Development of a general ecosystem model for a range of scales and ecosystems

Fitz, H. Carl; DeBellevue, E.B.; Costanza, Robert; Boumans, Roelof; Maxwell, Thomas; Wainger, Lisa A.; Sklar, Fred H.

doi:10.1016/0304-3800(95)00112-3

Cited by 129 publications

(45 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We identify two challenges: one related to the number of models and the other to the variety of models. With respect to the number of models, newly developed models often bear similarities to existing models ('reinventing the wheel') (e.g., Fitz et al 1996). In such cases, it would most likely be more efficient to apply or adopt an existing model instead of creating a new one.…”

Section: Introductionmentioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

View full text Add to dashboard Cite

A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: superindividual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

show abstract

Section: Introductionmentioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Various statistical estimation methods have also been used to fit land cover change models like the log-linear relationship based logistical models [58,60], various non linear fitting models [61,62] and artificial neural network based models [63]. Examples of dynamic process models would be process flow models [64][65][66], cellular models based on landscape and transitions [67,68] and agent based models focused on human actions [68,69]. It must also be noted that depending on the model limitations and the situation being evaluated, there are potentially other, more suitable analysis tools which could be incorporated [51], although for this research the modeling approach is used in a quite unique manner, where its weaknesses are utilized to better understand our landscape.…”

Section: Introductionmentioning

confidence: 99%

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

2012

View full text Add to dashboard Cite

Establishment of protected areas (PA) has been one of the leading tools in biodiversity conservation. Globally, these kinds of conservation interventions have given rise to an increase in PAs as well as the need to empirically evaluate the impact of these PAs on forest cover. Few of these empirical evaluations have been geared towards comparison of pre and post policy intervention landscapes. This paper provides a method to empirically evaluate such pre and post policy interventions by using a cellular automata-Markov model. This method is tested using remotely sensed data of Bannerghatta National park (BNP) and its surrounding, which have experienced various national level policy interventions (Indian National Forest Policy of 1988) and rapid land cover change between 1973 and 2007. The model constructs a hypothetical land cover scenario of BNP and its surroundings (1999 and 2007) in the absence of any policy intervention, when in reality there has been a significant potential policy intervention effect. The models predicted a decline in native forest cover and an increase in non forest cover post 1992 whereas the actual observed landscape experienced the reverse trend where after an initial decline from 1973 to 1992, the forest cover in BNP is towards recovery post 1992. Furthermore, the models show a higher deforestation and lower reforestation than the observed deforestation and reforestation patterns for BNP post 1992. Our results not only show the implication of national level policy changes on forest cover but also show the usefulness of our method in evaluating such conservation efforts.

show abstract

“…Because hydrology is of critical importance to water resource projects and the science is well developed, hydrologic modeling is frequently conducted during restoration project planning, e.g.,the restoration of the Florida Everglades (Fitz et al 1996). Numerical models can help in the planning process by facilitating sensitivity analysis of aspects of the system such as basin morphology and prediction of conditions such as hydroperiod (e.g., Burdick 2000, Yang et al 2010.…”

Section: Physical Modelsmentioning

confidence: 99%

Guidelines, processes and tools for coastal ecosystem restoration, with examples from the United States

Thom

Diefenderfer

Adkins

et al. 2010

Plankton Benthos Res

View full text Add to dashboard Cite

This paper presents a systematic approach to coastal restoration projects with five components: planning, implementation, performance assessment, adaptive management, and dissemination of results. Typical features of the iterative planning process are synthesized, beginning with a vision, a description of the ecosystem and landscape, and goals. The conceptual model and planning objectives are developed, prioritization techniques are used for site selection, and numerical models contribute to preliminary designs as needed. Within the planning process, cost analysis involves budgeting, scheduling, and financing. Performance criteria and reference sites are selected during design of the monitoring program. Particular emphasis is given to the monitoring program, used as a tool to assess project performance and identify problems affecting progression toward project goals, within an adaptive management framework. Key approaches to aspects of the monitoring program are reviewed and detailed with project examples. Another important means of strengthening the restoration project or program is documentation, peer review, and the distribution of information outside the planning team prior to finalizing construction plans, during implementation, and throughout monitoring and adaptive management. Introduction to the Restoration Project ComponentsIn planning a coastal restoration project, sound ecological science and engineering and rigorous planning procedures are equally important. A failure in any area can lead to costly retrofitting during or after project implementation (Noble et al. 2000). A range of planning methods and theories may be used, including, for example, rational, incremental, adaptive, and consensual approaches.Implementation may include many forms of construction "actions". Shreffler et al. (1995) list common actions including ground enhancement, rip rap installation, culvert installation, culvert cleanout and removal, channel cleaning, erosion control, vegetation planting, dike removal, dike/dam/levee building, and cattle fencing. In a national review of coastal restoration methods, Borde et al. (2003) describe innovative restoration methods in habitats including coral reefs, mangroves, salt marshes, and intertidal zones (seagrasses), including the placement of reef balls and other underwater structures.A monitoring program for performance assessment does not need to be complex and expensive to be effective (Kentula et al. 1992a). The National Research Council (1992) recommended that to assess the equivalency of the restored system to the antecedent one, wetland restoration monitoring programs should observe ten conditions. In developing monitoring protocols for assessment of two large estuaries on the West Coast U.S.A. (Simenstad et al. 1991, Roegner et al. 2009), we have found that these ten conditions are generally applicable to coastal restoration projects: (1) assessment criteria should include structural and functional attributes; (2) criteria should be based on known antecedent condi...

show abstract

Development of a general ecosystem model for a range of scales and ecosystems

Cited by 129 publications

References 33 publications

Challenges and opportunities for integrating lake ecosystem modelling approaches

Challenges and opportunities for integrating lake ecosystem modelling approaches

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

Guidelines, processes and tools for coastal ecosystem restoration, with examples from the United States

Contact Info

Product

Resources

About