Conservation priorities for Prunus africana defined with the aid of spatial analysis of genetic data and climatic variables

Abstract

Conservation priorities for Prunus africana, a tree species found across Afromontane regions, which is of great commercial interest internationally and of local value for rural communities, were defined with the aid of spatial analyses applied to a set of georeferenced molecular marker data (chloroplast and nuclear microsatellites) from 32 populations in 9 African countries. Two approaches for the selection of priority populations for conservation were used, differing in the way they optimize representation of intra-specific diversity of P. africana across a minimum number of populations. The first method (S1) was aimed at maximizing genetic diversity of the conservation units and their distinctiveness with regard to climatic conditions, the second method (S2) at optimizing representativeness of the genetic diversity found throughout the species' range. Populations in East African countries (especially Kenya and Tanzania) were found to be of great conservation value, as suggested by previous findings. These populations are complemented by those in Madagascar and Cameroon. The combination of the two methods for prioritization led to the identification of a set of 6 priority populations. The potential distribution of P. africana was then modeled based on a dataset of 1,500 georeferenced observations. This enabled an assessment of whether the priority populations identified are exposed to threats from agricultural expansion and climate change, and whether they are located within the boundaries of protected areas. The range of the species has been affected by past climate change and the modeled distribution of P. africana indicates that the species is likely to be negatively affected in future, with an expected decrease in distribution by 2050. Based on these insights, further research at the regional and national scale is recommended, in order to strengthen P. africana conservation efforts.

Figure 1. Prunus africana observations and modeled potential distribution.

Probability of occurrence of P. africana is determined on the basis of climatic/environmental parameters and indicated by different colors, from dark brown (high probability) to yellow (low probability).

Figure 2. Clustering of Prunus africana populations based on molecular marker data.

The 32 populations, represented by 30 minute grid cells, are grouped by Nei’s distance, based on similarity of haplotypes (cpSSR) (2a) and similarity of nuclear microsatellite (nSSRs) allelic composition (2b).

Figure 3. Prunus africana haplotype richness and allelic richness.

Haplotype (cpSSR) (3a) and allelic (nSSR) (3b) richness are determined for 32 populations, after rarefaction, using a 30 minute grid cell size.

Figure 4. Clustering of 1,500 Prunus africana observations based on level of similarity of bioclimatic variables.

Bioclimatic values for 19 variables were associated with all P. africana records. Bioclimatic values were extracted from 2.5 minute rasters obtained from the Worldclim website. The observation points are grouped (each cluster is highlighted with a different colour) by Euclidean distance.

Figure 5. Prunus africana modeled potential distribution in Kenya, Tanzania and Uganda with respect to croplands and protected areas.

P. africana modeled potential distribution is shown with respect to areas occupied by >50% croplands (5a), and to the location of protected areas (5b). Areas with expected high and low impact of climate change in 2050 are also highlighted (5b). In low impact areas (blue), no changes in species distribution are expected, while in areas of high impact (red), climatic conditions are expected to become unsuitable for P. Africana. The location of 19 populations, for which genetic data are available, is also shown.

Figure 6. Prunus africana modeled potential distribution under past, current and future conditions in 2050.

(6a) The spatial distribution of all P. africana observation points is shown. Areas in red are expected to be highly affected by future climate change; in low impact areas (blue) no changes in species distribution are expected; areas in green are expected to become suitable for P. africana. (6b) The past scenario refers to the last glacial maximum (LGM), about 21,000 years before present. Blue indicates areas with continued habitat suitability since LGM until present (original areas). Green indicates areas most likely unsuitable for P. africana at the LGM, but suitable at present (recent areas of expansion). Red represents areas suitable during LGM but no longer suitable at present (lost areas). The spatial distribution of the 32 sampled populations, for which genetic data are available, is indicated by yellow triangles.