Predicting the impacts of climate change on the distribution of threatened forest-restricted birds in Madagascar

The greatest common threat to birds in Madagascar has historically been from anthropogenic deforestation. During recent decades, global climate change is now also regarded as a significant threat to biodiversity. This study uses Maximum Entropy species distribution modeling to explore how potential climate change could affect the distribution of 17 threatened forest endemic bird species, using a range of climate variables from the Hadley Center's HadCM3 climate change model, for IPCC scenario B2a, for 2050. We explore the importance of forest cover as a modeling variable and we test the use of pseudo-presences drawn from extent of occurrence distributions. Inclusion of the forest cover variable improves the models and models derived from real-presence data with forest layer are better predictors than those from pseudo-presence data. Using real-presence data, we analyzed the impacts of climate change on the distribution of nine species. We could not predict the impact of climate change on eight species because of low numbers of occurrences. All nine species were predicted to experience reductions in their total range areas, and their maximum modeled probabilities of occurrence. In general, species range and altitudinal contractions follow the reductive trend of the Maximum presence probability. Only two species (Tyto soumagnei and Newtonia fanovanae) are expected to expand their altitude range. These results indicate that future availability of suitable habitat at different elevations is likely to be critical for species persistence through climate change. Five species (Eutriorchis astur, Neodrepanis hypoxantha, Mesitornis unicolor, Euryceros prevostii, and Oriola bernieri) are probably the most vulnerable to climate change. Four of them (E. astur, M. unicolor, E. prevostii, and O. bernieri) were found vulnerable to the forest fragmentation during previous research. Combination of these two threats in the future could negatively affect these species in a drastic way. Climate change is expected to act differently on each species and it is important to incorporate complex ecological variables into species distribution models.

Glacial to paraglacial history and forest recovery in the Oglio glacier system (Italian Alps) between 26 and 15 ka cal BP

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.

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.

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.