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Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly complete information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 May et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Studies examining phylogenetic community structure have become increasingly prevalent, yet little attention has been given to the influence of the input phylogeny on metrics that describe phylogenetic patterns of co-occurrence. Here, we examine the influence of branch length, tree reconstruction method, and amount of sequence data on measures of phylogenetic community structure, as well as the phylogenetic signal (Pagel's λ) in morphological traits, using Trichoptera larval communities from Churchill, Manitoba, Canada. We find that model-based tree reconstruction methods and the use of a backbone family-level phylogeny improve estimations of phylogenetic community structure. In addition, trees built using the barcode region of cytochrome c oxidase subunit I (COI) alone accurately predict metrics of phylogenetic community structure obtained from a multi-gene phylogeny. Input tree did not alter overall conclusions drawn for phylogenetic signal, as significant phylogenetic structure was detected in two body size traits across input trees. As the discipline of community phylogenetics continues to expand, it is important to investigate the best approaches to accurately estimate patterns. Our results suggest that emerging large datasets of DNA barcode sequences provide a vast resource for studying the structure of biological communities.

The region of Churchill, Manitoba, contains a wide variety of habitats representative of both the boreal forest and arctic tundra and has been used as a model site for biodiversity studies for nearly seven decades within Canada. Much previous work has been done in Churchill to study the Daphnia pulex species complex in particular, but no study has completed a wide-scale survey on the crustacean species that inhabit Churchill's aquatic ecosystems using molecular markers. We have employed DNA barcoding to study the diversity of the Branchiopoda (Crustacea) in a wide variety of freshwater habitats and to determine the likely origins of the Churchill fauna following the last glaciation. The standard animal barcode marker (COI) was sequenced for 327 specimens, and a 3% divergence threshold was used to delineate potential species. We found 42 provisional and valid branchiopod species from this survey alone, including several cryptic lineages, in comparison with the 25 previously recorded from previous ecological works. Using published sequence data, we explored the phylogeographic affinities of Churchill's branchiopods, finding that the Churchill fauna apparently originated from all directions from multiple glacial refugia (including southern, Beringian, and high arctic regions). Overall, these microcrustaceans are very diverse in Churchill and contain multiple species complexes. The present study introduces among the first sequences for some understudied genera, for which further work is required to delineate species boundaries and develop a more complete understanding of branchiopod diversity over a larger spatial scale.