Leaf contours delineate genetic control of leaf shape and predict ecological traits of trees
Wednesday, August 4, 2021
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Anshuman Swain and William F. Fagan, Department of Biology, University of Maryland, College Park, MD, Levi Fussell, Institute of Perception, Action and Behaviour, University of Edinburgh, Edinburgh, United Kingdom
Department of Biology, University of Maryland College Park, MD, USA
Background/Question/Methods Leaves serve as an interface between plants and their environment for gas exchange, light exposure, and thermoregulation, thereby directly affecting plant fitness. This intricate relationship between leaf and environment has led to massive diversification in leaf morphology, which influences function. For example, the extent of leaf margin dissection is inversely correlated with mean annual temperature, and paleo-botanists regularly use fossil leaf shape to predict paleo-climate. Leaf morphology and development also depend on other environmental factors like light quality, nutrient availability and humidity, and phylogenetically controlled genetic pathways and toolkits. Morphological patterns in leaves are measured using a variety of metrics that depict interconnected and complex parameters. Here, we utilize two contour complexity measures (fractal dimensionality and edge entropy) that compress many features of leaf shape into two statistics. We use these metrics to explore how leaf morphological changes affect or are affected by traits. Fractal dimensionality quantifies intricacy and repeated self-similarity of leaf contour, whereas edge entropy measures the non-uniformity of leaf edge, relative to its centroid. These two statistics have been mostly used in the past for machine learning tasks for identifying leaf specimens at the species level. Going further, we use these statistics to distinguish aspects of leaf morphology that connect the ecology and evolution of leaf contours in relation to the environmental factors that determined them. To explore these linkages, we used over 150,000 tree leaves, collected from various published sources, representing 141 plant families from 75 sites worldwide. Results/Conclusions We found a strong phylogenetic signal in the distribution of the two leaf complexity measures. We also found possible routes for this signal through developmental toolkits, especially the Knotted1-like homeobox (KNOX) and Reduced-Complexity (RCO) genes. We explore these linkages through a growth simulation model with parameters controlling fractality and overall shape. To contextualize this, trees like pines and junipers, which have duplicated KNOX genes (which is known to induce fractality in leaf margins) have some of the highest values of fractal dimensionality. Stoichiometric traits such as leaf C:N ratio, N:P ratio and plant nitrogen fixation ability were significantly correlated with fractal dimension. Other plant traits such as propensity to shed leaves and climatic variables were correlated with both fractal dimension and edge entropy, suggesting a link between climatic drivers and leaf development. Overall, deploying these two complexity measures in functional rather than taxonomic applications provide a new way of linking leaf econometrics and trait relations to morphology.