Easing the accessibility of Ecological Networks through in-silico approaches from open data

Ecological networks are complex systems that allow us to model the numerous interactions of each entity within an ecosystem. However, the quality and quantity of input data remain a common problem despite the possible interactions. This is mainly due to the data collection, often recorded directly by means of direct observation or, more recently, environmental DNA sampling. The intense effort, both in time and workforce, severely hinders the collection and availability of such ecological networks in the scientific literature, especially when compared with more established techniques, when focusing on specific habitats, locations, and network accessibility in open repositories. This thesis addresses these issues, seeking to facilitate and speed up the data collection process, especially for preliminary studies on a regional and urban scale, with two main topics: the simplification of food webs and the in-silico construction and analysis of potential ecological networks. At the same time, the easier accessibility of data and analysis was also the goal. Results suggest that pilot studies may be speeded up with an aprioristic taxonomic aggregation, especially when the objective is the food web architecture measured with topological indices at the node level. Metawebs built completely in-silico with open data may be a starting point to standardise and compare the performance of a real-world ecological network and point to specific biological entities whose importance can be glimpsed at the regional scale. This thesis explores the effect of taxonomical simplification and how a fully in-silico ecological network can be constructed and processed without fieldwork. The main findings of this project are that a certain degree of simplification can heavily influence the timing of topological studies of ecological networks and that an in-silico regional metaweb, acting as a potential ecological network, can summarise and be consistent with the actual state of the ecosystem. Both approaches rely on the collaboration of people and FAIR principles. New ways of facilitating and increasing the accessibility of ecological network data cannot be created out of thin air, but rely on the already established "level of openness" of the sector. Citizen science projects, global open repositories and golden open-access research are once again confirmed as impact ful resources for new models. This thesis also contributes to the open community data and enlightens the ecological network field, acting as positive feedback on the importance of open data.