Network analysis has revealed whole-network properties hypothesized to be general characteristics of ecosystems including pathway proliferation, and network non-locality, homogenization, amplification, mutualism, and synergism. Collectively these properties characterize the impact of indirect interactions among ecosystem elements. While ecosystem networks generally trace a thermodynamically conserved unit through the system, there appear to be several model classes. For example, trophic (TRO) networks are built upon a food web, usually follow energy or carbon, and are the most abundant in the literature. Biogeochemical cycling (BGC) networks trace nutrients like nitrogen or phosphorus and tend to have more recycling than TRO. We tested (1) the hypothesized generality of the properties in BGC networks and (2) that they tend to be more strongly expressed in BGC networks than in the TRO networks due to increased recycling. We compared the properties in 22 BGC and 57 TRO ecosystem networks from the literature using enaR. The results generally support the hypotheses. First, five of the properties occurred in all 22 BGC models, while network mutualism occurred in 86% of the models. Further, these results were generally robust to a $\pm$50% uncertainty in the model parameters. Second, the mean network statistics for the six properties were statistically significantly greater in the BGC models than the TRO models. These results (1) confirm the general presence of these properties in ecosystem networks, (2) highlight the significance of model types in determining property intensities, (3) reinforce the importance of recycling, and (4) provide a set of indicator benchmarks for future systems comparisons. Further, this work highlights how indirect effects distributed by network connectivity can transform whole-ecosystem functioning, and adds to the growing domain of network ecology.
Abstract Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g., stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics and their terminology across sub-disciplines; benchmarking new network algorithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular “model” data provenance, to increase the reproducibility of network models and analyses. We propose that applying these solutions will aid in synthesizing ecological subdisciplines and allied fields by improving the accessibility of network methods and models.
Summary Network analysis is a useful approach for investigating complex and relational data in many fields including ecology, molecular and evolutionary biology. Here, we introduce enaR , an r package for Ecosystem Network Analysis (ENA). ENA is an analytical tool set rooted in ecosystem ecology with over 30 years of development that examines the structure and dynamics of matter and energy movement between discrete ecological compartments (e.g. a food web). In addition to describing the primary functionality of the package, we highlight several features including a library of 100 empirical ecosystem models, the ability to analyse and compare multiple models simultaneously, and connections to other ecological network analysis tools in r .
