Computers and Electronics in Agriculture 116 (2015) 80–87

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Computers and Electronics in Agriculture

journal homepage: www.elsevier .com/locate /compag

A new SAS program for behavioral analysis of electrical penetrationgraph data

http://dx.doi.org/10.1016/j.compag.2015.06.0110168-1699/� 2015 Published by Elsevier B.V.

⇑ Corresponding author. Tel.: +863 956 8804.E-mail address: tebert@ufl.edu (T.A. Ebert).

1 Mention of trade names or commercial products in this publication is solely forthe purpose of providing specific information and does not imply recommendation orendorsement by the U.S. Department of Agriculture. USDA is an equal opportunityprovider and employer.

Timothy A. Ebert a,⇑, Elaine A. Backus b,1, Miguel Cid c, Alberto Fereres c, Michael E. Rogers a

a Department of Entomology & Nematology, Citrus Research and Education Center, University of Florida, 700 Experiment Station Rd., Lake Alfred, FL 33850, United Statesb USDA, Agricultural Research Service, USDA-ARS San Joaquin Valley Agricultural Sciences Center, 9611 South Riverbend Avenue, Parlier, CA, 93648-9757, United Statesc Departamento de Proteccion Vegetal, Instituto de Ciencias Agrarias (ICA), Consejo Superior de Investigaciones Científicas (CSIC), C/Serrano 115 dpdo, 28006 Madrid, Spain

a r t i c l e i n f o

Article history:Received 10 December 2014Received in revised form 11 May 2015Accepted 13 June 2015

Keywords:EPGSoftwareElectropenetography

a b s t r a c t

Monitoring feeding behaviors of insects whose piercing–sucking mouthparts are inserted into plant tis-sue is often done by making the insect part of an electronic circuit, using a technique called ElectricalPenetration Graph, or electropenetrography (both abbreviated EPG). Fluctuating voltage signals in the cir-cuit are graphed, and resulting waveforms are interpreted by a researcher as specific stylet activities.After measurement of waveforms, data consist of a list of different behaviors and associated durations.These data are further processed to yield hundreds of variables that are compiled and statisticallyanalyzed prior to publication. The goal of this study was to develop a program to make this process moreefficient for studies of aphids and related species, given the large quantity of data expected to begenerated. Herein, the three major existing programs that perform this function are reviewed. The oldestprogram (Backus 1.0) both compiles data and calculates a SAS-based statistical analysis; however it onlyworks with the original, recorded variables and is not tailored to aphid studies. The other programs (EPGCalc and the Sarria Excel� workbook) compile a more diverse suite of derived variables suitable for aphidsthan does Backus 1.0; however, they do not include statistical analyses. A new program (Ebert 1.0)introduced herein uses SAS to calculate the diverse suite of derived variables for aphids, and also providesstatistical analysis via powerful mixed-model ANOVA using a single software platform, similar to theBackus program. The code is open source, so that any researcher can adapt this program to deal withbehavioral idiosyncrasies of a particular study insect. The new program will be especially valuable forlarge experiments with many insect subjects.

The Backus 1.0 system for classifying variables required some modification in order to deal with all thederived variables for aphids. The new classification system has five levels: Cohort, Insect, Probe,Waveform, and Event. Within each of these levels, variables can be sequential or non-sequential, andthese are further subdivided into conditional and non-conditional. These changes will facilitate designof more complex experiments in the future, and the ultimate adaptation of this analysis techniquedesigned around aphids for use with other organisms. There is supplemental material included withthe manuscript to assist with understanding the nature of data generated using EPG methods, and thecomplex task of extracting knowledge from a vast quantity of data generated by these experiments.

� 2015 Published by Elsevier B.V.

1. Introduction

There is a methodological problem in assessing the stylet activi-ties of piercing–sucking arthropods because their mouthparts(stylets) are small and usually obscured by the opaque substrate

in which they are stylet-probing/-penetrating. For over 50 years,the primary solution to this problem has been the technologycalled electrical penetration graph or electropenetrography (bothabbreviated EPG). In most EPG studies to date, the organism hasbeen a hemipteroid insect (frequently aphids) stylet-probing/-penetrating a plant, but EPG can also be used with mosquitoes,ticks, spiders, and other organisms. EPG can equally be applied tooviposition, where an ovipositor inserts an egg into the tissues ofanother organism.

EPG involves making the organism a part of an electrical circuit,and measuring changes in voltage that result during feeding

T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87 81

(Walker and Backus, 2000). Analog electrical output signals (volt-ages) from EPG monitors are digitized by analog-to-digital (A/D)software and stored in a computer file as EPG waveform files. AllEPG recordings have sections with repetitive patterns of voltagefluctuations (called waveforms). A researcher then scrolls througheach file, second by second, and documents where the appearanceof the waveform’s pattern changes (indicating that the insect chan-ged its behavior or its stylet tip position) using each waveform’sestablished name known to the researcher. Sometimes one wave-form occurs only after others have taken place (Bonani et al.,2010; Ab Ghaffar et al., 2011; Civolani et al., 2011).

The biological meanings of waveforms are determined usingplant histology and related techniques and are unique to each spe-cies recorded. There is a diversity of conventions for naming wave-forms/behaviors. For example, researchers studying aphids andpsyllids use a similar waveform-naming convention, in partbecause both groups form a salivary sheath in the plant tissue astheir stylets penetrate intercellularly around plant cells to ulti-mately ingest from phloem sieve elements (Tjallingii and HogenEsch 1993; Bonani et al., 2010; Pearson et al., 2014). For insectsthat use a different feeding strategy, waveform names and biolog-ical meanings can be quite different from those used for aphids. Forexample, sharpshooter leafhoppers (sheath-feeders that penetratetheir stylets intracellularly to ingest from xylem cells) have at least12 waveforms (Sandanayaka and Backus, 2008), whose meaningsare very different compared with the eight waveforms for aphids.Thrips and lygus bugs (two types of cell rupture feeders that leaveno salivary sheaths) have even more different waveform namesand meanings (Cline and Backus, 2002; Kindt et al., 2003;Stafford et al., 2011).

The present project was inspired by the fact that EPG researchcan generate hundreds of recordings with thousands of events(individual occurrences of a waveform) per experiment, and there-fore efficient measurement and analysis of these large data sets iscritical. We began by examining the three computer programs pre-sently available for this activity (from Backus et al., 2007;Giordanengo, 2014, and Sarria et al., 2009), to identify their advan-tages and disadvantages. The program that we used most exten-sively was published in July 2009. It is an Excel workbook (Sarriaet al., 2009) (henceforth called the Sarria workbook). The programcalculates a variety of sequential and non-sequential variables (seeSupplemental Information, Section I for a list of variables). Thesecond-most-used program in our work was published in March2007 (Backus et al., 2007) (henceforth called Backus 1.0). Thisprogram both compiles and statistically analyzes non-sequentialvariables (those that do not take into account the order ofoccurrence of the waveforms). Lastly, EPG-Calc was cited for thefirst time in May 2007 as ‘‘Unpublished’’ (Ameline et al., 2007).The program was cited several times as ‘‘unpublished’’ or as theweb page where the program could be obtained, but it is nowdirectly citable through the peer reviewed literature(Giordanengo 2013, 2014). While there is some overlap in theresults produced by all three programs, each program also hassome unique features that will be described below. Finally, weacknowledge that there are at least two other programs, (vanGiessen and Jackson, 1998; Schwarzkopf et al., 2013) and anExcel macro named JKL 2.0 available at www.epgsystems.eu.However, we did not make use of these programs in this work.

The long-term goals of our research are to: (1) develop a holisticand user-adaptable suite of SAS programs for both compilation andstatistical analysis of EPG response variables for all types of insects,and in so doing, (2) develop a comprehensive system of categoriz-ing and naming those variables. The objectives of the present workwere to: (A) develop a SAS program that will both compile and sta-tistically analyze the variables from the Sarria workbook for aphidsand related insects; (B) develop a hierarchical classification and

naming scheme for these variables based upon the system ofBackus et al., (2007), originally designed for non-aphids (especiallyauchenorrhynchans and heteropterans); (C) develop a dictionary tosynonymize overlapping variable names among all three previouslypublished programs; and (D) understand and re-name variableswhere those with apparently similar names are outputted withdifferent values, depending upon which program was used.

2. Methods

2.1. EPG monitors and their waveforms

EPG monitors have undergone considerable changes since thefirst units were built in the early 1960s (McLean and Kinsey,1964). Initial monitors operated based on alternating current(AC) electronics (Ellsbury et al., 1994), then direct current (DC)monitors were developed (Schaefers, 1966; Tjallingii, 1978), andmore recently an instrument that utilizes either AC or DC(Backus and Bennett, 2009) has been designed. While many olderdesigns reside in various laboratories, only the Giga4 and Giga8DC monitors (Tjallingii, 2014) and the AC–DC monitor are currentlybeing manufactured.

To understand results and discussion on the program we intro-duce herein, one must know something about aphid and psyllidwaveforms. In aphids, there are eight waveforms that are mostcommonly used (Tjallingii, 1978, 1988; Prado and Tjallingii,1994): (1) NP: non-probing; (2) C: activities during stylet penetra-tion to the phloem or xylem (termed pathway activities); (3) pd:potential drop or intracellular puncture; (4) E1e: extracellular sali-vation; (5) E1: salivation in sieve element; (6) E2: ingestion insieve element; (7) G: ingestion in xylem; (8) F: derailed styletmechanics. Waveforms C and pd are sometimes further subdi-vided. A recording of a psyllid can be described using six wave-forms: (1) NP: Non-probing; (2) C: pathway; (3) D: first contactwith phloem; (4) E1: phloem salivation; (5) E2: phloem ingestion;and (6) G: xylem ingestion (Bonani et al., 2010). A hypotheticalsequence of waveforms is shown (Fig. 1) to assist with visualizingthe sequential progression of these waveform events. For eachwaveform in the figure, there is a temporal position in thesequence of events and a duration. For example, the fifth eventfor insect 1 in cohort 1 was G, and this was the first G of three Gevents performed by this insect.

2.2. Terminology

Parameter versus variable: The early literature collectively callsthe waveforms and derived values ‘‘parameters’’ (McLean andKinsey, 1968) and this terminology continues to the present(Prado and Tjallingii, 1999, 2007; Walker and Backus, 2000;Giordanengo, 2014; Pointeau et al., 2014). However, recently, someauthors have started using the term ‘‘response variable’’ or ‘‘vari-able’’ (Pelletier and Giguère, 2009; Serikawa et al., 2013;Tjallingii, 2014). In statistical nomenclature, the correct termwould be variable (Altman and Bland, 1999). A parameter in statis-tics is defined as ‘‘a numerical characteristic of a population, as dis-tinguished from a statistic obtained by sampling [the latter termedvariable] (OED, 2014a).’’ In mathematics, a variable is a valuethroughout a mathematical calculation that has the potential tochange (OED, 2014b). The terms variable and parameter are alsoused in computer science, where the correct term in our contextwould also be variable (Page and Didday, 1980; Kelley and Pohl,1984; Kernighan and Ritchie, 1988). In regards to historical prece-dence, we note that the OED gives a date of 1816 for the use of vari-able in mathematics while the first EPG monitor (hence earliestpossible date for use of the term parameter in EPG research) was

Fig. 1. A hypothetical set of behaviors from four insects in two cohorts nested within the hierarchical classification system. The figure thus emphasizes the linear nature ofEPG-recorded behaviors. Cohort is much like ‘‘treatment’’ though treatment implies an action while cohort could include something like male versus female. Insect is eachinsect in the experiment. Probe is the time from the end of one Np to the start of the next Np. Waveform is the behavior. Event is a specific instance of each behavior. Thearrows show how the data can be reorganized so that in the second probe of the first insect there is the first instance of C on through the fourth instance.

82 T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87

built around 1964 (McLean and Kinsey, 1964). Thus, for many rea-sons, variable is the correct term for EPG analyses.

Variable classification levels: The four hierarchical levels ofBackus et al. (2007) are: cohort (i.e., treatment), insect, probe,and waveform event (from highest to lowest). Within each level,variables are calculated either ‘‘by’’ or ‘‘per.’’

By versus per: A ‘‘by insect’’ variable calculates values for eachindividual insect in the study. In contrast, a ‘‘per insect’’ variablealways sums a value for all insects in the cohort, and then dividesby the number of insects, thus making a mean. One can distinguishbetween ‘‘Mean duration of C by insect’’ (for which there would beone value for each insect in the cohort) and ‘‘Mean duration of Cper insect’’ (for which there would be a single, representative valueacross all insects in the cohort). Thus, a duration that was by wave-form per probe per insect (by cohort) would, for each cohort, sumthe durations of each waveform event and divide by the number ofprobes, and would then take those values and next sum acrossinsect and divide by the number of insects.

Total versus mean: Both EPG-Calc and the Sarria workbook out-put by-insect values. Researchers must then calculate the means ofall of the totals for each insect, thus converting the by-insect totalsto per-insect means. Unfortunately, in subsequent publications,authors have commonly used the original Sarria/EPG-Calc variablename, despite displaying the later-generated mean not the originaltotal. Thus, a problem arises that is not with the Sarria or EPG-Calcprograms per se, but with the terminology to describe the meansgenerated after Sarria or EPG-Calc. The problem can be solved byone of two techniques. One can change the names of the variablesafter statistical analysis for use in the manuscript (e.g., Total dura-tion of C in the Sarria workbook, a ‘‘by insect’’ variable, becomes[Mean] Waveform Duration per Insect for C (Backus et al., 2007),a ‘‘per insect’’ variable). Alternatively, one can include a note inthe methods section of any manuscript that uses the Sarria variablenames (e.g., Stafford et al., 2011).

Sequential versus non-sequential variables: EPG data are by nat-ure sequential because there is always an order to events per-formed by the insect. The insect does one activity, then the nextactivity, and then the next. So, not only could a behavioralsequence be Np-C-Np-C-pd-C-pd-C-pd-E1-E2-Np-C, but one candiscuss the first C event versus the second C event (Fig. 1).Characterization of a variable as ‘‘sequential’’ is applied if the orderthat events appear is important. Mean duration of C by insect is anexample of a non-sequential variable. In calculation, you take alldurations of C, sum them and divide by the number of occurrences.In the calculation it does not matter whether you add the durationof the first C in the first probe to the third C in the second probe, orvice versa. One could also calculate Mean Duration of C by probe,where one would start by taking the average of all the C durationsfor C1 through C4 for the second probe for insect 1 (second set of

arrows in Fig. 1), and do likewise for all other probes. This too isnon-sequential because it does not account for the order of the Cbehaviors, nor does it utilize the order of the probes.

Conditional versus non-conditional variables: This is a new addi-tion to the description of these variables. Conditional variables usesome external value to select specific instances of a behavior. Anexample of a conditional variable is ‘‘Duration of E1 before a longE2.’’ The value of E1 that is used is conditional on the outcome ofthe next behavior. If that behavior is not a long duration E2, thenthe value of E1 is ignored in calculating this variable. The dic-tionary (Supplement 1) codifies all the Sarria variables in columnsR through V.

2.3. Experimental design for testing programs

As we developed our SAS program, we tested its performanceagainst the Sarria workbook to determine under what circum-stances the output results would be similar or different. Programperformance was evaluated using a set of 10 data sets that werecreated to have specific problems. We started by combining someaphid data (Aphis gossypii Glover, Cotton aphid) with some psylliddata (Diaphorina citri Kuwayama, Asian Citrus Psyllid). The D wave-form in the psyllid data was given code 11, which is the pd-L code(used for indication of long potential drops) in the Sarria work-book. We then generated other data sets by selectively deleting awaveform and fixing the resulting errors (for a list of types of errorsee Supplemental Information, Section II). A sequence likeNp-C-pd-C-pd-C-Np would become Np-C-C-C-Np if pd is deleted,but one cannot have repeated waveforms of the same type. Thiserror was fixed by reducing the sequence to Np-C-Np. There wereten data sets: (1) The Asian Citrus Psyllid was used with the Dwaveform coded as for pd-L. A recording from an aphid with pdbut lacking pd subphases was appended to these data; (2) Theentire recording was only NP. No other waveforms were present;(3) The entire recording alternates NP and C; (4) The recordingwas the same as 1, except all NP were deleted; (5) The recordingwas the same as 1, except all C were deleted; (6) This was the sameas 1, except all pd were deleted (including the pd-L from the psyl-lid); (7) This was the same as 1, except all G were deleted; (8) Thiswas the same as 1, except all E1 were deleted; (9) This was thesame as 1, except all E2 were deleted; (10) This was the same as1, except all pd-L were deleted. This approach checks to see howthe program handles data errors and tests whether problems inone part of the program cause problems in other parts of the pro-gram. Readers interested in reviewing the voluminous results ofthese tests may contact the senior author.

Even using the above example data sets, developing the pro-gram was not a simple activity. While the simple approach canwork 80% of the time, there are often special situations that need

T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87 83

to be addressed depending on the approach to each research prob-lem. Understanding these issues is part of error checking in pro-gramming. Thus, we also collaborated with beta testers (listed inAcknowledgments) to analyze their real-world research problems(not under the programmer’s control) to fine-tune the new Ebert1.0 program.

3. Results and discussion

3.1. Justification for naming variables based on a hierarchicalclassification scheme

EPG data can potentially produce thousands of variables.Consider the most basic set of behaviors for an aphid: Np, C, pd,E1, E2, and G. One can measure durations or counts of eachindividual event by waveform, and one can sum (by) or average(per) the durations or counts in a variety of different ways. (A probeincludes all the events between consecutive Np [non-probing/non-penetration] events.) One also can select some events byincluding conditions (e.g. Duration of Np before first E2 lasting morethan 10 min). Mostly one can substitute any of the waveforms inany of these variables to create more variables. More variables canbe created by calculating percentages (e.g., percentage of probingtime spent in xylem) or ratios (e.g., potential E2 Index). Therefore,six observed waveforms can become thousands of possible vari-ables that can be subjected to statistical tests. Keeping track ofnames, acronyms, and analyzing all these variables is a major task.

With complex data there are different ways that variables canbe calculated. For example, ‘‘Mean Duration of C’’ can be calculatedin at least two ways. One can sum all C events in a treatment anddivide by the number of C events in the treatment (calledWaveform Duration per Event in Backus et al. (2007)), or one cansum all the C events for each insect, divide by the number of eventsfor each insect (for the first mean), and then sum all the averagesand divide by the number of insects (for the second mean) (calledWaveform Duration per Event per Insect in Backus et al. (2007)). Itis unlikely that the two methods would both give the same numer-ical result unless exactly the same number of insects was recordedfor each cohort. Both of the above variables (i.e., a mean or amean-of-a-mean) are mathematically valid and potentially useful.A hierarchical classification scheme based on calculation methodcan facilitate selection of appropriate variable names. That said,the hierarchical system must not replace the more traditional sys-tem based on biological meaning of the waveforms (Sarria et al.,2009; Giordanengo, 2014; Tjallingii 2014). Both approaches arenecessary because the hierarchical system facilitates keeping trackof how variables are calculated while the biological classificationfacilitates understanding the biological implications of all the vari-ables. In order to understand how our program calculates variablesand assigns names, first we must assess the methods, advantages,and disadvantages of each of the previously published programs.

3.2. Published programs

Sarria Workbook version 4.4.3: This program can be downloadedat: http://cibio.ua.es/descargas/EPG_analysisworksheet_v4.4.3.xls.Sarria is a macro program written in Microsoft Excel�. The usercan copy-and-paste or import raw data files individually. The pro-gram imports data from a variety of sources: Windaq, Stylet+,MacStylet, or Probe. Advantages of this program are that it calcu-lates a broad range of sequential, non-sequential, conditional,and non-conditional variables. Sarria also identifies common errorsin aphid-type recordings, e.g., consecutive waveform errors, no E1before E2, and no beginning of probe. It facilitates finding theerrors by printing the line number with the error in the error

message. That program allows the user to create new variablesusing the formulae included in Microsoft Excel� without modifyingthe VisualBasic Macros. Disadvantages of the Sarria workbook arethat it does not do statistical analyses, the program can processonly 25 insects at a time (although it can be run multiple timesto process more than 25 insects), and each insect can have no morethan 974 events.

There are also some properties of the Sarria workbook thatcould be either advantages or disadvantages depending upon theuser’s needs or the insect recorded; they are merely differencescompared with the other programs. For example, Sarria is limitedto 11 aphid-type waveforms that are described in the user manual.Another property is that missing values are sometimes consideredmissing, sometimes zero, and sometimes the duration of recording.This variation is biologically justifiable based on choices made bythe authors of Sarria, to handle certain variables in certainways depending upon long experience with aphid biology.Nonetheless, it is the primary cause of differences in outputted val-ues for the same variable between Backus 1.0 and Sarria.

It is best to use the Sarria workbook to maintain historical con-sistency, especially for research on aphids and other sternorrhyn-chans, for which it was primarily designed. Sarria is also a goodchoice if one has no access to SAS, or if one is focused on a handfulof variables rather than all possible variables.

Backus 1.0: This is a SAS based program that did not start outwith a version number, so Backus 1.0 will be our designation forall pre-2015 versions of the program. This program requires a com-plete data file, as there is no by-insect import feature. The first partof this program checks for errors and compiles a chart of transi-tional probabilities: e.g., the probability that the next behaviorwould be XY2 given that it was XY1 now. The second step compilesthe means and standard errors, and then performs statisticalcomparisons among treatments using mixed model analysis ofvariance. The program assumes that missing values are missing,not zero. This program calculates a wide variety of non-sequentialvariables. Backus 1.0 has the fewest unintended consequences whenone or more of the eight rules developed for a useful EPG analysisprogram are violated (see Supplemental Information Section II forrules).

The advantages of Backus 1.0 are that it is easy to use, there areno limits on the number of insects or events that can be processedat one time, and it takes a concatenated raw data file and produces apublishable statistical output. For our purpose, however, its soledisadvantage is that it outputs no sequential variables. Finally, nei-ther an advantage nor disadvantage is that this program requiresthat there be a single non-probing behavior in order for most ofthe variables to calculate correctly, e.g., not NP (non-probing/standing still) and Z (non-probing/walking) (Serikawa et al.,2013). However, with minimal effort it is possible to get theprogram to analyze any set of waveforms from any insect.

EPG Calc-6.1: This is an online PHP program at http://www2.sophia.inra.fr/ID/SOFTS/epgNP/epg.php. Data from the programsProbe or Stylet (used with Giga 4 or 8 monitors) are imported ona ‘‘by insect’’ basis, and many sequential, non-sequential,conditional (see Supplemental Information Section III for definitionof conditional variables) and non-conditional variables are calcu-lated. The list of variables is the same as in Stylet+(Stylet+dv01.23 (11-07-2012, http://www.epgsystems.eu/downloads.php).EPG-Calc 6.1 checks for data input errors such as two sequentialidentical waveforms, negative durations, and E2 not preceded byE1. Maximum file size is 1 MB, and data downloading and analysismust be finished within 2 h of logging onto the system. This pro-gram is a good choice for longer recordings with more insects.The disadvantage is that you need to upload your data to theINRA web site instead of running the software on your owncomputer.

17

Backus 1.0 Sarria

EPG -Calc

7

6 26

42

52

69

Fig. 2. A Venn diagram showing the number of variables calculated by Backus 1.0,the Sarria Workbook, and EPG-Calc and the number of variables that they have incommon. The numbers are based on assumptions given in the text, and couldchange considerably using different assumptions.

84 T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87

Fig. 2 shows a comparison of the number of response variablescalculated by each of the published programs, and their degree ofoverlap. We did not count ‘‘by’’ and ‘‘per’’ variables as distinctvariables, because it is relatively easy to extract the ‘‘by insect’’variables from all three programs. We used the aphid waveformsNp, C, pd (the pd waveform can be further subdivided intothree subphases), E1, E2, G, E1e, and F. There would be greaterdifferences among the three programs if more waveforms or anon-aphid insect were used. Also, the Sarria workbook calculatesa number of variables on a ‘‘by hour’’ basis. The code for doing asimilar activity is present in Backus 1.0, but it is normally turnedoff. We did not count these as part of the number of variables inBackus 1.0 because we emphasized the main, non-sequentialvariables for which that program was originally designed.

There are functional tradeoffs among the three programs. Forexample, in the Sarria workbook and EPG-Calc, waveforms aregiven numerical codes. This makes it harder to have data entryerrors, but makes the programs less able to deal with a waveformcode 44 where the 4 key was accidentally hit twice. Backus 1.0 nowrelies on a program in Ebert 1.0 (described below) to check forerrors. The program prints out a list of all waveforms that itencountered. A person can scan the list to find waveforms thatshould not be there, and also can look at a transitional probabilitytable. The table lists the probability that C goes to pd, or Np, or anyother waveform. While the Sarria workbook and EPG-Calc willcatch some problems (like no E1 before E2), they will miss prob-lems like E2 going directly to G.

3.3. Revised hierarchical classification scheme

Categorizing variables by mathematical properties, as originallyconceived in Backus et al. (2007), facilitates understanding of themeaning of the variable. However, in the present endeavor, we dis-covered that we needed to revise the hierarchical system in Backuset al. (2007) to accommodate the sequential variables in the Sarriaworkbook and EPG-Calc (see Supplemental Information Section IIIfor all the revisions). The original hierarchy portrayed in Fig. 1 inBackus et al. (2007) was not intended to reflect perfectly the calcu-lation of variables, but instead was a heuristic means of under-standing the interpretation of values from those variables. Ourmodified hierarchical classification is depicted in Fig. 3, whichincludes only a selection of example variables.

Originally, Backus et al. (2007) used four levels and each levelcould have three states: by, per, or missing. Their concept of‘‘event’’ embodied both the waveform name and the duration ofa specific occurrence, or event, of that waveform, i.e., it was awaveform event. In our modified convention, the waveform issolely the name given to a behavior.

Finally we define herein for the first time that all variables canbe categorized as conditional or non-conditional. A variable basedon the state of some quantity is conditional. A variable like ‘‘num-ber of sustained E2’’ is conditional on the duration of E2,‘‘Duration of E2 in the second hour’’ is conditional on time, and‘‘Duration of E1 before second E2’’ is conditional on precedingthe second E2 event. In this latter case it is also sequentialbecause the variable that forms the condition is sequential. Allvariables, whether sequential or non-sequential, are composedof either durations or counts (numbers of) (Fig. 4). The figureshows that there are different paths that one can take in calculat-ing variables as shown by the connecting lines (e.g. betweensequential and conditional under ‘‘Waveform Duration’’). No onepath is inherently more useful than another. They are just differ-ent. For example, the conditional variable (Mean) Duration of theFirst E2 event (by insect) is logically related to (Mean) WaveformDuration per Event by Insect (Fig. 3) because both are the meansof selected event durations. Thus, the hierarchical classification

scheme can be useful for sequential variables both for their calcu-lation and their interpretation.

In summary, we define five hierarchical levels in SupplementalInformation Section III: Cohort, Insect, Probe, Waveform, and Event(Fig. 1 and 3). Within each of these categories, a variable can besequential or non-sequential. Below this, a variable can be condi-tional or non-conditional. Finally, with each of these categories avariable must be either ‘‘by’’ or ‘‘per’’ for any single hierarchicallevel, but the variable can be a mix of ‘‘by’’ and ‘‘per’’ across levels.One can also ignore some information. For example, in the Backusnaming convention, mean duration of C is named ‘‘WaveformDuration per event by insect for C’’ if one summed all C event dura-tions within each insect, then divided by the number of C eventsfor that individual insect. However, it is equally valid to sum allC durations within each probe within each insect, and then sumthose values before dividing by the number of insects. Thus, onedoes not ignore probe level information, but ultimately the probelevel information does not influence the end result (associativeproperty of addition). Therefore the variable is neither ‘‘by probe’’nor is it ‘‘per probe.’’ Probe is simply irrelevant in the calculation.

Finally, there are two aspects to the cohort level that are uniqueto that level. The cohort designation can be expanded into multiplecohort levels. So cohort includes multiple independent and multi-ple nested factors, and can become as complex as any statisticaldesign one can envision. The other unique feature was that theterm cohort is used rather than treatment because treatmentimplies an act while (herein) cohort includes both treatmentsand observational events like first generation versus third genera-tion of aphids on a new host, green versus brown color morphs inthe Asian Citrus Psyllid, or gender.

3.4. Our new Ebert 1.0 program (available for downloading athttp://www.crec.ifas.ufl.edu/extension/epg/

We began developing our program by observing how theBackus 1.0 program calculated the number of probes for eachinsect using only the raw data. This enabled a better understandingof how SAS handles data, and de novo derivations of this methodenabled calculation of all the sequential variables. To replicatethe Sarria outputs via a SAS program, we designed foursub-programs that work together to achieve our objectives. Thefirst part is a tool that concatenates the raw EPG data file(s) intoa single file. There are no limits to the size of the data files northe number of files. The second part helps the user check for dataentry errors due to such common problems as typographical

Fig. 3. A hierarchical classification of variables. A selection of variables is placed within the hierarchical framework to demonstrate how the hierarchical framework can beused to better understand the mathematical nature of these variables.

Fig. 4. Examples of hierarchical classifications for several variables. While Fig. 3 shows more explicitly the by and per difference in variables, this figure focuses on placingvariables within the hierarchical classification for both counts and durations.

T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87 85

errors, negative durations, and misread waveforms. The third(optional) sub-program removes artificially terminated events,i.e., the duration of the last event in a recording that ends becausethe scientist stopped recording. The last part compiles the data byinsect, calculates means and standard errors (or deviations), thenperforms statistical tests.

The error checking program includes a print out of transitionalprobabilities. Thus, all transitions can be clearly identified for anyinsect studied, no matter the waveform. It is up to the user todecide whether all of the transitions are correct. Technically,Ebert 1.0 assists users to catch more errors than the other pro-grams. It shifts to the user the burden of identifying a condition

86 T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87

as an error, in order achieve the flexibility to deal with insectsother than aphids. Especially if one is working with aphids, thiscould be considered a disadvantage of Ebert 1.0 compared withthe Sarria Workbook. It should be noted that if one wants to pub-lish transitional probabilities as part of the data analysis, the pro-gram will need to be rerun on the clean data file.

The fourth program performs statistical analyses and is run onlyafter finishing with the error checking program. To avoid ubiqui-tous problems with EPG data being non-normally distributed, thevariables are subjected to a restricted maximum likelihood estima-tion (REML) mean comparison procedure using Proc Glimmix(Serikawa et al., 2013). This type of mixed-model analysis of vari-ance (ANOVA) completely removes the assumption of normality inconventional ANOVA by simulation-modeling the actual distribu-tion of the dataset. Use of mixed-model ANOVA results inimproved power of statistical tests, especially compared withnon-parametric ANOVA (Gbur et al., 2012) such as the Mann–Whitney U or Kruskal–Wallis tests (commonly used by EPGresearchers). This is the default, but many other types of analysesare possible.

Ebert 1.0 mimics the Sarria workbook for aphids in terms of thevariables that are calculated and the order in which they are ana-lyzed. If a researcher wishes to adapt the program to non-aphidinsects, even a beginning SAS programmer can use the existingcode as a template and make the few changes necessary to adaptthe program to any insect. For example, if one had a waveform A,and wanted to calculate a variable like ‘‘Mean duration of A’’ thenone could go to the SAS program, search for a variable like ‘‘Meanduration of C’’ (or use the variable name ‘‘MnDurC’’) and then usethat SAS code after changing the waveform from ‘‘C’’ to ‘‘A’’, andchanging the variable name to MnDurA. One would then need togo to the analysis part of the program and copy, paste, and thenmodify one of the Proc Glimmix statements to include the newvariable, and make any transformations. This is not done in Ebert1.0 because the variable list in Ebert 1.0 is a mimic of the Sarriaworkbook and that program does not currently include otherwaveforms. However, people using the Sarria workbook can useExcel to calculate such variables on their own. The difference isonly in what kind of programming is easiest, and that will dependon the background of each person.

In comparing the outputted values for the same variable calcu-lated by Ebert 1.0 versus the other programs, we found that themain reason for occasional disagreements was due to the waymissing values are handled. In EPG data, it is often the case thatone insect will engage in a particular behavior while another willnot. The insect that does not engage in that behavior now has amissing value for that behavior. If there were an expectation thatat some point in the future the insect would engage in that behav-ior, then it would be reasonable to consider this a missing value.However, this is not always a reasonable expectation. In this case,it may be reasonable to record a zero rather than a missing value.There is a section in Ebert 1.0 entitled ‘‘Zeros and Missing Data.’’Search for this term to jump to that place in the program. The pro-gramming style in Ebert 1.0 will result in a missing value for allzeros. This is because separate data sets are created that includeonly those insects with a specific behavior, and those data setsare then used for calculating all variables associated with thatbehavior. However, as a mimic of the Sarria workbook, these valueshave been changed from the SAS default to the Sarria default. Thiscan be changed as appropriate by removing or modifying the rele-vant statements.

Variables associated with the C waveform generated by Backus1.0 may not give one the same answer as in those generated byEPG-Calc or Sarria. This is because Backus 1.0 does not combinemeasured pd and C waveforms into C, as does Sarria/EPG-Calc.Instead, Backus 1.0 directly uses whatever waveform names are

measured/annotated from the original waveform recording. Thisis because Backus 1.0 was designed to be used with any insect’sdata, using any waveform names, not just data from species likesternorrhynchans that make pds. Thus, if one measures/annotatesan aphid recording to include A, B, C, and pd waveforms in pathway(A is stylet contact with the plant, B is sheath salivation (vanHelden and Tjallingii, 1993)), the values outputted for ‘‘Mean dura-tion of C’’ by Sarria/EPG-Calc will be different from those of Backus1.0. Sarria/EPG-Calc will group all data together and call it C,whereas Backus 1.0 will separate the four waveforms and providethe duration only of the original C events measured. Understandingthis difference between programs means that, if one wishes to useBackus 1.0 to output the same ‘‘Mean duration of C’’ asSarria/EPG-Calc, one must merely lump all four waveformstogether during measurement/annotation and call the resultingdata waveform ‘‘C.’’ This also means that a person using Backus1.0 who records A, B, and C needs to be careful when comparingtheir result for C versus published results for C where A and B werenot separated. There is no right choice; both approaches are correctand the best approach must be judged by the needs of theresearcher. The goal of Ebert 1.0 is to mimic Sarria, therefore itlumps all pathway waveforms into C after measurement. This out-come is achieved by making a data set called OnlyC. If the defini-tion of C used in Backus 1.0 is desired, then change the data setused in this calculation. Also, note that Sarria does not includewaveforms A and B, and therefore Ebert 1.0 does not either. So ifthere is a recording with A, B, C, and pd, Ebert 1.0 will combine Cand pd, but will not know to also include A and B unless that partof the program is rewritten. The easiest way to do this is to modifythe code for generating the data set OnlyC.

Whichever program EPG researchers choose to use for dataanalysis, we recommend that they use one (or more) of these fourprograms, and cite both the program and the version number. Thiskind of programming is relatively complex and Ebert 1.0 benefitedgreatly from having previous programs available to check answers.All of these programs have been extensively tested by severalresearch groups, and this provides a measure of security in assess-ing the data analysis methodology. They are all freely available,although all except EPG-Calc require purchase of other software.

Herein, we have provided a comprehensive classificationscheme for all of the variables that have been calculated, and manythat have yet to be discovered. To this end, we modified the hier-archical classification scheme proposed by Backus et al. (2007) toinclude waveform and event as separate categories, and to addconditional and non-conditional classifications to the existingsequential and non-sequential classifications. We reviewed thethree main programs in the literature for assisting with EPG dataanalysis. We finished by introducing a new SAS program, Ebert1.0, that provides both compilation and statistical analysis of allthe variables in the Sarria workbook. We have designed manyimproved features into this SAS program compared with the otherprograms in use at present. It will be especially valuable for accel-erating the speed of processing large amounts of EPG data. A goalof our future research is to facilitate converting our program,mostly suited for analysis of aphid or psyllid data, to a more gen-eral program for analysis of EPG data from any arthropod species.

Acknowledgments

We appreciate the programming help provided by James Colee(University of Florida) and Amha Asfaw (University of Missouri),especially providing a couple of dozen lines of code to aid thesenior author in better understanding how SAS processes data.Charlie Mullin at the SAS technical help desk wrote the code thatenables SAS to read a raw .ana data file created by Stylet+. We alsothank Alana Jacobson (North Carolina State University), Danielle

T.A. Ebert et al. / Computers and Electronics in Agriculture 116 (2015) 80–87 87

Lightle (Oregon State University), Mitchell Stamm (University ofNebraska), Marco Pitino (John Innes Centre), and AlexanderColeman (John Innes Center) for help in testing earlier drafts ofthe Ebert 1.0 program using their EPG data sets. This researchwas conducted through funding received from the CitrusResearch and Development Foundation (Ebert and Rogers), andin-house funds of USDA-ARS (Backus). The contribution by Cidand Fereres was unfunded.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.compag.2015.06.011.

References

AbGhaffar, M.B., Pritchard, J., Ford-Lloyd, B., 2011. Brown planthopper (N. lugensstal) feeding behaviour on rice germplasm as an indicator of resistance. PlosOne 6, e22137, Doi: 10.1371.

Altman, D.G., Bland, J.M., 1999. Variables and parameters. BMJ 318, 1667.Ameline, A., Couty, A., Dugravot, S., Campan, E., Dubois, F., Giordanengo, P., 2007.

Immediate alteration of Macrosiphum euphorbiae host plant-selection behaviourafter biotic and abiotic damage inflicted to potato plants. Entomol. Exp. Appl.123, 129–137.

Backus, E.A., Bennett, W.H., 2009. The AC–DC correlation monitor: new EPG designwith flexible input resistors to detect both R and emf components for anypiercing–sucking hemipteran. J. Insect Physiol. 55, 869–884.

Backus, E.A., Cline, A.R., Ellerseick, M.R., Serrano, M.S., 2007. Lygus herperus(Hemiptera: Miridae) feeding on cotton: new methods and parameters foranalysis of nonsequential electrical penetration graph data. Ann. Entomol. Soc.Am. 100, 296–310.

Bonani, J.P., Fereres, A., Garzo, E., Miranda, M.P., Appezzato-Da-Gloria, B., Lopes,J.R.S., 2010. Characterization of electrical penetration graphs of the Asian citruspsyllid, Diaphorina citri, in sweet orange seedlings. Entomol. Exp. Appl. 134, 35–49.

Civolani, S., Leis, M., Grandi, G., Garzo, E., Pasqualini, E., Musacchi, S., Chicca, M.,Castaldelli, G., Rossi, R., Freddy Tjallingii, W., 2011. Stylet penetration ofCacopsylla pyri; an electrical penetration graph (EPG) study. J. Insect Physiol. 57,1407–1419.

Cline, A.R., Backus, E.A., 2002. Correlations among AC electronic monitoringwaveforms, body postures, and stylet penetration behaviors of Lygus hesperus(Hemiptera: Miridae). Environ. Entomol. 31, 538–549.

Ellsbury, M.M., Backus, E.A., Ullman, D.L., 1994. History, development, andapplication of AC electronic insect feeding monitors. In: Ashe, J.S. (Ed.),Thomas Say Publications in Entomology: Proceedings. Entomological Societyof America, Lanham, MD, pp. 245.

Gbur, E.E., Stroup, W.W., McCarter, K.S., Durham, S., Young, L.J., Christman, M., West,M., Kramer, M., 2012. Analysis of generalized linear mixed models in theagricultural and natural resources sciences. In: American Society of Agronomy,Crop Science Society of America, and Soil Science Society of America.

Giordanengo, P., 2013. EPG-Calc 6.1, php-based script to calculate EPG parameters<http://www2.sophia.inra.fr/ID/epg.php>. Sophia Agrobiotech Institute, SophiaAntipolis, France.

Giordanengo, P., 2014. EPG-Calc: a PHP-based script to calculate electricalpenetration graph (EPG) parameters. Arthropod-Plant Interact. 8, 163–169.

Kelley, A., Pohl, I., 1984. An Introduction to Programming in C: A Book on C. TheBenjamin/Cummings Publishing Company, Menlo Park, CA, USA.

Kernighan, B.W., Ritchie, D.M., 1988. The C Programming Language. Prentice Hall,Englewood Cliffs, NJ, USA.

Kindt, F., Joosten, N.N., Peters, D., Tjallingii, W.F., 2003. Characterisation of thefeeding behaviour of western flower thrips in terms of electrical penetrationgraph (EPG) waveforms. J. Insect. Physiol. 49, 183–191.

McLean, D.L., Kinsey, M.G., 1964. A technique for electronically recording aphidfeeding and salivation. Nature (London) 202, 1358–1359.

McLean, D.L., Kinsey, M.G., 1968. Probing behavior of the pea aphid, Acyrthosiphonpisum. II. Comparisons of salivation and ingestion in host and non-host plantleaves. Ann. Entomol. Soc. Am. 61, 730–739.

OED, 2014a. Oxford English Dictionary ‘‘parameter, n.’’. Oxford University Press.<http://www.oed.com/view/Entry/137519?redirectedFrom=parameter>.

OED, 2014b. Oxford English Dictionary ‘‘variable, adj. and n.’’. University Press.<http://www.oed.com/view/Entry/221514?redirectedFrom=variable Oxford>.

Page, R., Didday, R., 1980. Fortran 77 for Humans. West Publishing Co., St. Paul, MN,55165.

Pearson, C.C., Backus, E.A., Shugart, H.J., Munyaneza, J.E., 2014. Characterization andcorrelation of EPG waveforms of Bactericera cockerelli (Hemiptera: Triozidae):variability in waveform appearance in relation to applied signal. Ann. Entomol.Soc. Am. 107, 650–666.

Pelletier, Y., Giguère, M.A., 2009. Effect of manipulations on the host selectionbehavior of Sitobion avenae (Homoptera: Aphididae). J. Insect Behav. 22, 165–171.

Pointeau, S., Jaguenet, E., Couty, A., Dubois, F., Rambaud, C., Ameline, A., 2014.Differential performance and behavior of the corn leaf aphid, Rhopalosiphummaidis, on three species of the biomas crop Miscanthus. Ind. Crops Prod. 54,135–141.

Prado, E., Tjallingii, W.F., 1994. Aphid activities during sieve element punctures.Entomol. Exp. Appl. 72, 157–165.

Prado, E., Tjallingii, W.F., 1999. Effects of experimental stress factors on probingbehaviour by aphids. Entomol. Exp. Appl. 90, 289–300.

Prado, E., Tjallingii, W.F., 2007. Behavioral evidence for local reduction of aphid-induced resistance. J. Insect Sci. 7.48, Online: insectscience.org/7.48.

Sandanayaka, W.R.M., Backus, E.A., 2008. Quantitative comparison of styletpenetration behaviors of glassy-winged sharpshooter on selected hosts. J.Econ. Entomol. 101, 1183–1197.

Sarria, E.V., Cid, M., Garzo, E., Fereres, A., 2009. Excel workbook for automaticparameter calculation of EPG data. Comput. Electron. Agric. 67, 35–42.

Schaefers, G.A., 1966. The use of direct current for electronically recording aphidfeeding and salivation. Ann. Entomol. Soc. Am. 59, 1022–1024.

Schwarzkopf, A., Rosenberger, D., Niebergall, M., Gershenzon, J., Kunert, G., 2013. Tofeed or not to feed: Plant factors located in the epidermis, mesophyll, and sieveelements influence Pea Aphid’s ability to feed on legume species. Plos One 8, 9.http://dx.doi.org/10.1371/journal.pone.0075298.

Serikawa, R.H., Backus, E.A., Rogers, M.E., 2013. Probing behaviors of adult AsianCitrus Psyllid (Hemiptera Liviidae) are not appreciably affected by soilapplication of field rate aldicarb to citrus. Florida Entomol. 96, 1334–1342.

Stafford, C.A., Walker, G.P., Ullman, D.E., 2011. Infection with a plant virus modifiesvector feeding behavior. Proc. Nat. Acad. Sci. http://dx.doi.org/10.1073/pnas.1100773108.

Tjallingii, W.F., 1978. Electronic recording of penetration behaviour by aphids.Entomol. Exp. Appl. 24, 721–730.

Tjallingii, W.F., 1988. Electrical recording of stylet penetration activities. In: Minks,A.K., Harrewijn, P. (Eds.), Aphids, their Biology, Natural Enemies And Control.Elsevier Science Publications, Amsterdam, <http://library.wur.nl/WebQuery/wurpubs/lang/6451>.

Tjallingii, W.F., 2014. EPG Systems. <http://www.epgsystems.eu/products.php>.Tjallingii, W.F., Hogen Esch, T., 1993. Fine structure of aphid stylet routes in plant

tissues in correlation with EPG signals. Physiol. Entomol. 18, 317–328.van Giessen, W.A., Jackson, D.M., 1998. Rapid analysis of electronically monitored

homopteran feeding behavior. Ann. Entomol. Soc. Am. 91, 145–154.van Helden, M., Tjallingii, W.F., 1993. Tissue localisation of lettuce resistance to the

aphid Nasonovia ribisnigri using electrical penetration graphs. Entomol. Exp.Appl. 68, 269–278.

Walker, G.P., Backus, E.A., 2000. Principles and applications of electronic monitoringand other techniques in the study of Homopteran feeding behavior. In: Brown,J.W. (Ed.), Thomas Say Publications in Entomology: Proceedings. EntomologicalSociety of America, pp. 260.