DOI: 10.5061/dryad.jdfn2z377
title: LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately

description: In both basic and applied studies, quantification of herbivory on foliage
 is a key metric in characterizing plant-herbivore interactions, which
 underpin many ecological, evolutionary, and agricultural processes.
 Current methods of quantifying herbivory are slow or inaccurate. We
 present LeafByte, a free iOS application for measuring leaf area and
 herbivory. LeafByte can save data automatically, read and record barcodes,
 handle both light and dark colored plant tissue, and be used
 non-destructively.  We evaluate its accuracy and efficiency
 relative to existing herbivory assessment tools. LeafByte has the same
 accuracy as ImageJ, the field standard, but is 50% faster. Other tools,
 such as BioLeaf and grid quantification, are quick and accurate, but
 limited in the information they can provide. Visual estimation is
 quickest, but it only provides a coarse measure of leaf damage and tends
 to overestimate herbivory. LeafByte is a quick and accurate means of
 measuring leaf area and herbivory, making it a useful tool for research in
 fields such as ecology, entomology, agronomy, and plant science.
  
description: <strong>Methods for Testing
 LeafByte</strong>
 <em>Accuracy</em>
 Accuracy_Masterfile_.csv To confirm the
 accuracy of ImageJ and LeafByte, we used both methods to measure
 artificial "leaves" of known area. We printed out 16 black
 rectangles of known area with white "holes" of known size and
 analyzed them with both LeafByte and ImageJ, compared their results to the
 known area.  
 <em>Precision</em>
 Leafbyte_compare_users.csv We tested if
 different researchers analyzing the same leaves got the same results. To
 create herbivory, we excised thirty Solanum tuberosum leaves and allowed
 a single first instar Leptinotarsa decemlineata larvaeto feed for 24
 hours. Three independent researchers measured leaf area and herbivory
 using LeafByte.  
 <em>Comparisons of different methods</em>
 LeafByte Test.csv We collected 67 leaves from
 14 plant species (Supporting Information 3) from the Cornell Botanical
 Garden and grounds. Leaves were selected to represent a range of
 morphologies and were categorized by shape and margin type. If the leaf
 was undamaged, we created simple and complex artificial herbivory using
 hole punches and razor blades to remove 0-50% of the leaf. We recorded
 whether the leaf was damaged on the margin (n=36) or only internally
 (n=22). Herbivory was estimated visually and using grid
 quantification with 2mm2 grid paper (Coley 1983). For visual estimation,
 herbivory was estimated to the nearest 5%. Leaves
 with 0-2.5% herbivory were rounded to 5%. The leaves were then flattened
 between a sheet of printer paper with the scale printed on it and a
 Premium Matte Film Shield Screen Protector (J&D, Middleton, MA)
 and photographed. Each photograph was analyzed using LeafByte, BioLeaf,
 and ImageJ by at least two different researchers per method. LeafByte and
 ImageJ provided total leaf area, absolute herbivory, and percent
 herbivory. BioLeaf and visual quantification provided only percent
 herbivory, and the grid method provided only total herbivory. We also
 recorded the time it took to analyze each leaf and record the data. For
 ImageJ, we did not include the time it took to photograph and upload the
 pictures.     
 <strong>Statistics</strong> All
 statistics were performed using R, Version 3.5.2 (R Core Team, 2018). We
 built global mixed effects models using the nlme package (Pinheiro et al.,
 2018). We dropped non-significant predictors from the models in a
 backwards stepwise fashion, assessed pairwise differences between the
 methods using emmeans (Lenth, R., 2019), and adjusted for multiple
 comparisons using false discovery rate.   
 <em>Accuracy</em>  To test for
 differences in measurement accuracy between ImageJ and LeafByte, we ran
 linear mixed effects models with area and herbivory as response
 variables. Method was included as a fixed effect, and the known size of
 each artificial leaf was set as the reference value. Additionally, we used
 an equivalency test (TOSTER, Lakens 2017) to evaluate whether the methods
 produced the same results (as opposed to linear models that test for
 differences). We used ¼ of the standard deviation as upper and lower
 bounds of the model.  
 <em>Precision</em> Because
 data were non-normally distributed, we used a Kruskul Wallis test to
 assess the effect of individual users on estimates of leaf area and leaf
 area consumed.  
 <em>Comparisons of different methods</em>
 To analyze the effect of method on leaf area, we ran a linear
 mixed effects model with leaf area as the response variable and the
 interaction between method and leaf shape as predictor variables. Species
 and leaf ID were included as random effects in all models. Leaf areas were
 log transformed to meet assumptions of homoscedasticity. 
 To analyze the effect of method on herbivory, we ran a linear
 mixed effects model with herbivory as the response variable and the
 interaction between method and number of holes, and between method and
 presence of leaf margin herbivory as predictor variables. To analyze the
 effect of method on percent area consumed data, we ran a binomial
 generalized linear mixed effects model with herbivory as a response
 variable and the interaction between method and number of holes and the
 interaction between method and presence of leaf margin herbivory as fixed
 effects. Because low levels of herbivory (0-2.5%) were rounded to 5%
 rather than 0% when using visual quantification, we analyzed both the full
 data set and data where percent herbivory was greater than 5% to ensure
 that rounding did not skew our results.  
 <em>Comparing different LeafByte scale
 types</em> leafbyte_Line_v-dot_(2).csv
 Initially, we used a straight line
 of known length as the scale (Fig. 4A), similar to how ImageJ is often
 used. Testing and observations found that holding a phone at an angle
 (which happens commonly) leads to skewed results. To fix this problem, we
 designed a system with 4 dots in a square acting as the scale. This allows
 LeafByte to identify and correct skew. To compare the line and dots scale
 methods and validate that our skew-correction resulted in more accurate
 output, we photographed 46 leaves (Table 1) at 0, 15, and 30 degree angles
 as determined by the mobile level application ‘Bubble level for iPhone’
 (version 3.04). The leaves were photographed and analyzed using LeafByte
 (version 0.0.7) using a line as a scale, or LeafByte (version 1.0.0) using
 4 dots in a square as a scale.  All statistics were performed using R,
 Version 3.5.2 (R Core Team, 2018). We built linear mixed effects models
 using the nlme package (Pinheiro et al., 2018). We dropped non-significant
 predictors from the models in a backwards stepwise fashion, assessed
 pairwise differences between the methods using emmeans (Lenth, R., 2019),
 and adjusted for multiple comparisons using false discovery rate.
description: See read me file for details.