Spatial effects in parasite induced marine diseases of immobile hosts

Abstract

Marine infectious diseases are more prevalent in recent times due to climate change and other anthropogenic pressures, posing a substantial threat to marine ecosystems and the conservation of their biodiversity. An important subset of marine organisms are sessile, for which the most common mechanism for disease transmission is direct contact with waterborne parasites. Only recently, some deterministic compartmental models have been proposed to describe this kind of epidemics, being these models based on non-spatial descriptions where space is homogenised and parasite mobility is not explicitly accounted for. However, in realistic situations, epidemic transmission is conditioned by the spatial distribution of hosts and the parasites mobility patterns. Thus, the interplay between these factors is expected to have a crucial effect in the evolution of the epidemic, so calling for a explicit description of space. In this work we develop a spatially-explicit individual-based model to study disease transmission by waterborne parasites in sessile marine populations. We investigate the impact of spatial disease transmission, performing extensive numerical simulations and analytical approximations. Specifically, the effects of parasite mobility into the epidemic threshold and the temporal evolution of the epidemic are assessed. We show that larger values of pathogen mobility have two main implications: more severe epidemics, as the number of infections increases, and shorter time-scales to extinction. Moreover, an analytical expression for the basic reproduction number of the spatial model, , is derived as function of the non-spatial counterpart, R0, which characterises a transition between a disease-free and a propagation phase, in which the disease propagates over a large fraction of the system. This allows to determine a phase diagram for the epidemic model as function of the parasite mobility and the basic reproduction number of the non-spatial model.

Global predictions for the risk of establishment of Pierce’s disease of grapevines

“…All known areas where Xf is well-established in Europe (e.g. Apulia, Corsica, Balearic Islands, Region of Provence-Alpes Côte d’Azur (French Riviera), Alicante) are in the 96th percentile of the tracked sites, validating the strength of our mechanistic, non-correlative PD model predictions (test in [57]). In Supplementary Data S4 and Supplementary Table S8, we provide full details of the total vineyard areas currently at risk for each country and region.…”

“…This heuristic approach to obtaining PD risk maps yields results that are consistent with all the relevant data available [45]. It also allows us to quantitatively approximate the current potential growth rate of PD incidence in wine-growing regions under different transmission scenarios, as well as extrapolating the impact of PD by 2050 [57]. By estimating a lower global risk of PD, our study casts doubts on the potential impact predicted for other Xf-related diseases transmitted by P. spumarius [49], specially in Europe when vector distribution is taken into account.…”

“…In Europe, 92.1% of the territory is at nonrisk and 6.1% is included in the epidemic-risk zone, with only 1.9% showing a high-risk index and 1.5% a moderate risk (Supplementary Table S2). The model also reveals a progressive transition from areas without risk ( r ( t ) < 0) before 1990 to epidemic-risk zones potentially with low-risk indexes by 2019 ([57], see Movies), mainly affecting the basins of the rivers Po in Italy, Garonne and Rhone (France) and Douro/Duero (Portugal and Spain). This represents a mean increase of 0.21% y −1 in the risk-epidemic zone, a rate 3.5-times greater than that of the eastern United States, which could increase the likelihood of PD establishment in Europe in the coming decades.…”

“…4C vs Fig. 4A and [57], Movies). This distribution pattern holds and moves slightly northward over time in parallel to global warming, although the trend of PD latitudinal change is moderated by high- CDD values (i.e.…”

“…In addition, the disease extension also declines due to CDD interannual fluctuations in the simulations. Cold waves periodically occur that reach latitudes close to the Gulf, such as those that occurred in 1983, 1993, 1995, 2000, 2009 and 2013 (Movies at [57]), thus preventing PD expansion northward. The magnitude of this decrease is revealed after comparing the average annual increase of the areas between r ( t ) > 0 and CDD < 314 lines.…”