Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

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Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.
Kaczmarek, Thomas; Faye, Adama; Ilmi Ahmed, Aicha; Veltman, Margaretha; Thuillet, Anne-Celine; Scarcelli, Nora; Sultan, Benjamin; Vigouroux, Yves; Cubry, Philippe, 2025, "Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.", https://doi.org/10.23708/M9YGDC, DataSuds, V1, UNF:6:EdX+/x3auhCXDY0Q7OgZ4g== [fileUNF]
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Description

The ability of species and populations to adapt to their environment faces increasing challenges as climate change accelerates. Recent methods based on genomic offset (GO) statistics aim to quantify the risk of non-adaptation of populations to future climates. While several studies have evaluated the ability of different offset statistics to predict population (mal)adaptation, the impact of the chosen climate data —which could vary in relevance and quality— remains unexplored.

To explore this question, we analyzed the case of 157 pearl millet landraces cultivated in West Africa, for which fitness proxies were measured during field trials conducted over two years. We calculated geometric and gradient forest genomic offset statistics using three different historical climate datasets: bioclimatic variables from WorldClim (v.2.1), from CHELSA (v.2.1), and a dataset specifically designed for this study derived from the EWEMBI (v.1) climate database.

We provide here the climate data computed from the EWEMBI v.1 dataset, along with the code used to extract the climate crop-specific metrics.
(2025-09-05)
Subject Agricultural Sciences
Keyword climate change, climate modelling, fitness, genetic diversity, pearl millet, West Africa
Related Publication Thomas Kaczmarek, Adama Faye, Aicha Ilmi Ahmed, Margaretha A. Veltman, Anne-Céline Thuillet, Nora Scarcelli, Benjamin Sultan, Yves Vigouroux, Philippe Cubry. Genomic offset analyses: mind your climate variables. submitted to Global Change Biology Communications. UNDER REVIEW
Notes This work received support from the French National Research Agency projects AfrADAPT (grant number ANR-22-CE32-0008) and PEG2 (grant number ANR-22-CE45-0033).
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ReadMe_M9YGDC.txt
Plain Text - 1.8 KB
Published Sep 17, 2025
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MD5: b4beefe745809f4a1f5ca15b1dba090f
Contains citation, description and conditions of use of dataset data
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Preview "ReadMe_M9YGDC.txt"
climateMetrics_EWEMBI_liebmann_west_africa_1979-2013.nc
climateMetrics_files/Network Common Data Form - 123.6 MB
Published Sep 17, 2025
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MD5: 69a3987dbf4cc68d86230068ffeed8cc
A NetCDF file with climate crop-specific metrics stored for each pixel (resolution 0.5°) located within a defined area covering West Africa.
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climatemetrics_mil173Landraces_EWEMBI_Liebmann_1979-1990.tab
climateMetrics_files/Tabular Data - 508.5 KB
Published Sep 17, 2025
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185 Variables, 173 Observations UNF:6:EdX+/x3auhCXDY0Q7OgZ4g==
Precipatation, Temperature and radiation crop-specific climate variables computed and extracted from the EWEMBI (V1) dataset, for 173 populations of pearl millet.
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Preview "climateMetrics_files/climatemetrics_mil173Landraces_EWEMBI_Liebmann_1979-1990.tab"
Dictionary_cropmetrics.pdf
Dictionary_cropmetrics.pdf
Documentation/Adobe PDF - 25.0 KB
Published Sep 17, 2025
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MD5: baaa5328ffa5ac93176b65b0d2789aa8
Dictionary of crop climate metrics
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Preview "Documentation/Dictionary_cropmetrics.pdf"
extract_pixels_from_netcdf.py
script_python/Python Source Code - 6.7 KB
Published Sep 17, 2025
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MD5: bebbb223232f0f478feb8cdcd2685b46
A python script that extracts crop-specific metrics from the netCDF file, with a passport file containing geographical coordinates.
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