An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs

AbstractThe networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k-competition hypergraphs, m-step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario.

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A multispecies biomass dynamics model for investigating predator–prey interactions in the Bering Sea groundfish community

Deep Sea Research Part II Topical Studies in Oceanography ◽

10.1016/j.dsr2.2015.04.019 ◽

2016 ◽

Vol 134 ◽

pp. 331-349 ◽

Cited By ~ 4

Author(s):

Tadayasu Uchiyama ◽

Gordon H. Kruse ◽

Franz J. Mueter

Keyword(s):

Bering Sea ◽

Dynamics Model ◽

Predator Prey ◽

Biomass Dynamics ◽

The Bering Sea

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Intermediate- and Deep-Water Oxygenation History in the Subarctic North Pacific During the Last Deglacial Period

Frontiers in Earth Science ◽

10.3389/feart.2021.638069 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ekaterina Ovsepyan ◽

Elena Ivanova ◽

Martin Tetard ◽

Lars Max ◽

Ralf Tiedemann

Keyword(s):

Sea Level ◽

Deep Water ◽

Bering Sea ◽

North Pacific ◽

Intermediate Water ◽

Western Bering Sea ◽

Water Oxygen ◽

Wide Range ◽

The Bering Sea ◽

Oxygen Concentrations

Deglacial dissolved oxygen concentrations were semiquantitatively estimated for intermediate and deep waters in the western Bering Sea using the benthic foraminiferal-based transfer function developed by Tetard et al. (2017), Tetard et al. (2021a). Benthic foraminiferal assemblages were analyzed from two sediment cores, SO201-2-85KL (963 m below sea level (mbsl), the intermediate-water core) and SO201-2-77KL (2,163 mbsl, the deep-water core), collected from the Shirshov Ridge in the western Bering Sea. Intermediate waters were characterized by an oxygen content of ∼2.0 ml L−1 or more during the Last Glacial Maximum (LGM)–Heinrich 1 (H1), around 0.15 ml L−1 during the middle Bølling/Allerød (B/A)–Early Holocene (EH), and a slight increase in [O2] (∼0.20 ml L−1) at the beginning of the Younger Dryas (YD) mbsl. Deep-water oxygen concentrations ranged from 0.9 to 2.5 ml L−1 during the LGM–H1, hovered around 0.08 ml L−1 at the onset of B/A, and were within the 0.30–0.85 ml L−1 range from the middle B/A to the first half of YD and the 1.0–1.7 ml L−1 range from the middle to late Holocene. The [O2] variations remind the δ18O NGRIP record thereby providing evidence for a link between the Bering Sea oxygenation at intermediate depths and the deglacial North Atlantic climate. Changes in the deep-water oxygen concentrations mostly resemble the deglacial dynamics of the Southern Ocean upwelling intensity which is supposed to be closely coupled with the Antarctic climate variability. This coherence suggests that deglacial deep-water [O2] variations were primarily controlled by changes in the circulation of southern-sourced waters. Nevertheless, the signal from the south at the deeper site might be amplified by the Northern Hemisphere climate warming via an increase in sea-surface bioproductivity during the B/A and EH. A semi-enclosed position of the Bering Sea and sea-level oscillations might significantly contribute to the magnitude of oxygenation changes in the study area during the last deglaciation. Interregional correlation of different proxy data from a wide range of water depths indicates that deglacial oxygenation changes were more pronounced in the Bering and Okhotsk marginal seas than along the open-ocean continental margin and abyssal settings of the North Pacific.

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Inter-specific competition among trees in pythagorean fuzzy soft environment

Complex & Intelligent Systems ◽

10.1007/s40747-021-00470-2 ◽

2021 ◽

Author(s):

Muhammad Akram ◽

Hafiza Saba Nawaz

Keyword(s):

Fuzzy Set ◽

Mathematical Framework ◽

Forest Trees ◽

Mixed Conifer ◽

Pythagorean Fuzzy Set ◽

Plant Resources ◽

Competing Interactions ◽

Economic Competition ◽

Linguistic Communication ◽

Mixed Conifer Forests

AbstractA Pythagorean fuzzy set is very effective mathematical framework to represent parameter-wise imprecision which is the property of linguistic communication. A Pythagorean fuzzy soft graph is more potent than the intuitionistic fuzzy soft as well as the fuzzy soft graph as it depicts the interactions among the objects of a system using Pythagorean membership grades with respect to different parameters. This article addresses the content of competition graphs as well as economic competition graphs like k-competition graphs, m-step competition graphs and p-competition graphs in Pythagorean fuzzy soft environment. All these concepts are illustrated with examples and fascinating results. Furthermore, an application which describes the competition among distinct forest trees, that grow together in the mixed conifer forests of California, for plant resources is elaborated graphically. An algorithm is also designed for the construction of Pythagorean fuzzy soft competition graphs. It is worthwhile to express the competing and non-competing interactions in various networks with the help of Pythagorean fuzzy soft competition graphs wherein a variation in competition relative to different attributes is visible.

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