Global lake synchrony signals rising planetary-scale risks

General Information This repository contains the comprehensive collection of empirical lacustrine time series, sedimentary core records, standardized data matrices, and execution scripts required to fully replicate the figures, network topologies, and statistical null models presented in the associated manuscript. I. Software and Environment Requirements Python (v3.8+): Required libraries include numpy, pandas, matplotlib, scipy, statsmodels, and pymannkendall. R (v4.2+): Required libraries include wsyn, igraph, zoo, parallel, pbapply, ggplot2, and cowplot. Geospatial Platform: Esri ArcGIS Desktop (v10.8) or ArcGIS Pro (for cartographic rendering and vector layer manipulation). Computation Note: To mitigate boundary artifacts and edge effects inherent in chronological sliding-window operations, the final 12–24 data points of the generated time series are systematically excluded from the final trend evaluation. II. File Inventory and Component Descriptions 1. Empirical and Core Datasets (.csv) Global_Lake_Chlorophyll_a_Time_Series.csv Description: Long-term, multi-decadal monthly gridded chlorophyll-a concentration time series across global limnological cohorts, featuring unique lake identification codes as columns and sequential temporal intervals as rows. Global_Lake_Water_Color_Time_Series.csv Description: Normalized global lake water color time series quantified via the Forel-Ule Index (FUI) framework, structured identically to the chlorophyll dataset for multi-proxy comparison. Global_Lake_Sediment_Pigments.csv Description: Stratigraphic sedimentary core pigment records providing long-term retrospective evidence of historical limnological synchronization and baseline shifts. Figures_data.CSV Description: Consolidated and curated data matrices containing the exact values, coordinates, and regional groupings utilized to plot the core text figures. 2. Statistical Analysis and Mathematical Scripts (.py & .R) Pairwise correlation -based synchrony.py Description: Computes macro-scale spatial synchrony trends across lacustrine nodes using the standard pairwise correlation matrix stream following frequency-domain decomposition. Loreau φ Metric for lake synchrony.py Description: Execution script utilizing the classic Loreau-de Mazancourt φ metric to calculate multi-lake population-level synchrony across global and latitudinal cohorts. Sliding window sensitivity.py Description: Explores scale dependency and temporal robustness by executing the analytical data stream across varying sliding temporal windows (e.g., 2, 5, 8, 10, 15 time steps). Network and modularity analysis.R Description: Implements the wsyn continuous signed-power soft-thresholding paradigm and leverages igraph to partition similarity networks into topological communities, calculating decadal modularity . Permutation-based significance of synchrony trends.py Description: Performs Mann-Kendall trend tests on sliding-window synchrony series and runs empirical hypothesis testing to extract true directional shifts. Permutation-based significance of synchrony trends-Null_Model_Generator.py Description: Harnesses multi-core parallel processing to shuffle network weights 1,000 times, constructing empirical null distributions to validate the significance of observed network community dissolution. 3. Geospatial Visualization & Documentation (.rar & .txt) Figure 1.rar Description: Compressed archive containing all raw geospatial project databases, vector layer shapefiles (.shp), metadata tables, and cartographic layout definitions (.mxd) used to generate the global geographic distribution map of sample lakes (Figure 1). Compiled within Esri ArcGIS 10.8. Data_Sources_and_References.txt Description: A comprehensive standalone text file documenting the complete bibliographic literature sources, historical baselines, and corresponding DOI attributions compiled within the empirical datasets. III. Execution and Replication Workflow Data Cleaning & Detrending: Feed raw time series through Python/R scripts to execute STL harmonic regression models and filter low-frequency background signals. Synchrony Calculations: Execute the pairwise and Loreau metric scripts to plot continuous synchrony variations over time. Network Configurations: Run the R network script to output the high-resolution PCA community plots and decadal modularity comparisons. Significance Evaluation: Launch the null model generator to confirm that network configuration shifts significantly exceed random stochastic expectations (P < 0.001). Spatial Reconstruction: Extract Figure 1.rar into your local GIS directory to access, modify, or re-export the multi-layered baseline global sampling maps.