Clarke van Steenderen

Clarke van Steenderen

PhD student in the Department of Zoology and Entomology, Centre for Biological Control, Rhodes University

Publications

BinMat: A molecular genetics tool for processing binary data obtained from fragment analysis in R

Published in Biodiversity Data Journal, 2022

Processing and visualising trends in the binary data (presence or absence of electropherogram peaks), obtained from fragment analysis methods in molecular biology, can be a time-consuming and often cumbersome process. Scoring and analysing binary data (from methods, such as AFLPs, ISSRs and RFLPs) entail complex workflows that require a high level of computational and bioinformatic skills. The application presented here (BinMat) is a free, open-source and user-friendly R Shiny programme (https:// clarkevansteenderen.shinyapps.io/BINMAT/) that automates the analysis pipeline on one platform. It is also available as an R package on the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/web/packages/BinMat/index.html). BinMat consolidates replicate sample pairs of binary data into consensus reads, produces summary statistics and allows the user to visualise their data as ordination plots and clustering trees without having to use multiple programmes and input files or rely on previous programming experience. šŸ“ PDF

Recommended citation: van Steenderen, C.J.M. 2022. Biodiversity Data Journal (10) doi: 10.3897/BDJ.10.e77875 https://bdj.pensoft.net/article/77875/

SPEDE-sampler: an R Shiny application to assess how methodological choices and taxon-sampling can affect Generalised Mixed Yule Coalescent (GMYC) output and interpretation

Published in Molecular Ecology Resources, 2022

Species delimitation tools are vital to taxonomy and the discovery of new species. These tools can make use of genetic data to estimate species boundaries, where one of the most widely-used methods is the Generalised Mixed Yule Coalescent (GMYC) model. Despite its popularity, a number of factors are known to influence the performance and resulting inferences of the GMYC. Moreover, the few studies that have assessed model performance to date have been predominantly based on simulated datasets, where model assumptions are not violated. Here, we present a user-friendly R Shiny application, ā€œSPEDE-samplerā€ (SPEcies DElimitation sampler), that assesses the effect of computational and methodological choices, in combination with sampling effects, on the GMYC model. Output phylogenies are used to test the effect that 1) sample size, 2) BEAST and GMYC parameters (e.g. prior settings, single vs multiple threshold, clock model), and 3) singletons has on GMYC output. Optional predefined grouping information (e.g. morphospecies/ecotypes) can be uploaded in order to compare it to GMYC species and estimate percentage match scores. Additionally, predefined groups that contribute to inflated species richness estimates are identified by SPEDE-sampler, allowing for the further investigation of potential cryptic species or geographic sub-structuring in those groups. Merging by the GMYC is also recorded to identify where traditional taxonomy has overestimated species numbers. Four worked examples are provided to illustrate the functionality of the programā€™s workflow, and the variation that can arise when applying the GMYC model to empirical datasets. The R Shiny program is available for download on GitHub. šŸ“ PDF

Recommended citation: van Steenderen, C.J.M. and Sutton, G.F. 2022. SPEDE-sampler: an R Shiny application to assess how methodological choices and taxon-sampling can affect Generalised Mixed Yule Coalescent (GMYC) output and interpretation. Molecular Ecology Resources (22)2 doi: 10.1111/1755-0998.13591 https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13591

Addressing the red flags in cochineal identification: The use of molecular techniques to identify cochineal insects that are used as biological control agents for invasive alien cacti

Published in Biological Control, 2021

Invasive Cactaceae cause considerable damage to ecosystem function and agricultural practices around the world. The most successful biological control agents used to combat this group of weeds belong to the genus Dactylopius (Hemiptera: Dactylopiidae), commonly known as ā€˜cochinealā€™. Effective control relies on selecting the correct species, or in some cases, the most effective intraspecific lineage, of cochineal for the target cactus species. Many of the Dactylopius species are so morphologically similar, and in the case of intraspecific lineages, identical, that numerous misidentifications have been made in the past. These errors have resulted in failed attempts at the biological control of some cactus species. This study aimed to generate a multi-locus genetic database to enable the accurate identification of dactylopiids. Genetic characterization was achieved through the nucleotide sequencing of three gene regions (12S rRNA, 18S rRNA, and COI) and two inter-simple sequence repeats (ISSR). Nucleotide sequences were very effective for species-level and D. tomentosus lineage-level identification, but could not distinguish between the two lineages within D. opuntiae commonly used for biological control of various Opuntia spp. Fragment analysis through the use of ISSRs successfully addressed this issue. This is the first time that a method has been developed that can distinguish between these two D. opuntiae lineages. Using the methods developed in this study, biological control practitioners can ensure that the most effective agent species and lineages are used for each cactus target weed, thus maximizing the level of control. šŸ“ PDF

Recommended citation: van Steenderen, C.J.M., Paterson, I.D., Edwards, S., and Day, M.D. 2021. Addressing the red flags in cochineal identification: The use of molecular techniques to identify cochineal insects that are used as biological control agents for invasive alien cacti. Biological Control 104426. doi: 10.1016/j.biocontrol.2020.104426. https://www.sciencedirect.com/science/article/pii/S1049964420306538