Biology subjects

Elliott, T. A.

Publications and source records attributed to Elliott, T. A..

7 recordsLinked to original sources

bioRxiv2026

Predicting taxon-specific benthic cyanobacterial mat cover and anatoxin concentrations in northern California rivers

Ecological forecasts often rely on established relationships between the ecological process of interest and predictor variables that are more easily measured. Proliferations of benthic (i.e., bottom-dwelling) cyanobacteria have been increasingly observed in rivers globally and are an emerging ecological forecasting issue as they pose a public health threat due to the production of potent neurotoxins known as anatoxins. Controls on these benthic cyanobacteria are poorly understood, thus predicting or forecasting their extent and anatoxin production is a significant challenge. Here, we measured benthic cyanobacterial cover and anatoxin concentrations for two common taxa associated with anatoxins (Microcoleus and Anabaena) at biweekly to weekly intervals during June to September in 2022 and 2023 in three northern Californian rivers. We then built predictive models to test how incorporation of a biotic predictor (river reach-scale gross primary productivity [GPP]) affected predictive accuracy in addition to widely measured abiotic predictors (i.e., nutrients, discharge, and temperature). Temporal patterns in taxon-specific benthic cyanobacterial cover and anatoxin concentrations were highly variable among rivers and between taxa. While Microcoleus cover peaked in rivers during periods of relatively low GPP, there were no clear relationships between GPP and Anabaena cover nor anatoxin concentrations of either taxon among rivers. Among multiple reaches sampled weekly in the South Fork Eel River, magnitudes of taxon-specific benthic cyanobacterial cover and anatoxin concentrations differed, but the timing of peak taxon-specific cover and anatoxin concentrations were generally consistent. Furthermore, Anabaena displayed a hysteresis relationship where increases in cover were followed by increases in anatoxin concentrations while Microcoleus lacked such relationship. While incorporating GPP as a covariate improved our predictions of Anabaena cover, we had more success predicting Microcoleus cover than Anabaena cover due to its strong negative relationship with discharge. In contrast, our models predicting Anabaena anatoxin concentrations outperformed those predicting Microcoleus anatoxin concentrations due to the hysteresis relationship between Anabaena cover and anatoxins. Overall, our predictive modeling results highlight the application of ecological forecasting for benthic cyanobacterial cover and anatoxin concentrations in rivers and demonstrate the importance of incorporating taxon-specific predictors into future forecasts of benthic cyanobacteria.

ecology↗

bioRxiv2025

Periodical cicadas suffer legacy effects of century-old forest removal

Periodical cicada (Magicicada spp.) ranges have continued to decline even as their forest habitat has rebounded from historic lows. This counterintuitive trajectory may be a consequence of Magicicadas limited dispersal capabilities combined with their requirement that individual host trees survive across decades. We studied the relationship between >150 years of landscape change and brood XIII periodical cicada activity in Johnson County, IA. We found that the presence of Magicicada breeding populations was better predicted by tree cover in 1930 than by present-day tree cover, with cicadas primarily relegated to sites that did not fall below 17.4% tree cover. While total forest has increased since 1930, this is a net increase, inclusive of considerable turnover every 17 years, and cicada ranges have declined more than they have expanded. A model based on empirical measures of forest turnover and cicada dispersal distances suggests that cicada ranges will continue to decline under nearly all conditions. A managed relocation of 20,126 adult Magicicada to a restored forest failed, reinforcing that dispersal to new sites cannot be artificially accelerated to offset declines. Preservation of forest habitat across centuries may be the only bollard standing between periodical cicadas and their extinction.

ecology↗

bioRxiv2024

Building a Collaborative DNA Barcode Library for the Diptera of Churchill, Canada: A Resource for Northern Research

Churchill, Manitoba, Canada is a diverse subarctic region that has been the focus of DNA barcoding efforts and research for decades. Despite this effort, there are still taxa in this region that are underrepresented in public databases, particularly hyper diverse insect groups. We present a collaborative large-scale DNA barcode reference library for Diptera based on molecular data and expert taxonomic identification, and we explore habitat occupancy, biogeographic patterns, and molecular evolution. The reference library contains 16786 specimens sampled around Churchill from 2005 to 2011. Selected specimens were identified by morphological means, sequenced for the animal barcode marker, and sorted into 2225 Molecular Operational Taxonomic Units (using Barcode Index Numbers: BINs) representing 68 families and 1216 named species. Eighty-five species shared a BIN with another species, 143 species were split across several BINs, and 990 species were assigned to a unique BIN, including 129 BINs new to the Barcode of Life Data System. The intraspecific and nearest neighbour distances varied across families, but most species can be easily distinguished from their nearest neighbours. By combining molecular data with taxonomic expertise, the barcode library provides species-level information for 12608 specimens, supplying future researchers with detailed taxonomic information and the opportunity to perform barcode-based specimen identifications for large-scale studies through sequence matching. Comparison of the Diptera composition of Churchill to other subarctic regions reflected likely postglacial colonization northwards as well as a Beringian and circumpolar component. This study also provides an initial investigation of molecular evolution in the best-represented species and finds variability in the rate of molecular evolution, opening intriguing avenues for further investigation. This study provides a publicly available, detailed reference library for use in future research, as well as new insights into the current diversity and taxonomic composition of Diptera present among the diverse habitats of Churchill.

molecular biology↗

bioRxiv2022

Reconstruction of Small Subunit Ribosomal RNA from High-Throughput Sequencing Data: A Comparative Study of Metagenomics and Total RNA Sequencing

The small subunit (SSU) ribosomal RNA (rRNA) is the most commonly used marker for the identification of microbial taxa, but its full-length reconstruction from high-throughput sequencing (HTS) data remains challenging, especially for complex and diverse environmental samples. Metagenomics and total RNA sequencing (total RNA-Seq) are target-PCR-free HTS methods that are used to characterize microbial communities and simultaneously reconstruct SSU rRNA sequences. However, more testing is required to determine and improve their effectiveness. In this study, we processed metagenomics and total RNA-Seq data retrieved from a commercially available mock microbial community using 112 combinations of commonly used data-processing tools, determined SSU rRNA reconstruction completeness of both sequencing methods for each species in the mock community, and analyzed the impact of data-processing tools on SSU rRNA and genome completeness. Total RNA-Seq allowed for the complete or near-complete reconstruction of all mock community SSU rRNA sequences and outperformed metagenomics. SSU rRNA completeness of metagenomics strongly correlated with the genome size of mock community species. The impact of data-processing tools was overall low, although certain tools resulted in significantly lower SSU rRNA completeness. These results are promising for the high-throughput reconstruction of novel full-length SSU rRNA sequences and could advance the simultaneous application of multiple -omics approaches in routine environmental assessments to allow for more holistic assessments of ecosystems.

genomics↗

bioRxiv2021

DROP: Molecular voucher database for identification of Drosophila parasitoids

Molecular identification is increasingly used to speed up biodiversity surveys and laboratory experiments. However, many groups of organisms cannot be reliably identified using standard databases such as GenBank or BOLD due to lack of sequenced voucher specimens identified by experts. Sometimes a large number of sequences are available, but with too many errors to allow identification. Here we address this problem for parasitoids of Drosophila by introducing a curated open-access molecular reference database, DROP (Drosophila parasitoids). Identifying Drosophila parasitoids is challenging and poses a major impediment to realize the full potential of this model system in studies ranging from molecular mechanisms to food webs, and in biological control of Drosophila suzukii. In DROP (http://doi.org/10.5281/zenodo.4519656), genetic data are linked to voucher specimens and, where possible, the voucher specimens are identified by taxonomists and vetted through direct comparison with primary type material. To initiate DROP, we curated 154 laboratory strains, 856 vouchers, 554 DNA sequences, 16 genomes, 14 transcriptomes, and 6 proteomes drawn from a total of 183 operational taxonomic units (OTUs): 114 described Drosophila parasitoid species and 69 provisional species. We found species richness of Drosophila parasitoids to be heavily underestimated and provide an updated taxonomic catalogue for the community. DROP offers accurate molecular identification and improves cross-referencing between individual studies that we hope will catalyze research on this diverse and fascinating model system. Our effort should also serve as an example for researchers facing similar molecular identification problems in other groups of organisms.

genomics↗

bioRxiv2021

debar, a sequence-by-sequence denoiser for COI-5P DNA barcode data

DNA barcoding and metabarcoding are now widely used to advance species discovery and biodiversity assessments. High-throughput sequencing (HTS) has expanded the volume and scope of these analyses, but elevated error rates introduce noise into sequence records that can inflate estimates of biodiversity. Denoising --the separation of biological signal from instrument (technical) noise--of barcode and metabarcode data currently employs abundance-based methods which do not capitalize on the highly conserved structure of the cytochrome c oxidase subunit I (COI) region employed as the animal barcode. This manuscript introduces debar, an R package that utilizes a profile hidden Markov model to denoise indel errors in COI sequences introduced by instrument error. In silico studies demonstrated that debar recognized 95% of artificially introduced indels in COI sequences. When applied to real-world data, debar reduced indel errors in circular consensus sequences obtained with the Sequel platform by 75%, and those generated on the Ion Torrent S5 by 94%. The false correction rate was less than 0.1%, indicating that debar is receptive to the majority of true COI variation in the animal kingdom. In conclusion, the debar package improves DNA barcode and metabarcode workflows by aiding the generation of more accurate sequences aiding the characterization of species diversity.

bioinformatics↗

bioRxiv2019

coil: an R package for cytochrome C oxidase I (COI) DNA barcode data cleaning, translation, and error evaluation.

Biological conclusions based on DNA barcoding and metabarcoding analyses can be strongly influenced by the methods utilized for data generation and curation, leading to varying levels of success in the separation of biological variation from experimental error. The five-prime region of cytochrome c oxidase subunit I (COI-5P) is the most common barcode gene for animals, with conserved structure and function that allows for biologically informed error identification. Here, we present coil (https://CRAN.R-project.org/package=coil), an R package for the pre-processing and error assessment of COI-5P animal barcode and metabarcode sequence data. The package contains functions for placement of barcodes into a common reading frame, accurate translation of sequences to amino acids, and highlighting insertion and deletion errors. The analysis of 10,000 barcode sequences of varying quality demonstrated how coil can place barcode sequences in reading frame and distinguish sequences containing indel errors from error-free sequences with greater than 97.5% accuracy. Package limitations were tested through the analysis of COI-5P sequences from the plant and fungal kingdoms as well as the analysis of potential contaminants: nuclear mitochondrial pseudogenes and Wolbachia COI-5P sequences. Results demonstrated that coil is a strong technical error identification method but is not reliable for detecting all biological contaminants.

bioinformatics↗