Reconstructing (super)trees from data sets with missing distances: Not all is lost

The wealth of phylogenetic information accumulated over many decades of biological research, coupled with recent technological advances in molecular sequence generation, present significant opportunities for researchers to investigate relationships across and within the kingdoms of life. However, to make best use of this data wealth, several problems must first be overcome. One key problem is finding effective strategies to deal with missing data. Here, we introduce Lasso, a novel heuristic approach for reconstructing rooted phylogenetic trees from distance matrices with missing values, for datasets where a molecular clock may be assumed. Contrary to other phylogenetic methods on partial datasets, Lasso possesses desirable properties such as its reconstructed trees being both unique and edge-weighted. These properties are achieved by Lasso restricting its leaf set to a large subset of all possible taxa, which in many practical situations is the entire taxa set. Furthermore, the Lasso approach is distance-based, rendering it very fast to run and suitable for datasets of all sizes, including large datasets such as those generated by modern Next Generation Sequencing technologies. To better understand the performance of Lasso, we assessed it by means of artificial and real biological datasets, showing its effectiveness in the presence of missing data. Furthermore, by formulating the supermatrix problem as a particular case of the missing data problem, we assessed Lasso's ability to reconstruct supertrees. We demonstrate that, although not specifically designed for such a purpose, Lasso performs better than or comparably with five leading supertree algorithms on a challenging biological data set. Finally, we make freely available a software implementation of Lasso so that researchers may, for the first time, perform both rooted tree and supertree reconstruction with branch lengths on their own partial datasets

Impact of United States Corn-Based Ethanol Production on Land Use

This study measures the impact of corn-based ethanol production in the United States on land use in other countries, or indirect land use. Indirect land use is a change from non-cropland to cropland (e.g. deforestation) that may occur in response to increasing scarcity of cropland. As farmers worldwide respond to higher crop prices in order to maintain the global food supply and demand balance, pristine lands are cleared and converted to new cropland to replace the crops for feed and food that were diverted elsewhere to biofuel production. The results show that increasing ethanol production in the US has a positive and significant relation to U.S corn price. However, U.S. corn price does not have a significant impact on changes in corn acreage in Brazil and other countries such as Canada, Japan and China. Although many authors have hypothesized that increased ethanol production in the U.S. will increase corn prices, which will result in increased change in land use in other countries, these results suggest that the effect is minimal at best. This is important because although production of ethanol for fuel is often criticized for negatively impacting the environment because of indirect land use, this study was unable to prove the existence of indirect land use.ethanol, indirect land use, Agricultural and Food Policy, Demand and Price Analysis, Land Economics/Use, Marketing,

Land Use Implications of Expanding Biofuel Demand

The Renewable Fuel Standard mandates in the Energy Independence and Security Act of 2007 will require 36 billion gallons of ethanol to be produced in 2022. The mandates require that 16 of the 36 billion gallons must be produced from cellulosic feedstocks. The potential land use implications resulting from these mandates were examined using two methods, the POLYSYS model and a general equilibrium model. Results of the POLYSYS analysis indicated that 72.1 million tons of corn stover, 23.5 million tons of wheat straw, and 24.7 million acres would be used to produce 109 million tons of switchgrass in 2025 to meet the mandate. Results of the CGE analysis indicated that 10.9 billion bushels of corn grain, 71 million tons of corn stover, and 56,200 tons of switchgrass is needed to meet the mandate.cellulosic ethanol, corn stover, grain ethanol, renewable fuel standard, switchgrass, Crop Production/Industries, Demand and Price Analysis, Environmental Economics and Policy, Land Economics/Use, Resource /Energy Economics and Policy, Q15, Q42,