Climatic seasonality may affect ecological network structure: Food webs and mutualistic networks

TL;DR

This study investigates how climatic seasonality influences ecological network structures, revealing that increased seasonality generally enhances modularity and diversity, with effects varying by ecosystem type and climate factor.

Contribution

It provides empirical evidence linking climatic seasonality to changes in ecological network structures across different ecosystem types.

Findings

Rainfall seasonality increases modularity in freshwater food webs.

Temperature seasonality enhances diversity in mutualistic networks.

Climatic seasonality impacts network size and structure differently depending on ecosystem.

Abstract

Ecological networks exhibit non-random structural patterns, such as modularity and nestedness, which indicate ecosystem stability, species diversity, and connectance. Such structure-stability relationships are well known. However, another important perspective is less well understood: the relationship between the environment and structure. Inspired by theoretical studies that suggest that network structure can change due to environmental variability, we collected data on a number of empirical food webs and mutualistic networks and evaluated the effect of climatic seasonality on ecological network structure. As expected, we found that climatic seasonality affects ecological network structure. In particular, an increase in modularity due to climatic seasonality was observed in food webs; however, it is debatable whether this occurs in mutualistic networks. Interestingly, the type of climatic seasonality that affects network structure differs with ecosystem type. Rainfall and temperature seasonality influence freshwater food webs and mutualistic networks, respectively; food webs are smaller, and more modular, with increasing rainfall seasonality. Mutualistic networks exhibit a higher diversity (particularly of animals) with increasing temperature seasonality. These results confirm the theoretical prediction that stability increases with greater perturbation. Although these results are still debatable because of several limitations in the data analysis, they may enhance our understanding of environment-structure relationships.