Qualitative Network Models Research Articles

Qualitative network models in support of ecosystem approaches to bivalve aquaculture

Abstract Predicting the effects of aquaculture development for coastal ecosystems remains challenging, particularly for data-limited systems, and tools that account for complex ecological interactions are needed to support ecosystem approaches to aquaculture. Here, we used qualitative network models (QNMs) to examine the potential community effects of increasing bivalve aquaculture in South Puget Sound, a large estuarine system in Washington, United States. QNMs are formalized conceptual models that require only a qualitative understanding of how variables composing a system interact (that is, the sign of interactions: +, –, and 0) and are therefore well-suited to data-limited systems. Specifically, we examined community-wide responses to scenarios in which bivalve cultivation effort increased for three different bivalve species (Manila clam Venerupis philippinarum, Pacific oyster Crassostrea gigas, and geoduck Panopea generosa). Further, we evaluated community-wide responses to the removal of benthic bivalve predators, a future increase in nutrient loadings, and combinations of these scenarios acting simultaneously. The scenarios enabled identification of potential trade-offs between increased aquaculture and shifts in the abundance of community members and assessment of the possible effects of different management actions. We also analysed the QNM to identify key interactions that influence the sign outcome of community responses to press perturbations, highlighting potential points for management intervention and linkages deserving of more focused quantitative study. QNMs are mathematically robust and highly flexible, but remain underutilized. We suggest that they may serve as valuable tools for supporting ecosystem approaches to aquaculture.

A qualitative network model for understanding regional metabolism in the context of Social–Economic–Natural Complex Ecosystem theory

In Social–Economic–Natural Complex Ecosystem (SENCE) theory, regional metabolism is seen as an integral part of a complex network of interconnected natural, economic and societal subsystems. Analyzing regional metabolism within this network assists observers to understand the concept in a broader context. Some qualitative tools such as simple matrix operations have been adopted to explore such networks but related research is highly fragmented. Here the emphasis is put on a qualitative network model (QNM) that integrates the existing tools into a unified analytical framework. The tools in the QNM are organized into three levels: (1) dominance analysis focuses on how to measure the performance of each component of a network; (2) consistency analysis is used to test whether the interactions between network components are contradictory to the desired development direction for regional metabolism; and (3) sustainability analysis returns three sustainability benchmarks for regional metabolism. With the aid of these tools, QNM can comprehensively explore such networks from multiple perspectives. To test the QNM construct, we conducted a case study on the regional metabolism of bio-fuels from locally grown plant crops, a hotspot in regional environmental research. In the case study we screened key components for inconsistent variables, identified the relationships between components, and rated the sustainability of the regional bio-fuel metabolism system suggested by the model. This study demonstrated that our research approach can inform bio-fuel policy making. The QNM is helpful in the understanding of regional metabolism in the broader context of SENCE theory and may be used to improve regional environmental management and policy-making.

Testing Paradigms of Ecosystem Change under Climate Warming in Antarctica

Antarctic marine ecosystems have undergone significant changes as a result of human activities in the past and are now responding in varied and often complicated ways to climate change impacts. Recent years have seen the emergence of large-scale mechanistic explanations–or “paradigms of change”–that attempt to synthesize our understanding of past and current changes. In many cases, these paradigms are based on observations that are spatially and temporally patchy. The West Antarctic Peninsula (WAP), one of Earth’s most rapidly changing regions, has been an area of particular research focus. A recently proposed mechanistic explanation for observed changes in the WAP region relates changes in penguin populations to variability in krill biomass and regional warming. While this scheme is attractive for its simplicity and chronology, it may not account for complex spatio-temporal processes that drive ecosystem dynamics in the region. It might also be difficult to apply to other Antarctic regions that are experiencing some, though not all, of the changes documented for the WAP. We use qualitative network models of differing levels of complexity to test paradigms of change for the WAP ecosystem. Importantly, our approach captures the emergent effects of feedback processes in complex ecological networks and provides a means to identify and incorporate uncertain linkages between network elements. Our findings highlight key areas of uncertainty in the drivers of documented trends, and suggest that a greater level of model complexity is needed in devising explanations for ecosystem change in the Southern Ocean. We suggest that our network approach to evaluating a recent and widely cited paradigm of change for the Antarctic region could be broadly applied in hypothesis testing for other regions and research fields.

Research on Organization Network Model for Construction Project Based on Social Network Analysis

Current project organization is mostly non-professional and low maturity, which lack of mature and visual methods and tools. Engineering project has obvious social characteristics, which includes not only material operating activities also cooperation and collaboration of complex staff a large number of social activities, etc. This paper point out the organization of control of project management into the field of social network analysis(SNA), A qualitative social network model for construction project organization is established, and then this model is quantitatively analyzed with the help of UCINET, which can fully demonstrate the organization and management in the areas of project feasibility.

Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis

Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis and indicate that regulation by carbon/nitrogen metabolites occurs at multiple levels.

Inferring qualitative relations in genetic networks and metabolic pathways.

Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.

Algorithms for inferring qualitative models of biological networks.

Modeling genetic networks and metabolic networks is an important topic in bioinformatics. We propose a qualitative network model which is a combination of the Boolean network and qualitative reasoning, where qualitative reasoning is a kind of reasoning method well-studied in Artificial Intelligence. We also present algorithms for inferring qualitative networks from time series data and an algorithm for inferring S-systems (synergistic and saturable systems) from time series data, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems.

Initial Effects of the Alaska Halibut IFQ Program: Survey Comments of Alaska Fishermen

Previous articleNext article No AccessThalassoramaInitial Effects of the Alaska Halibut IFQ Program: Survey Comments of Alaska FishermenGUNNAR KNAPPGUNNAR KNAPP Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Marine Resource Economics Volume 12, Number 3Fall 1997 Article DOIhttps://doi.org/10.1086/mre.12.3.42629198 Views: 29Total views on this site Citations: 13Citations are reported from Crossref Copyright © 1997 Marine Resource FoundationPDF download Crossref reports the following articles citing this article:Marysia Szymkowiak, Melissa Rhodes-Reese, Sarah Kraak Examining the expansion of qualitative network models towards integrating multifaceted human dimensions, ICES Journal of Marine Science 78, no.77 (Jun 2021): 2395–2408.https://doi.org/10.1093/icesjms/fsab105Marysia Szymkowiak Adaptations and well-being: Gulf of Alaska fishing families in a changing landscape, Ocean & Coastal Management 197 (Nov 2020): 105321.https://doi.org/10.1016/j.ocecoaman.2020.105321Marysia Szymkowiak, Stephen Kasperski, Dan K. Lew Identifying community risk factors for quota share loss, Ocean & Coastal Management 178 (Aug 2019): 104851.https://doi.org/10.1016/j.ocecoaman.2019.104851Anne H. Beaudreau, Eric J. Ward, Richard E. Brenner, Andrew O. Shelton, Jordan T. Watson, Jennifer C. Womack, Sean C. Anderson, Alan C. Haynie, Kristin N. Marshall, Benjamin C. Williams Thirty years of change and the future of Alaskan fisheries: Shifts in fishing participation and diversification in response to environmental, regulatory and economic pressures, Fish and Fisheries 114 (Apr 2019).https://doi.org/10.1111/faf.12364Kailin Kroetz, James N. Sanchirico, Julio Peña-Torres, and David Corderi Novoa Evaluation of the Chilean Jack Mackerel ITQ System, Marine Resource Economics 32, no.22 (Feb 2017): 217–241.https://doi.org/10.1086/690771Daniel K. Lew, Amber Himes-Cornell, and Jean Lee Weighting and Imputation for Missing Data in a Cost and Earnings Fishery Survey, Marine Resource Economics 30, no.22 (Oct 2015): 219–230.https://doi.org/10.1086/679975Christina Package-Ward, Amber Himes-Cornell Utilizing Oral Histories to Understand the Social Networks of Oregon Fishermen in Alaska, Human Organization 73, no.33 (Sep 2014): 277–288.https://doi.org/10.17730/humo.73.3.x01174800236738lDietmar Grimm, Ivan Barkhorn, David Festa, Kate Bonzon, Judd Boomhower, Valerie Hovland, Jason Blau Assessing catch shares' effects evidence from Federal United States and associated British Columbian fisheries, Marine Policy 36, no.33 (May 2012): 644–657.https://doi.org/10.1016/j.marpol.2011.10.014Scott Crosson Resistance to alternative management in fisheries: Economic and cultural considerations of North Carolina's commercial fishers, Politics and the Life Sciences 30, no.0202 (Jan 2016): 31–42.https://doi.org/10.1017/S0730938400014039Scott Crosson Resistance to alternative management in fisheries: Economic and cultural considerations of North Carolina's commercial fishers, Politics and the Life Sciences 30, no.22 (Jan 2016): 31–42.https://doi.org/10.2990/30_2_31Suresh A Sethi Risk management for fisheries, Fish and Fisheries 11, no.44 (Oct 2010): 341–365.https://doi.org/10.1111/j.1467-2979.2010.00363.xRobert Dawson Vertical integration in the post-IFQ halibut fishery, Marine Policy 30, no.44 (Jul 2006): 341–346.https://doi.org/10.1016/j.marpol.2005.04.001Tom Tietenberg The Tradable Permits Approach to Protecting the Commons: What Have We Learned?, SSRN Electronic Journal (Jan 2002).https://doi.org/10.2139/ssrn.315500

On the Simulation Algorithm for Qualitative Network Model

プラントなどの大規模物理系に対する, 定性シミュレーションアルゴリズムを開発した.プラントのような大規模物理系においては, 状態変数が多く複雑に作用しあうため, 数値的なシミュレーションは不可能であったり, 数式に基づくモデルが得られない場合がある.そのため, 本論文では, 変数間の作用関係をアークとする定性ネットワークモデルを用い, ネットワーク上での定性シミュレーションについて考察した.そのアルゴリズムは基本的にはdekleerのアルゴリズムを定性ネットワーク上で応用したもので次に述べる特徴を持つ. (1) 値伝播を静的伝播と動的伝播に分けることで定性シミュレーションに特徴的な分岐の数を減らした. (2) 静的伝播に伴う決定不能な変数と発生をバランス機能により抑えた. (3) ネットワーク上で一貫性を保ちながら値伝播を行なうことができる.

A Method for Qualitative Model Building with Object-Oriented Approach

This paper implements a knowledge-acquisition system for building a diagnostic knowledge-base of large-scale processing plants. The large-scale plants are so complicated to analyze numerically, further in some cases it is impossible to formulate the numerical models expressed in term of the differential equations. In view of this circumstance, a great use is made of the qualitative simulation to generate diagnostic rules. In researches about the qualitative simulation, the method of model description is concerned with knowledge explanation and the method for simulation. In this paper, we propose a qualitative network model in which an object-oriented approach is taken. The inheritance mechanism and the newly introduced instance linking mechanism allow systematic building of large-scale models.

Symmetry, Change, Perturbation, and Observation Mode in Natural Communities

Like a kaleidoscope, nature presents continuously fleeting images of natural communities. It has been difficult to discern patterns of community structure in these elusive images. For both theoretical and applied considerations, however, holistic ecosystem properties need to be identified and quantified. An analogy of a kaleidoscope is used here to describe symmetry, change, and perturbation in the plankton community of Delaware Bay. Notions of symmetry are used extensively by physicists but much less so by biologist. For the ecological application give her, symmetry involves the notion of internal order and invariant pattern within the structure of natural communities. The concept of core structure is introduced to illustrate the inherent symmetry of plankton communities arising from consistent linkages and feedback relationships. The core is determined by using the most prevalent variables and links of a series of individual loop diagrams, each describing the community at a given point in time, to form a summary or composite model. Change refers to the seasonal alterations in community structure, whereas perturbation involves the source and entry location of environmental variation to the community network. Observing mode includes both the intrinsic (biological intuition) and extrinsic (loop analysis) models an observer uses to characterize symmetry, change, and perturbation. Loop analysis was used to model the Delaware Bay plankton community at 12 dates in an annual cycle. Loop diagrams consist of qualitative network models based on positive, negative, or zero interactions between variables in a pair for all pairs of variables. The fit of models to the data using the direct change technique resulted in 95% agreement. The core structure contained 18 variables in a three—tiered configuration with distinct subsystems of predators and small—sized algae and their associated herbivores. There was 3 nutrients, 5 algae, and 10 zooplankton variables in the core structure. It is proving to be consistent for several other marine communities. Various network properties related to community structure and stability were computed for the networks. The mean loop length varied from 1.6 to 2.8 variables of the individual networks; a network consisted of 22—31 loops of various lengths. Connectance ranged from 14 to 18%. The individual networks were stable; however, the stability of the core structure was questionable. The uniqueness problem, or verification of the single best—fit model, is presented as a concern central to all types of ecosystem modelling. The notion of three different—sized loop universes (sets of loop models) is introduced to illustrate one approach to this problem.