Dynamical transitions in a pollination-herbivory interaction: a conflict between mutualism and antagonism

Interaction Outcomes in Mutualism-Antagonism Continua: Context Dependency and Instantaneous Effects of the Interactions

AbstractIt is increasingly evident that most interactions are not static and move along a continuum ranging from pure mutualism (i.e., in which each species in the interaction has a net benefit in the long term) to pure antagonism (i.e., in which each species in the interaction has a net damage in the long term). Despite numerous experimental and theoretical works on this concept, predicting interaction outcomes within an ecological community continues to pose a significant challenge. This article aims to tackle this challenge by presenting a theoretical methodology for predicting the interaction outcomes within the common mutualism-antagonism modeling framework. Specifically, my main finding is to describe the influence of the population abundance of the species, the interaction effects, and the ecological context on the interaction outcomes and to quantify their relative contribution. I found that the interaction outcomes depend on the number of interacting species. In particular, when the number of interacting species increases, the trend is to skip situations where all species benefit from the interactions.

Dynamics of predatory effect on saturated plant-pollinator mutualistic relationship

In the realm of pollinator declination, understanding the dynamics of plant-pollinator interactions is a critical area of research to maintain healthy ecosystems. This study employs a mathematical modeling approach to investigate the dynamics of a saturated plant-pollinator mutualism, particularly aiming on the effect of predation on pollinator species. Using dynamical system theory, stability analysis of various ecological equilibria is investigated, and bifurcation phenomena such as transcritical and hopf are revealed. Furthermore, numerical results suggest that higher initial predator density can lead to pollinator extinction, although the predator population may not survive eventually. However, increased mutualistic strengths along with reduced predation rate can promote stability and support the sustainability of the plant-pollinator-predator ecosystem. These findings can be helpful for conservation strategies aimed at preserving pollinators and enhancing biodiversity.

Plant coexistence mediated by adaptive foraging preferences of exploiters or mutualists

Coexistence of plants depends on their competition for common resources and indirect interactions mediated by shared exploiters or mutualists. These interactions are driven either by changes in animal abundance (density-mediated interactions, e.g., apparent competition), or by changes in animal preferences for plants (behaviorally-mediated interactions). This article studies effects of behaviorally-mediated interactions on two plant population dynamics and animal preference dynamics when animal densities are fixed. Animals can be either adaptive exploiters or adaptive mutualists (e.g., herbivores or pollinators) that maximize their fitness. Analysis of the model shows that adaptive animal preferences for plants can lead to multiple outcomes of plant coexistence with different levels of specialization or generalism for the mediator animal species. In particular, exploiter generalism promotes plant coexistence even when inter-specific competition is too strong to make plant coexistence possible without exploiters, and mutualist specialization promotes plant coexistence at alternative stable states when plant inter-specific competition is weak. Introducing a new concept of generalized isoclines allows us to fully analyze the model with respect to the strength of competitive interactions between plants (weak or strong), and the type of interaction between plants and animals (exploitation or mutualism).

A unifying framework for interpreting and predicting mutualistic systems

Coarse-grained rules are widely used in chemistry, physics and engineering. In biology, however, such rules are less common and under-appreciated. This gap can be attributed to the difficulty in establishing general rules to encompass the immense diversity and complexity of biological systems. Furthermore, even when a rule is established, it is often challenging to map it to mechanistic details and to quantify these details. Here we report a framework that addresses these challenges for mutualistic systems. We first deduce a general rule that predicts the various outcomes of mutualistic systems, including coexistence and productivity. We further develop a standardized machine-learning-based calibration procedure to use the rule without the need to fully elucidate or characterize their mechanistic underpinnings. Our approach consistently provides explanatory and predictive power with various simulated and experimental mutualistic systems. Our strategy can pave the way for establishing and implementing other simple rules for biological systems.