O4.3 Monitoring strategy for plants and habitats with sample size, number and distribution in considered regions; guidelines to design effective monitoring programmes March 2015 1
Monitoring of biodiversity in the GREAT Med project had the main objective to provide spatial data on plant species and habitats in the five selected study areas, for assessing plant diversity and its vulnerability to threats. The study areas are generally characterized by high levels of biodiversity, and share critical conditions with regard to marine traffic and increasing urbanization. Each area comprises at least 10 km of coastline, which can be continuous or separated in 2 to 4 study sites (Fig. 1), selected to be different in terms of land cover, environmental characteristics, and human pressure. Because of differences between partner countries in terms of data availability, we developed a methodological framework adapted to individual specificities. In cases where no pre-existing or complete data were available for the study areas, we conceived an adaptive sampling protocol for field data collection. The French partners, instead, had access to extensive floristic databases, which were statistically analyzed to derive reliable information at the monitoring scale. This report describes the different components of the monitoring strategy: Criteria used for selecting and delimiting study sites within the study areas Protocol used for gathering floristic and habitat data Final indicators used for quantifying plant diversity at the species and habitat levels And provides some conclusive guidelines stemming from our cross-border experience. Fig. 1 - The study region (or area) is the whole region examined (e.g. Gulf of Cagliari), and include the individual case studies or study sites. Plots are distributed within the study sites. 2
1. MONITORING STRATEGY 1.1 SITE SELECTION Study sites were selected by a stratified approach so to be representative of different environmental and ecological conditions. In particular, partners agreed to consider the following criteria: Sites should be representative of the range of climatic conditions, lithological types, and natural and seminatural habitats occurring in the study areas Sites should show different levels of urbanization (low, moderate and high) Sites should include also some protected areas, in order to compare/estimate the role of the protected areas for conservation issues. The French team combined these criteria with the results of completeness analysis (see following sections), and selected accordingly four study sites (Cote Bleue, Calanques, Cap Sicié, Cap Camarat) that correspond to groups of well-sampled cells in different scenarios of human pressure and environmental conditions. Fig. 2 Results of completeness analysis in the Provence region at 1 km spatial resolution. Red circles indicate the location of the 4 selected study sites. In Lebanon, Tunisia and Italy, selection was based on pre-existing knowledge of the site characteristics. The Italian team selected two study sites within the Gulf of Cagliari: the area of Chia_Santa Margherita, which is more natural along the coastline and urbanized mainly by tourist resort or second-houses, and the area of Poetto-Capo Sant Elia, just off the city of Cagliari and its port. 3
The study sites were delimited using ecological land classification, which aims at identifying land units that are relatively homogeneous in terms of climatic, lithological and geomorphological features as well as mature vegetation (Blasi et al, 2000; Blasi & Frondoni, 2007). We applied this procedure especially to map the inland border of the coastal area to be sampled, since existing delimitations were inadequate, considering the limited time and resources for field data collection. The environmental stratification approach provided an ecologically meaningful mapping of study sites, and was basically based on the selection of lithological types and landforms that are consistent with the coastal environment and influential on the distribution and types of coastal vegetation (for further details, see Annex II of O1.4 Annual internal report). The inland border of the Italian study area (Gulf of Cagliari), instead, coincides with the delimitation of the coastal area by the 2006 Regional Landscape Plan of Sardinia (http://www.sardegnaterritorio.it). This area is defined as the portion of land in close relationship with the sea, ranging from 300 meters from the shoreline to few kilometres inward (internal reliefs), and was designed by the Sardinia Region on the basis of the following criteria: Geomorphology and vegetation Habitats of Community Importance (Habitats Directive) Physical and functional connectivity among environmental systems Fig. 3 Delimitation of study area (in grey) and study sites (in orange) in the Gulf of Cagliari, Italy. The inland border of the study area was delimited according to the Regional Landscape Plan of Sardinia (2006), whereas study sites were delimited using environmental stratification based on lithology and landform. 4
In Lebanon, the high urbanization and disturbance levels made challenging the choice of study sites. Even in areas where some coastal vegetation exists, many cells only contain privately owned gardens, which are not accessible. Byblos is a well-protected archaeological site, which preserved a part of biodiversity in this area. This low human pressure sites is compared to a second site, much more altered, in the south part of Beirut. In Tunisia, the selection procedure resulted in three sites differing in human impacts with Djerba being highly anthropized, Skhira moderately impacted and Gabès little impacted. Fig. 4 Study sites within the study area of coastal Lebanon (above) and within the Gulf of Gabès in Tunisia (right). 5
1.2 DATA GATHERING HABITAT DATA Habitat data refer to the whole study area, and are derived from regional land cover maps or, in case of absence, from the Global Land Cover Map (2010) that was used also for calculating fragmentation indexes. For the habitats of high conservation interest, France and Italy used the reference list of the EEC92/43 Habitats Directive. Since there is not an equivalent list of habitats of transnational importance for non-european countries, the Tunisian and Lebanese teams produced a list of threatened, rare or biogeographically important habitats in their respective countries, based on expert judgement and national literature, and with a view to the European Directive list and underlying criteria. These latter consider: Habitats in danger of disappearance in their natural range (rare habitats); Habitats that have a small natural range following their regression or by reason of their intrinsically restricted area (rare habitats); Habitats that are particularly rich in endemic species or species of biogeographical interest Any type of habitats deemed to be of national interest by a team of experts (providing also reasons for them to be considered). FLORISTIC DATA Floristic data of interest refer to richness of species within each grid cell and richness of species of high conservation interest. These latter included species listed in the IUCN Global Red List, Annex II and IV of the European Habitats Directive 92/43, Annex I of the Bern Convention, CITES (Washington Convention); species listed in national and regional red lists or species of national/regional interest according to team of experts (providing also reasons for these species to be considered). Case 1: Dealing with extensive databases For the French study area, the IMBE team used two different floristic databases: The CBN-SILENE database (http://flore.silene.eu/), which was used for the estimation of biodiversity indicators (Activity 4.2) and for niche modelling (Activity 4.3) The IMBE SIV database, used for modelling based on neutral theory (Activity 4.3) SILENE (System of Information and Localisation of native and invasive species) is a georeferenced database compiled by the National Mediterranean Botanical Conservatory (CBNMed). It contains about 4.5 million records on plant species occurrences in the French Mediterranean region (approx. 300 km coastline), which are derived from different sources (herbaria specimen, field data from different campaigns, etc.). No specific protocol was used for gathering data, and spatial and taxonomic biases can be present, as usually happens in extensive databases. To account for these biases and be able to further use presence data for the estimation of biodiversity indicators, the IMBE team conducted a thorough statistical 6
analysis of sampling completeness (Soberón et al, 2007), which will be presented in details in a scientific paper (in preparation). Completeness analysis relies on the estimation of the theoretical total species richness per cell using non-parametric indices (Chao), which is then compared to the observed species richness per cell. This analysis aims to identify: (i) the spatial scale and specific areas at which data are reliable and give reliable aggregated information, and (ii) areas that are under-sampled, where future monitoring effort should be given. Results have been transferred to regional partners (CBNMed, DREAL) to help identify zones for complementary field sampling. The SIV floristic database is based on systematic sampling every 500 metres in the National Park of Calanques, and contains several hundred individual relevés, covering approximately 16 km of coastline and 5 km inland. Sampling design consisted in complete relevés of 100 m 2 plots using the standard Braun-Blanquet cover scale (Braun-Blanquet 1932). To complement the SIV dataset for missing information, IMBE carried out fieldwork in autumn 2014, using the same sampling design, and plans another campaign in spring 2015. Case 2: No pre-existing database In Italy, Tunisia and Lebanon there were not enough existing data on plant species occurrences, so partners had to carry out field surveys under a common sampling design. In particular, considering the time and resource constraints, floristic data were collected only within the study sites. The IMBE team, WP coordinator and activity leader together with Sapienza, suggested using the same sampling design of the SIV database, in order to have comparable data. They suggested sampling circular plots of 100 square meters, in a systematic design every 500 m, and to include information on the presence of all vascular plants in the plot and their relative abundance. The Sapienza team proposed to adjust the number of plots according to the internal heterogeneity of cells, so to have one plot for each habitat type (excluding, therefore, urban and agricultural areas). Likewise, they decided to use smaller plots or plots that were not circular, for instance for the vegetation of coastal cliffs, in case extending the sampled area implied variations in the environmental conditions. Therefore, when a significant heterogeneity existed inside the grid cell, as often is the case in coastal areas, we varied the number of plots accordingly and, if necessary, we reduced the size of plot or reshape them (Braun-Blanquet 1932; Géhu, 1986). Lately, IMBE proposed to refer to a spatial grid of 1*1 km, because in the French study area, cells of 500x500m (25 ha) does not ensure a sampling density of at least 1 plot in any site, and correspond to small diversity of land uses. Moreover, the 1x1km grid is probably more adapted to harmonize biodiversity indices with the hazard indices developed in WP5. The particularity of the coastal environment, with its internal spatial heterogeneity and its generally narrow extent, hardly support coarser cell size. 7
The proposed sampling procedure was shared with partners, and agreed by all. Therefore, the common protocol states that: Sampling is based on a spatial grid of 1 km X 1 km, for reasons of data harmonization and for lighten the observation effort while increasing the sampling area The ideal sampling density is 1 plot per habitat type within each grid cell. This results in at least one relevé per cell, if only one habitat is present, else as much as relevés as habitat types in the cell. Plot size is left to the choice of partners, according to the minimum area representative of uniform habitat conditions (minimal area concept, see Braun Blanquet 1932). Sampling: preliminary results The Italian case combined a small existing data set with on purpose field sampling. Italian partners built a specific floristic database, which contains all geo-referenced floristic data, data on species conservation value, endemicity, and exotic species based on literature (65 floristic records from published floras, 71 endemic species, 161 published relevés). Sampling consisted in complete relevés in 100m2 plots using the standard Braun-Blanquet cover scale. One relevés per habitat type per grid cell was performed, according to a systematically stratified sampling protocol. Field survey took place in spring 2014 (61 original relevés) and is going to be complemented in spring 2015. Fig. 4 Spatial data in the Italian study sites in the Gulf of Cagliari: green dots refer to 143 literature data (phytosociological releves), yellow dots indicate 61 plots sampled in 2014 Tunisian case: no pre- 8
existing data has been used or compiled in 2014. In spring 2015, each site will be sampled in a band of 3 (4) km x 1 km, with a regular grid of 500x500m2. One relevé of 128 m² per habitat type is performed within each grid cell. Field sampling will end in May 2015 and data will be digitally available in June 2015. The minimum number of relevés estimated amounts to 40. Lebanese case: no pre-existing database could be used. The sampling scheme used relevés in a 1x1 km grid, in a band of approximately 1.5 km from the coastline. The sampling was irregular: many segments of the coastline were physically closed to public, which explains the difficulty to realize a plot at regular distance within the 10 km, or even to select randomly the sites. The fieldwork made was an attempt to have at least 1 plot in each 1km*1km grid. Sampling started in 2014 (35 plots on 3x3m in the Byblos area) and will be completed in spring 2015. Each relevé consists of a complete species list with cover-abundance values according to van der Maarel 1979, and notes on habitat characteristics. Fig. 5 Location of sample plots (in yellow) along the 10 km coastline of the Byblos study site (Lebanon) 9
1.3. SELECTED INDICATORS On the basis of data availability and feasibility of field data collection in the timeframe of the project, and after discussions arisen from testing the indicators, it was finally agreed to consider the following floristic indicators: Species richness Richness of species with high conservation value Species richness is defined as the total number of species observed within each cell of the 1km² grid. In cases that several plots are conducted within a cell, a unique list per cell should thus be estimated. Please note that floristic indicators are no longer considered as a proportion on the total regional pool, as considered in the Marseille meeting. However, to account for the species/terrestrial area relationship and for sea cover within surveyed cells, we decided to consider the log of each species indicator out of the log of the terrestrial surface within the cell. As for the habitat indicators, we used: Diversity of natural and seminatural ecosystems, based on the Shannon Index Relative cover for habitats of high conservation value. The maps derived from calculation of all biodiversity indicators per grid cell cover only the study sites, since floristic data were discontinuous and not systematically sampled over the whole study areas. 10
2. GUIDELINES We benefited from the experiences gained from the exchanges between the four countries and feel to provide the following specific guidelines for monitoring programs: Keep the monitoring strategy simple, when working on broad scales and with study areas differently covered by existing data Few, well selected indices that can be easily understood and refer to easily quantifiable biological information makes the procedure replicable in space and time, and helpful both to scientists and stakeholders. Notwithstanding particularities between countries, and variability of data from one case study to another, common indices should be used for all study sites. Though it might be possible to refer to different cell size for different indicators, it is crucial to use the same grid size for the same indicator, for having comparable data. Data availability is a crucial issues. In case of existing databases, one should pay attention to internal biases due to scale and protocol, which can be inadequate to the purposes of the ongoing study. Literature suggest to perform sampling completeness analysis to evaluate reliability of data at the monitoring scale. In case no previous data exist, an adapted protocol should be defined, balancing between spatial details and sampling extent, according to the goals of the project and the timeframe and resources available. To best reflect diversity in highly heterogeneous environments as coastal areas, we suggest a sampling protocol which is stratified per habitat type within each cell of the reference spatial grid. We suggest to use the criterion of minimal area from plant sociology, which implies uniform habitat conditions, to define plot size. To identify the monitoring sites, it might help us considering the following criteria: Sites should be selected in a stratified manner, in order to adequately represent underlying environmental conditions in the study areas. Ideally sites should have comparable biodiversity levels but contrasting effects of examined processes, such as urbanization. In any case it is important to consider different levels of intensity of such processes (small, moderate and important). Ecological land classification can be very helpful in the selection and delimitation of sites ecologically significant at the monitoring scale. The proposed strategy can be applied to similar programs, when there is the need to identify areas that are important in a similar way, appropriate to available data and including poorly known areas. 11
REFERENCES Blasi C., Carranza M.L., Frondoni R., Rosati L., 2000. Ecosystems classification and mapping: a proposal for Italian landscapes. Applied Vegetation Science 3: 233-242. Blasi C., Frondoni R., 2011. Modern perspectives for plant sociology: the case of ecological land classification and the ecoregions of Italy. Plant Biosystems 145(suppl): 30-37. Braun-Blanquet J. 1932. Plant sociology. Translated and edited by Henry S. Conard and George D. Fuller. New York: McGraw-Hill. Géhu JM. 1986. Des complexes de groupements végétaux à la phytosociologie paysagère contemporaine. Informatore Botanico Italiano 18: 53-83. Soberón J., Jimenez R., Golubiv J., Koleff P., 2007. Assessing completeness of biodiversity databases at different spatial scales. Ecography 30 (1): 152-160. van der Maarel E., 1979. Transformation of cover-abundance values in phytosociology and its effects on community similarity. Vegetatio 3 (2): 97-114 Regione Autonoma della Sardegna [Autonomous Regional Administration of Sardinia], 2006. Piano Paesaggistico Regionale: Relazione Comitato Scientifico (Regional Landscape Plan: Report of the Scientific Committee), available at http://www.sardegnaterritorio.it 12