Daniel L. Lindner

The fungus currently known as Laetiporus persicinus is a recognizable brown-rot decayer that is widespread on oak hosts in the southeastern United States. This species was first described as Polyporus persicinus in 1872 based on collections by Henry W. Ravenel from South Carolina. In this study, we elucidate the phylogenetic relationships of Laetiporus persicinus based on maximum likelihood and Bayesian inference analyses of a four-locus data set (18S, 28S, rpb2, and tef1) from taxa within the Fomitopsidaceae and Laetiporaceae. The internal transcribed spacer (ITS) region was analyzed separately because it was not possible to align this locus across a diverse data set that included taxa from multiple families. Our analysis and previous studies indicate that Laetiporus persicinus does not belong to Laetiporus sensu stricto, and we found a strongly supported relationship between Laetiporus persicinus and the African species Kusaghiporia usambarensis, despite the fact that the 28S phylogeny resolved a different (but unsupported) topology. Here, we propose Kusaghiporia persicinus, comb. nov., based on a combination of morphological and molecular data. Laetiporus persicinus shares many morphological features with K. usambarensis that are missing in other Laetiporus species, including centrally stipitate basidiomata, a brown to pinkish pileus surface, and a pore layer that bruises when touched. However, K. usambarensis and L. persicinus differ in basidiospore size and shape as well as their geographic distributions. We provide a revised taxonomic treatment for this common wood-decay fungus.

The extent to which persisting species may fill the functional role of extirpated or declining species has profound implications for the structure of biological communities and ecosystem functioning. In North America, arthropodivorous bats are threatened on a continent‐wide scale by the spread of white‐nose syndrome (WNS), a disease caused by the fungus Pseudogymnoascus destructans. We tested whether bat species that display lower mortality from this disease can partially fill the functional role of other bat species experiencing population declines. Specifically, we performed high‐throughput amplicon sequencing of guano from two generalist predators: the little brown bat (Myotis lucifugus) and big brown bat (Eptesicus fuscus). We then compared changes in prey consumption before versus after population declines related to WNS. Dietary niches contracted for both species after large and abrupt declines in little brown bats and smaller declines in big brown bats, but interspecific dietary overlap did not change. Furthermore, the incidence and taxonomic richness of agricultural pest taxa detected in diet samples decreased following bat population declines. Our results suggest that persisting generalist predators do not necessarily expand their dietary niches following population declines in other predators, providing further evidence that the functional roles of different generalist predators are ecologically distinct.

DNA analysis of predator faeces using high‐throughput amplicon sequencing (HTS) enhances our understanding of predator–prey interactions. However, conclusions drawn from this technique are constrained by biases that occur in multiple steps of the HTS workflow. To better characterize insectivorous animal diets, we used DNA from a diverse set of arthropods to assess PCR biases of commonly used and novel primer pairs for the mitochondrial gene, cytochrome oxidase C subunit 1 (COI). We compared diversity recovered from HTS of bat guano samples using a commonly used primer pair “ZBJ” to results using the novel primer pair “ANML.” To parameterize our bioinformatics pipeline, we created an arthropod mock community consisting of single‐copy (cloned) COI sequences. To examine biases associated with both PCR and HTS, mock community members were combined in equimolar amounts both pre‐ and post‐PCR. We validated our system using guano from bats fed known diets and using composite samples of morphologically identified insects collected in pitfall traps. In PCR tests, the ANML primer pair amplified 58 of 59 arthropod taxa (98%), whereas ZBJ amplified 24–40 of 59 taxa (41%–68%). Furthermore, in an HTS comparison of field‐collected samples, the ANML primers detected nearly fourfold more arthropod taxa than the ZBJ primers. The additional arthropods detected include medically and economically relevant insect groups such as mosquitoes. Results revealed biases at both the PCR and sequencing levels, demonstrating the pitfalls associated with using HTS read numbers as proxies for abundance. The use of an arthropod mock community allowed for improved bioinformatics pipeline parameterization.

High-throughput amplicon sequencing (HTAS) of conserved DNA regions is a powerful technique to characterize microbial communities. Recently, spike-in mock communities have been used to measure accuracy of sequencing platforms and data analysis pipelines. To assess the ability of sequencing platforms and data processing pipelines using fungal internal transcribed spacer (ITS) amplicons, we created two ITS spike-in control mock communities composed of cloned DNA in plasmids: a biological mock community, consisting of ITS sequences from fungal taxa, and a synthetic mock community (SynMock), consisting of non-biological ITS-like sequences. Using these spike-in controls we show that: (1) a non-biological synthetic control (e.g., SynMock) is the best solution for parameterizing bioinformatics pipelines, (2) pre-clustering steps for variable length amplicons are critically important, (3) a major source of bias is attributed to the initial polymerase chain reaction (PCR) and thus HTAS read abundances are typically not representative of starting values. We developed AMPtk, a versatile software solution equipped to deal with variable length amplicons and quality filter HTAS data based on spike-in controls. While we describe herein a non-biological SynMock community for ITS sequences, the concept and AMPtk software can be widely applied to any HTAS dataset to improve data quality.

Bat white-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans, has decimated North American hibernating bats since its emergence in 2006. Here, we utilize comparative genomics to examine the evolutionary history of this pathogen in comparison to six closely related nonpathogenic species. P. destructans displays a large reduction in carbohydrate-utilizing enzymes (CAZymes) and in the predicted secretome (~50%), and an increase in lineage-specific genes. The pathogen has lost a key enzyme, UVE1, in the alternate excision repair (AER) pathway, which is known to contribute to repair of DNA lesions induced by ultraviolet (UV) light. Consistent with a nonfunctional AER pathway, P. destructans is extremely sensitive to UV light, as well as the DNA alkylating agent methyl methanesulfonate (MMS). The differential susceptibility of P. destructans to UV light in comparison to other hibernacula-inhabiting fungi represents a potential “Achilles’ heel” of P. destructans that might be exploited for treatment of bats with WNS.

High throughput amplicon sequencing (HTAS) of conserved DNA regions is a powerful technique to characterize microbial communities. Recently, spike-in mock communities have been used to measure accuracy of sequencing platforms and data analysis pipelines. To assess the ability of sequencing platforms and data processing pipelines using fungal ITS amplicons, we created two ITS spike-in control mock communities composed of cloned DNA in plasmids: a biological mock community (BioMock), consisting of ITS sequences from fungal taxa, and a synthetic mock community (SynMock), consisting of non-biological ITS-like sequences. Using these spike-in controls we show that: 1) a non-biological synthetic control (e.g., SynMock) is the best solution for parameterizing bioinformatics pipelines, 2) pre-clustering steps for variable length amplicons are critically important, 3) a major source of bias is attributed to initial PCR reactions and thus HTAS read abundances are typically not representative of starting values. We developed AMPtk, a versatile software solution equipped to deal with variable length amplicons and quality filter HTAS data based on spike-in controls. While we describe herein a non-biological synthetic mock community for ITS sequences, the concept and AMPtk software can be widely applied to any HTAS dataset to improve data quality.