Environmental Engineering Reference
In-Depth Information
more achievable for ecosystem science thanks to these new technological advances
(
Kampe et al. 2010
).
Genetic and Molecular Data
New developments in methods and technologies such as environmental genomics/
metagenomics, transcriptomics, and proteomics will allow the gathering of much larger
amounts of molecular data than was previously possible. Using environmental genomics/
metagenomics methods, DNA sequences from environmental samples of microbial
communities can be collected and analyzed over large spatial and temporal scales.
Environmental genomics studies can include focused surveys of single genes from
environmental communities, or shotgun sequencing of all genes in an environmental
sample. In transcriptomics, RNA from individual organisms or microbial communities is
converted into complementary DNA libraries and sequenced, so that researchers can deter-
mine patterns of gene expression. Similarly, microarrays can also test for expression of
multiple targeted genes simultaneously. These technologies focusing on gene expression
are particularly useful in linking expression of functional genes to microbially driven pro-
cesses such as nitrogen mineralization. Finally, in proteomics, the complete set of proteins
expressed by a microbial community is analyzed, but this new method can be limited by
extraction quality and other technological challenges (
Vandenkoornhuyse et al. 2010
).
Improved technologies for high-throughput sequencing (the production of a large num-
ber of sequences at once), such as pyrosequencing and other next-generation tools, have
increased our capacity to collect molecular data on microbial communities. This allows us
to assess the composition of microbial communities in depth, and evaluate changes in
community structure over time or space, or in conjunction with ecosystem processes such
as the processing of nitrogen or carbon. Key challenges in using all of these technologies
in an ecological context include the development of computational and statistical tools
to store, quality check, annotate, and analyze these large data sets, and the improvement
of our understanding of previously undiscovered organisms, genes, and proteins of
unknown identity or function (
DeLong 2009; Vandenkoornhuyse et al. 2010
).
These technologies are, and will be, particularly useful in elucidating the structure and
function of microbial communities (including bacteria, fungi, archaea, microscopic eukar-
yotes, and viruses) that carry out so many ecosystem processes of interest. Therefore, they
could help ecosystem scientists open the proverbial “black box” of microbially driven
processes, linking biogeochemical transformations to cellular mechanisms, microbial
communities, and ecosystem function.
Manipulating and Analyzing Large Data Sets
Although sensor networks, remote sensing, and molecular advances have aided in our
understanding of ecosystems, the massive flow of data resulting from this intensive sam-
pling represents new challenges for data quality control and assurance, management, and
importantly, how science is conducted through data and idea sharing (
Fischer and
Zigmond 2010; Porter et al. 2012
). Developing national and international observatory
