plant surface microbiology.pdf

27 Applications of Quantitative Microscopy in Plant Surface Microbiology 541
Fig. 15. CMEIAS/GS+ analysis of spatial geostatistics (autocorrelation semivariogram)
for rhizobacteria during pioneer colonization of white clover seedlings grown in soil.
This graph indicates the highly significant, fundamentally new finding that pioneer colonization
of seedling roots by bacteria in soil has an in situ spatial dependence over a
spatial scale up to 52 mm
6.6 CMEIAS v. 3.0: Quantitative Autecological Biogeography of the
Rhizobium–Rice Association
In the fourth example, CMEIAS is being used to study the biogeography of R.
leguminosarum bv. trifolii strain E11, a plant growth-promoting endocolonizer
of rice roots isolated in the Nile delta where rice and berseem clover
have been rotated since antiquity (Yanni et al. 1997). We are using this strain
in a model study designed to define the autecological biogeography of rhizobial
PGPR endophytes of rice at two spatial scales, one relevant to the organisms
(its colonization of rice roots), and second relevant to the rice farmer
who would be using such strains as rice biofertilizer inoculants to enhance
rice production with less dependence on chemical fertilizer N (Yanni et al.
2001). Figure 16A is an SEM image quadrat of the rice root surface after gnotobiotic
cultivation with strain E11. Note that the root hair cells above the
plane of focus have obscured some of the root surface, and therefore the full
distribution of bacteria in this sampled area cannot be examined directly.
This problem in microbial biogeography is solved by a geostatistical analysis
of the spatial distribution data acquired by CMEIAS using a kriging analysis
to interpolate spatial dependence information on a continuous scale even in
areas not sampled. Figure 16B shows the 2-D krig map that provides a statistically
defendable interpolation of the spatial density of bacteria in a continuous
mode, even in these areas obscured by the overlying root hairs (Fig. 16B).
The power of CMEIAS geostatistical analysis!