Transitions and its indicators in mutualistic meta-networks: effects of network topology, size of metacommunities and species dispersal

Abstract

Gradual changes in the environment could cause dynamical ecological networks to suddenly shift from one state to an alternative state. When this happens ecosystem functions and services provided by ecological networks get disrupted. We, however, know very little about how the topology of such interaction networks can play a role in the transition of ecological networks when spatial interactions come into play. In the event of such unwanted transitions, little is known about how statistical metrics used to inform such impending transitions, measured at the species-level or at the community-level could relate to network architecture and the size of the metacommunity. Here, using hundred and one empirical plant-pollinator networks in a spatial setting, I evaluated the impact of network topology and spatial scale of species interactions on transitions, and on statistical metrics used as predictors to forecast such transitions. Using generalized Lotka-Volterra equations in a meta-network framework, I show that species dispersal rate and the size of the metacommunity can impact when a transition can occur. In addition, forecasting such unwanted transitions of meta-networks using statistical metrics of instability was also consequently dependent on the topology of the network, species dispersal rate, and the size of the metacommunity. The results indicated that the plant-pollinator meta-networks that could exhibit stronger statistical signals before collapse than others were dependent on their network architecture and on the spatial scale of species interactions.

Mutualistic network architecture and eco-evolutionary feedbacks modulate the occurrence of transitions and stability in response to rising temperature

“…This essentially was due to the simple fact that larger networks harbored more species than smaller networks, and as a result, maintained higher biomass than smaller networks even at very high temperatures (Fig. S5), and eventually delayed the collapse (Baruah 2023). As a consequence, species maintained higher density at higher local temperatures eventually delaying network collapse.…”

“…2023, Bascompte & Stouffer 2009, Revilla et al . 2015, Prakash & de Roos 2004, Baruah 2023, De Laender et al .). Due to rise in temperature, mutualistic interactions could be disrupted, as mismatches in plant-pollinator phenology for instance could occur, leading to the weakening of interactions (Hegland et al .…”

Reviving collapsed networks from a single species: the importance of trait variation and network architecture

“…Phenomenological statistical tools such as early warning signals have been suggested to help in forecasting such unwanted transitions (9, 10, 11, 12). However, there are many drawbacks associated with using such tools as most often these signals imperfectly forecast transition (13, 14).…”