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Short-term response of macroalgal communities to ocean warming in the Southern Bay of Biscay
Arriaga, O.; Wawrzynkowski, P.; Ibáñez, H.; Muguerza, N.; Díez, I.; Pérez-Ruzafa, I.; Gorostiaga, J.M.; Quintano, E.; Becerro, M.A. (2023). Short-term response of macroalgal communities to ocean warming in the Southern Bay of Biscay. Mar. Environ. Res. 190: 106098. https://dx.doi.org/10.1016/j.marenvres.2023.106098
In: Marine Environmental Research. Applied Science Publishers: Barking. ISSN 0141-1136; e-ISSN 1879-0291, meer
Peer reviewed article  
Beschikbaar in  Auteurs 
Trefwoorden
    ANE, Europe, Iberian Peninsula
    Climate change
    Monitoring
    Temperature
    Iberian Peninsula [Marine Regions]
    Marien/Kust
Author keywords
    Macroalgae; Community temperature index (CTI); Canopy; Subtidal
Auteurs  Top 
  • Arriaga, O.
  • Wawrzynkowski, P.
  • Ibáñez, H.
  • Muguerza, N.
  • Díez, I.
  • Pérez-Ruzafa, I.
  • Gorostiaga, J.M.
  • Quintano, E.
  • Becerro, M.A.
Abstract
    Climate change is causing significant shifts in biological communities worldwide, including the degradation of marine communities. Previous research has predicted that southern Bay of Biscay canopy-forming subtidal macroalgal communities will shift into turf-forming Mediterranean-like communities by the end of the century. These predictions were based on a community-environment relationship model that used macroalgal abundance data and IPCC environmental projections. We have tested the short-term accuracy of that model by resampling the same communities and locations four years later and found the short-term predictions to be consistent with the observed communities. Changes in sea surface temperature were positively correlated with changes in the Community Temperature Index, suggesting that macroalgal communities had responded quickly to global warming. The changes over four years were significant, but canopy-forming macroalgae were more resilient in local sites with favourable temperature conditions. Our study demonstrated that updating predictive models with new data has the potential to yield reliable predictions and inform effective conservation strategies.
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