What are the three commonly described patterns of plant pathogens?
How would you test the correlation of variables of interest in R? What does it mean if the values all fall between 0 and 1?
Control of Verticillium wilt is difficult. Provide a reason for this. Why would studying the spatial pattern be of interest?
Studying spatial patterns can be especially interesting with rust pathogens. Why is that and why is this characteristic of rusts important?
Does Cercospora beticola, the causal agent of Cercospora leaf spot of sugar beet, tend to spread among rows or within rows of sugar beet?
If disease is randomly distributed in a field, which will be better predictors of disease severity on a particular individual
Which of the following options most accurately describes the use of linear regression in modeling the spatial pattern of plant disease?
Disease severity values of the plants adjacent to a given plant are used to estimate the disease severity value of the given plant. The estimated disease severity is compared to the actual disease severity to determine whether disease severity can be accurately predicted based on spatial arrangement of plants.
Disease severity values of plants in a population are analyzed to look for random or clustering patterns. In a clustered pattern, plants disease severity values are expected to be more similar to each other at shorter distances away from one another whereas values are expected to be more different at longer distances away from one another.
Two specific types of moves were described for identifying nearest-neighbors. What were they and how do they differ?
How do the variance and mean relate with respect to different spatial patterns?
Why is the beta-binomial distribution more appropriate for fitting aggregated patterns of plant diseases when compared to the binomial distribution?
How can we fit the beta-binomial likelihood using R? Besides the likelihood function, what is the other key piece of information that we need to have?
Module Topics
Can apply or modify R-code in order to run presented examples.
Understands the mathematical concepts and assumptions for application with plant pathological data.
Understands output and can give a biological interpretation of the results.
Understands mathematical concepts but understanding of model assumptions is incomplete.
Understands output but biological interpretation may not be complete.
Interpretation of model output is incomplete because of incomplete knowledge of mathematical concept or biological meaning.
Cannot apply program, cannot interpret output.
Questions and Quizzes
Correctly answers 9-10 of 10 questions and provides appropriate details.
Correctly answers 7-8 of 10 questions and provides adequate but not complete detail.
Correctly answers 5-6 of 10 questions, provides some detail.
Correctly answers <5 of 10 questions, level of detail is unsatisfactory.
References