Open Access
Aquat. Living Resour.
Volume 37, 2024
Article Number 4
Number of page(s) 12
Published online 19 March 2024
  • Abdou K, Aubin J, Romdhane MS, Le Loc'h F, Lasram FBR. 2017. Environmental assessment of seabass (Dicentrarchus labrax) and seabream (Sparus aurata) farming from a life cycle perspective: a case study of a Tunisian aquaculture farm. Aquaculture 471: 204–212. [CrossRef] [Google Scholar]
  • Anderson RP, Gonzalez Jr I. 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol Modell 222: 2796–2811. [CrossRef] [Google Scholar]
  • Araújo R, Vázquez Calderón F, Sánchez López J, Azevedo IC, Bruhn A, Fluch S, Garcia Tasende M, Ghaderiardakani F, Ilmjärv T, Laurans M, Mac Monagail M. 2021. Current status of the algae production industry in Europe: an emerging sector of the blue bioeconomy. Front Mar Sci 7: 626389. [CrossRef] [Google Scholar]
  • Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrão EA, De Clerck O. 2018. Bio‐ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob Ecol Biogeogr 27: 277–284. [CrossRef] [Google Scholar]
  • Baldwin RA. 2009. Use of maximum entropy modeling in wildlife research. Entropy 11: 854–866. [CrossRef] [Google Scholar]
  • Barrington K, Chopin T, Robinson S. 2009. Integrated multi-trophic aquaculture (IMTA) in marine temperate waters, in D. Soto (Ed.), Integrated mariculture: a global review. FAO Fisheries and Aquaculture Technical Paper. No. 529. Rome: FAO, pp. 7–46. [Google Scholar]
  • Bivand RS, Pebesma EJ, Gomez-Rubio V. 2013. Applied spatial data analysis with R, Second edition. Springer, NY. [CrossRef] [Google Scholar]
  • Bivand RS, Keitt T, Rowlingson B. 2021. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package[r1] version 1.5-23. [Google Scholar]
  • Boria RA, Olson LE, Goodman SM, Anderson RP. 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model 275: 73–77. [CrossRef] [Google Scholar]
  • Bosch S, Fernandez S. 2022. sdmpredictors: Species Distribution Modelling Predictor Datasets. R package version 0.2.12. [Google Scholar]
  • Brown JL, Bennett JR, French CM. 2017. SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ 5: e4095. [CrossRef] [PubMed] [Google Scholar]
  • Brown AR, Daniels C, Jeffery K, Tyler CR, Brown AR, Daniels C, Jeffery K, Tyler CR. 2020. Developing general rules to facilitate evidence-based policy for mariculture development in and around Marine Protected Areas (MPAs) in England Final Report to Research England (Strategic Priorities Fund) September 2020. [Google Scholar]
  • Buck BH, Troell MF, Krause G, Angel DL, Grote B, Chopin T. 2018. State of the art and challenges for offshore integrated multi-trophic aquaculture (IMTA). Front Mar Sci 5: 165. [Google Scholar]
  • Chopin T, Buschmann AH, Halling C, Troell M, Kautsky N, Neori A, Kraemer GP, Zertuche‐González JA, Yarish C, Neefus C. 2001. Integrating seaweeds into marine aquaculture systems: a key toward sustainability. J Phycol 37: 975–986. [CrossRef] [Google Scholar]
  • Chopin T. 2014. Seaweeds: top mariculture crop, ecosystem service provider. Glob Aquacult Advocate 17: 54–56. [Google Scholar]
  • Collins C, Bresnan E, Brown L, Falconer L, Guilder J, Jones L, Kennerley A, Malham S, Murray A, Stanley M. 2020. Impacts of climate change on aquaculture. MCCIP Sci Rev 2020: 482–520. [Google Scholar]
  • Duarte CM, Wu J, Xiao X, Bruhn A, Krause-Jensen D. 2017. Can seaweed farming play a role in climate change mitigation and adaptation? Front Mar Sci 4: 100. [Google Scholar]
  • Elith J, Graham HC, Anderson R, Dudík M, Ferrier S, Guisan A, Hijmans R, Huettmann F, Leathwick J, Lehmann A, Li J. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129–151. [CrossRef] [Google Scholar]
  • EMODnet Biology. 2023. Basic Occurrence Data downloaded from the EMODnet Biology project. Available online at: (Accessed 21 April 2023). [Google Scholar]
  • EMODnet Human Activities. 2022. EMODnet_HA_EMSA_Route_Density_Map_20191111. (Accessed 28 March 2023). [Google Scholar]
  • ESRI. 2020. ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute. [Google Scholar]
  • ESRI. 2021. “World Countries” [basemap]. Scale Not Given. “World Countries”. (April 18, 2023). [Google Scholar]
  • Ezeh AC, Bongaarts J, Mberu B. 2012. Global population trends and policy options. The Lancet 380: 142–148. [Google Scholar]
  • FAO. 2022. The State of World Fisheries and Aquaculture 2022. Towards Blue Transformation. Rome, FAO. [Google Scholar]
  • Fielding AH, Bell JF. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24: 38–49. [CrossRef] [Google Scholar]
  • Filgueira R, Fernández-Reiriz MJ, Labarta U. 2009. Clearance rate of the mussel Mytilus galloprovincialis. I. Response to extreme Chlorophyll ranges Tasa de aclaramiento del mejillón Mytilus galloprovincialis. I. Respuesta a intervalos extremos de clorofila. Ciencias Marinas 35: 405–417. [CrossRef] [Google Scholar]
  • Folke C, Kautsky N. 1992. Aquaculture with its environment: prospects for sustainability. Ocean Coastal Manag 17: 5–24. [CrossRef] [Google Scholar]
  • Fourcade Y, Engler JO, Rödder D, Secondi J. 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One 9: e97122. [Google Scholar]
  • Garcia MJL. 2015. Recent warming in the Balearic Sea and Spanish Mediterranean coast. Towards an earlier and longer summer. Atmósfera 28: 149–160. [CrossRef] [Google Scholar]
  • 2023a. GBIF Home Page.> (Accessed: 21 April 2023) [Google Scholar]
  • GBIF. 2023b. GBIF Occurrence Download. (Accessed: 21 April 2023) [Google Scholar]
  • GBIF. 2023c. GBIF Occurrence Download. (Accessed: 21 April 2023) [Google Scholar]
  • GBIF. 2023d. GBIF Occurrence Download. (Accessed: 21 April 2023) [Google Scholar]
  • Hijmans RJ. 2021. raster: Geographic Data Analysis and Modeling. R package version 3.4-13. [Google Scholar]
  • Hughes, King, 2023. Habitat[r2] suitability modelling for an integrated multi-trophic aquaculture (IMTA) system along Europe’s Atlantic coast [Google Scholar]
  • Kim JK, Yarish C, Hwang EK, Park M, Kim Y, Kim JK, Yarish C, Hwang EK, Park M, Kim Y 2017. Seaweed aquaculture: cultivation technologies, challenges and its ecosystem services. Algae 32: 1–13. [CrossRef] [Google Scholar]
  • Kleitou P, Kletou D, David J. 2018. Is Europe ready for integrated multi-trophic aquaculture? A survey on the perspectives of European farmers and scientists with IMTA experience. Aquaculture 490: 136–148. [CrossRef] [Google Scholar]
  • Korsøen ØJ, Fosseidengen JE, Kristiansen TS, Oppedal F, Bui S, Dempster T. 2012. Atlantic salmon (Salmo salar L.) in a submerged sea-cage adapt rapidly to re-fill their swim bladders in an underwater air filled dome. Aquacult Eng 51: 1–6. [CrossRef] [Google Scholar]
  • Korzen L, Abelson A, Israel A. 2016. Growth, protein and carbohydrate contents in Ulva rigida and Gracilaria bursa-pastoris integrated with an offshore fish farm. J Appl Phycol 28: 1835–1845. [CrossRef] [Google Scholar]
  • Marinho G, Nunes C, Sousa-Pinto I, Pereira R, Rema P, Valente LM. 2013. The IMTA-cultivated Chlorophyta ulva spp. as a sustainable ingredient in Nile tilapia (Oreochromis niloticus) diets. J Appl Phycol 25: 1359–1367. [CrossRef] [Google Scholar]
  • Martinez-Porchas M, Martinez-Cordova LR. 2012. World aquaculture: environmental impacts and troubleshooting alternatives[r3]. Scientific World J 2012. [Google Scholar]
  • National Geospatial-Intelligence Agency, Word Port Index. 2016. National Geospatial-Intelligence Agency [producer and distributor], Data © Copyright 2017 By the United States Government. Published to by Geospatial Geoscience Ltd. (January, 2023). [Google Scholar]
  • Neori A, Chopin T, Troell M, Buschmann AH, Kraemer GP, Halling C, Shpigel M, Yarish C. 2004. Integrated aquaculture: rationale, evolution and state of the art emphasizing seaweed biofiltration in modern mariculture. Aquaculture 231: 361–391. [CrossRef] [Google Scholar]
  • Pebesma EJ, Bivand RS. 2005. Classes and methods for spatial data in R. R News 5 (2), [Google Scholar]
  • Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190: 231–259. [CrossRef] [Google Scholar]
  • Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S. 2009. Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecol Appl 19: 181–197. [CrossRef] [PubMed] [Google Scholar]
  • Phillips SJ. 2017. A Brief Tutorial on Maxent. (Accessed on 2023-1-5) [Google Scholar]
  • Pillay TVR. 2008. Aquaculture and the Environment, John[r4] Wiley & Sons, 2008, pp. 2–3. [Google Scholar]
  • Prestinicola L, Boglione C, Cataudella S. 2014. Relationship between uninflated swim bladder and skeletal anomalies in reared gilthead seabream (Sparus aurata). Aquaculture 432: 462–469. [CrossRef] [Google Scholar]
  • R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Google Scholar]
  • Radosavljevic A, Anderson RP. 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J Biogeogr 41: 629–643. [CrossRef] [Google Scholar]
  • Sarà G, Zenone A, Tomasello A. 2009. Growth of Mytilus galloprovincialis (mollusca, bivalvia) close to fish farms: a case of integrated multi-trophic aquaculture within the Tyrrhenian Sea. Hydrobiologia 636: 129–136. [CrossRef] [Google Scholar]
  • Sbrocco EJ, Barber PH. 2013. MARSPEC: ocean climate layers for marine spatial ecology. Ecology 94: 979. [CrossRef] [Google Scholar]
  • Shcheglovitova M, Anderson RP. 2013. Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecol Modell 269: 9–17. [CrossRef] [Google Scholar]
  • Tidwell JH, Allan GL. 2001. Fish as food: aquaculture's contribution. EMBO Rep 2: 958–963. [CrossRef] [PubMed] [Google Scholar]
  • Townsend Peterson A, Papeş M, Eaton M. 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30: 550–560. [CrossRef] [Google Scholar]
  • Troell M, Joyce A, Chopin T, Neori A, Buschmann AH, Fang JG. 2009. Ecological engineering in aquaculture—potential for integrated multi-trophic aquaculture (IMTA) in marine offshore systems. Aquaculture 297: 1–9. [CrossRef] [Google Scholar]
  • Troell M, Naylor RL, Metian M, Beveridge M, Tyedmers PH, Folke C, Arrow KJ, Barrett S, Crépin AS, Ehrlich PR, Gren Å. 2014. Does aquaculture add resilience to the global food system? Proc Natl Acad Sci 111: 13257–13263. [CrossRef] [PubMed] [Google Scholar]
  • Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O. 2012. Bio‐ORACLE: a global environmental dataset for marine species distribution modelling. Glob Ecol Biogeogr 21: 272–281. [CrossRef] [Google Scholar]
  • UNEP-WCMC & IUCN. 2023. Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) [Online], January 2023, Cambridge, UK: UNEP-WCMC & IUCN. Available at: [Google Scholar]
  • United Nations Department of Economic and Social Affairs, Population Division. 2022. World Population Prospects 2022: Summary of Results. UN DESA/POP/2022/TR/NO. 3. [Google Scholar]
  • Veloz SD. 2009. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models. J Biogeogr 36: 2290–2299. [CrossRef] [Google Scholar]
  • Wei B, Wang R, Hou K, Wang X, Wu W. 2018. Predicting the current and future cultivation regions of Carthamus tinctorius L. using MaxEnt model under climate change in China. Glob Ecol Conserv 16: e00477. [MathSciNet] [Google Scholar]
  • Williamson P, Turley CM, Ostle C. 2017. Ocean acidification. MCCIP Sci Rev. 2017. [Google Scholar]
  • Wiltshire KH, Tanner JE. 2020. Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species. Ecol Modell 429: 109071. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.