DOI: 10.5281/zenodo.16020394
title: Quantifying Ecological Surprises: A Bayesian Framework Decoding Multi-Stressor Interactions in Marine Population Dynamics

description: 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.