Aggregating a Plankton Food Web: Mathematical versus Biological Approaches

Abstract

Species are embedded in a web of intricate trophic interactions. Understanding the functional role of species in food webs is of fundamental interests. This is related to food web position, so positional similarity may provide information about functional overlap. Defining and quantifying similar trophic functioning can be addressed in different ways. We consider two approaches. One is of mathematical nature involving network analysis where unique species can be defined as those whose topological position is very different to others in the same food web. A species is unique if it has very different connection pattern compared to others. The second approach is of biological nature, based on trait-based aggregations. Unique species are not easy to aggregate with others because their traits are not in common with the ones of most others. Our goal here is to illustrate how mathematics can provide an alternative perspective on species aggregation, and how this is related to its biological counterpart. We illustrate these approaches using a toy food web and a real food web and demonstrate the sensitive relationships between those approaches. The trait-based aggregation focusing on the trait values of size (sv) can be best predicted by the mathematical aggregation algorithms.

Mesoscale network properties in ecological system models

“…In a more functional sense, similar food web positions (i.e. similar interaction neighborhood) or trait-based similarities may detect functional redundancy [42,61] which is more in line with the concept of degeneracy [65,66]. In this latter case, elements of different origin perform similar or overlapping functions and this may result in their replaceability.…”

“…One question is how to choose k nodes in such a way that most of the other n-k nodes are reachable (or fragmented). One of the challenges in applying these mesoscale approaches is how to standardize the aggregation of food webs (how to define graph nodes in ecological networks, see [61]), which is a highly context-dependent problem [56,57].…”

From metabarcoding time series to plankton food webs: The hidden role of trophic hierarchy in providing ecological resilience

“…Our results may have been influenced by the methodology used to derive co-occurrence networks (e.g., filtration of the less frequent taxa to avoid spurious correlations) and by the aggregation at genus level, as the aggregation criterion can affect the results obtained (Giacomuzzo & Jordán, 2021;Jordán et al, 2018). Nonetheless, we highlight that congeneric species often show similar (if not the same) trophic characteristics (body size and diets, among all) and the genus-aggregated metaB data allowed us studying at greater depth the potential inter-dependence of planktonic taxa showing significantly different body sizes and dietary behaviors.…”

“…Moreover, the plankton food web from the GoN was modular and such feature was driven by the body size and trophic habits of planktonic taxa rather than by their seasonality or habitat preference, as shown in other systems (Krause et al, 2003) and, for what concerns the time‐aggregation of taxa within specific seasons, in the GoN (Di Capua et al, 2021; Piredda et al, 2017; Ribera d'Alcalà et al, 2004). Our results may have been influenced by the methodology used to derive co‐occurrence networks (e.g., filtration of the less frequent taxa to avoid spurious correlations) and by the aggregation at genus level, as the aggregation criterion can affect the results obtained (Giacomuzzo & Jordán, 2021; Jordán et al, 2018). Nonetheless, we highlight that congeneric species often show similar (if not the same) trophic characteristics (body size and diets, among all) and the genus‐aggregated metaB data allowed us studying at greater depth the potential inter‐dependence of planktonic taxa showing significantly different body sizes and dietary behaviors.…”

Scale-free characteristics of the global marine food web

“…Additionally, the degree distribution strongly depends on network size 22 , so it is not independent of food web aggregation practices 23,24 . This means that interpretation of degree distribution patterns must consider whether graph nodes depict species or functional groups in trophic networks.…”