Drivers of diversity gradients of a highly mobile marine assemblage in a mesoscale seascape

Guillermo M. Svendsen*, Matias Ocampo Reinaldo, Maria Alejandra Romero, Gabriela Williams, Anne Magurran, Sandra Luque, Raul A. Gonzalez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.
Original languageEnglish
Pages (from-to)149-164
Number of pages16
JournalMarine Ecology Progress Series
Volume638
DOIs
Publication statusE-pub ahead of print - 19 Mar 2020

Keywords

  • Demersal fishes
  • Species-energy relationships
  • Spatial structure
  • Marine fronts
  • Alpha-diversity
  • Bay effect
  • Boosted regression trees
  • Connectivity

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