Data_Sheet_1_Local working collections as the foundation for an integrated conservation of Theobroma cacao L. in Latin America.PDF

The intraspecific diversity of cacao has traditionally been preserved in genebanks. However, these establishments face various challenges, notably insufficient funding, accession redundancy, misidentification and lack of wild cacao population samples. In natural environments, it is expected that unknown varieties of cacao may still be found, but wild populations of cacao are increasingly threatened by climate change, deforestation, habitat loss, land use changes and poor knowledge. Farmers also retain diversity, but on-farm conservation is affected by geopolitical, economic, management and cultural issues, that are influenced at multiple scales, from the household to the international market. Taking separately, ex situ, in situ and on-farm conservation have not achieved adequate conservation fostering the inclusion of all stakeholders and the broad use of cacao diversity. We analyze the use of the traditional conservation strategies (ex situ, in situ and on-farm) and propose an integrated approach based on local working collections to secure cacao diversity in the long term. We argue that national conservation networks should be implemented in countries of origin to simultaneously maximize alpha (diversity held in any given working collection), beta (the change in diversity between working collections in different regions) and gamma diversity (overall diversity in a country).</p

Observed locally common alleles compared to current and future modeled distribution of cacao.

<p>Upper: predicted changes in cacao habitat suitability from present until 2050; red areas represent potential habitat suitability at present but no longer by 2050 (high impact or restriction areas); green indicates areas with continued habitat suitability from present until 2050 (low impact or stable areas); and blue indicates areas which are currently unsuitable for cacao, but may become suitable by 2050 (new or expansion areas) Lower: distribution of areas with modeled habitat suitability of cacao by 2050, overlaid with the location of currently existing protected areas.</p

Spatial variation of different genetic parameters, represented at a resolution of ten minute grid cells and a circular neighborhood of 1 degree.

<p>Highest values are consistently observed in the extensive bean-shaped Amazonian area covering both the Peruvian-Brazilian border, and the southern part of the Colombian-Brazilian border, as well as Amazonian Ecuador.</p

Ordination diagram of a Principal Coordinate Analysis applied on the cacao dataset, using Nei’s distance.

<p>The first two axes show 69% of the variation in data. Environmental variables were added <i>a posteriori</i> through vector fitting. Arrows point in the direction of most rapid change in the variable and their length is proportional to the correlation between ordination and variable. According to the classification used by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047676#pone.0047676-Motamayor2" target="_blank">[9]</a>, Cluster1 = Purus; Cluster2 = Criollo; Cluster3 = Guiana; Cluster4 = Marañon-Amazon River; Cluster5 = Amelonado; Cluster6 = Contamana + Nacional (+Purus); Cluster7 = Marañon-Rondônia; Cluster8 = Iquitos (+Purus); Cluster9 = Nanay; Cluster10 = Curaray (alt = altitude; BIO1 = Annual mean temperature; BIO2 = Mean diurnal range (max temp – min temp) (monthly average); BIO3 = Isothermality (BIO1/BIO7) * 100; BIO4 = Temperature Seasonality (Coefficient of Variation); BIO5 = Max Temperature of Warmest Period; BIO6 = Min Temperature of Coldest Period; BIO7 = Temperature Annual Range (BIO5–BIO6); BIO8 = Mean Temperature of Wettest Quarter; BIO9 = Mean Temperature of Driest Quarter; BIO10 = Mean Temperature of Warmest Quarter; BIO11 = Mean Temperature of Coldest Quarter; BIO12 = Annual Precipitation; BIO13 = Precipitation of Wettest Period; BIO14 = Precipitation of Driest Period; BIO15 = Precipitation Seasonality (Coefficient of Variation); BIO16 = Precipitation of Wettest Quarter; BIO17 = Precipitation of Driest Quarter; BIO18 = Precipitation of Warmest Quarter; BIO19 = Precipitation of Coldest Quarter).</p

Cluster richness, i.e. the number of different clusters shown in <b>figure 5</b> that occur in a given area.

<p>Cluster richness, i.e. the number of different clusters shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047676#pone-0047676-g005" target="_blank"><b>figure 5</b></a> that occur in a given area.</p

Overview of the different locations of the ten clusters identified by k-means clustering.

<p>The three subclusters of cluster 6 are highlighted with different colours, clearly distinguishing the group that is largely composed of the Nacional cultivar of the Ecuadorean coastal plains (red colour).</p

Averages of genetic parameters per locus for clusters 1 to 10, based on 1,000 bootstrap samples of 35 trees (i.e. the size of the smallest cluster).

<p>Four highest values for each of the parameters are underlined.</p

Complete linkage clustering based on Nei’s distance (cophenetic correlation = 0.92).

<p>Complete linkage clustering based on Nei’s distance (cophenetic correlation = 0.92).</p