Untangling the environmental drivers of gross primary productivity in African rangelands
dc.contributor.author | Lomax, GA | |
dc.contributor.author | Powell, TWR | |
dc.contributor.author | Lenton, TM | |
dc.contributor.author | Economou, T | |
dc.contributor.author | Cunliffe, AM | |
dc.date.accessioned | 2024-09-24T15:19:56Z | |
dc.date.issued | 2024-09-12 | |
dc.date.updated | 2024-09-24T14:00:03Z | |
dc.description.abstract | Precipitation variability is forecast to increase under climate change but its impacts on vegetation productivity are complex. Here, we use generalised additive models and remote sensing-derived datasets to quantify the effect of precipitation amount, distribution, and intensity on the gross primary productivity of dry rangelands across sub-Saharan Africa from 2000 to 2019 and differentiate these effects from other variables. We find that total precipitation is the primary driver of productivity, but that more variable rainfall has a small negative effect across vegetation types and rainfall regimes. Temperature and soil nitrogen also have strong effects, especially in drier rangelands. Shrublands and grasslands are more sensitive to environmental variability than savannas. Our findings support a model in which the main constraints on productivity are maintenance of soil moisture and minimisation of plant water stress. This highlights the risks of climate warming and increasing variability for productivity in water-limited grass and shrublands but suggests savannas may have greater resilience in Africa. | en_GB |
dc.description.sponsorship | Oppenheimer Programme in African Landscape Systems (OPALS) | en_GB |
dc.description.sponsorship | University of Exeter | en_GB |
dc.description.sponsorship | Sarah Turvill | en_GB |
dc.description.sponsorship | Oppenheimer Generations Research and Conservation | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | A. G. Leventis Foundation | en_GB |
dc.description.sponsorship | Leverhulme Trust | en_GB |
dc.description.sponsorship | Alan Turing Institute | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.description.sponsorship | Cyprus Government | en_GB |
dc.identifier.citation | Vol. 5(1), article 500 | en_GB |
dc.identifier.doi | https://doi.org/10.1038/s43247-024-01664-5 | |
dc.identifier.grantnumber | EP/S022074/1 | en_GB |
dc.identifier.grantnumber | RPG-2018-046 | en_GB |
dc.identifier.grantnumber | 856612 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/137524 | |
dc.identifier | ORCID: 0000-0002-6725-7498 (Lenton, Timothy M) | |
dc.identifier | ORCID: 0000-0001-8697-1518 (Economou, Theo) | |
dc.identifier | ORCID: 0000-0002-8346-4278 (Cunliffe, Andrew M) | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.relation.url | https://github.com/gee-hydro/gee_PML | en_GB |
dc.relation.url | https://www.chc.ucsb.edu/data/chirps | en_GB |
dc.relation.url | https://lpdaac.usgs.gov/products/mcd12q1v006/ | en_GB |
dc.relation.url | https://lpdaac.usgs.gov/products/mcd64a1v006/ | en_GB |
dc.relation.url | https://doi.org/10.5281/zenodo.13294238 | en_GB |
dc.relation.url | https://doi.org/10.5281/zenodo.7024961 | en_GB |
dc.relation.url | https://code.earthengine.google.com/?accept_repo=users/guylomax01/africa_rangeland_ppt_gpp_analysis | en_GB |
dc.relation.url | https://code.earthengine.google.com/?asset=projects/ee-guylomax01/assets/africa_rangeland_precipitation_gpp | en_GB |
dc.rights | © The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.title | Untangling the environmental drivers of gross primary productivity in African rangelands | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2024-09-24T15:19:56Z | |
dc.identifier.issn | 2662-4435 | |
exeter.article-number | 500 | |
dc.description | This is the final version. Available on open access from Nature Research via the DOI in this record | en_GB |
dc.description | Data availability: All data used in this study are freely available and can be accessed in online repositories. The latest PML_V2 product39 is available through Google Earth Engine from https://github.com/gee-hydro/gee_PML. CHIRPS daily precipitation data38 is available at https://www.chc.ucsb.edu/data/chirps. ERA5-Land reanalysis data95 are available from the European Centre for Medium-Range Weather Forecasts at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means. The following MODIS datasets are available from the Land Processes Distributed Active Archive Center (LP DAAC): land cover classes90 (https://lpdaac.usgs.gov/products/mcd12q1v006/) and burned area96 (https://lpdaac.usgs.gov/products/mcd64a1v006/). The iSDAsoil dataset60 can be accessed at https://isda-africa.com/isdasoil. All datasets are also available through Google Earth Engine (Google account required) and are linked to in the Google Earth Engine repository detailed below. Processed data used for fitting the annual and multi-annual GAMs, as well as model output data used to generate Figs. 1–4, are available at https://doi.org/10.5281/zenodo.13294238. | en_GB |
dc.description | Code availability: Data preparation and analysis code is available at https://doi.org/10.5281/zenodo.7024961. The Google Earth Engine repository for data pre-processing can be accessed by registered Earth Engine users at https://code.earthengine.google.com/?accept_repo=users/guylomax01/africa_rangeland_ppt_gpp_analysis. Intermediate data pre-processing Earth Engine assets can also be accessed at https://code.earthengine.google.com/?asset=projects/ee-guylomax01/assets/africa_rangeland_precipitation_gpp. | en_GB |
dc.identifier.eissn | 2662-4435 | |
dc.identifier.journal | Communications Earth & Environment | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2024-08-29 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2024-09-12 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2024-09-24T15:15:19Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2024-09-24T15:20:48Z | |
refterms.panel | C | en_GB |
refterms.dateFirstOnline | 2024-09-12 |
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