TY - JOUR
T1 - Scale-dependence of environmental and socioeconomic drivers of albizia invasion in Hawaii
AU - Niemiec, R. M.
AU - Asner, G. P.
AU - Brodrick, P. G.
AU - Gaertner, J. A.
AU - Ardoin, N. M.
N1 - Funding Information:
This study was supported by a National Science Foundation Graduate Research Fellowship (DGE-114747) to the first author. The Worldview-2 imagery used in this analysis was provided by USDA-NRCS National Geospatial Center of Excellence. The imagery was obtained from the Spatial Data Analysis and Visualization Lab at the University of Hawaii Hilo. We thank Peter Vitousek, Flint Hughes, Chris Balzotti, Franny Brewer, and Springer Kaye for their guidance on this work.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2018/1
Y1 - 2018/1
N2 - To reduce the spread and impacts of invasive species in human-dominated landscapes, numerous and diverse residents often need to engage in individual as well as collective invasive species control efforts on their property and in their community. Combating invasion thus requires an understanding of what socioeconomic factors, in addition to ecological factors, may slow an invasive species or facilitate its spread. However, few studies have examined associations between invasive species and multi-scale socioeconomic and environmental factors, which may influence residents’ decisions to control invaders. We combine spatially explicit social and environmental datasets, and we apply gradient boosting regression to examine the socioeconomic, land use, and environmental factors associated with the distribution of albizia (Falcataria moluccana), an invasive tree species, in Hawaii. We find that environmental factors are the dominant controls on albizia cover at the landscape scale, but socioeconomic variables lead to a modest improvement in the ability to predict albizia distribution at the housing subdivision scale. At the subdivision scale, albizia is more common on properties with absentee and/or less-wealthy landowners. Albizia is also more common on smaller properties and non-agricultural land. Our study provides policy recommendations for reducing the spread of invaders and outlines an approach using computation machine learning for examining multiple socioeconomic and environmental factors associated with biological invasion in complex social landscapes.
AB - To reduce the spread and impacts of invasive species in human-dominated landscapes, numerous and diverse residents often need to engage in individual as well as collective invasive species control efforts on their property and in their community. Combating invasion thus requires an understanding of what socioeconomic factors, in addition to ecological factors, may slow an invasive species or facilitate its spread. However, few studies have examined associations between invasive species and multi-scale socioeconomic and environmental factors, which may influence residents’ decisions to control invaders. We combine spatially explicit social and environmental datasets, and we apply gradient boosting regression to examine the socioeconomic, land use, and environmental factors associated with the distribution of albizia (Falcataria moluccana), an invasive tree species, in Hawaii. We find that environmental factors are the dominant controls on albizia cover at the landscape scale, but socioeconomic variables lead to a modest improvement in the ability to predict albizia distribution at the housing subdivision scale. At the subdivision scale, albizia is more common on properties with absentee and/or less-wealthy landowners. Albizia is also more common on smaller properties and non-agricultural land. Our study provides policy recommendations for reducing the spread of invaders and outlines an approach using computation machine learning for examining multiple socioeconomic and environmental factors associated with biological invasion in complex social landscapes.
KW - Boosted regression trees
KW - Conservation behavior
KW - Invasive species
KW - Private lands conservation
KW - Species distribution models
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U2 - 10.1016/j.landurbplan.2017.08.008
DO - 10.1016/j.landurbplan.2017.08.008
M3 - Article
AN - SCOPUS:85028561787
SN - 0169-2046
VL - 169
SP - 70
EP - 80
JO - Landscape Planning
JF - Landscape Planning
ER -