Rethinking the Complexity and Uncertainty of Spatial Networks Applied to Forest Ecology

Abstract

Characterizing tree spatial patterns and interactions are helpful to reveal underlying processes assembling forest communities. Spatial networks, despite its complexity, are powerful to examine spatial interactions at an individual level using well defined patterns. However, complex tree networks introduce uncertainties. Validation methods are needed in order to assess whether network-based metrics can clearly identify different processes. Here, we constructed three types of tree networks, which reflect various aspects of tree competition. Based on five types of spatial null models and 199 Monte-Carlo simulations, we were able to select network-based metrics that exhibited well performances on distinguishing different processes. This technique was then applied to forest dataset Tuscany of Italy. We found that the average node degree and the cluster coefficient are ideal metrics like the Ripley’s K and paired correlation function. In addition, network approach can identify fine-scale spatial variations of tree competition and its underlying causes. Our analyzes also indicate that a bit of caution is needed when defining the network structure as well as designing network-based metrics. We suggested that validation techniques using corresponding spatial null models are critically important to reduce the negative effects caused by uncertainties of the network.