Johnson DL, Domier JEJ, Johnson DN. 2005. Reflections on the nature of soil and its biomantle. Annals of the Association of American Geographers 95: 11-31.
These authors advocate a new paradigm to underlie studies of soil science and related fields, based on increased recognition of processes occurring in and responsible for the production of the “biomantle”, the upper layer of soil composed of and formed by the actions of organisms. Many of these processes are based on movements of soil and soil components, and so are dominated by animals, especially active burrowers that are responsible for large vertical movements of soil in some environments. The biomantle is defined as being composed of “biofabric”, or materials that owe their existence to the actions of organisms, from the bodies of these organisms themselves, to the materials released by the organisms, to the minerals created by biological processes, to the voids created by their movements and the gases filling those voids released by their metabolisms.
These authors trace their ideas from the writings of Darwin, particularly his final work involving the activity of worms in “vegetable mould”, a late-18th century term for what we now call soil.
A biomantle layer, residing chiefly in the A horizon (or topsoil) can be more easily recognized in some landscapes than others. Humid tropical soils especially may show very thick and distinct biomantles, in two layers. The upper, thicker layer is composed of relatively fine materials, resting on a basal layer of coarser material; this basal layer is referred to here as the stonelayer. Below the stonelayer is non-biomantle, typically a B horizon (or subsoil). The hypothesized process creating this two-layer biomantle is the action of “conveyor belt” animals, especially termites that carry small particles upwards but are unable to move larger stones, thus eventually sorting the soil mineral material.
In other soils, such as loess-derived sandy soils without a large component of gravel and larger stones, such a two-layer biomantle may not form, or may be very weakly developed and difficult to identify as such. Nonetheless, bioturbation activity by burrowing animals is usually apparent, for example in the form of “krotovina”, in-filled animal burrows.
Besides advocating for a view of soils and their processes with an animal-based, biomantle point of view, these authors spend some time dismissing subaqueous soils (e.g. marine sediments) as simplistic, uncomplicated places lacking many of the key (and very complex) processes that occur in subaerial soils. Their list of such processes near the end of the paper, taken as a kind of justification for their uncited and unsupported dismissal of subaqueous soils, is composed entirely of those processes relating to changing water amounts in terrestrial soils, such as groundwater flow and wetting and drying events. I found their argument unconvincing, as they do not describe any aqueous-only processes such as changes in dissolved-O2 concentrations or the sorting action of water currents, and their blithe disregard for marine biodiversity in statements about how much more diverse the life in terrestrial soils must be, is the proverbial icing on the insult cake. Johnson et al.: please cite some evidence to support such sweeping generalizations.
Showing posts with label Concepts and Philosophy of Science. Show all posts
Showing posts with label Concepts and Philosophy of Science. Show all posts
Monday, August 16, 2010
Wednesday, February 24, 2010
Siciliano et al. 2007
Siciliano SD, Ma W, Powell S. 2007. Evaluation of quantitative polymerase chain reaction to assess nosZ gene prevalence in mixed microbial communities. Canadian Journal of Microbiology 53: 636-642.
These authors examined the usefulness of qPCR in studying populations of soil bacteria, especially denitrifiers using the gene nosZ that codes for nitrous oxide reductase. This enzyme catalyzes the final reaction in the process of denitrification, converting N2O to N2. Normally, it is expressed only in severely anaerobic conditions, as it allows the use of N2O as the terminal electron acceptor during metabolism.
There are a number of factors that control the efficiency of PCR in quantitative PCR applications. The efficiency is a major component of the calculations that allow qPCR to estimate gene copy numbers in samples and thus to be used to examine population dynamics of non-culturable microorganisms from environmental samples. Of particular importance is consistency of efficiency between the amplification of the standard DNA template and the amplification of all templates in the unknown samples. Variation between the standard and the unknowns can lead to severe under- or over-estimation of target populations, while variation in efficiency between different templates within the unknown samples can lead to misestimations of relative proportions of organisms.
These authors evaluated the efficiency of qPCR in a range of experimental templates, and in a range of combinations simulating mixed populations. Little variance in efficiency was found, and this variance was not associated with genetic distance from a reference organism. The experimental design did not allow a direct examination of the influence of the geographical differences in the sources of the test sequences (Arctic, temperate-grassland, Antarctic), but this lack of association with the reference organism does indicate low or no variation among PCR efficiencies associated with some other variable.
The influence of varying PCR efficiencies among templates within a sample becomes less severe as the number of different templates rises. In a typical soil sample with perhaps 1000 different templates, no one template can utterly dominate amplification by outcompeting for primers, thus the resulting mix of amplicons at the end of 40 rounds of PCR will most likely be representative of the population mixture in the environment.
This paper is of obvious high utility to my own work, not least because the individual machine used to perform qPCR is the same individual machine that I will be using. For this and other reasons, this paper was suggested to me, repeatedly. Future reference to this paper, when I am developing my methods and when I am writing up the next paper or two, seems likely.
These authors examined the usefulness of qPCR in studying populations of soil bacteria, especially denitrifiers using the gene nosZ that codes for nitrous oxide reductase. This enzyme catalyzes the final reaction in the process of denitrification, converting N2O to N2. Normally, it is expressed only in severely anaerobic conditions, as it allows the use of N2O as the terminal electron acceptor during metabolism.
There are a number of factors that control the efficiency of PCR in quantitative PCR applications. The efficiency is a major component of the calculations that allow qPCR to estimate gene copy numbers in samples and thus to be used to examine population dynamics of non-culturable microorganisms from environmental samples. Of particular importance is consistency of efficiency between the amplification of the standard DNA template and the amplification of all templates in the unknown samples. Variation between the standard and the unknowns can lead to severe under- or over-estimation of target populations, while variation in efficiency between different templates within the unknown samples can lead to misestimations of relative proportions of organisms.
These authors evaluated the efficiency of qPCR in a range of experimental templates, and in a range of combinations simulating mixed populations. Little variance in efficiency was found, and this variance was not associated with genetic distance from a reference organism. The experimental design did not allow a direct examination of the influence of the geographical differences in the sources of the test sequences (Arctic, temperate-grassland, Antarctic), but this lack of association with the reference organism does indicate low or no variation among PCR efficiencies associated with some other variable.
The influence of varying PCR efficiencies among templates within a sample becomes less severe as the number of different templates rises. In a typical soil sample with perhaps 1000 different templates, no one template can utterly dominate amplification by outcompeting for primers, thus the resulting mix of amplicons at the end of 40 rounds of PCR will most likely be representative of the population mixture in the environment.
This paper is of obvious high utility to my own work, not least because the individual machine used to perform qPCR is the same individual machine that I will be using. For this and other reasons, this paper was suggested to me, repeatedly. Future reference to this paper, when I am developing my methods and when I am writing up the next paper or two, seems likely.
Monday, February 22, 2010
Dandie et al. 2007
Dandie CE, Miller MN, Burton DL, Zebarth BJ, Trevors JT, Goyer C. 2007. Nitric oxide reductase-targeted real-time PCR quantification of denitrifier populations in soil. Applied and Environmental Microbiology 73: 4250-4258.
These authors examined the responses of two major components of the denitrifying bacteria fraction of soil bacteria to the addition of labile carbon (glucose) under denitrifying conditions. Denitrification is presented as a four-step process, with enzymes responsible for shuttling nitrate to N2 via nitrite, nitric oxide, and nitrous oxide. In this study, one of the enzymes responsible for the reduction of NO to N2O, cNOR, was examined using primers optimized for two different groups of denitrifying bacteria. This gene is found only in denitrifiers, unlike another enzyme, qNOR, found in many microorganisms and associated with detoxification, rather than utilization, of dangerous nitric oxide.
Primers for qPCR are presented in a table. Specific primers for the two variants of cNOR were developed in this study for use with SYBR green-based qPCR. 16s rRNA sequences were also studied, to examine the total population of soil bacteria; for these qPCR reactions, the TaqMan primers-plus-probe system was used, based on oligonucleotides published by Suzuki et al. 2000.
Two experiments were carried out. In the first, a preliminary experiment to establish the utility of qPCR in this area was based on inoculating soils with cultures of bacteria of known cell density, followed by qPCR evaluation of those soils. Under most conditions qPCR performed well, though at low cell densities of some genera of bacteria the signal was not distinguishable from the background noise also associated with sterilized soil. The second experiment forms the main body of work of this paper, and is an examination of the population dynamics of soil bacteria, divided into the hierarchical categories “denitrifiers” and “all bacteria”, under denitrifying conditions and with varying levels of added labile carbon in the form of glucose solutions in distilled water. In the second experiment, soil nitrate was maintained at a high level, to ensure sufficient raw material for detectable denitrification activity. As N2O accumulation was one of the measures of activity, nitrous oxide reductase activity that would reduce N2O to N2 was inhibited by maintaining an atmosphere of 10% acetylene in culture jars. Soils were maintained at 70% WFPS to encourage denitrification.
Total microbial biomass was also measured, using the CHCl3 fumigation-extraction technique. While cNOR sequences are almost certainly restricted to one copy per genome, 16s rRNA sequences may range in copy number up to 15 per genome, thus estimates of bacterial populations by qPCR of 16s may have a large error associated with it. Fumigation-extraction captures all carbon associated with cells, thus contributions by archaea and fungi will not be found by molecular methods such as 16s qPCR that are specific to bacteria. However, in this study, estimates of total bacterial population by the two methods were well correlated, with r2 = 0.69.
Denitrification occurred in this study. Soils treated with additional glucose showed greater depletion of nitrate, as expected when denitrifiers increase their activity in response to a food supply and conditions already favour denitrification. These authors provide two possible mechanisms, non-mutually-exclusive, that could lead to increased denitrification activity under added glucose. First, the population of denitrifiers could expand, through both additional cell replication and activation of dormant cells. This would increase the proportion of the bacterial population composed of denitrifiers. Second, the total population of soil organisms could increase, leading to increased respiration, a decrease in oxygenation, and establishment of anaerobic conditions more favourable for denitrification. This would not necessarily change the proportion of the population composed of denitrifiers. In this study, denitrifiers increased their proportion of the population as measured by comparative qPCR from less than 1% to about 2.4% of cell numbers.
This change in population components is central to the approach using qPCR advocated in this paper. As these authors state:
This paper is of great value to my studies. The qPCR methods are directly applicable, for example the primers presented here will be useful if I decide to examine multiple components of the denitrification pathway. The approach, as described above, is also useful. And the reference list is composed almost entirely of papers I am surprised I have not yet found in my literature searches.
These authors examined the responses of two major components of the denitrifying bacteria fraction of soil bacteria to the addition of labile carbon (glucose) under denitrifying conditions. Denitrification is presented as a four-step process, with enzymes responsible for shuttling nitrate to N2 via nitrite, nitric oxide, and nitrous oxide. In this study, one of the enzymes responsible for the reduction of NO to N2O, cNOR, was examined using primers optimized for two different groups of denitrifying bacteria. This gene is found only in denitrifiers, unlike another enzyme, qNOR, found in many microorganisms and associated with detoxification, rather than utilization, of dangerous nitric oxide.
Primers for qPCR are presented in a table. Specific primers for the two variants of cNOR were developed in this study for use with SYBR green-based qPCR. 16s rRNA sequences were also studied, to examine the total population of soil bacteria; for these qPCR reactions, the TaqMan primers-plus-probe system was used, based on oligonucleotides published by Suzuki et al. 2000.
Two experiments were carried out. In the first, a preliminary experiment to establish the utility of qPCR in this area was based on inoculating soils with cultures of bacteria of known cell density, followed by qPCR evaluation of those soils. Under most conditions qPCR performed well, though at low cell densities of some genera of bacteria the signal was not distinguishable from the background noise also associated with sterilized soil. The second experiment forms the main body of work of this paper, and is an examination of the population dynamics of soil bacteria, divided into the hierarchical categories “denitrifiers” and “all bacteria”, under denitrifying conditions and with varying levels of added labile carbon in the form of glucose solutions in distilled water. In the second experiment, soil nitrate was maintained at a high level, to ensure sufficient raw material for detectable denitrification activity. As N2O accumulation was one of the measures of activity, nitrous oxide reductase activity that would reduce N2O to N2 was inhibited by maintaining an atmosphere of 10% acetylene in culture jars. Soils were maintained at 70% WFPS to encourage denitrification.
Total microbial biomass was also measured, using the CHCl3 fumigation-extraction technique. While cNOR sequences are almost certainly restricted to one copy per genome, 16s rRNA sequences may range in copy number up to 15 per genome, thus estimates of bacterial populations by qPCR of 16s may have a large error associated with it. Fumigation-extraction captures all carbon associated with cells, thus contributions by archaea and fungi will not be found by molecular methods such as 16s qPCR that are specific to bacteria. However, in this study, estimates of total bacterial population by the two methods were well correlated, with r2 = 0.69.
Denitrification occurred in this study. Soils treated with additional glucose showed greater depletion of nitrate, as expected when denitrifiers increase their activity in response to a food supply and conditions already favour denitrification. These authors provide two possible mechanisms, non-mutually-exclusive, that could lead to increased denitrification activity under added glucose. First, the population of denitrifiers could expand, through both additional cell replication and activation of dormant cells. This would increase the proportion of the bacterial population composed of denitrifiers. Second, the total population of soil organisms could increase, leading to increased respiration, a decrease in oxygenation, and establishment of anaerobic conditions more favourable for denitrification. This would not necessarily change the proportion of the population composed of denitrifiers. In this study, denitrifiers increased their proportion of the population as measured by comparative qPCR from less than 1% to about 2.4% of cell numbers.
This change in population components is central to the approach using qPCR advocated in this paper. As these authors state:
“Although absolute numbers may not be achievable, gross differences and changes in population size are still detectable. The differences observed between the two denitrifier populations studied are then real differences in the responses of these populations to the conditions tested.”This general approach of examining relative changes in populations is applicable to a very wide array of studies of environmental microbiology, including my own planned studies in which the environmental factor under examination is biogeographical (i.e. latitude) and the functional diversity response is in terms of greenhouse gas cycling."
This paper is of great value to my studies. The qPCR methods are directly applicable, for example the primers presented here will be useful if I decide to examine multiple components of the denitrification pathway. The approach, as described above, is also useful. And the reference list is composed almost entirely of papers I am surprised I have not yet found in my literature searches.
Friday, February 19, 2010
Liptzin 2006
Liptzin D. 2006. A banded vegetation pattern in a High Arctic community on Axel Heiberg Island, Nunavut, Canada. Arctic, Antarctic, and Alpine Research 38: 216-223.
This author attempted to explain the observation of banded vegetation on a slope that lacked the usual factors that generate such patterns. In temperate and tropical locations, banded vegetation, also known as “tiger stripes”, forms on shallow slopes in dry areas with a consistent direction of water flow. Plants at a position on the slope increase water retention and facilitate further colonization by plants. Similarly, some locations experience consistent wind direction carrying sea spray that kills trees at some positions. In cold environments, patterned ground from cryoturbation on shallow slopes can also lead to banded vegetation. However, the study site in this paper lacks all of these features, including cryoturbation despite the presence of permafrost within 50cm at most locations.
Some aspect of soil properties is the obvious explanatory hypothesis, which this author explores after describing the transects measuring plant diversity and the soil pits used to examine soil properties. In general, features that would normally be expected to influence plant diversity and abundance such as soil moisture or exchangeable cation levels, had no significant impact in the various statistical tests employed in this study. However, soil type did have some effect, as a few species of plants were found only on sandy soil, and nitrogen levels were negatively correlated with species richness.
The discussion section of this paper is an excellent example of a chain of logical reasoning working through a series of potential explanations. While this paper is interesting, it’s only relevant to my own studies in a narrow area around potential starting points in looking for explanations for whatever patterns I may find in my biogeography studies in 2010. However, this paper seems remarkably suitable as an introduction to the basics of modern soil science research, and may be relevant to my not-quite-mothballed interest in an undergraduate course about the current state of the scientific literature.
This author attempted to explain the observation of banded vegetation on a slope that lacked the usual factors that generate such patterns. In temperate and tropical locations, banded vegetation, also known as “tiger stripes”, forms on shallow slopes in dry areas with a consistent direction of water flow. Plants at a position on the slope increase water retention and facilitate further colonization by plants. Similarly, some locations experience consistent wind direction carrying sea spray that kills trees at some positions. In cold environments, patterned ground from cryoturbation on shallow slopes can also lead to banded vegetation. However, the study site in this paper lacks all of these features, including cryoturbation despite the presence of permafrost within 50cm at most locations.
Some aspect of soil properties is the obvious explanatory hypothesis, which this author explores after describing the transects measuring plant diversity and the soil pits used to examine soil properties. In general, features that would normally be expected to influence plant diversity and abundance such as soil moisture or exchangeable cation levels, had no significant impact in the various statistical tests employed in this study. However, soil type did have some effect, as a few species of plants were found only on sandy soil, and nitrogen levels were negatively correlated with species richness.
The discussion section of this paper is an excellent example of a chain of logical reasoning working through a series of potential explanations. While this paper is interesting, it’s only relevant to my own studies in a narrow area around potential starting points in looking for explanations for whatever patterns I may find in my biogeography studies in 2010. However, this paper seems remarkably suitable as an introduction to the basics of modern soil science research, and may be relevant to my not-quite-mothballed interest in an undergraduate course about the current state of the scientific literature.
Wednesday, February 3, 2010
Wardle and Nilsson 1997
Wardle DA, Nilsson M-C. 1997. Microbe-plant competition, allelopathy and arctic plants. Oecologia 109: 291-293.
These authors critique Michelsen et al. (1995), a study that came to several important conclusions regarding the interactions between Arctic plants and the soil microbial communities. This is a very negative review of that paper, in which these authors question almost all of the conclusions of Michelsen et al. (1995).
These authors make two main criticisms. First, they question the measures of soil microbial activity used by the earlier paper. Second, they question the conclusions regarding the allelopathy of Empetrum hermaphroditum. Soil microbial activity was measured by Michelsen et al. (1995) in two ways: soil respiration, and soil ergosterol content. Neither approach is necessarily informative about one of Michelsen et al.’s (1995) main claims, that soil microbial biomass was increased by the addition of plant leaf extracts. There are a number of studies, many of them with Nilsson as a co-author, in which a lack of association between microbial biomass and soil respiration was demonstrated. Furthermore, ergosterol is presented by Michelsen et al. (1995) as an indicator of fungal biomass, but previous work by Newell and colleagues (e.g. Newell and Fallon, 1991; Newell 1992) showed that ergosterol is not a reliable indicator of biomass nor is it useful as a proxy measure of soil fungal activity; the ratio of ergosterol to fungal biomass is highly variable.
From the reference list in this short paper, it appears that Wardle and Nilsson had, by early 1997, completed a considerable body of work regarding the allelopathic and other ecological interactions of E. hermaphroditum in sub-arctic environments. The conclusion by Michelsen et al. (1995) that the chemicals released by this plant have a greater impact on microbial communities than potential surface-plant competitors is not supported by this work by Wardle, Nilsson, and their colleagues.
The conclusion of Michelsen et al. (1995) that currently has the most direct bearing on my own work is that key plant traits often possessed by prostrate shrubs in tundra ecosystems such as a high root: shoot ratio and storage of nutrients such as nitrogen in the roots allow those plants to escape from or outcompete soil microorganisms. This conclusion was not addressed by these authors, but given the devastation inflicted upon the other conclusions, my confidence in the utility of Michelsen et al. (1995) in addressing issues of interactions between Cassiope tetragonal and soil microbial communities has been shaken.
These authors critique Michelsen et al. (1995), a study that came to several important conclusions regarding the interactions between Arctic plants and the soil microbial communities. This is a very negative review of that paper, in which these authors question almost all of the conclusions of Michelsen et al. (1995).
These authors make two main criticisms. First, they question the measures of soil microbial activity used by the earlier paper. Second, they question the conclusions regarding the allelopathy of Empetrum hermaphroditum. Soil microbial activity was measured by Michelsen et al. (1995) in two ways: soil respiration, and soil ergosterol content. Neither approach is necessarily informative about one of Michelsen et al.’s (1995) main claims, that soil microbial biomass was increased by the addition of plant leaf extracts. There are a number of studies, many of them with Nilsson as a co-author, in which a lack of association between microbial biomass and soil respiration was demonstrated. Furthermore, ergosterol is presented by Michelsen et al. (1995) as an indicator of fungal biomass, but previous work by Newell and colleagues (e.g. Newell and Fallon, 1991; Newell 1992) showed that ergosterol is not a reliable indicator of biomass nor is it useful as a proxy measure of soil fungal activity; the ratio of ergosterol to fungal biomass is highly variable.
From the reference list in this short paper, it appears that Wardle and Nilsson had, by early 1997, completed a considerable body of work regarding the allelopathic and other ecological interactions of E. hermaphroditum in sub-arctic environments. The conclusion by Michelsen et al. (1995) that the chemicals released by this plant have a greater impact on microbial communities than potential surface-plant competitors is not supported by this work by Wardle, Nilsson, and their colleagues.
The conclusion of Michelsen et al. (1995) that currently has the most direct bearing on my own work is that key plant traits often possessed by prostrate shrubs in tundra ecosystems such as a high root: shoot ratio and storage of nutrients such as nitrogen in the roots allow those plants to escape from or outcompete soil microorganisms. This conclusion was not addressed by these authors, but given the devastation inflicted upon the other conclusions, my confidence in the utility of Michelsen et al. (1995) in addressing issues of interactions between Cassiope tetragonal and soil microbial communities has been shaken.
Tuesday, January 26, 2010
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:
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.
Tuesday, January 19, 2010
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.
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.
Wednesday, January 6, 2010
Siciliano et al. 2009
Siciliano SD, Ma WK, Ferguson S, Farrell RE. 2009. Nitrifier dominance of Arctic soil nitrous oxide emissions arises to due fungal competition with denitrifiers for nitrate. Soil Biology and Biochemistry 41: 1104-1110.
These authors examined the nitrous oxide emissions, microbial communities, and some components of nitrogen cycling in soils from three landforms at Truelove Lowland, on Devon Island. Previous results (Ma et al. 2007) had indicated that Arctic nitrous oxide emissions are not sensitive to soil moisture, at least in the range of 50% to saturated water filled pore space. This study includes a series of incubations of soil samples at a range of temperatures similar to ambient conditions, and treatments to disrupt fungi or particular types of prokaryotes.
Large differences in community composition were found between the three landforms, with the highest biomass and fungi:bacteria ratio in the wet sedge meadow and lowest in the raised beach crest (the lower foreslope was intermediate by these measures). Competition between fungi and denitrifiers for soil nitrate pools was inferred as the mechanism allowing dominance of emitted N2O by nitrifiers; fungi and denitrifiers are busy scavenging every available electron acceptor starting with nitrate and running all the way down to N2 gas, so almost any N2O that escapes was generated by nitrifiers in conditions not favoured by either of the other major groups.
This paper serves to demonstrate the very complex nature of soil biology, especially regarding the multiple and interacting pathways that may produce or consume materials of interest such as N2O. The references in this paper should be useful for digging into this complexity.
These authors examined the nitrous oxide emissions, microbial communities, and some components of nitrogen cycling in soils from three landforms at Truelove Lowland, on Devon Island. Previous results (Ma et al. 2007) had indicated that Arctic nitrous oxide emissions are not sensitive to soil moisture, at least in the range of 50% to saturated water filled pore space. This study includes a series of incubations of soil samples at a range of temperatures similar to ambient conditions, and treatments to disrupt fungi or particular types of prokaryotes.
Large differences in community composition were found between the three landforms, with the highest biomass and fungi:bacteria ratio in the wet sedge meadow and lowest in the raised beach crest (the lower foreslope was intermediate by these measures). Competition between fungi and denitrifiers for soil nitrate pools was inferred as the mechanism allowing dominance of emitted N2O by nitrifiers; fungi and denitrifiers are busy scavenging every available electron acceptor starting with nitrate and running all the way down to N2 gas, so almost any N2O that escapes was generated by nitrifiers in conditions not favoured by either of the other major groups.
This paper serves to demonstrate the very complex nature of soil biology, especially regarding the multiple and interacting pathways that may produce or consume materials of interest such as N2O. The references in this paper should be useful for digging into this complexity.
Tuesday, September 15, 2009
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.
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.
Sunday, May 11, 2008
Miller et al. 2007
Miller KB, Alarie Y, Whiting MF. 2007. Description of the larva of Notaticus fasciatus (Coleoptera: Dytiscidae) associated with adults using DNA sequence data. Annals of the Entomological Society of America 100: 787-797.
These authors were able to associate some larvae collected in French Guiana to a species widespread in lowland South America that did not previously have described larvae. This represents the first description of larvae of the tribe Aubehydrini, though this tribe contains only one described genus with two possibly synonymous species. This is the last tribe in the subfamily Dytiscinae to be so described.
The general methods closely follow those of Miller et al. (2005), though there is little in the way of phylogenetic discussion because, as stated by the authors, this work forms part of the basis of ongoing and future projects to examine dytiscine phylogeny in detail.
The larvae were clearly identified as belonging to the genus Notaticus, and are most likely members of the species N. fasciatus, with sequence differences well below the usual 2% threshold relative to adults of that species. However, the adults used in the comparison came from geographically distant populations in Bolivia, and the sequences of the larvae are sister to the sequences of the adults, rather than nested within in the cladogram. Thus, it is possible though unlikely that these larvae are actually members of a different species within the genus, either N. confusus or some as-yet-undescribed species. In addition, the adults that have been described of the two species of Notaticus are morphologically very similar, and may actually represent members of one species.
These authors were able to associate some larvae collected in French Guiana to a species widespread in lowland South America that did not previously have described larvae. This represents the first description of larvae of the tribe Aubehydrini, though this tribe contains only one described genus with two possibly synonymous species. This is the last tribe in the subfamily Dytiscinae to be so described.
The general methods closely follow those of Miller et al. (2005), though there is little in the way of phylogenetic discussion because, as stated by the authors, this work forms part of the basis of ongoing and future projects to examine dytiscine phylogeny in detail.
The larvae were clearly identified as belonging to the genus Notaticus, and are most likely members of the species N. fasciatus, with sequence differences well below the usual 2% threshold relative to adults of that species. However, the adults used in the comparison came from geographically distant populations in Bolivia, and the sequences of the larvae are sister to the sequences of the adults, rather than nested within in the cladogram. Thus, it is possible though unlikely that these larvae are actually members of a different species within the genus, either N. confusus or some as-yet-undescribed species. In addition, the adults that have been described of the two species of Notaticus are morphologically very similar, and may actually represent members of one species.
Miller et al. 2005
Miller KB, Alarie Y, Wolfe GW, Whiting MF. 2005. Association of insect life stages using DNA sequences: the larvae of Philodytes umbrinus (Motschulsky) (Coleoptera: Dytsicidae). Systematic Entomology 30: 499-509.
These authors used DNA sequence data in the form of 806 bp of mtCOI, to associate unidentified larvae to a described species. The insects were collected from pools in the desert region of the Skeleton Coast of northern Namibia. The larvae were morphologically associated with one tribe of dytiscids, and were collected with adults and larvae of two other species in the tribe in the genus Laccophilus, but were larger-bodied than known larvae of that genus.
Between species differences in sequence ranged between 1.9 and 19.9%, similar to values reported for other insect species. Within species differences were between 0 and 0.82%, and the differences in sequences between the larvae and the adults of their assigned species was 0 to 0.14%, providing clear evidence of the association.
The authors provide a very detailed list of morphological characters that are diagnostic for larvae of this genus (Philodytes), and a longer and even more detailed list of characters diagnostic for the species (P. umbrinus). They state near the end of the paper that their goal was not to provide molecular diagnostic features, but to use DNA sequence data to work backwards to find diagnostic morphological features which had not previously been described. There is some additional discussion of the uses, abuses, and limits of DNA barcoding in a taxonomic context, with a final point that taxonomy without morphology would not be as interesting.
These authors used DNA sequence data in the form of 806 bp of mtCOI, to associate unidentified larvae to a described species. The insects were collected from pools in the desert region of the Skeleton Coast of northern Namibia. The larvae were morphologically associated with one tribe of dytiscids, and were collected with adults and larvae of two other species in the tribe in the genus Laccophilus, but were larger-bodied than known larvae of that genus.
Between species differences in sequence ranged between 1.9 and 19.9%, similar to values reported for other insect species. Within species differences were between 0 and 0.82%, and the differences in sequences between the larvae and the adults of their assigned species was 0 to 0.14%, providing clear evidence of the association.
The authors provide a very detailed list of morphological characters that are diagnostic for larvae of this genus (Philodytes), and a longer and even more detailed list of characters diagnostic for the species (P. umbrinus). They state near the end of the paper that their goal was not to provide molecular diagnostic features, but to use DNA sequence data to work backwards to find diagnostic morphological features which had not previously been described. There is some additional discussion of the uses, abuses, and limits of DNA barcoding in a taxonomic context, with a final point that taxonomy without morphology would not be as interesting.
Tuesday, February 19, 2008
Gerstein et al. 2007
Gerstein MB, Bruce C, Rozowksy JS, Zheng D, Du J, Korbel JO, Emanuelsson O, Zhang ZD, Weissman S, Snyder M. 2007. What is a gene, post-ENCODO? History and updated definition. Genome Research 17: 669-681.
These authors reacted to some of the findings of the ongoing ENCODE project by redefining “gene”. Their new definition of this sometimes-contentious word is “"A gene is a union of genomic sequences encoding a coeherent set of potentially overlapping functional products."
They explain in the text first, a brief history of changing definitions of “gene”, from the coining of the term around 1900 to just prior to the publication of the ENCODE consortium (The ENCODE Project Consortium, 2007). Second, they describe complications and phenomena discovered by ENCODE and other research that render the current definition problematic. Third, they describe the important criteria in determining a new definition. These criteria are described as “backwards compatible”, organism-independent, statement of a simple idea, practical, and compatible with other biological nomenclature. The new definition meets these criteria.
The new definition also raises a more difficult question about function, and defining function in a biochemical and molecular context. The hard part is “what does this gene do?”, which can be answered (or not) at multiple levels.
Much of this paper reads like a corrolary to a strongly (though not strictly) adaptationist view of evolution, in addition to its strong (though not exclusive) human focus. One particular minor annoyance is a description of Ohno’s 1972 work, coining the term “junk DNA”, as intrinsically dismissive of all function of non-coding elements; this is not actually what Ohno (1972) said. Other adaptationist-leaning-statements include reference to the potential for many (most?) unannotated transcripts to represent transcriptional “noise” only in passing, seemingly as an afterthought, though Tress et al. (2007) are cited; I have not yet read that paper.
Overall, this new, ENCODE-informed definition seems useful in most contexts that I am likely to encounter the term “gene”.
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