Accelerating the discovery of rare tree species in Amazonian forests: integrating long monitoring tree plot data with metabolomics and phylogenetics for the description of a new species in the hyperdiverse genus Inga Mill

Author:

Guevara Andino Juan Ernesto1,Hernández Consuelo2,Valencia Renato3,Forrister Dale45,Endara María-José1

Affiliation:

1. Grupo de Investigación en Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Americas, Quito, Ecuador

2. Laboratorio de Ecología de Plantas. Herbario QCA, Escuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Quito, Ecuador

3. Laboratorio de Ecología de Plantas. Herbario QCA, Escuela de Ciencias Biológicas,, Pontificia Universidad Católica del Ecuador, Quito, Ecuador

4. Department of Biology, University of Utah, Salt Lake City, United States of America

5. Estación de Biodiversidad Tiputini, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito—USFQ, Quito, Ecuador

Abstract

In species-rich regions and highly speciose genera, the need for species identification and taxonomic recognition has led to the development of emergent technologies. Here, we combine long-term plot data with untargated metabolomics, and morphological and phylogenetic data to describe a new rare species in the hyperdiverse genus of trees Inga Mill. Our combined data show that Inga coleyana is a new lineage splitting from their closest relatives I. coruscans and I. cylindrica. Moreover, analyses of the chemical defensive profile demonstrate that I. coleyana has a very distinctive chemistry from their closest relatives, with I. coleyana having a chemistry based on saponins and I. cylindrica and I. coruscans producing a series of dihydroflavonols in addition to saponins. Finally, data from our network of plots suggest that I. coleyana is a rare and probably endemic taxon in the hyper-diverse genus Inga. Thus, the synergy produced by different approaches, such as long-term plot data and metabolomics, could accelerate taxonomic recognition in challenging tropical biomes.

Funder

Universidad de las Américas research grant

Artificial Intelligence for Species Discovery National Geographic Grant

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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