Changes in transmission rates drive seasonal patterns of shrimp black gill disease
Data files
Jul 29, 2025 version files 901.39 KB
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All_Estuaries_Trim_date.csv
13.03 KB
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All_models_all_distributions_bestfit2.R
39.49 KB
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all_mods_best_fit_betas.csv
101.93 KB
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Allmods_sensitivity_analysis_model_fitsV2.R
2.43 KB
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Best_fit_model_params_processingV5.R
62.47 KB
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Best_fit_params2.zip
51.39 KB
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Beta_sense_params_june_start.csv
14.84 KB
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Catch_level_prevalence.csv
400.62 KB
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Dens_Dep_Sens_Analysis_fits3.csv
60.10 KB
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Freq_Dep_Sens_Analysis_fits3.csv
17.44 KB
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generating_beta_sense_values.R
3.14 KB
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lambda_mu_gamma_est4.csv
1.27 KB
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M4_PreTreatment_Scope_PCR.csv
4.46 KB
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Mortality_4_KMdata.csv
3.63 KB
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Mortality_Recovery_Exp_StatsV2.R
7.45 KB
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Par_values_from_experiments_v3_June_startV2.R
8.98 KB
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Phil_Trans_tables_and_figures.R
22.49 KB
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Plotting_shrimp_pop_dynamics.R
5.87 KB
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README.md
17.65 KB
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Reservoir_Sens_Analysis_fits3.csv
21.30 KB
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Sensitivity_analysis_execution.R
15.28 KB
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Sentinel_Shrimp_Full_dataset.csv
16.76 KB
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Transmission_Exp_StatsV2.R
9.37 KB
Abstract
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.
Dataset DOI: 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