We examine spatially explicit models described by reaction-diffusion partial differential equations for the study of predator-prey population dynamics. The numerical methods we propose are based on the coupling of a finite difference/element spatial discretization and a suitable partitioned Runge-Kutta scheme for the approximation in time. The RK scheme here implemented uses an implicit scheme for the stiff diffusive term and a partitioned RK symplectic scheme for the reaction term (IMSP scheme). We revisit some results provided in literature for the classical Lotka-Volterra system and the Rosenzweig-MacArthur model. We then extend the approach to metapopulation dynamics in order to numerically investigate the effect of migration through a corridor connecting two habitat patches. Moreover, we analyze the synchronization properties of subpopulation dynamics, when the migration occurs through corridors of variable size.

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/ semi-automatic translation framework of LC/ LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed.

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth Observation (EO) data offers a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of Land Cover (LC) and/or Land Use (LU) classification systems. Yet, these are not as clearly relatable to biodiversity in comparison to habitat classifications, and provide less scope for monitoring. While a conversion from LC/LU classification to habitat classification can be of great utility, differences in definitions and criteria have so far limited the establishment of a unified approach for such translation between these two classification systems. Focusing on five Mediterranean NATURA 2000 sites, this paper considers the scope for three of the most commonly used global LC/LU taxonomies - CORINE Land Cover (CLC), the Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS) and the International Geosphere-Biosphere Programme (IGBP) to be translated to habitat taxonomies. Through both quantitative and expert knowledge based qualitative analysis of selected taxonomies, FAO-LCCS turns out to be the best candidate to cope with the complexity of habitat description and provides a framework for EO and in-situ data integration for habitat mapping, reducing uncertainties and class overlaps and bridging the gap between LC/LU and habitats domains for landscape monitoring - a major issue for conservation. This study also highlights the need to modify the FAO-LCCS hierarchical class description process to permit the addition of attributes based on class-specific expert knowledge to select multi-temporal (seasonal) EO data and improve classification. An application of LC/LU to habitat mapping is provided for a coastal Natura 2000 site with high classification accuracy as a result Key words: Mapping; land cover; land use; habitat; earth observation; taxonomies; Natura 2000; classification schemes

One of the core European Union environmental policies is the creation and monitoring of the Natura 2000 network of protected areas. This network has been explicitly established for the preservation of conservation priority habitat types and species. Still the concept of habitat is a key concept for ecologists that remains ill defined and is notoriously hard to quantify and measure. Several classification schemes have been put forward, but their relative strengths and weaknesses remain less well examined. In this study we analyzed 8 different Natura 2000 sites (3 Greek, 2 Italian, 2 Portuguese, 1 British). Our study sites reflect a variety of ecosystems, most of them are Mediterranean (7 of the 8) and most of them are wetlands (6 of the 8). In each site, we classified habitats according to 4 different classification schemes (Annex I of the Habitats Directive, Corine Biotopes, EUNIS and General Habitat categories). Also, we used three other widely used land cover classification schemes (namely Corine Land Cover, FAO Land Cover Classification System and IGBP DIS scheme). We found that the different schemes produced considerably different values of landscape diversity leading even to different ranking of the sites according to their diversity. Furthermore, when comparing the landscape composition among sites according to the different schemes, they led to different inferences. Our results imply that the classification scheme used for estimating habitat composition plays an important role for the monitoring of protected areas, perhaps more important than previously assumed.

At a global level, protected sites have been established for the primary purpose of conserving biodiversity, with survey and monitoring of habitats undertaken largely within their boundaries. However, because of increasing human populations with greater access to resources, there is a need to now consider monitoring anthropic activities in the surrounding landscapes as pressures and disturbances are impacting on the functioning and biodiversity values of many protected sites. Earth Observation (EO) data acquired across a range of spatial and temporal scales offer new opportunities for monitoring biodiversity over varying time-scales, either through direct or indirect mapping of species or habitats. However, Land Cover (LC) and/or Land Use (LU), rather than habitat maps are generated in many national and international programs and, whilst the translation from one classification to the other is desirable, differences in definitions and criteria have so far limited the establishment of a unified approach. Focusing on both natural and non-natural environments associated with Natura 2000 sites in the Mediterranean, this paper considers the extent to which three common LC/LU taxonomies (CORINE, the Food and Agricultural Organisation (FAO) Land Cover Classification System (FAO-LCCS) and the IGBP) can be translated to habitat taxonomies with minimum use of additional environmental attributes and/or in situ data. A qualitative and quantitative analysis based on the Jaccard's index established the FAOLCCS as being the most useful taxonomy for harmonizing LC/LU maps with different legends and dealing with the complexity of habitat description and as a framework for translating EO-derived LC/LU to habitat categories. As demonstration, a habitat map of a wetland site is obtained through translation of the LCCS taxonomy.

The assessment of the ecosystem state, in terms of the functionality of the soil and of the soil/vegetation interactions is a relevant step of the procedures for monitoring the impact of human activities and climate changes on landscapes. The focus is here on the connectivity of the landscape with respect to the flux of different elements like water, sediment and fauna. The deliverable is organized into two parts: a) the first one introduces a new landscape, plants, landslides and erosion model, which exploits the Landscape Function Analysis framework to deal with the hydrogeological connectivity. As a result of the proposed methodology we provide an example of scenario analysis, applied to a BIO_SOS study site; b) the second one deals with the connectivity related to fauna fluxes. We introduce a new mathematical model, built on the metapopulation dynamics approach, to study predator-prey populations dynamics. The study has been motivated by the conservation issues related to the wolf-wild boar pair populating the Alta Murgia BIO_SOS study site.

Protected areas are continuously subjected to ecological change due to anthropic pressures. Analyses of changes in the extent and intensity of pressures over time are essential for adaptive management, yet such analyses are rarely conceptualized or performed in a well-defined, standardized way, with a frequent lack of clarity in development, definition and measurement. Over-time remote sensing data has great potential for mapping spatial pattern of pressures and their impacts. Some pressures can be mapped directly (e.g. land use dynamics, some invasive species), whereas for others the nature, intensity and spatial pattern of impacts can be used to infer on pressures. This paper develops a framework within which remote sensing datasets in combination with GIS and ecological modeling may be used to identify potential pressure growth through either direct detection or indirect monitoring of impacts on landscapes, land cover/habitat types, communities and species through multi-temporal remote sensing image series. The use of this framework is illustrated through categorization of pressure-derived impacts on protected areas in six countries - Greece, Italy, Portugal, Wales, The Netherlands, and India - located in diverse biogeographic, environmental and social-ecological contexts, and facing a different range of pressures. The framework is conceptually robust, geographically invariant, scalable and spatially-explicit, connecting to the growing data sets from remote sources, and we urge it to be tested over a wide range of pressures and social-ecological settings.

Spatially explicit models consisting of reaction-diffusion partial differential equations are considered in order to model prey-predator interactions, since it is known that the role of spatial processes reveals of great interest in the study of the effects of habitat fragmentation on biodiversity. As almost all of the realistic models in biology, these models are nonlinear and their solution is not knwon is closed form. Our aim is approximating the solution itself by means of exponential Runge-Kutta integrators. Moreover, we apply the shift-and-invert Krylov approach in order to evaluate the entire functions needed for implementing the exponential method. This numerical procedure reveals to be very efficient in avoiding numerical instability during the simulation, since it allows us to adopt high order in the accuracy.