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

Individual CWR level: whether the target CWR taxa are adequately represented by active in situ conservation.
Ecogeographic level: whether the whole ecogeographic range of the CWR is represented in 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 in situ.
Trait level: whether specific CWR populations that contain a particular trait of interest (e.g. resistance to drought etc.) are adequately conserved in situ.

In situ gap analysis includes the following five main steps:

Select the occurrence data to be used in the analysis.
Identify the CWR that do not occur within the existing network of protected areas (individual CWR taxon level).
Identify the CWR that do occur within existing protected areas but are not actively managed and conserved (individual CWR taxon level).
CWR distribution modelling and field confirmation.
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 levels 1 to 3 from here).

2. Identify the CWR that do not occur within the existing network of protected areas (individual CWR taxon level)

This task involves:

Surveying the environment and agriculture communities in order to find out whether the target CWR are known to occur within any existing PAs, as this information might not have been picked out during the collation of occurrence data.
Overlaying the CWR taxa distribution with the network of protected areas in a GIS (e.g. DIVA-GIS, ArcGIS etc.).

Further analysis is required for any CWR found only to occur outside protected areas, to confirm their absence (see point 4 'CWR distribution modelling and field confirmation' below).

3. Identify the CWR that do occur within existing protected areas but are not actively managed and conserved (individual CWR taxon level)

In these cases, CWR are only passively conserved. GAPS = CWR taxa within protected areas but lacking management.

4. CWR distribution modelling and field confirmation

If target CWR do not occur within any existing PA, their distribution can be predicted using species distribution modelling techniques (see Box 'Species distribution models'). Modelled distributions of CWR can then be matched with the existing network of PA. If the species is then predicted to occur within an existing PA, field work should be undertaken to confirm this. If confirmed, it should then be evaluated to determine if it is actively managed and conserved (though this is very unlikely, as the previous analysis will have shown that there were no records of the species in the PA). If the species is not managed and actively conserved, then this is a gap in its conservation (GAPS = CWR taxa within protected areas but lacking management). If the species is not predicted to occur within a PA, then this is also a gap (GAPS = CWR taxa not actively conserved in situ).

5. Identify gaps at infra-species level, i.e. ecogeographic diversity, genetic and trait levels

At ecogeographic diversity level: determine the range of ecogeographic CWR diversity and identify where it is conserved in situ. GAPS = CWR ecogeographic areas not already conserved in situ. This can be achieved using ecogeographic land characterization maps (ELC maps) and the ecogeographic diversity analysis already outlined here. 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 in situ conservation activities.

After creating the ELC map (either species-specific or generalist) (to know more about these two different types of ELC maps, 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 populations of the species that are already actively conserved in situ.
Gaps in in situ conservation can then be identified for each target taxon.
Priority areas for in situ conservation can then be identified by producing richness maps of in situ ecogeographic gaps (DIVA-GIS can be used to produce such maps) or by complementarity analysis using the Complementa tool of the CAPFITOGEN tools (Parra-Quijano et al. 2016). Both methods use the occurrence points of each priority CWR that are located in the identified ecogeographic gaps, and the richest or most complementary areas for each under-conserved ELC category etc. are determined. How the ecogeographic diversity gaps are incorporated into in situ conservation is the responsibility of the researcher.

At genetic / trait level: compare CWR distribution with genetic/trait diversity data and determine which populations are actively conserved. GAPS = specific CWR populations with genetic diversity/traits of interest not conserved in situ. For more information on genetic diversity analysis of priority CWR, click here.
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 in situ. For further information, see this document (166 KB) that has been produced in the context of the SADC Crop Wild Relative 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.