911 resultados para boreal forest
Resumo:
The integrated stratigraphic, radiocarbon and palynological record from an end-moraine system of the Oglio valley glacier (Italian Alps), propagating a lobe upstream in a lateral reach, provided evidence for a complete cycle of glacial advance, culmination and withdrawal during the Last Glacial Maximum and early Lateglacial. The glacier culminated in the end moraine shortly after 25.8 +/- 0.8 ka cal BP, and cleared the valley floor 18.3-17.2 +/- 0.3 ka cal BP. A primary paraglacial phase is then recorded by fast progradation of the valley floor.
As early as 16.7 +/- 0.3 ka cal BP, early stabilization of alluvial fans and lake filling promoted expansion of cembran pine. This is an unprecedented evidence of direct tree response to depletion of paraglacial activity during the early Lateglacial, and also documents the cembran pine survival in the mountain belt of the Italian Alps during the last glaciation. Between 16.1 and 14.6 +/- 0.5 ka cal BP, debris cones emplacement points to a moisture increase favouring tree Betula and Pinus sylvestris-mugo. A climate perturbation renewed paraglacial activity. According to cosmogenic ages on glacial deposits and AMS radiocarbon ages from lake records in South-Eastern Alps such phase compares favourably with the Gschnitz stadial and with the oscillations recorded at lakes Ragogna. Langsee and Jeserzersee, most probably forced by the latest freshening phases of the Heinrich Event 1.
A further sharp pine rise marks the subsequent onset of Bolling interstadial. The chronology of the Oglio glacier compares closely with major piedmont glaciers on the Central and Eastern Alpine forelands. On the other hand, the results of the present study imply a chronostratigraphic re-assessment of the recent geological mapping of the Central Italian Alps. (C) 2012 Elsevier Ltd. All rights reserved.
Resumo:
Background: A previous review showed that high stress increases the risk of occupational injury by three- to five-fold. However, most of the prior studies have relied on short follow-ups. In this prospective cohort study we examined the effect of stress on recorded hospitalised injuries in an 8-year follow-up.
Methods: A total of 16,385 employees of a Finnish forest company responded to the questionnaire. Perceived stress was measured with a validated single-item measure, and analysed in relation recorded hospitalised injuries from 1986 to 2008. We used Cox proportional hazard regression models to examine the prospective associations between work stress, injuries and confounding factors.
Results: Highly stressed participants were approximately 40% more likely to be hospitalised due to injury over the follow-up period than participants with low stress. This association remained significant after adjustment for age, gender, marital status, occupational status, educational level, and physical work environment.
Conclusions: High stress is associated with an increased risk of severe injury.
Resumo:
The emerging tephrostratigraphy of NW Europe spanning the last termination (ca. 15–9 ka) provides the potential for synchronizing marine, ice-core and terrestrial records, but is currently compromised by stratigraphic complications, geochemical ambiguity and imprecise age estimates for some layers. Here we present new tephrostratigraphic, radiocarbon and chironomid-based
palaeotemperature data from Abernethy Forest, Scotland, that refine the ages and stratigraphic positions of the Borrobol and Penifiler tephras. The Borrobol Tephra (14.14–13.95 cal ka BP) was deposited in a relatively warm period equated with Greenland Interstadial sub-stage GI-1e. The younger Penifiler Tephra (14.09–13.65 cal ka BP) is closely associated with a cold oscillation equated with GI-
1d. We also present evidence for a previously undescribed tephra layer that has a major-element chemical signature identical to the Vedde Ash. It is associated with the warming trend at the end of the Younger Dryas, and dates between 11.79 and 11.20 cal ka BP.
Resumo:
Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.