Cyanobacterial ecology across environmental gradients and
spatial scales in China’s hot and cold deserts
Kimberley A. Warren-Rhodes1, Kevin L. Rhodes2, Linda Ng Boyle3, Stephen B. Pointing4, Yong Chen3,
Shuangjiang Liu5, Peijin Zhuo5 & Christopher P. McKay1
1
NASA-Ames Research Center, Moffett Field, CA, USA; 2College of Agriculture, Forestry and Natural Resource Management, The University of Hawaii at
Hilo, HI, USA; 3Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, IA, USA; 4School of Biological Sciences,
The University of Hong Kong, Hong Kong SAR, China; and 5Chinese Academy of Sciences, Institute of Microbiology, Beijing, China
Correspondence: Kimberley A. WarrenRhodes, NASA-Ames Research Center,
Mail Stop 245-3, Moffett Field, CA 94035,
USA. Tel/fax.: 11 5302726002; e-mail:
[email protected]
Received 11 February 2007; revised 30 April
2007; accepted 4 May 2007.
First published online August 2007.
DOI:10.1111/j.1574-6941.2007.00351.x
Editor: Patricia Sobecky
Keywords
hypolithic; photoautotrophs; hyperarid desert;
landscape ecology; patchiness; trigger-transferresponse-pulse framework.
Abstract
Lithic photoautotrophic communities function as principal primary producers in
the world’s driest deserts, yet many aspects of their ecology remain unknown. This
is particularly true for Asia, where some of the Earth’s oldest and driest deserts
occur. Using methods derived from plant landscape ecology, we measured the
abundance and spatial distribution of cyanobacterial colonization on quartz stony
pavement across environmental gradients of rainfall and temperature in the
isolated Taklimakan and Qaidam Basin deserts of western China. Colonization
within available habitat ranged from 0.37 0.16% to 12.6 1.8%, with cold dry
desert sites exhibiting the lowest abundance. Variation between sites was most
strongly correlated with moisture-related variables and was independent of
substrate availability. Cyanobacterial communities were spatially aggregated at
multiple scales in patterns distinct from the underlying rock pattern. Site-level
differences in cyanobacterial spatial pattern (e.g. mean inter-patch distance) were
linked with rainfall, whereas patchiness within sites was correlated with local
geology (greater colonization frequency of large rocks) and biology (dispersal
during rainfall). We suggest that cyanobacterial patchiness may also in part be selforganized – that is, an outcome of soil water-biological feedbacks. We propose that
landscape ecology concepts and models linking desert vegetation, biological
feedbacks and ecohydrological processes are applicable to microbial communities.
Introduction
Stony desert pavements, composed of surface soils mantled
by gravels and bedrock debris, are important microbial
habitats in arid environments (Friedmann & Galun, 1974;
Golubic et al., 1981). Translucent rocks support unique
lithic bacterial communities that are often the principal
primary producers in hyperarid deserts (Allen, 1997; Warren-Rhodes et al., 2006). These communities are dominated
by an apparently ubiquitous cyanobacterium of the formgenus Chroococcidiopsis that is extremely resistant to ionizing radiation and desiccation (Billi et al., 2000), often in
association with other filamentous cyanobacterial and heterotrophic taxa (Pointing et al., 2007). They are referred to
as lithic (‘lithobiontic’, ‘lithophytic’, Table 1) cyanobacterial
communities (LCC) to reflect the importance of the rock
niche and photoautotrophic component. By inhabiting
diaphanous rocks and minerals (e.g. quartz, granite, gyp2007 Federation of European Microbiological Societies
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sum, halite, sandstone) these organisms gain protection
from solar radiation and receive increased moisture relative
to the bare soil (Vogel, 1955; Friedmann et al., 1967; Cockell
& Stokes, 2004; Cockell et al., 2005).
Many aspects of LCC ecology, including photosynthetic
carbon fixation, nitrogen cycling, and biodiversity have been
investigated in hot and cold arid and hyper-arid desert
environments (Friedmann et al., 1967; Allen, 1997; Smith
et al., 2000; Garcia-Pichel et al., 2001; Matthes et al., 2001;
Schlesinger et al., 2003; Boison et al., 2004; Cockell & Stokes,
2006; Omelon et al., 2006; Warren-Rhodes et al., 2006).
However, there is a limited understanding of their spatial
distribution and the effects of moisture, temperature and
substrate parameters (e.g. rock size) on their abundance and
diversity. Moreover, consistent sampling and statistically
rigorous design has largely been absent from past studies
(Matthes et al., 2001), thus restricting comparisons between
and an understanding of LCC and the physical environment,
FEMS Microbiol Ecol 61 (2007) 470–482
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Cyanobacterial ecology in China’s deserts
Table 1. List of abbreviations
Name
Abbreviation
Lithic (lithophytic, lithobiontic)
cyanobacterial communities
Mean annual precipitation
Mean annual air temperature
Air temperature
Relative humidity (air)
Soil temperature
Relative humidity (soil)
Soil liquid water (Hobos logger reads RHS Z95%)
Rock surface liquid water (Campbell grid reads Volts
Z0.005)
China Meteorological Administration
Scanning electron microscopy
Extracellular polymeric substances
LCC
MAP
MATA
TA
RHA
TS
RHS
LWS
LWR
CMA
SEM
EPS
ANOVA
ANOVA
Index of dispersion
Cochrane–Mantel–Haenszel
ID
CMH
especially on larger scales. Schlesinger et al. (2003), for
example, measured a single plot in the warm semi-arid
Mojave Desert, which showed 100% of quartz rocks – a
common desert pavement substrate – were colonized by
hypoliths. This result was similar to that reported by Cockell
& Stokes (2006) for opaque rocks in the cold, wet Arctic
environment. In contrast, the warm arid Atacama supported
28% and the warm hyper-arid core of the Atacama o 0.1%
colonization (Warren-Rhodes et al., 2006). These studies
provide an interesting glimpse into possible geographical
patterns and highlight the need for robust statistical approaches and comparative ecological investigation. It is also
tantalizing to hypothesize that proliferation of LCC in
extremely arid conditions may involve biological feedback
mechanisms to maximize use of scarce water resources, such
as those demonstrated by higher plants in deserts (Rietkerk
et al., 2004; Ludwig et al., 2005). While laboratory testing of
some aspects of LCC ecology is possible, many ecological
hypotheses in microbial systems require testing in ‘natural
laboratories,’ where relatively few key variables exist and
trophic interactions are simpler. The western deserts of
China comprise swathes of contiguous quartz desert pavement spanning thousands of kilometers across wide climatic
gradients, and as such provide an ideal region for investigating not only LCC ecology but also for examining issues of
broader relevance to microbial ecology.
To more fully understand LCC ecology we carried out
multiple ecological studies at four desert locations in
western China. The sites all supported a predominantly
quartz pavement substrate, thus minimizing variability, but
varied in their level of hyperaridity by virtue of different
long-term mean annual temperature and rainfall. Here we
FEMS Microbiol Ecol 61 (2007) 470–482
report both landscape-scale and small-scale abundance
patterns and identify possible linkages between ecohydrological processes and LCC across multiple scales. We interpret
our observations in terms of macro-ecological theory as
applied to microbial distributions and biological feedbacks.
Materials and methods
Field locales and sampling
China’s Northwest contains some of the oldest, driest, and
most isolated deserts on Earth (Guo et al., 2002; Sun & Liu,
2006). Within the region, desert pavement occurs on the
periphery of hyperarid basins and flanks most major mountain ranges (Fig. 1) (Walker, 1982). From preliminary
surveys and precipitation data, we chose three locations
(10–100 km scale) for study (Fig. 1): (1) Tokesun, northern
Turpan Depression; (2) Ruoqiang, southern Taklimakan
Desert; and (3) Sorkuli (also Suoerkuli) Qaidam Basin,
Qinghai-Tibetan Plateau (Table 2).
To determine the environmental influence on inter-site
LCC abundance and diversity, sites (1–10 km scale) within
each location were chosen based on mean annual precipitation (MAP) and mean annual air temperature (MATA) to
allow wet and dry, and hot and cold site comparisons (Table
2). Inter-site geological variation was minimized by choosing silicious desert pavement sites of similar soil and rock
type, particularly quartz, which forms the bulk of LCC
habitats in our study. Four main sampling sites were chosen
within the three locations (Table 2).
Fig. 1. Map of China, with field locations highlighted.
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472
K.A. Warren-Rhodes et al.
Table 2. General site characteristics, China
Temperature category
Moisture category Site/
Parameter
Latitude
Longitude
Elevation (m)
MAP (mm year1)
MATA ( 1C)
Hot
Cold
Dry Tokesun,
Turpan Depression
Wet Ruoqiang,
Taklimakan Desert
Dry Sorkuli 01,
Qaidam Basin
Wet Sorkuli 03,
Qaidam Basin
N42142.915 0
E88136.966 0
119
10.0
14.6
N38124.233 0
E86153.806 0
1217
23.3
13.1
N38155.410 0
E92116.703 0
2882
15.1
3.2
N39100.350 0
E91153.434 0
3440
N/A
0.25
Mean annual precipitation (MAP) and mean annual temperature (MAT) are 13-year historical averages for Ruoqiang and Sorkuli from the China
Meteorological Administration (CMA). Ruoqiang site data are from the Qiemo CMA station (30 km away), and Sorkuli data are from the Lenghu CMA
station (100 km away). Tokesun site MAT data (13-year average) are from the Turpan CMA station (70 km away), but Tokesun MAP data (10-year
average) are from the local Tokesun meteorological bureau.
Environmental variables
In situ environmental data, including air temperature (TA),
relative humidity (RHA) (Onset Computer, Hobo PROs)
and rainfall (Onset Computer, RG2-M), were collected 8
August 2001 to 12 September 2002 (Warren-Rhodes et al.,
2007). Soil temperature (TS) and pore-space relative humidity (RHS) dataloggers (Onset Computer, Hobo PROs)
were placed under rocks such that sensors were directly
below visible hypolith communities (1–5 cm below soil
surface, depending on hypolith location) and 10 cm (to
monitor soil moisture retention). Soil liquid water (LWS)
was considered present at RHS Z95% (Warren-Rhodes
et al., 2007). The presence of rock surface liquid water
(LWR) – a proxy for moisture available to chasmolithic
communities – was measured with a moisture-sensing grid
(Campbell Scientific, 237-L). Historical climate data are
courtesy of the China Meteorological Administration (CMA).
Microscopy
Microscopic examination was carried out using light microscopy (BX50 compound microscope, Olympus, Tokyo,
Japan) and scanning electron microscopy (SEM) (Stereoscan S440, Leica Cambridge, UK). Colonized quartz fragments were stored in a silica-gel desiccator for 48 h prior to
gold sputter-coating (SCD 005, BAL-TEC, Lichtenstein) and
observation under low vacuum (to avoid artifacts generated
during chemical fixation). Working depth for imaging was
20 mm, and beam current 12.0 kV.
Cultivation and phylogenetic analysis
A sterile scalpel was used to scrape a small amount of
colonized quartz from each pebble into BG11 cyanobacterial
growth medium and enrichment cultures were incubated at
25 1C and 250 mmol m2 s1 photosynthetically active radiation (12-h light-dark cycle) with periodic subculture (Castenholz, 1988). In all samples a single cyanobacterial
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morphotype of the form-genus Chroococcidiopsis was the
only taxon recovered and these were further characterized by
16S rRNA gene sequence. Sequencing was achieved using
primers with Escherichia coli equivalent nucleotide positions
of 27F-1492R as previously described (de la Torre et al.,
2003). Amplicons were purified (GFX, Amersham, Bucks,
UK) prior to automated sequencing using the BigDye
Terminator Cycle Sequencing kit and ABI 3730 Genetic
Analyzer (Applied Biosystems, CA). Approximate phylogenetic affiliations were determined by BLAST searches of the
NCBI GenBank database (http://www.ncbi.nlm.nih.gov/).
Multiple alignments were then created with reference to
selected GenBank sequences using CLUSTAL v.1.81 (Thompson et al., 1997). Maximum likelihood analysis using PAUP
4.0b8 (Swofford, 2001) was used to illustrate the relationship of sequences to representative taxa. Bootstrap values for
1000 simulations were calculated and are shown for branch
nodes supported by more than 50% of the trees. Sequences
from this study have been deposited in the NCBI GenBank
database under accession numbers DQ914863–DQ914866.
Field measurements of colonization
Abundance was defined as percent colonization [hereafter,
colonization = (# of colonized quartz rocks/total quartz
rocks) 100]. The term LCC or cyanobacteria hereafter
refers to hypolithic (subsurfaces) or chasmolithic (rock
cracks, crevices) forms, unless otherwise noted. Sampling
design and field methods from plant and animal landscape
ecology were used to measure LCC abundance and spatial
pattern (Andrew & Mapstone, 1987; Krebs, 1989; Waite,
2000). A pilot study was conducted to optimize sampling
unit shape, size and number of replicates (Krebs, 1989). As
site remoteness precluded conducting pilot studies at all
sites, Tokesun was chosen as a proxy. The results indicated
that approximately six 1 m 50 m transects were needed
(Fig. 2) to obtain a representative site measure of abundance
at the desired precision (0.25). However, sampling at other
FEMS Microbiol Ecol 61 (2007) 470–482
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Cyanobacterial ecology in China’s deserts
Fig. 2. Mean and SE plotted as a cumulative abundance curve for the
pilot transect study (Tokesun 2001, China). Rectangular belt transects
were 1 m 50 m. Results showed that five 1 m by 50 m transects were
required to measure site abundance at the 0.25 precision level. Subsequent analysis revealed some sites required additional transects, such
that 10 transects were used for the main study (2002). The figure
demonstrates that standard plant and animal ecology methods to
measure abundance can be applied to obtain statistically robust field
measures of microbial abundance.
sites revealed subsequently that additional transects were
needed to achieve the stated precision at all sites, such that
10 transects were used for all sites in the final study. The 10
transects of 50 m2 were randomly distributed over a
0.5 km2 area, and all quartz rocks were counted, measured
(maximum length, nearest 0.5 cm) and inspected visually
for colonization. For spatial distribution, LCC location was
recorded to nearest centimeter (x-axis along 50 m transect).
Underlying rock pattern was determined by photo-analyses
of randomly selected 1 m2 quadrats within each transect.
LCC climate, abundance and spatial distribution were
measured across inter-site (Z1 km) and intra-site (o 1 km)
scales. The latter consisted of landscape (50–100s of m,
inter-transect), area (1–50 m, inter-quadrats) and rock
scales (o 1 m to 1 mm, intra-quadrat). The influence of
quartz rock density (= habitat availability = quartz rocks per
50 m2) and rock size on LCC abundance were measured at
the transect level. The effects of rock size on colonization,
along with soil moisture, temperature, LCC location and
depth, were measured for individual rock scales.
Statistical analyses
ANOVA (Devore, 2004; Box et al., 2005) was used to determine
inter-site variation for percent colonization and rock density, with Pearson’s correlation coefficients used to compare
these parameters over multiple scales. Within-site rock size
standard deviations were compared using F-tests. A twotailed Z-test examined colonized rocks vs. overall rock
proportions (dry and wet sites), with a post hoc Cochrane–
Mantel–Haenszel (CMH) test used for inter-site differences
in rock size. The index of dispersion (ID) tested LCC spatial
randomness, with quadrat size and number chosen to
FEMS Microbiol Ecol 61 (2007) 470–482
ensure the validity of the w2 test (Diggle, 2003). For sites
with sufficient colonized rock samples, the scale(s) of
aggregation was analyzed with the Three-Term Local Quadrat Variance (3TLQV) method (Dale, 1999). Site-level
relationships between overall vs. colonized rock spatial
distributions were tested with product-moment correlation
analyses (Upton & Fingleton, 1985). Analyses of underlying
rock patterns from photo-quadrats used two-dimensional
(2-D) point pattern analyses, with calculation of Ripley’s K
and nearest neighbor functions and comparison with Monte
Carlo simulation values (n = 999) for a completely random
(Poisson) process (Upton & Fingleton, 1985; Ripley, 1988;
Cressie, 1993).
Logistic regression models were used to predict sitespecific colonization likelihood, based on environmental
variables such as moisture and temperature (Hosmer &
Lemeshow, 2000). Odds ratios are computed from the
coefficients of the model to assess the magnitude of the
relationships. All tests were adjusted for unequal sample size
and variance and transformed as needed to meet normality
and heterogeneity of variance assumptions. Statistical significance was assessed at P 0.05.
Results
Environmental monitoring
The study sites are roughly divided by climate into hot
(Tokesun and Ruoqiang) and cold (Sorkuli 01 and 03)
deserts (Table 1 Supplementary). In situ TS indicated a
minimum range of thermal tolerance for hypoliths of
23.8–53.8 1C at the sites. Long-term MAP and soil moisture availability divided sites into dry (Tokesun, Sorkuli 01)
( 15 mm MAP and 500 h year1 of LWS) and wet
(Ruoqiang, Sorkuli 03) (4 15 mm MAP and 500 h year1
of LWS) categories (supplementary Table 1). Spring/summer
rainfall was the sole source of LWS to hypoliths at all sites but
Ruoqiang, where winter precipitation (e.g. snowmelt) also
played a dominant role (supplementary Table 1). No dew or
fog was recorded. Annual liquid water available to chasmoliths indicated a range of LWR from 297 to 1397 h and 302 to
1839 h for hypoliths (LWS). Roughly 60% of available water
occurred under conditions suitable for photosynthesis
(daylight, TA 4 6 1C) (200–922 h year1, supplementary
Table 1) (Warren-Rhodes et al., 2007).
LCC identification and colonization
Visual examination revealed all colonized rocks supported a
single coccoid morphotype corresponding to Chroococcidiopsis, and the presence of an apparent extracellular polymeric substance (EPS) surrounding (either partially or fully)
cells was indicated for all samples examined (Fig. 3), similar
to observations for other cyanobacterial habitats (Allen,
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K.A. Warren-Rhodes et al.
LCC colonization and habitat availability
Characteristics of all quartz rocks at the sites
Fig. 3. (a) Scanning electron micrograph of colonized quartz illustrating
extracellular polymeric substance associated with cells, scale bar = 2 mm.
(b) Light microscopy image of Chroococcidiopsis cells from colonized
quartz, scale bar = 5 mm.
1997; Philippis & Vincenzini, 1998; de los Rı́os et al., 2004).
Cracks that appeared in the EPS demonstrate its relatively
thick nature, and the omission of any chemical fixing step
precludes the possibility that EPS was an artifact of sample
preparation. Phylogenetic analysis of the 16S rRNA gene for
cultivated strains from the sites indicated that they affiliated
into a clade of closely related phylotypes within the known
desiccation-resistant (Grilli-Caiola et al., 1993) and arid
environment inhabiting (Schlesinger et al., 2003; WarrenRhodes et al., 2006; Pointing et al., 2007) genus Chroococcidiopsis (Fig. 4), thus concurring with visual observations.
The morphological and phylogenetic data supports the
validity of the field sampling as being focused upon the
same types of assemblage dominated by this form-genus at
each location.
During 2001–2002, over 10 000 quartz rocks and 3000 m2
of desert pavement were investigated. Significant differences
in percent colonization (Table 3) between all sites were
observed (ANOVA, df = 3,36, F = 46.7, P o 0.0001), with
abundance ranging from 0.37 0.16% (SE) at Sorkuli 01 to
12.6 1.8% at Ruoqiang. High spatial variability in abundance was observed not only across sites, but also within
sites. For example, colonization measured within individual
transects (i.e. 50 m scale) at Tokesun 01 and Tokesun 02
ranged from 1% to 12%, although mean site colonization
(n = 10 transects) did not significantly differ for the two sites
(10 km apart) (ANOVA, df = 1,8, F = 0.001, P = 0.97). This
observed high variability in abundance signals significant
spatial patchiness in LCC distributions at multiple scales
both within and across sites.
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No significant differences in quartz rock density were
observed among sites (ANOVA, log transformed, df = 4,20,
F = 2.4, P = 0.083) and rock density was not correlated with
colonization either at or within sites (n = 25, r = 0.154,
P = 0.9) (supplementary Table 2). Therefore, a greater availability of quartz rock habitat did not equate to higher LCC
abundance at any of the scales investigated.
All sites were dominated by small rocks 5 cm (Table 3),
although significant differences were observed for mean rock
size (supplementary Table 3) and rock size distribution
among sites (F-test, df = 596,1318, F = 8.24, P o 0.0001;
Cochrane–Mantel–Haenszel statistic, df = 4, CMH w2 = 190.2,
P o 0.0001, respectively). A significant correlation was
shown between percent colonization and percent large rocks
within sites (supplementary Table 2, r = 0.54, P = 0.0058,
n = 25 transects), but not across sites (r = 0.135, P = 0.83,
n = 5 sites). Thus, variability in rock size distributions partly
explained LCC abundance at intra-site but not inter-site
scales, indicating that spatial heterogeneity in rock characteristics (i.e. size) is a partial control on LCC abundance at
scales o 1 km.
Characteristics of colonized rocks at the sites
Colonized rock size ranged from 1 to 26 cm. Mean colonized
rock size (Table 3) differed significantly from the mean size
of all quartz rocks at each site (Z 4 1.96, P o 0.01, all sites),
with colonized rocks consistently larger than the available
underlying rock size (Fig. 5). The shift to larger colonized
rocks is further evident in Table 4 and Fig. 6, which show a
significant and discernible trend towards larger size classes
(4 5 cm) for colonized rocks at all sites, indicating that
colonized rock size is not merely a reflection of the background quartz rock size of a site. At Ruoqiang, for example,
only 6.4% of all quartz rocks were large, yet 48% of
colonized rocks were large (Table 4).
The data above indicate that cyanobacteria in hyperarid
environments do not randomly inhabit available quartz rock
habitat, but instead tend to colonize larger rocks within the
desert pavement. This preference may, in part, explain the
high spatial heterogeneity in abundance observed for all sites
and the correlation between areas with higher percentages of
large rocks (e.g. across and within transects) and higher
percent colonization. Such an ecological response by LCC to
colonizing large rocks is likely explained by the greater
efficiency of larger rocks in collecting and retaining scarce
water under hyperarid conditions and the variable temperature and light that affect moisture availability (e.g. undersides of rocks as moisture reservoirs, Friedmann & Galun,
FEMS Microbiol Ecol 61 (2007) 470–482
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Cyanobacterial ecology in China’s deserts
Fig. 4. Phylogenetic affiliation of Chroococcidiopsis from hyperarid desert locations in western China based upon maximum likelihood analysis of the
complete 16S rRNA gene. Tree topology is supported by bootstrap values based upon 1000 simulations. Scale bar represents 0.1 nucleotide changes per
position.
1974). Interestingly, no significant differences were observed
between the mean size of colonized rocks at ‘dry’ vs. ‘wet’
sites (test for unequal variances: t186 = 0.67, P 4 0.05).
Rock size also influenced LCC colonization type (i.e.
hypolith, chasmolith), which varied significantly between
sites (Cochrane–Mantel–Haenszel Statistic (df = 6, CMH
w2 = 191.56, P o 0.001). Multinomial logistic regression
modeling (supplementary Table 4) showed larger rocks as
more likely to support endolithic (within interstitial pore
spaces) and chasmolithic forms, which are more prevalent at
Tokesun and Ruoqiang, than the relatively smaller rocks at
Sorkuli 01 and 03, where hypolithic forms dominate.
Table 3. Ecological results for the China desert sites (mean SE)
Percent colonization (abundance)
Total no. of quartz rocks counted
Total no. of colonized rocks
Mean no. of quartz rocks/m2
Mean no. of colonized rocks per m2
Mean colonized rock size (cm)
Percent of rocks 5 cm
Percent colonized hypolithic
Percent colonized chasmolithic
Tokesun
Ruoqiang
Sorkuli 01
Sorkuli 03
0.99 0.2
5198
50
10.3 1.0
0.10 0.02
6.9 0.4
79.6
46
54
12.6 1.8
1779
215
3.1 0.3
0.43 0.05
5.6 0.2
93.6
74.8
25.2
0.37 0.16
3205
10
6.4 0.7
0.02 0.00
4.7 0.6
96.5
100
0
8.5 1.6
1802
142
3.3 0.4
0.28 0.05
3.2 0.1
95.5
99.3
0.7
Two sites within Tokesun (01 and 02) were also compared to assess intra-location variability in quartz rock size and density.
With the exception of these variables, or unless otherwise noted, Tokesun refers to the main study site, Tokesun 01.
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K.A. Warren-Rhodes et al.
Mean rock size (cm)
Table 4. Percent size distribution of rocks and Z-test results
All rocks (%)
Colonized (%)
Site
4 5 cm
n
4 5 cm
n
Z test
Ruoqiang
Tokesun 01
Tokesun 02
Sorkuli 01
Sorkuli 03
6.4
20.4
10.8
3.5
4.5
1045
597
1010
1319
858
47.7
72.7
27.7
27.8
36.8
86
33
47
18
57
5.54
3.52
2.20
1.96
4.02
To compare the percent of colonized rocks that were large with the
percent of all rocks that were large within a site, a Z-test was used with
the Z critical = 1/ 1.96 for a/2 = 0.025 for two-tailed test.
Significant at P 0.05. These results show that, at all sites, a significantly greater proportion of all rocks were small, whereas a greater
proportion of colonized rocks were large.
Site
Fig. 5. Mean rock size and SE. Solid line = colonized rocks; dashed
line = all rocks.
Table 5. Site-level rock spatial pattern results
Tokesun
(a)
Percent frequency
Colonized rocks
No. of quadrats (n)
50
100
10
100
Index of dispersion (ID)
1.6735
1.9767
1.5926
2.7768
P-value of w2 test
0.0022 o 0.0001
0.1110 o 0.0001
All rocks
No. of quadrats (n)
10
10
10
10
ID
52.9
11.83
42.1
20.2
P-value of w2 test
o 0.0001 o 0.0001 o 0.0001 o 0.0001
Product-moment
0.2727
0.2268 0.1611 0.0351
correlation coefficient, r
Z-test statistic, z
0.8017
0.6586 0.4617 0.0995
Probability, P
0.4459
0.5286
0.6566
0.9232
v
ID 4 1 = aggregated spatial pattern. For w2 test, if P 0.05, pattern is
significantly aggregated.
Product moment correlation tests whether the two rock distributions
(colonized and all rocks) are independent, have negative association
(repulsion, r o 0) or positive association (attraction, r 4 0) Upton &
Fingleton (1985).
Size class (cm)
Percent frequency
(b)
v
v
Size class (cm)
Fig. 6. Frequency distribution of (a) all rocks and (b) colonized rocks by
size class.
LCC spatial distribution
Site level analyses consistently showed that LCC are spatially
aggregated (patchy), as is the underlying rock pattern (Table
5). However, these two patterns were not significantly
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Ruoqiang Sorkuli 01 Sorkuli 03
correlated (Table 5). At smaller (1 m2) scales, point pattern
analyses for underlying rock distributions indicate greater
heterogeneity, with random and aggregated spatial patterns
occurring. At Ruoqiang and Sorkuli 03, where colonized
rock sample size allowed analysis, the scale of LCC aggregation was 2–5 m or 12–16 m, or both.
Analysis of site-level LCC spatial patterns (Fig. 7) indicates that LCC ‘island-patches’ (as defined by Belnap et al.,
2005) consisted of one to seven colonized rocks/patch, were
1–6 m2 in area, and were separated by mean interspaces of
4–20 m (linear distance). These site-level patterns and parameters (Table 6) were strongly tied to water availability, with
patch area significantly decreasing, and inter-patch distance
increasing with lower MAP/LWS levels (Table 6). These
changes are manifested visually with LCC site pattern shifts
from relatively abundant cover and stripes (e.g. Ruoqiang,
Fig. 7, top) to mostly spots (e.g. Tokesun, Fig. 7, bottom),
FEMS Microbiol Ecol 61 (2007) 470–482
477
Cyanobacterial ecology in China’s deserts
(a)
0 2 4 6 8
A
7
B
C
D 2 2
3
E 22
F 3
2
G
2
5
H
I
3
J
2 3 2 4
(b)
0
2
4
6
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
4
2
2
2
2
2
2
2 2
2
3
3
2
4
2
3
2
3
3
2
2
2
3 2
2
2
8
2
2
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
2
A
B
C
D
E
F
G
H
I
J
2
3
4
2
3
2
2
Fig. 7. Colonized rock spatial pattern maps for 10 randomly placed 1 m by 50 m transects (a–j) within each site: (a) Ruoqiang (23.3 mm MAP);
(b) Tokesun (10.0 mm MAP). Black squares indicate one colonized rock per 1 m2 quadrat, while numbers within the square indicate 4 1 colonized rock
in the quadrat. Horizontal stripes, gaps (interspaces between colonized patches), and spots are evident.
Table 6. LCC patch data from transects (mean SE) with decreasing MAP (left to right)
Parameter/site
2
No. of patches per transect (50 m )
Mean patch area (m2)
Mean inter-patch distance (m)
Mean no. of colonized quartz/patch
Ruoqiang
Sorkuli 03
Tokesun
Sorkuli 01
9.8 0.8
1.5 0.1
4.4 0.5
2.2 0.2
6.9 0.7
1.4 0.1
6.0 0.6
2.0 0.2
3.5 0.5
1.1 0.1
7.6 0.8
1.5 0.3
0.8 0.3
1.0 0.3
20.1 2.5
1.1 0.6
reminiscent of patch mosaic pattern shifts exhibited by
desert plants with declining rainfall (Rietkerk et al., 2004).
Because site-level spatial patterns were linked to MAP, we
hypothesized that one underlying mechanism for LCC
patchiness was dispersal via rainfall. To further test this
hypothesis, we randomly selected small rock clusters
(1 m2) within three sites (Table 7). In each case, clustered
rocks (facilitating potential short-range dispersal) had higher colonization than either non-clustered rocks or the site
mean, with the disparity growing as water scarcity intensified (Table 7). Colonized rock clusters at the two driest sites
had 20 times higher abundance than the overall site,
whereas at the wettest site (Ruoqiang) the difference was
less pronounced.
Relationships between rainfall and other climate conditions and LCC abundance were further examined using the
odds ratio model. Results showed that hot, wet sites such as
Ruoqiang are 43.9 times more likely to be colonized than
FEMS Microbiol Ecol 61 (2007) 470–482
cold, dry sites such as Sorkuli 01 (Table 8). To investigate the
separate influence of rainfall and temperature, we further
segmented the data by MATA and MAP (or LWS) to show
that LCC abundance follows the trend hot-wet 4 cold-wet
4 hot-dry 4 cold-dry. When MAP is held constant, colonization was greater at hot vs. cold sites [n = 11 984, goodness of fit (based on log-likelihood) = 382.88, P o 0.0001],
which supports previous findings by Allen (1997) for Gobi
Desert hypoliths. Logistic regression models indicated water
availability (MAP, LWS or LWR), MATA and MATS to be the
most significant positive influence on LCC abundance, with
MAP exerting the largest effect.
Discussion
Lithic photoautotrophic communities in deserts provide a
unique opportunity to explore key facets of microbial
ecology within a relatively ‘simple’ system – namely, one
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478
K.A. Warren-Rhodes et al.
Table 7. Individual rock cluster studies (mean SE), with decreasing MAP (left to right)
Parameter
Sorkuli 03
Tokesun
Sorkuli 01
No. of patches in study
Mean no. of quartz stones (uncolonized)/patch
Mean patch area (m2)
Mean no. of colonized quartz/patch
Mean % colonization, individual patches
Percent of all patches colonized
Mean site percent colonization
Patch-to-site percent colonization ratio
5
30 5.6
0.9 0.3
3.2 1.9
9.5 4.4
60
8.5 1.6
1.1
14
38 6.6
1.0 0.2
6.3 1.9
20.9 4.8
100
0.99 0.2
20.3
14
19 2.9
1.2 0.2
1.1 0.4
9.1 3.0
50
0.37 0.16
24.6
Table 8. Results for odds ratio model that indicate the likelihood of
colonization compared to baseline environmental conditions
Likelihood of site colonization
Baseline
Ruoqiang
Ruoqiang
Sorkuli 01
Sorkuli 03
Tokesun 01
43.921
1.607
14.154
Sorkuli 01
Sorkuli 03
Tokesun 01
0.023
0.622
27.331
0.071
3.103
0.114
0.037
0.322
8.807
In the odds ratio model, based on MAP and MAT, Ruoqiang and Sorkuli
03 are classified as ‘wet’ sites and Tokesun and Sorkuli 01 as ‘dry’ sites;
Tokesun and Ruoqiang are classified as ‘hot’ sites and Sorkuli 01 and
Sorkuli 03 as ‘cold’ sites. Numbers in columns indicate the odds ratio
results for the site listed in the column heading compared with the site
listed in the row heading, e.g. Ruoqiang is 43.9 times more likely to be
colonized than Sorkuli 01. The above results show the following trend in
colonization likelihood: hot, wet site (Ruoqiang) 4 cold, wet site (Sorkuli
03) 4 hot, dry site (Tokesun 01) 4 cold, dry site (Sorkuli 01). Alternatively, a rock at Ruoqiang is 1.6 times more likely to be colonized by LCC
than at Sorkuli 03; a rock at Sorkuli 03 is 8.8 times more likely to be
colonized than at Tokesun 01; and a rock at Tokesun 01 is 3.1 times more
likely to be colonized than at Sorkuli 01.
lacking in plants and higher organisms. Despite this relative
simplicity, a complex relationship between LCC and their
environment exists, and its understanding requires the
intersection of fundamental theoretical ecology frameworks
and concepts, including patch dynamics, ‘trigger-transferreserve-pulse’ (TTRP) models, self-organization, biological
feedback, hierarchy theory, and heterogeneity and scale
(O’Neill et al., 1986; Grundmann, 2004; Rietkerk et al.,
2004; Ludwig et al., 2005).
Our data support the following major findings:
(i) MAP (and soil moisture, as LWS) determines LCC
abundance across hyperarid sites, and when MAP is held
constant, higher abundance is likely at hot sites;
(ii) abundance increases with rock size within (but not
between) sites: however, greater quartz availability
(= habitat) per se does not equate to increased abundance;
(iii) LCC spatial pattern is aggregated (patchy) and not
correlated with the underlying rock pattern;
(iv) LCC spatial pattern is linked to MAP.
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These results are explained in terms of the aridity gradient
at the site level, but at smaller scales require an incorporation of various desert plant ecology models, particularly
with regard to LCC spatial patterns. We suggest that three
main mechanisms – rainfall dispersal, ecohydrological variables and self-sustaining biological feedbacks between LCC
(via EPS) and soil water – are operating independently or in
concert to create LCC patchiness.
By viewing LCC through the lens of plant ecology, namely
the Trigger-Transfer-Reserve-Pulse (TTRP) framework (Belnap et al., 2005; Ludwig et al., 2005), it is possible to
facilitate interpretation of the results, gain new theoretical
insights into LCC survival and distribution, and identify
new hypotheses for future testing. We provide a graphical
adaptation of the TTRP framework that can be applied to
LCC systems (Fig. 8). Within sites, ‘local’ ecohydrological
factors, e.g. topography, soil/rock properties (i.e. size), and
water availability partly explain LCC spatial pattern. Such
local variables can operate at multiple hierarchical scales and
exhibit marked temporal and/or spatial heterogeneity (Rodriguez-Iturbe, 2000; Lookingbill & Urban, 2004).
Reserves: LCC- island patch
Desert plant ecology frameworks (e.g. TTRP) (Ludwig &
Tongway, 1997; Breshears & Barnes, 1999; Ludwig et al.,
2005) explain the complex, coupled interactions between
organisms and ecohydrological processes (Belnap et al.,
2005; Bowker et al., 2006). Our data suggest that, like desert
plants, LCC ecosystems function as ‘island-patches’, or
‘reserves’ (Belnap et al., 2005), amidst a sea of ‘inter-patch’
rock/soil pavement (Fig. 8). We extrapolate our results to
the TTRP framework – aware that some specific aspects
described require additional testing.
In China, a diversity of LCC patch patterns were measured that were reminiscent of plant patch mosaics – from
abundant cover and lines/gaps at wet sites to mostly spots/
solitary patches at dry sites. These spatial pattern transitions
were significantly linked to MAP, with significant differences
in mean patch number, area and inter-patch distance
FEMS Microbiol Ecol 61 (2007) 470–482
479
Cyanobacterial ecology in China’s deserts
transfer
E
I
T
L
Transfers: horizontal and vertical water
movement, nutrients and biota
P
trigger
RO
RO/RN
∆S
pulse
soil
Fig. 8. Trigger-Transfer-Reserve-Pulse diagram adapted for LCC
from Ludwig et al. (2005). Precipitation (trigger) results in runoff (RO,
transfer) from the inter-patch, which can be captured as run-on (RN,
transfer) by LCC patches (reserves). Transfers (spatial redistribution) of
water, soil and biota can follow trigger events, and large events may
move these resources overland to connect patches. Water is stored in
soil (DS) layers, especially the rock-soil interface, and in LCC extracellular
polymeric substances (EPS), at rates dependent on EPS properties,
soil infiltration (I) and rock/soil properties, such as soil texture (T). Soil
water is lost by evaporation (E) or leaching (L). Transfers may result in
pulses of LCC activity (C, N) and, possibly, colonization by LCC to new
rocks and areas.
observed. Whether these changes reflect shifts in ecosystem
states as described for desert vegetation (Rietkerk et al.,
2004), or alternatively, simply declines in LCC abundance
with MAP, is currently unclear.
Rainfall-mediated dispersal of LCC is indicated by declines in inter-patch distance, larger mean patch size and
decreasing ratios of percent colonization of rock clusters to
mean site abundance with increasing MAP. Further support
exists in the aggregated vs. random LCC spatial pattern
measured in this study. LCC dispersal during wetting events
has been described previously for individual rocks (Bell,
1993; Allen, 1997), whereby EPS (see below) expands with
water and cells are ejected and dispersed to new areas along a
rock. We suggest a ‘stepping stone’ process, whereby LCC
disperse via water through the wet soil matrix to adjacent
soils and rocks to create larger scale patches, a mechanism
possibly contributing to self-organized patchiness (Rietkerk
et al., 2004) by concentrating biomass (and EPS) within
small geographical spaces.
Triggers
Rainfall is the key trigger for surface runoff, LCC activity
and possibly dispersal. Its stochastic nature was evident
(Warren-Rhodes et al., 2007), resulting in similarly high
variability in water inputs (‘run-on’) to LCC patches. Winter
precipitation (e.g. frost or snowmelt) is also a likely trigger
at some sites (e.g. Ruoqiang).
FEMS Microbiol Ecol 61 (2007) 470–482
Precipitation can trigger runoff that may be received by LCC
as run-on, stored in soil layers (e.g. rock/soil interface), or
lost through leaching or evaporation (Fig. 8). The extent and
rates of these processes depend on physical factors (e.g.
event intensity, temperature, light, terrain), rock/soil properties (texture, depth, rock orientation/size), and biology
(EPS) acting to control water availability (Fernandez-Illescas
et al., 2001; Guswa et al., 2002; Lookingbill & Urban, 2004;
Belnap et al., 2005; Bowker et al., 2006).
Such processes and factors are highlighted by the strong
positive correlation of intra-site percent colonization (i.e.
patch existence) and rock size. We hypothesize that microscale positive water concentration control mechanisms
influence colonization, which can be understood via the
TTRP model. Like plants, rocks obstruct water flow and
collect run-on at the surface and rock–soil interface, where
rainwater penetrates more deeply and may persist as reservoirs (Friedmann & Galun, 1974; Ludwig et al., 2005).
Indeed, Mehuys et al. (1975) reported many important
moderating micro-scale effects of surface rocks, including a
milder thermal regime with less temperature oscillations
beneath rocks – similar to plant canopies (Domingo et al.,
2000; Puigdefábregas, 2005) – and temperature differences
that facilitate condensation and lower overall water loss.
Rocks thus create for organisms (relative to bare soil)
‘islands where water would be more available when the
desert is dry’ (Mehuys et al., 1975, p. 441).
Soil moisture availability and retention benefits of the rock
habitat will vary (temporally and spatially) based on myriad
factors, including seasonally variable temperature and light,
rock size/orientation, and the extent and position of LCC
colonization. The 1–3 cm subsurface location of many hypolithic communities likely reflects this control of soil-moisture
availability, as well as light capture/protection requirements.
We further suggest that large rocks enhance LCC survival in
hyperarid deserts not only by collecting but also by retaining
more run-on/soil moisture – reducing evaporation, accumulating more water, and having lower surface temperatures
beneath them (i.e. thermal inertia effects) (Mehuys et al.,
1975) – than bare ground or smaller rocks. These hypotheses
require further testing, but this would explain why larger
rocks, and areas within our sites with higher proportions of
large rocks, had higher percent colonization. Moreover, in our
study, where rocks were closely clustered ( 1 m2 scales),
abundance increased, demonstrating that rock spacing is also
important. Rock orientation, depth and location (microtopography) are also likely important for colonization at
smaller scales owing to soil-moisture effects.
The presence of EPS, a common feature of most cyanobacterial communities, has several well-described benefits:
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480
(i) water absorption and retention (slows desiccation); (ii)
soil adhesion (increases macroporosity and infiltrability);
(iii) reduced evaporation; and (iv) nutrient capture and aid
in biogeochemical processes (Foster, 1981; Lynch & Bragg,
1985; Grilli-Caiola et al., 1993; Mazor et al., 1996; Allen,
1997; Philippis & Vincenzini, 1998; de los Rı́os et al., 2004).
We suggest that EPS plays a role in biological feedbacks (and
thus LCC patchiness) that concentrate soil water by capturing runoff and enhancing infiltration (via macroporosity),
storage and retention of run-on in the LCC rock/soil
environment. As LCC disperse, EPS accumulates to fill and
stabilize soils, which further enhances the water concentration feedback. This process and its effects may extend
beyond individual rocks to create larger-scale LCC patches.
Pulse
Light and temperature, along with water, determine photosynthetic and metabolic rates and balances (Allen, 1997;
Belnap et al., 2005). For this study, 200–922 h year1 were
available for carbon fixation. Water inputs probably initiate
pulses of activity within LCC patches but likely also supply
metabolic products to nearby soil microorganisms, as C
accumulates at higher levels in the LCC ecosystem than in
inter-patch areas (Warren-Rhodes et al., 2006). In hyperarid
deserts, LCC patches thus function as islands of fertility
(Schlesinger et al., 1990), concentrating C and other nutrients (which may also be present in soils via additional
mechanisms, such as aeolian deposition) through metabolism and decomposition.
Feedbacks between transfer, reserve and pulse
The C and N pulses stemming from LCC activity during
precipitation presumably create more biomass and EPS,
which further intensifies feedback loops at the individual
rock scale. Significant rainfall events probably also enable
LCC dispersal, thereby increasing patch size. Whether this,
in turn, leads to even greater positive EPS feedback at these
larger-scales (cm to m) is presently unknown.
Conclusions
Much has been learned from the systematic study of desert
plants and soil dynamics in landscape ecology and ecohydrology. To date, the application of such techniques has been
limited to only a few soil microbial communities (Nunan
et al., 2002; Barrett et al., 2004; Bowker et al., 2006) and is
especially lacking for extremely dry and/or hot deserts
(Belnap et al., 2005). This study represents a significant step
toward understanding microbial ecohydrology and is the
first to quantify LCC ecology systematically across significant geographical and climatic scales. The results provide
novel insights into a well-studied micro-habitat but poorly
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K.A. Warren-Rhodes et al.
understood landscape ecosystem. Of particular importance
is the finding that significant LCC patchiness is displayed at
multiple scales. The data support the idea that physical
factors (e.g. rock size) and biological mechanisms (e.g.
rainfall-related dispersal) offer underlying explanations of
LCC spatial pattern in deserts. The data also raise the
possibility that EPS-soil water feedback further contributes
to LCC patchiness – i.e. that LCC patches are, in part, selforganized. Further study of LCC at landscape to finer scales
is needed to test and quantify these effects (e.g. Ludwig et al.,
2005) and to understand how LCC survive within the harsh
and dynamic physical environments of the world’s driest
deserts.
Acknowledgements
The authors acknowledge the financial and logistical support of the National Research Council and the Institute of
Microbiology, the Chinese Academy of Sciences and Hong
Kong Research Grants Council (HKU 7573/05M). We thank
P. Gong (University of California-Berkeley) and the China
Meteorological Administration for long-term climate data,
and the two anonymous reviewers for their insightful
comments. This article is dedicated to the memory of
Rosalie and E. Imre Friedmann.
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Supplementary material
The following supplementary material is available for this
article:
Table S1. Environmental parameters at the China study
sites. All temperatures (T) in 1C.
Table S2. Pearson correlation coefficients (r) between
LCC abundance and (a) quartz rock density and (b) percent
of large rocks.
Table S3. Mean rock sizes and Z-test results for all sites.
Table S4. Logistic regression results for colonization
form and rock size.
This material is available as part of the online article
from: http://www.blackwell-synergy.com/doi/abs/10.1111/
j.1574-6941.2007.00351.x (This link will take you to the
article abstract).
Please note: Blackwell Publishing are not responsible
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missing material) should be directed to the corresponding
author for the article.
FEMS Microbiol Ecol 61 (2007) 470–482