posal process. In some cases, these goals appear to be deceptively straightforward. For example, for the Comprehensive Everglades Restoration Plan (see Chapter 4), “The overarching objective of the Plan is the restoration, preservation, and protection of the South Florida ecosystem while providing for other water-related needs of the region, including water supply and flood protection” (Water Resources Development Act of 2000). However, enormous amounts of time and energy have been—and continue to be—invested to define what “restoration” constitutes, and what the end points might be. Major multidisciplinary scientific initiatives wrestle with integrating multiple objectives, such as understanding fundamental processes such as streamflow generation, investigating scaling relationships of observations over time and space, understanding behavior under extreme conditions, and developing new instrumentation. The more multidisciplinary the project the more difficult—and more critical—it is to establish one’s goals at the onset. In many cases, it is essential to allow the goals to change over time as new methods are developed, new ideas evolve, and new researchers add to both the needs and the capabilities of the project.
Building a strong, interdisciplinary team, when the project spans disciplines, is as essential as it is supremely challenging. As one participant in an NRC workshop expressed it, “A ‘multidisciplinary’ team is put together and they work in isolation until the very end, when they fight.” Some of the difficulties are neither scientific, nor institutional, but personal. Keys to success in putting together an interdisciplinary group include finding colleagues who work at institutions that have policies and practices that lower barriers to interdisciplinary scholarship, and are willing to “immerse themselves in the languages, cultures, and knowledge of their collaborators” (NRC, 2004).
Designing a project to achieve the goals set out, whether narrow or broad, specific or flexible, is the next step. An overall approach for the particular needs of interdisciplinary collaborations is described in Benda et al. (2002) as follows:
[T]he success of interdisciplinary collaborations among scientists can be increased by adopting a formal methodology that considers the structure of knowledge in cooperating disciplines. For our purposes, the structure of knowledge comprises five categories of information: (1) disciplinary history and attendant forms of available scientific knowledge; (2) spatial and temporal scales at which that knowledge applies; (3) precision (i.e., qualitative versus quantitative nature of understanding across different scales); (4) accuracy of predictions; and (5) availability of data to construct, calibrate, and test predictive models. By definition, therefore, evaluating a structure of knowledge reveals limitations in scientific understanding, such as what knowledge is lacking or what temporal or spatial scale mismatches exist among disciplines.