peruzzi et al. 2014).

Differences among the three status categories for each group of conservation species were evaluated using Kruskal–Wallis test and post hoc test through the package dunn.test (Dinno 2017).

Finally, we aggregated the assessments for all sampling units in each different Natura 2000 site following the general approach of Sipkova et al. (2010), here adapted to the site scale. If more than 25% of the sampling units were categorised as bad, the Natura 2000 site was judged as bad, whereas if more than 75% of the sampling units were categorised as favourable the Natura 2000 site was judged as favourable, everything in between resulted as inadequate. Finally, we compared our results with the habitat conservation assessment indicated in the standard data forms of the Natura 2000 sites corresponding to the study areas (accessed through in December 2017). Although also the standard data form assessment derives from the evaluation of habitat structure and functions (see Explanatory Notes available at:, its categories do not perfectly match those of the Article 17 reporting, and use excellent (A), good (B) and average or reduced (C) as categories coarsely similar to favourable, inadequate, bad.

3 Results

Among the ten potential indicators, only four were retained by the model, of which only two were significantly related to the compositional change, i.e. the number of habitat diagnostic species and the relative cover of woody species (Fig. 2).
Fig. 2

Regression coefficients and confidence intervals of the four potential indicators retained by the linear mixed effects model when explaining the compositional change. The two indicators significantly related to the compositional change are those that do not cross the vertical dotted line, i.e. the number of habitat diagnostic species and the relative cover of the woody species

By combining the values of these two indicators (Fig. 3), 80 sampling units were associated with a favourable degree of conservation, while 32 resulted as inadequate, and 20 in a bad degree of conservation.
Fig. 3

Conservation status assessment based on the combination of the two indicators retained by the model that resulted as significant in explaining the compositional change

The average number of species of conservation concern (orchids, policy, threatened, and endemic species) increased substantially when shifting from the bad to the inadequate and favourable categories, with significant differences especially between the two unfavourable categories and the favourable one (Fig. 4).
Fig. 4

Average number of orchids, policy species (Habitats Directive, Bern Convention, CITES), threatened species (from global, national and regional Red lists), and endemic species per sampling units in each status category. Letters on bars indicate significant differences among groups as resulted from the post hoc test (same letter indicates no significant differences between the two categories considered)

When aggregating results to the site level, our assessment only partially corresponds with the one reported for Natura 2000 sites in the standard data forms. Only one site resulted as in favourable degree of conservation after the data aggregation, while the status for most sites resulted as inadequate. Two sites show a bad degree of conservation, while in the standard data forms the habitat assessment results as excellent or good (Table 3).
Table 3

Conservation status assessment of the seven study areas based on the two selected indicators, i.e. number of habitat diagnostic species and relative cover of woody species

Study area

Natura 2000 code

Habitat area (ha)

Standard data form

No. of units

Present study

Site level

Sampling units level (%)



































































For each study area, we reported the number of sampling units and the percentage of the sampling units that resulted in the three status categories, and the aggregate assessment for each site. We also reported info about the Natura 2000 site that overlaps each study areas: code, habitat area (ha), and the habitat conservation assessment (A excellent, B good, C average or reduced), as reported in the standard data form updated in Jan 2017

Acronyms stand for: MC Mt. Catria, VA Valsorda, MP Mt. Pennino, MS Mt. Subasio, GS Gran Sasso, MV Mt. Velino, MA Mt. Alpi

4 Discussion

4.1 Diagnostic and woody species could effectively be monitored to assess the degree of conservation of the habitat

Among the potential indicators, the two that emerged as efficient in assessing the 6210 habitat degree of conservation are both related to the species composition and structure features of the habitat, reflecting the recommendations of the Habitats Directive (Council Directive 93/42/EEC). Analogous results emerged for other grassland habitats, such as those included in the priority habitat 2130, where the analysis of species composition and structure turned out as the most straightforward tool to assess the degree of conservation of the habitat (Silan et al. 2017). In our case, the number of habitat diagnostic species and the relative cover of woody species indicate different facets and/or stages of the change in habitat degree of conservation. Indeed, when the semi-natural grasslands are subjected to compositional changes, especially in relation to successional dynamics, grassland typical species decrease in frequency and abundance, this process can be associated with an increase in the cover of woody species depending on environmental and historical factors (Andersen et al. 2013; Timmermann et al. 2015; Giarrizzo et al. 2017).

The habitat diagnostic species we considered were selected from those listed as characteristic species of the habitat 6210 in the European and Italian interpretation manual (Biondi et al. 2009; European Commission 2013); therefore they represent those species commonly used to identify the habitat and to distinguish it from others (Del Vecchio et al. 2015). Here, we highlighted that, being related to the habitat function and ability of persist over time, these species can also play a significant role in the degree of conservation assessment for the habitat, as it was already found for chalk grassland (Stroh et al. 2017) pointing to an overlap with the concept of typical species sensu Article 17 (DG Environment 2017).

The other indicator of the habitat degree of conservation, i.e. the relative cover of woody species, gives relevant information on the probability of the habitat to exist in the future (Kallimanis et al. 2017). In semi-natural grasslands, the cover of woody species indicates a striking change in the structure of the habitat. This is related to ongoing vegetation dynamics (Gigante et al. 2016b; Giarrizzo et al. 2017) associated with the absence of continuous management (JNCC 2004; BfN 2011).

In general, the two indicators selected by the model give a measure of the extent to which the habitat differs from a desirable condition. Thus, they represent a good basis for the prioritisation of areas to put under active management or on which restoration actions are necessary. Importantly, these two indicators are basic descriptors that are cost-effective and easy to sample in the field, complying with the requirements of the Habitats Directive, and of biological indicators in general (Noss 1990). In a cost-efficiency perspective, both of them can be sampled by personnel specifically trained on the identification of a limited number of species, rather than involving highly specialised botanists in charge of a complete sample of species composition. In fact, habitat diagnostic species are usually common and in some cases abundant within the target communities; therefore, personnel may be easily trained on their identification. Similarly, within a grassland habitat, woody species are typically distinguishable from the herbaceous species also by non-expert personnel (Kallimanis et al. 2017). The monitoring of distinctive, readily identifiable species that have a marked sensitivity to habitat change provides a useful tool for the rapid assessment and monitoring of site quality and allows for widespread monitoring activities, increasing the probability of recognising habitat degradation processes over large areas (Stroh et al. 2017). Using few easy indicators for habitat monitoring was already acknowledged as an effective approach (JNCC 2004; Yera Posa and Ascaso Martorell 2009; BfN 2011), that proved to be reliable also when data are recorded by non-specialised collectors, especially at the site level (Kallimanis et al. 2017).

The classification into status categories we performed reflected the occurrence of species of conservation concern that in turn are relevant in assessing the habitat biodiversity value at local and broader spatial scales (Marignani and Blasi 2012; Geven et al. 2016; Carli et al. 2018). The number of orchids and policy species increased significantly from the two inadequate categories to the favourable one, underlining the relevance of our assessment in the framework of the Habitats Directive conservation efforts. On the other hand, among the species of conservation concern the number of threatened species (IUCN) is not completely consistent with our categorization: their number increased from bad to favourable but not significantly. This result reflects the low congruency between the Habitats Directive and the IUCN assessment at the local spatial scale, as depicted by Moser et al. (2016) at the EU level, even if contrasting results were found at the national level (Fenu et al. 2017). The number of endemics, which in Italy is in general particularly high (Peruzzi et al. 2014), significantly increased from bad to favourable category, supporting the idea that areas with high number of endemism are important for conservation purposes (Blasi et al. 2011; Bacchetta et al. 2012; Trigas et al. 2012; Hobohm et al. 2014).

Our results only partly confirm the assessment of the habitat conservation at site level reported in the standard data forms of the Natura 2000 sites, posing some uncertainty on the expert-based evaluation that usually integrates this information. With our approach, we provided a quantitative basis for the site level assessment, and we depicted a situation similar to the national condition highlighted by the last reporting (Genovesi et al. 2014). Moreover, also at the site level, it is important to account for the categorization of the sampling units since in an active conservation perspective, also within a site whose degree of conservation is generally favourable, patches in a bad or inadequate degree of conservation can be identified and be subjected to specific conservation measures.

4.2 A multi-temporal perspective strongly benefits the assessment of habitat conservation status

Different authors stressed the importance of defining a reference state in the assessment of the conservation status of a habitat (Reynoldson and Wright 2000; Gigante et al. 2016a), with this reference state usually deriving from the species composition of the community types to which the habitat under investigation should be referred (Silan et al. 2017). For instance, the use of existing databases as a reference for habitat interpretation is currently being promoted through the Natura 2000 biogeographical process (, and could also be used as a baseline for the monitoring activities on which the Habitats Directive reporting should be based. Within this framework, a multi-temporal perspective may be extremely powerful for the conservation status assessment, since it would compare newly collected information to a concrete reference from the past, allowing for a long-term perspective that otherwise is often not possible to accomplish. By considering historical species pools, it is possible to obtain a measure of change from a reference compositional state, which in our cases represents the favourable status of the habitat 6210. The limit of using habitat-specific species pools is related to the fact that their composition, as well as conservation targets, is unique for each region and habitat. However, a standardised workflow was recently proposed to estimate the composition of habitat-specific species pools (Helm et al. 2015). Moreover, large vegetation-plot databases are becoming increasingly available (Landucci et al. 2012; Chytrý et al. 2016), and it is likely that in the near future they will contribute to overcome data limitations and allow studies on biological diversity that encompasses different temporal scales (Sabatini et al. 2017). Through re-visitation, we added strength to this approach because we considered as reference state the communities in favourable status that originally occupied the sites where the assessment was performed.

We explored and tested the possibility of using re-visitation not only to measure the degree of change to which habitat patches have been subjected, but also to identify effective condition indicators for future monitoring activities. A similar approach was adopted also by Stroh et al. (2017), which, through re-visitation, identified one distinctive species that acted as a useful indicator species for the monitoring and assessment of site quality in calcareous semi-natural grasslands in Southern and South-Eastern England.

Supplementary material

12210_2018_707_MOESM1_ESM.xlsx (11 kb)
Supplementary material 1 (XLSX 10 kb)
12210_2018_707_MOESM2_ESM.xlsx (9 kb)
Supplementary material 2 (XLSX 9 kb)


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© Accademia Nazionale dei Lincei 2018

Authors and Affiliations

  1. 1.Department of Environmental BiologySapienza University of RomeRomeItaly
  2. 2.Institute of Agricultural and Environmental Sciences, Estonian University of Life SciencesTartuEstonia
  3. 3.Department of Bioscience and TerritoryUniversity of MolisePescheItaly
  4. 4.Department of Agricultural, Environmental and Food SciencesUniversity of MoliseCampobassoItaly
  5. 5.TivoliItaly

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