title: Global patterns and predictors of fish species richness in estuaries.
abstract: 1. Knowledge of global patterns of biodiversity and regulating variables is indispensable to develop predictive models. 2. The present study used predictive modelling approaches to investigate hypotheses that explain the variation in fish species richness between estuaries over a worldwide spatial extent. Ultimately, such models will allow assessment of future changes in ecosystem structure and function as a result of environmental changes. 3. A comprehensive worldwide data base was compiled of the fish assemblage composition and environmental characteristics of estuaries. Generalized Linear Models were used to quantify how variation in species richness among estuaries is related to historical events, energy dynamics and ecosystem characteristics, while controlling for sampling effects. 4. At the global extent, species richness differed among marine biogeographic realms and continents and increased with mean sea surface temperature, terrestrial net primary productivity and the stability of connectivity with a marine ecosystem (open vs. temporarily open estuaries). At a smaller extent (within a marine biogeographic realm or continent), other characteristics were also important in predicting variation in species richness, with species richness increasing with estuary area and continental shelf width. 5. The results suggest that species richness in an estuary is defined by predictors that are spatially hierarchical. Over the largest spatial extents, species richness is influenced by the broader distributions and habitat use patterns of marine and freshwater species that can colonize estuaries, which are in turn governed by history contingency, energy dynamics and productivity variables. Species richness is also influenced by more regional and local parameters that can further affect the process of community colonization in an estuary including the connectivity of the estuary with the adjacent marine habitat, and, over smaller spatial extents, the size of these habitats. In summary, patterns of species richness in estuaries across large spatial extents seem to reflect from global to local processes acting on community colonization. The importance of considering spatial extent, sampling effects and of combining history and contemporary environmental characteristics when exploring biodiversity is highlighted.
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Rita P
authors: RP
authors: Vasconcelos
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Sofia
authors: S
authors: Henriques
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Susana
authors: S
authors: França
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Stéphanie
authors: S
authors: Pasquaud
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Inês
authors: I
authors: Cardoso
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Marina
authors: M
authors: Laborde
authors: MARE - Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.
authors: Henrique N
authors: HN
authors: Cabral
mesh_terms: D000818:Animals; D044822:Biodiversity; D003247:Conservation of Natural Resources; D017753:Ecosystem; D063366:Estuaries; D005399:Fishes / Q000502:physiology*; D016014:Linear Models; D008954:Models, Biological
keywords: alpha-diversity; coastal; community; meta-analysis; predictive models; review; species distribution models