Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

Standard Area Diagrams for Pecan Leaf Scab: Effect of Rater Experience, Location, and Leaf Size on Reliability and Accuracy of Visual Estimates

Assessments of the severity of scab (Venturia effusa), an economically significant disease of pecan, are critical for determining pecan cultivar susceptibility, disease epidemiology, and integrated disease management approaches. We developed a standard area diagram (SAD) set to aid in assessments of pecan leaflet scab. Leaflets with scab lesions were harvested and scanned using a flatbed scanner at 600 dpi, and Fiji (ImageJ) was used to determine the actual percent disease severity. The SADs had 10 leaflets ranging in severity from 0.2 to 48.9%. Forty "small" (1.34 to 7.43 cm2) and 40 "large" (7.67 to 25.9 cm2) leaflet images were randomized for rater assessments. The images were assessed twice by 36 raters, first without and then with the SADs as a guide. Data were subjected to analysis using Lin's concordance correlation coefficient (LCC, pc) to determine the accuracy of ratings and by intraclass correlation coefficient (ICC) analysis to determine interrater reliability. The effects of rater experience, rater location, and leaflet size were also determined. The SADs significantly improved the agreement between raters and the actual values (LCC, pc = 0.70 and 0.84 without and with the SADs, respectively). The reliability of estimates was improved (ICC = 0.54 and 0.82 without and with the SADs, respectively). The effect of rater location on overall concordance was significant without and with the SADs based on an analysis of variance using a generalized linear model and lsmeans separation (P < 0.05). A generalized linear mixed model analysis revealed that there was a significant interaction between rater location, experience, and the use of the SADs, with some raters having greater improvement in generalized bias and concordance. Raters had a significantly better accuracy when rating "small" leaves (LCC, pc = 0.86) compared with "large" leaves (LCC, pc = 0.82) when using the SADs, highlighting the impact of psychophysics on field evaluations of plant disease severity. The proposed SADs will serve as an improved tool for performing pecan leaflet scab assessments by the pecan research community.

Survival Analysis as a Basis for Testing Hypotheses when Using Quantitative Ordinal Scale Disease Severity Data

Disease severity in plant pathology is often measured by the amount of a plant or plant part that exhibits disease symptoms. This is typically assessed using a numerical scale, which allows a standardized, convenient, and quick method of rating. These scales, known as quantitative ordinal scales (QOS), divide the percentage scale into a predetermined number of intervals. There are various ways to analyze these ordinal data, with traditional methods involving the use of midpoint conversion to represent the interval. However, this may not be precise enough, as it is only an estimate of the true value. In this case, the data may be considered interval-censored, meaning that we have some knowledge of the value but not an exact measurement. This type of uncertainty is known as censoring, and techniques that address censoring, such as survival analysis (SA), use all available information and account for this uncertainty. To investigate the pros and cons of using SA with QOS measurements, we conducted a simulation based on three pathosystems. The results showed that SA almost always outperformed midpoint conversion with data analyzed using a t test, particularly when data were not normally distributed. Midpoint conversion is currently a standard procedure. In certain cases, the midpoint approach required a 400% increase in sample size to achieve the same power as the SA method. However, as the mean severity increases, fewer additional samples are needed (approximately an additional 100%), regardless of the assessment method used. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.

Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry

Two decades have passed since the strawberry (Fragaria x ananassa) disease caused by Macrophomina phaseolina, a necrotrophic soilborne fungal pathogen, began surfacing in California, Florida, and elsewhere. This disease has since become one of the most common causes of plant death and yield losses in strawberry. The Macrophomina problem emerged and expanded in the wake of the global phase-out of soil fumigation with methyl bromide and appears to have been aggravated by an increase in climate change-associated abiotic stresses. Here we show that sources of resistance to this pathogen are rare in gene banks and that the favorable alleles they carry are phenotypically unobvious. The latter were exposed by transgressive segregation and selection in populations phenotyped for resistance to Macrophomina under heat and drought stress. The genetic gains were immediate and dramatic. The frequency of highly resistant individuals increased from 1% in selection cycle 0 to 74% in selection cycle 2. Using GWAS and survival analysis, we found that phenotypic selection had increased the frequencies of favorable alleles among 10 loci associated with resistance and that favorable alleles had to be accumulated among four or more of these loci for an individual to acquire resistance. An unexpectedly straightforward solution to the Macrophomina disease resistance breeding problem emerged from our studies, which showed that highly resistant cultivars can be developed by genomic selection per se or marker-assisted stacking of favorable alleles among a comparatively small number of large-effect loci.

Coinfection with a virus constrains within-host infection load but increases transmission potential of a highly virulent fungal plant pathogen

The trade-off between within-host infection rate and transmission to new hosts is predicted to constrain pathogen evolution, and to maintain polymorphism in pathogen populations. Pathogen life-history stages and their correlations that underpin infection development may change under coinfection with other parasites as they compete for the same limited host resources. Cross-kingdom interactions are common among pathogens in both natural and cultivated systems, yet their impacts on disease ecology and evolution are rarely studied. The host plant Plantago lanceolata is naturally infected by both Phomopsis subordinaria, a seed killing fungus, as well as Plantago lanceolata latent virus (PlLV) in the Åland Islands, SW Finland. We performed an inoculation assay to test whether coinfection with PlLV affects performance of two P. subordinaria strains, and the correlation between within-host infection rate and transmission potential. The strains differed in the measured life-history traits and their correlations. Moreover, we found that under virus coinfection, within-host infection rate of P. subordinaria was smaller but transmission potential was higher compared to strains under single infection. The negative correlation between within-host infection rate and transmission potential detected under single infection became positive under coinfection with PlLV. To understand whether within-host and between-host dynamics are correlated in wild populations, we surveyed 260 natural populations of P. lanceolata for P. subordinaria infection occurrence. When infections were found, we estimated between-hosts dynamics by determining pathogen population size as the proportion of infected individuals, and within-host dynamics by counting the proportion of infected flower stalks in 10 infected plants. In wild populations, the proportion of infected flower stalks was positively associated with pathogen population size. Jointly, our results suggest that the trade-off between within-host infection load and transmission may be strain specific, and that the pathogen life-history that underpin epidemics may change depending on the diversity of infection, generating variation in disease dynamics.