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Ex situ gap analysis can be carried out at different levels:

Individual CWR level: whether the target CWR taxa are adequately represented by existing ex situ accessions.
Ecogeographic level: whether the whole ecogeographic range of the CWR is represented ex situ. Ecogeographic diversity can be used as an indicator of genetic diversity, the assumption being that the conservation of maximum ecogeographic diversity will result in the conservation of maximum genetic diversity. Characterizing populations according to the environmental conditions in which they grow can also help to identify useful abiotic traits such as extreme temperatures, drought etc.
Genetic level: whether specific CWR populations that contain genetic diversity of interest (e.g. high genetic diversity) are conserved ex situ.
Trait level: whether specific CWR populations that contain a particular trait of interest (e.g. resistance to drought etc.) are adequately conserved ex situ.

Ex situ gap analysis includes the following three main steps, which are very similar to those carried out in an in situ gap analysis:

Select the occurrence data to be used in the analysis.
Identify the CWR that are not conserved ex situ (individual CWR taxon level).
Identify gaps at infra-species level, i.e. ecogeographic diversity, genetic and trait levels.

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).

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 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:

Ecogeographic land characterization maps (ELC maps) and the ecogeographic diversity analysis already outlined here can be used as the basis for a gap analysis. This method identifies known populations of the target CWR that are within ecogeographic gaps (ecogeographic area not adequately conserved) and also helps to determine appropriate areas for ex situ conservation activities.
- After creating an ELC map (either species-specific or generalist) (to know more about these two different types of ELC map, click here), the Representa tool of the CAPFITOGEN tool set (Parra-Quijano et al. 2016) can be used to compare the ELC categories in which the species occurs with the ELC categories containing ex situ accessions of the species that are already conserved ex situ.
- Gaps in ex situ conservation can then be identified for each target taxon.
- Priority areas for ex situ collection can then be identified by producing richness maps of ex situ ecogeographic gaps (DIVA-GIS can be used to produce such maps) or by complementarity analysis using the Complementa tool of the CAPFITOGEN tool set (Parra-Quijano et al. 2016). Both methods use the occurrence points of each priority CWR that have been identified as ecogeographic gaps.

The Ramírez-Villegas et al. (2010) methodology, which consists of identifying gaps in ex situ collections based on a combination of sampling, geographic and environmental gaps, can also be used. This method involves the creation of potential distribution models for each target taxon and results in the identification of species of high, medium and low priority for collecting as well as identifying potential collecting areas for priority species (this is in contrast to the previous method, which only considered known occurrences).

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 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 (166 KB) that has been produced in the context of the SADC Crop Wild Relatives project with some guidelines on how to undertake a predictive characterization study.

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Species distribution models

Species distribution models

Species distribution models

Species distribution models (SDM) are a useful tool to predict potential areas of distribution of priority CWR. They have been commonly used to answer questions related to ecology, evolution and conservation (Elith et al. 2006). SDM have been employed to aid conservation decisions (e.g. Dockerty et al. 2003, Midgley et al. 2003), to direct field surveys towards locations where taxa are likely to be found (e.g. Engler et al. 2004), to establish baseline information for predicting a species’ response to landscape alterations and/or climate change (e.g. Huntley et al. 1995, Beaumont and Hughes 2002, Thuiller 2003, Thomas et al. 2004, Hijmans and Graham 2006) and to identify high‐priority sites for ex situ and in situ conservation (e.g. Araújo and Williams 2000, Loiselle et al. 2003, Castañeda-Álvarez et al. 2016).

There is a wide range of methods for modelling species distribution. These include classification and regression trees (CART) (e.g. Breiman et al. 1984), generalized linear models (GLM) (McCullagh and Nelder 1989), generalized additive models (GAM) (Hastie and Tibshirani 1990), climatic envelope models (CEM) (e.g. BIOCLIM) (Busby 1991), Gower‐similarity models (e.g. DOMAIN) (e.g. Carpenter et al. 1993), artificial neural networks (ANN) (e.g. Mastrorillo et al. 1997), ecological niche factor analysis (ENFA) (e.g. Hirzel et al. 2001, freely available from here), generalized dissimilarity models (GDM) (e.g. Ferrier 2002) and maximum entropy models (e.g. MaxEnt by Phillips et al. 2006, freely available from here). These models vary in how they model distribution responses, select relevant climatic parameters, define fitted functions for each parameter, weight different parameter contributions, allow for interactions and predict geographic patterns of occurrence (Guisan and Zimmerman 2000, Burgman et al. 2005). See Brotons et al. (2004), Segurado and Araújo (2004) and Elith et al. (2006) for detailed reviews and comparison of existing modelling methods, and Thuiller et al. (2005) for discussion on the ecological principles and assumptions of each model, as well as their limitations and decisions inherent to the evaluation of these models.

The Interactive Toolkit for Crop Wild Relative Conservation Planning was developed within the framework of the SADC CWR project www.cropwildrelatives.org/sadc-cwr-project (2014-2016),
which was co-funded by the European Union and implemented through ACP-EU Co-operation Programme in Science and Technology (S&T II) by the African, Caribbean and Pacific (ACP) Group of States.
Grant agreement no FED/2013/330-210.

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Species distribution models