Museum specimen data reveal emergence of a plant disease may be linked to increases in the insect vector population

Abstract

The emergence rate of new plant diseases is increasing due to novel introductions, climate change, and changes in vector populations, posing risks to agricultural sustainability. Assessing and managing future disease risks depends on understanding the causes of contemporary and historical emergence events. Since the mid-1990s, potato growers in the western United States, Mexico, and Central America have experienced severe yield loss from Zebra Chip disease and have responded by increasing insecticide use to suppress populations of the insect vector, the potato psyllid, Bactericera cockerelli (Hemiptera: Triozidae). Despite the severe nature of Zebra Chip outbreaks, the causes of emergence remain unknown. We tested the hypotheses that (1) B. cockerelli occupancy has increased over the last century in California and (2) such increases are related to climate change, specifically warmer winters. We compiled a data set of 87,000 museum specimen occurrence records across the order Hemiptera collected between 1900 and 2014. We then analyzed changes in B. cockerelli distribution using a hierarchical occupancy model using changes in background species lists to correct for collecting effort. We found evidence that B. cockerelli occupancy has increased over the last century. However, these changes appear to be unrelated to climate changes, at least at the scale of our analysis. To the extent that species occupancy is related to abundance, our analysis provides the first quantitative support for the hypothesis that B. cockerelli population abundance has increased, but further work is needed to link B. cockerelli population dynamics to Zebra Chip epidemics. Finally, we demonstrate how this historical macro-ecological approach provides a general framework for comparative risk assessment of future pest and insect vector outbreaks.

Keywords: NIMBLE; Bayesian analysis; Candidatus Liberibacter solanacearum; list length analysis; occupancy model; opportunistic ecological data.