Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Biomass Supply Chains for Bioenergy and Biorefining highlights the emergence of energy generation through the use of biomass and the ways it is becoming more widely used. The supply chains that produce the feedstocks, harvest, transport, store, and prepare them for combustion or refinement into other forms of fuel are long and complex, often differing from feedstock to feedstock. Biomass Supply Chains for Bioenergy and Biorefining considers every aspect of these supply chains, including their design, management, socioeconomic, and environmental impacts. The first part of the book introduces supply chains, biomass feedstocks, and their analysis, while the second part looks at the harvesting, handling, storage, and transportation of biomass. The third part studies the modeling of supply chains and their management, with the final section discussing, in minute detail, the supply chains involved in the production and usage of individual feedstocks, such as wood and sugar starches, oil crops, industrial biomass wastes, and municipal sewage stocks. - Focuses on the complex supply chains of the various potential feedstocks for biomass energy generation - Studies a wide range of biomass feedstocks, including woody energy crops, sugar and starch crops, lignocellulosic crops, oil crops, grass crops, algae, and biomass waste - Reviews the modeling and optimization, standards, quality control and traceability, socioeconomic, and environmental impacts of supply chains