Martin 2002

Martin AP. 2002. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Applied and Environmental Microbiology 68: 3673-3682.

This author presents a synthesis of a set of statistical techniques for detailed analysis of biodiversity in the context of microbial communities. One new test, the P-test (for phylogenetics) is combined with the FST test to generate inferences about the quantified levels of difference in community composition when examining multiple microbial communities.

A review of existing methods for quantifying diversity is provided first, rapidly pointing out the not-unlikely circumstances under which inter-community differences would be either under- or over-estimated in the absence of explicit phylogenetic inference. Other types of phylogenetic inference in this context are examined, but one main problem with techniques such as the Shannon-Wiener index is its dependency on accurate information about frequency of taxa. The P-test, novel to this paper as far as I can tell, avoids this pitfall, and instead is based on an examination of the covariance between a phylogeny and the distribution of taxa in communities.Figure 3 from Martin (2002). The basis of the P test is the covariance between which community a sequence was found in, and the positions of sequences on the phylogenetic tree.

The P test is combined with the FST test to examine the partitioning of sequence variation between communities. A P test on its own is not particularly informative, because it says little about how variation is partitioned between communities vs. the total pool.The 2x2 grid of comparison of P test and FST test results, from Figure 4 of Martin (2002). Each possible outcome of significance for the two tests allows inference about the evolutionary and ecological history of a particular situation of microbial communities.

The raw data for the P test is sequence data, typically 16S rDNA. This author advocates whole-gene sequences for comparison, to provide the maximum data and maximum compatibility between different studies, but acknowledges the trade-off between sequence length and number of sequences that can be produced. These are also the raw data for FST, but how those raw data are treated before going into each test varies.

Under the P test, the sequence data are used to construct a phylogeny, incorporating all sequence data from all communities. This phylogeny is set to equal total branch lengths from the root to the tips (the tips being the currently-measured sequences), and a null model of branching through time (lineage-per-time) is built. Then the community occurrence of each sequence is mapped onto the phylogeny, and the covariance calculated.

The FST test takes in Theta values as its meat of calculation. Theta is the total genetic variation in a sample, and in FST the grand total theta for all communities combined is compared to the average within-community theta for all communities under consideration.

This combined approach is intended to be complimentary to existing methods of examining microbial diversity, such as methods for estimating species richness, and methods for examining microbial phylogenies. I think the author’s own words at the beginning of the discussion section provide a good summary:

“In this study I used standard quantitative methods of analysis borrowed from population genetics and systematics for describing and comparing microbial communities. Information gained from analysis of DNA sequences provided the basis for statistical analysis of communities in ways that advance inferences about the processes that may govern the compositions and functions of microbial communities. Furthermore, the analytical approaches advocated here make it possible to accomplish broad comparisons of ecological communities. For instance, a comparison of lineage-per-time plots across a diverse set of ecosystems might reveal differences in the phylogenetic compositions of ecological communities that would be invisible with standard ecological statistics that ignore the magnitude of genetic differences among sampled sequences.”

I think I would like to use this approach in the analysis of microbial communities I will conduct based on soil samples from the polar desert. This method seems at this point like a useful way to quantify diversity across the gradient of latitude I will be covering.

Nannipieri et al. 2003

Nannipieri P, Ascher J, Ceccherini MT, Landi L, Pietramellara G, Renella G. 2003. Microbial diversity and soil functions. European Journal of Soil Science 54: 655-670.

These authors present a review of the current state of knowledge about microbial diversity and ecosystem function such as organic matter decomposition in soils. They devote sections of the paper to the structure of soil as a habitat for microbes and other soil-dwelling organisms, methods of measuring microbial diversity, measuring soil functions, the current understanding of these methods and prominent results, and how these measures fit together in various contexts.

Unlike above-ground systems, soils appear to have no link between function and microbial diversity, or the direction and magnitude of the relationship varies considerably with which function is studied. General ecology theory and results suggest there should be a hump-shaped relationship between biodiversity (species richness and evenness) and productivity, such that productivity increases with diversity to some point, before declining. This relationship has not typically been found in soil systems, though there are relatively few studies of this relationship specifically in soils.
Measuring function in soils is complicated by the structure of soil. It is largely composed of non-living matter, some of which such as clay surfaces are capable of catalyzing reactions normally associated with living cells. In addition, these surfaces can adsorb large organic molecules such as enzymes and nucleic acids and protect them from degradation while still allowing some catalytic activity. Thus, even after all cells have been killed in a soil sample, enzymatic activity may be detectable. Distinguishing between biotic and abiotic chemical reactions in natural soil systems is therefore extremely difficult.

Measuring biodiversity in soil is not much easier than measuring function. Plate-count methods have been widely criticized because they will measure only culturable organisms, variously estimated to compose a small fraction of actual biodiversity. Countering this criticism, some researchers have suggested that the biomass, rather than species richness, of unculturable microbes is a minority, rendering plate counts of culturable species much more relevant to ecological studies. However, much attention has been focused recently on molecular methods, further divided into DNA-based techniques and fatty-acid based techniques.

DNA-based techniques deployed to study microbial diversity in soils often include a PCR step. However, DNA extraction methods for soil must balance several trade-offs, for example gram positive bacteria have very tough cell wall structures that require harsh treatment to break down and access their DNA. This same harsh treatment can shred DNA from less-tough cells to under 1kb fragments, which will often form chimeras during PCR, especially when using universal primers for such popular markers as 16s rDNA. Similarly, high-efficiency methods of DNA extraction and isolation are also efficient at extracting humic acids, which interfere with PCR. Despite these concerns, a large number of studies based on PCR of soil-derived DNA templates have been published, providing a large database of sequences for phylogenetic comparison.

Fatty-acid based techniques avoid the PCR-based concerns of DNA methods, but are less specific in their results: fatty acid composition is generally not species-specific the way DNA sequence data can be. However, techniques such as PFLA provide useful estimates of soil microbial biomass.

There is an ecological puzzle in the observed high biodiversity of near-surface soils. Two competing, though probably not mutually-exclusive hypotheses centre on a lack of competition among soil microbes. Under the first hypothesis, microbial microhabitats tend to be isolated from each other, preventing contact and competition. Community mixing occurs when water droplets bridge the gaps between soil aggregates, as during rainfall when soil pore spaces are filled. Countering this hypothesis is the observation that much of the near-surface soil environment is not especially prone to pore-drying, for example the plant root-soil interfaces, yet contains high species richness. The second hypothesis suggests that high specialization for organic substrates (i.e. microbe food) prevents competition among cells in close physical proximity. There are higher quantity and diversities of organic molecules in surface soils compared to greater depths, but flow channels such as cracks, fissures, and worm burrows also have high levels of organic molecules, and high microbial biomass, but do not show higher diversity than the surrounding bulk soil. The puzzle remains unsolved.

Much of the discussion of various measurements in this paper is of direct relevance to my own work. The various methods for assessing soil function, for example, are almost all measures of enzyme activity, which is precisely what my gas-flux measurements are as well. I intend to measure biodiversity, by molecular means, and the references and discussion here are valuable. Overall, this review paper does a good job of providing an overview of some issues I will also be exploring.

Freeman et al. 2009

Freeman KR, Pescador MY, Reed SC, Costello EK, Robeson MS, Schmidt SK. 2009. Soil CO2 flux and photoautotrophic community composition in high-elevation, ‘barren’ soil. Environmental Microbiology 11: 674-686.

These authors measured photosynthetic carbon fixation and microbial community composition in sub-nival barren soils in the Colorado Front Range of the United States, at 40ºN latitude and approximately 3600m altitude. Like polar desert soils, these sub-nival soils lack conspicuous macrophytic vegetation (vascular plants and bryophytes) and are snow-covered for most of the year. Previous examinations of these systems had suggested the majority of carbon input to these soils was derived from wind-blown dust, but this study demonstrated a much larger input of carbon from in-situ photosynthesis.

Net carbon fixation was estimated by subtracting in-light measurements from in-dark measurements of CO2 flux. All measurements were made using an IRGA system with a 1.18L transparent chamber; dark measurements were made by covering the chamber with a dark, opaque cloth. After measurement of CO2 flux, one site was carefully dug up and transported to the laboratory for molecular-phylogenetic analysis.

The soil was divided into 2 depths: 0-2cm and 2-4cm, then DNA was extracted and PCR using universal bacterial primers for the 16s region was carried out, followed by sequencing. This generated more than 1000 sequences, in 4 bacterial divisions containing known photoautotrophic microorganisms, plus some sequences from eukaryotic green algae.

The most intriguing group of bacteria found were the Chloroflexi, an understudied group found in both depth layers. The taxa composition found in the deeper layer was highly different from the community found in the surface, light-receiving zone, and the authors suggest, based on a few studies done of Chloroflexi in hot-springs environments, that this group may use longer-wavelength light which penetrates deeper in soils. These authors do not make it, but this suggests to me the microphotoautotrophs in this system may be partitioning their environment in both space (depth) and spectrum (red).

This paper includes a large number of references and introductory descriptions for techniques and findings I will need to incorporate into the planning stages (at least) of my future studies in the polar desert. In particular, the molecular approach to the phylogenetics and biodiversity of the soil photoautotrophs seems both powerful and relatively uncomplicated. There are many procedures to carry out, to be sure, but the justification for each is clear, and the sequence of operations appears to be linear.

Uchida et al. 2002

Uchida M, Muraoka H, Nakatsubo T, Bekku Y, Ueno T, Kanda H, Koizumi H. 2002. Net photosynthesis, respiration, and production of the moss Sanionia uncinata on a glacier foreland in the High Arctic, Ny-Ålesund, Svalbard. Arctic, Antarctic, and Alpine Research 34: 287-292.

These authors constructed a model of moss physiology that uses meteorological data to estimate productivity, based on data collected during one field season at Svalbard. In 2000, these authors measured the response of a common High Arctic moss species to water content, temperature, and light, then determined the relationship between those variables and available meteorological data, then applied previous-years meteorological data to their model and estimated previous-years productivity. These estimates suggest a great deal of variation in year-to-year productivity, driven largely by differences in water availability. Water content of fresh moss tissue was the single most important controlling variable in moss photosynthesis rates. The response to temperature was nearly flat between 7 and 23ºC, with near-freezing photosynthetic rates still a large fraction of maximum under saturating light conditions. Saturating light conditions were estimated at near 800µmol/m^2/s, which is not uncommon on sunny days in this environment.

The glacial foreground in question is at 79º North, but is not polar desert as it receives approximately 360mm of precipitation per year. The moss species studied is dominant in the local ecosystem, but appears to represent an intermediate successional stage, with high-productivity vascular plants replacing bryophytes in older sites in the area (i.e. further from the toe of the glacier).

Floyd et al. 2002

Floyd R, Abebe E, Papert A, Blaxter M. 2002. Molecular barcodes for soil nematode identification. Molecular Ecology 11: 839-50.

These authors present a detailed description of and theory behind the MOTU concept. This analysis technique uses molecular sequence data to identify taxonomic units, hence the name Molecular Operational Taxonomic Unit. This paper uses the MOTU concept to examine and draw inferences about a collection of nematodes from a Scottish farm, finding high levels of species richness, and demonstrating a set of methods for rapid, inexpensive phylogenetics of a taxonomically-difficult group of animals.

Büdel et al. 2009

Büdel B, Darienko T, Deutschewitz K, Dojani S, Friedl T, Mohr KI, Salisch M, Reisser W, Weber B. 2009. Southern African biological soil crusts are ubiquitous and highly diverse in drylands, being restricted by rainfall frequency. Microbial Ecology 57: 229-247.

These authors examined biological soil crusts (BSCs) along a 2000km transect running roughly north-south through Namibia and South Africa. A number of hypotheses relating to BSC composition, frequency, and succession were proposed and tested, with most hypotheses partly confirmed. In general, BSCs are an important and abundant component of these dryland ecosystems, and show patterns of biodiversity associated with biomass, as measured by chlorophyll-a concentrations and species counts.

The major finding of this study, as implied in the title, is that BSC distribution and composition is primarily controlled by patterns of rainfall, but not total rainfall. Species richness and successional stage of BSCs was highest in the winter rain zone, which has a shorter dry season though less total annual rainfall than the summer rain zone. This implies that most BSC organisms are limited by drought tolerance rather than annual water input.

This study is interesting to me for a number of reasons. First, it includes in the references a number of reviews of BSCs and methods to study them, such as protocols for measuring chlorophyll-a concentrations per square metre, and molecular methods for species richness estimation. Second, because BSCs are expected to be the major photosynthetic organisms in the polar desert, I need to know what patterns of their distribution and diversity I should expect. This paper’s Hypothesis 4, that biomass (and productivity) of BSCs increases with species richness, which was essentially confirmed, is of particular interest in this context, as it provides another layer of background expected pattern in addition to my general expectation of a species-richness gradient associated with latitude, particularly as one crosses Lancaster Sound north of Baffin Island. This paper provides some ideas for ways to measure species richness in BSCs, which (third) contribute strongly to the overall biodiversity of dryland regions and therefore will be interesting in their own right in studies of Arctic Biogeography.

Pilegaard et al. 2006

Pilegaard K, Skiba U, Ambus P, Beier C, Bruggemann N, Butterbach-Bahl, Dick J, Dorsey J, Duyzer J, Gallagher M, Gasche R, Horvath L, Kitzler B, Leip A, Pihlatie MK, Rosenkranz P, Seufert G, Vesala T, Westrate H, Zechmeister-Boltenstern S. 2006. Factors controlling regional differences in forest soil emission of nitrogen oxides (NO and N2O). Biogeosciences 3: 651-661.

These (abundant) authors present an analysis of a large combined dataset covering NO and N2O emissions from a range of forest systems in Europe. The measurements contributing to this large dataset were continuous measures (at least daily, usually hourly or better) and run at least one year. This provides a high-quality dataset that includes variation induced by seasonality.

One of the most interesting findings in this study is a scale-dependent relationship between soil parameters and N2O emissions. Within-forests, soil temperature and moisture were highly predictive of N2O flux, but not at scales encompassing multiple forests in comparison. At larger spatial scales, stand age and C/N ratio were much better predictors.

Chen et al. 2008

Chen Y, Dumont MG, Neufeld JD, Bodrossy L, Stralis-Pavese N, McNamara NP, Ostle N, Briones MJI, Murrell JC. 2008. Revealing the uncultivated majority: combining DNA stable-isotope probing, multiple displacement amplification and metagenomic analyses of uncultivated Methylocystis in acidic peatlands. Environmental Microbiology 10: 2609-2622.

These authors used a multiple-methods approach to isolate and identify DNA from a group of methanotrophic prokaryotes that have previously resisted attempts at culture. These microbes were previously estimated to be highly abundant in peatland soils, and were found in soils from a range of peatlands in Europe.

The three methods used to investigate these microbes were 1. a microarray built using sequences derived from a key enzyme in the methanotrophic process, 2. DNA-SIP, DNA Stable-Isotope Probing, used to examine DNA replicated with an injection of 13C-labelled CH4 (such that only methanotrophs would be able to use the carbon in their metabolisms), and 3. MDA, Multiple-Displacement Amplification to generate sufficient template DNA for fosmid-library construction and subsequent DGGE and cladistic analysis.

This triple-combined approach allowed the isolation, identification, and some basic phylogenetic analysis of a group of ecologically-important microbes previously unstudied in such a way. From my perspective, currently the most useful parts of this paper are the references (containing reviews of metagenomics and microarrays) and the methods section, as I may be attempting similar analyses of polar desert soils.

Barrett et al. 2006

Barrett JE, Virginia RA, Wall DH, Cary SC, Adams BJ, Hacker AL, Aislabie JM. 2006. Co-variation in soil biodiversity and biogeochemistry in northern and southern Victoria Land, Antarctica. Antarctic Science 18: 535-548.

These authors examined soil biota at three sites across about 7 degrees of latitude in the drier part of Antarctica. The latitudinal gradient here covers a range of different ecosystems, from relatively wet, more northern and coastal systems to extremely arid and barren southern systems. Here, latitude is not studied for its effects on ecosystems; its effects on ecosystems are exploited to cover the widest available range of conditions. Within each of the three sites, one wet and one dry location were chosen a priori based on obvious surface features such as meltwater drainage channels and the presence of moss beds. At each location, a set of transects were laid out and samples were collected. To quote them directly:

"We investigated the structure (bacterial and metazoan diversity) and functioning (soil respiration) of soil communities and the influence of soil biogeochemical properties (organic matter, inorganic nutrients, physicochemical properties) on habitat suitability."

This is quite similar in many respects to my planned investigations in the Arctic polar desert. The list of molecular techniques used in this paper, for example, serves as a useful guide or checklist of the procedures I intend to use.

Soil invertebrates were investigated using both morphological and molecular techniques. There are only four metazoan phyla with any significant presence in Antarctica’s soils (Arthropoda, Nematoda, Rotifera, and Tardigrada), all of which are difficult to identify to species using standard morphological methods. While these authors were able to identify nematodes to species, rotifers, tardigrades, and mites were handled as MOTUs, molecular operational taxonomic units (Floyd et al. 2002). These MOTUs, based on ribosomal DNA sequences, were also used to generate a series of cladograms used to assess biodiversity at the research sites. Unfortunately, while the text descriptions of the methods and results are reasonably clear, the figures relating to the biodiversity and metazoan-molecular work are confusing and poorly described.

The results of the microbial analyses are broadly similar to the metazoan dataset. These authors were able to examine biodiversity at the level of microbes, and compare this diversity to both soil chemical characteristics such as water content and C:N ratio, and to the metazoan diversity. Perhaps surprisingly, they found no evidence to support the hypothesis of top-down control on bacterial populations by metazoan predators, as there was no correlation between DGGE and FAME-derived estimates of bacterial population size and species richness and cladogram and sugar-extract-derived estimates of nematode biodiversity. Bacterial diversity is described as not varying across sites, though community composition does. I take this to mean that while total species richness of bacteria (as measured by DGGE) was constant across sites, species turnover (Beta-diversity) was high. This is interesting to me, though not mentioned in the paper, because it seems to directly contradict the microbial-biogeography hypothesis of “everything is everywhere”. There is a mention in the description of this comparison of the importance of geography in structuring biodiversity, which reads to me like an opportunity to apply explicit geospatial techniques to their dataset.

Overall, while it is true that Antarctic soil ecosystems are extremely simple relative to other systems, there are a great many complex and variable interactions between even the few components of these systems, creating a great deal of complexity. This paper will be very useful to me in structuring some of my own investigations in the Arctic.

Ettema & Wardle 2002

Ettema CH, Wardle DA. 2002. Spatial soil ecology. Trends in Ecology & Evolution 17: 177-183.

These authors review the growing use of explicit geospatial analysis techniques in soil biology. As this is a TREE article, there are several helpful boxes that explain fundamentals of geospatial analysis such as the terminology and key case studies. This is also a review article, so there are descriptions of various previous studies that include evidence useful in answering the questions set out in this paper. These questions are 1) What are the scales, patterns and causes of spatial variability in soil organism distributions? 2) What are the implications of spatial variability for the structure and function of soil communities? 3) How do spatial properties of the soil biota influence plant communities?

Regarding question 1, the scales and patterns of spatial variability in soil organisms range from 10s and 100s of metres down to millimetres. Studies of soil microbes including methanogenic Archaea have included soil corers of 1mm diameter (based on a hollow needle) and aggregations of organisms separated by distances of 2 to 4mm.

Soil communities and their influence on plant communities were found to be highly non-uniform, and show predictable though complex spatial patterns. However, while much was made of the role of individual plants (especially trees) to structure the soils around them and create spatial patterns of microbes and invertebrates on the same scale as the trees themselves are distributed, very little was made of the role that small-scale aggregations play in structuring larger patterns. This is surprising, given the highly biased view of soil processes in this paper and more generally in the soil science literature: soil is viewed as something that exists primarily to support plants, rather than a system of its own independent importance. That is the impression I have gotten, at least.

This paper is a very useful overview of geospatial analysis, and the reference list includes a number of similarly useful papers. In particular, further exploration of the statistics of semivariance patterns seems useful.

Garten et al. 2007

Garten CT Jr., Kang S, Brice DJ, Schadt CW, Zhou J. 2007. Variability in soil properties at different spatial scales (1m-1km) in a deciduous forest ecosystem. Soil Biology & Biochemistry 39: 2621-2627.

These authors examined some of the fundamental assumptions of geospatial analysis as applied to soil properties, in a pair of transects that I strongly suspect have been used repeatedly for many studies in Tennessee (see, e.g. Zhou et al. 2008). One of the fundamental assumptions is of spatial autocorrelation, that is, samples in close proximity will be more similar to each other than samples separated by greater distances. In this study, this assumption was stated as the null hypothesis “there are no differences in variance at different spatial scales”; a rejection of this null hypothesis can be interpreted as support for the “common sense” (their wording) principle of spatial autocorrelation, at least among the spatial scales discussed here (i.e. metres to kilometres). This and other important assumptions of geospatial analysis come from a series of papers applying these principles to soils, which I should probably read soon.

The 11 soil variables examined in this paper were distinctly non-orthogonal in their relationships. Many of the variables were calculated directly from other variables, and the majority takes the form of either ratios (such as C-to-N) or fractions (such as silt content). The Principle Components Analysis (PCA) these authors conducted on their final, grand-total dataset indicated that the usual suspects of soil properties were important – soil Carbon, soil Nitrogen, and soil Texture are one way to summarize the first three PC variables.

I looked up and read this paper mainly because of the statistical tests used here and the discussion of them. They conducted 5 main statistical tests.
1. Bartlett’s test for equal variances at different distances.
2. Bartlett’s test is sensitive to non-normal data, so they also used the non-parametric Spearman’s Rank Correlation between coefficients of variation (100 x S.D./mean). The other reason a non-parametric test was used was that the functional relationship (linear vs. non-linear) between variance and sampling distance was unknown.
3. Mantel and Partial Mantel tests. These were the central analysis, I think, and provided most of the key results regarding the tests of the main hypotheses. Apparently, Burrough (1993) recommends semivariogram analysis, but the present data set was not amenable to such.
4. PCA, as mentioned above.
5. Power analysis. How many samples would they need to collect to be more certain of being close to the true mean value in their estimates?
Besides the PCA, which produced utterly unsurprising results, I think the statistical tests deployed here will serve as models for my own analysis of 2009 and putative 2010 datasets from the High Arctic. In particular, the Mantel tests and the Power analysis should be very useful in my own examinations.

Overall, the geospatial assumption of spatial autocorrelation was not very well supported by this study. Many soil properties appear to be highly variable at small spatial scales, such that samples collected within a few metres of each other are as variable as samples collected from up to a kilometre away, at least in a temperate forest ecosystem as studied here. This is particularly surprising in light of the consideration of the structure of such a forest, where individual trees presumably have strong impacts on soil properties within perhaps 5 to 10 metres of their trunks.

Zhou et al. 2008

Zhou J, Kang S, Schadt CW, Garten CT Jr. 2008. Spatial scaling of functional gene diversity across various microbial taxa. Proceedings of the National Academy of Sciences of the USA 105: 7768-7773.

These authors used a microarray-based technique to estimate biodiversity of soil microbes across a pair of transects in a forest in Tennessee. Their analysis found rates of species turnover through space much lower than rates for macroorganisms such as “higher” plants and animals.

The species-area relationship, generalized to the Taxa-Area-Relationship (TAR), is S=cA^z, where S is the number of species, A is the area, c is the intercept in log-log space, and z is a measure of the rate of species turnover across space. Values of z for macroorganisms have been estimated close the theoretically derived value of 0.25, while previous estimates for microbes have been often much lower, but occasionally much higher. This study found a range of z-values, all a bit less than 0.1.

The key technique used in this study was the GeoChip, a microarray with nearly 25000 50-mer probes for more than 10000 genes in functional groups such as denitrification or heavy-metal resistance. As such, it represents an excellent tool for such investigations, because it reduces or avoids many of the microbe-diversity sampling artifacts such as undersampling that plague other methods.

A large fraction of the observed variation in sequences across the transects was unexplained. These authors speculate that a fraction of this unexplained variation may be driven by unexamined patterns and processes including biotic interactions (competition, trophic interaction), abiotic interactions (O2 concentrations, labile C pool), and microscale effects below 1m scales.

One interesting suggestion by these authors is to use metagenomic approaches to characterize key sequences of interest in a particular system, and then examine biodiversity using a microarray customized for these sequences. This is in line with what I was thinking in regards to using such techniques in polar desert soils – first, characterize what is there; second, look at biodiversity and patterns within diversity relating to groups of interest.

Broll et al. 1999

Broll G, Tarnocai C, Mueller G. 1999. Interactions between vegetation, nutrients and moisture in soils in the Pangnirtung Pass area, Baffin island, Canada. Permafrost and Periglacial Processes 10: 265-277.

These authors examined soils from 6 pedons in Pangnirtung Pass, a north-south pass between mountains on Cumberland Peninsula. Three pedons were from moist soils, and three from dry soils. The moisture content drove a major difference in soil structure: dry soils are not cryoturbated, resulting in strong differences in nutrient content and mineralization rates.

The goal of the study was to compare in detail these differences between dry and moist soils. This seems very similar to my PhD goals surrounding examinations of Polar Desert soils. This study thus represents a possible template for some of my own investigations.

Bockheim 1979

Bockheim JG. 1979. Properties and relative age of soils of southwestern Cumberland peninsula, Baffin island, N.W.T., Canada. Arctic and Alpine Research 11: 289-306.

This author sampled soils from more than 60 sites on the Cumberland peninsula of Baffin Island, mostly near the hamlet of Pangnirtung. This covered soils from two tundra vegetations (Dwarf shrub-sedge-moss-lichen on lowlands and coastal, stony sedge-moss-lichen in highlands and northern fjords) and the Polar Desert of Baffin island. The tundra soils ranged from mesic to subxeric, while the desert near Penny icecap was xeric. A similar gradient driven by latitude rather than altitude is referenced in Tedrow (1973).

Descriptions are made of the pH and various exchangeable and free minerals in the soils, along with how those components change with depth in each area. pH increases with depth, for example, especially in the Polar Desert. Phosphorus was found in surprisingly high levels in all soils. The active layer, or at least the layer above the permafrost, is much deeper than found on Ellesmere island, and appears to be deeper than 1m everywhere studied in this paper.

Niederberger et al. 2008

Neiderberger TD, McDonald IR, Hacker AL, Soo RM, Barrett JE, Wall DH, Cary SC. 2008. Microbial community composition in soils of Northern Victoria Land, Antarctica. Environmental Microbiology 10: 1713-1724.

These authors present an analysis of a large collection of data regarding both microbial and metazoan biodiversity at relatively small scales in one part of Taylor Valley, Antarctica, one of the famous Dry Valleys. This contributes to both the Latitudinal Gradient Project, an international effort to characterize Antarctica, and to the biogeographical debate regarding the distribution and community assemblages of microbes and soil microfauna.

Biodiversity was higher than expected based on the physical characteristics of this extreme environment, and was much more variable at small (~200m) spatial scales. While the microbes identified by 16s sequences were not particularly surprising, the changes in community composition between study sites was high. This supports the hypothesis that extreme environments “select for” particular microbial physiologies, and that differences in soil physical features such as moisture and temperature are highly important, in distinct contrast to the “everything is everywhere” hypothesis of microbial biogeography.


NB October 1 2009: the “everything is everwhere” hypothesis (Beijerinck 1913) includes the second clause “the environment selects”, which implies my earlier impressions, above, are incorrect. This paper’s demonstration that extreme environments select for particular soil communities, and that local-scale variables such as moisture and temperature, rather than regional-scale variables such as climate factors, actually supports Beijerinck’s (1913) hypothesis, rather than countering it.