Fred D. Singer – författare

Providing students with a solid understanding of core ecological concepts while explaining how ecologists raise and answer real-world questions, this second edition weaves together classic and cutting-edge case studies to bring the subject to life. It is fully updated throughout, including two chapters devoted to climate change ecology, along with extensive coverage of disease ecology, and has been designed specifically to equip students with the tools to analyze and interpret real data. Each chapter emphasizes the linkage between observations, ideas, questions, hypotheses, predictions, results, and conclusions. Additional summary sections describe the development and evolution of research programs in each of ecology's core areas, providing students with essential context. Integrated discussion questions, along with end-of-chapter questions, encourage active learning. These are supported by online resources including tutorials that teach students to use the R programming language for statistical analyses of data presented in the text.

Taking a fresh approach to integrating key concepts and research processes, this undergraduate textbook encourages students to develop an understanding of how ecologists raise and answer real-world questions. Four unique chapters describe the development and evolution of different research programs in each of ecology''s core areas, showing students that research is undertaken by real people who are profoundly influenced by their social and political environments. Beginning with a case study to capture student interest, each chapter emphasizes the linkage between observations, ideas, questions, hypotheses, predictions, results, and conclusions. Discussion questions, integrated within the text, encourage active participation, and a range of end-of-chapter questions reinforce knowledge and encourage application of analytical and critical thinking skills to real ecological questions. Students are asked to analyze and interpret real data, with support from online tutorials demonstrating the R programming language for statistical analysis.