Quantifying Ecological Surprises: A Bayesian Framework Decoding Multi-Stressor Interactions in Marine Population Dynamics

The data package contains several csv files, the first two of which can be used directly to build the Bayesian hierarchical spatio-temporal modeling (BHSM) framework to quantify and classify multi-stressor interactions in dynamic populations. 'All_Hane_D&B.csv' details the standardized biomass data on Harpodon nehereus and their occurrence obtained from fishery resource surveys conducted in the Yangtze Estuary between 2011 and 2022. 'Lon': longitude of the survey station 'Lat': latitude of the survey station 'Year': the survey year 'Weight': standardized biomass of H. nehereus 'Presence': presence of H. nehereus in the survey, “1” for presence, “0” for absence 'ENV&STR.csv' contains data collected from environmental surveys implemented in conjunction with fishery resource surveys. It also contains stressor data from multiple sources. 'FP': annual fisheries pressure 'PDO': Pacific Decadal Oscillation 'VoCC': velocity of climate change 'RV': runoff volume of the Yangtze River into the Yangtze Estuary 'SL': sediment load by Yangtze River runoff into the Yangtze Estuary 'NE': nutrient enrichment 'UW': urban wastewater 'WQ': water quality 'SWV': sea surface water velocity 'D': water depth 'SS': sea surface salinity 'BS': bottom salinity 'ST': sea surface temperature 'BT': bottom temperature 'DO': Dissolved oxygen concentration in seawater 'pH': pH of seawater 'Chl_a': Sea surface chlorophyll a concentration 'NE_PCA.csv' contains nutrient enrichment by station within the waters of the Yangtze Estuary for each year. 'TN': total nitrogen in seawater 'TP': Total phosphorus in seawater 'PC1 (61.86%)': the specific magnitude of the first principal component axis (PC1) for the TN and TP data, and a significant explanatory rate 'PC2 (38.14%)': the specific magnitude of the second principal component axis (PC2) for the TN and TP data, and a significant explanatory rate 'UW_PCA.csv' contains emission information for pollution sources directly entering the sea in Shanghai, as well as the Eigenvalues and Explanation Rates from Principal Component Analysis (PCA). 'Year': year of pollution emission 'Sewage (×108t)': annual volume of wastewater discharged directly to the sea in Shanghai 'Petroleum (t)': Petroleum emissions 'COD (×104t)': Chemical Oxygen Demand (COD) discharged into the sea 'NH4 (×104t)': total ammonia nitrogen discharged into the sea 'TP (t)': total phosphorus discharged into the sea 'PC1 (74.70%)': specific magnitude of the PC1 for Sewage, Petroleum, COD, NH4, and TP data, and a significant explanatory rate 'PC2 (13.57%)': specific magnitude of the PC2 for Sewage, Petroleum, COD, NH4, and TP data, and a significant explanatory rate ' WQ_PCA.csv' ncludes annual water quality information for each station in the Yangtze Estuary. 'DIP': Dissolved inorganic phosphorus content 'COD': COD in water 'DIN': Dissolved inorganic nitrogen content 'PC1 (55.38%)': specific magnitude of the PC1 for DIP, COD, and DIN data, and a significant explanatory rate 'PC2 (33.61%)': specific magnitude of the PC2 for DIP, COD, and DIN data, and a significant explanatory rate Code/Software R is required to run INLA; the script was created using version 4.2.3. Annotations are provided throughout the script to load the library and datasets, run the analyses, and recreate the figures.