Arboriculture & Urban Forestry 48(1): January 2022 that the leaf can be used to correctly identify the tax- onomic section to which it belongs (sections Quercus and Lobatae of the subgenus Quercus were tested). However, it is very likely that volunteer training could yield similar results without an app (Kosmala et al. 2016; Roman et al. 2017; Bancks et al. 2018). While taxonomic section-level identification of the tree is often not specific enough to properly manage or understand the implications of the tree on the site (and conversely the site on the tree), it can help to suf- ficiently reduce the number of potential candidates and make further identification markedly easier. In addition to the Fagaceae and the Juglandaceae, trees in the Platanaceae, Sapindaceae, Malvaceae, and Cupressaceae all have a highly confident identifica- tion to genus (above 95.00%). On the other end of this spectrum, it is important to note that certain taxonomic groups can be seen as chronic underperformers, and therefore their identifi- cations should not be inherently trusted. Species in the Betulaceae (and specifically the genus Betula) collectively have some of the lowest identification percentages by leaf photos, but conversely have one of the highest identification percentages by bark (85.00%). The lowest percent accuracy determined through this study was in regard to the genus Magnolia, which had only a 37.50% accuracy to genus with leaf photos and a meager 5.00% accuracy to genus with bark photos. While not inherently surprising given the dif- ficulty even for trained foresters to distinguish Mag- nolia species without specific characteristics, it is clear that these apps do not seem to offer any reliabil- ity for this taxon in particular. This is likely due, in part, to the inability for any of the apps to utilize any other sensory characteristics in their identifications (e.g., the presence and quality of trichomes, smell of crushed leaves, and sound of snapping needles); all characteristics which are often relied on heavily in the training of professionals in the field. The taxonomic groups listed in Table 4 were lim- ited in order to attempt to ensure that the data would not be completely unrepresentative of the group. For instance, including percentages for a group such as the Lamiales, for which our study only considered Fraxinus species, would not be indicative of the apps’ abilities to identify any species within the Lamiales, but instead just indicate their ability to identify Frax- inus species: it is unknown whether the inclusion of species in the genera Olea and Syringa (also within 39 the Lamiales) would have greatly changed the total percentages for the entire order. Similarly, all genera with only 1 tested species were excluded, as the app’s ability to identify 1 species is not necessarily indica- tive of its ability to identify another species within the same genus. Some attention should also be paid to those spe- cies that were offered incorrectly as the primary iden- tification very frequently throughout the study: Carya glabra (identified incorrectly 45 times), Fraxinus americana (39 times), Betula pendula (34 times), Liquidambar styraciflua (32 times), and Acer plata- noides (29 times) were all erroneously offered very frequently. While these misidentifications were mostly due to incorrect identifications of bark photos, it is important to understand which species are frequently suggested so that it is understood that even though some species might have extremely high correct iden- tification percentages, not every identification can be trusted. For example, Acer platanoides has an impres- sive correct identification rate to species of 100.00% for leaf photos, however, 9 additional leaf photos (all of Acer saccharum) were incorrectly identified as Acer platanoides. The apps also frequently identified species that are not native to North America and are almost exclusively found in the planted landscape, such as Betula pendula (34 times), Carpinus betulus (27 times), and Quercus robur (27 times), which can often be excluded quickly by form or site conditions if working in the natural landscape, especially those of European origins. Again, compared to earlier train- ing studies such as Jones (2020) and the occurrences of Q. robur as a suggestion, there is an artifact of training and a rationality to consider with choice of application, which has to be balanced with the varied selections of urban landscapes. The unfortunate point to be made, however, is that we can make these obser- vations from a vantage point of already possessing a positive identification before using the apps. The person needing or using the apps cannot be expected to know in such detail what to trust or avoid, otherwise they would not be likely to use the app in the first place (unless they were, for example, in a supervised train- ing event with an expert to guide the process as a teaching tool). CONCLUSION For our purposes, the use of PictureThis would most likely offer the most accurate identifications for ©2022 International Society of Arboriculture
January 2022
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