A gap analysis modelling framework to prioritize collecting for ex situ conservation of crop landraces

Abstract

Aim:The conservation and effective use of crop genetic diversity are 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 modelling and ex situ conservation gap analysis modelling framework for crop landraces, using common bean (Phaseolus vulgaris L.) as a case study. Location: The Americas. Methods: The modelling framework includes five main steps: (a) determining relevant landrace groups using literature to develop and test classification models; (b) modelling the potential geographic distributions of these groups using occurrence (landrace presences) combined with environmental and socioeconomic predictor data; (c) calculating geographic and environmental gap scores for current genebank collections; (d) mapping ex situ conservation gaps; and (e) compiling expert inputs. Results: Modelled 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, Venezuela. Mesoamerican landrace collecting priorities are concentrated in Mexico, Belize and Guatemala. Conclusions:The modelling 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. | 731RAMIREZ-VILLEGAS Et AL.

Germplasm Acquisition and Distribution by CGIAR Genebanks

“…Second, spatial analyses have been undertaken using a method to assess the ecogeographic gaps and coverage of current CGIAR crop collections. The method, which works best for collections with a high percentage of available information on the latitude–longitude of the origin of accessions, looks for relationships between geographic patterns in crop distribution with the genetic structuring, and uses these relationships to build distribution models for crop landraces [ 41 ]. Third, a method for trait-based gap analyses focuses on the analysis of the distribution of adaptive priority traits in relation to the environment using machine learning to make predictions; it works best where landraces have been associated with an environment for long enough for their traits to become associated with their environment, and presupposes well-characterized collections.…”

International collaboration between collections‐based institutes for halting biodiversity loss and unlocking the useful properties of plants and fungi

“…Developing the concept of the metacollection of living plants across the botanic gardens community and the seed collections held in global and national genebanks promotes a positive exchange of data and material between collections. Several methods for undertaking gap analyses at the population or provenance level have been developed and used to prioritize collection of the known landraces of Phaseolus vulgaris L. (Ramirez‐Villegas et al., 2020), a sample of socioeconomically important South American trees (van Zonneveld et al., 2018) and more broadly for a large sample of c. 7,000 useful wild plants (Khoury et al., 2019).…”

GapAnalysis: an R package to calculate conservation indicators using spatial information

“…The methods implemented in GapAnalysis have only been applied to wild plant species thus far, in particular crop wild relatives and other wild flora of socioeconomic and cultural value, for two reasons. First, modeling distributions of domesticated plants and of animals (wild or domesticated) present unique challenges, for crop varieties because their ranges are determined by cultural as well as ecological factors (Ramírez‐Villegas et al 2020), and for livestock because of data gaps (FAO 2015, McGowan et al 2018). Second, measuring in situ conservation of domesticated biodiversity necessitates a methodology based on on‐farm and other forms of agricultural or rangeland conservation, rather than WDPA protected areas (Altieri and Merrick 1987, Bellon 2004).…”