1. CIBIO/InBIOIICT Miguel Porto, Pedro Beja, Rui Figueira

2.  Ecological systems are highly intricate networks: every species may be related, in different ways, to every other species  Much of the knowledge on ecological networks is at the conceptual level, not at the factual level changes in the abundance of any one species may affect, directly or indirectly, N other species

3.  Biodiversity data has traditionally been based on species occurrences  Biodiversity databases, as a norm, fail to document relations  Ecological relations, like species, have a spatial and temporal dimension, i.e. they occur

4. Why not store relationship occurrences rather than species occurrences? (the former includes the latter, anyway) Cytinus ruber parasitizing rockrose at 38.7654 ºN 7.9866 ºW it might look like a detail but it makes a huge difference in the amount of fundamental ecological information that is recorded

5.  An infrastructure to store and manage occurrences of ecological relations that:  is connected bidirectionally to existing species occurrence databases  strictly follows the data standards for existing types of data (e.g. DarwinCore for species occurrence data)  proposes new standards for describing ecological/ biological relationship data  provides an array of relationship-based web services to allow interoperability with existing platforms

6.  Raw data: published relationship data comes in an immense array of formats and with varying levels of detail and aggregation ▪ Orobanche gracilis parasitizing Retama sphaerocarpa ▪ Blackbird feeding on the fruits of ivy in Portugal ▪ Fish of genus Barbus feeding on filamentous algae in Tagus river, in Spring ▪ Beetle pollinating an unidentified red flower at 38.4532 ºN 8.2456 ºW in May-2007 data model able to accommodate all kinds of raw data without loss of information or generalization

7.  Computational resources: for example, a small country like Portugal may have ca. 40000 species which can all potentially interact  relations are directional and may be of several types and subtypes: ▪ Feeding on ▪ Parasitizing ▪ Dispersing ▪ Pollinating ▪ Co-occurring ▪...  … and relations may have different weights/strengths (e.g. species A is more frequently found feeding on species B than on C)  … and occurr at different places and dates  … and pertain to different body parts

8.  Computational resources  The network easily attains great complexity because ▪ different data facets are covered – taxonomy, morphology, ontology,... ▪ data is highly structured in each facet ▪ relationship occurrences are stored – not “conceptual” relationships – which leads to large amounts of data accumulating over time  but it needs to be efficiently traversed and summarized in an intelligible and meaningful way

9. To build a virtual lab infrastructure for  storing ecological relationship data  conducting network-based analyses  testing ecological network-based hypotheses aim

10.  Data is either compiled from published studies or from direct observation (citizen science platform)  Provides services and interfaces for querying, visualizing, summarizing and analyzing the network

11.  Highly flexible as to the nature of underlying data:  relations may be solely “conceptual” without further details, as obtained from classical bibliography (e.g. species A parasitizes species B), but this is far from ideal  relations may have precise geographical coordinates and timestamp  relations may connect any two entities of any taxonomical rank (e.g. species, genus, family, order...)  relations may connect entities which are not necessarily taxonomic (e.g. arbitrary trait-based entities)  relations may refer to precise organs, structures or life stages (e.g. caterpillar feeding on the leaves of species A)

12.  A virtual lab infrastructure for conducting ecological network-based analyses  Analyze the spatial patterns of relations and their relationship with environmental drivers, and predict network-level changes upon environmental change  Infer functional relationship patterns from documented relationship occurrences  Predict the n-th order impacts of removing nodes (e.g. species) in the integrity of ecological networks  Test the ecological significance of observed relationship patterns using simulated random networks