Data from: Bridging Data Gaps in Risk Assessments of Threatened and Endangered Plants: The Role of Reproductive Strategies and Pollinator Traits

Risk assessments for pesticides (or plant protection products) for species listed under the United States Endangered Species Act (ESA) are often hindered by limited data on the biology of many species, particularly for plant species. We investigate the integration of publicly available plant and pollinator trait data to enhance ESA risk assessments, with an emphasis on the potential indirect effects to plants that may result from insecticide use affecting pollination. We relied on publicly accessible data bases to compile trait information across 408 ESA-listed plant species which were identified as listed plants with biotic pollination in U.S. Fish and Wildlife Service Biological Opinion (BiOp) for the insecticide malathion. The compilation provides a structured overview of available information by plant species and trait, and facilitates the addition of new data. We conducted a cluster analysis with a subset of nine traits for which complete information for the majority of plant species could be compiled. However, the grouping into clusters did not provide relevant additional information. In addition to the traits of the listed plants, we collated their reported pollinator taxa and compiled pollinator traits relevant in the context of pesticide exposure potential, i.e., traits that could correspond to risk modifiers. Bees were identified as the predominant pollinators of ESA-listed plants addressed in our study. We demonstrate the utility of public databases in addressing critical knowledge gaps for pesticide risk assessments. However, challenges persist due to the limitations and variability in available data. Quantitative trait data are rarely available and considerable data gaps remain. In addition, understanding pollinator diversity and vulnerability is essential for accurately evaluating the potential for insecticide exposure and effects on pollinators, and in turn, the potential indirect risks to listed plant species. The provided data sets can be used directly for future risk assessments, be extended with new data, and provide a basis for development of trait-based approaches such as population modeling.