Pennock 2004

Pennock DJ. 2004. Designing field studies in soil science. Canadian Journal of Soil Science 84: 1-10.

This author reviews the major issues surrounding field-based (as opposed to strictly laboratory-based) research, focusing on issues specific or of greatest importance to soil science. Soil science’s history could perhaps be described as a fusion of physical geography and geology with agronomy, and many published studies in the soil science journals show these roots. Following the lead of previous authors, who have included ecologists, statisticians, and philosophers and historians of science, this author divides field research into 2 major categories, broadly manipulative studies and mensurative studies. Manipulative studies are, under some definitions including one tentatively employed in this paper, the only type of study that qualify for the name “experiment”, and involve complete control over experimental conditions by the researcher. Treatments in an experiment are directly related to replication, and can be applied with great precision. Mensurative studies are those that at least partly use features of the environment beyond the control of the researcher to test hypotheses or discover new information. The key feature of a mensurative study is that the features of interest are clearly defined but not controlled (i.e. not randomized) by the person conducting the study.

Replication, and avoiding pseudoreplication, is of great importance in all types of studies. However, the replication built into a manipulative experiment in the form of repeated application of treatments is distinct from the replication of a mensurative study using repeated features of the environment. That these are different types of replication is stated in this paper, but I found no more detail or explanation than that. Pseudoreplication in this paper is discussed little in the context of independence of samples; rather the discussed risk is of attempting to draw inferences beyond the inference space of the study. This is a problem in both major types of study, and can be avoided by carefully determining and describing the inference space, and expanding that space by greater replication; too-small sample sizes are quite simply labeled as unpublishable in this paper, a sentiment I can agree with.

Determining the required sample size is a major issue for all types of studies. In this author’s presentation, this is an early step in the design of the study, after the biological and statistical questions have been established but before data collection begins. There is some discussion here as well of statistical power (the chance of avoiding a Type II error, that is of failing to reject a false null hypothesis) and recommendations of flexibility regarding especially alpha values (the chance of making a Type I error, that is of rejecting a null hypothesis that is not false). For a number of reasons, some of which are practical and logistical, alpha values larger than the ubiquitous 0.05 are encouraged, because in many cases the consequences of the 2 types of error are not even, and one may wish to concentrate on reducing the probability of a Type II error.

This paper describes 10 commonly-encountered study designs in soil science and related disciplines, and then discusses study-design concerns common to all such as replication and the need to clearly define study units, samples, populations, and other important aspects. Finally, this author presents the conclusions from all of these examples and considerations in the form of a short list of key recommendations. Quoting directly:

1. A clear definition of the research question is the initial (and most critical) step. This definition dictates the type of research design that is appropriate and the specific design issues associated with different research types.
2. The appropriateness of a given research design can be judged only after a thorough review of what is known about the research question. Exploratory pattern studies can be very informative at an early stage of research, but yield little new information for well-established research topics. Equally, the imposition of a set of treatments if little is known of the processes controlling responses is unlikely to produce comprehensive interpretations.
3. There is never a good reason for haphazard sampling – the rationale for selecting sampling points in pedological, soil geomorphic, or inventory studies should be clearly stated.
4. A clear definition of the population and the elements that comprise the population under study is very important.
5. The definition of the population dictates the extent of the study and the physical or temporal space that the results pertain to, which is critical to avoid pseudoreplication.
6. The sample support, spacing, and extent of the study must be consistent with what is known of the processes controlling the phenomena being studied.
7. The construction of hypotheses for formal testing should be based on sound physical or biological reasoning, and sufficient samples should be taken to allow reliable testing of the alternative hypotheses.
8. The exclusion of phenomena because they cannot be replicated is inherently limiting to the expansion of our knowledge of soils. Innovative approaches must continue to be developed and applied so that we can expand the scale at which field studies can be undertaken.

Bremer et al. 2009

Bremer C, Braker G, Matthies D, Beierkuhnlein C, Conrad R. 2009. Plant presence and species combination, but not diversity, influence denitrifier activity and the composition of nirk-type denitrifier communities in grassland soil. FEMS Microbiology and Ecology 70: 377-387.

These authors ran a manipulative, common-garden experiment to examine interactions between surface-plant community and soil denitrifier community diversities. The major finding of this paper, as described in the title, is that denitrifier community diversity is influenced by the species of plants in the system, but not how many species are there.

I saw 2 major problems with this paper that calls their major finding into question at least in my mind. First, the single greatest effect on denitrifier community composition found here was the very distinct community in the control, no-plant plots. These authors never acknowledge that zero plants is a point on their spectrum of species diversity; they state their range of plant community species richness was 2 to 8, but it was actually 0 to 8, with a strong effect of the 0 community. They do not analyze their data in this way that I can see, so I do not know if this 0-community effect does or does not reverse their conclusion that plant species diversity does not influence denitrifier diversity.

Second, the single most distinct with-plants community that was included in analysis is probably an outlier and should be excluded, because the plant community was 2 species of grasses (rather than mixed grass / forb), which also had the highest productivity in one of the study years. Furthermore that year was a high-temperature drought year across much of Europe including the study site. This probably-outlier result is acknowledged by these authors as having some of these problems, but their discussion of the probable role of greater niche-space in the soil under the more-diverse-but-same-species-richness plots does not suggest they have considered the confounding effects.

Despite the suspect results, this paper provides a useful overview, especially in the introduction and large parts of the discussion sections of denitrifier communities in soils and their probable interactions with plant communities.

Bedard-Haughn et al. 2006

Bedard-Haughn A, Matson AL, Pennock DJ. 2006. Land use effects on gross nitrogen mineralization, nitrification, and N2O emissions in ephemeral wetlands. Soil Biology and Biochemistry 38: 3398-3406.

These authors used a combination of stable-isotope and measurements of chemical pools and emissions from soils techniques to examine the role of various microbial-mediated processes in contributing to N2O production in an agricultural landscape. N2O emissions are the result of a complicated suite of metabolic activity in soils, with local oxygen concentrations, driven by soil moisture, and concentrations of reactants in these chemical pathways both contributing to net processes.

In the Canadian prairies, gross N2O production is positively correlated with soil moisture, with the highest emissions associated with lower-slope and wetland soils. This is consistent with the major contributing process in N2O emissions being denitrification, the process that reduces NO3- under anaerobic conditions. However, nitrification, the production of NO3- from NH4+, has also been observed to contribute to N2O emissions, especially from drier and aerobic soils.
Simple measurements of NO3- and NH4+ concentrations in soils will not capture information about the processes cycling N between these and other pools of soil matter. Used in conjunction with measurements of those processes, such as the 15N technique used here, does provide information about the factors controlling those processes. In this case, little variation through time or space in either pool combined with patterned variation in N2O emission and 15N movements allowed these authors to infer that both nitrification and denitrification are not limited by the substrate pools, despite the quite different other aspects of these processes.

This paper provides a very detailed description of the 15N procedures used, as well as a clear discussion of the various N-cycling processes in soils.

Zuur et al. 2010

Zuur AF, Ieno EN, Elphick CS. 2010. A protocol for data exploration to avoid common statistical problems. Methods in Ecology & Evolution doi: 10.1111/j.2041-210X.2009.00001.x

These authors present a step-by-step guide and recommendations for data exploration, a procedure in analysis of statistical data that should be carried out before primary statistical techniques such as regression. The point of data exploration is to look for errors in measurement, calculation or data-entry, to remove outliers, and to ensure no critical assumptions are being violated. Data exploration is not an instantaneous process, and may take up to 50% of the time spent on data analysis.

Their Figure 1 shows the steps in data exploration. Not all steps need be conducted for every dataset, for example, PCA is not sensitive to normal distribution, so the construction of histograms to evaluate normality is not necessary. On the other hand, almost all statistical techniques are very sensitive to violations of the assumption of independence.

(To avoid potential copyright issues, I have not pasted Fig. 1 from the paper here)

Figure 1 from Zuur et al. (2010). The procedures in italics are described in detail in this paper.
This paper was assigned reading for a course I am taking, Plant Sciences 813, Statistical Methods in the Life Sciences. I think the advice and instructions here will be useful.

Michalyna 1971

Michalyna W. 1971. Distribution of various forms of aluminum, iron and manganese in the orthic gray wooded, gleyed orthic gray wooded and related gleysolic soils in Manitoba. Canadian Journal of Soil Science 51: 23-36.

This author examined soil Al, Fe, and Mn in some poorly drained soils of the Gleysolic order in western Manitoba, looking for indicators for soil classification that would cover some of the deficiencies of the previous criteria. The distribution of these metals, including the ratio of oxalate-extractable to dithionite-extractable iron representing amorphous and total Fe(III)-oxide forms, respectively, was a useful criterion for classification.

Iron of all types was concentrated in the upper horizons of these soils, with the highest levels in the BA and B horizons. The ratio of amorphous to total Fe(III) was also highest in these horizons, and declined with depth. This suggests amorphous Fe(III) is most abundant in relatively oxidizing conditions in these wet soils. Water content is not reported, except to note that some soils are “imperfectly drained”, others are “poorly drained”.

The ratio of amorphous to total Fe(III) found in these soils ranged from about 0.1 to about 1.2, with most measurements between 0.4 and 0.8. My own measurements, converted to the same ratio, range between 0.14 and 0.83.

Howarth 1979

Howarth RW. 1979. Pyrite: Its rapid formation in a salt marsh and its importance in ecosystem metabolism. Science 203: 49-51.

This author investigated sulfur and iron dynamics in a salt marsh in the United States. Previous work by other authors had suggested pyrite (FeS2), one end-product in sulfur reduction, forms slowly over years or decades in marine sediments. This paper includes an experiment involving buried Teflon bags in which pyrite formation was detected after 48 hours. From this and other measurements, an estimate of total marshland bacterial sulfur-driven respiration was formed that is of a similar magnitude in CO2 release as is total net productivity of the marshland.

Iron metabolism in this system involves formation of amorphous iron compounds under oxidizing conditions, with a predominance of crystalline forms only under more reducing conditions.

Soulides and Allison 1961

Soulides DA, Allison FE. 1961. Effect of drying and freezing soils on carbon dioxide production, available mineral nutrients, aggregation, and bacterial population. Soil Science 91: 291-298.

These authors conducted a series of experiments to investigate previously reported claims of a burst of CO2 production following drying or freezing of soils, with an associated change in soil bacterial populations. Drying soils killed large fractions of bacterial populations; freezing as well, to a lesser extent. Combined drying and freezing killed many bacteria, but did not sterilize soils. CO2 production was raised 20 to 40% over controls following drying and rewetting under different schemes, which these authors attribute to rapid breakdown of organic material by large numbers of bacterial cells in an early growth phase; CO2 production declines as populations stabilize.

I was hoping this paper would provide clues to the soil water levels tolerable by bacteria, but these authors do not discuss critical levels of moisture or temperature, beyond noting that severe drying is detrimental, and temperatures below 2ºC prevent most bacterial growth.