Current estimates place the total number of plant and animal species on Earth somewhere between 10 and 30 million (Myers 1995). Because many of these species reside in rain forests, it is also estimated that about 20% of all species will become extinct within 30 years mainly due to habitat destruction. The background extinction rate (no anthropogenic influences) is about one species every four years. As opposed to most other environmental problems, extinction is not reversible - evolutionary processes would require 200,000 human generations to re-evolve the present diversity of life. Such biodepletion does not appear to be limited to very small or very obscure species - the media frequently carries stories of the endangered status of many large animals including sharks, whales, siberian tigers, and rhinos.
To stop or at least slow this destruction, effective ecosystem protection policies need to be enacted by the ecosystem-affecting countries. How can such policies be enacted and maintained for long periods of time? Fiorino (1995, ch. 7), in a discussion of the U.S. Environmental Protection Agency (USEPA), states that:
... Policy makers need better information on environmental trends and on the results of their efforts at solving environmental problems. Information is often uneven and does not allow us to link measures we have to performance and results.
... To set the policy agenda, policy makers need not only good analysis but also mechanisms for consulting with the public. Reliable and acceptable measures of progress support analysis as a way to make more informed, risk-based decisions. Institutions that can integrate across policy sectors will be able to design more comprehensive, cost-effective programs. Better analysis will help agencies determine which policy instruments to use in carrying out what kinds of strategies. Innovative approaches to dealing with industry and the public build stronger democratic institutions and restore public confidence in those institutions. Greater confidence in institutions can lead to better dialogue about analytical methods and their acceptable uses.
In the above, Fiorino echoes principles of sustainable development (see Krehbiel et al. (1999)); namely that in order to achieve long-term protection of an ecosystem, regulatory policies need to be developed that are (a) effective at protecting the ecosystem, (b) enjoy the maximum amount of public support in all ecosystem-affecting countries, and (c) are as economically efficient as possible. Minimal necessary ingredients for the creation of such policies and regulations are first; the public having developed a sense of ownership in the policy or regulation by having been involved at a grass-roots level in the development of the policy from the start, second; public recognition of the exact nature of the danger to the ecosystem, and third; empirical studies that suggest a proposed policy will be effective.
Given the political realities of the present international order, it may be naive to expect such principles to ever become reality. Nonetheless, this article develops models, a statistical inference method, and software systems that would help implement sustainable development. Specifically, enacting effective and widely supported ecosystem management policy would be aided if there were a publically-accessible Ecosystem Management System (EMS). Such a a system would be able to use all relevant data to assess both current ecosystem health and the effect of proposed ecosystem management policies. To be used by a broad spectrum of society, this system would need to be easy to use and produce analyses that were understandable by individuals with little training in quantitative modeling. Such a system would increase the public's awareness and involvement in finding economical and politically feasible ways to protect an ecosystem. Currently, the most convenient means of making information and management systems available to the widest spectrum of stakeholders is through the World Wide Web (hereafter, the web).
From the perspective of present-day ecosystem management wherein most analysis and decision making is carried out within government agencies, easier to use EMS's would increase the amount and, most importantly, the timeliness of governmental studies of ecosystem status and regulatory policy impacts.
Currently, there is mixed reliance on rational decision making systems for making ecosystem management decisions. If such management is to be shared and effort expended to cooperatively develop an ecosystem model that produces optimal management options, then the ecosystem-affecting countries would need to follow through and use the EMS-recommended options. Otherwise, as Fiorino mentions, the public would lose faith in a government's stated commitment to sharing ecosystem management decision making. One way to capture the public's trust is for each country to enact legislation that mandates all ecosystem management decisions to be based on EMS-derived recommendations. Such legislation would contain wording to the effect that:
To be implemented, an ecosystem management policy must be supported by predictions of the policy's impacts computed from a probabilistic model of the ecosystem. This ecosystem model is contained within an open-access EMS. The parameters of the ecosystem model are to be estimated from a combination of expert opinion and monitoring data. The policies recommended by this model will be the policies that the regulatory agency proposes to implement. Complete documentation of the model and all EMS-based policy impact predictions will be made available at the EMS web site.The intention of this legislation is that the reasoning behind the proposed policy would be made public in a nonambiguous form - a quantitative, probabilistic decision making system.
Governments of course, would be reluctant to surrender any decision making latitude to a software system and hence would need to be convinced that the benefits of increased effectiveness and increased public support for environmental policy initiatives would outweigh the partial loss of control over policy formulation.
Currently, most environmental management decisions are made by combining expert opinion, model predictions, and organizational politics. The combination of the first two of these elements, expert opinion and model predictions is needed because either available models only represent a subunit of the entire process being managed or, worse, no model and/or data exists for the process in question. For example, complex waterbody eutrophication models (e.g. Shen and Kuo (1996)) typically only represent waterbody mechanisms without explicitly representing nutrient sources or nutrient transport buffering effects.
The effect of organizational politics on decision making can take many forms including consensus building and satisficing (see Hogarth (1987, p. 65)). Use of such methods can lead to suboptimal decisions and hence it is important to develop effective, accountable, and accessible decision making procedures that provide an alternative to these less effective methods of reaching ecosystem management decisions.
Political influences aside, the rules for combining expert opinion and model predictions are often a mix of ad-hoc personal and group-derived rules. This lack of an objective and explicit procedure for combining expert opinion and model predictions to reach ecosystem management decisions make such decisions difficult to defend. This article gives one solution to this problem by providing a methodology for combining expert opinion and model predictions into one quantitative, probabilistic model of the entire ecosystem management decision making process. The essential idea is to embed both expert opinion and quantitative model input/outputs into a single, comprehensive influence diagram (Howard and Matheson 1981).
To demonstrate feasibility of the web-based EMS approach to ecosystem
management, this article describes a functioning EMS for cheetah
(Acinonyx jubatus) management in Kenya.
This web site (www.uwm.edu/
haas/ems-cheetah/) is experimental
but includes working examples of all EMS capabilities proposed herein.
This article is organized as follows. Section 2 describes the proposed EMS and its influence diagram architecture. Section 3 describes, estimates, and exercises the cheetah EMS. Parameter estimation is carried out with a statistical inference method called consistency analysis (Haas 1991a, 1991b, 1997). Consistency analysis is a nonbayesian way of incorporating prior, ecological theory of ecosystem function into parameter estimation when only an incomplete sample is available. Section 4 concludes.