Is a single item stress measure independently associated with subsequent severe injury: a prospective cohort study of 16,385 forest industry employees

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.

New age estimates and climatostratigraphic correlations for the Borrobol and Penifiler Tephras: evidence from Abernethy Forest, Scotland

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.

Étude des impacts écologiques du dynamisme spatio-temporel des habitats naturels sur la faune menacée du Complexe Zones Humides Mahavavy-Kinkony, Madagascar.

This study of the Mahavavy-Kinkony Wetland Complex (MKWC) assesses the impacts of habitat change on the resident globally threatened fauna. Located in Boeny Region, northwest Madagascar, the Complex encompasses a range of habitats including freshwater lakes, rivers, marshes, mangrove forests, and deciduous forest. Spatial modelling and analysis tools were used to (i) identify the important habitats for selected, threatened fauna, (ii) assess their change from 1950 to 2005, (iii) detect the causes of change, (iv) simulate changes to 2050 and (v) evaluate the impacts of change. The approach for prioritising potential habitats for threatened species used ecological science techniques assisted by the decision support software Marxan. Nineteen species were analysed: nine birds, three primates, three fish, three bats and one reptile. Based on knowledge of local land use, supervised classification of Landsat images from 2005 was used to classify the land use of the Complex. Simulations of land use change to 2050 were carried out based on the Land Change Modeler module in Idrisi Andes with the neural network algorithm. Changes in land use at site level have occurred over time but they are not significant. However, reductions in the extent of reed marshes at Lake Kinkony and forests at Tsiombikibo and Marofandroboka directly threaten the species that depend on these habitats. Long term change monitoring is recommended for the Mahavavy Delta, in order to evaluate the predictions through time. The future change of Andohaomby forest is of great concern and conservation actions are recommended as a high priority. Abnormal physicochemical properties were detected in lake Kinkony due to erosion of the four watersheds to the south, therefore an anti-erosion management plan is required for these watersheds. Among the species of global conservation concern, Sakalava rail (Amaurornis olivieri), Crowned sifaka (Propithecus coronatus) and dambabe (Paretroplus dambabe) are estimated the most affected, but at the site level Decken’s sifaka (Propithecus deckeni), kotsovato (Paretroplus kieneri) and Madagascan big-headed turtle (Erymnochelys madagascariensis) are also threatened. Local enforcement of national legislation on hunting means that MKWC is among the sites where the flying fox (Pteropus rufus) and Madagascan rousette (Rousettus madagascariensis) are well protected. Ecological restoration, ecological research and actions to reduce anthropogenic pressures are recommended.

Predicting forest attributes In southeast Alaska using artificial neural networks

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.