Phylogenetic relationships between thirteen species of downy mildew and 103 species of Phytophthora (plant-pathogenic oomycetes) were investigated with two nuclear and four mitochondrial loci, using several likelihood-based approaches. Three Phytophthora taxa and all downy mildew taxa were excluded from the previously recognized subgeneric clades of Phytophthora, though all were strongly supported within the paraphyletic genus. Downy mildews appear to be polyphyletic, with graminicolous downy mildews (GDM), brassicolous downy mildews (BDM) and downy mildews with colored conidia (DMCC) forming a clade with the previously unplaced Phytophthora taxon totara; downy mildews with pyriform haustoria (DMPH) were placed in their own clade with affinities to the obligate biotrophic P. cyperi. Results suggest the recognition of four additional clades within Phytophthora, but few relationships between clades could be resolved. Trees containing all twenty extant downy mildew genera were produced by adding partial coverage of seventeen additional downy mildew taxa; these trees supported the monophyly of the BDMs, DMCCs and DMPHs but suggested that the GDMs are paraphyletic in respect to the BDMs or polyphyletic. Incongruence between nuclear-only and mitochondrial-only trees suggests introgression may have occurred between several clades, particularly those containing biotrophs, questioning whether obligate biotrophic parasitism and other traits with polyphyletic distributions arose independently or were horizontally transferred. Phylogenetic approaches may be limited in their ability to resolve some of the complex relationships between the “subgeneric” clades of Phytophthora, which include twenty downy mildew genera and hundreds of species.

Laurel wilt kills members of the Lauraceae plant family in the southeastern United States. It is caused by Raffaelea lauricola T.C. Harr., Fraedrich and Aghayeva, a nutritional fungal symbiont of an invasive Asian ambrosia beetle, Xyleborus glabratus Eichhoff, which was detected in Port Wentworth, Georgia, in 2002. The beetle is the primary vector of R. lauricola in forests along the southeastern coastal plain of the United States, but other ambrosia beetle species that obtained the pathogen after the initial introduction may play a role in the avocado (Persea americana Miller) pathosystem. Susceptible taxa are naïve (new-encounter) hosts that originated outside Asia. In the southeastern United States, over 300 million trees of redbay (P. borbonia (L.) Spreng.) have been lost, and other North American endemics, non-Asian ornamentals and avocado-an important crop that originated in MesoAmerica-are also affected. However, there are no reports of laurel wilt on the significant number of lauraceous endemics that occur in the Asian homeland of R. lauricola and X. glabratus; coevolved resistance to the disease in the region has been hypothesized. The rapid spread of laurel wilt in the United States is due to an efficient vector, X. glabratus, and the movement of wood infested with the insect and pathogen. These factors, the absence of fully resistant genotypes, and the paucity of effective control measures severely constrain the disease's management in forest ecosystems and avocado production areas.

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.

The challenge of maintaining sufficient food, feed, fiber, and forests, for a projected end of century population of between 9-10 billion in the context of a climate averaging 2-4 • C warmer, is a global imperative. However, climate change is likely to alter the geographic ranges and impacts for a variety of insect pests, plant pathogens, and weeds, and the consequences for managed systems, particularly agriculture, remain uncertain. That uncertainty is related, in part, to whether pest management practices (e.g., biological, chemical, cultural, etc.) can adapt to climate/CO 2 induced changes in pest biology to minimize potential loss. The ongoing and projected changes in CO 2 , environment, managed plant systems, and pest interactions, necessitates an assessment of current management practices and, if warranted, development of viable alternative strategies to counter damage from invasive alien species and evolving native pest populations. We provide an overview of the interactions regarding pest biology and climate/CO 2 ; assess these interactions currently using coffee as a case study; identify the potential vulnerabilities regarding future pest impacts; and discuss possible adaptive strategies, including early detection and rapid response via EDDMapS (Early Detection & Distribution Mapping System), and integrated pest management (IPM), as adaptive means to improve monitoring pest movements and minimizing biotic losses while improving the efficacy of pest control.can encompass simple to sophisticated strategies (e.g., from hoeing to modifying the environment to utilize ecosystem services) to manage pest populations and ensuing damage.Recent and projected increases in atmospheric carbon dioxide (CO 2 ) concentration are expected to continue with a potential 2× increase over current CO 2 levels, and subsequent, concomitant increases in average temperature between 0.15 and 0.3 • C, per decade, by 2100 [3,4]. Such projections, as well as recent potential changes in extreme events, increase the degree of uncertainty of how these environmental changes could impact pest biology (insects, plant pathogens, weeds), and the consequences for future biotic losses from managed plant systems.Recent and projected increases in atmospheric CO 2 could change pest biology in two essential ways. The first is related to physical changes in the environment incurred as CO 2 increases. Such increases, along with other radiation trapping gases (e.g., CH 4 , N 2 O), will increase surface temperatures [5], cause changes in precipitation frequency [6], and alter the diurnal temperature range (DTR) [7], as well as the magnitude and distribution of extreme weather events [8]. A second essential consequence is the "fertilization" effect of rising CO 2 on plant photosynthesis; approximately 95% of plant species, those that rely solely on the C 3 photosynthetic pathway, could increase growth and reproduction as CO 2 increases, including agronomic and invasive weeds. There are hundreds of studies and several meta-analyses showing that both recen...

Peronospora effusa is an obligate oomycete that causes downy mildew of spinach. Downy mildew threatens sustainable production of fresh market organic spinach in California, and routine fungicide sprays are often necessary for conventional production. In this study, airborne P. effusa spores were collected using rotating arm impaction spore trap samplers at four sites in the Salinas Valley between late January and early June in 2013 and 2014. Levels of P. effusa DNA were determined by a species-specific quantitative polymerase chain reaction assay. Peronospora effusa was detected prior to and during the growing season in both years. Nonlinear time series analyses on the data suggested that the within-season dynamics of P. effusa airborne inoculum are characterized by a mixture of chaotic, deterministic, and stochastic features, with successive data points somewhat predictable from the previous values in the series. Analyses of concentrations of airborne P. effusa suggest both an exponential increase in concentration over the course of the season and oscillations around the increasing average value that had season-specific periodicity around 30, 45, and 75 days, values that are close to whole multiples of the combined pathogen latent and infectious periods. Each unit increase in temperature was correlated with 1.7 to 6% increased odds of an increase in DNA copy numbers, while each unit decrease in wind speed was correlated with 4 to 12.7% increased odds of an increase in DNA copy numbers. Disease incidence was correlated with airborne P. effusa levels and weather variables, and a receiver operating characteristic curve analysis suggested that P. effusa DNA copy numbers determined from the spore traps nine days prior to disease rating could predict disease incidence.