Citizen science helps predict risk of emerging infectious disease

Abstract

Engaging citizen scientists is becoming an increasingly popular technique for collecting large amounts of ecological data while also creating an avenue for outreach and public support for research. Here we describe a unique study, in which citizen scientists played a key role in the spatial prediction of an emerging infectious disease. The yearly citizen‐science program called “Sudden Oak Death (SOD) Blitz” engages and educates volunteers in detecting the causal pathogen during peak windows of seasonal disease expression. We used these data – many of which were collected from under‐sampled urban ecosystems – to develop predictive maps of disease risk and to inform stakeholders on where they should prioritize management efforts. We found that continuing the SOD Blitz program over 6 consecutive years improved our understanding of disease dynamics and increased the accuracy of our predictive models. We also found that self‐identified non‐professionals were just as capable of detecting the disease as were professionals. Our results indicate that using long‐term citizen‐science data to predict the risk of emerging infectious plant diseases in urban ecosystems holds substantial promise.

Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California

“…and tanoak (Notholithocarpus densiflorus) in CA since first detection in 1995 (16). The epidemic has been intensively monitored (17), and much is now known about PR epidemiology. However, questions remain about the feasibility of statewide control, introducing uncertainty and confusion into identifying regional management objectives.…”

Environmental Factors Driving the Recovery of Bay Laurels from Phytophthora ramorum Infections: An Application of Numerical Ecology to Citizen Science

“…Samplings and laboratory analyses leading to the realization of the "SODmap" database were performed within the "SOD Blitz" program, whose details were previously published [17]. From 2005 to 2015 volunteer citizens were recruited to collect water, soil or plant tissue samples.…”

“…The above MC simulation was iterated 10 3 times. Only the input needed by the Φ Λ algorithm was retained from the original "SODmap" database, which was filtered to remove all unnecessary information [17,48]. Sampled trees coordinates were reprojected into UTM NAD83 zone 11N (EPSG 4326) and included within a polygon shapefile in a GIS environment [65].…”

“…Assuming that trees coordinates had been collected by volunteers endowed with GPS sensors embedded in smartphones, iPhones or comparable devices [17,48], two scenarios were analyzed depending on the GPS median radial error ε*. Based on thresholds reported in previous studies [52], the minimum and maximum ε* were set to 10 m and 500 m, respectively, defining two alternative scenarios (i.e., scenario-10 m and scenario-500 m).…”

“…The interest of forest pathologists in unraveling environmental factors driving plant diseases has been amplified in the last decades by the onset of relevant epidemics caused by emerging pathogens such as Phytophthora ramorum Werres, De Cock and Man in't Veld in Western North America, Heterobasidion irregulare Garbelotto and Otrosina, Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz and Hosoya, and Gnomoniopsis castaneae G. Tamietti in Europe, just to cite a few relevant examples [3][4][5][6][7][8][9][10][11][12]. The main environmental drivers underlying the success of such novel epidemics have often been identified through a numerical ecology approach, based on computational and multivariate statistical techniques suitable to deal with complex ecological datasets [10,11,[13][14][15][16][17][18][19][20][21][22].…”