Christian Frølund Damgaard’s research while affiliated with Aarhus University and other places

The Gini coefficient, while widely used to quantify inequality in biological size distributions, lacks the capacity to resolve directional asymmetry inherent in Lorenz curves, a critical limitation for understanding skewed resource allocation strategies. To address this, we extend our prior geometric framework of the rotated and right-shifted Lorenz curve (RRLC) by introducing two original asymmetry metrics: the positional shift ratio (PL, defined as xc/2, where xc is the x-coordinate of the RRLC’s maximum value point) and the area ratio (PA, defined as AL/(AL + AR), where AL and AR denote the areas under the left and right segments of the RRLC). These indices uniquely dissect contributions of dominant versus small individuals to overall inequality, with PL reflecting the peak position of the RRLC and PA quantifying the area dominance of its left segment. Theoretically, PL directly links to the classical Lorenz asymmetry coefficient S (defined as S=xc′+yc′, where xc′,yc′ is the tangent point on the original Lorenz curve with a 45° slope) through S = 2 − 2PL, bridging geometric transformation and parametric asymmetry analysis. Applied to 480 Shibataea chinensis Nakai shoots, our analysis revealed that over 99% exhibited pronounced left-skewed distributions, where abundant large leaves drove the majority of leaf area inequality, challenging assumptions of symmetry in plant canopy resource allocation. The framework’s robustness was further validated by the strong correlation between PA and PL. By transforming abstract Lorenz curves into interpretable bell-shaped performance curves, this work provides a novel toolkit for analyzing asymmetric size distributions in ecology. The proposed metrics can be applied to refine light-use models, monitor phenotypic plasticity under environmental stress, and scale trait variations across biological hierarchies, thereby advancing both theoretical and applied research in plant ecology.

Nitrogen was manipulated in a dune heath ecosystem and using time-series pin-point data it was demonstrated that both Lotka-Volterra type interspecific competition and frequency dependency play significant roles in determining plant growth. For modelling simplicity, plant taxa were divided into heather, wavy hair-grass, and all other vascular species. Significant interspecific competition was observed among all species, except wavy hair-grass on the growth of all other vascular species, and nitrogen addition was found to increase the competitive effect of heather on the growth of all other vascular species. Both heather and wavy hair-grass showed positive feedback dynamics on growth when they were relatively dominant at the plot scale and the effect increased with added nitrogen. Such positive feedback dynamics may lead to the formation of patches, which are a characteristic feature of heath ecosystems. Oppositely, there was a beneficial effect of being relatively rare on the growth of all other vascular species in plots with added nitrogen. The study highlights the importance of the combined effects of interspecific competition and frequency dependency in regulating plant communities, and consequently undermine both theoretical and empirical conclusions of modern coexistence theory.

Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here, we used nine bands from multispectral high-to-medium resolution (10–60 m) satellite data (Sentinel-2) and machine learning to predict local vegetation plot characteristics over a broad area (approx. 30,000 km²) in terms of plants’ preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants’ abiotic soil preferences, and using solely satellite data we achieved predictive powers between 26 and 59%, improving to around 70% when habitat information was included as a predictor. This shows that plants’ abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing—if at all possible—needs further methodological and data development.

Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here we used 9 bands from multispectral high-to-medium resolution (10–60 m) satellite data (Sentinel-2) and machine learning to predict the local vegetation plot characteristics at broad extent (approx. 30.000 km2) in terms of plants’ preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants abiotic soil preferences and using solely satellite data we achieved predictive powers between 26–59% improving to about 70% when habitat information was included as a predictor. This show that plants abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing – if at all possible – needs further methodological and data development.

Heathland vegetation has undergone significant changes in the past century, e.g., due to airborne pollutants and a lack of proper management. Understanding the interactions between these factors in combination is pivotal for heathland conservation. Here, we studied the vegetation changes at a dune heath in a four-year manipulation experiment analysing the combined effects of nitrogen deposition, mowing, and deer grazing. Our results showed no significant effect of nitrogen deposition and deer grazing on plant growth and cover of dwarf shrubs within the experimental plots. However, high loads of nitrogen decreased bryophyte cover and increased the growth and cover of sand sedge Carex arenaria L. Mowing adversely affected the dwarf shrub community, e.g., the dwarf shrub species crowberry Empetrum nigrum L., and facilitated increased cover and plant growth of graminoids. Plant growth and the cover of C. arenaria increased in plots without deer grazing, whereas bryophyte cover decreased significantly without grazing. We do not recommend intensive mowing of vegetation as a conservation method for dune heaths because it promotes graminoids. From a conservation aspect, it is essential to consider the effect of deer on heathlands because they both impede some species and benefit others and mitigate the adverse effects of nitrogen deposition on dune heaths.