More precise measurements of the complexity of leaf shapes can open new pathways to understanding plant adaptation and resilience in the face of global changes. We present a method to measure the complexity of plant leaf shapes by relating their fractal dimension to topological entropy. Our method relies on ‘segmental fractal complexity’ and stems from a fractal-thermodynamic system analogy. The complexity of plant leaf shapes is an algebraic combination of the fractal dimension of the components, leaf lamina, background, and leaf edge. We applied this method to leaf forms of 42 tropical plant species. Segmental fractal complexity is an improvement over the leaf dissection index because of its ability to capture the spatial positioning of a leaf’s lamina, edge, and background. It is also an advancement over conventional geometric and fractal dimension-based measures of leaf complexity because it does not entail information loss due to the pre-processing and is perceptibly simple.

Shape is an objective characteristic of an object. A boundary separates a physical object from its surroundings. It defines the shape and regulates energy flux into and from an object. Visual perception of a definite shape (geometry) of physical objects is an abstraction. While the perceived geometry at an object’s sharp interface (macro) creates a Euclidian illusion of actual shape, the notion of diffuse interfaces (micro) allows an understanding of the realistic form of objects. Here, we formulate a dimensionless geometric entropy of plant leaves (S_L) by a 2-D description of a phase-field function. We applied this method to 112 tropical plant leaf images. S_L was estimated from the leaf perimeter (P) and leaf area (A). It correlates positively with a fractal dimensional measure of leaf complexity, viz., segmental fractal complexity. Leaves with a higher P: A ratio have higher S_L and possess complex morphology. The univariate cluster analysis of S_L reveals the taxonomic relationship among the leaf shapes at the genus level. An increase in S_L of plant leaves could be an evolutionary strategy. The results of morphological complexity presented in this paper will trigger discussion on the causal links between leaf adaptive stability/efficiency and complexity. We present S_L as a derived plant trait to describe plant leaf complexity and adaptive stability. Integrating S_L into other leaf physiological measures will help to understand the dynamics of energy flow between plants and their environment.

Anthrophony is an important determinant of habitat quality in the Anthropocene. Acoustic adaptation of birds at lower levels of anthrophony is known. However, threshold anthrophony, beyond which biophony starts decreasing, is less explored. Here, we present empirical results of the relationship between anthrophony and biophony in four terrestrial soundscapes. The constancy of the predicted threshold vector normalised anthropogenic power spectral density (~ 0.40 Watts/Hz) at all the study sites is intriguing. We propose the threshold value of anthropogenic power spectral density as an indicator of the avian acoustic tolerance level in the study sites. The findings pave the way to determine permissible sound levels within protected landscapes and directly contribute to conservation planning.

Despite similar universal primary physiological functions, plant leaves exhibit myriad shapes and sizes. Understanding this morphological variation is invaluable in plant taxonomy, ecology, evolution, and biomimetics. Achieving a comprehensive understanding of eco-evo-devo research requires diverse leaf-image datasets collected across regions and over time. While many datasets support morphometric studies using advanced imaging and machine learning, few provide standardised leaf images that enable uniform interspecific comparisons. We present a dataset of 161 high-quality RGB images of leaves of wild and cultivated tree species from Kerala, India, collected between 2020 and 2023. All leaves, including their petioles, were scanned using a digital scanner (Epson L360), centrally framed on a white background, and uniformly scaled to 1024 × 1024 pixels. In addition, the dataset comprises codes to compute the leaf morphometry using two novel objective morphometric measures: Segmental fractal complexity ( D _ΣS ) and Geometric entropy ( S _L ). These metrics were validated against the leaf dataset, showing strong correlations between D _ΣS and leaf dissection index ( LDI ) ( ρ = 0.94) and between S _L and D _ΣS ( ρ = 0.94), confirming the relationship between leaf patterns and leaf lobiness, pinnation, and serration. D _ΣS surpasses LDI by incorporating spatial positioning of leaflets, lobes and fine serration features. Both D _ΣS and S _L outperform geometric morphometric techniques, which are limited to intraspecific comparisons. Their objectivity, ease of use, and lack of statistical preprocessing make D _ΣS and S _L reliable metrics for interspecific leaf comparisons. We encourage researchers to expand or replicate our analysis using codes and leaf datasets from diverse locations. This dataset supports the development and validation of future leaf morphometric techniques. Despite limitations in high-resolution imaging and intraspecific variability, it remains valuable for advancing research and fostering collaboration across taxonomy, ecology, and computer vision.