Assessing the phylogenetic host breadth of millet pathogens and its implication for disease spillover

Abstract

Abstract1. Increasing agriculture intensification has led to dramatically improved crop yields; however, this shift in agricultural practice has been accompanied by increasing threats from new and emerging plant pathogens. While the pathogens associated with crop species are often well studied, especially within North America and Europe, less is known about pathogen pressures on crops elsewhere, and our ability to predict the emergence of novel pathogens is limited. Here, we model phylogenetic constraints on the distribution of pathogens of millet – one of the most important crops in Africa.2. We conducted a literature review to compile a database of common millet pathogens and the non‐millet host crops associated with each. We then characterized the phylogenetic host range for each pathogen using measures of mean pairwise distance (MPD) and mean nearest taxon distance (MNTD) separating crop hosts.3. We detected robust phylogenetic clustering for both metrics of phylogenetic dispersion (MPD and MNTD). Evidence for phylogenetic clustering tended to be stronger (more negative standard effect sizes) and more variable for MPD than for MNTD.4. Although patterns for individual pathogens were variable, we did not find significant differences in phylogenetic dispersion of hosts among pathogen types (bacteria, viruses and fungi). However, in several cases, we observed evidence of phylogenetic clustering in evolutionarily distant host clades, a possible signal of occasional large phylogenetic host jumps.5. We show that pathogens cluster on closely related hosts, and it is thus likely that closely related millets also share similar pathogen communities. On average, the probability of a pathogen host shift may, therefore, be predicted by the phylogenetic relatedness between host species. However, host shifts between distantly related hosts are not infrequent. This finding has relevance not only for the design of agronomic systems to reduce disease spillover but also for biological control agents risk analysis, quarantine regulations in international trade and our understanding of the distribution and abundance of plants in natural systems.