Challenges for Ecological Modelling
Pressing contemporary ecological issues emphasise questions about how we should go about modelling ecological systems. In their preface to the latest volume of Ecological Modelling, Solidoro et al. suggest three main challenges for modellers with regards to applied environmental problems:
"A first challenge is to meet the legitimate expectations of the scientific community and society, providing solid expertise, reliable tools and critical interpretation of model results. Many questions need an answer here and now, and sometime[s] there is no point in saying ‘there are not enough data, information, knowledge’. To ask for more time, or to declare that no rigorous scientific conclusion can be drawn, will simply made those people needing an answer turn and look for someone else – qualified or not – willing to provide a suggestion. We have to be rigorous, to remind of limits and approximations implicit in any model and of uncertainties (and errors) implicit in any prediction. Nevertheless, if a model has to be made and/or used, ‘who if not us’, and ‘when if not now?’
A second challenge is neither generating false expectations, by promising what cannot be achieved, nor permitting others to do that, or to put such expectations on modelling. Within a society which regards magicians more than scientists, sometimes it might seem a good idea to wear a magician hat. However, modellers are not magicians, and models are not crystal bowls. And, once lost, it would be very hard to gain scientific credibility again.
A third point to remember is that the goal is knowledge, and models are only instruments. Even if its role in science is more central than in the past, ecological modelling should keep on staying open to contamination and to interbreeding with other scientific fields. Obviously, this includes confrontation with data and with the knowledge of people who collect them. Surely, it is true that reality is not the data but what data stand for, however experimental observations still remain the only link between theory and reality."
The first point above is largely consistent with those I highlighted in my recent book review for Landscape Ecology (now in print); when data and understanding are sparse, modellers may just need to scale-back their modeling aims and objectives. When faced with pressing environmental issues we may need to settle for models that work - models that we can use to help make decisions rather than those that 'prove' (quantitatively) specific aspects of system function or ecological theory. In such a situation it may well be the case that 'no rigorous scientific conclusion' can be made in the short-term (when decisions are required) and, as the second point above implies, we shouldn't try to disguise that. But that doesn't mean people 'needing an answer' should be forced to look elsewhere (unless of course the answer they are looking for is 42).
Rather than focusing on the scientific results (numbers) of the model as a product, modellers in this situation might seek to captialise on the use of the process of modelling as a means to facilitate consensus-building and decision-making by providing a platform for communication about (potentially complex) systems interactions. Alternatively, they may use a model to foster better understanding about potential outcomes by examining how modelled systems behave qualitatively under different scenarios. Accurate quantitative predictions can be very persuasive, but when resources are in short supply we may not have the luxury of being able to produce them.
Solidoro et al. (2009) Challenges for ecological modelling in a changing world: Global Changes, Sustainability and Ecosystem Based Management Ecological Modelling 220(21) 2825-2827 doi:10.1016/j.ecolmodel.2009.08.018
"A first challenge is to meet the legitimate expectations of the scientific community and society, providing solid expertise, reliable tools and critical interpretation of model results. Many questions need an answer here and now, and sometime[s] there is no point in saying ‘there are not enough data, information, knowledge’. To ask for more time, or to declare that no rigorous scientific conclusion can be drawn, will simply made those people needing an answer turn and look for someone else – qualified or not – willing to provide a suggestion. We have to be rigorous, to remind of limits and approximations implicit in any model and of uncertainties (and errors) implicit in any prediction. Nevertheless, if a model has to be made and/or used, ‘who if not us’, and ‘when if not now?’
A second challenge is neither generating false expectations, by promising what cannot be achieved, nor permitting others to do that, or to put such expectations on modelling. Within a society which regards magicians more than scientists, sometimes it might seem a good idea to wear a magician hat. However, modellers are not magicians, and models are not crystal bowls. And, once lost, it would be very hard to gain scientific credibility again.
A third point to remember is that the goal is knowledge, and models are only instruments. Even if its role in science is more central than in the past, ecological modelling should keep on staying open to contamination and to interbreeding with other scientific fields. Obviously, this includes confrontation with data and with the knowledge of people who collect them. Surely, it is true that reality is not the data but what data stand for, however experimental observations still remain the only link between theory and reality."
The first point above is largely consistent with those I highlighted in my recent book review for Landscape Ecology (now in print); when data and understanding are sparse, modellers may just need to scale-back their modeling aims and objectives. When faced with pressing environmental issues we may need to settle for models that work - models that we can use to help make decisions rather than those that 'prove' (quantitatively) specific aspects of system function or ecological theory. In such a situation it may well be the case that 'no rigorous scientific conclusion' can be made in the short-term (when decisions are required) and, as the second point above implies, we shouldn't try to disguise that. But that doesn't mean people 'needing an answer' should be forced to look elsewhere (unless of course the answer they are looking for is 42).
Rather than focusing on the scientific results (numbers) of the model as a product, modellers in this situation might seek to captialise on the use of the process of modelling as a means to facilitate consensus-building and decision-making by providing a platform for communication about (potentially complex) systems interactions. Alternatively, they may use a model to foster better understanding about potential outcomes by examining how modelled systems behave qualitatively under different scenarios. Accurate quantitative predictions can be very persuasive, but when resources are in short supply we may not have the luxury of being able to produce them.
Solidoro et al. (2009) Challenges for ecological modelling in a changing world: Global Changes, Sustainability and Ecosystem Based Management Ecological Modelling 220(21) 2825-2827 doi:10.1016/j.ecolmodel.2009.08.018
Labels: Ecological, Modelling, Sustainability
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
posted by James at Saturday, October 24, 2009
2 Comments:
It seems to me that there are some big IFs in the arguments being made here.
The suggestion that "modellers... might seek to captialise on the use of the process of modelling as a means to facilitate consensus-building and decision-making by providing a platform for communication about (potentially complex) systems interactions" depends on the process being conducted in particular ways. Of particular concern is whether a model constructed of computer code accessible only to those with specialized knowledge is a viable path to transparency, which some might posit as a prerequisite for valid consensus.
Yes there are lots of IFs. Just as ecologists answer all their questions "It depends...", modellers like to say "If we consider..."
Understanding how a model works is certainly an important part of building consensus - people won't (or at least shouldn't) agree with something they don't understand. That's true as much for 'simple' box-and-arrow diagram models as for those constructed with thousands of lines code.
If we consider models away from the simple box-and-arrow diagrams end of the spectrum (not that those are always simple to understand, of course!) some authors have pointed out that as our get models more complex we run the risk of producing models as complex as the systems they are supposed to represent (and simplify; e.g., O'Sullivan 2004). So yes, your question is a pertinent one; if we are to use (potentially complex) models as a means to facilitate consensus building, how do we ensure we communicate our model structure and results transparently?
I touched on some of these issues in the paper I submitted to Journal of Land Use science this summer [and still haven't blogged about or heard anything back about]. I wanted to investigate how well non-modellers that had not been involved in model construction could evaluate a model of a system they knew intimately (the Spanish agricultural landscape in which they live and work). One of the things I found was that some participants had trouble distinguishing the between model structure and scenario boundary conditions it was used to investigate. Issues like these will certainly need to be addressed before any kind of consensus-building can start.
Of course, another approach is the companion modelling approach that involves decision-makers right from the start and let's THEM build the model (and appreciate the enlightening process for themselves).
Before I sign off I just want to reiterate that I am not advocating this approach in all circumstances. As I pointed out in my post, an accurate quantitative prediction can be very persuasive. But in circumstances where decisions are needed faster than the appropriate quantitative model can be constructed or the necessary data collected (like many of the models in the Hobbs and Suding book I reviewed), alternative modelling approaches might lead to more prompt, and hopefully better, decision-making.
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