e-Services

The available services are:

• • Atlas of Phytoplankton: a guide for the identification of marine and freshwater species. It includes pictures, synonyms, morphological, morphometrical and ecological characters and geographical distribution of the taxa. It also provides formulas to calculate the biovolume and surface area based on linear dimensions according to the organism view (e.g., lateral, frontal, etc.).
  • Atlas of Shapes: a catalogue of geometrical models for calculating biovolume and surface area of phytoplankton taxa. It includes illustrative schemes of simple and complex shapes.
  • Thesauri: two complementary semantic resources, the Traits Thesaurus and the Phytoplankton VRE Thesaurus, designed to harmonize data related to phytoplankton ecology and traits.
  • Workflow 1: Integration of existing computational services within the Phytoplankton VRE.
    This analytical workflow combines and harmonises six core services available in PhytoVRE: Traits Computation, Size Class Distribution, Size–Density Relationships, Data Filtering, Community Matrix and Analysis, and Community Indices.
  • Workflow 2: Analysis of phytoplankton responses to environmental change.
    This workflow examines phytoplankton variation in relation to abiotic environmental parameters using both traditional and trait-based approaches.
  • Workflow 3: Interspecific coexistence and niche partitioning in phytoplankton.
    This workflow investigates the mechanisms underlying species coexistence and the partitioning of ecological niches within phytoplankton communities.
  • Workflow 4: The role of rare phytoplankton species in community structure.
    This workflow focuses on the ecological significance of rare species in phytoplankton communities and their contribution to community structure.
  • Workflow 5: Phytoplankton community dynamics across time and space.
    This workflow analyses temporal and spatial patterns in phytoplankton community composition, abundance, and diversity.
  • Workflow 6: Forecasting future phytoplankton dynamics using predictive models.
    This workflow applies statistical, mechanistic, and machine-learning models to predict future phytoplankton community dynamics under different future scenarios.
  • Additional Services
    These are indipendent e-services supporting workflow analysis and can be integrated into workflows.