Integrating diverse model results into decision support for good environmental status and blue growth

Uusitalo, Laura; Blenckner, Thorsten; Puntila-Dodd, Riikka; Skyttä, Annaliina; Jernberg, Susanna; Voss, Rudi; Müller‐Karulis, Bärbel; Tomczak, Maciej; Möllmann, Christian; Peltonen, Heikki

doi:10.1016/j.scitotenv.2021.150450

Cited by 15 publications

(20 citation statements)

References 60 publications

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“…For example, to answer similar questions, we could have employed a random forests approach [46]. The strength of the BN approach is that it includes explicit description of the interaction of dependent factors [47]; this was relevant in our study to evaluate the importance of biotopic information that directly affected cod biomass in the Baltic BN. In contrast, the other classes of fisheries/ecosystem models are those that are based on dynamical system representations of food-web interactions and abiotic factors.…”

Section: Discussionmentioning

confidence: 99%

“…Fisheries decisions that are based on environmental flows, fish catch statistics, or simple trophic indicators are limited in their management confidence [47]. Including a range of parameters that can characterize the linkage of fish dynamics as a function of overall system characteristics is more likely to generate meaningful advice on fish catch quotas and restrictions.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the BN's behavior was improved by adhering to rules of simplicity for bin number and linkage density [14,15]. Future improvements in exploring the model complexity are likely to be fruitful for fisheries management [47].…”

Section: Discussionmentioning

confidence: 99%

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Is Diversity the Missing Link in Coastal Fisheries Management?

et al. 2022

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Fisheries management has historically focused on the population elasticity of target fish based primarily on demographic modeling, with the key assumptions of stability in environmental conditions and static trophic relationships. The predictive capacity of this fisheries framework is poor, especially in closed systems where the benthic diversity and boundary effects are important and the stock levels are low. Here, we present a probabilistic model that couples key fish populations with a complex suite of trophic, environmental, and geomorphological factors. Using 41 years of observations we model the changes in eastern Baltic cod (Gadus morhua), herring (Clupea harengus), and Baltic sprat (Sprattus sprattus balticus) for the Baltic Sea within a Bayesian network. The model predictions are spatially explicit and show the changes of the central Baltic Sea from cod- to sprat-dominated ecology over the 41 years. This also highlights how the years 2004 to 2014 deviate in terms of the typical cod–environment relationship, with environmental factors such as salinity being less influential on cod population abundance than in previous periods. The role of macrozoobenthos abundance, biotopic rugosity, and flatfish biomass showed an increased influence in predicting cod biomass in the last decade of the study. Fisheries management that is able to accommodate shifting ecological and environmental conditions relevant to biotopic information will be more effective and realistic. Non-stationary modelling for all of the homogeneous biotope regions, while acknowledging that each has a specific ecology relevant to understanding the fish population dynamics, is essential for fisheries science and sustainable management of fish stocks.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Is Diversity the Missing Link in Coastal Fisheries Management?

et al. 2022

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“…Alva-Basurto and Arias-González, 2014; Capitani et al, 2021a;Dahood et al, 2020Dahood et al, , 2019de Mutsert et al, 2021;Espinosa-Romero et al, 2011;Fretzer, 2015;Frisk et al, 2011;Goncalves et al, 2021 Bauer et al, 2019Bauer et al, , 2018Beecham et al, 2015;Costalago et al, 2019;Daskalov, 2002;de Mutsert et al, 2016;Ehrnsten et al, 2019;Hansson et al, 2007;Hyytiäinen et al, 2021;Kao et al, 2014;Libralato et al, 2015;Ma et al, 2010;Niiranen et al, 2013;Okey et al, 2004;Österblom et al, 2007;Sakamoto and Shirakihara, 2017;Uusitalo et al, 2022;Wang et al, 2021;Zhu et al, 2020 • Forcing functions for primary production or producer biomass (e.g., based on satellite data or biogeochemical models) • Shading of benthic vegetation as forced mortality (fishing) • Deoxygenation as forcing functions, e.g., modifying fish egg production based on water volumes with oxygen levels above a recruitment threshold…”

Section: Habitat Loss and Restorationmentioning

confidence: 99%

Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions

Stock¹,

Murray²,

Gregr³

et al. 2022

Preprint

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Understanding the cumulative effects of multiple stressors is a research priority in environmental science. Ecological models are a key component of tackling this challenge because they can simulate interactions between the components of an ecosystem. Here, we ask, how has the popular modeling platform Ecopath with Ecosim – well-known for applications in fisheries – been used to model human impacts related to climate change, land and sea use, pollution, and invasive species? We conducted an exhaustive, systematic literature review encompassing 163 studies covering mostly aquatic ecosystems. The most modeled stressors were climate change (59 studies), habitat loss (21), species introductions (21), and eutrophication (19), using a range of modeling techniques. Despite this apparently comprehensive coverage, we identify four substantial gaps. First, only 13% of studies investigated three or more stressors, with most studies focusing on single stressors. Compounding this incomplete view, many studies modeled only one of many pathways through which each stressor is known to affect ecosystems. Second, various methods have been applied to define environmental response functions representing the effects of single stressors on species groups. These functions can have a large effect on the timing and magnitude of ecological changes, but best practices for deriving them are yet to emerge. Third, human dimensions of environmental change – except for fisheries – were rarely considered. For example, only 3% of studies reported indicators representing non-extractive ecosystem services. Fourth, only 3% of studies used statistical research designs that allow attribution of ecosystem changes to stressors’ direct effects and interactions, such as factorial (computational) experiments. None made full use of the statistical possibilities that arise when simulations can be repeated 1,000s of times with controlled changes to the inputs. We argue that all four gaps are feasibly filled by integrating ecological modeling with advances in other subfields of environmental science and in computational statistics.

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“…Since its conceptualization in the early 1980s, the Internet of Things (IoT), has revolutionized the way people work, move, trade, communicate or even live. During the last decade, concepts such as smart cities [1], smart factories [2], or smart agriculture [3] are part of our daily lives with a great impact on circular production [4] and blue growth [5], among others. Furthermore, advances in technologies such as 5G and parallel processing as well as data analysis techniques (i.e., machine learning, deep learning) enable the design and operation of large-scale digital manufacturing processes [6] and digital twins, even of an entire city [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

Spandonidis

Sedikos²,

Giannopoulos

et al. 2022

Signals

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Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations.

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Integrating diverse model results into decision support for good environmental status and blue growth

Cited by 15 publications

References 60 publications

Is Diversity the Missing Link in Coastal Fisheries Management?

Is Diversity the Missing Link in Coastal Fisheries Management?

Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

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