A probabilistic approach for plant diversity monitoring in a European Natura 2000 network

1. A probabilistic approach for plant diversity monitoring in a European Natura2000 network Alessandro Chiarucci, Giovanni Bacaro, Duccio Rocchini* Department of Environmental Science “G. Sarfatti” University of Siena, Italy *TerraData Environmetrics, www.terradata.it

2. Nature Reserve Networks A high number of protected areas is spread in European countries, often arranged in territorial networks. In Europe, the Natura 2000 is the most important network pf protected areas and it is supposed to preserve almost all the terrestrial species and habitats. The following specific objective has been set: “to achieve by 2010 a significant reduction of the current rate of biodiversity loss at the global, regional and national level". To understand if the existing reserve networks can achieveme of this objective we need to quantify in an affordable way how much biodiversity is present within it and how it is changing through time!

3. Plant species diversity Vascular plants are the most important component of most terrestrial ecosystems because of their functional and structural role. The quantification and the monitoring of plant species diversity represents thus essential steps for the management of protected areas and for understanding how these are changing through time. With this project we aimed to develop and test a method for evaluating and monitoring plant species diversity within a territorial Natura2000 Network, based on a sample approach. This method is based on a constrained sampling effort and a high potential for spatial inference.

4. Assessment of plant species diversity Assessing and monitoring plant species diversity over very large and fragmented areas is a really difficult task. Large databases are presently available but they present many problems for spatial and temporal inference. Some national and local monitoring programs use remote sensing, mapping or structural indicators. Taxonomic information is still an essential data source for describing and monitoring, biodiversity, at least until the potential use of other indicators will be clearer. However, it is virtually impossible to get complete lists of species for large areas. It is also very difficult to use the records when they are collected on non-homogeneous criteria.

5. Study Area As study area we used the network of SCIs present in the province of Siena, Tuscany. Descriptive data: ► 20 SCIs ► ≈ 593 km2 ► 5 - 137 km2 each ► Altitudinal range: 65 -1685

6. Existing Networks The whole country was divided into cells of 1x1 km I.N.F.C. In each cell a random point was selected Data were collected by using a three-stage sampling design

7. Sampling design In each point vascular plants were sampled by using a 10x10 m plot, divided into 16 subplots. In preliminary tests, this plot size was found to represent the best compromise between local species richness and sampling accuracy.

8. Data Collection Points were localised with a high precision GPS system and the spatial data then submitted to differential correction. Each plot was sampled by a team made by at two experienced botanists and herbarium specimens were collected.

9. Data storage Floristic data were stored on a web-based relational database that guarantees the preservation of the data and their easy access to all the authorised users, for both research and management aims.

10. SCIs in Siena Province SCIs sampled in 2006 SCIs sampled in 2005 Sampling Summary

11. Data Summary

12. 2005 Results

13. 2005 Results

14. Inventory Diversity Differentiation diversity Scale of sampling Alpha1 Beta1 (within stands) Subplots Alpha2 Beta2 (among stands) Plots Alpha3 Beta3 among sites SCI Alpha4 Network Additive partitioning of diversity To test the significance of alfai and betai components, samples at level i-1 were randomly allocated among those samples at level i that belong to the same sample unit at i+1.

15. 100% 90% 80% 70% 60% 50% Proportion of species diversity 40% 30% 20% 10% 0% Obs Ran Obs Ran Obs Ran Obs Ran AMIA LUCC PIGE RIPA 2005 Results Alfa 1 Beta 1 Beta 2

16. α4= 362 sp. α3= 137.8 sp. α2= 25.5 sp. 2005 Results β3 = 224.2 sp. β2 = 112.3 sp.

17. 2005 Results

18. Conclusions • Species composition data collected by a probabilistic sample were useful in evaluating the partitioning of species diversity in a network of protected areas and gave useful insights for its future monitoring. • Species diversity is largely due to large scale variation, both at the within-site and the among sites levels. • The monitoring of plant species diversity in a network of protected areas should be performed by using a high number of plots, rather using few larger sites. • To provide spatial and temporal inference of plant species diversity, a limited number of plots selected with a probabilistic approach should be preferred over larger databases of preferentially selected plots. • Additive partitioning of species diversity combined with rarefaction curves can provide a useful method to quantify and monitoring plant species diversity.

19. We acknowledge all the students and colleagues that contributed to the project: Andrea Billi, Arianna Vannini, Elisa Baragatti, Elisa Santi, Fernando Cortés Selva, Francesco Geri, Giulia Bennati, Lia Pignotti, Mauro Taormina, Patrizia Mosca, Sara Ghisleni.