Staff

Emanuele Giacomuzzo

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Emanuele Giacomuzzo

PhD student

Department Aquatic Ecology

About Me

I am a PhD student in the Altermatt lab, where I study how flows of non-living resources connect ecosystems. Ecosystems are intrinsically linked by such flows; for example, leaves falling from forest trees into streams, bears transporting fish remains deep into forests, or wind carrying nutrients across the Atlantic to link the Sahara Desert with the Amazon rainforest. Despite the ubiquity of these connections, how the size of ecosystems influences these resource flows remains poorly understood.


Take, for instance, leaves falling from forests into streams. Does the size of the forest determine the type of leaves that fall? And do streams of different sizes respond differently to these inputs? Answering such questions in natural environments is challenging due to the many confounding variables.

To address this, my PhD focuses on disentangling these complexities through controlled laboratory experiments. I use model protist communities to simulate ecosystems that exchange non-living resources, enabling me to investigate how ecosystem size influences resource flows and their effects in a simplified yet meaningful way (proof-of-concept).

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Curriculum Vitae

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Publications

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Food web aggregation: effects on key positions

Food webs are often simulated dynamically to explore how trophic interactions influence resource and consumer abundances. As large trophic networks cannot be simulated in their original size – it would be too computationally expansive – they are shrunk by aggregating species together. However, key species may get lumped during this process, masking their unique role in their ecosystem. Therefore, a more systematic understanding of the aggregation effects on key positions is needed. Here, we study how six aggregation methods change 24 importance indices used to find key species in food webs. Our work was carried out on 76 aquatic food webs from the Ecopath with Ecosim database (EcoBase). The aggregation methods we considered were: 1) hierarchical clustering with the Jaccard index; 2) hierarchical clustering with the REGE index; 3) clustering within classic food web modules, which we refer to as 'density-based' modules; 4) clustering within 'predator-based modules' in which species fed on the same preys; 5) clustering within 'prey-based modules' in which species are fed upon by the same predators; and 6) clustering within 'groups' in which species share the same probability to interact with other groups. Hierarchical clustering with the REGE index produced the best results. Therefore, we recommend using it if we were interested in maintaining the identity of key species. The other algorithms could also be used to study specific network processes. However, we need to consider the bias they produce when masking important species.
Giacomuzzo, E.; Jordán, F. (2021) Food web aggregation: effects on key positions, Oikos, 130(12), 2170-2181, doi:10.1111/oik.08541, Institutional Repository

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Address

E-Mail: emanuele.giacomuzzo@eawag.ch
Phone: +41 58 765 6854
Fax: +41 58 765 5802
Address: Eawag
Überlandstrasse 133
8600 Dübendorf
Office: BU G09
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Research Group

Altermatt Lab

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