MSB-FRA: Testing abiotic drivers of activity, abundance, and diversity of ground-dwelling arthropod communities at a continental scale

Insects are among the most abundant and ecologically important animals in the biosphere. They include serious crop pests and invasive species that cause millions in damage. A key goal of this project is to understand how and when the number, and activity of insects change as one moves from place to place across the U.S., and why those numbers fluctuate from year to year. Such an understanding can help predict insect pest outbreaks, the spread of invasive species, and changes in an ecosystems ability to provide food and fiber and conserve soil nutrients. This project will use insect samples collected by the National Ecological Observatory Network (NEON) at 47 locations spanning the U.S.'s major ecosystems to determine how abundance, activity, and diversity of soil insects vary across the U.S. The project will use environmental barcoding (analyzing insect DNA from the samples' preservative) and image analysis techniques to train computer algorithms to count and identify insects preserved in the samples. Together, these technologies will automate and streamline NEON's monitoring network, providing the first such nationwide dataset on abundance, activity, and diversity of the U.S.'s soil insects. In doing so they will serve a variety of stakeholders: ecologists testing and refining models that predict future insect communities; land managers who want to know the likelihood of a pest eruption; conservation biologists and urban planners hoping to anticipate spread of invasive ants and beetles.

A key aim of this project is to quantify and predict how Earth's great abiotic drivers--temperature, precipitation, and biogeochemistry--govern how ecological communities of soil insects vary from place to place. Community data at continental extents vastly underrepresent the terrestrial arthropods in part due to the immense effort required to count, size, and identify taxa ranging from mites to ants to beetles to spiders. Yet the few existing arthropod datasets suggest that as one travels from deserts to rainforests, terrestrial arthropod communities vary by orders of magnitudes in abundance (the number of individuals), size (mass per individual), activity (the rate at which individuals do work on the system), and diversity (the number of species/forms). Combined, these four variables help predict how arthropods regulate ecosystem processes like decomposition, herbivory, and seed dispersal. This knowledge gap will be filled by the analysis of samples from the NEON pitfall network (arrays of traps, sunk in the soil, that capture and store biweekly samples of arthropods in ethanol). It will develop two complementary methods to do so. Environmental Bar Coding samples and identifies pitfall taxa from extracts of ethanol. Image Analysis uses machine learning to count, size, and classify arthropods in a sample. Pitfall samples containing key orders of Earth's arthropods will be analyzed from NEON's 47 sites, and Environmental Bar Coding and Image Analysis pipelines that count, size, and identify taxa from these samples will be developed, tested, honed, and validated, and then used to analyze two years of NEON samples.