Abstract
Abstract Plants lose a remarkable amount of energy to herbivorous animals, and this damage has substantial impacts on plant fitness and species' distributions. There are many ways ecologists can measure leaf damage, with some methods being more time‐consuming than others. Due to a high variance in herbivory, accurate quantification of damage at the population level requires sampling of many leaves. A simple yet effective solution to this problem is to estimate leaf damage visually. Visually estimating leaf damage may be less accurate than scanning methods, but visual estimates of leaf damage are much faster than digital measurements. Using simulations, we show that gathering larger quantities of data at a slightly higher level of inaccuracy gives a more accurate estimate of a population's overall leaf damage than fewer, exact measurements. We then introduce the ZAX Herbivory Trainer, a free online application that teaches researchers to accurately visually estimate leaf damage. On average, users took ~9 min and 48 images to complete our trainer which significantly decreased their estimate inaccuracy from 13.2% to 6%. This low level of inaccuracy can be retained up to 3 months post‐training so researchers can use the ZAX Herbivory Trainer once prior to short fieldwork or every 3 months for extensive fieldwork. We also recommend a cut‐off point, whereby if a person has not completed the app in 17.5 min or 85 images (90th percentile), they may not be suitable to estimate herbivory for research purposes. The ZAX Herbivory Trainer will allow researchers of any experience level to assess herbivory quickly and accurately in a globally standardised way. International collaborators, students and citizen scientists can all find use in this app, no matter the scale of their projects. From this we can gather better data to address big picture questions in ecology such as patterns in herbivory relating to latitude or climate change.
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