The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is crucial for managing and conserving genetic resources in forest tree species. By combining tools from population genetics, landscape ecology and spatial statistics, landscape genetics thus represents a powerful method for evaluating the geographic patterns of genetic resources at the population level. In this study, we explore the possibility of combining genetic diversity data, spatial statistic tools and GIS technologies to map the genetic divergence and diversity of 31 Castanea sativa populations collected in Spain, Italy, Greece, and Turkey. The IDW technique was used to interpolate the diversity values and divergence indices as expected hetereozygosity (He), allelic richness (Rs), private allelic richness (PRs), and membership values (Q) of each population to different clusters. Genetic diversity maps and a synthetic map of the spatial genetic structure of European chestnut populations were produced. Spatial coincidences between landscape elements and statistically significant genetic discontinuities between populations were investigated. Evidence is provided of the significance of cartographic outputs produced in the study and on their usefulness in managing genetic resources.
The current distribution of species was shaped by complex interactions occurring over time and space between biological, physical and socio-cultural processes. Understanding the biogeographical distribution, the diversity patterns of living species and the underlying evolutionary processes are key to being able to conserve and manage genetic resources [
In the last decades, interdisciplinary methods have thus been developed in order to understand the complex relationships between natural and human processes. Landscape genetics has emerged as a discipline that combines the concepts and methods of landscape ecology, population genetics, geography and spatial statistics [
Landscape genetics could therefore be crucial for estimating the impact of landscape composition on the spatial genetic structure of natural populations of plants and for new conservation genetic programs. Progress in molecular analysis, spatial analytical methods, GIS technologies and the acquisition of spatially-explicit datasets of environmental variables has contributed to the development and diffusion of landscape genetics [
Natural forests are key ecosystems with a high level of biodiversity [
We focus on Castanea sativa, a tree of great economic and ecological importance, and the only species of the genus Castanea in Europe. The current patterns of biogeographic distribution of C. sativa are known to be a response to the evolutionary history of the species, in relation both to the climatic and geomorphological processes that have occurred over the geological time scale and to the anthropic activities over the centuries.
Although there are many studies on the genetic structure and diversity of chestnut across Europe [
In this study, we explore the possibility of combining spatial analysis techniques with molecular data to map the genetic diversity of a large set of European C. sativa populations along the distribution area of the species. We used the landscape genetic overlay technique [
In the last three decades, the CNR Institute of Agro-environmental and Forest Biology (IBAF, Porano, Italy) has been involved in genetic diversity study of European chestnut populations, assembling a large collection of C. sativa populations. In this study, we performed spatial analysis of 31 European chestnut populations (779 wild trees), using a set of genetic data previously obtained by Mattioni [
Allelic richness (Rs) and private allelic richness (PRs) were computed by the rarefaction method with HP-RARE software [
A Bayesian clustering approach implemented in STRUCTURE 2.3.3 software [
genetic structure of C. sativa populations. A complete description of the method was reported by Mattioni [
The geospatial interpolation of genetic diversity indices enabled us to produce new spatial data representative of the genetic diversity of the European chestnut. Expected heterozygosity (He), allelic richness (Rs) and private allelic richness (PRs) values (data reported in [
Eastern Turkish populations showed intermediate values of expected heterozygosity (He), as well as the highest values of allelic richness (Rs) and private allelic richness (PRs). Various western Turkish populations and two populations from central and northern Italy also showed high values of expected heterozygosity (He) and allelic richness (Rs) (
The spatial mapping of genetic diversity also contributed to the identification of priority units for the conservation of genetic resources. Thus outputs from the spatial overlay of the three maps of expected heterozygosity (He), allelic richness (Rs) and private allelic richness (PRs) provided critical information on the diversity of chestnut populations across Europe and can be assumed as basic criteria for identifying areas for conservation. Given that allelic richness is considered a key measurement in analyzing the conservation of the genetic diversity of species [
Our results indicated that the main centers of genetic diversity for the European chestnut are located in the easternmost areas of Turkey, in western Turkey, and in central and northern Italy. We therefore observed a spatial matching between these sites and the geographical areas identified by Krebs [
In a previous work [
The first cluster, in green in
The presence of populations belonging to three different gene pools is highlighted in
Although the synthetic map of genetic structure provides an exhaustive geographical representation of the main gene pools in a single map, it has limitations. In fact, if the K number of clusters inferred by STRUCTURE analysis is greater than three, it cannot be graphically displayed and it is necessary to map K different clustering surface maps. These limits are derived from the combination of up to three color bands (red-green-blue) at a time, to graphically display the multiband raster dataset representing the genetic structure. However, it is still possible to represent the K clustering surface map, regardless of the K number of clusters inferred by STRUCTURE.
Mapping the spatial clustering of European chestnut populations highlighted three distinct gene pools, in agreement with those we had detected previously [
Three significant genetic barriers were identified using Monmonier’s maximum difference algorithm, with bootstrap support >50%. Populations from Italy and Spain were separated from Greek and Turkish populations by the main genetic barrier (“a” in
contributed to the detection of this main barrier. The second and third discontinuities (“b” and “c” in
Considering the structural and geomorphological complexity of the Mediterranean regions, it is likely that geographical barriers have interfered with the gene flow between chestnut populations. There is a geographical correspondence between the main genetic barrier, detected in this study, and the Dinaric Alps, which could act as an obstacle to the gene flow between eastern and western European populations of C. sativa, and the Adriatic Sea could also have impeded the exchange of germplasm between the Balkan and Italian peninsulas. In addition, genetic discontinuities between western and central-eastern Turkish populations may indicate the Taurus Mountains as a putative physical barrier to the gene flow between Turkish populations.
In this study, the spatial patterns of genetic diversity and structure of natural C. sativa populations were analyzed at the European scale. This interdisciplinary approach enabled us to combine data and methods from population genetics, landscape ecology and spatial analysis to explicitly quantify the effect of landscape configuration on genetic variation. The use of GIS contributed significantly, due both to its ability to store, manage and integrate molecular and spatial data, and to extract more in depth information from existing data. GIS is also useful for spatial analysis, modeling, data visualization and mapping.
The spatial analyses, such as the interpolation of diversity indices and the Q-membership coefficient value of each population to K clusters inferred by STRUCTURE, produce more visually clear and intuitive representations of the intra population genetic diversity and structure of European chestnut populations. The landscape genetic overlay technique enabled us to better display areas with a different genetic diversity, to highlight the geographical distribution of different gene pools and to show the overlap of genetic and geographic barriers.
The results also enabled us to indicate areas as reservoirs of genetic diversity, to speculate on the effects of the landscape structure on gene flow and genetic variability, and to provide suggestions for conservation planning. All these spatial outputs support the results of our previous publications, indicating the divergence between eastern and western populations of European chestnut, and thus confirming the presence of two different gene pools and an introgression zone in Turkey [
In conclusion, we have demonstrated how landscape genetics, combining spatial analysis techniques with molecular data in studying the genetic diversity of natural chestnut populations, can contribute to increasing the knowledge and understanding of the biogeographic history and distribution of C. sativa in Europe. The significant outputs produced are easily understandable and usable in the inventory, conservation, and management of genetic resources.
This paper is a part of the PhD thesis of the first author in Sciences and Technologies for Forest and Environ- mental Management supported by the project CISIA (Conoscenze Integrate per Sostenibilitàed Innovazione del Made in Italy Agroalimentare). The authors are grateful to Marco Ciolfi for his critical review of the manuscript and figures. M. A. Martin is grateful to the Secretaría General de Ciencia, Tecnología e Innovación de la Consejería de Economía e Infraestructuras from the Regional Government of Extremadura (Spain) for the financial support.
Francesca Chiocchini,Claudia Mattioni,Paola Pollegioni,Ilaria Lusini,Maria Angela Martín,Marcello Cherubini,Marco Lauteri,Fiorella Villani, (2016) Mapping the Genetic Diversity of Castanea sativa: Exploiting Spatial Analysis for Biogeography and Conservation Studies. Journal of Geographic Information System,08,248-259. doi: 10.4236/jgis.2016.82022
Country | Region | Sampling site | IDa | Latitude | Longitude | N |
---|---|---|---|---|---|---|
Spain | Galicia | Sierra do Faro | ES_01 | 42˚36'29''N | 07˚51'59''W | 23 |
Andalucia | Sierra do Ronda | ES_02 | 36˚32'20''N | 0.5˚18'34''W | 26 | |
Asturias | Navaliego | ES_03 | 43˚13'01''N | 05˚45'50''W | 29 | |
Galicia | Costa Atlanticas | ES_04 | 43˚17'09''N | 08˚22'11"W | 21 | |
Italy | Sicilia | Madonie | I_01 | 37˚49'56''N | 14˚05'12''E | 26 |
Calabria | SilaPiccola | I_02 | 39˚02'55"N | 16˚43'03"E | 26 | |
Basilicata | Mt. Vulture | I_03 | 40˚54'56''N | 15˚36'50"E | 25 | |
Marche | Mt. dellaLaga | I_04 | 42˚44'05''N | 13˚24'45''E | 26 | |
Lazio | Mt. Cimini- | I_05 | 42˚24'50''N | 12˚11'38''E | 23 | |
Toscana | Mugello | I_06 | 43˚58'07''N | 11˚34'03''E | 19 | |
Piemonte | Trontano | I_07 | 46˚07'12''N | 08˚20'07''E | 26 | |
Piemonte | V. Pellice | I_08 | 44˚48'02''N | 07˚08'17''E | 26 | |
Friuli | V. del Natisone | I_09 | 46˚07'17''N | 13˚33'19''E | 26 | |
Greece | S-E-Macedonia | Holomontas | GR_01 | 40˚32'01''N | 23˚44'44''E | 26 |
C-Macedonia | Hortiatis | GR_02 | 40˚35'40''N | 22˚22'50''E | 26 | |
W-Macedonia | Dafni | GR_03 | 40˚16'38''N | 21˚08'37''E | 26 | |
N-Macedonia | Paiko | GR_04 | 40˚57'09''N | 22˚22'44''E | 26 | |
Eastern Turkey | Duzce | Akcakoca | TR_01 | 41˚05'15"N | 31˚07'25"E | 24 |
Sinop | Sinop | TR_02 | 42˚01'36"N | 35˚09'01"E | 31 | |
Artvin | Hopa | TR_03 | 41˚24'01"N | 41˚29'23"E | 22 | |
Trabzon | Meryem Ana | TR_04 | 40˚44'50"N | 39˚34'50"E | 30 | |
Giresun | Giresun | TR_05 | 40˚54'24"N | 38˚31'22"E | 26 | |
Central Turkey | Sakarya | Karadere | TR_06 | 40˚43'56"N | 30˚51'43"E | 21 |
Kocaeli | Sardala | TR_07 | 41˚02'09"N | 29˚57'13"E | 23 | |
Yalova | Cinarcik | TR_08 | 40˚38'42"N | 29˚07'12"E | 26 | |
Kocaeli | Golcuk | TR_09 | 40˚43'20"N | 29˚48'54"E | 25 | |
Sakarya | Sapanca | TR_10 | 40˚41'29"N | 30˚16'06"E | 24 | |
Western Turkey | Bursa | Bursa | TR_11 | 40˚11'40"N | 29˚03'35"E | 25 |
Canakkale | Bayramic | TR_12 | 39˚48'36"N | 26˚36'37"E | 31 | |
Izmir | Kemalpasa | TR_13 | 38˚26'07"N | 27˚15'44"E | 23 | |
Manisa | Demirci | TR_14 | 39˚02'41"N | 28˚39'01"E | 22 |
aID = Identity code of population.
*Corresponding author.
1List of sampling location of 31European chestnut populations.
*Corresponding author.
1List of sampling location of 31European chestnut populations.