DOI: 10.5061/dryad.fqz612jwf
title: A reconstruction of parasite burden reveals one century of climate-associated parasite decline

description: Long-term data allow ecologists to assess trajectories of population
 abundance. Without this context, it is impossible to know whether a taxon
 is thriving or declining to extinction. For parasites of wildlife, there
 are few long-term data – a gap that creates an impediment to managing
 parasite biodiversity and infectious threats in a changing world. We
 produced a century-scale time series of metazoan parasite abundance and
 used it to test whether parasitism is changing in Puget Sound, USA and, if
 so, why. We performed parasitological dissection of fluid-preserved
 specimens held in natural history collections for eight fish species
 collected between 1880 and 2019. We found that parasite taxa using three
 or more obligately required host species – a group that comprised 52% of
 the parasite taxa we detected – declined in abundance at a rate of 10.9%
 per decade, whereas no change in abundance was detected for parasites
 using one or two obligately required host species. We tested several
 potential mechanisms for the decline in 3+-host parasites and found that
 parasite abundance was negatively correlated with sea surface temperature,
 diminishing at a rate of 38% for every 1°C increase. Although the
 temperature effect was strong, it did not explain all variability in
 parasite burden, suggesting that other factors may also have contributed
 to the long-term declines we observed. These data document one century of
 climate-associated parasite decline in Puget Sound – a massive loss of
 biodiversity, undetected until now.
description: Specimen selection and
 collection site history We focused on the
 metazoan parasites of eight fish species from Puget Sound, Washington,
 USA, selecting host species that were well-represented in natural history
 collections and that encompassed a broad range of trophic levels, body
 sizes, habitats, functional feeding groups, and vulnerability to human
 impacts (Supplementary Information Table S1). Specimens were sourced
 primarily from the University of Washington Fish Collection (UWFC) at the
 Burke Museum of Natural History and Culture, with additional specimens
 from other collections to increase temporal scope and resolution (Figure
 1b; Supplementary Information Table S2). For each specimen examined, we
 noted locality data from the natural history collection databases and
 measured the specimen’s total length (TL) in cm. When the collection
 location was descriptive but lacked coordinates, we estimated the
 collection location in decimal degrees using Google Maps.
 Parasitological
 dissections Each fish was subjected to a
 comprehensive parasitological dissection (see details in Fiorenza et al.
 [2020] and Snover et al. [2005]). For each parasite identified, we noted
 its broad taxonomic grouping (Subclass Copepoda, Subclass Hirudinea, Class
 Monogenea, Class Trematoda, Class Cestoda, Phylum Nematoda, Class
 Acanthocephala; Supplementary Information Table S3). For flatworms, we
 stained and mounted specimens before identification (Cable et al. 1963).
 For nematodes, we cleared specimens before identification (Cable et al.
 1963). We identified each parasite to the finest taxonomic resolution,
 which in most cases was family or genus level, and classified them into
 one of two transmission strategies: directly transmitted (i.e., parasites
 that can be transmitted between conspecific hosts) or complex life cycle
 (i.e., parasites that are transmitted from one host species to another
 host species in an obligately required sequence). For complex life cycle
 parasites, we also estimated the number of obligately required host
 species based on natural history information (Supplementary Information
 Table S3). Parasites that were identified to species and found in more
 than one host were recorded under the same parasite taxon name (e.g.,
 <em>Derogenes</em> <em>varicus</em>). Larval
 nematodes of the genera <em>Anisakis</em> and
 <em>Contracaecum</em> are known to be host generalists for
 their fish intermediate/paratenic hosts (Mattiucci et al. 2006; Kuhn et
 al. 2013; Shamsi 2019); therefore, even though these worms could not be
 identified to species, we also recorded these under the same parasite
 taxon name across host species (i.e., <em>Contracaecum</em>
 sp., <em>Anisakis</em> sp.). For all other parasites that were
 not identified to species, we assumed that individuals found in one host
 were of a different species than those found in another host, and named
 them accordingly (Sasal et al. 1998; Benesh et al. 2021; e.g.,
 <em>Lepeophtheirus</em> sp. of Walleye Pollock versus
 <em>Lepeophtheirus</em> sp. of Surf Smelt).
 Potential environmental
 drivers In a retrospective study like
 this one, it is not possible to definitively identify the causal drivers
 of change in parasite abundance. However, we had access to several
 long-term environmental datasets and sought to assess the correlation
 between environmental variables and parasite burden. Our environmental
 datasets included information on sea surface temperature (Wan 2022), heavy
 metal and organic pollutants (Brandenberger et al. 2008), and fish host
 density (Greene et al. 2015; Essington et al. 2021) within Puget
 Sound. The temperature and pollutant datasets reflect
 prevailing conditions in Puget Sound that might have affected all hosts
 and parasites, while fish density data reflect conditions pertaining
 primarily to parasites within that fish host. Data on sea surface
 temperature were from a continuous record (1921–2019) collected at Race
 Rocks lighthouse (Wan 2022); we extracted average monthly sea surface
 temperature in degrees Celsius, discarded any year in which more than one
 month was missing data (n = 4 of 98 years), and obtained an annual average
 for each year. For each host individual, we matched the year of the host’s
 collection to the corresponding year from the temperature dataset. Data on
 pollutants were from a continuous record (1774–2005) obtained by coring
 Puget Sound sediments (Brandenberger et al. 2008), which yielded values
 for the concentration of lead, arsenic, zinc, nickel, vanadium, chromium,
 copper, barium, and beryllium in micrograms per gram of sediment, as well
 as concentrations of lignin and soil biomarkers, which indicate inputs of
 terrestrial organic matter. We extracted annual values for each variable
 from two cores taken near Tacoma and Seattle, WA in Puget Sound (PS-1 near
 Tacoma = 47.347167, -122.409667; PS-4 near Seattle = 47.614967,
 -122.449017; Brandenberger et al. 2008), averaged values within each year
 across the two cores, and interpolated among years to bridge temporal
 gaps. Some of the 12 pollutant variables were collinear with one another
 (Supplementary Information Figure S3), so we performed a principal
 components analysis to reduce the dimensionality of the pollutant dataset
 and found that the first two principal components explained 74% of
 variation (Supplementary Information Figure S4). To account for annual
 measurement error, we ran a LOESS smoother on each principal component and
 then matched the year of each host individual’s collection to the
 corresponding year from the first and second principal
 components. We also had access to data on the density
 of the fish hosts for six of the eight host species we examined:
 <em>G</em>. <em>chalcogrammus</em>,
 <em>H</em>. <em>colliei</em>,
 <em>M</em>. <em>productus</em>, and
 <em>P</em>. <em>vetulus</em> from Essington et al.
 [2021] and <em>C</em>. <em>pallasii</em> and
 <em>H</em>. <em>pretiosus</em> from Greene et al.
 [2015]. Data from Essington et al. [2021] were annual projections of
 density based on historical data collected from 1946 to 1977, while data
 from Greene et al. [2015] were estimates of catch per unit effort (CPUE)
 for various Puget Sound basins sampled between 1972 and 2011. For Greene
 et al. [2015], estimates were averaged across basins within each year to
 obtain an annual estimate of abundance across Puget Sound. For each fish
 species’ time series, we interpolated among years to bridge temporal gaps
 and matched the year of each host individual’s collection to the
 corresponding year from the host density dataset, matching host species to
 corresponding parasites (i.e., <em>P</em>.
 <em>vetulus</em> density was recorded for
 <em>P</em>. <em>vetulus</em> parasites
 only). Literature
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