A natural history of networks: Modeling higher-order interactions in geohistorical data

Abstract

Paleobiologists often employ network-based methods to analyze the inherently complex data retrieved from geohistorical records. Because they lack a common framework for designing, performing, evaluating, and communicating network-based studies, reproducibility and interdisciplinary research are hampered. The high-dimensional and spatiotemporally resolved data also raise questions about the limitations of standard network models. They risk obscuring paleontological patterns by washing out higher-order node interactions when assuming independent pairwise links. Recently introduced higher-order representations and models better suited for the complex relational structure of geohistorical data provide an opportunity to move paleobiology research beyond these challenges. Higher-order models can represent the spatiotemporal constraints on the information paths underlying geohistorical data, capturing the high-dimensional patterns more accurately. Here we describe how to use the Map Equation framework for designing higher-order models of geohistorical data, address some practical decisions involved in modeling complex dependencies, and discuss critical methodological and conceptual issues that make it difficult to compare results across studies in the growing body of network paleobiology research. We illustrate multilayer networks, hypergraphs, and varying Markov time models for higher-order networks in case studies on gradient analysis, bioregionalization, and macroevolution, and delineate future research directions for current challenges in the emerging field of network paleobiology.