DOI: 10.5061/dryad.mw6m9068b
title: Changes in transmission rates drive seasonal patterns of shrimp black gill disease

description: Quantifying the processes affecting disease dynamics is critical for
 informing management strategies of fisheries. We present the results of a
 series of experiments and mechanistic models to disentangle the roles of
 transmission, mortality, and recovery in driving seasonal prevalence of
 shrimp Black Gill (sBG) disease in penaeid shrimp. We quantified seasonal
 sBG transmission to uninfected sentinel shrimp deployed into the
 environment. Next, we manipulated temperature and infection status in
 laboratory experiments to quantify drivers of host mortality and recovery.
 We then utilized these experiments to parameterize a series of mechanistic
 models to determine if disease dynamics were driven by host
 density-dependent or independent factors. Transmission was highest during
 the summer, with 75-91% of shrimp acquiring infection, but declined
 substantially in all other seasons (0-10% infection prevalence). In our
 lab experiments, we observed little disease-induced mortality and complete
 recovery from infection in all treatments. Our models revealed that host
 density did not drive disease dynamics. Together, this suggests that the
 seasonal change in sBG is due to high transmission rates in the summer
 months, followed by gradual recovery when transmission rates are low. Our
 methodology provides a framework to quantify drivers of seasonal variation
 in disease prevalence in fisheries.
description: # Changes in transmission rates drive seasonal patterns of shrimp black
 gill disease Dataset DOI:
 [10.5061/dryad.mw6m9068b](10.5061/dryad.mw6m9068b) ## Description of the
 data and file structure The code for the manuscript contains three parts:
 a field-based transmission experiment, an in-lab mortality and recovery
 experiment, and a mathematical model. The information below is divided to
 reflect this structure of the manuscript. ### Files and variables ##
 Transmission Experiment #### File: Transmission_Exp_StatsV2.R
 **Description:** This code contains the R script used to produce the
 results for the Transmission Experiment section and to create Figure 1,
 Appendix 3, and Table S1. #### File: All_Estuaries_Trim_date.csv
 **Description:** This file contains the diagnostic information for shrimp
 collected from the Wild and is used to compare the prevalence of deployed
 shrimp after the transmission experiments conclusion. ##### Variables *
 PCR_binom: binary variable indicating *Hyalophysa lynni* DNA detection
 (“1”) or no detection (“0”) via PCR. * Estuary.System: The name of the
 Sound in Georgia (USA) where the shrimp was collected * Clx_date: The date
 the shrimp was collected * Month: The numeric month of collection *
 Year: The year of collection * Collection_type: The name of the research
 vessel used for collection * Month2: relabeled month to match transmission
 experiment deployment months for comparison * Treatment: the treatment
 group of the shrimp,  all shrimp in this dataframe are labeled as a “Wild”
 treatment to compare to the “Control” and “Deployed” treatments in the
 transmission experiment. #### File: Sentinel_Shrimp_Full_dataset.csv
 **Description:** This file contains the information collected for the
 shrimp used in the *Transmission Experiment*, including sBG diagnostics,
 shrimp size, sex, and collection date. ##### Variables *
 Experiment_date_range: the date range when shrimp were either deployed in
 the estuary or maintained in the lab as controls * Month: the experiment
 month * Season: the season of the experiment * Treatment: the treatment
 group of the shrimp, either “Control” or “Deployed” * Group: either the
 deployment group or control group (not included in this analysis) *
 Shrimp_Number: the individual ID number of the shrimp * PCR_754: a binary
 variable indicating that *H. lynni* DNA was either detected (“1”) or
 undetected (“0”) in the sampled gill tissue * Visual_Scale: Qualitative
 assessment of the shrimp gill coloration (not included in analysis) * Sex:
 The determined sex of the sampled shrimp, either “F”, “M”, or undetermined
 “” * Length_mm: the measured length of the shrimp from the tip of the
 rostrum to the end of the tail to the nearest millimeter * Sample_area_mm:
 the area of the sampled gill tissue as measured by ImageJ *
 total_bands: the number of dark bands detected per gill tissue sample (not
 included in analysis) * total_trophonts: the number of trophonts (feeding
 stage of *H. lynni*) detected microscopically in the gill tissue sample *
 PCR_date: the date the PCR analysis was performed on that sample *
 Scope_date: the date the microscope analysis was performed on that sample
 ## Recovery and Mortality Experiment #### File:
 Mortality_Recovery_Exp_StatsV2.R **Description:** This code contains the R
 script used to produce the results for the *Recovery and Mortality
 Experiment*, Tables 1 and 2, Figure 2, and Appendix 5. #### File:
 M4_PreTreatment_Scope_PCR.csv **Description:** This file contains the
 diagnostic, shrimp size, sex, and collection information for shrimp in the
 Pre-treatment samples. ##### Variables * Sample: Individual sample ID
 corresponds to individual shrimp * Visual: Qualitative assessment of the
 shrimp gill coloration (not included in analysis) * Med_group: The
 experimental treatment group of the sampled shrimp. *
 PRE-TREATMENT_NOT_CURED are referred to in the text as the
 *pre-application unmedicated* sample * PRE-TREATMENT_CURED are shrimp
 sampled upon arrival at the Skidaway Institute of Oceanography (not
 included in analysis) * PRE-TREATMENT_CURED2 are referred to in the text
 as the *pre-application medicated* sample * CURED is referred to in the
 text as the *post-application medicated* sample * NOT CURED are referred
 to in the text as the *post-application unmedicated* sample * PCR: binary
 variable indicating *Hyalophysa lynni* DNA detection (“1”) or no detection
 (“0”) via PCR. * gill_tissue_area_mm2: the area of the sampled gill tissue
 as measured by ImageJ * total_bands: the number of dark bands detected per
 gill tissue sample (not included in analysis) * total_trophonts: the
 number of trophonts (feeding stage of *H. lynni*) detected microscopically
 in the gill tissue sample * band_density: the resulting value from
 dividing the total_bands column by the gill_tissue_area_mm2 column (not
 included in analysis) * trophont_density: the resulting value from
 dividing the total_trophonts column by the gill_tissue_area_mm2 column
 #### File: Mortality_4_KMdata.csv **Description:** Contains survival and
 infection data from the temperature treatments in the mortality
 experiment. ##### Variables * ShrimpNum: Individual sample ID, corresponds
 to individual shrimp * PCR_binom: binary variable indicating *Hyalophysa
 lynni* DNA detection (“1”) or no detection (“0”) via PCR. * band_binom:
 binary variable indicating melanized bands detected (“1”) or not detected
 (“0”) via microscopy (not included in analysis) * trophont_binom: binary
 variable indicating trophonts detected (“1”) or not detected (“0”) via
 microscopy * band_density: the resulting value from dividing the
 total_bands column by the sampled gill tissue area (not included in
 analysis) * trophont_density: the resulting value from dividing the
 total_trophonts column by the sampled gill tissue area * Temp: Temperature
 treatment, either “Heated” to 30 °C or “Ambient” at 23.5 °C * Drug: The
 medication treatment the shrimp was subjected to, either “I” for
 unmedicated or “C” for medicated * Molts: The number of times a molted
 shrimp exoskeleton was found * Event_time: The day of the experiment where
 the shrimp were either censored (i.e., survived the entire experiment) or
 experienced mortality * Event: A binary variable indicating that the
 shrimp either died during the experiment (1) or were censored (0). ##
 Mechanistic Model  The code below should be run in the order presented to
 recreate the analysis. However, depending on the system used, this code
 can take several hours to complete. The CSV files included are produced by
 the code below and can be utilized by users who do not wish to run all the
 scripts to recreate the analysis. #### File:
 Par_values_from_experiments_v3_June_startV2.R **Description:** This code
 generates the parameter values listed in Table 3 and produces figures S1a
 and c. #### File: Plotting_shrimp_pop_dynamics.R **Description:** This
 code generates S1b and d. It is not necessary to run this code to
 reproduce the analysis. #### File: All_models_all_distributions_bestfit2.R
 **Description:** This code is used to execute the models described in the
 *Mechanistic models* section. It produces the parameters necessary to
 produce the best-fit beta function for each model class and outputs CSVs
 for every model class. These CSVs are stored in the Best_fit_params2
 subfolder. Note: this code takes several hours to run on a typical home
 computer. #### File: Best_fit_model_params_processingV5.R
 **Description:** This file condenses the CSVs into one dataframe and plots
 each model to evaluate fit. It also produces a CSV with the best-fit model
 parameterizations. None of the plots appear in the manuscript but are
 instead used for model diagnostics. #### File:
 Phil_Trans_tables_and_figures.R **Description:** This file produces the
 data in Table S4, Table S3, and Figure 3. #### File:
 generating_beta_sense_values.R **Description:** This file alters each
 parameter tested in the sensitivity analysis and creates a CSV containing
 these altered values. #### File: Sensitivity_analysis_execution.R
 **Description:** This file contains the code necessary to run the
 sensitivity analysis. It produces three CSVs that contain the best-fit
 model parameters for each sensitivity analysis scenario. This code takes
 several hours to run on a typical home computer. #### File:
 Allmods_sensitivity_analysis_model_fitsV2.R **Description:** This script
 produces Tables S5-7 – sensitivity analysis results. #### File:
 lambda_mu_gamma_est4.csv **Description:** this file contains the parameter
 estimates generated from Par_values_from_experiments_v3_June_start.R It
 creates Table 3 and Figure S1(a) and (c). ##### Variables * lambda:
 Estimated migration rate per month * mu: Estimated natural mortality rate
 by month * month: the numeric month of the year * cpue_diff: the
 difference in the mean Catch Per Unit Effort (CPUE) between months (i.e.,
 the difference between meancpue and cpue_m1) * meancpue: the average
 number of shrimp caught per tow per month on the Georgia coast as
 collected by the Georgia Natural Resources Coastal Resources Division
 Environmental Monitoring Trawl Survey * cpue_m1: the following month CPUE
 * alpha: the disease-induced mortality rate (static across the year) *
 gamma: estimated recovery rate per month #### File:
 Catch_level_prevalence.csv **Description:** This file contains the
 prevalence estimates collected from wild shrimp and is used to evaluate
 model fit. It is used in the script
 All_models_all_distributions_bestfit2.R ##### Variables * RefNum: The tow
 reference number * Sound: Two-letter code indicating which Sound in
 Georgia (USA) the tow was collected in: (CU – Cumberland, OS – Ossabaw, SA
 – St. Andrews, SP – Sapelo, SS – St. Simons, WA – Wassaw) * Month – the
 month the tow was collected in * Year – the year the tow was collected in
 * Sample_size – how many shrimp were collected in the 15-minute tow *
 sBGPrev – the prevalence of shrimp black gill in the tow (the number of
 shrimp with black gills divided by the total number of shrimp collected)
 #### File: Best_fit_params2.zip folder **Description:** This folder
 contains CSVs of the parameters necessary to produce the best-fit beta
 function for each model class. The lowest 10 SSE parameter sets are
 contained here. All CSVs contain the same columns. The file names indicate
 the transmission function (DD: density-dependent, FD: frequency-dependent,
 Res: reservoir), the *βᵢₘ* generating function (either Normal (Normal),
 Brière (Briere), and Modified Brière (ModBriere)), and whether the
 transmission rate (B) and/or recovery rate (G) was variable (Vary) or
 remained static (Static) across the year. For example, the CSV
 corresponding to the best fit parameter values for the density-dependent
 transmission using the normal *βᵢₘ* generating function that varied
 transmission but held recovery rate static is named
 “Best_fit_DD_VaryB_StaticG.csv”. ##### Variables * V1: The first parameter
 in the beta generating function (see Table S2 for definition) * V2: The
 second parameter in the beta generating function (see Table S2 for
 definition). * V3: The third parameter in the beta generating function
 (see Table S2 for definition) * V4: The fourth parameter in the beta
 generating function (see Table S2 for definition; note that CSVs
 corresponding to normal distribution fits lack this column) * ssqcalcs:
 the calculated sum-of-squared errors from the modeled prevalence compared
 to field data. * rank: The ranked fit based on ssqcalcs (lowest ssqcalcs
 is the highest rank, some csvs contain identical ssqcalcs, in which case
 the first ten fits were chosen for further analysis) #### File:
 all_mods_best_fit_betas.csv **Description:** This file contains the final
 best-fit models for each model class. ##### Variables * NA: row names *
 V1: The first parameter in the beta generating function (see Table S2 for
 definition) * V2: The second parameter in the beta generating function
 (see Table S2 for definition). * V3: The third parameter in the beta
 generating function (see Table S2 for definition) * V4: The fourth
 parameter in the beta generating function (see Table S2 for definition;
 note that the normal distribution fits lack this column) * ssqcalcs: the
 calculated sum-of-squared errors from the modeled prevalence compared to
 field data. * rank: The ranked fit based on ssqcalcs (lowest ssqcalcs is
 the highest rank, some csvs contain identical ssqcalcs, in which case the
 first ten fits were chosen for further analysis) * Model: The transmission
 function corresponding to the model run. * Gamma: Indicates whether
 recovery was variable or static over the course of the simulation. *
 Beta_Fxn: Which beta-generating function was used in the model run
 (Briere, Modified Briere, or normal) * Beta: Whether the transmission term
 was variable or static over the course of the simulation. * Transmission:
 Which type of transmission function was used (Density Dependent, Frequency
 Dependent, or Reservoir) * modkey: Model index that indicates the
 transmission function, the beta-generating function, and if transmission
 and/or recovery was variable or static. #### File:
 Beta_sense_params_june_start.csv **Description:** This file contains the
 parameters for the sensitivity analysis. ##### Variables * Lambda: the
 migration rate * Mu: natural mortality rate * meancpue: starting
 population * alpha: disease-induced mortality rate * gamma: recovery rate
 * month: the model-month (add 5 to get calendar month) * key: the
 sensitivity analysis scenario. #### File:
 Reservoir_Sens_Analysis_fits3.csv **Description:** This file contains the
 results from the sensitivity analysis for the reservoir transmission
 model. ##### Variables * V1: The first parameter in the beta generating
 function (see Table S2 for definition) * V2: The second parameter in the
 beta generating function (see Table S2 for definition). * V3: The third
 parameter in the beta generating function (see Table S2 for definition) *
 V4: The fourth parameter in the beta generating function (see Table S2 for
 definition) * ssqcalcs: the calculated sum-of-squared errors from the
 modeled prevalence compared to field data. * Key: The sensitivity analysis
 scenario. See the main text for more details. #### File:
 Freq_Dep_Sens_Analysis_fits3.csv **Description:** This file contains the
 results from the sensitivity analysis for the Density Dependent
 transmission model. ##### Variables * V1: The first parameter in the beta
 generating function (see Table S2 for definition) * V2: The second
 parameter in the beta generating function (see Table S2 for definition). *
 V3: The third parameter in the beta generating function (see Table S2 for
 definition) * ssqcalcs: the calculated sum-of-squared errors from the
 modeled prevalence compared to field data. * key: The sensitivity analysis
 scenario. See the main text for more details. #### File:
 Dens_Dep_Sens_Analysis_fits3.csv **Description:** This file contains the
 results from the sensitivity analysis for the Density Dependent
 transmission model ##### Variables * V1: The first parameter in the beta
 generating function (see Table S2 for definition) * V2: The second
 parameter in the beta generating function (see Table S2 for definition). *
 V3: The third parameter in the beta generating function (see Table S2 for
 definition) * ssqcalcs: The calculated sum-of-squared errors from the
 modeled prevalence compared to field data. * key: The sensitivity analysis
 scenario. See the main text for more details ## Code/software These were
 created using R version 4.4.2 (2024-10-31 ucrt) -- "Pile of
 Leaves" ``` 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
 LC_MONETARY=English_United States.utf8 [4] LC_NUMERIC=C
 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] MuMIn_1.48.4
 survminer_0.5.0 survival_3.7-0 scales_1.3.0 binom_1.1-1.1 extrafont_0.19
 lhs_1.2.0 [8] ggpubr_0.6.0 paletteer_1.6.0 plotrix_3.8-4 lubridate_1.9.4
 forcats_1.0.0 purrr_1.0.2 readr_2.1.5 [15] tibble_3.2.1 tidyverse_2.0.0
 gridExtra_2.3 stringr_1.5.1 dplyr_1.1.4 tidyr_1.3.1 ggplot2_3.5.1 [22]
 deSolve_1.40 loaded via a namespace (and not attached): [1] gtable_0.3.6
 xfun_0.51 rstatix_0.7.2 lattice_0.22-6 tzdb_0.4.0 vctrs_0.6.5 [7]
 tools_4.4.2 generics_0.1.3 stats4_4.4.2 pkgconfig_2.0.3 Matrix_1.7-1
 data.table_1.16.4 [13] lifecycle_1.0.4 compiler_4.4.2 munsell_0.5.1
 carData_3.0-5 Rttf2pt1_1.3.12 Formula_1.2-5 [19] pillar_1.10.1 car_3.1-3
 extrafontdb_1.0 abind_1.4-8 nlme_3.1-166 km.ci_0.5-6 [25] tidyselect_1.2.1
 stringi_1.8.4 rematch2_2.1.2 splines_4.4.2 grid_4.4.2 colorspace_2.1-1
 [31] cli_3.6.3 magrittr_2.0.3 broom_1.0.7 withr_3.0.2 backports_1.5.0
 timechange_0.3.0 [37] ggsignif_0.6.4 zoo_1.8-12 hms_1.1.3 evaluate_1.0.3
 knitr_1.49 KMsurv_0.1-5 [43] survMisc_0.5.6 rlang_1.1.5 Rcpp_1.0.14
 xtable_1.8-4 glue_1.8.0 rstudioapi_0.17.1 [49] R6_2.6.1 ```