Mesoscale network properties in ecological system models

Abstract

Network models are among the most powerful tools in systems ecology. Since trophic relationships (i.e. who eats whom) are among the most frequent interspecific interactions, food webs serve well as system models. In order to better understand ecosystem dynamics, neither strictly local (focusing on individual species) nor strictly global (focusing on the whole ecosystem) approaches are adequate. This mesoscale view on network links suggests to quantify indirect interactions up to some reasonable range and a mesoscale view on network nodes suggests to identify a small set of nodes that are in the most important network positions. We present some examples taking this mesoscale view in ecosystem modelling and use these to discuss the mesoscale perspective. For systems-based conservation management, we suggest to focus on keystone species complexes that are determined considering their indirect interaction neighbourhood. This approach provides a systems-based alternative that hopefully increases to efficiency of future conservation efforts: a small set of system components are targeted in such a way that a large set of the remaining elements are benefited. Challenges Using systems models in ecology has quite a long history [1,2], supporting the view that ecology is essentially the science of coexistence among multiple players. Different kinds of interactions among organisms are the grist for the mill of network modelling: trophic networks describe carbon flows between producers and consumers [3], pollination networks represent inter-specific effects between plants and pollinators [4,5] and co-occurrence networks summarize statistically inferred interactions, typically between microbes [6]. In all of these networks, whatever is the definition of nodes (species, functional groups, OTUs) and links (predation, association), dependencies are represented, being either directional or mutual. If the network is wisely defined, it is a holistic model of a more or less "whole" system. A general strategy of systems approaches in biology is to cross levels of hierarchical organization (i.e. individual, population, community, ecosystem; infraindividual levels not considered in this paper) by integrating pieces of local knowledge and looking for emergent properties [7]. Network analysis offer possibilities to study and quantify part-to-whole relationships: how can smaller components (like species) compose a system (like a lake community) and how can system-level properties (e.g. food web connectance) constrain the behaviour of its components (by various mechanisms including energetics, informational

Evolutionary histories impart structure into marine fish heterospecific co‐occurrence networks

“…In network theory, a hub is a species (vertex) with many connections (edges), such as the potential keystone species identified in this study (species with high eigenvector centrality scores). These hub species provide integrity to co‐occurrence network structure, because removal of these species is predicted to result in extensive alteration of network structure (Tylianakis et al, ; Banerjee, Schlaeppi, & Heijden, ; Jordán et al, ). The structure of the co‐occurrence networks from the temperate locations may indicate that these communities are resilient to random species loss, but more vulnerable than the tropical communities when the loss is of keystone species.…”

Socio-Ecological Network Structures from Process Graphs

“…Early examples include input-output 3 models similar to those used to describe economic structures [1]. Metrics to describe the 4 structure of ecological networks naturally flowed from the use of such quantitative tools 5 (e.g., [2]). These tools provide a lens for the analysis of complex interactions that arise 6 from interactions among ecosystem components.…”

“…Such models are extended from ENA 17 methods through linkage with a network model of a human community at an 18 appropriate scale. Network techniques are useful for understanding emergent behavior 19 that arises from complex interactions among system components [5]. Such system-level 20 behavior is often not immediately evident from the local properties of individual 21 components, and failure to account for them can often lead to unexpected results [3].…”

Socio-ecological network structures from process graphs

“…Such models are extended from ecological network analysis methods through linkage with a network model of a human community at an appropriate scale. Network techniques are useful for understanding emergent behavior that arises from complex interactions among system components [5]. Such system-level behavior is often not immediately evident from the local properties of individual components, and failure to account for them can often lead to unexpected results [3].…”