Predicting adult mosquito (Culex pipiens) emergence in response to temperature, nutrients, and salinity

The zip-file contains the data and code accompanying the paper 'Predicting adult mosquito (Culex pipiens) emergence in response to the combined effects of temperature, nutrients and salinity'. Together, these files should allow for the replication of the results. The 'raw_data' folder contains all experimental data that were used in this study. The files 'observations_A.csv', 'observations_B.csv', and 'observations_C.csv' contain the mesocosm data for respectively experiments A, B, and C (see the 'Methods' section in the manuscript for a detailed description of experiments A, B, and C). The file 'observations_description.txt' provides a complete description of the columns in the data files. The 'derived_data' folder contains all data that were derived from the observational data.The files 'parameters_A.txt', 'parameters_B_20.txt', 'parameters_B_200.txt', 'parameters_B_2000_trtmt4.txt', 'parameters_B_2000_trtmt8.txt', 'parameters_B_2000_trtmt12.txt', and 'parameters_C.txt' contain the non-formatted survival rates, development rates, and detection probabilities from the respective algorithm optimization run for the respective experimental data. The letters A, B and C indicate the experiment, (for experiment B) the number indicates the initial larval density, and (for density 2000) the 'trtmt' indicates treatment number. The files 'dataframe_A,csv', 'dataframe_B.csv', and 'dataframe_C.csv' contain the survival rates, development rates and detection probabilities for experiments A, B and C, with corresponding temperature, concentration, salinity and density values as well as treatment number and stage. The files 'table_svv_A.csv', 'table_svv_B.csv', and 'table_svv_C.csv' contain the survival rates for resp. experiment A, B and C.The files 'table_dev_A.csv', 'table_dev_B.csv', and 'table_dev_C.csv' contain the development rates for resp. experiment A, B and C.The files 'table_det_A.csv', 'table_det_B.csv', and 'table_det_C.csv' contain the detection probabilities for resp. experiment A, B and C.The files 'RE_table.csv', 'FE_table.csv', and 'SE_table.csv' contain resp. the random effects, fixed effects and standard errors of the fixed effects for the statistical analysis to derive continuous response relationships for the survival and development rates. The 'scripts' folder contains all scripts that should allow for replication of the results. The files 'calculate_parameters_A.py', 'calculate_parameters_B.py', 'calculate_parameters_B.py', 'calculate_parameters_B_2000.py', and 'calculate_parameters_C.py' apply the optimization algorithm (developed by Dellar et al. (2022)) to the respective dataset in order to calculate survival rates, development rates, and detection probabilities. For dataset B, we split the dataset according to initial larval density ('calculate_parameters_B.py' applies the algorithm to densities 20 and 200, 'calculate_parameters_B_2000.py' applies the algorithm to density 2000) and, in order to prevent convergence issues, we furthermore split the data according to treatment for density = 2000.The files 'prepare_data_A.py', 'prepare_data_B.py' and 'prepare_data_C.py' transform the raw parameter values into tables with the survival rates, development rates and detection probabilities, including the corresponding temperature, nutrient concentration, salinity and initial larval density.The file 'perform_analysis.R' performs the statistical analysis to obtain continuous response relationships for the vital rates (i.e. survival and development rates), and the file 'fixed_effects.R' contains all combinations of fixed effects factors that are required for the statistical analysis. The file 'predict_abundance.R' predicts the mosquito response by using two emergence indicators (the emergence time and probability) for combinations of temperature, nutrients, salinity, and initial larval density. The 'figures' folder contains all figures that are included in this study.Figure 1 is produced with the 'run_model.py' script. Figures 2 and 3 and figures S4.1-S4.3 are produced with the 'predict_abundance.py' script. Figures S6.1-S6.2 are produced with the 'perform_analysis.R' script.