Analyzing plant disease epidemics with the R package epiphy

Abstract

This paper presents the R package epiphy which provides a common framework for spatialized plant disease intensity data collected at one or more time points. Many statistical methods developed over the last decades to describe and quantify plant disease epidemics are implemented. The paper is an introduction to the framework concepts and the provided tools using different sample codes to illustrate possible workflows.

Keywords: plant disease epidemics, phytopathology, spatial data, spatial aggregation, aggregation index, beta-binomial distribution, Taylor’s power law, binary power law, spatial hierarchy analysis, SADIE, R.

Introduction

Performing analyses of plant disease intensity data is a common task for many field phytopathologists. Different softwares have been developped to make computationally available some statistical methods, but there are separated programs and are sometimes restricted to a specific computer system.

This paper describes the version 0.5.0 of the package epiphy for R (R Core Team 2015), which offers an uniform and coherent framework to perform spatial analyses in plant disease epidemics. Efforts were made to ensure that the users can fluently and fluidly carry out the data analyses that meet their needs, making it possible to generalize and automate such tasks, and piece together a sequence of operations, while limiting the need for reimplementing methods described in the scientific literature to save time and reduce the potential for error. Another key advantage of this package is to allow users unfamiliar with these methods (e.g. students, other scientists) to use them with safeguards against misuses of methods specific for a given kind of data set for instance. While firstly intented to be a toolbox for phytopathologists, implemented methods may be easily translatable to other data contexts.

This package consists of three components: a bundle of historical data sets in plant disease epidemiology, a set of relevant data classes to reliably and efficiently handle date sets, and statistical methods developped over the last few decades to extract information from collected data sets. Information about these different components are given in this paper, but the main amphasis is made on the practical tools using code examples.

As the epiphy package is not yet available on CRAN, one needs to use the following lines to install it:

install.packages("devtools") # If not already installed.
devtools::install_github("chgigot/epiphy") # Note: Same command for the updates.
library(epiphy)

Data sets

The package epiphy is provided with a bundle of more than 10 historical data sets that were mainly published in plant disease epidemiology literature. There is, for example, counts of arthropods made in a wheat field in UK in 1996 (Holland, Winder, and Perry 1999) or incidence of tomato spotted wilt virus (TSWV) disease recorded in field trials at the Waite Institute (Australia) in 1929 (Cochran 1936). These two data sets (called arthropods and tomato_tswv$field_1929 in epiphy) will be used throughout this paper. To take a look at all the available data sets, type data(package = "epiphy"). Each data set is supported by relevant documentation specifying briefly the context of data collection, the data structure and the published references. Note that you do not need to use the function data to load a provided data set because all of them are already lazily loaded when epiphy is loaded. This means that they will not occupy any memory until you use them.

str(arthropods)

## 'data.frame':    378 obs. of  6 variables:
##  $ x : int  1 2 3 4 5 6 7 1 2 3 ...
##  $ y : int  1 1 1 1 1 1 1 2 2 2 ...
##  $ xm: int  0 30 60 90 120 150 180 0 30 60 ...
##  $ ym: int  0 0 0 0 0 0 0 30 30 30 ...
##  $ t : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ i : int  29 32 2 24 20 10 26 21 25 12 ...

str(tomato_tswv$field_1929)

## 'data.frame':    4320 obs. of  5 variables:
##  $ x: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ y: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ t: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ i: int  0 1 0 1 0 0 0 0 1 0 ...
##  $ n: int  1 1 1 1 1 1 1 1 1 1 ...

In these two examples, \(x\) and \(y\) correspond to the spatial coordinates of the sampling units distributed in a regular two-dimensional grid. Specifically \(x\) and \(y\) are the row id and the within-row sampling unit id, respectively. \(t\) variable stands for the assessment time or date. There are six and three recording dates for the counts of arthropods and the TSWV incidence data sets, respectively. \(i\) variable corresponds to the number of recorded individuals (arhtropods or diseased plants) in each sampling unit. \(n\), which is only there in incidence data sets, is the total number of individuals in a sampling unit. As in the raw TSWV incidence data set, \(n = 1\) everywhere, this means that each sampling unit contains only one plant and so \(i\) can only be equal to 0 (the plant is healthy) or 1 (the plant is diseased).

Conceptual framework

In order to collect spatial plant disease data, an appropriate sampling unit (or quadrat or sample unit or cluster) corresponding to a location where assessments are carried out must be chosen. It may be a plant unit (typically a leaf in the case of foliar pathogens), an indivudal plant or a cluster of nearby plants. The different ways of recording disease levels (or disease intensity) were sometimes confusing in the literature. We strive to stick to the nomenclature proposed by McRoberts, Hughes, and Madden ( 2003) where the count of visible symptoms (such as lesions), the presence or absence of disease, and the assessment of the proportion of plant tissue diseased correspond to count, incidence and severity data, respectively. To reliably handle such kinds of observational data sets, the package epiphy relies on the definition of a set of relevant classes. There is a mother class, named intensity, which makes it possible to format disease data sets for further analyses and check that everything is fine with the provided data (e.g. the input data must be a data frame). Object creation is only possible for one of the three subclasses of intensity which are count, incidence and severity for eponymous kinds of data. (Note that there is no implemented methods for the severity class at the moment.) Count data are integers starting from zero with no upper limit, while incidence data differ only by a upper limit set equal to the number of individuals in the sampling unit. Severity data correspond to percentages ranging from 0 to 100%.

When creating an object of one of the subclasses of intensity, it is necessary to perform variable mapping to describe how variables in the input data frame will be mapped to spatial, temporal and observational properties of the analysis methods described in a later section. The reserved variable names for spatial information correspond to the three spatial dimensions, x, y and z. (Note that no currently implemented method deals with the third dimension z). t is used to map temporal information. Finally, i and n are reserved for the so-called observational properties. They stand for recorded intensity and number of individuals in a sampling unit, respectively. Variable mapping can be implicit, if (some of) the reserved names are already present in the column names of the input data frame, or explicit, if the user make the links between the reserved names and the column names using the function mapping. Note that this paradigm is similar to the one used in ggplot2 package with the function aes.

# Count data
# We will use only the last assessment date for the arthropods data set:
arthropods_t6 <- arthropods[arthropods$t == 3, ]
# - Explicit mapping:
(cou_t3 <- count(arthropods_t6, mapping(x = x, y = y, t = t, i = i)))

## # A mapped object: count class
## # dim: 2 space, 1 time, 1 obs
##   [x] [y]   .  . [t] [i]
##     x   y  xm ym   t   i
## 1   1   1   0  0   3   3
## 2   2   1  30  0   3  26
## 3   3   1  60  0   3  10
## 4   4   1  90  0   3   8
## 5   5   1 120  0   3  16
## 6   6   1 150  0   3  12
## # ... with 57 more records (rows)

# - Total implicit mapping:
cou_t3_bis <- count(arthropods_t6)
# - Partial implicit mapping:
cou_t3_ter <- count(arthropods_t6, mapping(i = i))
all(identical(cou_t3, cou_t3_bis), identical(cou_t3, cou_t3_ter))

## [1] TRUE

# Implicit mapping for incidence data:
(inc <- incidence(tomato_tswv$field_1929))

## # A mapped object: incidence class
## # dim: 2 space, 1 time, 2 obs
##   [x] [y] [t] [i] [n]
##     x   y   t   i   n
## 1   1   1   1   0   1
## 2   1   2   1   1   1
## 3   1   3   1   0   1
## 4   1   4   1   1   1
## 5   1   5   1   0   1
## 6   1   6   1   0   1
## # ... with 4314 more records (rows)

Some useful information are displayed when you print an intensity object, such as the exact nature of this object (count, incidence or severity). Mapping variables (with squared brackets) and mapped variables (just below mapping variables) are also printed. You can also plot such objects to vizualize all the data in a convenient way.

plot(cou_t3, tile = FALSE, size = 5)

Figure 1. Observation sequences of maps of of the counts of arthropods over time.

It is possible to perform useful data transformation directly with intensity objects. For example, the clump function can be used to regroup adjacent sampling units into bigger ones, and thus redefine what is a sampling unit in a given data set. An extended version of split was also implemented to deal with intensity objects in an efficient way. In addition, you can use as.data.frame anytime you want to retrieve the underlying data frame of an intensity object (without any mapping).

inc9 <- clump(inc, unit_size = c(x = 3, y = 3))
plot(inc)
plot(inc9)

Figure 2. Observation sequences of maps of TSWV incidence data over time for two definition of a sampling unit. A sampling unit contains either only one tomato plant (above), or a set of 9 plants (below). In each case, the three maps correspond to the same field at different dates.

inc9_t1 <- split(inc9, by = "t")[[1]]
inc9_t1_sub <- split(inc9_t1, unit_size = c(x = 4, y = 5))[[6]]
plot(inc9_t1)
plot(inc9_t1_sub)

Figure 3. Different sub-parts of the TSWV incidence data set, with only what was observed for the first scoring time (left) and for a subpart of this same scoring time (right).

Statistical methods

A collection of statistical methods has been implemented in epiphy. At the moment, the available tools include several indices of aggregation (e.g. Fisher’s, Lloyd’s and Morisita’s indices), distribution fitting to reveal any spatial aggregation in data sets, power law analysis (Taylor’s and binary forms) and an early version of Spatial Analysis by Distance IndicEs (SADIE). We strived to mention most relevant scientific literature related to each method in the corresponding R help pages.

Most of the time, a function dedicated to some methods is clever enough to know which “flavor” of the method needs to be used with the provided data set. For example, if you use the function power_law with a count data set, the regular Taylor’s power law will be called, whereas in the case of incidence data, it will be the binary form of the power law. That is the other reason why, in addition to performing initial compliance tests, a set of dedicated classes was implemented to handle different kinds of intensity data sets. In any case, the function outputs will let you know what flavor was used to perform the analysis.

(inc9_t1_idx <- agg_index(inc9_t1))

## Fisher's index of dispersion:
## (Version for incidence data)
## 1.4

chisq.test(inc9_t1_idx)

## 
##  Chi-squared test for (N - 1)*index following a chi-squared distribution
##  (df = N - 1)
## 
## data:  inc9_t1_idx
## X-squared = 222.56, df = 159, p-value = 0.0006578

z.test(inc9_t1_idx)

## 
##  One-sample z-test
## 
## data:  inc9_t1_idx
## z = 3.7803, p-value = 0.0001566
## alternative hypothesis: two.sided

calpha.test(inc9_t1_idx)

## 
##  C(alpha) test
## 
## data:  inc9_t1_idx
## z = 3.7092, p-value = 0.0002079

cou_t3_distr <- fit_two_distr(cou_t3)
summary(cou_t3_distr)

## Fitting of two distributions by maximum likelihood
## for 'count' data.
## Parameter estimates:
## 
## (1) Poisson (random):
##        Estimate  Std.Err Z value    Pr(>z)    
## lambda 11.68254  0.43062  27.129 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (2) Negative binomial (aggregated):
##       Estimate   Std.Err Z value    Pr(>z)    
## k     3.308038  0.742318  4.4564 8.336e-06 ***
## mu   11.682540  0.916690 12.7443 < 2.2e-16 ***
## prob  0.220675  0.040883  5.3977 6.748e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

inc9_t1_distr <- fit_two_distr(inc9_t1)
summary(inc9_t1_distr)

## Fitting of two distributions by maximum likelihood
## for 'incidence' data.
## Parameter estimates:
## 
## (1) Binomial (random):
##      Estimate  Std.Err Z value    Pr(>z)    
## prob 0.181250 0.010151  17.855 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (2) Beta-binomial (aggregated):
##        Estimate   Std.Err Z value    Pr(>z)    
## alpha  3.671093  1.457761  2.5183  0.011792 *  
## beta  16.574710  6.665468  2.4867  0.012895 *  
## prob   0.181326  0.011913 15.2204 < 2.2e-16 ***
## rho    0.047068  0.017943  2.6232  0.008711 ** 
## theta  0.049393  0.019759  2.4997  0.012428 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

plot(cou_t3_distr, breaks = 17)
plot(inc9_t1_distr)

cou <- count(arthropods[arthropods$x <= 6, ])
cou <- split(cou, unit_size = c(x = 3, y = 3))
cou_plaw <- power_law(cou)
coef(summary(cou_plaw))

##                             Estimate Std. Error    t value     Pr(>|t|)
## (Intercept): log_base(Ar) -0.1191721  0.5650426 -0.2109082 8.342179e-01
## log(x): b                  1.5752164  0.2010455  7.8351249 4.035639e-09

inc9_spl <- split(inc9, unit_size = c(x = 4, y = 5))
inc_plaw <- power_law(inc9_spl)
coef(summary(inc_plaw))

##                             Estimate Std. Error   t value     Pr(>|t|)
## (Intercept): log_base(Ar)  0.5533101 1.04346689 0.5302613 6.012425e-01
## log(x): b                  1.0994981 0.27222117 4.0389883 5.484494e-04
## Ai                         1.7389998 1.81458867 0.9583438 3.483037e-01
## ai                         0.1552786 0.06965083 2.2293866 3.631573e-02
## AI                         1.1230753 0.19158067 5.8621535 6.727429e-06
## aI                        12.5775689 5.64171735 2.2293866 3.631573e-02

plot(cou_plaw)
plot(inc_plaw)

plot(inc9_t1)
plot(threshold(inc9_t1))

inc_low     <- split(inc9, unit_size = c(x = 4, y = 5, t = 1))
inc_high    <- lapply(inc_low, threshold)
(inc_sphier <- spatial_hier(inc_low, inc_high))

## Spatial hierarchy analysis for 'incidence' data.
## 
## Parameter estimate:
##        Estimate Std. Error
## log_nu 2.052793 0.08445315
## nu     7.789630 0.65785878

plot(inc_sphier)

set.seed(123)
cou_t3_m <- remap(cou_t3, mapping(x = xm, y = ym))
plot(cou_t3_m)
res <- sadie(cou_t3_m)

## Computation of Perry's indices:

summary(res)

## 
## Call:
## sadie.count(data = cou_t3_m)
## 
## First 6 rows of clustering indices:
##     x y  i cost_flows      idx_P idx_LMX prob
## 1   0 0  3  -30.00000 -0.4708909      NA   NA
## 2  30 0 26   40.23662  0.7460930      NA   NA
## 3  60 0 10  -53.08882 -1.2326789      NA   NA
## 4  90 0  8    0.00000  0.0000000      NA   NA
## 5 120 0 16   30.00000  0.7601079      NA   NA
## 6 150 0 12   30.00000  0.8107080      NA   NA
## 
## Summary indices:
##                       overall    inflow  outflow
## Perry's index        1.171517 -1.310935 1.073955
## Li-Madden-Xu's index       NA        NA       NA
## 
## Main outputs:
## Ia: 1.1414 (Pa = 0.14)
## 
## 'Total cost': 1274.105
## Number of permutations: 100

plot(res)
plot(res, isoclines = TRUE)

set.seed(123)
res <- mapcomp(cou_t3_m, delta = 4, bandwidth = 60)
res

## Map Comparison analysis (mapcomp)
## 
## Call:
## mapcomp.count(data = cou_t3_m, delta = 4, bandwidth = 60)
## 
## Stat: 0.0989 (P = 0.019802)

plot(res)

Conclusion

The package epiphy implements currently many of the methods described in the chapter 9 of the book “The study of plant disease epidemics” (Madden, Hughes, and Bosch 2007). We hope that such statistical methods packaged in a consistent way to be easily used in an open statistical environment will facilitate their use and spread in the phytopathology community, and even beyond.

Acknowledgments

The authors are grateful to Prof. Xiangming Xu for discussion and advice regarding the SADIE procedure.

Annexes

Most of the functions in the package epiphy have been designed to be compatible with pipeline coding. Using the package magrittr, you can pipe the analyses as in the following examples.

library(epiphy)
library(magrittr)

incidence(tomato_tswv$field_1929) %>%
    split(by = "t") %>%
    getElement(1) %>% # To keep the first assessment time.
    clump(unit_size = c(x = 3, y = 3)) %>%
    fit_two_distr() %T>%
    plot() %>%
    summary()

## Fitting of two distributions by maximum likelihood
## for 'incidence' data.
## Parameter estimates:
## 
## (1) Binomial (random):
##      Estimate  Std.Err Z value    Pr(>z)    
## prob 0.181250 0.010151  17.855 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (2) Beta-binomial (aggregated):
##        Estimate   Std.Err Z value    Pr(>z)    
## alpha  3.671093  1.457761  2.5183  0.011792 *  
## beta  16.574710  6.665468  2.4867  0.012895 *  
## prob   0.181326  0.011913 15.2204 < 2.2e-16 ***
## rho    0.047068  0.017943  2.6232  0.008711 ** 
## theta  0.049393  0.019759  2.4997  0.012428 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

For information, below are the same analyses without pipes.

my_data <- incidence(tomato_tswv$field_1929)
my_data <- split(my_data, by = "t")
my_data <- my_data[[1]]
my_data <- clump(my_data, unit_size = c(x = 3, y = 3))
my_res  <- fit_two_distr(my_data)
plot(my_res)
summary(my_res)

Here is another example:

count(arthropods) %>%
    clump(unit_size = c(x = 3, y = 3)) %>%
    split(by = "t") %>%
    lapply(agg_index) %T>%
    (function(x) plot(sapply(x, function(xx) xx$index), type = "b",
                      xlab = "Observation sequence",
                      ylab = "Aggregation index")) %>%
    sapply(function(x) chisq.test(x)$p.value)

##            1            2            3            4            5            6 
## 1.523216e-07 5.594987e-08 1.549744e-07 2.979618e-13 9.080714e-19 3.287662e-07