Understanding the spatial distribution of genetic diversity
within a species range is of considerable importance to conservation
and sustainable wildlife management. Genetic diversity provides
the basis for both adaptation to changing environmental conditions
and to future evolutionary change. As such, its conservation
is an essential part of more general programs to protect biodiversity.
Under the IPGRI project
on "Conservation, management and sustainable use of forest
genetic resources", we have collaborated closely with
in Argentina and the forest genetic resources program in IPGRI
to develop methods for mapping genetic diversity in a landscape
based on environmental interactions.
Assessing patterns of genetic diversity is notoriously difficult
however, because studies must extensively survey and analyze
data from all the areas of a species range that are of interest.
This is especially difficult and laboratory-intensive for
adaptive traits, because it requires the identification and
surveying of appropriate Quantitative Trait Loci (QTLs) or
the cultivation and analysis of biochemical/morphological
traits under common garden conditions. As such, the means
to rapidly predict patterns of adaptive diversity can be of
considerable aid in both prioritizing research and planning
conservation projects to account for diversity (e.g., seed
zone designation, optimal areas for replanting, or reserve
Because the diversity of adaptive traits in a given population
is the product of its particular evolutionary history, it
is possible to predict genetic patterns from an understanding
of the evolutionary factors that shape them. For adaptive
traits, the most important evolutionary forces are selection
(which eliminates non-adaptive traits) and gene flow (which
redistributes traits from one place to another). By modeling
the interaction between these forces, it should be possible
to predict patterns of spatial genetic structure.
Using the Andean Monkey Puzzle tree Araucaria araucana
as an example, we mapped out likely selection pressures (drought
stress, Figure 2), and modeled patterns of gene flow (pollination
by wind). We hypothesized that the highest levels of genetic
diversity would be found in regions with high environmental
heterogeneity within the range from which pollen could travel.
Comparing this " effective heterogeneity" to levels
of diversity in populations of A. araucana, we found
high correlation (r = 0.902), indicating a strong relationship
and a good predictor.
We then used these models to map out effective heterogeneity
in the entire region inhabited by A. araucana. This
rapid and inexpensive method has allowed the prediction of
patterns of adaptive genetic diversity throughout the species
range, which will be of considerable use to Argentinean and
Chilean conservation projects attempting to preserve habitat
and ensure sustainable reproduction. The results of this study
have been submitted to the journal "Evolution" for
Figure 1. Distribution of Araucaria araucana created
by reclassifying LANSAT satellite imagery.
Figure 2. Drought stress in the region inhabited by Araucaria
Figure 3. Effective heterogeneity in the region inhabited
by Araucaria araucana.
Figure 4. Detail of effective heterogeneity in the region
inhabited by Araucaria araucana.