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Spatial database model of ichthyofauna bioindicators of coastal environment

Spatial database model of ichthyofauna bioindicators of coastal environment. Jorge Brenner and José A. Jiménez Coastal Zone Management Group Universitat Politècnica de Catalunya. Ocean Biodiversity Informatics Conference Hamburg, Germany December 1, 2004. Contents.

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Spatial database model of ichthyofauna bioindicators of coastal environment

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  1. Spatial database model of ichthyofauna bioindicators of coastal environment Jorge Brenner and José A. Jiménez Coastal Zone Management Group Universitat Politècnica de Catalunya Ocean Biodiversity Informatics Conference Hamburg, Germany December 1, 2004

  2. Contents • Objectives and motivation • Case of study • Conceptual approach • Data model • Pre-implementation OBI, Hamburg, Dec. 1, 2004

  3. Objectives At this moment: To develop an ichthyofauna indicator spatial data model Is fish diversity a good/useful indicator of the coastal environment? In a broader scope: To develop an indicator framework for assessing the environmental condition of the Calatonian coast. OBI, Hamburg, Dec. 1, 2004

  4. Reseach motivation • Develop a bioindicator framework for: • Envision the complexity • Understand the role of biodiversity function • Assess the system ecological condition • Identify conservation priorities • Develop a monitoring/management tool Science based CZ/Ocean Management • Local issues: • Several legal motivations (EU Water Dir., 2006) • Other community based bioindicators • Address coastal resources state • Mitigate human competition for coastal • resources OBI, Hamburg, Dec. 1, 2004

  5. Study area • Catalonian coastal area: • 848 km long coastline • 44 % of total population (2.8 mill.) living in the coastal municipalities • One of the largest ports in the Mediterranean • A global tourist coastal destination Catalonia Mediterranean Sea Ebro River delta Continental shelf OBI, Hamburg, Dec. 1, 2004

  6. Conceptual approach B. Desired/sustainable state • Probability of accomplish depends on system’s stability, given by: • Structure • Function System’s condition Ecological resilience A. Unknown transitional state - Multiscale – accross scales - OBI, Hamburg, Dec. 1, 2004

  7. Functional diversity Functions Fish biodiversity Memory Link Fish functional diversity Diversity groups Ecosystem resilience Response - Diversity (interaction) buffer variability - OBI, Hamburg, Dec. 1, 2004

  8. The functional model Functional diversity groups Structure template Output Resilience algorithm Input Taxa occurrence 1 . . . N Link Fish Criteria Criteria Resilience Response Community unit (1 . . . N) Memory • Marine communities • Pressure – impacts • Vulnerability GIS sub-models - Ecological resilience: distribution of functional groups at accross scales - OBI, Hamburg, Dec. 1, 2004

  9. The data model: general Management tools Objectives Core groups Dependent • Indicator (s): • Condition • Management Fish diversity Bio-physical General S y s t e m m o d u l e s Specific Socio-economic Independent Independent External: Data + Applications M e t a d a t a G I S OBI, Hamburg, Dec. 1, 2004

  10. The data model: conceptual Resilience assessment Spatial domain structured Community * * Fish diversity 1..* Functional group Vulnerability Impact S p a t i a l r e l a t i o n s h i p s EO 1..* Pressure 1..* Ecological Taxonomic OBI, Hamburg, Dec. 1, 2004

  11. Database implementation Fish species: CBR-CSIC + literature + Fishbase: 265 species in 93 families 46 species with some degree of concern (30 families) 93 maximum EO in sample point 2598 total EOs in analysis area OBI, Hamburg, Dec. 1, 2004

  12. Species – environment Analysis: • Mantel’s simple correlation between spp EOs and • Independent variables. B. RDA analysis with automatic selection among “all parameters:” 15 % of variance. • Conceptual models (131 spp @ 999 permutations): • Pressure indexes • Bio-physico-chemical parameters • Hybrid model • 0.174 • p=0.001 • M = Indexes -> Parameters • species • Examples of species found related to: • NO3-M: • Cetorhinus maximus (Cetorhinidae; very low) • Syngnathus phlegon (Syngnathidae; medium) • Helicolenus d. Dactylopterus (Sebastidae; ?) • Alosa fallax nilotica (Clupeidae; medium) • FC-M: • Chelidonichthys lucernus (Triglidae; low) • Callionymus risso (Callionymidae; high) • Scomber japonicus (Scombridae; medium) • Spondyliosoma cantharus (Sparidae; medium) • Polyacanthonotus rissoanu (Notacanthidae; ?) • Pomatoschistus microps (Gobiidae; high) • 0.0713 • p=0.015 0.079 p=0.004 OBI, Hamburg, Dec. 1, 2004

  13. Final ideas • Structure controlled fish species can represent specific functional groups at macroecology level • Ecological resilience can be a reasonable proxy of the ecological condition at multiple scales of the marine environment • The design (model) of species behaviours is directly influenced by data depth, breath and quality and determines the implementation of the data conceptual model • Species presence only data relation to environmental factors and coastal originated human impacts is scale dependent of the biophysical model OBI, Hamburg, Dec. 1, 2004

  14. On going work • Improve the coastal/marine biophysical model in order to develop species distribution models • Identify the functional research clusters based on specific structural criteria • Assess the coastal/marine probable resilience at community level OBI, Hamburg, Dec. 1, 2004

  15. Thanks for their support to: • Marine Engineering Lab (LIM) – UPC • Fishbase Project (www.fishbase.org) • Agencia Catalana de l’Aigua (DMAiH) - GenCat THANK YOU Jorge Brenner +34-934017392 jorge.brenner@upc.edu OBI, Hamburg, Dec. 1, 2004

  16. Structure template Trophic level (1 … N) • Local • Frequent Occurrence type OBI, Hamburg, Dec. 1, 2004 * Group of parameters

  17. Indicator Pressure attributes Impact factor Industry Nuclear plant / other 1-1000 m Aquaculture Surface / type / organism / intensity 1000 m Coastal Tourism Beach length >= 100 m / high use / urban Beach length Submarine waste outfalls Diameter / long / category / status Outfall length Ports Type / surface class 2000 m Coastal Urban Pressure Municipal urban surface / municipality coastal length Coastal length Pressure - impact model Land originated P- I: Possible impact species: 66.7 % spp (177) 54.5 % SCS (6) Possible impact area: 32.8 % EOs (854) 10.7 % hexagons (331) OBI, Hamburg, Dec. 1, 2004

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