Trait‐based risk assessment for invasive species: high performance across diverse taxonomic groups,geographic ranges and machine learning/statistical tools

Aim Trait‐based risk assessment for invasive species is becoming an important tool for identifying non‐indigenous species that are likely to cause harm. Despite this, concerns remain that the invasion process is too complex for accurate predictions to be made. Our goal was to test risk assessment performance across a range of taxonomic and geographical scales, at different points in the invasion process, with a range of statistical and machine learning algorithms. Location Regional to global data sets. Methods We selected six data sets differing in size, geography and taxonomic scope. For each data set, we created seven risk assessment tools using a range of statistical and machine learning algorithms. Performance of tools was compared to determine the effects of data set size and scale, the algorithm used, and to determine overall performance of the trait‐based risk assessment approach. Results Risk assessment tools with good performance were generated for all data sets. Random forests (RF) and logistic regression (LR) consistently produced tools with high performance. Other algorithms had varied performance. Despite their greater power and flexibility, machine learning algorithms did not systematically outperform statistical algorithms. Geographic scope of the data set, and size of the data set, did not systematically affect risk assessment performance. Main conclusions Across six representative data sets, we were able to create risk assessment tools with high performance. Additional data sets could be generated for other taxonomic groups and regions, and these could support efforts to prevent the arrival of new invaders. Random forests and LR approaches performed well for all data sets and could be used as a standard approach to risk assessment development.     

Shotgun mitogenomics across body size classes in a local assemblage of tropical Diptera: Phylogeny,species diversity and mitochondrial abundance spectrum

Mitochondrial genomes can be assembled readily from shotgun‐sequenced DNA mixtures of mass‐trapped arthropods (“mitochondrial metagenomics”), speeding up the taxonomic characterization. Bulk sequencing was conducted on some 800 individuals of Diptera obtained by canopy fogging of a single tree in Borneo dominated by small (<1.5 mm) individuals. Specimens were split into five body size classes for DNA extraction, to equalize read numbers across specimens and to study how body size, a key ecological trait, interacts with species and phylogenetic diversity. Genome assembly produced 304 orthologous mitochondrial contigs presumed to each represent a different species. The small‐bodied fraction was the by far most species‐rich (187 contigs). Identification of contigs was through phylogenetic analysis together with 56 reference mitogenomes, which placed most of the Bornean community into seven clades of small‐bodied species, indicating phylogenetic conservation of body size. Mapping of shotgun reads against the mitogenomes showed wide ranges of read abundances within each size class. Ranked read abundance plots were largely log‐linear, indicating a uniformly filled abundance spectrum, especially for small‐bodied species. Small‐bodied species differed greatly from other size classes in neutral metacommunity parameters, exhibiting greater levels of immigration, besides greater total community size. We suggest that the established uses of mitochondrial metagenomics for analysis of species and phylogenetic diversity can be extended to parameterize recent theories of community ecology and biodiversity, and by focusing on the number mitochondria, rather than individuals, a new theoretical framework for analysis of mitochondrial abundance spectra can be developed that incorporates metabolic activity approximated by the count of mitochondria.     

Learning to see the wood for the trees: machine learning,decision trees,and the classification of isolated theropod teeth

Taxonomic identification of fossils based on morphometric data traditionally relies on the use of standard linear models to classify such data. Machine learning and decision trees offer powerful alternative approaches to this problem but are not widely used in palaeontology. Here, we apply these techniques to published morphometric data of isolated theropod teeth in order to explore their utility in tackling taxonomic problems. We chose two published datasets consisting of 886 teeth from 14 taxa and 3020 teeth from 17 taxa, respectively, each with five morphometric variables per tooth. We also explored the effects that missing data have on the final classification accuracy. Our results suggest that machine learning and decision trees yield superior classification results over a wide range of data permutations, with decision trees achieving accuracies of 96% in classifying test data in some cases. Missing data or attempts to generate synthetic data to overcome missing data seriously degrade all classifiers predictive accuracy. The results of our analyses also indicate that using ensemble classifiers combining different classification techniques and the examination of posterior probabilities is a useful aid in checking final class assignments. The application of such techniques to isolated theropod teeth demonstrate that simple morphometric data can be used to yield statistically robust taxonomic classifications and that lower classification accuracy is more likely to reflect preservational limitations of the data or poor application of the methods.     

The genus Alterosa Blahnik, 2005 (Trichoptera,Philopotamidae, Philopotaminae) in northeastern Brazil,including the description of three new species and an identification key for the genus

Alterosa Blahnik, 2005 contains 35 described species distributed in southern and southeastern Brazil. Three new species of Alterosa from northeastern Brazil are described and illustrated, Alterosa amadoi sp. n., Alterosa castroalvesi sp. n. and Alterosa caymmii sp. n., the first records of the genus from northeastern Brazil. An identification key for all known species of the genus is also presented.     

Ancyronyx Erichson, 1847 (Coleoptera,Elmidae) from Mindoro,Philippines, with description of the larvae and two new species using DNA sequences for the assignment of the developmental stages

Ancyronyx buhid sp. n. and Ancyronyx tamaraw sp. n. are described based on adults and larvae, matched using their cox1 or cob DNA sequence data. Additional records of Ancyronyx schillhammeri Jäch, 1994 and Ancyronyx minerva Freitag & Jäch, 2007 from Mindoro are listed. The previously unknown larva of Ancyronyx schillhammeri is also described here, aided by cox1 data. The new species and larval stages are described in detail and illustrated by SEM and stacked microscopic images. Keys to the adult and larval Ancyronyx species of Mindoro and an updated checklist of Philippine Ancyronyx species are provided. The usefulness as bioindicators, the phylogenetic relationships and biogeographic aspects affecting the distribution patterns are briefly discussed.