The spread of a wild plant pathogen is driven by the road network
Elina Numminen and Anna-Liisa Laine. 2020
Elina Numminen and Anna-Liisa Laine. 2020
Spatial analyses of pathogen occurrence in their natural surroundings
entail unique opportunities for assessing in vivo drivers of disease
epidemiology. Such studies are however confronted by the complexity of
the landscape driving epidemic spread and disease persistence. Since
relevant information on how the landscape influences epidemiological
dynamics is rarely available, simple spatial models of spread are often
used. In the current study we demonstrate both how more complex
transmission pathways could be incorpoted to epidemiological analyses
and how this can offer novel insights into understanding disease spread
across the landscape. Our study is focused on Podosphaera plantaginis,
a powdery mildew pathogen that transmits from one host plant to another
by wind-dispersed spores. Its host populations often reside next to
roads and thus we hypothesize that the road network influences the
epidemiology of P. plantaginis. To analyse the impact of roads
on the transmission dynamics, we consider a spatial dataset on the
presence-absence records on the pathogen collected from a fragmented
landscape of host populations. Using both mechanistic transmission
modeling and statistical modeling with road-network summary statistics
as predictors, we conclude the evident role of the road network in the
progression of the epidemics: a phenomena which is manifested both in
the enhanced transmission along the roads and in infections typically
occurring at the central hub locations of the road network. We also
demonstrate how the road network affects the spread of the pathogen
using simulations. Jointly our results highlight how human alteration of
natural landscapes may increase disease spread.
The two computed centrality measures, betweenness (A) and closeness (B),
for the considered host populations, computed based on their projection
to the closest point in the road network. The correlation between the
Euclidean- and shortest distance by road for a random set of pairs of
host populations (C) and the relationship between the computed
betweenness summary-statistic and the presence and absence of pathogen
in different years (D). The roadmaps in the background were created
using data produced by National Land Survey of Finland.
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