DOI: 10.5061/dryad.5qfttdzh8
title: Host traits and temperature predict biogeographic variation in seagrass disease prevalence

description: Diseases are ubiquitous in natural systems, with broad effects across
 populations, communities, and ecosystems. However, the drivers of many
 diseases remain poorly understood, particularly in marine environments,
 inhibiting effective conservation and management measures. We examined
 biogeographic patterns of infection in the foundational seagrass Zostera
 marina by the parasitic protist Labyrinthula zosterae, the causative agent
 of seagrass wasting disease, across &gt;20° of latitude in two ocean
 basins. We then identified and characterized relationships among wasting
 disease prevalence and a suite of host traits and environmental variables.
 Host characteristics and transmission dynamics explained most of the
 variance in prevalence across our survey, yet the particular host traits
 underlying these relationships varied between oceans, with host size and
 nitrogen content important in the Pacific and host size and density most
 important in the Atlantic. Temperature was also a key predictor of
 prevalence, particularly in the Pacific Ocean. The strength and shape of
 the relationships between prevalence and some predictors differed in our
 large-scale survey vs previous experimental and site-specific work. These
 results show that both host characteristics and environment influence
 host-parasite interactions, and that some such effects scale up
 predictably, whereas others appear to depend on regional or local context.
description: <em>Study sites: </em>In the summer of 2015, we
 surveyed 17 discrete, monospecific eelgrass beds across the northern
 hemisphere, mostly along the coasts of North America (9 in the Atlantic
 Ocean, 8 in the Pacific Ocean). All of the surveyed sites were in
 protected, shallow waters (0 - 2 m depth at low tide).
 <em>Wasting disease survey:</em> At each site, we
 collected a total of 20 individual vegetative eelgrass shoots at least two
 meters apart along two 10 meter transects running parallel to shore. We
 clipped shoots at the base in the field and placed them in individual
 plastic bags. We timed sampling to target peak wasting disease prevalence
 based on local knowledge of system dynamics, which resulted in most sites
 being surveyed in mid-summer near peak eelgrass biomass.
 Within five hours of collection in the field, we scored the third
 youngest leaf of each shoot for signs of wasting disease (i.e., lesions).
 We focused on this leaf because it has been shown to harbor the highest
 intensity of wasting disease (Bockelmann et al. 2013) and remains
 sufficiently free of epibionts, making lesions clearly distinguishable. At
 each site, we used a standardized protocol including published
 descriptions and photographs of wasting disease to guide scoring (Burdick
 et al. 1993, Groner et al. 2014, Groner et al. 2016). Leaf lesions
 typically indicate intense wasting disease infections, and thus we
 interpret lesion distribution as a reliable reflection of <em>L.
 zosterae</em> distribution (Dawkins et al. 2018, Brakel et al.
 2019). However, leaf lesions could also reflect processes other than
 parasite abundance. For example, though black and brown lesions are
 established visual signs of wasting disease, other stressors can also
 cause lesions, and low levels of <em>L. zosterae</em> parasite
 may be present in asymptomatic leaves (Bockelmann et al. 2013, Groner et
 al. 2016). Because we had local experts familiar with the signs of other
 stressors specific to each site (e.g., UV damage or grazing), we minimized
 mis-classification between lesions associated with disease and lesions
 caused by other stressors. We also collected notes and representative
 photographs of putative wasting disease lesions as necessary to ensure
 consistency of scoring. <em>Host and
 environmental surveys:</em> In separate surveys at these same sites
 in 2014, we collected data on host and environmental factors that could
 mediate wasting disease prevalence. These data were collected from 20,
 1-m<sup>2</sup> plots spaced roughly 2 m apart and included
 eelgrass density (measured as shoots per m<sup>2</sup>), blade
 length (measured from the meristem to the tip of the longest leaf), leaf N
 content (from young leaf material of 5 pooled shoots in each plot run on a
 CHN analyser), allelic richness (calculated from 5 shoots per plot at 24
 DNA microsatellite loci), periphyton (grams of total dried algae,
 microbes, and detritus present on the leaf surface per grams of dried
 eelgrass), and mesograzer abundance (number of mesograzers per grams of
 eelgrass) following methods described in greater detail in Duffy et al.
 (2015, 2022). Many of these variables are generally consistent at a given
 site from year to year when sampled in the same season (Douglass et al.
 2010, Bertelli et al. 2021), and sometimes consistent across many years
 (Reynolds et al. 2017). In addition, previous studies have demonstrated
 that variables measured months prior can be as or more predictive of
 wasting disease prevalence than those measured coincident with sampling of
 wasting disease (Groner et al. 2021, Graham et al. 2023). However, we may
 be underestimating the predictive power of host and environmental factors
 on wasting disease by sampling them in consecutive years rather than the
 same year. To quantify temperature and salinity at each
 site, we extracted estimates of the maximum mean monthly sea surface
 temperature and minimum mean monthly salinity from the Bio-ORACLE data set
 for 2000-2014 (Tyberghein et al. 2012, Assis et al. 2018;
 9.6-km<sup>2</sup> resolution). We chose maximum temperature
 to capture summer conditions when past epidemics have been observed
 (Rasmussen 1977, Burdick et al. 1993) and minimum salinity because lower
 salinities inhibit the parasite (McKone & Tanner 2009). We used
 the raster package in R (Hijmans 2019) to extract these data from all
 cells within 10 km of each site, and we averaged these estimates to
 generate site-level predictors. As a potential complement to these
 long-term data, we also measured <em>in situ</em> water
 temperature and salinity simultaneous with the wasting disease
 survey.
description: # Host traits and temperature predict biogeographic variation in seagrass
 disease prevalence
 [https://doi.org/10.5061/dryad.5qfttdzh8](https://doi.org/10.5061/dryad.5qfttdzh8) ## Description of the data and file structure Dataset to reproduce analyses in "Host traits and temperature predict biogeographic variation in seagrass disease prevalence" ### Files and variables #### File: Schenck\_etal\_data\_repository.csv ##### Variables * site: The site name. * site.code: The unique two digit abbreviation for each site name. * region: The geographic region where the site is located. * ocean: The ocean where the site is located. * disease.prevalence: The proportion of Zostera marina third leaf blades on which lesions characteristic of eelgrass wasting disease are present * latitude: The latitude of the site in decimal degrees. * longitude: The longitude of the site in decimal degrees. * shoot.density: Mean density of Zostera marina shoots per square meter (from sampling hoops or other methods) from the Zostera Experimental Network (ZEN) * blade.length: Mean length from the meristem to the tip of the longest leaf from the Zostera Experimental Network (ZEN). * s.t.a.t.o.s: Seawater temperature in degrees Celsius taken in the middle of the seagrass canopy height during sampling. * s.a.t.o.s: Salinity in parts per thousand taken in the middle of the seagrass canopy height during sampling. * allelic.richness: Allelic richness of Zostera marina from one shoot per plot. Provided by J.Olsen: Originally reported as Â (n = 7 genets) "Allelic diversity normalized to 7 genets". Think of as "allelic richness: average # alleles per locus, normalized to 7 genets" from the Zostera Experimental Network (ZEN). * leaf.N: Mean leaf percent nitrogen in the Zostera marina tissue from the Zostera Experimental Network (ZEN). * periphyton.load: Mean standardized loading of periphyton (g dried periphyton/dry g Zostera) from the Zostera Experimental Network (ZEN). * grazer.abundance: Standardized mean abundance of total mesograzers (number of mesograzers divided by the total dry mass of macrophytes from the epifaunal sample) from the Zostera Experimental Network (ZEN). * lt.m.s.s.t: Estimates of the maximum mean monthly sea surface temperature in degrees Celsius from the Bio-ORACLE data set for 2000-2014 (Tyberghein et al. 2012, Assis et al. 2018; 9.6-km2 resolution). * lt.m.s.s.s: Estimates of the minimum mean monthly sea surface salinity in parts per thousand from the Bio-ORACLE data set for 2000-2014 (Tyberghein et al. 2012, Assis et al. 2018; 9.6-km2 resolution). ## Code/software Analyses were conducted using the randomForest package in R version 3.6.1 (Liaw & Wiener 2002). ## Access information Other publicly accessible locations of the data: * [https://hdl.handle.net/2047/D20664000](https://hdl.handle.net/2047/D20664000)