Optimal Network Architectures for Spatially Structured Populations with Heterogeneous Diffusion

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.

New insights into the combined effect of dispersal and local dynamics in a two-patch population model

Understanding the effect of dispersal on fragmented populations has drawn the attention of ecologists and managers in recent years, and great efforts have been made to understand the impact of dispersal on the total population size. All previous numerical and theoretical findings determined that the possible response scenarios of the overall population size to increasing dispersal are monotonic or hump-shaped, which has become a common assumption in ecology. Against this, we show in this paper that many other response scenarios are possible by using a simple two-patch discrete-time model. This fact evidences the interplay of local dynamics and dispersal and has significant consequences from a management perspective that will be discussed.

The role of the spatial topology in trophic metacommunities: Species with reduced mobility and total population size

A central question in ecology is understanding the influence of the spatial topology on the dynamics of a metacommunity. This is not an easy task, as most fragmented ecosystems have trophic interactions involving many species and patches. Recent attempts to solve this challenge have introduced certain simplifying assumptions or focused on a limited set of examples. These simplifications make the models mathematically tractable but keep away from real-world problems. In this paper, we provide a novel methodology to describe the influence of the spatial topology on the total population size of the species when the dispersal rates are small. The main conclusion is that the influence of the spatial topology is the result of the influence of each path in isolation. Here, a path refers to a pairwise connection between two patches. Our framework can be readily used with any metacommunity, and therefore represents a unification of biological insights. We also discuss several applications regarding the construction of ecological corridors.

The Role of Movement Patterns in Epidemic Models on Complex Networks

In this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.