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• W2894096905 abstract "Abstract One major challenge to delimiting species with genetic data is successfully differentiating species divergences from population structure, with some current methods biased towards overestimating species numbers. Many fields of science are now utilizing machine learning (ML) approaches, and in systematics and evolutionary biology, supervised ML algorithms have recently been incorporated to infer species boundaries. However, these methods require the creation of training data with associated labels. Unsupervised ML, on the other hand, uses the inherent structure in data and hence does not require any user-specified training labels, thus providing a more objective approach to species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus ). First, we combine mitochondrial data with examination of male genitalic morphology to identify a priori species hypotheses. Then we use single nucleotide polymorphism data derived from sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML algorithms in successfully identifying a priori species, comparing results to commonly used genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses using UCE data. We find that unsupervised ML approaches successfully cluster samples according to species level divergences and not to high levels of population structure, while standard model-based validation methods over-split species, in some instances suggesting that all sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the benefits of better data visualization in two-dimensional space and the ability to accommodate various data types. We argue that ML methods may be better suited for species delimitation relative to currently used model-based validation methods, and that species delimitation in a truly integrative framework provides more robust final species hypotheses relative to separating delimitation into distinct “discovery” and “validation” phases. Unsupervised ML is a powerful analytical approach that can be incorporated into many aspects of systematic biology, including species delimitation. Based on results of our empirical dataset, we make several taxonomic changes including description of a new species." @default.
• W2894096905 created "2018-10-05" @default.
• W2894096905 creator A5060820355 @default.
• W2894096905 creator A5062127953 @default.
• W2894096905 creator A5076916016 @default.
• W2894096905 creator A5084204507 @default.
• W2894096905 creator A5084608068 @default.
• W2894096905 date "2018-09-28" @default.
• W2894096905 modified "2023-10-18" @default.
• W2894096905 title "An Empirical Demonstration of Unsupervised Machine Learning in Species Delimitation" @default.
• W2894096905 cites W1482808801 @default.
• W2894096905 cites W1513618424 @default.
• W2894096905 cites W151377110 @default.
• W2894096905 cites W1529325410 @default.
• W2894096905 cites W1575700397 @default.
• W2894096905 cites W1769408182 @default.
• W2894096905 cites W1907561617 @default.
• W2894096905 cites W1946356339 @default.
• W2894096905 cites W1949176574 @default.
• W2894096905 cites W1963957860 @default.
• W2894096905 cites W1965092590 @default.
• W2894096905 cites W1965213652 @default.
• W2894096905 cites W1979597031 @default.
• W2894096905 cites W1987971958 @default.
• W2894096905 cites W1992962234 @default.
• W2894096905 cites W2003070761 @default.
• W2894096905 cites W2004444667 @default.
• W2894096905 cites W2026062398 @default.
• W2894096905 cites W2027319309 @default.
• W2894096905 cites W2035839217 @default.
• W2894096905 cites W2041026583 @default.
• W2894096905 cites W2049567459 @default.
• W2894096905 cites W2060300932 @default.
• W2894096905 cites W2103441770 @default.
• W2894096905 cites W2103521631 @default.
• W2894096905 cites W2107382411 @default.
• W2894096905 cites W2108234281 @default.
• W2894096905 cites W2109628770 @default.
• W2894096905 cites W2110910793 @default.
• W2894096905 cites W2113375225 @default.
• W2894096905 cites W2116585889 @default.
• W2894096905 cites W2119180969 @default.
• W2894096905 cites W2119182375 @default.
• W2894096905 cites W2126419817 @default.
• W2894096905 cites W2128880918 @default.
• W2894096905 cites W2133445144 @default.
• W2894096905 cites W2134403593 @default.
• W2894096905 cites W2136540392 @default.
• W2894096905 cites W2139050004 @default.
• W2894096905 cites W2141052558 @default.
• W2894096905 cites W2141283603 @default.
• W2894096905 cites W2145535342 @default.
• W2894096905 cites W2146290595 @default.
• W2894096905 cites W2147299002 @default.
• W2894096905 cites W2149159956 @default.
• W2894096905 cites W2149992227 @default.
• W2894096905 cites W2159957100 @default.
• W2894096905 cites W2160378127 @default.
• W2894096905 cites W2161339576 @default.
• W2894096905 cites W2161759962 @default.
• W2894096905 cites W2162307412 @default.
• W2894096905 cites W2162744774 @default.
• W2894096905 cites W2165484512 @default.
• W2894096905 cites W2166972836 @default.
• W2894096905 cites W2185547054 @default.
• W2894096905 cites W2260449022 @default.
• W2894096905 cites W2266821187 @default.
• W2894096905 cites W2346630322 @default.
• W2894096905 cites W2403024067 @default.
• W2894096905 cites W2515298508 @default.
• W2894096905 cites W2518112906 @default.
• W2894096905 cites W2518867337 @default.
• W2894096905 cites W2543701912 @default.
• W2894096905 cites W2552697893 @default.
• W2894096905 cites W2580606745 @default.
• W2894096905 cites W2580899508 @default.
• W2894096905 cites W2588873228 @default.
• W2894096905 cites W2625093593 @default.
• W2894096905 cites W2636917114 @default.
• W2894096905 cites W2727609679 @default.
• W2894096905 cites W2783880833 @default.
• W2894096905 cites W2793555634 @default.
• W2894096905 cites W2799413939 @default.
• W2894096905 cites W2806634205 @default.
• W2894096905 cites W2809178512 @default.
• W2894096905 cites W2811175355 @default.
• W2894096905 cites W2884303974 @default.
• W2894096905 cites W2889326414 @default.
• W2894096905 cites W2889839918 @default.
• W2894096905 cites W2911964244 @default.
• W2894096905 cites W2949942731 @default.
• W2894096905 cites W2950022062 @default.
• W2894096905 cites W2950732129 @default.
• W2894096905 cites W2951054803 @default.
• W2894096905 cites W2952084747 @default.
• W2894096905 cites W3100627343 @default.
• W2894096905 cites W3106324661 @default.
• W2894096905 cites W4210723357 @default.
• W2894096905 cites W4212883601 @default.
• W2894096905 cites W4255582690 @default.

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