spatial database model of ichthyofauna bioindicators of coastal environment jorge brenner and josé...
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Spatial database model of ichthyofauna bioindicators of coastal environment
Jorge Brenner and José A. JiménezCoastal Zone Management Group
Universitat Politècnica de Catalunya
Ocean Biodiversity Informatics Conference
Hamburg, Germany
December 1, 2004
OBI, Hamburg, Dec. 1, 2004 2
Contents
• Objectives and motivation
• Case of study
• Conceptual approach
• Data model
•Pre-implementation
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Objectives
To develop an ichthyofaunaindicator spatial data model
To develop an indicator framework for assessing theenvironmental condition of the Calatonian coast.
At this moment:
In a broader scope:
Is fish diversity a good/usefulindicator of the coastal environment?
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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
Sci
ence
bas
ed C
Z/O
cean
Man
agem
ent
Sci
ence
bas
ed C
Z/O
cean
Man
agem
ent
Local issues:• Several legal motivations (EU Water Dir., 2006)• Other community based bioindicators• Address coastal resources state• Mitigate human competition for coastalresources
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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
Continental shelf
Mediterranean SeaEbro Riverdelta
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Conceptual approach
System’scondition
B. Desired/sustainable state
A. Unknown transitional state
Probability of accomplish depends on system’s stability, given by:
• Structure• Function
- Multiscale – accross scales -
Eco
log
ical
res
ilie
nce
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Functional diversity
ResponseLi
nk
Mem
ory
Diversitygroups
Fish functionaldiversity
Ecosystemresilience
Fish biodiversity
- Diversity (interaction) buffer variability -- Diversity (interaction) buffer variability -
Functions
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The functional model
- Ecological resilience: distribution of functional groups at accross scales -- Ecological resilience: distribution of functional groups at accross scales -
Link
Response
Memory
Criteria Criteria Resilience
Functionaldiversitygroups
Co
mm
un
ity
un
it (
Co
mm
un
ity
un
it (
1 .
. .
N)
1 .
. .
N)
Taxaoccurrence
1...N
OutputInput Structuretemplate
Resiliencealgorithm
GIS
sub
-mo
del
s
Fis
h
• Marine communities• Pressure – impacts• Vulnerability
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The data model: generalS
y s
t e
m
m o
d u
l e
sS
y s
t e
m
m o
d u
l e
s
Independent
Gen
eral
Spe
cific
Dependent
Independent
Fish diversityBio-physical
Socio-economic
M e t a d a t a
Objectives Core groupsManagement
tools
Indicator (s):
• Condition
• Management
Data + ApplicationsExternal:
G I S
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The data model: conceptualS
p a
t i
a l
r
e l
a t
i o
n s
h i
p s
S p
a t
i a
l
r e
l a
t i
o n
s h
i p
s
ImpactVulnerability
Community
Functionalgroup
1..*Pressure
EO
TaxonomicEcological
1..*
*
*
1..*Fish diversityFish diversity
Resilience assessmentResilience assessment Spatial domainstructured
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Database implementation
480
850
2542
158 105 164
1904-1980 1981-1990 1991-1995 1996-2000
Eos
Spp
Fish species:
CBR-CSIC + literature + Fishbase:
265 species in 93 families46 species with some degree of concern (30 families)93 maximum EO in sample point2598 total EOs in analysis area
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Species – environment
Analysis:
A. Mantel’s simple correlation between spp EOs andIndependent variables.
Conceptual models (131 spp @ 999 permutations):
A) Pressure indexesB) Bio-physico-chemical parametersC) Hybrid model
0.174 p=0.001
M = Indexes -> Parameters species
0.079p=0.004
-0.0713p=0.015
B. RDA analysis with automatic selectionamong “all parameters:” 15 % of variance.
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)
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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
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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
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Thanks for their support to:
– Marine Engineering Lab (LIM) – UPC– Fishbase Project (www.fishbase.org)– Agencia Catalana de l’Aigua (DMAiH) - GenCat
THANK YOUTHANK YOU
Jorge Brenner
+34-934017392
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Structure template
Tro
ph
ic le
vel
(1
… N
) LINK
• Swimming mode
• Max weigth• TL• Depth range• Environment
MEMORY
• Reproduction type *• Growth *• Swimming mode• Feeding habit• TL
RESPONSE
• Reproduction type *• Growth *• Feeding habit• Depth range
Occurrence type
* Group of parameters
• Local• Frequent
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Pressure - impact model
Land originated P- I: Possible impact area:
32.8 % EOs (854)
10.7 % hexagons (331)
Possible impact species:
66.7 % spp (177)
54.5 % SCS (6)
Water pro pertiesIndus tryAquacultureTourismWaste discharge tubesP ortsCU P 2001
EOs012 - 34 - 2123 - 3739 - 93
Land
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