INTERACTIVE TOOLKIT FOR
CROP WILD RELATIVE CONSERVATION PLANNING version 1.0
Ex situ gap analysis can be carried out at different levels:
Ex situ gap analysis includes the following three main steps, which are very similar to those carried out in an in situ gap analysis:
1. Select the occurrence data to use in the analysis
If different levels of geographic precision have been ascribed to each species’ occurrences, only the most accurate should be used (e.g use the levels 1 to 3 from here [1]).
2. Identify the CWR that are not conserved ex situ (individual CWR taxon level)
This task involves comparing priority CWR taxa with ex situ accessions of those taxa that are adequately conserved in genebanks and field genebanks (this information should already have been collated at this [2] step). This enables detection of CWR not adequately conserved ex situ. Note that only ex situ accessions conserved under suitable genebank conditions, with enough seed, and that are adequately labelled with good quality passport information (where it is possible to track its original collection site) should be considered, as these are the only accessions that can be utilized. GAPS = CWR taxa not adequately conserved ex situ.
3. Identify gaps at infra-species level, i.e. ecogeographic diversity, genetic and trait levels.
At ecogeographic diversity level: compare the ecogeographic diversity of priority CWR and the ecogeographic diversity that has already been collected and conserved ex situ for those CWR. GAPS = CWR ecogeographic areas not yet conserved ex situ. There are a few alternative methodologies that can be used to carry out an ecogeographic diversity gap analysis:
At genetic/trait level: compare CWR distribution with genetic/trait diversity data and determine which populations have been previously collected and conserved ex situ. GAPS = specific CWR populations with genetic diversity/the traits of interest not conserved ex situ. See here [8] for more on genetic diversity analysis of priority CWR.
Regarding the trait level analysis, predictive characterization is a technique that enables the identification of CWR populations (in situ or from ex situ collections) that potentially harbour traits of interest (e.g. drought tolerance, insect pest resistance) (Thormann et al. 2014). It uses ecogeographical and climatic data derived from the specific location of a collecting or observation site to predict traits of interest in order to inform conservation and use options (Thormann et al. 2014). Once these traits have been identified, it is possible to assess whether the populations in which they are thought to occur are actually conserved adequately ex situ. For further information, see this document [9] (166 KB) that has been produced in the context of the SADC Crop Wild Relatives project [10] with some guidelines on how to undertake a predictive characterization study.