TY - JOUR
T1 - Six central questions about biological invasions to which NEON data science is poised to contribute
AU - Gill, Nathan S.
AU - Mahood, Adam L.
AU - Meier, Courtney L.
AU - Muthukrishnan, Ranjan
AU - Nagy, R. Chelsea
AU - Stricker, Eva
AU - Duffy, Katharyn A.
AU - Petri, Laís
AU - Morisette, Jeffrey T.
N1 - Funding Information:
The authors thank the organizers of the 2019 NEON Science Summit—Jennifer K. Balch, R. Chelsea Nagy, Dawn Umpleby, and the NEON Science Summit Steering Committee. This research was supported by National Science Foundation Awards # DBI‐1906144 and DEB #1557135. Additional support came from Earth Lab, CIRES, and the Grand Challenge Initiative at the University of Colorado Boulder, and Texas Tech University’s College of Agricultural Sciences and Natural Resources Summer Salary Support Program. Special thanks also to Julien Brun for contributing to the early conceptualization of the ideas presented in this manuscript. N. S. Gill and A. L. Mahood contributed equally to the work reported here. The authors have no conflicts of interest to disclose.
Funding Information:
The authors thank the organizers of the 2019 NEON Science Summit—Jennifer K. Balch, R. Chelsea Nagy, Dawn Umpleby, and the NEON Science Summit Steering Committee. This research was supported by National Science Foundation Awards # DBI-1906144 and DEB #1557135. Additional support came from Earth Lab, CIRES, and the Grand Challenge Initiative at the University of Colorado Boulder, and Texas Tech University’s College of Agricultural Sciences and Natural Resources Summer Salary Support Program. Special thanks also to Julien Brun for contributing to the early conceptualization of the ideas presented in this manuscript. N. S. Gill and A. L. Mahood contributed equally to the work reported here. The authors have no conflicts of interest to disclose.
Publisher Copyright:
© 2021 The Authors.
PY - 2021/9
Y1 - 2021/9
N2 - Biological invasions are a leading cause of rapid ecological change and often present a significant financial burden. As a vibrant discipline, invasion biology has made important strides in identifying, mapping, and beginning to manage invasions, but questions remain surrounding the mechanisms by which invasive species spread and the impacts they bring about. Frequent, multiscalar ecological monitoring such as that provided through the National Ecological Observatory Network (NEON) can be an important tool for addressing some of these questions. We articulate a set of major outstanding questions in invasion biology, consider how NEON data science is positioned to contribute to addressing these questions, and provide suggestions to help equip a growing contingent of NEON data users in solving invasion biology problems. We demonstrate these ideas through four case studies examining the mechanisms of plant invasions in the U.S. Intermountain West. In Case Study I, we evaluate the relationships between native species richness, non-native species richness, and probability of invasion across scales. In Case Studies II and III, we explore the relationship between environmental factors and non-native species presence to understand invasion mechanisms. Case Study IV outlines a method for improving the ability to distinguish invasive plants from native vegetation in remotely sensed data by leveraging temporal patterns of phenology. There are many novel elements in the NEON sampling design that make it uniquely poised to shed light on the mechanisms that can help us understand invasibility, prediction, and progression, as well as on the variability, longevity, and interactions of multiple invasive species’ impacts. Thus, knowledge gained through analysis of NEON data is expected to inform sound decision-making in unique ways for managers of systems experiencing biological invasions.
AB - Biological invasions are a leading cause of rapid ecological change and often present a significant financial burden. As a vibrant discipline, invasion biology has made important strides in identifying, mapping, and beginning to manage invasions, but questions remain surrounding the mechanisms by which invasive species spread and the impacts they bring about. Frequent, multiscalar ecological monitoring such as that provided through the National Ecological Observatory Network (NEON) can be an important tool for addressing some of these questions. We articulate a set of major outstanding questions in invasion biology, consider how NEON data science is positioned to contribute to addressing these questions, and provide suggestions to help equip a growing contingent of NEON data users in solving invasion biology problems. We demonstrate these ideas through four case studies examining the mechanisms of plant invasions in the U.S. Intermountain West. In Case Study I, we evaluate the relationships between native species richness, non-native species richness, and probability of invasion across scales. In Case Studies II and III, we explore the relationship between environmental factors and non-native species presence to understand invasion mechanisms. Case Study IV outlines a method for improving the ability to distinguish invasive plants from native vegetation in remotely sensed data by leveraging temporal patterns of phenology. There are many novel elements in the NEON sampling design that make it uniquely poised to shed light on the mechanisms that can help us understand invasibility, prediction, and progression, as well as on the variability, longevity, and interactions of multiple invasive species’ impacts. Thus, knowledge gained through analysis of NEON data is expected to inform sound decision-making in unique ways for managers of systems experiencing biological invasions.
KW - Bromus tectorum
KW - Eragrostis lehmanniana
KW - National Ecological Observatory Network (NEON)
KW - Special Feature: Harnessing the NEON Data Revolution
KW - biotic resistance
KW - exotic plants
KW - invasion biology
KW - invasion impacts
KW - invasive species
KW - macroecology
KW - macrosystems
KW - scale
UR - http://www.scopus.com/inward/record.url?scp=85115876082&partnerID=8YFLogxK
U2 - 10.1002/ecs2.3728
DO - 10.1002/ecs2.3728
M3 - Article
AN - SCOPUS:85115876082
VL - 12
JO - Ecosphere
JF - Ecosphere
SN - 2150-8925
IS - 9
M1 - e03728
ER -