Search

Pierce's disease (PD) is a vector-borne disease caused by the bacteria Xylella fastidiosa, which affects grapevines in the Americas. Currently, vineyards in continental Europe, the world's largest producer of quality wine, have not yet been affected by PD. However, climate change may alter this situation. Here we incorporate the latest regional climate change projections into a climate-driven epidemiological model to assess the risk of PD epidemics in Europe for different levels of global warming. We found a significant increase in risk above + 2 ∘ C in the main wine-producing regions of France, Italy and Portugal, in addition to a critical tipping point above + 3 ∘ C for the possible spread of PD beyond the Mediterranean. The model identifies decreasing risk trends in Spain, as well as contrasting patterns across the continent with different velocities of risk change and epidemic growth rates. Although there is some uncertainty in model projections over time, spatial patterns of risk are consistent across different climate models. Our study provides a comprehensive analysis of the future of PD at multiple spatial scales (country, Protected Designation of Origin and vineyard), revealing where, why and when PD could become a new threat to the European wine industry. [ABSTRACT FROM AUTHOR]

The bacterium Xylella fastidiosa is mainly transmitted by the meadow spittlebug Philaenus spumarius in Europe, where it has caused significant economic damage to olive and almond trees. Understanding the factors that determine disease dynamics in pathosystems that share similarities can help to design control strategies focused on minimizing transmission chains. Here, we introduce a compartmental model for X. fastidiosa-caused diseases in Europe that accounts for the main relevant epidemiological processes, including the seasonal dynamics of P. spumarius. The model was confronted with epidemiological data from the two major outbreaks of X. fastidiosa in Europe, the olive quick disease syndrome in Apulia, Italy, caused by the subspecies pauca, and the almond leaf scorch disease in Mallorca, Spain, caused by subspecies multiplex and fastidiosa. Using a Bayesian inference framework, we show how the model successfully reproduces the general field data in both diseases. In a global sensitivity analysis, the vector-to-plant and plant-to-vector transmission rates, together with the vector removal rate, were the most influential parameters in determining the time of the infectious host population peak, the incidence peak, and the final number of dead hosts. We also used our model to check different vector-based control strategies, showing that a joint strategy focused on increasing the rate of vector removal while lowering the number of annual newborn vectors is optimal for disease control. [ABSTRACT FROM AUTHOR]

The decreasing seawater pH trend associated with increasing atmospheric carbon dioxide levels is an issue of concern due to possible negative consequences for marine organisms, especially calcifiers. Globally, coastal areas represent important transitional land-ocean zones with complex interactions between biological, physical and chemical processes. Here, we evaluated the pH variability at two sites in the coastal area of the Balearic Sea (Western Mediterranean). High resolution pH data along with temperature, salinity, and also dissolved oxygen were obtained with autonomous sensors from 2018 to 2021 in order to determine the temporal pH variability and the principal drivers involved. By using environmental datasets of temperature, salinity and dissolved oxygen, Recurrent Neural Networks were trained to predict pH and fill data gaps. Longer environmental time series (2012–2021) were used to obtain the pH trend using reconstructed data. The best predictions show a rate of - 0.0020 ± 0.00054 pH units year - 1 , which is in good agreement with other observations of pH rates in coastal areas. The methodology presented here opens the possibility to obtain pH trends when only limited pH observations are available, if other variables are accessible. Potentially, this could be a way to reliably fill the unavoidable gaps present in time series data provided by sensors. [ABSTRACT FROM AUTHOR]

The state of the art of epidemic modelling in terrestrial ecosystems is the compartmental SIR model and its extensions from the now classical work of Kermack–Mackendrick. In contrast, epidemic modelling of marine ecosystems is a bit behind, and compartmental models have been introduced only recently. One of the reasons is that many epidemic processes in terrestrial ecosystems can be described through a contact process, while modelling marine epidemics is more subtle in many cases. Here we present a model describing disease outbreaks caused by parasites in bivalve populations. The SIRP model is a multicompartmental model with four compartments, three of which describe the different states of the host, susceptible (i.e. healthy), S, infected, I, and removed (dead), R, and one compartment for the parasite in the marine medium, P, written as a 4-dimensional dynamical system. Even if this is the simplest model one can write to describe this system, it is still too complicated for both direct analytical manipulation and direct comparison with experimental observations, as it depends on four parameters to be fitted. We show that it is possible to simplify the model, including a reduction to the standard SIR model if the parameters fulfil certain conditions. The model is validated with available data for the recent Mass Mortality Event of the noble pen shell Pinna nobilis , a disease caused by the parasite Haplosporidium pinnae , showing that the reduced SIR model is able to fit the data. So, we show that a model in which the species that suffers the epidemics (host) cannot move, and contagion occurs through parasites, can be reduced to the standard SIR model that represents epidemic transmission between mobile hosts. The fit indicates that the assumptions made to simplify the model are reasonable in practice, although it leads to an indeterminacy in three of the original parameters. This opens the possibility of performing direct experiments to be able to solve this question. [Display omitted] • Marine diseases transmitted through waterborne parasites are increasingly prevalent. • State-of-the-art mathematical compartmental models are scarce in the field. • We present a SIR-like compartmental model to describe bivalve infectious diseases. • The model describes disease transmission explicitly through waterborne parasites. • Data from Pinna nobilis individuals kept in a tank validate the model. [ABSTRACT FROM AUTHOR]