Plant-herbivore networks

Workflow to establish diet identification with barcodes

I was surprised about this number but its true: plants and their associated insect herbivores, represent more than 50% of all known species on earth. This immense biodiversity is in parts the result of coevolutionary processes that have taken place between these two ecological partners

In order to better understand these ecological and evolutionary interactions and how they create and maintain biological diversity we need to determine the associations and networks between insect herbivores and their host plants. In the past there have been a few strategies to identify insect herbivore diets. The most accessible one seems to be direct observations of herbivores in the field but they are problematic in habitats that are difficult to access. Furthermore they are also greatly limited by the ability of the researcher to correctly identify the species involved. Scientists have also use feeding trials in the lab which unfortunately led to overestimates of breadth of diets as insects were often feeding on host plants not normally consumed in the wild. That leaves us with morphological analysis and stable isotope techniques applied to gut content. However, the taxonomic and ecological resolution was often very limited.

The resulting plant-herbivore network

Researchers from the Smithsonian Institution in Washington DC, have now tested a DNA Barcoding based alternative to determine insect-host plant associations for an entire guild of insect herbivores using plant DNA extracted from insect gut contents. Their study is a very convincing contribution to the increasing number of ecological applications of DNA Barcoding. It represents a very nice case study generated for a particular study system (rolled-leaf beetles). I can only agree with the author's conclusions:

Our study demonstrates that host plant identifications at the species-level using DNA barcodes are feasible, cost-effective, and reliable, and that reconstructing plant-herbivore networks with these methods will become the standard for a detailed understanding of these interactions.