Reply to: Caution over the use of ecological big data for conservation

Queiroz, Nuno; Humphries, Nicolas E.; Couto, Ana; Vedor, Marisa; Costa, Ivo da; Sequeira, Ana M. M.; Mucientes, Gonzalo; Santos, António M.; Abascal, Francisco J.; Abercrombie, Debra L.; Abrantes, Kátya G.; Acuña-Marrero, David; Afonso, André S.; Afonso, Pedro; Anders, Darrell; Araújo, Gonzalo; Arauz, Rándall; Bach, Pascal; Barnett, Adam; Bernal, Diego; Berumen, Michael L.; Lion, Sandra Bessudo; Bezerra, Natalia Priscila Alves; Blaison, Antonin; Block, Barbara A.; Bond, Mark E.; Bonfil, Ramón; Braun, Camrin D.; Brooks, Edward J.; Brooks, Annabelle; Brown, Judith; Byrne, Michael E.; Campana, Steven E.; Carlisle, Aaron B.; Chapman, Demian D.; Chapple, Taylor K.; Chisholm, John; Clarke, Christopher R.; Clua, Eric; Cochran, Jesse E. M.; Crochelet, Estelle; Dagorn, Laurent; Daly, Ryan; Cortés, Daniel Devia; Doyle, Thomas K.; Drew, Michael; Duffy, Clinton; Erikson, Thor; Espinoza, Eduardo; Ferreira, Luciana C.; Ferretti, Francesco; Filmalter, John David; Fischer, G. Chris; Fitzpatrick, Richard; Fontes, Jorge; Forget, Fabien; Fowler, Mark; Francis, Malcolm P.; Gallagher, Austin J.; Gennari, Enrico; Goldsworthy, Simon; Gollock, Matthew; Green, Jonathan R.; Gustafson, Johan A.; Guttridge, Tristan L.; Guzmán, Héctor M.; Hammerschlag, Neil; Harman, Luke; Hazin, Fábio Hissa Vieira; Heard, Matthew S.; Hearn, Alex; Holdsworth, John C.; Holmes, Bonnie J.; Howey, Lucy A.; Hoyos-Padilla, Mauricio; Hueter, Robert E.; Hussey, Nigel E.; Huveneers, Charlie; Irion, Dylan T.; Jacoby, David; Jewell, Oliver J. D.; Johnson, Ryan; Jordan, Lance K. B.; Joyce, Warren; Daly, Clare A. Keating; Ketchum, James T.; Klimley, A. Peter; Kock, Alison A.; Koen, Pieter; Ladino, Felipe; Lana, Fernanda O.; Lea, James S. E.; Llewellyn, Fiona; Lyon, Warrick S.; MacDonnell, Anna; Macena, Bruno C. L.; Marshall, Heather; McAllister, Jaime D.; Meÿer, Michael A.; Morris, John; Nelson, Emily R.; Papastamatiou, Yannis P.; Peñaherrera-Palma, César; Pierce, Simon J.; Poisson, François; Quintero, Lina Maria; Richardson, Andrew J.; Rogers, Paul; Rohner, Christoph; Rowat, David; Samoilys, Melita; Semmens, Jayson M.; Sheaves, Marcus; Shillinger, George L.; Shivji, Mahmood S.; Singh, Sarika; Skomal, Gregory B.; Smale, Malcolm J.; Snyders, Laurenne B.; Soler, Germán; Soria, Marc; Stehfest, Kilian M.; Thorrold, Simon R.; Tolotti, Mariana Travassos; Towner, Alison V.; Travassos, Paulo; Tyminski, John P.; Vandeperre, Frédéric; Vaudo, Jeremy J.; Watanabe, Yuuki; Weber, Sam; Wetherbee, Bradley M.; White, Timothy D.; Williams, Sean; Zárate, Patricia; Harcourt, Robert; Hays, Graeme C.; Meekan, Mark G.; Thums, Michele; Irigoien, Xabier; Eguı́luz, Vı́ctor M.; Duarte, Carlos M.; Sousa, Lara L.; Simpson, Samantha J.; Southall, Emily J.; Sims, David

doi:10.1038/s41586-021-03464-9

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…In formula (7), M i represents the weight coefficient of each metabolic diffusion feature, and conα represents the observation sequence of metabolic diffusion state under complex network intrusion [23], which needs to meet the conditions of i = 1, 2, 3, 4 ⋯ .…”

Section: Realize the Metabolic Grey Prediction Modelmentioning

confidence: 99%

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The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

Ren

Sun

Shan

et al. 2022

Wireless Communications and Mobile Computing

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In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.

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Section: Realize the Metabolic Grey Prediction Modelmentioning

confidence: 99%

“…Big data has 5 V characteristics: volume, velocity, variety, value, and veracity. Big data itself is an abstract concept [6][7][8]. Relying on the development of the Internet and cloud computing, big data plays an increasingly important role in all walks of life [9].…”

Section: Introductionmentioning

confidence: 99%

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

Ren

Sun

Shan

et al. 2022

Wireless Communications and Mobile Computing

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An open science automatic workflow for multi-model species distribution estimation

Coro,

Sana,

Bove

2024

Int J Data Sci Anal

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Integrated Environmental Assessment systems and ecosystem models study the links between anthropogenic and climatic pressures on marine ecosystems and help understand how to manage the effects of the unsustainable exploitation of ocean resources. However, these models have long implementation times, data and model interoperability issues and require heterogeneous competencies. Therefore, they would benefit from simplification, automatisation, and enhanced integrability of the underlying models. Artificial Intelligence can help overcome several limitations by speeding up the modelling of crucial functional parts, e.g. estimating the environmental conditions fostering a species’ persistence and proliferation in an area (the species’ ecological niche) and, consequently, its geographical distribution. This paper presents a full-automatic workflow to estimate species’ distributions through statistical and machine learning models. It embeds four ecological niche models with complementary approaches, i.e. Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps. It automatically estimates the optimal model parametrisations and decision thresholds to distinguish between suitable- and unsuitable-habitat locations and combines the models within one ensemble model. Finally, it combines several ensemble models to produce a species richness map (biodiversity index). The software is open-source, Open Science compliant, and available as a Web Processing Service-standardised cloud computing service that enhances efficiency, integrability, cross-domain reusability, and experimental reproduction and repetition. We first assess workflow stability and sensitivity and then demonstrate effectiveness by producing a biodiversity index for the Mediterranean based on $$\sim $$ ∼ 1500 species data. Moreover, we predict the spread of the invasive Siganus rivulatus in the Mediterranean and its current and future overlap with the native Sarpa salpa under different climate change scenarios.

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Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic

Coro

Bove

Ellenbroek

2022

Ecological Informatics

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Reply to: Caution over the use of ecological big data for conservation

Cited by 10 publications

References 8 publications

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

An open science automatic workflow for multi-model species distribution estimation

Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic

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