Trophic mediation and ecosystem stability: An assessment using qualitative network models

Abstract

Nontrophic interactions can contribute to negative and positive feedbacks within a community, thus affecting likelihood of regime shifts; however, assessing the nature and importance of these effects in a network remains challenging, especially for pelagic ecosystems. Here, we present a qualitative modeling approach for assessing the importance of different effects and resultant feedbacks for community stability, using a Southern Ocean example. A potentially important positive feedback in the Southern Ocean ecosystem involves production of a chemical cue, dimethyl sulfide (DMS), by some phytoplankton. Production of DMS can promote phytoplankton growth by attracting predators of phytoplankton-grazers, and nutrients released as feces from those predators help fertilize the water column. We explored how uncertainties in the nature of this feedback affect community stability in a set of small, community models. We found that stability varied substantially depending on how the community was modeled, but that the interactions most important for determining stability were consistent across all models. Model stability was sensitive to the strength of phytoplankton competition, controls on phytoplankton, DMS production and release, and predator attraction to DMS, suggesting that the community could be destabilized by perturbation affecting these interactions. Incorporating DMS-mediated feedbacks into a larger Southern Ocean network had a moderate impact on stability characteristics and altered the trophic level at which the system would be most vulnerable to perturbation.

Uncertainties in forecasts of ecosystem persistence under climate change

“…For unconstrained edges, we drew random numbers from a uniform distribution ranging [0,-1] if the sign of the interaction coefficient was negative, and [0,1] if the sign of the interaction coefficient was positive 7 . Where the relative strength of edge interactions within the model were constrained (i.e., sediment supply and dam biophysical scenarios; Extended Data Table 1), constraints were defined using partial ordering, and topological sorting that ensured interaction weights were parameterised to meet the constraint conditions 39 . For all other edges that were constrained (i.e., tidal range, ecological connectivity, and coastal squeeze), the range of the uniform distribution used for parameterisation was binned (Extended Data Table 1).…”

Nonlinear dynamics, resilience, and regime shifts in aquatic communities and ecosystems: an overview

“…Stability is a feature of resilience reflecting the ability to recover from temporary perturbations. Models of Southern Ocean plankton point to the importance of feedbacks among phytoplankton, their chemical cues (dimethyl sulfide), and grazers, in maintaining stability (Ward et al 2022). Experiments with macroalgae removal quantify the ability of species to recover relative to the size of perturbations, and the long‐term study of a set of manipulations uncovered features that stabilize alternative states (Dudgeon and Petraitis 2022).…”