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Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence

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  • Melo-Merino, Sara M.
  • Reyes-Bonilla, Héctor
  • Lira-Noriega, Andrés

Abstract

In recent years, the use of ecological niche models (ENMs) and species distribution models (SDMs) to explore the patterns and processes behind observed distribution of species has experienced an explosive growth. Although the use of these methods has been less common and more recent in marine ecosystems than in a terrestrial context, they have shown significant increases in use and applications. Herein, we provide a systematic review of 328 articles on marine ENMs and SDMs published between 1990 and 2016, aiming to identify their main applications and the diversity of methodological frameworks in which they are developed, including spatial scale, geographic realm, taxonomic groups assessed, algorithms implemented, and data sources. Of the 328 studies, 48 % were at local scales, with a hotspot of research effort in the North Atlantic Ocean. Most studies were based on correlative approaches and were used to answer ecological or biogeographic questions about mechanisms underlying geographic ranges (64 %). A few attempted to evaluate impacts of climate change (19 %) or to develop strategies for conservation (11 %). Several correlative techniques have been used, but most common was the machine-learning approach Maxent (46 %) and statistical approaches such as generalized additive models GAMs (22 %) and generalized linear models, GLMs (14 %). The groups most studied were fish (23 %), molluscs (16 %), and marine mammals (14 %), the first two with commercial importance and the last important for conservation. We noted a lack of clarity regarding the definitions of ENMs versus SDMs, and a rather consistent failure to differentiate between them. This review exposed a need to know, reduce, and report error and uncertainty associated with species’ occurrence records and environmental data. In addition, particular to marine realms, a third dimension should be incorporated into the modelling process, referring to the vertical position of the species, which will improve the precision and utility of these models. So too is of paramount importance the consideration of temporal and spatial resolution of environmental layers to adequately represent the dynamic nature of marine ecosystems, especially in the case of highly mobile species.

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  • Melo-Merino, Sara M. & Reyes-Bonilla, Héctor & Lira-Noriega, Andrés, 2020. "Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence," Ecological Modelling, Elsevier, vol. 415(C).
  • Handle: RePEc:eee:ecomod:v:415:y:2020:i:c:s030438001930345x
    DOI: 10.1016/j.ecolmodel.2019.108837
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    6. Rodrigues, Lucas dos Santos & Daudt, Nicholas Winterle & Cardoso, Luis Gustavo & Kinas, Paul Gerhard & Conesa, David & Pennino, Maria Grazia, 2023. "Species distribution modelling in the Southwestern Atlantic Ocean: A systematic review and trends," Ecological Modelling, Elsevier, vol. 486(C).
    7. Azuaje-Rodríguez, Roxiris A. & Silva, Sofia Marques & Carlos, Caio J., 2022. "Not going with the flow: Ecological niche of a migratory seabird, the South American Tern Sterna hirundinacea," Ecological Modelling, Elsevier, vol. 463(C).
    8. Barber-O'Malley, Betsy & Lassalle, Géraldine & Chust, Guillem & Diaz, Estibaliz & O'Malley, Andrew & Paradinas Blázquez, César & Pórtoles Marquina, Javier & Lambert, Patrick, 2022. "HyDiaD: A hybrid species distribution model combining dispersal, multi-habitat suitability, and population dynamics for diadromous species under climate change scenarios," Ecological Modelling, Elsevier, vol. 470(C).
    9. Brown, Christian H. & Griscom, Heather P., 2022. "Differentiating between distribution and suitable habitat in ecological niche models: A red spruce (Picea rubens) case study," Ecological Modelling, Elsevier, vol. 472(C).
    10. Pinto, Miguel & Albo-Puigserver, Marta & Bueno-Pardo, Juan & Monteiro, João Nuno & Teodósio, Maria Alexandra & Leitão, Francisco, 2023. "Eco-socio-economic vulnerability assessment of Portuguese fisheries to climate change," Ecological Economics, Elsevier, vol. 212(C).
    11. Varos Petrosyan & Fedor Osipov & Vladimir Bobrov & Natalia Dergunova & Andrey Omelchenko & Alexander Varshavskiy & Felix Danielyan & Marine Arakelyan, 2020. "Species Distribution Models and Niche Partitioning among Unisexual Darevskia dahli and Its Parental Bisexual ( D. portschinskii , D. mixta ) Rock Lizards in the Caucasus," Mathematics, MDPI, vol. 8(8), pages 1-21, August.
    12. Rotllan-Puig, Xavier & Traveset, Anna, 2021. "Determining the minimal background area for species distribution models: MinBAR package," Ecological Modelling, Elsevier, vol. 439(C).
    13. Damiana Ravasi & Francesca Mangili & David Huber & Laura Azzimonti & Lukas Engeler & Nicola Vermes & Giacomo Del Rio & Valeria Guidi & Mauro Tonolla & Eleonora Flacio, 2022. "Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito Aedes albopictus in Switzerland," IJERPH, MDPI, vol. 19(6), pages 1-22, March.
    14. Barbosa, Charles H.X.B. & Dias, Claudia M. & Pastore, Dayse H. & Silva, José C.R. & Costa, Anna R.C. & Santos, Isaac P. & Azevedo, Ramoni Z.S. & Figueira, Raquel M.A. & Fortunato, Humberto F.M., 2023. "Analysis of a mathematical model for golden mussels infestation," Ecological Modelling, Elsevier, vol. 486(C).

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