Extending the late holocene tephrochronology of the central Kenai Peninsula, Alaska

Tephrochronology, the reconstruction of past volcanic ash deposition, provides a valuable method for dating sediments and determining long-term volcanic history. Tephra layers are highly numerous in Alaska, but knowledge of their occurrence and distribution is incomplete. This study expands the regional tephrochronology for the Kenai Peninsula of southcentral Alaska by investigating the tephrostratigraphy of two peatland sites. We located seven visible tephras and seven microtephras and investigated the particle size and geochemistry of the visible tephras. Radiocarbon dates were used to estimate the timescale of each core. Geochemical comparison showed that the visible tephras originated from late Holocene eruptions of Augustine, Crater Peak-Mt. Spurr, and Hayes volcanoes. Some of the tephras had been documented previously, and these new findings expand their known range. Others represent eruptions not previously reported, including a Crater Peak-Mt. Spurr eruption around 430 cal. BP. The results provide new tephra data for the region, illustrate the spatial heterogeneity of tephra deposition, and show the potential of microtephras for expanding the regional tephra record

Disturbance and succession in early to mid-holocene northern english forests:Palaeoecological evidence for disturbance of woodland ecosystems by mesolithic hunter-gatherers

Forest succession can be monitored in the present, modelled for the future, but also reconstructed in the past on the records of forest history, including through the use of palaeo-ecological techniques. Longer-term records from pollen data can show changes over centennial and millennial timescales that are impacted by climate, migration or soil development. Having knowledge of previous phases of post-disturbance seral stages of woodland regeneration however, as after fire, can provide insights regarding successional process and function over short-term decadal timescales. The aim of this paper is to test the high-resolution pollen record as a source of new insights into processes of succession, assisted by the supplementary data of microscopic charcoal analyses. On short-term timescales, multiple phases of forest disturbance and then recovery have been identified in early to mid-Holocene peat records in northern England, many from the uplands but also from lowland areas. We identify and describe a typology of recovery patterns, including the composition and rate of recovery, and then test the processes and factors that impacted on different seral trajectories, concentrating on fire disturbance which might have had a natural origin, or might have been caused by pre-agricultural Mesolithic hunter-gatherers. Factors considered include the spatial location and intensity of the fire event, the duration of the disturbance phase, the structure and dynamics of the successional regeneration vegetation communities and the pre-disturbance tree cover. Data from examples of fire disturbance of woodland have been examined from both upland and lowland sites in northern England and indicate that they had different successional pathways after disturbance. Fire disturbances in the denser lowland forests were mostly single burn events followed by natural successions and regeneration to forest, whereas fire disturbances in the upland woods usually showed continued or repetitive fire pressure after the initial burning, arresting succession so that vegetation was maintained in a shrub phase, often dominated by Corylus, for an extended period of time until disturbance ceased. This creation of a kind of prolonged, almost plagioclimax, ‘fire-coppice’ hazel stage suggests controlled rather than natural successional pathways, and strongly suggests that Mesolithic foragers were the fire starters in the upland English woodlands where hazel was naturally common and could be maintained in abundance in later-stage successions, along with other edible plants, for human use. All post-fire seral stages would have been attractive to game animals, providing a reliable food source that would have been of great benefit to hunter-gatherer populations

Sorting Strategies in an Extensive Forage Utilization Beef Production System

One hundred sixty crossbred steer calves were stratified by weight and allotted into four groups to test three sorting strategies against an unsorted control to compare methods of sorting long yearling steers to decrease variation in carcass weight and fat thickness, increase pounds of carcass weight sold, and increase profitability. Sorting by weight upon entry into the feedlot or by weight and fat thickness at the end of the feeding period increased average carcass weight. No statistical differences in variation or profitability were found, although numerical differences were present

Sorting Strategies for Yearlings

One hundred sixty medium-framed English-cross steers were used in each year of a two-year study to determine effects of three sorting strategies on performance, carcass characteristics and profitability in an extensive beef production system. Sorting by weight before the grazing period or entering the feedlot decreased variation in carcass weight. Sorting by weight before the grazing period increased marbling scores and resulted in significantly higher premiums. However, no sorting strategy significantly increased carcass weight or improved profitability

Sorting Strategies for Yearlings

One hundred sixty medium-framed English-cross steers were used in each year of a two-year study to determine effects of three sorting strategies on performance, carcass characteristics and profitability in an extensive beef production system. Sorting by weight before the grazing period or entering the feedlot decreased variation in carcass weight. Sorting by weight before the grazing period increased marbling scores and resulted in significantly higher premiums. However, no sorting strategy significantly increased carcass weight or improved profitability

Testing peatland testate amoeba transfer functions: Appropriate methods for clustered training-sets

Transfer functions are widely used in palaeoecology to infer past environmental conditions from fossil remains of many groups of organisms. In contrast to traditional training-set design with one observation per site, some training-sets, including those for peatland testate amoeba-hydrology transfer functions, have a clustered structure with many observations from each site. Here we show that this clustered design causes standard performance statistics to be overly optimistic. Model performance when applied to independent data sets is considerably weaker than suggested by statistical cross-validation. We discuss the reasons for these problems and describe leave-one-site-out cross-validation and the cluster bootstrap as appropriate methods for clustered training-sets. Using these methods we show that the performance of most testate amoeba-hydrology transfer functions is worse than previously assumed and reconstructions are more uncertain

Significance testing testate amoeba water table reconstructions

Transfer functions are valuable tools in palaeoecology, but their output may not always be meaningful. A recently-developed statistical test ('randomTF') offers the potential to distinguish among reconstructions which are more likely to be useful, and those less so. We applied this test to a large number of reconstructions of peatland water table depth based on testate amoebae. Contrary to our expectations, a substantial majority (25 of 30) of these reconstructions gave non-significant results (P > 0.05). The underlying reasons for this outcome are unclear. We found no significant correlation between randomTF P-value and transfer function performance, the properties of the training set and reconstruction, or measures of transfer function fit. These results give cause for concern but we believe it would be extremely premature to discount the results of non-significant reconstructions. We stress the need for more critical assessment of transfer function output, replication of results and ecologically-informed interpretation of palaeoecological data

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p