No abstract

Biodiversity drives ecological functioning, ultimately providing ecosystem services.Ecosystem processes are favored by greater functional diversity, particularly when groups of functionally different species interact synergistically. Many of such functions are performed by insects, among which dung beetles stand out for their important role in dung decomposition. However, anthropogenic disturbances are negatively affecting their ecological dynamics and ecosystem services. We conducted a manipulative field study to evaluate the effect of human disturbance on dung beetle diversity (abundance, species richness, and functional group richness) and dung removal rates, comparing perturbed and conserved forests in three regions of Colombia (Caribbean, Andes, and Amazon). We also assess the relationship between dung beetle diversity and dung removal rates. Dung beetle diversity was assessed using pitfall traps, and specimens were measured and assigned to functional groups according to body size and dung relocation strategy. We used exclusion control units and experimental units to assess dung degradation with and without dung beetle activity and evaluate differences in removal rates between two dung removal strategies: paracoprids and telecoprids. Dung removal rates, abundance, and functional group richness were lower in perturbed forests compared to conserved forests. Dung removal increased with abundance, species richness, and functional group richness. Moreover, dung removal performed by telecoprids increased with species richness of telecoprids and

Molecular phylogenetics has transitioned into the phylogenomic era, with data derived from next-generation sequencing technologies allowing unprecedented phylogenetic resolution in all animal groups, including understudied invertebrate taxa. Within the most diverse harvestmen suborder, Laniatores, most relationships at all taxonomic levels have yet to be explored from a phylogenomics perspective. Travunioidea is an early-diverging lineage of laniatorean harvestmen with a Laurasian distribution, with species distributed in eastern Asia, eastern and western North America, and south-central Europe. This clade has had a challenging taxonomic history, but the current classification consists of ~77 species in three families, the Travuniidae, Paranonychidae, and Nippononychidae. Travunioidea classification has traditionally been based on structure of the tarsal claws of the hind legs. However, it is now clear that tarsal claw structure is a poor taxonomic character due to homoplasy at all taxonomic levels. Here, we utilize DNA sequences derived from capture of ultraconserved elements (UCEs) to reconstruct travunioid relationships. Data matrices consisting of 317–677 loci were used in maximum likelihood, Bayesian, and species tree analyses. Resulting phylogenies recover four consistent and highly supported clades; the phylogenetic position and taxonomic status of the enigmatic genus Yuria is less certain. Based on the resulting phylogenies, a revision of Travunioidea is proposed, now consisting of the Travuniidae, Cladonychiidae, Paranonychidae (Nippononychidae is synonymized), and the new family Cryptomastridae Derkarabetian & Hedin, fam. n., diagnosed here. The phylogenetic utility and diagnostic features of the intestinal complex and male genitalia are discussed in light of phylogenomic results, and the inappropriateness of the tarsal claw in diagnosing higher-level taxa is further corroborated.

22One major challenge to delimiting species with genetic data is successfully differentiating 23 species divergences from population structure, with some current methods biased towards 24 overestimating species numbers. Many fields of science are now utilizing machine learning (ML) 25 approaches, and in systematics and evolutionary biology, supervised ML algorithms have 26 recently been incorporated to infer species boundaries. However, these methods require the 27 creation of training data with associated labels. Unsupervised ML, on the other hand, uses the 28 inherent structure in data and hence does not require any user-specified training labels, thus 29 providing a more objective approach to species delimitation. In the context of integrative 30 taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random 31 forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species 32 delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus). First, 33 we combine mitochondrial data with examination of male genitalic morphology to identify a 34 priori species hypotheses. Then we use single nucleotide polymorphism data derived from 35 sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML 36 algorithms in successfully identifying a priori species, comparing results to commonly used 37 genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses 38 using UCE data. We find that unsupervised ML approaches successfully cluster samples 39 according to species level divergences and not to high levels of population structure, while 40 standard model-based validation methods over-split species, in some instances suggesting that all 41 sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the 42 benefits of better data visualization in two-dimensional space and the ability to accommodate 43 various data types. We argue that ML methods may be better suited for species delimitation 44 3 relative to currently used model-based validation methods, and that species delimitation in a truly 45 integrative framework provides more robust final species hypotheses relative to separating 46 delimitation into distinct "discovery" and "validation" phases. Unsupervised ML is a powerful 47 analytical approach that can be incorporated into many aspects of systematic biology, including 48 species delimitation. Based on results of our empirical dataset, we make several taxonomic 49 changes including description of a new species. 50 51 52 Key Words: Random Forest, t-SNE, Variational Autoencoders, ultraconserved elements, 53 integrative taxonomy, Opiliones 54 4 Modern species delimitation is becoming increasingly objective relying on, for example, 55 statistical thresholds and/or clustering algorithms to identify species in multivariate 56 morphological space (e.g., Ezard et al. 2010;Seifert et al. 2014), or using the multispecies 57 coalescent to ident...