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Habitat richness, that is, the diversity of ecosystem types, is a complex, spatially explicit aspect of biodiversity, which is affected by bioclimatic, geographic, and anthropogenic variables. The distribution of habitat types is a key component for understanding broad‐scale biodiversity and for developing conservation strategies. We used data on the distribution of European Union (EU) habitats to answer the following questions: (i) how do bioclimatic, geographic, and anthropogenic variables affect habitat richness? (ii) Which of those factors is the most important? (iii) How do interactions among these variables influence habitat richness and which combinations produce the strongest interactions? The distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network were used to calculate habitat richness for the 10 km × 10 km EU grid map. We then investigated how environmental variables affect habitat richness, using generalized linear models, generalized additive models, and boosted regression trees. The main factors associated with habitat richness were geographic variables, with negative relationships observed for both latitude and longitude, and a positive relationship for terrain ruggedness. Bioclimatic variables played a secondary role, with habitat richness increasing slightly with annual mean temperature and overall annual precipitation. We also found an interaction between anthropogenic variables, with the combination of increased landscape fragmentation and increased population density strongly decreasing habitat richness. This is the first attempt to disentangle spatial patterns of habitat richness at the continental scale, as a key tool for protecting biodiversity. The number of European habitats is related to geography more than climate and human pressure, reflecting a major component of biogeographical patterns similar to the drivers observed at the species level. The interaction between anthropogenic variables highlights the need for coordinated, continental‐scale management plans for biodiversity conservation., We modeled EU habitat richness at continental scale as a function of geographic, climatic, and anthropogenic variables. We found geographical variables were by far the most strongly correlated with habitat richness, followed by climate. However, anthropogenic variables gained importance when considering their interactions, with important implications for conservation planning.

The article 17 of the 92/43/EEC Habitats Directive imposes to the Members States to produce periodic reports (every 6 years) on the conservation status of habitats and species at national level. In this context, in view of preparing the 4th National Report, the Italian National Institute for Environmental Protection and Research (ISPRA) drafted the national guidelines for habitats monitoring, focusing on data collection at site level. The intention of this paper is to provide an overview about the development, in Italy, on Habitat types monitoring methodologies, in particular detailing the current state-of-the-art for the parameter structure and functions, which is the most controversial among those required by the Habitats Directive for the assessment of conservation status. The paper concludes that, although habitat types monitoring programs could be carried out using individual species of fauna and/or flora, vegetation surveys represent the best choice as it allows to collect standardised and objective data, useful for the definition of more specific indicators, first of all the presence and abundance of typical species.

Aim: Ecoinformatics offer new opportunity to test islands as biogeographic and eco- logical models. In this paper we predicted three hypotheses: (1) plot-based data issu- ing from vegetation surveys can be used to predict Island Species-Area Relationships (ISARs) or island similarity; (2) the habitat area is an independent predictor of species richness patterns within island; (3) species richness and composition are more de- pendent on habitat type than island identity in land-bridge islands. Area: Tuscan Archipelago, Italy. Methods: We assembled a database of all the vegetation plots available for the archi- pelago. For the first hypothesis we calculated ISARs, using Arrhenius model, and Beta Diversity, using Jaccard dissimilarity, on both published floras and cumulative plot data. For the second hypothesis, we modelled Habitat Species-Area Relationships (HSARs), using Arrhenius model. For the third hypothesis, we used additive partition- ing of species richness, NMDS and PERMANOVA. Results: Island Species-Area Relationships based on plot data mirrored those on pub- lished floras, but absolute values of c and z parameters were different. Beta diversity based on plot data resembled those of published floras, but was higher. Species rich- ness was significantly related to the habitat area. The total species richness of the archipelago was linked to large scale drivers, such as island identity, while plot species composition was driven by both habitat type and island identity. Conclusions: Data assembled issuing from vegetation surveys are useful to describe biogeographic patterns. Species richness in the archipelago is driven by spatial fac- tors such as the amount of habitats and the differences among islands, while the spe- cies composition of local assemblages is largely driven by habitat filters rather than by island identity, as expected in land-bridge islands.