Biodiversity Monitoring in Sites of Community Importance (Monitoraggio della Biodiversità nei Siti di Importanza Comunitaria - MoBiSIC) 

This project was performed throughout the whole set of Protected Areas (PAs) of the province of Siena (Tuscany, Italy), including Nature Reserves (NRs, designated under national or regional regulations) and SCIs (Sites of Community Importance). The size of these PAs ranges from 0.4 (Lago di Montepulciano) to 137.47 km2 (Montagnola Senese), while their elevations ranges from 101 to 1.685 m a.s.l.. The cumulative area of the PAs is 596.32 km2 (i.e. 16% of the whole province). A probabilistic sampling procedure is proposed and tested for quantifying and monitoring plant species diversity within the network of protected areas of the Siena Province, Italy.

The sampling design applied for this investigation was based on a probabilistic design in order to gather information which are representative of the plant species composition for the whole network of protected areas. In particular, this sampling design is based a grid of adjacent square non-overlapping cells of 1 km x 1 km, with a random point located within each of these cells (sampling point). Basically, this is unaligned systematic sampling (EPA 2002, Fattorini et al. 2006), which ensures a nominal sampling density of 1 sample point per 1 km2 of surface area of PA (PhD Thesis, Landi S. 2013).

In each of these points, once located through a high precision GPS, a sampling unit made of a square with sides of 10 m (plot) was centred at the sampling point. The entire plot, whose surface is 100 m2, was divided into 4 quadrants with sides of length 5 m, each of which was then divided into 4 sectors, each with sides of length 2.5 m. Each quadrant was identified by a specific orientation (NW, NE, SE and SW), while the sectors were numbered from 1 to 16. The field data collection was performed field from April to June, during the years 2005-2009. The total number of sampled plots was 604 points (PhD Thesis, Landi S. 2013).

Information on coverage (%) of the three main layers of vegetation (trees, shrubs and grasses), topography (exposure and slope), habitat conditions and the time needed to sample were recorded for each plot. Then, the presence of all species (or subspecies) of vascular plants was recorded within each of the 16 sectors. Specimens were collected for species which were not directly identifiable in the field. Field survey took place in late spring-early summer, therefore some autumnal species that flower in late winter-early spring and then disappear could have been overlooked (PhD Thesis, Landi S. 2013).

This sample covering with the same sampling density all the protected areas present in the province represent one of the few cases, possibly a unique one, in Europe. This data set has already been used for several analyses but offer many other possibilities for macroecological and biogeographical tests.

Abstracts of some published articles (in numerical order) are shown below, developed using data obtained from the MoBiSIC project:

1) Assessing the effects of the spatial components on species diversity in a network of protected areas represents an important step for assessing its conservation ‘‘capacity’’. A clear evaluation on how α- β-, and γ-diversity are partitioned among and within spatial scales can help to drive manager decisions and provide method for monitoring species diversity. Moving from these concepts, a probabilistic sample of plant species composition was here applied for quantifying plant species diversity within the Sites of Community Importance (SCIs) of the Natura 2000 network in the Siena Province. All analyses were performed separately for all species and those species defined as ‘‘focal’’ (included in regional, national or continental ‘‘red’’ lists). The results indicated that species richness of the SCIs differed from one location to another one independently from the sampling efforts. Diversity partitioning indicated that most of the flora diversity within the network was given by larger-scale β-diversity, i.e. the differences in species composition among SCIs. β-diversity was then decomposed in two components: β Area (due to the differences in area among SCIs) and β Replacement (due to the compositional differences across SCIs). β Area was particularly important for all species, while β Replacement was the most important factor for focal species. The consequent implications for monitoring and nature conservation strategies are discussed (Chiarucci et al. 2008 a).

2) Even if the establishment of nature reserves is to date a reality and the increase of protected areas is going to grow year after year, monitoring programs aiming to assess the effectiveness of the established protected areas for biodiversity conservation are still needed. That is the case for the Natura 2000 network in Europe, for which monitoring methods and programs are not yet well-established. A probabilistic sampling procedure is proposed and tested for quantifying and monitoring plant species diversity within a local network of protected areas, namely the Natura 2000 network in the Siena Province, Italy. On the basis of a sampling strategy of one 100 m plot randomly located in each 1 km × 1 km cell, four Sites of Community Importance (SCIs) were investigated in 2005. The gradients in species composition at the plot scale were largely related to elevation and forest cover. The species richness values of the four SCIs were compared by means of sample-based rarefaction curves. Then, additive partitioning of species richness was applied to determine the most important spatial components in determining the total species richness of the network. Compositional differences among the plots within each SCI were the most responsible of the total species richness. These methodologies can be adopted for assessing plant species richness within a large region or within a reserve network and, if combined with additive partitioning, they can be used as a set of large scale indicators of species diversity (Chiarucci et al. 2008 b).

3) Additive partition of diversity as standard for the selection of protected areas in the natural reserves network of the Siena province (Italy). Estimating the effects of the spatial components on species diversity represents an important step to establish the conservation “capacity” in a network of protected areas. A clear evaluation on how α, β, and γ diversity are partitioned among and within spatial (and temporal) scales can help us to drive managing decisions and provide methods for monitoring species diversity. Moving from these concepts, the probabilistic sample of MoBiSIC was applied for quantifying plant species diversity at different spatial scales within the network of protected areas existing in the Siena Province (Central Italy), and to evaluate the effects on the species diversity of the whole network due to the addition of two new protected areas (Ripa d’Orcia and Bogatto). Focusing on both common species and those defined as “focal” (included in regional, national or continental “red” lists), our results indicated that species richness of the protected areas differed each other independently from the sampling efforts. Diversity partitioning indicated that most of the diversity within the network is due to larger-scale β-diversity, i.e., the differences in species composition among reserves. Moreover, total β-diversity was decomposed into two components: β area (due to the differences in area among reserves) and β replacement (due to the compositional differences across protected areas). β area was particularly important for all species, while β replacement was the most important factor for the set of focal species. Noteworthy, the analyzed variation in diversity components due to the inclusion of the two new natural reserves into the network (Ripa d’Orcia and Bogatto) resulted in a proportional increase in β area for the whole network, and, on the other hand, in a reduction for β replacement diversity component. Based on these quantitative results, criteria for the selection and the inclusion of protected areas in existing networks of natural reserves (such as the Natura 2000 Network) can be achieved (Marcantonio et al. 2010).

4) The Floristic Quality Analysis (FQA) is a method to assess the quality of a flora based on the assignment of scores to plant species and subsequent calculation of indices. This method is widely applied, but inadequate investigation has been devoted to test its potential problems due to human factors. This work is aimed to specifically test how the human factor can affect the calculation of the FQA indices, by addressing three questions: (i) Are the scores given to plant species consistent among different experts?; (ii) Are the floristic quality indices calculated by different experts consistent in ordering individual sites?; and (iii) Does the use of an appropriate statistics change the ordering of individual sites? To answer these questions, a list of species obtained in 136 plots in central Italy was submitted to nine experts, who scored each species. The FQA indices were then calculated from the scores of each of the experts. The results showed that: (i) the scores given to the species by the experts were not consistent and the derived floristic quality indices were statistically different; (ii) the floristic quality indices calculated for each plot were significantly different among experts, but the ranking of these plots based on their floristic quality was rather consistent; and (iii) the use of ordinal statistics, which is more adequate for this type of data, did not change the results. This study demonstrated that the Floristic Quality Analysis does not provide reliable and objective tools to assess the quality of the flora in a human-managed ecosystem. The application of these indices should be preceded with resolution of the methodological problems associated with the use of inappropriate statistics, and by procedures to reduce the degree of subjectivity in assigning the CC scores (Landi e Chiarucci, 2010).

5) Despite the widely recognised importance of reserve networks, their effectiveness in encompassing and maintaining biodiversity is still debated. Species diversity is one of the most affordable measures of biodiversity, but it is difficult to survey such data over large scales. This research aimed to perform a sample-based assessment of species richness of groups of plants with different conservation value (alien species, protected species, and all species) within a reserve network, testing the use of partitioning as a tool for assessing diversity at different spatial scales, from the plot to the entire network. Plant diversity patterns differed for the groups of species for most of the investigated spatial scales. Despite these patterns assumed divergent tendencies when different species groups were considered, most of the species richness within the network was given by larger scale b-diversity for both alien and protected species, as well for all species. Diversity partitioning proved an effective tool to quantify the role of spatial scales in structuring the total species richness of the network, and is helpful in planning reserve networks (Chiarucci et al. 2012).

6) As a consequence of multiple cycles of deforestation and reforestation, most forest landscapes in Europe consist of a complex mosaic of patches of different successional ages. Despite the biogeographical distinctiveness of the Mediterranean region, studies on the effects of forest age on plant species diversity and composition are almost lacking for this area. This paper evaluates the influence of forest successional age on plant species richness and composition in various forest types of Mediterranean Italy (Amici et al. 2013).

7) Since landscape attributes show different patterns at different spatial extents, it is fundamental to identify how the relation between landscape structure and plant species diversity at local scale varies with scale. Then, it is fundamental to assess the appropriate extent at which landscape factors affect plant species richness at the local scale. To investigate this relation, data on plant species richness of forest communities at plot scale were extracted from a large data set and landscape metrics were calculated around the same plots for a range of extents (250–3000 m). Then, multiple regression models and variance partitioning techniques were applied to assess the amount of variance explained by the landscape metrics on plant species richness for a range of extents. In general, we found that increasing extent of the surrounding landscape analyzed, improved the strength of relationship between the landscape metrics and the properties of plant communities at plot scale. The medium-large extent was most informative as it combined a decent total variance explained with high variance explained by the pure fractions of complexity, fragmentation and disturbance and the minimum of collinearity. In conclusion, we found that it is possible and beneficial to identify a specific extent, where the redundancy in the predictor variables is minimized and the explanatory power of the pure fractions (or single groups) maximized, when examining landscape structure effects on local plant species richness (Amici et al. 2015).

8) Changes in land use are among the forces shaping Earth’s surface. In many industrialized areas, the loss of a traditional state of dynamic equilibrium between traditional management and natural dynamics is followed by abandonment to regeneration processes. This can reduce ecological complexity at the landscape scale and negatively affect biodiversity patterns. In this study, we investigate the relation between land use change and plant species diversity in the network of protected areas (PAs) of the province of Siena (Tuscany, Central Italy). This is an area characterized by long-lasting human activities and highly renowned cultural landscapes. We used remotely sensed, mapping and ground based plant compositional data, to investigate the present pattern of plant species diversity, the changes of landscape structure and changes in forest habitats. Most of the plant diversity present in this network of PAs is due to broad scale gradients due to ecological diversity but also to human management. Most of the area is currently covered by forests and analysis of a historical sequence of spatial data reveals that this is largely a consequence of the abandonment of traditional management during the last decades. Finally, focusing on forest succession as a consequence of land use change, we demonstrate that species richness significantly declines with increasing age of forest stands. Taken together, our results confirm that the recent trends of rural abandonment are leading to homogenization and biodiversity loss in traditional landscapes of Mediterranean Europe. We discuss implications for policy, and suggest that PA management in cultural and historical landscapes should pay increasing attention traditional anthropic practices (Amici et al. 2015).

9) The identification of shape and size of sampling units that maximizes the number of plant species recorded in multiscale sampling designs has major implications in conservation planning and monitoring actions. In this paper we tested the effect of three sampling shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded species. We used a large dataset derived from the network of protected areas in the Siena Province, Italy. This dataset is composed of plant species occurrence data recorded from 604 plots (10 m × 10 m), each divided in a grid of 16 contiguous subplot units (2.5 m × 2.5 m). Moreover, we evaluated the effect of plot orientation along the main environmental gradient, to examine how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. A significantly higher species richness was recorded by the random arrangement of 4 subplots within each plot in comparison to the ‘rectangle’ and ‘square’ shapes. Although the rectangular shape captured a significant larger number of species than squared ones, plot orientation along the main environmental gradient did not show a systematic effect on the number of recorded species. We concluded that the choice of whether or not using elongated (rectangular) versus squared plots should dependent upon the objectives of the specific survey with squared plots being more suitable for assessing species composition of more homogeneous vegetation units and rectangular plots being more suited for recording more species in the pooled sample of a large area (Bacaro et al. 2015).




1) Chiarucci A., Bacaro G., Rocchini D. 2008 a. Quantifying plant species diversity in a Natura 2000 network: Old ideas and new proposals. Biological Conservation 141:2608-2618.

2) Chiarucci A., Bacaro G., Vannini A., Rocchini D. 2008 b. Quantifying species richness at multiple spatial scales in a Natura 2000 network. Community Ecology 9 (2): 185-192.

3) Marcantonio M., Bacaro G., Filibeck G., Scoppola A., Nonis D., Gasparini P., Rocchini D., Santi E., Landi S., Maccherini S., Chiarucci A. 2010. Partizione additiva della diversità come criterio per la selezione di aree protette: un esempio per la rete di riserve naturali della Provincia di Siena. Forest@ 7: 28-43 [online: 2010-04-01] URL:

4) Landi S., Chiarucci S. 2010. Is floristic quality assessment reliable in human-managed ecosystems? Systematics and Biodiversity, 8: 269–28

5) Chiarucci A., Bacaro G. , Filibeck G., Landi S., Maccherini S., Scoppola A. Scale dependence of plant species richness in a network of protected areas. Biodiversity and  Conservation (2012) 21:503-516. DOI 10.1007/s10531-011-0196-8

6) Amici V., Santi E., Filibeck G., Diekmann M., Geri F., Landi S., Scoppola A., Chiarucci A. 2013. Influence of secondary forest succession on plant diversity patterns in a Mediterranean landscape. Journal of Biogeography 40: 2335-2347.

7)  Amici V., Rocchini D., Filibeck G., Bacaro G., Santi E., Geri F., Landi S., Scoppola A., Chiarucci A. 2015. Landscape structure effects on forest plant diversity at local scale: Exploring the role of spatial extent. Ecological Complexity 21: 44-52.

8) Amici V., Landi S., Frascaroli F., Rocchini D., Santi E., Chiarucci A. 2015. Anthropogenic drivers of plant diversity: perspective on land use change in a dynamic cultural landscape. Biodiversity and Conservation 24 (13): 3185-3199.

9) Bacaro G., Rocchini D., Diekmann M., Gasparini P., Gioria M., Maccherini S., Marcantonio M., Tordoni E., Amici V., Landi S., Torri D., Castello M., Altobelli A., Chiarucci A. 2015. Shape matters in sampling plant diversity: Evidence from the field. Ecological Complexity 24: 37-45.


Other references

EPA 2000. Guidance for choosing a sampling design for environmental data collection. EPA QA/G-5S, Washington, D.C.: US Environmental Protection Agency 1-155.

Fattorini L., Marcheselli M., Pisani C. 2006. A three-phase sampling strategy for large-scale multiresource forest inventories. Journal of Agricultural, Biological, and Environmental Statistics 11: 296-316.