Maccracken, S. A. | bioRxiv Science

bioRxiv2026

Solving Paleobotany's 200-year problem with fluorescence and artificial intelligence

Paleobotany has a 200-year problem: leaf fossils are often too poorly preserved to make proper taxonomic identification. Though they are the most abundant, and well-studied macroscopic fossil plant organ, many fossil leaves-- including specimens held in museum and university collections-- appear to lack the detailed features needed to take crucial next steps, like placement into phylogenetic context, generation of ecological interpretations, or reconstruction of paleoclimate. However, we have discovered that under UV and laser light [~]50% of leaves across a diverse survey of fossil deposits fluoresce in a way that reveals spectacularly detailed leaf venation and gross morphology that is invisible under white light. These 'Fossil Ciphers' bring new life into each step of the paleobotanical method, unlocking previously unattainable taxonomic classifications, quantitative morphometrics, and promising new avenues for the incorporation of AI into fossil leaf analyses. With the aid of fluorescence techniques, we can help solve paleobotany's 200-year problem.

paleontology↗

bioRxiv2022

Sampling bias and the robustness of ecological metrics for plant-damage-type association networks

Plants and their insect herbivores have been a dominant component of the terrestrial ecological landscape for the past 410 million years and feature intricate evolutionary patterns and co-dependencies. A complex systems perspective allows for both detailed resolution of these evolutionary relationships as well as comparison and synthesis across systems. Using proxy data of insect herbivore damage (denoted by the damage type or DT) preserved on fossil leaves, functional bipartite network representations provide insights into how plant-insect associations depend on geological time, paleogeographical space, and environmental variables such as temperature and precipitation. However, the metrics measured from such networks are prone to sampling bias. Such sensitivity is of special concern for plant-DT association networks in paleontological settings where sampling effort is often severely limited. Here, we explore the sensitivity of functional bipartite network metrics to sampling intensity and identify sampling thresholds above which metrics appear robust to sampling effort. Across a broad range of sampling efforts, we find network metrics to be less affected by sampling bias and/or sample size than richness metrics, which are routinely used in studies of fossil plant-DT interactions. These results provide reassurance that cross-comparisons of plant-DT networks offer insights into network structure and function and support their widespread use in paleoecology. Moreover, these findings suggest novel opportunities for using plant-DT networks in neontological terrestrial ecology to understand functional aspects of insect herbivory across geological time, environmental perturbations, and geographic space.

ecology↗