Given the difficulty of separating the three Picea species—P. glauca, P. mariana, and P. rubens (white, black, and red spruce)—in the pollen record, little is known about their unique histories in eastern North America following deglaciation. Here we report the first use of a classification tree analysis (CART) to distinguish pollen grains of these species. It was successfully applied to fossil pollen from eight sites in Maine and one in Massachusetts. We focused on the late glacial/early Holocene (14,000 to 8000 cal yr B.P.) and the late Holocene (1400 cal yr B.P. to present)—the two key periods since deglaciation when Picea has been abundant in the region. The result shows a shift from a Picea forest of P. glauca and P. mariana in the late glacial to a forest of P. rubens and P. mariana in the late Holocene. The small number of P. rubens grains identified from the late glacial/early Holocene samples (<5%) suggests that that species was either absent or rare at most of the sites. The occurrence and distribution of the three species do not reveal any geographic or temporal trend during late glacial time, but the data suggest that they were distributed in local patches on the landscape. The results of this study indicate that the recent population expansion of Picea (1000 to 500 cal yr B.P.) was likely the first time since deglaciation that P. rubens was abundant in the region.

Glyphosate-resistant (GR) crops have been rapidly adopted in the United States and the evolution of GR weeds throughout the world has also been on the rise. With experience, weed scientists and crop advisers develop “intuition” on the basis of field history and current in-field conditions for predicting whether escaped weed biotypes may be herbicide resistant. However, there are no previous reports on the association of in-field crop management factors with the prediction of herbicide resistance. By using in-field survey data, we tested the accuracy of predicting glyphosate resistance in late-season horseweed escapes. We hypothesized that glyphosate resistance in late-season horseweed populations found in soybean fields could be predicted using in-field knowledge of crop residues and the appearance and distribution of weeds in the field. Field survey data were collected to determine the distribution and frequency of GR horseweed populations in Indiana soybean fields during September and October of 2003, 2004, and 2005. After the in-field survey, soil properties for sampled field locations were also collected from the U.S. Department of Agriculture Natural Resources Conservation Service Web Soil Survey. GR horseweed predictions used in-field presence of crop residues and the appearance, abundance, and distribution of weeds in the field. The significance of independent data factors were determined by chi-square statistics. The interactions and relative significance of multiple factors were modeled using classification and regression tree analysis. Our results indicated that the most important factor for predicting GR populations was the identification of an altered plant phenotype after injury from POST glyphosate. This was followed by crop rotation, field distribution, and the presence of other escaped weed species in the field in a model with a classification rate of 0.68.

Understanding the ecological distribution range of exotic trees in an arboreal ecosystem is essential to managing natural forest resources sustainably. Forest resource mapping can be applied as a powerful tool in the identification of forest resource threat patterns, and in monitoring ongoing changes associated with a landscape. This study offers an insight on Russian-olive and its impact on a spatially bound ecosystem, namely, Bismarck–Mandan Wildland–Urban Interface (BMWUI). Data from the National Agricultural Imagery Program collected in 2005 and 2010 and in situ reference data were used to estimate the potential habitat of Russian-olive using ArcGIS ArcInfo® 9.3 (ESRI, Redlands, CA). Russian-olive plants are discernible on aerial photographs with a fine spatial resolution because of silvery gray-green leaves in the upper strata of their canopies. Results showed that Russian-olive occupied 110 ha (272 acres) in BMWUI in 2005 and of that, 13 ha (12%) was in inundated habitats. In addition, Russian-olive in 2010 covered 125 ha within the BMWUI and of that, 25 ha (20%) was in inundated habitats. Russian-olive showed a close association with the silt loam and silty clay soil type, which occurs along the Missouri River floodplain. Our findings revealed that the species is well established in riparian habitats and other open habitats such as roadside and agricultural lands. There is a greater likelihood of lateral spread of Russian-olive throughout the BMWUI that may require active management to avert undesirable conservation impacts.

Picea is an important taxon in late-glacial pollen records from eastern North America, but little is known about which species of Picea were present. We apply a recently developed palynological method for discriminating the three Picea species in eastern North America to three records from New England. Picea glauca was dominant at ∼ 14,500–14,000 cal yr BP, followed by a transition to Picea mariana between ∼ 14,000 and 13,500 cal yr BP. Comparison of the pollen data with hydrogen isotope data shows clearly that this transition began before the beginning of the Younger Dryas Chronozone. The ecological changes of the late-glacial interval were not a simple oscillation in the position of a single species' range, but rather major changes in vegetation structure and composition occurring during an interval of variations in several environmental factors, including climate, edaphic conditions, and atmospheric CO2 levels.

A stochastic algorithm for bound-constrained global optimization is described. The method can be applied to objective functions that are nonsmooth or even discontinuous. The algorithm forms a partition on the search region using classification and regression trees (CART), which defines a region where the objective function is relatively low. Further points are drawn directly from the low region before a new partition is formed. Alternating between partition and sampling phases provides an effective method for nonsmooth global optimization. The sequence of iterates generated by the algorithm is shown to converge to an essential global minimizer with probability one under mild conditions. Nonprobabilistic results are also given when random sampling is replaced with points taken from the Halton sequence. Numerical results are presented for both smooth and nonsmooth problems and show that the method is effective and competitive in practice.

This paper deals with variable selection in regression and binary classificationframeworks. It proposes an automatic and exhaustive procedure which relies on the use ofthe CART algorithm and on model selection via penalization. This work, of theoreticalnature, aims at determining adequate penalties, i.e. penalties whichallow achievement of oracle type inequalities justifying the performance of the proposedprocedure. Since the exhaustive procedure cannot be realized when the number of variablesis too large, a more practical procedure is also proposed and still theoreticallyvalidated. A simulation study completes the theoretical results.

We deal with the problem of choosing a piecewise constant estimator of a regression function s mapping $\mathcal{X}$ into $\mathbb{R}$ .We consider a non Gaussian regression framework with deterministic design points, and we adopt the non asymptotic approach of model selection via penalization developed by Birgé and Massart. Given a collection of partitions of $\mathcal{X}$ , with possibly exponential complexity,and the corresponding collection of piecewise constant estimators, we propose a penalized least squares criterion which selects a partition whose associated estimator performs approximately as well as the best one, in the sense that its quadratic risk is close to the infimum of the risks. The risk bound we provide is non asymptotic.

The chance discovery of a chariot burial shows Iron Age Scotland to be in direct contact with the European continent.