1. Merging Spectral and Phylogenetic Diversity to Assess Macrophyte Traits and Functions Along Ecological Gradients
- Author
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Paolo Villa a, Andrea Coppi b, Rossano Bolpagni c, Maria Beatrice Castellani b, Alice Dalla Vecchia c, and Lorenzo Lastrucci d
- Subjects
remote sensing ,aquatic ecology ,phylogenetic diversity ,functional traits ,macrophytes ,biodiversity - Abstract
As littoral and riparian environments are in decline and survival of many aquatic plants is threatened by anthropic activities all over the globe, the conservation of macrophyte diversity should be considered a priority, because of their key role in freshwater ecosystems. High-throughput techniques, such as remote sensing spectroscopy, genetics and phylogenetics, have been explored in the last decade to support and enhance operational diversity monitoring. These techniques have opened new ways of measuring biodiversity, especially in forest and grassland systems, but a sound link between spectral and phylogenetic features with plant functional characteristics has yet to be established. The idea behind macroDIVERSITY, a new national project, funded by the Italian Ministry of Education, University and Research (2020-2023), is that phylogenetic and spectral diversity measures can be integrated into a multidimensional data-driven framework for mapping plant traits and functions across scales and gradients. To this objective, we will collect data on macrophyte traits, diversity, and spectral reflectance from plots sampled over selected lakes and wetlands in Italy, according to robust experimental design. A fully resolved supertree, obtained from DNA markers analysis integrating available data and new sequencing, will be used for assessing the evolutionary diversity of the species assemblage, highlighting the phylogenetic signal in the context of the community traits information. Hyperspectral imaging data acquired from proximal platforms and airborne drones at centimetre scale will be used for modelling bio-chemical and functional macrophyte traits (e.g. canopy morphology, productivity, pigments and nutrients content). Environmental parameters collected and diversity metrics derived will be eventually merged into a machine-learning framework for mapping macrophyte functional diversity (richness and divergence). The project outcomes are expected to impact on applied ecology studies focusing on delineating plant diversity using remote sensing data, and investigating the role played by species interactions and community complexity in regulating aquatic ecosystem quality.
- Published
- 2020