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A modern pollen dataset from China and Mongolia (18-52 degrees N, 74-132 degrees E) is investigated for its potential use in climate reconstructions. The dataset includes 2559 samples, 229 terrestrial pollen taxa and four climatic variables - mean annual precipitation (P-ann): 35-2091 mm, mean annual temperature (T-ann): -12.1-25.8 degrees C, mean temperature in the coldest month (Mt(co).): -33.8-21.7 degrees C, and mean temperature in the warmest month (Mt(wa)): 03-29.8 degrees C. Modern pollen-climate relationships are assessed using canonical correspondence analysis (CCA), Huisman-Olff-Fresco (HOF) models, the modern analogue technique (MAT), and weighted averaging partial least squares (WA-PLS). Results indicate that P-ann is the most important climatic determinant of pollen distribution and the most promising climate variable for reconstructions, as assessed by the coefficient of determination between observed and predicted environmental values (r(2)) and root mean square error of prediction (RMSEP). Mt(co) and Mt(wa) may be reconstructed too, but with caution. Samples from different depositional environments influence the performance of cross-validation differently, with samples from lake sediment-surfaces and moss polsters having the best fit with the lowest RMSEP. The better model performances of MAT are most probably caused by spatial autocorrelation. Accordingly, the WA-PLS models of this dataset are deemed most suitable for reconstructing past climate quantitatively because of their more reliable predictive power. (C) 2014 Elsevier B.V. All rights reserved.
AimFossil pollen spectra from lake sediments in central and western Mongolia have been used to interpret past climatic variations, but hitherto no suitable modern pollen-climate calibration set has been available to infer past climate changes quantitatively. We established such a modern pollen dataset and used it to develop a transfer function model that we applied to a fossil pollen record in order to investigate: (1) whether there was a significant moisture response to the Younger Dryas event in north-western Mongolia; and (2) whether the early Holocene was characterized by dry or wet climatic conditions.
LocationCentral and western Mongolia.
MethodsWe analysed pollen data from surface sediments from 90 lakes. A transfer function for mean annual precipitation (P-ann) was developed with weighted averaging partial least squares regression (WA-PLS) and applied to a fossil pollen record from Lake Bayan Nuur (49.98 degrees N, 93.95 degrees E, 932m a.s.l.). Statistical approaches were used to investigate the modern pollen-climate relationships and assess model performance and reconstruction output.
ResultsRedundancy analysis shows that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Spatial autocorrelation and significance tests of environmental variables show that the WA-PLS model for P-ann is the most valid function for our dataset, and possesses the lowest root mean squared error of prediction.
Main conclusionsPrecipitation is the most important predictor of pollen and vegetation distributions in our study area. Our quantitative climate reconstruction indicates a dry Younger Dryas, a relatively dry early Holocene, a wet mid-Holocene and a dry late Holocene.
Aim: Fossil pollen spectra from lake sediments on the Tibetan Plateau have been used for qualitative climate reconstruction, but no modern pollen-climate calibration set based on lake sediments is available to infer past climate quantitatively. This study aims to develop such a dataset and apply it to fossil data. Location: The Tibetan Plateau, between 30 and 40 degrees N and 87 and 103 degrees E. Methods: We collected surface sediments from 112 lakes and analysed them palynologically. The lakes span a wide range of mean annual precipitation (P-ann; 31-1022 mm), mean annual temperature (T-ann; -6.5 to 1 degrees C), and mean July temperature (T-July; 2.6-19.7 degrees C). Redundancy analysis showed that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Transfer functions for P-ann, T-ann and T-July were developed with weighted averaging partial least squares. Model performance was assessed by leave-one-out cross-validation. Results: The root mean square errors of prediction (RMSEP) were 104 mm (P-ann), 1.18 degrees C (T-ann) and 1.17 degrees C (T-July). The RMSEPs, when expressed as percentages of the gradient sampled, were 10.6% (P-ann), 15.7% (T-ann) and 11.9% (T-July). These low values indicate the good performance of our models. An application of the models to fossil pollen spectra covering the last c. 50 kyr yielded realistic results for Luanhaizi Lake in the Qilian Mountains on the north-eastern Tibetan Plateau (modern P-ann 480 mm; T-ann-1 degrees C). T-ann and P-ann values similar to present ones were reconstructed for late Marine Isotope Stage 3, with minimum values for the Last Glacial Maximum (c. 300 mm and 2 degrees C below present), and maximum values for the early Holocene (c. 70 mm and 0.5 degrees C greater than present). Main conclusions: The modern pollen-climate calibration set will potentially be useful for quantitative climate reconstructions from lake-sediment pollen spectra from the Tibetan Plateau, an area of considerable climatic and biogeographical importance.
We address the problem of belief change in (nonmonotonic) logic programming under answer set semantics. Our formal techniques are analogous to those of distance-based belief revision in propositional logic. In particular, we build upon the model theory of logic programs furnished by SE interpretations, where an SE interpretation is a model of a logic program in the same way that a classical interpretation is a model of a propositional formula. Hence we extend techniques from the area of belief revision based on distance between models to belief change in logic programs.
We first consider belief revision: for logic programs P and Q, the goal is to determine a program R that corresponds to the revision of P by Q, denoted P * Q. We investigate several operators, including (logic program) expansion and two revision operators based on the distance between the SE models of logic programs. It proves to be the case that expansion is an interesting operator in its own right, unlike in classical belief revision where it is relatively uninteresting. Expansion and revision are shown to satisfy a suite of interesting properties; in particular, our revision operators satisfy all or nearly all of the AGM postulates for revision.
We next consider approaches for merging a set of logic programs, P-1,...,P-n. Again, our formal techniques are based on notions of relative distance between the SE models of the logic programs. Two approaches are examined. The first informally selects for each program P-i those models of P-i that vary the least from models of the other programs. The second approach informally selects those models of a program P-0 that are closest to the models of programs P-1,...,P-n. In this case, P-0 can be thought of as a set of database integrity constraints. We examine these operators with regards to how they satisfy relevant postulate sets.
Last, we present encodings for computing the revision as well as the merging of logic programs within the same logic programming framework. This gives rise to a direct implementation of our approach in terms of off-the-shelf answer set solvers. These encodings also reflect the fact that our change operators do not increase the complexity of the base formalism.