Determining the long-term changes in biodiversity and provisioning services along a transect from Central Europe to the Mediterranean

Climate, land use and fire are strong determinants of plant diversity, potentially resulting in local extinctions, including rare endemic and economically valuable species. While climate and land use are decisive for vegetation composition and thus the species pool, fire disturbance can lead to landscape fragmentation, affecting the provisioning of important ecosystem services such as timber and raw natural resources. We use multi-proxy palaeoecological data with high taxonomic and temporal resolution across an environmental gradient to assess the long-term impact of major climate shifts, land use and fire disturbance on past vegetation openness and plant diversity (evenness and richness). Evenness of taxa is inferred by calculating the probability of interspecific encounter (PIE) of pollen and spores and species richness by palynological richness (PRI). To account for evenness distortions of PRI, we developed a new palaeodiversity measure, which is evenness-detrended palynological richness (DE-PRI). Reconstructed species richness increases from north to south regardless of time, mirroring the biodiversity increase across the gradient from temperate deciduous to subtropical evergreen vegetation. Climatic changes after the end of the last ice age contributed to biodiversity dynamics, usually by promoting species richness and evenness in response to warming. The data reveal that the promotion of diverse open-land ecosystems increased when human disturbance became determinant, while forests became less diverse. Our results imply that the today’s biodiversity has been shaped by anthropogenic forcing over the millennia. Future management strategies aiming at a successful conservation of biodiversity should therefore consider the millennia-lasting role of anthropogenic fire and human activities.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.