Spatially assessing plant diversity for conservation: A Mediterranean case study

Accepted Manuscript Title: Spatially assessing plant diversity for conservation: a Mediterranean case study Authors: Emanuela Carli, Raffaella Frondoni, Maria Silvia Pinna, Gianluigi Bacchetta, Giuseppe Fenu, Mauro Fois, Michela Marignani, Selena Puddu, Carlo Blasi PII: DOI: Reference:

S1617-1381(17)30055-9 https://doi.org/10.1016/j.jnc.2017.11.003 JNC 25599

To appear in: Received date: Revised date: Accepted date:

2-2-2017 8-9-2017 6-11-2017

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SPATIALLY

ASSESSING

PLANT

DIVERSITY

FOR

CONSERVATION:

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MEDITERRANEAN CASE STUDY Emanuela Carli*a, Raffaella Frondoni*a, Maria Silvia Pinnab, Gianluigi Bacchettab, Giuseppe Fenub, Mauro Foisb, Michela Marignanib, Selena Puddub, Carlo Blasia * Equally credited authors a

Dept of Environmental Biology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome,

b

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Italy Dept of Life and Environmental Sciences, University of Cagliari, Viale S. Ignazio 13, 09123

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Cagliari, Italy Corresponding author [email protected]

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Abstract

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In this paper, we present a spatially explicit procedure for mapping and assessing coastal plant

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diversity value in the context of biodiversity monitoring and conservation planning. Our objective

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was to devise a replicable and easy to understand methodology framework, which can represent an

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expedient tool for coastal management and decision making at spatial scales between 1:25,000 and 1:50,000. For this purpose, we adopted a small number of key descriptors that refer to easily

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quantifiable information on species and habitats: plant species richness, species of conservation value, floristic consistency, habitat diversity, and habitats of conservation interest under the Council

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Directive 92/43/EEC. We built an expedient sampling strategy that combines systematic sampling by grid cells of fixed size with stratification per habitat type, and apply a plain equal weighting scoring

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system for assessing overall plant diversity. All floristic and habitat data were entered into a spatial database built within a GIS environment and referred to a 1x1 square km spatial grid overlaid on two selected test sites in southern Sardinia (Italy). The descriptors we chose were successful surrogates of plant diversity, as they were able to represent the known conservation importance of both test sites and of specific areas within them, both individually and in combination. In particular, our results show that integrating indicators at different 1

levels of biodiversity enabled to represent aspects with marked differences in distribution as well as to compensate possible biases in data collection, as habitat data are more easily available than floristic information and spatially continuous even in less accessible areas. Being based on well-known criteria and policies, and on data that are most widely and consistently available, our assessment procedure proved effective and easily transferable, and provides a spatial reference framework for systematically evaluating and monitoring coastal plant diversity at national

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level and across the Mediterranean Basin. Keywords : Plant diversity conservation; species indicators; habitat indicators; spatial grid; coastal

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areas; southern Sardinia 1. Introduction

Spatial assessment of biodiversity value provides the baseline information for monitoring biodiversity

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in space and time, and identifying priority areas in the context of conservation planning (Henle et al.,

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2013; Margules & Pressey, 2000; Myers, Mittermeier, Mittermeier, da Fonseca, & Kent, 2000). The

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need for quantifying conservation value has rapidly emerged along with the increasing awareness of

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the decline in biodiversity, due particularly to land cover change, overexploitation, pollution, and

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invasive species (Marchese, 2015; MEA, 2005; SCBD, 2005). Typically, conservation prioritisation focuses on the protection of particular species and communities, areas of high species richness or

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high endemism, and functioning ecosystems (Noss, 1990; Pressey, Cabeza, Watts, Cowling, & Wilson, 2007; Wilson, Carwardine, & Possingham, 2009). In addition, the modern perspective of

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systematic conservation planning integrates conservation priorities with socio-economic and political considerations, in order to maximise the biological benefits as well as the return on investments (Freudenberger et al., 2013; Kukkala & Moilanen, 2013; Naidoo et al., 2006). Biodiversity is too

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complex to be directly measurable, therefore spatial conservation planning usually relies on surrogates, which should represent multiple levels of organisation and be relatively easy to measure (Margules, Pressey, & Williams, 2002; Rodrigues & Brooks, 2007; Sarkar et al., 2006). Indicators based on endangered plants and animals, vegetation communities or habitat types have been extensively used as biodiversity descriptors, as data on these groups are generally more widely and 2

regularly available (Brooks, Da Fonseca, & Rodrigues, 2004; Brown & Williams, 2016; Wu et al., 2014). Environmental data based on abiotic features or combinations of physical attributes and vegetation have also been explored as surrogates of higher levels of biodiversity, with controversial results (Beier et al., 2015; Engelbrecht, Robertson, Stoltz, & Joubert, 2016; Hermoso, JanuchowskiHartley, & Pressey, 2013). Species-based and environmental-based strategies can be seen as complementary, as the metrics based on the spatial pattern of environmental classes can be used as

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coarse filters that account better for ecological processes and ecosystem functioning, whilst specieslevel proxies provide biological precision and detail at finer levels (Bonn & Gaston, 2005; Brooks et

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al., 2004; Noss, 1990). However, in practice the choice of indicators largely depends on the existing datasets and the spatial scale of interest, because quantitative georeferenced data are usually available only for some features and fine-scale assessments are generally costly in terms of resources and time

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(Álvarez-Berastegui et al., 2014; Cañadas et al., 2014; Payet, Rouget, Lagabrielle, & Esler, 2010).

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Coastal areas generally host a high diversity of plant species and communities as consequence of

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environmental heterogeneity, but are amongst the most threatened in Europe and in the Mediterranean

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by increasing human pressure due to urbanisation, tourism and industrial development (Abdelaal,

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Fois, & Fenu, 2017; Cuttelod et al., 2008; Doxa, Albert, Leriche, & Saatkamp, 2017; EEA, 2013; Myers et al., 2000). Though several studies examined coastal habitats and plant communities in Italy

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(Farris, Pisanu, Ceccherelli, & Filigheddu, 2013; Malavasi, Santoro, Cutini, Acosta, & Carranza, 2016; Pinna, Cogoni, Fenu, & Bacchetta, 2015), to the best of our knowledge there is no national or

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regional systematic spatial assessment of coastal biodiversity, which is instead crucial for monitoring and reviewing conservation actions consistently. Within this general context, we developed a spatial assessment procedure for plant diversity, using

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two coastal areas in the Gulf of Cagliari (southern Sardinia, Italy) as test sites. We focused on plant diversity because plants are highly representative of local environmental conditions and their associated ecosystems, and because of the present concern for plant diversity loss raised by the Global Strategy for Plant Conservation (Blasi et al., 2011). Our final objective was to devise a method that is relatively easy to understand for local stakeholders and conservation consultants, and supportive 3

for environmental planning at scales between 1:25,000 and 1:50,000. Therefore, we assumed a simple set of basic data, key criteria, and well-known policies, as well as an intermediate level between fineand coarse-filters strategies based on the integration of species and habitat information. In building our proposal, we benefited from the experience we gained during an international project that aimed at developing a toolkit for risk analysis and integrated coastal management in critical areas of the Mediterranean Basin (Marignani et al., 2017). In particular, the wider Mediterranean perspective

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helped keep the procedure as plain and universal as possible, thanks to insights on data availability and on the need of shared protocols that emerged from the collaboration with partners from France,

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Lebanon, and Tunisia.

In this paper, we describe our joint analysis of species and habitat data and discuss its strength,

assessment of plant diversity in coastal areas, which is:

Capable of representing the heterogeneous pattern of coastal ecosystems whilst optimising

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

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data collection and design effort;

Easy to understand and operational as expedient tool for management and decision making at

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

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weakness, and potential for replicability. Our aim is to propose a plain procedure for spatial

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spatial and thematic scales between the national level and the strictly local; Helpful as spatial reference framework for systematically assessing and monitoring coastal plant

2. Study area

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diversity at national (e.g. Italy) and trans-national (e.g. Mediterranean Basin) levels.

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The Gulf of Cagliari lies in southern Sardinia (Italy), between Capo Spartivento in the west and Capo Carbonara in the east. It is a popular tourist destination in summer, and houses the city of Cagliari (150,000 inhabitants), an important commercial and recreational port, and the oil refinery of Sarroch,

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which is one of the biggest in Europe (Cortis et al., 2016). On the other hand, it hosts 15 Natura 2000 sites, two Ramsar sites and one Marine Protected Area, which overall demonstrate the remarkable biodiversity value of the area. Within the Gulf, we selected two study sites, which are already known as valuable for plant species and vegetation, and are representative of different environmental and socio-economic characteristics. 4

These are the area of Poetto Molentargius Capo Sant’Elia (PMC hereafter), just off the city of Cagliari, and the coastline stretching between Chia and Santa Margherita di Pula (CSM hereafter), towards the western limit of the Gulf (Fig. 1). Fig. 1 We used lithological and geomorphological data to apply an ecological land classification approach and delimit the study sites according to environmental boundaries (Blasi, Carranza, Frondoni, &

topographic

and

geological

maps

at

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Rosati, 2000; Capotorti, Guida, Siervo, Smiraglia, & Blasi, 2012) (Fig. 2). In particular, we used 1:25,000

scale to

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(http://webgis.regione.sardegna.it/scaricocartografiaETL/cartaGeologica/geologiaAreali.zip)

derive geomorphological data and a simplified lithological map. In the case of PMC, the inner border coincides with the boundary between lithological classes of coastal or alluvial origin and the

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anthropogenic sediments of the urban area of Cagliari. As for CSM, due to the more varied and

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interspersed pattern of lithological classes, we used the contour line of 40 m a.s.l. to delimit the inland

of

the

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Plan

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extent. Both sites are completely enclosed within the coastal zone delimited by the 2006 Landscape Sardinia

Region

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(http://webgis2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:KEODD). Fig. 2

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The CSM site extends over 1744 hectares and represents one of the best examples of coastal vegetation in southern Sardinia (Bacchetta, 2006). It contains two Sites of Community Importance

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under the Habitats Directive 92/43/EEC (ITB042231, ITB042230) and includes dune systems, wetlands and rocky habitats. The PMC site has an area of 2145 hectares and encompasses the long sandy beach of Poetto, the

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important wetlands of the Molentargius area, and the carbonate promontory of Capo Sant’Elia, characterised by great floristic diversity and plant species of biogeographic importance (De Martis, Mulas, Malavasi, & Marignani, 2016; Gargano, Fenu, Medagli, Sciandrello, & Bernardo, 2007). The site falls within two Special Protection Areas (ITB044002, ITB04400) and comprises five Sites of Community Importance (ITB042242, ITB042243, ITB040021, ITB040022, and ITB040023). 5

3. Materials and methods 3.1 Data collection To allow comparisons among different areas in space and time, we collected data using a spatial grid with fixed cell size (1km×1km). We overlapped our reference grid with the study sites and excluded those edge cells where the site covers less than 1 hectare or where artificial and/or agricultural areas cover more than 90%, unless pre-existing floristic data from natural and semi-natural habitats were

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available. We then created a spatial database of species and habitats for 59 cells (Fig. 1), using QGIS Wien 2.8.4

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(QGIS Development Team, 2015).

For habitat data, we referred to the 2008 regional land cover map at scale 1:25,000, which depicts 77 cover classes at the third, fourth and fifth levels of the national CORINE Land Cover nomenclature

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(http://webgis2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:WBMEW). We also

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referred to the 2010 Map of Nature by ISPRA at scale 1:50,000 (Camarda et al., 2015; ISPRA, 2013),

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and to the 1:10,000 habitat maps of the Natura2000 sites (2008-2014) (official data provided by the

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Department of Environmental Protection of Regione Sardegna).

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Floristic data came from original field surveys and from relevant literature for the study area. Field survey took place in summer 2014 and spring 2015, and resulted in 118 original vegetation relevés.

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They were carried out within environmentally homogeneous areas (in terms of vegetation canopy, slope, aspect, and other site factors) that were representative of the habitat types, and consisted of

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complete lists of all species present. Plot size varied according to habitat type following Chytry & Optykova (2003). To make the effort uniform, sampling consisted in one plot per habitat type per grid cell. This approach, which resulted in as many plots as the habitats per cell, best reflects habitat

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heterogeneity and vegetation diversity (Schweiger, Irl, Steinbauer, Dengler, & Beierkuhnlein, 2016). Literature data included only records from 1950 onwards, which were already georeferenced or could be univocally assigned to specific grid cells, because of explicit topographical and physical data and of vegetation description. To minimise errors due to possible changes over time, we checked that the habitats they were referred to in the literature were still present in the specific cells. All the 6

bibliographic data we used involved plant species that were found in similar habitats in other cells of the study area, which made us more confident on data reliability. Moreover, local experts verified the current distribution of species of concern. Overall, we collected 136 floristic records and 161 vegetation relevés (Bacchetta, 2006; Bacchetta, Brullo, Giusso del Galdo, & Guarino, 2005; Bartolo et al., 1989; Biondi & Mossa, 1992; Bocchieri, 1984; Chiesura Lorenzoni & Lorenzoni, 1977; De Marco & Mossa, 1980; De Martis & Mulas, 2008; De Martis & Serri, 2009; Martinoli, 1969, 1950).

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3.2 Plant diversity assessment and mapping To quantify and map plant diversity, we used three floristic indicators (species richness, richness of species

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of conservation interest, and floristic consistency) and two habitat indicators (habitat diversity and relative cover of habitats of high conservation value) (Table 1). All descriptors were calculated on a cell basis. Each 1 km×1 km grid cell received individual scores for each descriptor considered, as well as an overall

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score given by their sum, which quantifies overall plant diversity.

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We tested correlation among indicators using the Kendall rank correlation coefficient in R software (R

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the tau-b coefficient, which accounts for ties.

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Core Team, 2016). In particular, as an individual indicator may have equal scores among cells, we used

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To simplify spatial representation, we ranked the range of scores of each descriptor into three classes using Jenks natural breaks, as already done in previous studies on biodiversity assessment (Blasi et al., 2011;

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Game, Kareiva, & Possingham, 2013; Vimal et al., 2012). For comparison purposes among cells and indicators, we interpreted these three classes in relative terms as low, medium and high value.

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Table 1

3.2.1 Species indicators

Species richness (SR) represents the total number of plant species recorded in each cell and, due to our

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sampling strategy, refers only to natural and semi-natural habitats. Measures of species richness enable comparisons among sites and time intervals, but give equal importance to all species, regardless the fact that they could be native or introduced, ubiquitous or habitat specific, rare or common (Taft, Hauser, & Robertson, 2006). To gain more insight into composition, from the list of species per cell we calculated the richness in species of high conservation 7

interest (HCSR) and floristic consistency (FC), which refers to the number of species that can be considered habitat specific (Carli et al., 2016). This indicator helps to reduce biases in the interpretation of species richness, such as underestimating plant diversity in cells that host speciespoor communities with highly specialised flora (such as coastal cliffs, or halophile vegetation) or, to the other end, overestimating diversity in cells with high floristic richness but presence of cosmopolitan, ruderal or even exotic plants.

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For the species of conservation interest, due to the lack of complete and updated national Red lists, we used a comprehensive criterion that include levels from global to regional. We also considered

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plant endemics that are not listed in official policies, in order to adapt this indicator to the specific local context and account for the exceptionally abundant endemic flora of Sardinia (Bacchetta, Farris, & Pontecorvo, 2012; Cañadas et al., 2014). In particular, we considered the following categories: (i)

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threatened species listed in global, national and regional Red lists (Conti, Manzi, & Pedrotti, 1992,

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1997; IUCN, 2016; Rossi et al., 2016); (ii) policy species from international Conventions and

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Directives (Habitats Directive, Bern Convention, CITES), which might be unthreatened but represent

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priorities for conservation, as well (Blasi et al., 2011; Fenu et al., 2017); and (iii) endemic species,

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from national to local (Fenu, Fois, Cañadas, & Bacchetta, 2014). Floristic consistency was inspired by the floristic quality assessment procedure, which is based on

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species sensitivity to anthropogenic disturbance and their likelihood of being found in remnant natural areas (Andreas, Mack, & McCormac, 2004; Taft, Wilhelm, Ladd, & Masters, 1997). However, to

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reduce inherent biases depending on the a priori subjective knowledge of plant species affinity to habitats (Landi & Chiarucci, 2010), we referred to standard lists used for vegetation description and habitat interpretation. In particular, we considered the diagnostic species listed in the European

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Interpretation Manual of Habitats (European Commission, 2013), in the Italian Manual (Biondi et al., 2009), and in the CORINE Biotopes Manual (Devillers, Devillers-Terschuren, & Ledant, 1991). We also took into account the frequent and/or diagnostic species that are listed for coastal plant communities in the Prodrome of the Italian vegetation (Biondi et al., 2014; http://www.prodromo-

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vegetazione-italia.org). Moreover, to improve easiness, we directly evaluated the number of habitat specific components within the floristic pool, instead of calculating individual scores for each species. All floristic indicators were calculated as the ratio of the logarithm of the number of species in each cell to the logarithm of the terrestrial surface within that cell, in order to consider only terrestrial habitats and account for species-area relationships. 3.2.2 Habitat indicators

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To measure habitat diversity (HD), we took into account the classes falling under the CORINE Land Cover categories “Forests and semi-natural areas”, “Wetlands” and “Water bodies” on the 2008

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regional land cover map, and calculated the Shannon Index of Diversity (H) = ∑P[piln(pi)], where pi is the relative cover value of each land cover type in the cell (Taft et al., 2006).

To account for differences in composition, as in the case of species, we considered the relative cover

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of terrestrial habitats of conservation value (HCV) listed in Annex I of the Habitats Directive, which

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is crucial in Europe for nature conservation (Álvarez-Berastegui et al., 2014; Gauthier, Foulon,

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Jupille, & Thompson, 2013; Panitsa, Koutsias, Tsiripidis, Zotos, & Dimopoulos, 2011). We

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calculated the ratio of the logarithm of the area covered by important habitats to the logarithm of the

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terrestrial surface on a cell basis. To maximise the impact of this indicator, considering the known vulnerability of coastal areas in Italy, we included both priority and non-priority habitats. For areas

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outside Natura2000 sites, we reclassified the categories of the Map of Nature by ISPRA into Natura 2000 habitats, and used our expert knowledge to refine their spatial extent.

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4. Results

The two study sites show similar values of most descriptors at the site level in terms of number of habitats, floristic richness, presence of habitat specific species (35% of total species in PMC, 37% in

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CSM), and amount of species of conservation interest (10.5% in PMC, 8.5% in CSM, see Table 2). However, this apparent similarity in plant diversity value hinders differences in composition and, for habitats, relative cover. Only 30% of the total 471 plant species is common to both sites, and only nine species of high conservation value (out of 50) are found in both (Table 2 and supplementary material). For instance, when considering endemic Genista species under threat, Genista corsica is 9

found in both sites, whereas Genista bocchieri and Genista valsecchiae occur only in CSM. On the other hand, Sarcopoterium spinosum, a threatened species at the western border of its distribution area, is present only on the carbonate promontory of Capo Sant’Elia (PMC site). Similarly, out of 23 terrestrial habitats listed in the Habitats Directive and present in at least one site, only seven are in common, whereas 13 are mapped only in CSM and four only in PMC (see supplementary material). Table 2

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The spatial distribution of descriptors within sites reveals clear differences in patterns for each aspect, except for species richness (SR) and floristic consistency (FC) (Fig. 3a). Only one cell in CSM and

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two in PMC has lower relative scores for FC than SR, whereas the contrary occurs in one cell in PMC. These results indicate general good quality of flora and habitats, probably due to existing conservation measures. In this situation, it is likely that most of the species in the cell are also

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representative of the habitats that host them. High species richness occurs in 18 out of 26 cells in

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CSM, and in 11 out of 33 cells in PMC. High scores are well distributed along the entire CSM site

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and concentrated in the southern area (Capo Sant’Elia) and in the Molentargius area in PMC. Low

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scores are particularly notable in four cells largely covered by agricultural areas along the inner border

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of CSM and in six cells at the western border of the Molentargius area, which is more influenced by artificial areas. Richness in species of high conservation interest (HCSR) is much more selective as

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descriptor, and has the highest amount of low scores (no species of interest) and a few cells with high scores. These latter consist of four cells spatially interspersed in the CSM site and five cells highly

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clustered at Capo Sant’Elia in PMC (Fig. 3a). However, all top ranking cells for HCSR also have high scores for floristic consistency and species richness. On the other hand, a small cluster of cells in the Molentargius area is top-ranked for SR and FQ but not for HCSR.

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As for the maps of habitat indicators (Fig. 3b), 26 out of 33 cells in the PMC site show high relative cover of habitats of conservation value (HCV). Nine of them show high diversity of habitats (HD), due to the natural environmental heterogeneity of the area, whereas three cells in the Molentargius area have low diversity, as they host a single important habitat type. In the CSM site, only one cell has low relative cover of habitats of conservation value, whereas the remaining 25 are equally 10

distributed between high and medium scores. In particular, the top ranking cells for HCV are concentrated in the Chia area, which is also important for HD, and at the northeastern border of the study site, which is instead less naturally heterogeneous. Overall plant diversity scores highlight and confirm the remarkable value of both study sites, with low values concentrated in only four marginal cells out of 26 in the CSM area, and in four edge cells out of 33 in the PMC site (Fig. 3c). The spatial distribution of plant diversity scores indicates a

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continuous area from Chia to Santa Margherita, plus two adjacent cells in the area of Pula, as particularly valuable in CSM. In PMC, the values of overall plant diversity result more spatially

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interspersed compared with the pattern of individual indicators, but highlight priorities at Capo Sant’Elia and in the Molentargius area (Fig. 3c).

Values of Kendall rank correlation coefficient show positive correlations among floristic indicators,

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particularly SR and FC, and no significant relationships among habitat indicators or between the two

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groups of descriptors, floristic and habitat (Table 3). Overall plant diversity shows the strongest

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with habitats of conservation concern is weak.

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positive correlation with habitat diversity and, secondarily, floristic consistency, whereas correlation

5. Discussion

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The task of developing a replicable and easy to understand methodology framework for spatially assessing plant diversity value in coastal areas, at scales between 1:25,000 and 1:50,000, involved

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different critical questions. We put special effort to take into account conservation policies and strategies that are most familiar to local administrators and stakeholders, and to employ criteria and data that are generally well known, most widely available, and adequate to depict biodiversity at this

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operational level. For this reason, we adopted a small number of key descriptors, which refer to easily quantifiable information on species and habitats. Likewise, we built an expedient sampling strategy that optimises data collection, and apply a plain equal weighting scoring system for assessing overall plant diversity (Blasi et al., 2011; Gauthier, Debussche, & Thompson, 2010; Tasser, Sternbach, & Tappeiner, 2008). 11

Combining systematic sampling by grid cells of fixed size with stratification per habitat type ensured consistent effort throughout the study area, comparability among cells, and effective representation of within-cell variability, all aspects that are especially important when dealing with species data (Brooks et al., 2004; Brown & Williams, 2016; Fleishman, Noss, & Noon, 2006). Though the availability of detailed land cover and habitat maps greatly help organising the survey campaign, existing information rarely match the fine scale of natural heterogeneity in coastal areas,

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and therefore may not properly represent the fine-grained mosaics of habitat types, as in the case of psammophilous vegetation. For this reason, and for possible inaccuracies in the maps, it is more

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convenient to decide the number and actual location of sampling plots within cells once in the field. The use of existing species data to combine with field information poses questions on reliability, especially as records may be out of date. In our case, we adopted some restrictions to gain confidence,

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and used expert judgement to verify in particular the present occurrence of species of conservation

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concern in the cells. This enabled us to benefit from a larger database while minimising bias.

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The descriptors we chose were successful surrogates of plant diversity, as they were able to represent

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the known conservation importance of both test sites and of specific areas within them, both

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individually and in combination. Most indicators are indeed positively correlated with overall plant diversity, whereas floristic and habitat indicators are not significantly correlated among them, which

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stresses the advantage of considering both levels of biological organisation. Floristic consistency, which is based on the number of species that are somehow representative,

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though not exclusive, of the habitats that host them, is an important descriptor of species composition. It can help to better interpret the species richness value, first because it excludes ubiquitous or exotic species (which are even a threat to plant diversity) and secondly because it accounts for habitats that

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are naturally poor in species, such as sea cliffs or marshes. Moreover, plant species identified as habitat specific could prove particularly useful as key indicators in monitoring strategies (Talhouk et al., 2005). Limiting sampling to natural and semi-natural land cover classes, and dealing with coastal habitats that are selective for plant species and in general good state of conservation, can result in similar patterns of species richness and floristic consistency, as in the case of our test sites, where 12

only a few cells are ranked differently for these descriptors. However, this difference is informative, as three cells with ranked value of SR relatively higher than FC host degraded psammophilous or wetland vegetation in most exploited areas, whereas the only cell where SR is relatively lower than FC hosts saltpans and marshes with poor but representative floristic pool. When examining the spatial variability of indicators within each site, our results highlight the importance of considering two different levels of biodiversity to represent aspects with marked

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differences in distribution. For instance, in the area of Capo Sant’Elia (PMC) five adjacent cells are top ranked for floristic measures, but only three have high plant diversity when combining also habitat

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indicators. On the other hand, the importance for plant diversity of part of the Molentargius area, which is less accessible and naturally poor in species, is mostly due to the significant cover of habitat indicators.

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Moreover, the joint analysis of species and habitats compensates possible biases in data collection,

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as habitat data are more easily available than floristic information and spatially continuous even in

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less accessible areas. In this regard, it is likely that habitat diversity contributes most to overall plant

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diversity also because it is the only descriptor with no zero scores in the cells, as may happen instead

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for species and habitats of concern, or in undersampled cells. A further advantage of habitat data is that they account for both occurrence and extent. In our approach, cells covered by natural habitats

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that have inherent differences in species richness (as species-poor saltpans and species rich annual grasslands) will be equally sampled, and this would result in marked differences in floristic indicators.

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However, including habitat information may adjust the overall score for the species-poor cells, if they host habitats of conservation concern with significant extent. This sort of calibration applies also to cells that are more rich in habitat types or that, being more accessible, were oversampled in the

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literature. In all cases, combining information of different nature and largely independent proved an effective strategy for balancing biases in data quality and availability and adjusting overall plant diversity consistently.

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Obviously, the real efficiency of the method we proposed and its replicability can be assessed only if it will be applied in other sites, yet there are some key points that underline the potential of our approach for being adapted and used elsewhere. First, the method is straightforward and based on data that can be produced quite easily. Certainly, collection of species and habitat data is often costly in time and resources. However, using an expedient, though not exhaustive, sampling design and joint species and habitat data proved a good

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strategy for gathering significant information. In absence of habitat maps, land cover maps can be adequate for identifying areas to be sampled and for deriving habitat information, as description of classes

can

generally

be

translated

into

habitat

types

(see

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cover

https://www.eea.europa.eu/themes/biodiversity/eunis/eunis-habitat-classification).

for

instance

This

is

particularly true when land cover maps are analysed in conjunction with an ecological land

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classification that delimit homogeneous land units in terms of physical features and potential

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vegetation. In this case, we can reasonably assume that the patches of land cover type A within a

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homogeneous land unit will probably represent the same habitat (Blasi et al., 2000; Capotorti et al.,

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2012). Therefore, the ecological land classification approach can help to delimit the ecological

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boundaries of coastal areas, as in our case, as well as to stratify sampling and data collection. Secondly, the approach is flexible enough to be adapted to local contexts and needs, and to other

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spatial scales based on same indicators. However, when doing so, one should check that the spatial and thematic resolution of existing data is still adequate, and carefully consider the constraints

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imposed by resolution, extent and time. The level of detail of data we used and the maps they were derived from are consistent with scales that range between 1:25,000 and 1:50,000, which are frequently used for environmental planning and sustainable management of large areas at the

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administrative level of municipalities and provinces (Blasi et al., 2008; Frondoni, Mollo, & Capotorti, 2011; Tasser et al., 2008). Application of the procedure at local scale such as 1:5,000-1:10,000 might, for instance consider a finer spatial grid for collecting data and reflecting heterogeneity (which is scale-dependent), as well as different ranks within descriptors, such as differentiating between narrow or broad endemics for species of concern or between priority and no-priority habitats. 14

Finally, references to policies and documents that are widely used at global, European, and national scales (IUCN Red Lists, Habitats Directive, Vegetation Prodrome) provide a standard and objective framework, which can be largely shared across European countries (Gauthier et al., 2013; Panitsa et al., 2011) and the Mediterranean Basin (Marignani et al., 2017). For sure, the approach can be easily transferred to coastal areas of Italy, for which many of the required data and reference documents already exist, to provide a national spatial assessment of

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coastal plant diversity at the regional scale, which can be used to review conservation actions and as

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reference layer for monitoring.

6. Concluding remarks

The methodology described herein provides an operative and relatively easy to understand framework

U

for spatially assessing plant diversity in coastal areas, which includes an expedient sampling design,

N

simple descriptors and a plain scoring system.

A

To keep the methodological framework relatively rapid and replicable in space and time, we

M

recommend selecting a small set of significant descriptors that refer to “easily” quantifiable information at species and habitat levels. The joint analysis of species and habitat data proved in fact

ED

helpful to represent different aspects of plant diversity as well as to balance biases in data quality and availability. This is a crucial issue in any spatial biodiversity assessment, as habitat information is

PT

generally more widely and consistently available, at least in European countries, or more easily

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derivable. On the contrary, floristic data are usually incomplete and rarely gathered with uniform protocols. In this regard, we suggest an optimised strategy for plant data collection that combines systematic sampling by grid cell with stratification per habitat type (within each cell). This ensures a

A

consistent effort throughout the study area as well as the best representation of the high heterogeneity of coastal areas. Finally, we recommend analysing species composition by using descriptors that accounts for quality and representativeness, as in the case of the floristic consistency indicator, which results more predictive of plant diversity value than species richness alone.

15

Whereas further refinements are possible for adapting the procedure to finer detail and larger data, we believe that our approach, based on simple but sound information and well-known concepts and criteria, has the potential to become a standard methodology for spatially assessing and monitoring plant diversity in the coastal regions of Italy and across the Mediterranean Basin.

Acknowledgements

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This paper has been elaborated in the framework of the GREAT Med project (Generating a Risk and Ecological Analysis Toolkit for the Mediterranean) funded by the ENPI CBC Mediterranean Sea

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Basin Programme (Grant Agreement no. 39/2377).

We wish to thank all our partners, and particularly Arne Saatkamp, Aggeliki Doxa and Emilia Queller (IMBE, University of Aix-Marseille), for enlightening discussions.

U

We are grateful to Regione Autonoma della Sardegna-Assessorato della Difesa dell’Ambiente for

N

kindly providing the habitat maps of Natura 2000 sites.

A

Finally, we would like to thank two anonymous reviewers for their constructive comments and

ED

M

suggestions.

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Table 1 Species and habitat descriptors used for assessing biodiversity Species indicators Species richness (SR) Log(species richness)/Log(terrestrial surface) Richness in species of conservation Log(richness of species of interest)/Log(terrestrial interest (HCSR) surface) Floristic consistency (FC) Log(diagnostic species)/Log(terrestrial surface) Habitat indicators Diversity of natural and semi-natural Habitat diversity index on natural and semi-natural habitats (HD) habitats Relative cover of habitat of conservation Log(cover of habitat of conservation value (HCV) value)/Log(terrestrial surface)

A

N

U

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Table 2 Floristic and habitat measures for the individual study sites and overall study area PMC CSM PMC+CSM Floristic measures Total number of species 323 294 471 Number of species of high conservation interest 34 25 50 Number of habitat specific species 113 110 155 Habitat measures Types of natural and semi-natural habitats 12 13 Number of habitats of Community interest in each site 11 19 23 (92/43/ECC)

0.872 0.627

PT

0.606

HD

HCV

0.240 0.253 0.244

0.008 0.027 0.008 0.160

Overall diversity

plant

0.447 0.392 0.452 0.636 0.338

A

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SR HCSR FC HD HCV

FC

ED

HCSR

M

Table 3 Correlation among indicators. The values in the cells correspond to Kendall’s tau-b coefficients. Values in bold are statistically significant for p<0.01.

26

FIGURE CAPTIONS Fig.1 Location of the two study sites (in dark grey) within the Gulf of Cagliari (southern Sardinia, Italy). The box shows the position of key places named in the text and the 1 km×1 km spatial grid that was analysed for plant diversity descriptors (59 cells). The area in light grey indicates the coastal zone

delimited

by

the

2006

Regional

Landscape

Plan

of

Sardinia

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(http://webgis2.regione.sardegna.it/catalogodati/card.jsp?uuid=R_SARDEG:KEODD). Fig.2 Map of ecological land units in the two study sites. The ecological land classification approach

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was used to ensure the ecologically meaningful delimitation of sites

Fig.3 Spatial patterns of floristic indicators (a), habitat indicators (b) and overall plant diversity value (c) in the two study sites. The original scores of each indicator are ranked by natural breaks

U

classification in three classes (as in the legend under each indicator), which for comparison purposes

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can be considered as low, medium and high (as in the legend on the bottom right corner)

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Wu, R., Long, Y., Malanson, G. P., Garber, P. A., Zhang, S., Li, D., … Duo, H. (2014). Optimized spatial priorities for biodiversity conservation in China: A systematic conservation planning perspective. PLoS ONE, 9(7), 1–11. http://doi.org/10.1371/journal.pone.0103783

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