10.5061/DRYAD.866T1G1N0
Ramirez-Villegas, Julian
0000-0002-8044-583X
International Center for Tropical Agriculture
Khoury, Colin K
0000-0001-7893-5744
International Center for Tropical Agriculture
Achicanoy, Harold A
International Center for Tropical Agriculture
Mendez, Andres C
International Center for Tropical Agriculture
Sosa, Chrystian C
International Center for Tropical Agriculture
Debouck, Daniel G
International Center for Tropical Agriculture
Kehel, Zakaria
International Center for Agricultural Research in the Dry Areas
Guarino, Luigi
Global Crop Diversity Trust
A gap analysis modeling framework to prioritize collecting for ex situ
conservation of crop landraces
Dryad
dataset
2020
Landrace
plant genetic resources
Gap analysis
common bean
Ex-situ conservation
CGIAR Genebanks Platform
2021-02-06T00:00:00Z
2021-02-06T00:00:00Z
en
8393815 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Aim: The conservation and effective use of crop genetic diversity is
crucial to overcome challenges related to human nutrition and agricultural
sustainability. Farmers’ traditional varieties (“landraces”) are major
sources of genetic variation. The degree of representation of crop
landrace diversity in ex situ conservation is poorly understood, partly
due to a lack of methods that can negotiate both the anthropogenic and
environmental determinants of their geographic distributions. Here we
describe a novel spatial modeling and ex situ conservation gap analysis
modeling framework for crop landraces, using common bean (Phaseolus
vulgaris L.) as a case study. Location: The Americas Methods: The modeling
framework includes five main steps: (1) determining relevant landrace
groups using literature to develop and test classification models; (2)
modeling the potential geographic distributions of these groups using
occurrence (landrace presences) combined with environmental and
socioeconomic predictor data; (3) calculating geographic and environmental
gap scores for current genebank collections; (4) mapping ex situ
conservation gaps; and (5) compiling expert inputs. Results: Modeled
distributions and conservation gaps for the two genepools of common bean
(Andean and Mesoamerican) were robustly predicted and align well with
expert opinions. Both genepools are relatively well conserved, with Andean
ex situ collections representing 78.5% and Mesoamerican 98.2% of their
predicted geographic distributions. Modelling revealed additional
collection priorities for Andean landraces occur primarily in Chile, Peru,
Colombia and, to a lesser extent, in Venezuela. Mesoamerican landrace
collecting priorities are concentrated in Mexico, Belize, and Guatemala.
Conclusions: The modeling framework represents an advance in tools that
can be deployed to model the geographic distributions of cultivated crop
diversity, to assess the comprehensiveness of conservation of this
diversity ex situ, and to highlight geographic areas where further
collecting may be conducted to fill gaps in ex situ conservation.
Our distribution modeling and conservation gap analysis modeling framework
requires geographic occurrence (presence) data for landraces, and
information on the locations where these landraces have been previously
collected for conservation ex situ, as well as characterization data on
the landrace accessions. To assess the world’s common bean landrace
collections, we compiled available genebank accession-level passport
(i.e., site where collected) data from major online germplasm databases,
including the Genesys plant genetic resources portal and the United
Nations Food and Agriculture Organization World Information and Early
Warning System on Plant Genetic Resources for Food and Agriculture
(WIEWS). To ensure inclusion of the crop’s majorgermplasm collections, we
specifically gathered occurrence and characterization data from the CIAT
database, freely available at and from the United States Department of
Agriculture (USDA) Genetic Resources Information Network (GRIN)–Global.
Additional occurrences were gathered from the Global Biodiversity
Information Facility (GBIF), which contained 25,670 observations from
herbaria, botanic gardens, and other plant repositories, to provide
independent data from non-genebank sources. We compiled the datasets into
a single database and performed a thorough quality check of all records.
Duplicated observations were eliminated with preference to maintain
original data, e.g., USDA-GRIN or CGIAR records included in Genesys or
WIEWS were discarded. Coordinates were corrected, or if not possible,
eliminated, when latitude and longitude were equal to zero, located in
inland water bodies or in the ocean, located in the wrong country, had an
inverted sign in the latitude and/or longitude, or had low coordinate
precision (i.e. with less than 2 decimal places). With the aim of
compiling a robust global dataset of important environmental and
anthropogenic drivers of the geographic distributions of crop landraces,
we gathered and/or calculated spatially explicit (gridded) information for
a total of 50 potential predictors, including climate, topography,
diversity and domestication, and socioeconomic variables. These were
extracted for each occurrence point location. For climate, we used a total
of 40 variables, derived from a combination of the WorldClim version 2and
the Environmental Rasters for Ecological Modeling (ENVIREM) databases. We
included topography from the Shuttle Radar Topography Mission (SRTM)
dataset of the CGIAR-Consortium on Geospatial Information (CSI) portal.
Two crop genetic diversity and domestication proxy variables were
included, namely, the distance to known common bean wild relative
populations, and the distance to human settlements before year AD 1500.
Regarding socioeconomic variables (8 in total) we included datasets on the
geographic distribution of ethnic groups; crop yield, harvested area, and
crop production quantity (You et al., 2017); population density;
population accessibility; distance to navigable rivers; and percentage of
area under irrigation. All spatial predictor data were scaled to or
computed on a common 2.5 arc-min grid, using the Geographic Coordinate
System (GCS) with WGS84 as datum.
See Readme file.