Tropical limestone forest resilience and late Pleistocene foraging during MIS-2 in the Tràng An massif, Vietnam

In this paper we present a multi-proxy study of tropical limestone forest and its utilization by human groups during the major climatic and environmental upheavals of MIS-2 (29-11.7 kBP). Our data are drawn from new field research within the Tràng An World Heritage property on the edge of the Red River Delta, northern Vietnam. Key findings from this study include 1) that limestone forest formations were resilient to the large-scale landscape transformation of the Sunda continent at the end of the last glaciation; 2) that prehistoric human groups were probably present in this habitat through-out MIS-2; and 3) that the forested, insular, karst of Tràng An provided foragers with a stable resource-base in a wider changing landscape. These results have implications for our understanding of the prehistoric utilization of karst environments, and resonance for their conservation in the face of climate and environmental change today.

A 50,000-year record of Late Pleistocene tropical vegetation and human impact in lowland Borneo.

There has been considerable uncertainty about the nature of Pleistocene environments colonised by the first modern humans in Island SE Asia, and about the vegetation of the Last Glacial Maximum (LGM) in the region. Here, the palynology from a series of exposures in the Great Cave of Niah, Sarawak, Malaysian Borneo, spanning a period from ca. 52,000 to 5000 BP is described. Vegetation during this period was climate-driven and often highly unstable. Interstadials are marked by lowland forest, sometimes rather dry and at times by mangroves. Stadials are indicated by taxa characteristic of open environments or, as at the LGM, by highly disturbed rather open forest. Stadials are also characterised by taxa now restricted to 1000-1600 m above sea level, suggesting temperature declines of ca 7-9 C relative to present, by comparison with modern lapse rates. The practice of biomass burning appears associated with the earliest human activity in the cave.

The 'human revolution' in lowland tropical Southeast Asia:the antiquity and behavior of anatomically modern humans at Niah Cave (Sarawak, Borneo)

Recent research in Europe, Africa, and Southeast Asia suggests that we can no longer assume a direct and exclusive link between anatomically modern humans and behavioral modernity (the 'human revolution'), and assume that the presence of either one implies the presence of the other: discussions of the emergence of cultural complexity have to proceed with greater scrutiny of the evidence on a site-by-site basis to establish secure associations between the archaeology present there and the hominins who created it. This paper presents one such case study: Niah Cave in Sarawak on the island of Borneo, famous for the discovery in 1958 in the West Mouth of the Great Cave of a modern human skull, the 'Deep Skull,' controversially associated with radiocarbon dates of ca. 40,000 years before the present. A new chronostratigraphy has been developed through a re-investigation of the lithostratigraphy left by the earlier excavations, AMS-dating using three different comparative pre-treatments including ABOX of charcoal, and U-series using the Diffusion-Absorption model applied to fragments of bones from the Deep Skull itself. Stratigraphic reasons for earlier uncertainties about the antiquity of the skull are examined, and it is shown not to be an `intrusive' artifact. It was probably excavated from fluvial-pond-desiccation deposits that accumulated episodically in a shallow basin immediately behind the cave entrance lip, in a climate that ranged from times of comparative aridity with complete desiccation, to episodes of greater surface wetness, changes attributed to regional climatic fluctuations. Vegetation outside the cave varied significantly over time, including wet lowland forest, montane forest, savannah, and grassland. The new dates and the lithostratigraphy relate the Deep Skull to evidence of episodes of human activity that range in date from ca. 46,000 to ca. 34,000 years ago. Initial investigations of sediment scorching, pollen, palynomorphs, phytoliths, plant macrofossils, and starch grains recovered from existing exposures, and of vertebrates from the current and the earlier excavations, suggest that human foraging during these times was marked by habitat-tailored hunting technologies, the collection and processing of toxic plants for consumption, and, perhaps, the use of fire at some forest-edges. The Niah evidence demonstrates the sophisticated nature of the subsistence behavior developed by modern humans to exploit the tropical environments that they encountered in Southeast Asia, including rainforest. (c) 2006 Elsevier Ltd. All rights reserved.

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.