DOI: 10.5061/dryad.4j0zpc8jx
title: Data from: Warmer is deadlier: A meta-analysis reveals increasing temperatures accentuate disease impacts on fisheries hosts

description: Rapid warming could drastically alter host-parasite relationships, which
 is especially important for fisheries crucial to human nutrition and
 economic livelihoods, yet we lack a synthetic understanding of how warming
 influences parasite-induced mortality in these systems. We conducted a
 meta-analysis using 266 effect sizes from 52 empirical papers on harvested
 aquatic species and determined the relationship between parasite-induced
 host mortality and temperature and how this relationship was altered by
 host, parasite, and study design traits. Overall, higher temperatures
 increased parasite-induced host mortality; however, the magnitude of this
 relationship varied. Hosts from the order Salmoniformes experienced a
 greater increase in parasite-induced mortality with temperature than the
 average response to temperature across fish orders. Opportunistic
 parasites were associated with a greater increase in infected host
 mortality with temperature than the average across parasite strategies,
 while bacterial parasites were associated with lower infected host
 mortality as temperature increased than the average across parasite types.
 Thus, parasites will generally increase host mortality as the environment
 warms; however, this effect will vary among systems.
description: We compiled data from experimental studies on fisheries species
 that compared mortality of parasitized and unparasitized hosts at a static
 temperature. We defined fisheries species to include both invertebrate and
 vertebrate species that are harvested commercially or recreationally. In
 Fall 2019, we searched Web of Science following PRISMA protocols (Foo et
 al. 2021) using key terms that would return papers focused on harvested
 aquatic species, parasites, and diseases, but would exclude papers that
 were focused on human, environmental or domestic animal health (see
 Appendix S1 in Supporting Information). This search yielded 1,201 papers.
 We then screened the abstracts of these papers, and retained only papers
 that satisfied four criteria: 1) an experiment was performed that included
 at least one parasite exposure treatment paired with an unexposed control
 group, 2) temperatures were intended to be constant and not intentionally
 varied, 3) hosts were from species that constitute a fishery, including
 those in aquaculture, and 4) estimates of survival or mortality were
 reported for both infected and uninfected hosts at each temperature
 treatment.  This selection process reduced the number of studies to 386
 (Appendix S1 and Figure S1).  We then screened the
 abstracts of these papers, and retained only papers that satisfied four
 criteria: 1) an experiment was performed that included at least one
 parasite exposure treatment paired with an unexposed control group, 2)
 temperatures were intended to be constant within a temperature treatment
 and not intentionally varied, 3) hosts were from species that constitute a
 fishery, including those in aquaculture, and 4) estimates of survival or
 mortality were reported for both infected and uninfected hosts at each
 temperature treatment.  This selection process reduced the number of
 papers to 386 (Appendix S1 and Figure S1).  We obtained
 full versions of 358 papers (28 papers from the original 386 were
 unobtainable).  We then screened the full text of these papers to ensure a
 match to our four criteria, which reduced the 358 papers to 70. To
 increase statistical power to estimate the effect of host Order on
 parasite-induced mortality, we excluded experiments from hosts in Orders
 with fewer than two experiments with ≥ 2 temperature treatments. We also
 removed experiments with zero mortality in infected and uninfected
 treatments at all experimental temperatures. These experiments provided no
 information about how parasite-induced mortality changed with temperature.
 This reduced the number of papers included in our dataset from 70 to 52
 and yielded a total of 266 effect sizes from 121 experiments (several
 papers included more than one experiment; Appendix S1 and S2, Figure
 S1).  At least two people extracted data from each
 paper to reduce extraction error. If extracted values differed, the data
 were re-extracted until there was agreement between the two extractors.
 For data that were displayed in a graphical format only, we used
 WebPlotDigitizer (Rohatgi 2022) to extract data. Data (which may have been
 presented as mortality rates, or proportion surviving) were converted to
 numbers of host individuals that were dead and alive at the end of the
 experiment. We also extracted information about the paper itself,
 including the source of the hosts used in the paper and the motivation for
 conducting the experiment (see Appendix S1). Finally, we collected
 additional information about host and parasite traits from outside sources
 (e.g., other peer reviewed papers, government reports) when necessary to
 obtain moderator variables (Table 1, Appendix S1 and S2). The moderators
 (Table 1) were used to test <em>a priori </em>hypotheses
 regarding how host, parasite, and study design traits influenced how
 temperature affected parasite-induced mortality.
 Because our focus was on parasite-induced mortality, we used log
 odds ratios and the variance surrouding log odds ratio as our effect size
 to compare host survival in the parasitized vs unparasitized
 treatments. Similar to Sauer <em>et al.
 </em>(2020), we used a mixed effect meta-analysis (<em>metafor
 </em>package, <em>rma.mv</em> function; Viechtbauer
 2010) to analyze our data. This model accounts for heterogeneity among
 experiments by allowing the relationship between temperature and LOR to
 have a different intercept for each experiment. The overall slope provides
 an estimate of how LOR changes with temperature on average.  Additionally,
 the random intercepts account for variation in the relationship between
 LOR and temperature that results from factors we were unable to model
 explicitly (e.g., dosage, geographic source of host or parasite,
 laboratory facilities).This model accounts for heterogeneity among
 experiments by allowing the relationship between temperature and LOR to
 have a different intercept for each experiment. The overall slope provides
 an estimate of how LOR changes with temperature on average.  Additionally,
 the random intercepts  account for variation in the relationship between
 LOR and temperature that results from factors we were unable to model
 explicitly (e.g., dosage, geographic source of host or parasite,
 laboratory facilities). We explored twelve versions of
 equation 3 that avoided collinearity that arose between host taxonomic
 information and parasite or host traits. In addition to running the above
 models on the full dataset, we also applied two trait-based models, single
 categorical trait models, and a null model on a reduced dataset that was
 restricted to the Order Salmoniformes, the most common Order in the data
 set (comprising 26 % of the effect sizes). This allowed us to explore
 temperature effects in a well-studied, and economically important,
 group.  Excluding the null models, we used backwards
 elimination to produce a model that included only factors with significant
 interactions with temperature (<em>p</em> &lt; 0.1) in
 both the full and Salmoniformes datasets.  All the models generated in
 this process were compared using an Information-Theoretic approach that
 utilizes Akaike Information Criterion corrected for sample size (AICc;
 Akaike 1973; Blankenship <em>et al.</em> 2002; Whittingham
 <em>et al.</em> 2006) to select the best fit model. We also
 calculated McFadden’s
 Pseudo–<em>R<sup>2</sup></em> (Hemmert
 <em>et al.</em> 2018) and Nakagawa’s
 <em>Pseudo-R<sup>2</sup> </em>for generalized
 linear mixed-effects models (<em>orchaRd </em>package,
 <em>r2_ml</em> function;<em> </em>Nakagawa et
 al<em>.</em> 2023; Nakagawa & Schielzeth 2013) which
 estimates the amount of additional variation explained by the
 mixed-effects model compared to the null model. Nakagawa’s
 <em>Pseudo-R<sup>2</sup> </em>provides a
 goodness-of-fit metric for both the fixed-effects of the model (Marginal
 <em>R<sup>2</sup></em>) and for the full
 mixed-effects model (Conditional
 <em>R<sup>2</sup></em>). While our models estimate
 both main effects and interactions with temperature of our fixed effects,
 we discuss only the interactions because these are the estimates that
 reflect how the effects of temperature on parasite-induced mortality are
 influenced by host, parasite, and study design traits.
 We additionally evaluated publication bias and the degree of
 variance caused by heterogeneity in the true effect sizes compared to the
 variance caused by sampling error in our data. To evaluate publication
 bias we assess the effect of low sample size studies on the overall effect
 and if the overall effect declines with publication year using the
 procedures outlined in Nakagawa <em>et al.</em> 2022 (Appendix
 S3) for both the full and Salmoniformes datasets. We calculated the
 typical sampling error variance from studies contained in our
 meta-analysis to estimate the statistical noise in our data
 (<em>sigma2_v</em> function; Yang <em>et al.</em>
 2023) as well as the <em>I<sup>2</sup> </em>metric
 to interpret the heterogeneity in our data and determine the variability
 in the true effect attributed to each level of our random effects (Higgins
 & Thompson 2002; <em>orchaRd</em> package,
 <em>i2_ml</em> funciton, Nakagawa <em>et al.</em>
 2023.) All analyses were performed using R version 4.4.2 (R Core Team
 2024).
description: # Data from: Warmer is deadlier: A meta-analysis reveals increasing
 temperatures accentuate disease impacts on fisheries hosts
 [https://doi.org/10.5061/dryad.4j0zpc8jx](https://doi.org/10.5061/dryad.4j0zpc8jx) Update May 30, 2025: updated code to remove unnecessary libraries, updated ReadME to reflect session information.  Update May 7, 2025: Updated Tables and Figures Full Analysis5V2 to fix formatting issue in Table S2.  Update April 8, 2025: Updated code and data based on data editor review: changed random effect structure and excluded studies that did not meet updated criteria. This includes new analysis, new code, new figures, and a new dataset that was updated from the previous version.  Update November 12, 2024: Updated colors in TM1R plot*, updated plot labels in Salmoniformes*_figures plot, renamed files to be more reflective of figure descriptions in manuscript. Updated names of files at the end of the READ ME document. ## Description of the data and file structure The attached csv file is the compiled dataset used to perform the meta-analysis described in the manuscript. These data include columns not utilized in the text as these categorical variables were later simplified to increase sample size. These columns were retained in this dataset for transparency purposes. Sources for additional information outside of what was provided in the original studies are described in Appendix S2 and full citations are available in Appendix S4. The column descriptions are as follows: Study: In-text citation for the original manuscript where the mortality data were sourced (See Appendix S2 and S5) Group: the experiment associated with that row of mortality data (see Methods) Temp_C: the temperature at which the experiment was performed in degrees Celsius. Days_in_study: the duration of the experiment in days. Reg_DE: estimated number of dead infected hosts Reg_DC: estimated number of dead uninfected hosts Reg_AE: estimated number of alive infected hosts Reg_AC: estimated number of alive uninfected hosts Order: Order of the host species used Class: Class of the host species used Phylum: Phylum of the host species used Superfamily: Superfamily of the host species used Host_mobility: If adult host was mobile in the water column (See Appendix S1) Vertebrae: If adult host has a vertebrae (See Appendix S1) LH_clean: Life stage listed in source paper (See Appendix S1) Temp_zone: Host distribution (See Appendix S1) Salinity: Salinity tolerance of host (See Appendix S1), later simplified into Salinity_simple which was the variable used in the meta-analysis. Parasite_Type: Taxonomic group of Parasite used (See Appendix S1), later simplified into Parasite_Type_simple which was the variable used in the meta-analysis. Host_source: The local source of the experimental animals as described in the paper (See Appendix S1), later simplified into Host_source_simple which was the variable used in the meta-analysis. Motivation_code_2: The motivation of the researchers performing the original study (See Appendix S1). Salinity_simple: Simplified salinity tolerance (See Methods, Table 1, and Appendix S1). LH_simple: Life history of the hosts simplified (See Methods, Table 1, and Appendix S1). Parasite: The parasite used in the study (Appendix S2). Parasite_Type_simple: The simplified parasite taxonomy used in the study (See Methods, Table 1, and Appendix S1). Parasite_transmission3: The mode of transportation of the parasite (See Methods, Table 1, and Appendix S1). Pathogen_type: The life history strategy of the parasite (See Methods, Table 1, and Appendix S1). Parasite_location: If the parasite was an external or internal parasite (See Methods, Table 1, and Appendix S1). Parasite_Transmission_simple: Simplified parasite transmission into single or multiple transmission modes. Not used in the meta-analysis Host_source_simple: Simplified Host source (See Methods, Table 1, and Appendix S1). Temp_Cent: mean-centered temperature in degrees Celsius. TrueLOR: the calculated log odds ratio from that experiment (see Methods) TrueLORVar: the calculated variance of the log odds ratios (see Methods) obsID: the observation ID number ## Sharing/Access information Data was derived from the sources listed in Appendix S2 and Appendix S5 in the manuscript. ## Code/Software Attached are R scripts to produce the statistical models and all figures in the manuscript. These were created using R version 4.4.2 (2024-10-31 ucrt) -- "Pile of Leaves" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 LOR_temp_calc: script to calculate Log Odds Ratio, the Variance in Log Odds Ratio, and centered temperature. This script is not necessary to run analysis but instead is provided for transparency purposes.  Full_analysis5: script to recreate full meta-analysis statistics and publication bias evaluation, including figures Tables_and_Figures_Full_analysis5: script to recreate tables and figures provided in text and supplementary material. ``` &gt; sessionInfo() R version 4.4.2 (2024-10-31 ucrt) Platform: x86_64-w64-mingw32/x64 Running under: Windows 11 x64 (build 26100) Matrix products: default locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C [5] LC_TIME=English_United States.utf8 time zone: America/New_York tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] extrafont_0.19 ggplot2_3.5.1 stringr_1.5.1 orchaRd_2.0 dplyr_1.1.4 [6] metafor_4.8-0 numDeriv_2016.8-1.1 metadat_1.4-0 Matrix_1.7-1 loaded via a namespace (and not attached): [1] crayon_1.5.3 vctrs_0.6.5 nlme_3.1-166 cli_3.6.3 rlang_1.1.5 stringi_1.8.4 [7] Rttf2pt1_1.3.12 generics_0.1.3 labeling_0.4.3 glue_1.8.0 colorspace_2.1-1 extrafontdb_1.0 [13] scales_1.3.0 grid_4.4.2 munsell_0.5.1 tibble_3.2.1 lifecycle_1.0.4 compiler_4.4.2 [19] mathjaxr_1.6-0 pkgconfig_2.0.3 rstudioapi_0.17.1 farver_2.1.2 lattice_0.22-6 digest_0.6.37 [25] R6_2.6.1 tidyselect_1.2.1 pillar_1.10.1 magrittr_2.0.3 withr_3.0.2 tools_4.4.2 [31] gtable_0.3.6 ```