Improving sustainable crop protection using population genetics concepts

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Evolutionary Epidemiology Consequences of Trait-Dependent Control of Heterogeneous Parasites

AbstractDisease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. In particular, we consider a parasite expressing continuous levels of a trait-determining resource exploitation and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimize both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.

Dynamics of the rice yellow mottle disease in western Burkina Faso: Epidemic monitoring, spatio-temporal variation of viral diversity, and pathogenicity in a disease hotspot

The rice yellow mottle virus (RYMV) is a model in plant virus molecular epidemiology, with the reconstruction of historical introduction routes at the scale of the African continent. However, information on patterns of viral prevalence and viral diversity over multiple years at a local scale remains scarce, in spite of potential implications for crop protection. Here, we describe a 5-year (2015-9) monitoring of RYMV prevalence in six sites from western Burkina Faso (geographic areas of Bama, Banzon, and Karfiguela). It confirmed one irrigated site as a disease hotspot and also found one rainfed lowland (RL) site with occasional high prevalence levels. Within the studied fields, a pattern of disease aggregation was evidenced at a 5-m distance, as expected for a mechanically transmitted virus. Next, we monitored RYMV genetic diversity in the irrigated disease hotspot site, revealing a high viral diversity, with the current coexistence of various distinct genetic groups at the site scale (ca. 520 ha) and also within various specific fields (25 m side). One genetic lineage, named S1bzn, is the most recently emerged group and increased in frequency over the studied period (from 20 per cent or less in 2015-6 to more than 65 per cent in 2019). Its genome results from a recombination between two other lineages (S1wa and S1ca). Finally, experimental work revealed that three rice varieties commonly cultivated in Burkina Faso were not different in terms of resistance level, and we also found no significant effect of RYMV genetic groups on symptom expression and viral load. We found, however, that infection outcome depended on the specific RYMV isolate, with two isolates from the lineage S1bzn accumulating at the highest level at early infections. Overall, this study documents a case of high viral prevalence, high viral diversity, and co-occurrence of divergent genetic lineages at a small geographic scale. A recently emerged lineage, which comprises viral isolates inducing severe symptoms and high accumulation under controlled conditions, could be recently rising through natural selection. Following up the monitoring of RYMV diversity is required to confirm this trend and further understand the factors driving the local maintenance of viral diversity.