The selection of paleointensity data is a challenging, but essential step for establishing data reliability. There is, however, no consensus as to how best to quantify paleointensity data and which data selection processes are most effective. To address these issues, we begin to lay the foundations for a more unified and theoretically justified approach to the selection of paleointensity data. We present a new compilation of standard definitions for paleointensity statistics to help remove ambiguities in their calculation. We also compile the largest-to-date data set of raw paleointensity data from historical locations and laboratory control experiments with which to test the effectiveness of commonly used sets of selection criteria. Although most currently used criteria are capable of increasing the proportion of accurate results accepted, criteria that are better at excluding inaccurate results tend to perform poorly at including accurate results and vice versa. In the extreme case, one widely used set of criteria, which is used by default in the ThellierTool software (v4.22), excludes so many accurate results that it is often statistically indistinguishable from randomly selecting data. We demonstrate that, when modified according to recent single domain paleointensity predictions, criteria sets that are no better than a random selector can produce statistically significant increases in the acceptance of accurate results and represent effective selection criteria. The use of such theoretically derived modifications places the selection of paleointensity data on a more justifiable theoretical foundation and we encourage the use of the modified criteria over their original forms.
We present results for a series of hysteresis measurements that provide information about remanent, induced, transient‐free, and transient magnetization components. These measurements, and differences between measurement types, enable production of six types of first‐order reversal curve (FORC)‐like diagrams which only double the number of measurements involved in a conventional FORC measurement. These diagrams can be used to distinguish magnetic signatures associated with each domain state. When analyzing samples with complex magnetic mineral mixtures, the contrasting domain state signatures are mixed together in a traditional FORC diagram, but these signatures can be identified individually when using the various FORC diagrams discussed here. The ability to make different FORC measurements and to identify separately each magnetic component by investigating different magnetization types can provide much‐improved understanding of the information provided by FORC diagrams. In particular, the transient hysteresis FORC diagram provides a method to measure the nucleation field of magnetic vortices and domain walls. We provide a simple explanation for FORC results from natural multidomain samples that are not explained by conventional domain wall pinning models. We also provide software for processing the different types of FORC data.
The origin and evolution of magnetoreception, which in diverse prokaryotes and protozoa is known as magnetotaxis and enables these microorganisms to detect Earth’s magnetic field for orientation and navigation, is not well understood in evolutionary biology. The only known prokaryotes capable of sensing the geomagnetic field are magnetotactic bacteria (MTB), motile microorganisms that biomineralize intracellular, membrane-bounded magnetic single-domain crystals of either magnetite (Fe3O4) or greigite (Fe3S4) called magnetosomes. Magnetosomes are responsible for magnetotaxis in MTB. Here we report the first large-scale metagenomic survey of MTB from both northern and southern hemispheres combined with 28 genomes from uncultivated MTB. These genomes expand greatly the coverage of MTB in the Proteobacteria, Nitrospirae, and Omnitrophica phyla, and provide the first genomic evidence of MTB belonging to the Zetaproteobacteria and “Candidatus Lambdaproteobacteria” classes. The gene content and organization of magnetosome gene clusters, which are physically grouped genes that encode proteins for magnetosome biosynthesis and organization, are more conserved within phylogenetically similar groups than between different taxonomic lineages. Moreover, the phylogenies of core magnetosome proteins form monophyletic clades. Together, these results suggest a common ancient origin of iron-based (Fe3O4 and Fe3S4) magnetotaxis in the domain Bacteria that underwent lineage-specific evolution, shedding new light on the origin and evolution of biomineralization and magnetotaxis, and expanding significantly the phylogenomic representation of MTB.
The Earth's inner core grows by the freezing of liquid iron at its surface. The point in history at which this process initiated marks a step-change in the thermal evolution of the planet. Recent computational and experimental studies have presented radically differing estimates of the thermal conductivity of the Earth's core, resulting in estimates of the timing of inner-core nucleation ranging from less than half a billion to nearly two billion years ago. Recent inner-core nucleation (high thermal conductivity) requires high outer-core temperatures in the early Earth that complicate models of thermal evolution. The nucleation of the core leads to a different convective regime and potentially different magnetic field structures that produce an observable signal in the palaeomagnetic record and allow the date of inner-core nucleation to be estimated directly. Previous studies searching for this signature have been hampered by the paucity of palaeomagnetic intensity measurements, by the lack of an effective means of assessing their reliability, and by shorter-timescale geomagnetic variations. Here we examine results from an expanded Precambrian database of palaeomagnetic intensity measurements selected using a new set of reliability criteria. Our analysis provides intensity-based support for the dominant dipolarity of the time-averaged Precambrian field, a crucial requirement for palaeomagnetic reconstructions of continents. We also present firm evidence for the existence of very long-term variations in geomagnetic strength. The most prominent and robust transition in the record is an increase in both average field strength and variability that is observed to occur between a billion and 1.5 billion years ago. This observation is most readily explained by the nucleation of the inner core occurring during this interval; the timing would tend to favour a modest value of core thermal conductivity and supports a simple thermal evolution model for the Earth.
Grain size distribution (GSD) data are widely used in Earth sciences and although large data sets are regularly generated, detailed numerical analyses are not routine. Unmixing GSDs into components can help understand sediment provenance and depositional regimes/processes. End‐member analysis (EMA), which fits one set of end‐members to a given data set, is a powerful way to unmix GSDs into geologically meaningful parts. EMA estimates end‐members based on covariability within a data set and can be considered as a nonparametric approach. Available EMA algorithms, however, either produce suboptimal solutions or are time consuming. We introduce unmixing algorithms inspired by hyperspectral image analysis that can be applied to GSD data and which provide an improvement over current techniques. Nonparametric EMA is often unable to identify unimodal grain size subpopulations that correspond to single sediment sources. An alternative approach is single‐specimen unmixing (SSU), which unmixes individual GSDs into unimodal parametric distributions (e.g., lognormal). We demonstrate that the inherent nonuniqueness of SSU solutions renders this approach unviable for estimating underlying mixing processes. To overcome this, we develop a new algorithm to perform parametric EMA, whereby an entire data set can be unmixed into unimodal parametric end‐members (e.g., Weibull distributions). This makes it easier to identify individual grain size subpopulations in highly mixed data sets. To aid investigators in applying these methods, all of the new algorithms are available in AnalySize, which is GUI software for processing and unmixing grain size data.
Microbes that synthesize minerals, a process known as microbial biomineralization, contributed substantially to the evolution of current planetary environments through numerous important geochemical processes. Despite its geological significance, the origin and evolution of microbial biomineralization remain poorly understood. Through combined metagenomic and phylogenetic analyses of deep-branching magnetotactic bacteria from the Nitrospirae phylum, and using a Bayesian molecular clock-dating method, we show here that the gene cluster responsible for biomineralization of magnetosomes, and the arrangement of magnetosome chain(s) within cells, both originated before or near the Archean divergence between the Nitrospirae and Proteobacteria. This phylogenetic divergence occurred well before the Great Oxygenation Event. Magnetotaxis likely evolved due to environmental pressures conferring an evolutionary advantage to navigation via the geomagnetic field. Earth's dynamo must therefore have been sufficiently strong to sustain microbial magnetotaxis in the Archean, suggesting that magnetotaxis coevolved with the geodynamo over geological time.Archean | microbial biomineralization | magnetotaxis | magnetotactic bacteria | geodynamo
 Detecting and excluding non-ideal behavior during paleointensity experiments is critical to asserting the reliability of data. Our knowledge of detecting non-ideal behavior, in particular the influence of multidomain (MD) grains, has expanded considerably over the past decade and experimental procedures now commonly incorporate checks to detect the effects of MD behavior. However, many older studies were carried out before these checks were devised and provide no quantifiable means of testing for the presence of MD grains. An estimated one third of all entries in the most recent paleointensity database do not include some form of check for MD behavior. The reliability of these results is therefore questionable and can only hinder efforts to understand the evolution of the geomagnetic field and the geodynamo. I propose a simple phenomenological check that can be applied to previous studies, provided that the raw data are available, that will allow the exclusion of MD behavior and provide a means of identifying reliable data. The check is a quantification of the curvature, k, of data points on an Arai plot, a feature commonly associated with MD behavior. Analysis of paleointensity data from samples with known grain size indicates that this new parameter is significantly correlated with grain size and with the accuracy of the paleointensity estimates made from both limbs of the curved data. Analysis of 181 samples from five historical data sets indicates that k is significantly correlated with experimentally obtained MD and alteration check parameters, and the accuracy of the paleointensity estimate. A threshold selection value of k ≤ 0.164 can be defined using the samples with known grain sizes. Applying this cut-off value, combined with a threshold on the quality of the circle fit and a commonly used alteration check, to the historical data yields an accurate result with low scatter. When compared with previously published selection criteria that incorporate experimental checks for non-ideal behavior, the result of applying the criteria proposed here is an improvement. The application of these three criteria rejects over 65% of all inaccurate results and has the highest concentration of accurate results when compared with the other criteria sets tested. Other selection criteria can be subsequently used to improve on this result. While modern studies should always include experimental checks to identify MD behavior, this new criterion will provide a useful tool for future studies and, importantly, a method to assess the reliability of previously published data.
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