DOI: 10.5061/dryad.q2bvq83xg
title: Data and code from: Biogeography of ubiquitous <em>Labyrinthula zosterae</em> and emergence of a novel seagrass pathogen

description: The biogeography of host-pathogen interactions shapes disease dynamics by
 influencing pathogen presence, host susceptibility, and environmental
 factors. This study investigates the genetic diversity of the protist
 Labyrinthula in eelgrass (Zostera marina) ecosystems, a model for
 host-pathogen research. Labyrinthula zosterae (Lz), the pathogen
 responsible for eelgrass wasting disease, causes leaf lesions that lead to
 necrosis, shoot mortality, and habitat loss. We collected eelgrass samples
 from Alaska to Southern California and performed high-throughput 18S gene
 sequencing. We identified two dominant pathogenic strains: one in the Lz
 clade, previously linked to Pacific and Atlantic outbreaks, and a novel
 strain associated with crescent-shaped lesions. While pathogenic Lz
 dominated 10 sites, non-pathogenic strains were more prevalent at 11 other
 sites. Notably, the novel crescent strain dominated Northern California
 sites. The observation that 67 % of ASVs were unique to individual regions
 suggests localized environmental or ecological drivers of diversity, while
 the widespread occurrence of the remaining 33 % indicates the ability of
 certain Labyrinthula taxa to persist across diverse habitats. This
 research enhances our understanding of the diversity of Lz, supporting
 eelgrass ecosystem conservation and restoration efforts, and it increases
 our understanding of biogeographic patterns of host-pathogen systems.
description: # Data from: Biogeography of ubiquitous *Labyrinthula zosterae* and
 emergence of a novel seagrass pathogen
 ([https://doi.org/10.5061/dryad.q2bvq83xg](https://doi.org/10.5061/dryad.q2bvq83xg)) We have submitted our raw data (**otu_data_normalized_032125.csv**), top 20 ASVs (**otu_data_normalized_top20_032125.csv**, and **18S_ASVs_top20_normalized_Laby_only_rare_032125.txt**), alpha diversity file (**alphadiv_823.csv**), taxonomy table for phylogenetic tree (**taxonomy.tsv**), R scripts (**6_DA_analysis.R, alpha_diversity_plots_032425_anon.R, alpha_statsA_032125_anon.Rmd, alpha_statsB_032425_anon.R, Dominant_strain_barplot_032425_anon.R, Heat_map_032425_anon.R, PCoA_032425_anon.R, PERMANOVA_032425_anon.R, PERMDISP_pairwise_032425_anon.R, Rarecurve_032025.R, RDA_031225_anon.R,** and **3_dada2_18S.R**), phyloseq file (**ps_18S_lulu.RDS**), and Linux scripts; **2_cutadapt.sh, 1_fastqc.sh, 4_rescript_taxonomy.sh**). The file **README_laby_021226.txt** is a copy of this README. ## Descriptions ### 18S_ASVs_top20_normalized_Laby_only_rare_032125.txt This is a text file with genomic data of 18S strains of *Labyrinthula* previously identified in the literature, and top 20 ASVs of *Labyrinthula* identified in our study. ### alphadiv_823.csv ##### Variables * sample: name of sample * observed: alpha observed diversity value * shannon: alpha shannon diversity value * Sample: sample name with dashes * Person: individual who extracted DNA * Year: year of sample collection * State: location/state/provence of collection * Site: alpha site name within state/provence * Sample_Type: G (green/healthy) or L (lesioned/symptomatic) leaf classification * Sample_number: replicate at each site (1-10) * Matched: does the sample have a matched lesion/green from the same leaf. * Sample_Type: green (healthy) or lesioned (symptomatic) leaf classification * Lesion_Type: crescent, not crescent, or not sure lesion type. All healthy plants are classified as non-crescent ### otu_data_normalized_032125.csv ##### Variables * First row is ASV name * First column is sample name * All numbers within the table are ASV abundance data that were transformed into relative abundances * A zero value depicts that the ASV was not present in the sample ## Code/Software R and Linux are required to run *.R* and *.Rmd* and *.sh*, respectively; the R scripts were created using version 2024.04.2 Annotations are provided throughout the script through 1) data loading and cleaning, 4) analyses, and 5) visualization creation.