Data on Distribution and Abundance: Monitoring for Research and Management

In the first chapter of this book we identified the interdependence of method, data and theory as an important influence on the progress of science. The first several chapters focused mostly on progress in theory, in the areas of integrating spatial and temporal complexity into ecological analysis, the emergence of landscape ecology and its transformation into a multi-scale gradient-based science. These chapters weaved in some discussion about the interrelationships between method and these theoretical approaches. In particular, we discussed how powerful computing, large spatial databases and GIS cross-fertilized ecological theory by enabling new kinds of analyses and new scopes of investigation. However, up to this point we have given relatively little attention to the third leg of this triad, data. This and following chapters focus explicitly on data. The next several chapters discuss the advances in broad-scale data collection and analysis enabled by remote sensing, molecular genomics and satellite GPS telemetry, and how these data have made fundamental contributions to virtually all branches of ecology, especially spatial ecology, landscape ecology, and global scale research. The goal of this chapter is to establish a framework for how data collection and management can best be designed to interact with modeling and analysis across both space and time. The chapter is divided into five sections. First, we discuss the fundamental importance of quality, large sample, spatially referenced, broadly distributed data for reliable inferences to advance research and to guide adaptive management. Second, we explore the challenge posed by limited quality, quantity and extent of data on species and environmental conditions over space and time and discuss the limitations this poses to effective monitoring to guide research and adaptive management. This then provides motivation for a discussion of the importance of monitoring resources themselves, in context and in particulate. We then introduce the concept of the four-dimensional monitoring data-cube, and argue that by collecting accurate data at a fine spatial scale and across large geo-