PARENT SESSION
Contributed Oral Session 132: Invasive Species: Management and Indicator Species
Thursday, August 11, 1:30 PM - 5:00 PM, Meeting Room 516 A, Level 5, Palais des congrès de Montréal

A Framework for Synthesizing Non-Native Species Databases within the United States.

Crall, Alycia¹, Meyerson, Laura², Stohlgren, Thomas^{1, 3}, Crosier, Catherine^{1, 3}, Newman, Gregory¹, O'Malley, Robin², ¹ Colorado State University, Fort Collins, CO² Heinz Center, Washington, D.C.³ USGS, Fort Collins, CO

ABSTRACT- Invasion by non-native species has adversely affected many ecosystems in the United States, threatening biodiversity, ecosystem functioning, human health, and the economy. Because organisms continue to be introduced from other countries via trade and transportation, there is a growing need for early detection and rapid response to new invaders. Thus, it has become increasingly important to synthesize existing data on non-native species abundance and distributions. However, little is currently known regarding what data exist on non-native species, and there have been few efforts to improve collaboration and data synergy among governmental agencies, non-governmental organizations, industry, academic researchers, and other non-native species networks. Therefore, a primary goal of non-native species research should be to facilitate non-native species data sharing among these different research groups. We conducted a thorough review of existing non-native species data in the United States from state, multi-state region, national, and global scales. Through this effort, we aimed to provide a better understanding of what data currently exist for non-native species and to determine where data gaps exist (taxonomically, spatially, and temporally) to guide future survey, research, and spatial predictive modelling efforts. We provided a framework that we hope will increase collaboration among various research organizations to begin to more efficiently tackle the non-native species problem.

Key words: non-native species, databases, data synergy, predictive models