Modeling the Unique Attributes of Desert Ecosystems: Potentials and Limitations Based on Lessons from the Jornada Basin

JAMES F. REYNOLDS; PAUL R. KEMP; KIONA OGLE; ROBERTO J. FERNANDEZ; QIONG GAO; JIANGUO WU

Structure and Function of a Chihuahuan Desert Ecosystem: The Jornada Basin Long Term Ecological Research Site

Oxford University Press

Lugar: New York; Año: 2006; p. 321 - 353

In this chapter, we have emphasized the tenet that ecosystem dynamics of the Jornada Basin are complex. We have argued that traditional, simple linear paradigms have limitations for understanding the unique attributes and dynamics of these ecosystems. We suggest that this uniqueness derives from complex interactions and feedbacks among the components of the ecosystem. Thus, we employ a hierarchical, process-based modeling methodology to identify and tease apart the underlying mechanisms that govern principal interactions, which we believe to be among the most important determinants of the transient states that characterize these and other dryland ecosystems. In this chapter, we summarized important take-home lessons regarding aspects of ecosystem dynamics learned at various hierarchical levels—from the leaf and plant, to the patch and landscape scale. We now turn to the limitations of this modeling approach. Model formulation is based on assumptions and conceptualizations about how systems behave and which factors, relationships, and interactions are important in governing the structure and function of that system. Models depend upon a basic level of system understanding that is built upon by testing, evaluation, and reformulation. Significant gaps remain in our knowledge concerning how dryland plants and ecosystems function and especially how they may respond to the human-induced disturbances now imposed upon them. As Reynolds et al. (2001) have noted, this presents an interesting paradox. On the one hand, an empirical approach to the problem is not only impractical but potentially misleading. Natural and human disturbances may affect plants and ecosystems in many unknown and complex ways, and it is impossible to design more than a small fraction of the many combinations of experiments needed to sort out the plethora of interactions likely to occur in the future. Technological and logistic restrictions usually result in most experiments being designed at relatively small spatial scales and over short periods of time, so we are unable to observe long-term changes that may lead to new feedbacks, homeostasis or shifts in species. On the other hand, there is a danger that, in the absence of full understanding, untested hypotheses can readily become incorporated into models (and assumed valid), and important processes will be ignored with unknown and potentially large consequences (Reynolds et al. 1996a). This result is particularly true for complex ecosystems characterized by slow or potentially sudden rates of change, such as in arid lands. Attempts to predict future behavior of dryland ecosystems are operating at the frontier of what is amenable to the scientific method today. How do we proceed? Briefly, our approach, which has been described previously (Reynolds and Leadley 1992; Reynolds et al. 1993; Reynolds et al. 1997; Reynolds and Wu 1999; Reynolds et al. 2001) is to avoid complex, all-purpose models. Rather, we suggest the development of mechanistic models to fully explore processes at lower hierarchical levels, which can then be scaled up to higher levels through a filtering or simplification process (examples in Reynolds et al. 1993). The crucial aspect of this process is the validation of model against observations across the spatial and temporal hierarchy of interest. The long-term studies and observations exemplified by the Jornada Basin LTER program offer tremendous opportunity in the case of dryland ecosystems for this validation process. In spite of these limitations, we must continue to develop models to address the questions being posed by resource managers and policymakers at local, regional and global scales. Current models represent an uneven blend of state-of-the-art knowledge and assumptions based on both practical and theoretical considerations as well as educated guesses. It is important to recognize that while we should not necessarily trust models to accurately predict dryland responses to climatic change or other human perturbations, model simulations can serve as sensitivity experiments for an indication of how sensitive drylands may be to projected human impacts. As our knowledge improves, the models will improve. By linking models with emerging technologies, like remote sensing and geographical information systems, we will be able to make predictions of environmental-biotic interactions under differing sets of assumptions and climatic change scenarios that will be useful to policymakers.