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Biological Invasions

, Volume 20, Issue 12, pp 3545–3562 | Cite as

Spatial modeling improves understanding patterns of invasive species defoliation by a biocontrol herbivore

  • Annie L. Henry
  • Eduardo González
  • W. Wright Robinson
  • Bérenger Bourgeois
  • Anna A. Sher
Original Paper

Abstract

Spatial modeling has proven to be useful in understanding the drivers of plant populations in the field of ecology, but has yet to be applied to understanding variation in biocontrol impact. In this study, we employ multi-scale analysis (Moran’s Eigenvector Maps) to better understand the variation in tree canopy exposed to defoliation by a biocontrol beetle (Diorhabda spp.). The control of the exotic tree Tamarix in riparian areas has long been a priority for land managers and ecologists in the American southwest. Diorhabda spp. was introduced as a bio-control agent beginning in 2001 and has since become an inseparable part of Tamarix-dominated river systems in the southwest. Between 2013 and 2016 tamarisk dieback was assessed at 79 sites across Grand County, Utah, arguably the epicenter of Diorhabda impact in the U.S. Canopy cover of Tamarix was between 73 and 81% at these sites, with the percent that was live cover fluctuating by year with a minimum of 42%. Using a traditional general linear model, we found that readily and commonly measured environmental factors could explain only up to 26% of the variation in Tamarix live canopy each year. The number of defoliations was correlated with an increase rather than a decrease in percent live canopy, suggesting compensatory growth. Spatial structure alone explained 22–40% of variation. We found fine scale spatial structure at less than 10 km and broad scale spatial structure from 10 to 30 km. Combining both traditional and novel spatial statistical methods we increased that percentage to 43–63%, depending on year. These results suggest that scientists and land managers must look beyond commonly measured environmental variables to explain non-random biocontrol impact in this system. In particular, this study points to the potential for biotic interactions and variation in flood cycles for further exploration of the identified spatial structure.

Keywords

Biological control Diorhabda spp. Moran’s Eigenvector Maps Spatial modeling Tamarix spp. 

Notes

Acknowledgements

We would like to thank the following funding sources for supporting this project: Val A. Browning Foundation, Utah Department of Agriculture and Food, Utah Weed Supervisor’s Association (APHIS funds), Tamarisk Coalition, National Park Service, Grand County, Utah. Eduardo González’s participation in this work was partially supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme (ESFFORES Project Grant Number 299044) and the U.S. Geological Survey, Invasive Species Program. Tim Higgs, Grand County Weed Department—Supervisor, oversaw the collection and release of beetles as well as all subsequent monitoring projects within the county. Field assistant George Gerhart contributed to the bulk of data collection with Tim Graham helping out at several locations and always being ready to assist with identifying unknown groundcover plants growing on survey lines. We would also like to thank all the undergraduate assistants in data entry and management, specifically Grace Fierle, Carlie McGuire, April Rose, James Sheinbaum, and Maddie Sligh, as well as graduate student Lisa Buie Clark. Joe Ryan from the Center for Statistics and Visualization at the University of Denver also helped on this project by creating web applications to visualize beetle defoliation over time. We are also are indebted to Melissa Islam at the Kathryn Kalmbach Herbarium at the Denver Botanic Gardens, and Eric Tabacchi at EcoLab (UPS-INP-CNRS) for helping to identify and verify plants. Finally, we thank our associate editor and two anonymous reviewers, whose suggestions greatly improved the manuscript.

Data Availability

The data associated with this paper have been deposited in a Dryad digital repository  https://doi.org/10.5061/dryad.2ts54jj.

Supplementary material

10530_2018_1794_MOESM1_ESM.pdf (557 kb)
S1 Environmental variable transformations. This figure shows the frequency distribution and normal quantile plots for the environmental variables that were log transformed. (PDF 556 kb)
10530_2018_1794_MOESM2_ESM.pdf (61 kb)
S2 Distributions of percent live canopy in each year. This figure shows the frequency distribution and normal quantile plots for percent live canopy in each year. (PDF 60 kb)
10530_2018_1794_MOESM3_ESM.jpg (238 kb)
Supplementary material 3 (JPEG 238 kb)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Annie L. Henry
    • 1
  • Eduardo González
    • 1
    • 2
  • W. Wright Robinson
    • 3
  • Bérenger Bourgeois
    • 4
    • 5
  • Anna A. Sher
    • 1
  1. 1.Department of Biological SciencesUniversity of DenverDenverUSA
  2. 2.Department of BiologyColorado State UniversityFort CollinsUSA
  3. 3.Grand County Weed DepartmentMoabUSA
  4. 4.Département de Phytologie, Faculté des Sciences de l’Agriculture et de l’AlimentationUniversité LavalQuebecCanada
  5. 5.Department of Biology, Québec Centre for Biodiversity ScienceMcGill UniversityMontréalCanada

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