Abstract
Surveying invasive species can be highly resource intensive, yet near-real-time evaluations of invasion progress are important resources for management planning. In the case of the soybean rust invasion of the United States, a linked monitoring, prediction, and communication network saved U.S. soybean growers approximately $200 M/yr. Modeling of future movement of the pathogen (Phakopsora pachyrhizi) was based on data about current disease locations from an extensive network of sentinel plots. We developed a dynamic network model for U.S. soybean rust epidemics, with counties as nodes and link weights a function of host hectarage and wind speed and direction. We used the network model to compare four strategies for selecting an optimal subset of sentinel plots, listed here in order of increasing performance: random selection, zonal selection (based on more heavily weighting regions nearer the south, where the pathogen overwinters), frequency-based selection (based on how frequently the county had been infected in the past), and frequency-based selection weighted by the node strength of the sentinel plot in the network model. When dynamic network properties such as node strength are characterized for invasive species, this information can be used to reduce the resources necessary to survey and predict invasion progress.
Highlights
Invasive species are a global problem in natural, agricultural, and human health systems, and there is a corresponding great need for optimized strategies for detection of invasive species movement [1]
Beginning with the complete sentinel plot network, we evaluated the error in predictions when a smaller subset of the sentinel plots was retained following each of the following four strategies: (i) random selection of the subset, (ii) weighted probability of inclusion based on geographic zone, (iii) selection of the subset based on historical frequency of infection, and (iv) selection based on frequency of infection weighted by node strength
We developed and validated what is, to our knowledge, the first national-scale dynamic network model for an invasive species moving outside of human transportation networks
Summary
Invasive species are a global problem in natural, agricultural, and human health systems, and there is a corresponding great need for optimized strategies for detection of invasive species movement [1]. The trend toward large research networks, such as the US National Ecological Observatory Network (NEON), is another motivation for identifying optimal approaches to sampling across large linked ecological systems [2]. Sampling strategies and strategies for identifying points for management of invasive species will often have significant overlap. The spatial structure of landscapes to be sampled is an important consideration in constructing ecological sampling designs and measures of invasion [3,4]. Taking into account the dispersal mechanisms of invasive species in combination with the connectivity of the landscape for invasion has the potential to improve management efforts [5,6]. Human transport hubs may have important roles in invasions [9] and models of vaccination programs in human and other animal populations may often be relevant to invasions in plant landscapes [10,11,12,13,14]
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