Examples and applied use

Large population of Trifolium pratense and T. repens, found in the Arctic Circle, northern Norway. (Photo: Jade Phillips)

Diversity and in situ and ex situ gap analyses were carried out for priority CWR in Norway. Species occurrence data were gathered from GBIF. Observed distribution maps for individual taxa were created and predictive species distribution models were obtained in MaxEnt to undertake a gap analysis that determined how many taxa were predicted to be conserved in situ within the current protected areas (PA) network in Norway. The CAPFITOGEN tools were used to create an ecogeographic land characterization (ELC) map and to identify complementary in situ genetic reserves and ex situ collecting priorities which target the full range of ecogeographic diversity of priority taxa. The areas with the highest taxa richness were found to be around Oslo and the south-east coast of Norway, with up to 131 different priority CWR. The most taxa rich PAs were those in the Oslo and Østfold region, Kristiansand and the islands in Vestfold. A network of 19 complementary grids (≈ 10 Km2) contain 201 priority CWR, and a separate analysis identified a network of 23 complementary existing protected areas that contain 181 priority taxa. Ex situ gap analysis identified 177 taxa not conserved ex situ and of the 24 with accessions, 15 had the minimum of five populations conserved throughout their ecogeographic range.

Source: Phillips et al. 2016

Ecogeographic land characterization (ELC) maps are based on bioclimatic, geophysical and edaphic variables, which are combined to form a map that categorizes the target geographic area of study into various environmental adaptive scenarios. This approach has been used to identify genetic reserves for beet CWR in Europe by combining population density maps, ecogeographic data and species distribution models. The process is summarized in the following model:

  1. A map of population density of the selected species was developed as a starting point.
  2. Locations with a richness of at least two species were selected.
  3. Areas representating the most ecogeographic units for a group of species were selected.
  4. Sites located within existing protected areas, with the greatest number of populations of the target species and representing both common and marginal ecogeographic units were identified.

The premise of this approach is that the conservation of species across sites with the greatest ecogeographic variability implies that the maximum range of genetic diversity of adaptive importance within and among these species is conserved and, possibly, also the most interesting allelic variation in the genes of interest for crop improvement. Below (a) shows an ELC map for Beta species with 50 ecogeographic categories and (b) shows the potential species richness map for three Beta species.

Source: Parra-Quijano et al. (2008, 2012b)

Strategies for the development of core collections based on ecogeographic data

The authors determined the suitability of core collections based solely on ecogeographic data. Sixteen ecogeographic core collections were evaluated for six Lupinus spp. occurring on the Iberian Peninsula and the Balearic Islands. A Ward-Modified Location Model (Ward-MLM) and a two-step clustering (TSC) with proportional allocation strategy (P) produced the most representative core collections for the target taxa. In addition, a highly representative ecogeographic core collection was obtained by a simpler procedure by grouping according to ELC maps with P allocation. Ecogeographic data were thus used to create representative core collections with similar strategies to those used with genotypic or phenotypic data or simpler strategies such as CEM, and this method is easy to apply and update.

Source: Parra-Quijano et al. (2011)

Spanish populations of Lupinus luteus were characterized ecogeographically as follows:

  1. Selection of good quality georeferenced presence data.
  2. Collation of ecogeographical GIS layers/variables (from passport data and by extracting information from georeferencing collecting sites).
  3. Selection of the most relevant ecogeographic variables both through consultation with experts and by analyzing their relative statistical significance.
  4. Performing a Principal Component Analysis (PCA) in order to reduce the number of variables.
  5. Creation of tables with accessions and their corresponding ecological descriptors.
  6. Estimation of ecogeographical distances between all pairs of accessions (by using the Gower similarity coefficient).
  7. Performing a cluster analysis on the distance matrix and UPGMA agglomerative method to create dendrograms that represented ecogeographic similarities between accessions.
  8. Ecogeographic groups (EG) were then obtained from the cluster analysis using the new variables obtained with the PCA (PCA1 related to thermopluviometric factors, PCA2 related to temperature, PCA3 related to edaphic factors).
  9. To each accession, its corresponding EG was assigned and visualized in a map.

Source: Parra-Quijano et al. (2008)

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.