{'fraction_non_alphanumeric': 0.022316043425814235, 'fraction_numerical': 0.013268998793727383, 'mean_word_length': 5.35632183908046, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Molecular identification of roots from a grassland community using size differences in fluorescently labelled PCR amplicons of three cpDNA regions

Elucidating patterns of root growth is essential for a better understanding of the functioning of plant‐dominated ecosystems. To this end, reliable and inexpensive methods are required to determine species compositions of root samples containing multiple species. Previous studies use a range of PCR‐based approaches, but none have examined a species pool greater than 10 or 30 when evaluating mixed and single species samples, respectively. We present a method that evaluates size differences in fluorescently labelled PCR amplicons (fluorescent fragment length polymorphism) of the trnL intron and the trnT‐trnL and trnL‐trnF intergenic spacers. Amplification success of the trnT‐trnL spacer was limited, but variation in the trnL intron and the trnL‐trnF spacer was sufficient to distinguish over 80% of the 95 species (97% of the 77 genera) evaluated from a diverse fescue grassland community. Moreover, we identified species known to be present in mixed samples of 4, 8, 12, and 16 species on average 82% of the time. However, this approach is sensitive to detecting species known to be absent (false positives) when using our key of 95 species. Comparing unknowns to a limited species pool ameliorates this problem, comparable to a researcher using prior knowledge of what species could be found in a sample to constrain the identification of species. Comparisons to other methods and future improvements are discussed. This method is efficient, cost‐ effective and broadly applicable to many ecosystems.