The involvement of weakly adsorbed water
in multiple mechanistic steps is also consistent
with the large reaction order (1.3). DFT calculations also indicate that a second adsorbed water
molecule in the vicinity of the *COOH species
facilitates the proton transfer, which only needs
to overcome the thermodynamic barrier (DE =
0.70 eV, Ea = 0.70 eV, SM 6.6). Further, at higher
water coverage, rapid proton mobility (33) can
explain the shift to an equilibrium isotope effect.
*COOH decomposition has also been identified
as the RDS in the related WGS reaction on Cu
and Pt (34, 35), and this elementary step is
consistent with reports of NaOH promoting CO
oxidation over Au catalysts (17, 36).
The CO oxidation mechanism shown in Fig. 4,
along with the structural model of support OH
groups anchoring and activating water near Au
particles, provides a fresh framework for interpreting previous results. This model provides a
single active-site description that unifies some
very disparate mechanistic information, accounts
for the promotional effects of water, and is consistent with previously reported isotope exchange
studies (21, 27) that indicate that CO and O2 must
react directly on the Au particles without exchanging O atoms with the support or adsorbed water.
At the same time, it maintains the importance of
the support OH groups and the metal-support
interface without directly involving them in the
reaction mechanism. The likely active sites bear
a strong resemblance to the WGS mechanism over
Au catalysts, where the support anchors water
near Au-CO sites (6).
This proposed mechanism explains why the O2
adsorption and activation steps, which are widely
regarded as the critical mechanistic steps, have
been so difficult to characterize. The fast room-temperature catalysis mechanism requires both
water and CO for O2 binding and activation. Experiments performed without water, particularly
ultrahigh-vacuum and DFT studies, ultimately
probe different reaction mechanisms than what
appears to be the dominant room-temperature
pathway on supported catalysts. Similarly, most
traditional catalyst studies rarely control or report feedwater contents, which has likely contributed to the wide range of reported CO oxidation
activities for Au/TiO2 catalysts and to the difficulties in understanding the key features of the best
catalysts. Finally, this new mechanism brings
the interpretation of traditional supported catalyst experiments more in line with computational and surface-science studies, which have
largely indicated that the key reaction steps
occur on Au (7, 9–14, 17, 29), without direct participation of the support.
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We gratefully acknowledge the U.S. National Science Foundation
(grants CBET-1160217, CHE-1012395, and CHE-1300619) and the
U.S. Department of Energy (NSF/DOE CBET-1258688) for financial
support of this work. We also thank R. Rioux (Pennsylvania State
University) and Z. Tonzetich (University of Texas at San Antonio)
for assistance with transmission electron microscopy and
thermogravimetric analysis measurements, respectively. Additional
financial support was provided to L.C.G. and H.A.D. through a
University of Houston New Faculty Grant. Use of the computational
resources at the Center for Nanoscale Materials was supported by
the U.S. DOE, Office of Science, Office of Basic Energy Sciences,
under contract no. DE-AC02-06CH11357. This work also used the
Extreme Science and Engineering Discovery Environment (XSEDE,
supported by NSF grant OCI-1053575) and the Kraken computing
resource at the National Institute for Computational Sciences
(supported by NSF grants 0711134, 0933959, 1041709, and
1041710 and the University of Tennessee). We acknowledge the
Texas Advanced Computing Center (TACC) at The University of
Texas at Austin for providing high-performance computing
resources that have contributed to the research results reported in
this paper. Additional computational resources were provided by
the Center for Advanced Computing and Data Systems (CACDS,
formerly Texas Learning and Computation Center, TLC2) and the
Research Computing Center (RCC) at the University of Houston.
Materials and Methods
Figs. S1 to S12
Tables S1 to S8
13 May 2014; accepted 8 August 2014
Published online 4 September 2014;
Environmental filtering explains
variation in plant diversity along
Etienne Laliberté,1 Graham Zemunik,1 Benjamin L. Turner2
The mechanisms that shape plant diversity along resource gradients remain unresolved
because competing theories have been evaluated in isolation. By testing multiple theories
simultaneously across a >2-million-year dune chronosequence in an Australian biodiversity
hotspot, we show that variation in plant diversity is not explained by local resource
heterogeneity, resource partitioning, nutrient stoichiometry, or soil fertility along this
strong resource gradient. Rather, our results suggest that diversity is determined by
environmental filtering from the regional flora, driven by soil acidification during long-term
pedogenesis. This finding challenges the prevailing view that resource competition controls
local plant diversity along resource gradients, and instead reflects processes shaping
species pools over evolutionary time scales.
For decades, ecologists have sought to under- stand patterns in terrestrial plant diversity alongenvironmental gradients (1). Prominent heories emphasize resource competition as a key driver of diversity (2–4). Alterna-
tively, it has been proposed that variation in local
plant diversity along gradients reflects the fil-
tering of species that are poorly adapted to local
environmental conditions (5–7), highlighting the
importance of long-term evolutionary processes
in shaping species pools and present-day patterns
of plant diversity. These competing hypotheses
have been considered in isolation, and further
progress can be made only by considering the
1602 26 SEPTEMBER 2014 • VOL 345 ISSUE 6204
RESEARCH | REPORTS