Water Temperature Regime
6.4 Discussion and conclusions .1 General considerations
7.1.3 Modelling and adaptive management
Currently, there is a perception that "adaptive management has been more influential as an idea than as a practical means of gaining insight into the behaviour of ecosystems utilized and inhabited by humans" (Lee 1999). Criticism has been levelled at the adaptive management paradigm, for having had limited practical success (Johnson 1999b). While institutional barriers and inertia pose the greatest threat to its successful implementation (WaIters 1997, WaIters et al. 2000), additional criticisms include too much focus on the models while ignoring the problems, scale linkages between different models (WaIters 1997; Jewitt and Gorgens 2000b), and the definition of appropriate goals (Johnson 1999a).
Jewitt and Gorgens (2000a) highlight that for a fish modelling exercise that was part of the management programme of the Kruger National Park, the main challenge centred on issues of scale and interdisciplinary collaboration.
Models approximate the real world (Beven 2001), and therefore function as hypotheses or problem-solving tools (Starfield and Bleloch 1991; Starfield 1997) for stimulating thinking about a system. Hilborn and Mangel (1997) envisage four components to the modelling process, viz. a set of hypotheses; "good" data; a goodness of fit (how well the description of the world fits the observations); and numerical procedures to explore the goodness of fit of other models. Models should not be seen as the definitive understanding of a system, but rather as tools that help to expose gaps in the data, screen policy options (Waiterset al.
2000), and predict the probable consequences of management actions (Quinn 1998). This is of greatest significance under conditions where time is limited and systems are sensitive (Walters et al. 2000). Indeed, "models are nothing but simple theories about the cause of observed patterns" (Scheffer 1999). While model outputs may approximate the real world situation (i.e. there is significant correlation between observed and simulated data), it is important that the mechanisms underlying the model output are also correct (Scheffer 1999; Beven 2001; Snowling and Kramer 2001),which allows for greater confidence when transferring a model to another situation. A model is most useful to natural resource managers if its inputs can be coupled with different scenarios, and the outputs compared against a meaningful threshold.
Newton's first rule of hypothesizing, which states that "we are to admit no more causes of natural things such as are both true and sufficient to explain their appearances" (Forster 1998), and the application of Occam's razor! (Forster 1998), reinforce the idea of parsimony, and should guide model development.
Simple, pragmatic models that require relatively fewer parameters than complex models (Jeppesen and Iversen 1987) form a basis for more complex models that are designed to promote management. In this way, suites of relatively simple models can be added together into a "tool-box" of decision-making techniques. However, bringing together different models at different spatial and temporal scales is a daunting task, and involves finding common units, such as fish habitats (biotic models) and geomorphological units (abiotic models) (Jewitt and Gorgens 2000b). Lessons learned during the course of a research programme in the Kruger National Park that lasted several years were that in order to achieve efficient interdisciplinary collaboration, model detail may be sacrificed to
IOckham (1285-1347) -"Entities are not to be multiplied beyond necessity"
allow scientists from different disciplines to work within a common framework, at compatible scales, to understand broad-scale catchment patterns (Jewitt and Gorgens 2000a). Such an approach has also proved to be useful in highlighting certain management issues within the Colorado River ecosystem (Walters et al. 2000), where a suit of small models at multiple temporal and spatial scales were used to assist scientists and managers in "measuring" system response under different scenarios.
The idea of management as a "game" involving different roleplayers, can reveal important general patterns of system behaviour, as illustrated by the "Nonpoint" model developed by Carpenter et al. (1999). This simple system model involving few roleplayers, serves to show the interaction between fast and slow variables (multiple time scales), and illustrates that continual learning is crucial for maintaining sustainability in ecosystems.
Finally, model development should be driven by the objectives of the management programme, rather than the available data (Starfield and Bleloch 1991). "In a decision- making context, the ultimate test of a model is not how accurate or truthful it is, but only whether one is likely to make a better decision with it than without it" (Starfield 1997). Thus, the management objectives define the temporal and spatial scales at which a model is developed, with the recognition that the scale of application can restrict the generality and utility of the findings (Lovell et al. 2002). Dominant processes and physical laws change with scale, and thus observations should be made at the scales at which the processes and physical laws are taking place (Lovell et al. 2002). Generic solutions to environmental problems seldom exist, and there should preferably be long-term environmental management programmes, especially in areas with variable climatic or environmental conditions, where short projects may fail to detect processes occurring over longer time scales (Lovell et al. 2002).
7.2 The Chiloglanis modelling system
One of the management goals of the Kruger National Park is the maintenance ofbiodiversity (Rogers and Bestbier 1997). Central to achieving this goal in the adaptive management process is that the rivers of the Kruger National Park conform to a desired future state.
Maintaining this state is facilitated through TPCs (cf. Sections 6.1.1-2), which inturncan be monitored using suitably chosen biological indicators. As discussed in Chapter 6, a TPC was
suggested for river temperature in the Sabie River, with two species of Chiloglanid fish chosen to act as indicators of this TPC.