Aggregation error in ecological models

O'Neill, R. V.; Rust, Bert W.

doi:10.1016/0304-3800(79)90001-2

Cited by 94 publications

(35 citation statements)

References 2 publications

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“…Pooling functional groups is less successful than omitting the least important groups entirely (Fulton 2001). This result is commensurate with the general guidelines of earlier work on the effects of model aggregation (Wiegert 1977, O'Neill & Rust 1979, Cale & Odell 1980, Gardner et al 1982.…”

Section: Trophic Complexitysupporting

confidence: 89%

“…Too much complexity leads to too much uncertainty and problems with interpretation of the model's dynamics and predictions, while too little detail results in models that cannot produce realistic behaviours. These studies have also identified some important guidelines which can usefully extend or augment those proposed in earlier works on ecological models (Wiegert 1977, O'Neill & Rust 1979, Cale & Odell 1980, Gardner et al 1982, Halfon 1983a,b, Innis & Rexstad 1983, Iwasa et al 1987): (1) Explicit physiological detail of every trophic group is not always necessary. However, the use of explicit physiological detail can be important in certain circumstances (such as oligotrophic conditions when simple assumptions about nutrient uptake and the ratio of limiting nutrients are violated).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the study of the general properties of aggregation, and its limitations as a tool for use in model development and application, has received some attention in theoretical systems over the last 40 yr. A number of researchers have considered the effects of aggregation from a theoretical standpoint and have produced some useful guidelines (Zeigler 1976, O'Neill 1979, O'Neill & Rust 1979, Cale & Odell 1980, Gardner et al 1982, Cale et al 1983, Iwasa et al 1987, Bartell et al 1988, Rastetter et al 1992, Fulton 2001. The 2 most important guidelines are: (1) do not aggregate serially linked groups (predator and prey) (Gardner et al 1982, Fulton 2001; (2) do not aggregate species, age classes or functional groups with rate constants more than 2-to 3-fold (Wiegert 1977, O'Neill & Rust 1979, Cale & Odell 1980, Gardner et al 1982, Fulton 2001. Ignoring either of these guidelines is likely to result in a significant decline in model performance.…”

Section: Trophic Aggregationmentioning

confidence: 99%

See 2 more Smart Citations

Effect of complexity on marine ecosystem models

Fulton¹,

Smith²,

Johnson³

2003

Mar. Ecol. Prog. Ser.

278

174

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Ecosystem rather than species management has become an explicit part of policies that feature in international treaties and national legislation. Many of the tools that will be needed to fulfil the requirements of these policies are still in an early stage of development. One such tool is trophic ecosystem modelling. These models have been put forward to aid system-level understanding and provide insight into the potential impacts of human activities. Despite this, there are many gaps in knowledge of their strengths and weaknesses. In particular, little is known about the effect of the level of detail in a model on its performance. There has been some consideration of the effects of model formulation, as well as the effects of the physical, biological and chemical scope of multispecies and ecosystem models on their performance. A review of existing research indicates that there is a humped relationship between model detail and performance for these models, and that there are some guiding principles to consider during model development. This review gives some insight into which model structures and assumptions are likely to aid understanding and management, and which may be unnecessary. Further, it provides some understanding of whether some models can capture properties of real systems that other models cannot. The main recommendation is that the use of a single 'ultimate' ecosystem model is ill-advised, while the comparative and confirmatory use of multiple 'minimum-realistic' models is strongly recommended.

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Section: Trophic Complexitysupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Trophic Aggregationmentioning

confidence: 99%

See 1 more Smart Citation

Effect of complexity on marine ecosystem models

Fulton¹,

Smith²,

Johnson³

2003

Mar. Ecol. Prog. Ser.

278

174

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“…Some work on the errors of aggregation resulting from the approximation of a three-dimensional spatial continuum by a two-dimensional model representation has been reported by McLaughlin [1985] for groundwater systems. Others have investigated in depth the errors arising from the aggregation of groups of heterogeneous biological species into single ecological "compartments" [O'Neill and Rust, 1979] (see also section 7). Second, the errors of model structure, although the focus of much discussion throughout the review, are in fact extremely difficult to quantify in a formal manner.…”

Section: Class Imentioning

confidence: 99%

Water quality modeling: A review of the analysis of uncertainty

Beck¹

1987

Water Resources Research

861

431

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This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. More specifically, four problem areas are examined in detail: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model. The review is rather lengthy, and it has therefore been prepared in effect as two papers. There is a shorter, largely nontechnical version, which gives a quick impression of the current and future issues in the analysis of uncertainty in water quality modeling. Enclosed by this shorter discussion is the main body of the review dealing in turn with (1) identifiability and experimental design, (2) the generation of preliminary model hypotheses under conditions of sparse, grossly uncertain field data, (3) the selection and evaluation of model structure, (4) parameter estimation (model calibration), (5) checks and balances on the identified model, i.e., model “verification” and model discrimination, and (6) prediction error propagation. Much time is spent in discussing the algorithms of system identification, in particular, the methods of recursive estimation, and in relating these algorithms and the subject of identification to the problems of prediction uncertainty and first‐order error analysis. There are two obvious omissions from the review. It is not concerned primarily with either the development and solution of stochastic differential equations or the issue of decision making under uncertainty, although clearly some reference must be made to these topics. In brief, the review concludes (not surprisingly) that much work has been done on the analysis of uncertainty in the development of mathematical models of water quality, and much remains to be done. A lack of model identifiability has been an outstanding difficulty in the interpretation and explanation of past observed system behavior, and there is ample evidence to show that the “larger,” more “comprehensive” models are easily capable of generating highly uncertain predictions of future behavior. For the future of the subject, it is speculated that there is the possibility of progress in the development of novel algorithms for model structure identification, a need for new questions to be posed in the problem of prediction, and a distinct challenge to the conventional views of this review in the new forms of knowledge representation and manipulation now emerging from the field of artificial intelligence.

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“…Besides the methods discussed here, there are other dynamic modeling techniques also relevant to spatial upscaling. For examples, the methods of model simplification or model aggregation (e.g., O'Neill and Rust 1979, Iwasa et al 1989, Cale 1995 are useful for upscaling especially when they directly address the problem of spatial aggregation. Metamodelingdeveloping coarse-scale models based on fine-scale models -is directly relevant to upscaling (e.g., de Vries et al 1998, Urban et al 1999, Bierkens et al 2000.…”

Section: Discussionmentioning

confidence: 99%

Perspectives and Methods of Scaling

2006

Scaling and Uncertainty Analysis in Ecology

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Aggregation error in ecological models

Cited by 94 publications

References 2 publications

Effect of complexity on marine ecosystem models

Effect of complexity on marine ecosystem models

Water quality modeling: A review of the analysis of uncertainty

Perspectives and Methods of Scaling

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