Six general ecosystem properties are more intense in biogeochemical cycling networks than trophic based webs
Academic Article
Overview
Overview
Abstract
Network analysis has revealed several whole-network properties hypothesized to be general characteristics of ecosystems including pathway proliferation, and network non-locality, homogenization, amplification, mutualism, and synergism. Collectively these six 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 around a food web, usually follow energy or carbon, and are the most abundant models in the literature. Biogeochemical cycling (BGC) networks trace nutrients like nitrogen or phosphorus and tend to have more aggregated nodes, less dissipation, and more recycling than TRO. We tested (1) the hypothesized generality of the properties in BGC networks and (2) that the properties tend to be more strongly expressed in BGC networks than in the TRO networks due to increased recycling. We compared the properties in 22 biogeochemical and 51 trophic ecosystem networks from the literature using enaR, and evaluated the robustness of these results with an uncertainty analysis. The results generally support the hypotheses. First, five of the properties occurred in varying degrees in all 22 BGC models, while network mutualism occurred in 86% of the models. Further, these results were generally robust to a +/-50% uncertainty in the model parameters. Second, the average 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 different model types in determining property intensities, and (3) provide a set of indicator benchmarks for future systems comparisons. Further, this work highlights how indirect effects distributed by network connectivity can transform species relationships, making them more mutualistic, and changing whole-ecosystem development and functioning.
