Transcript John J. Kineman Physical Scientist/Ecologist E C O S Y S T E M I N F O R M A T I C S (National Geophysical Data Center) Research Associate (University of Colorado) [email protected] www.ngdc.noaa.gov/seg/ecosys.shtml.

John J. Kineman
Physical Scientist/Ecologist
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(National Geophysical Data Center)
Research Associate
(University of Colorado)
[email protected]
www.ngdc.noaa.gov/seg/ecosys.shtml
Biodiversity and Ecosystem Informatics
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PCAST: “Research on, development of, and use of technological, sociological,
and organizational tools and approaches for the dynamic acquisition, indexing,
dissemination, storage, querying, retrieval, visualization, integration,
analysis, synthesis, sharing (which includes electronic means of collaboration),
and publication of data such that economic and other benefits may be derived
from the information by users from all sectors of society.”
NSF/NBII-2/BDEI: “Until recently, little attention has been paid to computer and
information science and technology research in the biodiversity and
ecosystem domain. The interdisciplinary field of biodiversity and ecosystem
informatics (BDEI) is attempting to change that.”
Report: Dave Maier, Eric Landis, Judy Cushing, Anne Frondorf, Avi Silberschatz, Mike
Frame, and John L. Schnase (Editors). 2001. Research Directions in Biodieversity and
Ecosystem Informatics. Report of an NSF, USGS, NASA Workshop on Biodiversity and
Ecosystem Informatics held at NASA Goddard Space Fight Center, June 22-23, 2000.1 31pp.
Ecological Indicators, Assessment and
Monitoring
NRC - National Ecological Indicators
Heinz - The State of the Nations Ecosystems
IPCC - Climate Change 2001
UNEP - Global Environmental Outlook - 3
WRI - Pilot Assessment of Global Ecosystems
WRI - World Resources 2000-2001
GOOS - Coastal Ocean Observing System
World Bank - World Development Report 2004
WRI - Reefs at Risk
CORIS - Coral Reef Action Strategy
Millennium Ecosystem Assessment
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"In all five ecosystem types PAGE analyzed,
ecosystem capacity is decreasing over a range of
goods and services, not just one or two.“
(Pilot Analysis of Global Ecosystems)
Problem:
 Human demand for ecosystem goods and
services is growing dramatically
 We have made, and are making, changes to
ecosystems of unprecedented magnitude
Biodiversity underlies all other goods and
services and provides “goods” in its own
right.
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An estimated
10-15% of the
world’s
species will
be committed
to extinction
over the next
30 years.
Ecosystem "Goods and Services"
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Ecosystem services: the conditions and processes
supported by biodiversity through which ecosystems
sustain and fulfil human life…
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Biological Goods: e.g. food, water, fibre, fuel, other
biological products and biotechnology
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Ecological Functions: e.g. biodiversity, pollination,
waste treatment, biogeochemical cycling
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Human Values: e.g. cultural, aesthetic, social,
psychological, and ethical
Integrated Ecosystem Assessment
“Optimizing” Multi-Sector “Tradeoffs”
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Key
Condition
Food-Fiber Production
Excellent
Good
Water Quality
Fair
Poor
Water Quantity
Bad
Not Assessed
Biodiversity
Carbon Storage
Changing
Capacity
Increasing
Decreasing
Mixed
IPCC: "Human activity is significantly affecting the
climate system."
But what do we know about
Ecosystems?
WRI: “…the PAGE study faced limitations in the basic data
needed to determine the condition of global ecosystems.”
UNEP/GEO-3: “Missing data and data of uncertain
quality are seriously hindering integrated environmental
assessment at global and regional levels"
Regional Ecosystems Assessment Database
A Data "Collaboratory"
Regional
Ecosystems
 High Quality Publication
of Case-study Data
 Modeling Support
Assessment
Database
 Evaluation of Observing
System Data
Pacific Basin Coastal Ecosystems
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 Ecosystem Decline and Vulnerability
 Causes and Effects
 Management Options
 Hotspot Detection and Early Warning
 Ecological Indicators and Monitoring
Regional Ecosystems Assessment Database
Components
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Data Quality and Structure
Documentation / Metadata
Scientific Design / Linkage
Spatial Models
Publication / Archive
Field Assistance
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Niche Modeling
0.4
Temperature
We combine probability
distributions to model
the niche in “character”
space.
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10
20
30
40
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Salinity
0.2
Co mbi ned p ro bab il it y
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0
10
20
30
40
50
x
ECOSYSTEM INFORMATICS
Model Interface
Multi-variate Stratification
 temp
 precip
 swhc
 PAR
 LUI
 [N]
 elev
 Vrel
 PET
 LAI
Pden
s
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100
50
75
50
50
45
76
86
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25
10<23
300<700
=
=
<10
=
=
=
=
<20
50<
linear
linear
linear
linear
log
sqrt
linear
sqr
e(3x-7)
1/x
linear
 Cluster analysis
 Maximum liklihood
 Baysian probabilities
 gradient analysis
 Define Classes
 Define training
 Define thresholds
 System defined
Preview
FD = 1.3420
Previous Next
Niche Model
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Decadal
Annual
Seasonal
MonthlyDaily
Hourly
Coars
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Fine
Line fractal
dimension (FD)
Delineation Method
Spatial
Scale of unit
User Definition
Output
Time
Scale
Response
function
Range
Weight
factor
Select
data layers
Input
1.2D
1.7D
P
start time
Set preview background image
end time
Add data layer / time period
Save Model and Execute
time step
(run avg.)
Remove data layer
Reset defaults
link FD with
spatial scale
ECOSYSTEM INFORMATICS
Adaptive Ecological Mapping
Multi-variate Stratification
 temp
 precip
 swhc
 PAR
ÿ LUI
ÿ [N]
ÿ elev
ÿ Vrel
ÿ PET
ÿ LAI
ÿPdens
100
100
50
75
50
50
45
76
86
45
25
linear
linear
linear
linear
log
sqrt
linear
sqr
e(3x-7)
1/x
linear
ÿ Define C lasses
ÿ Define training
ÿ Define thresholds
 System defined
Preview
Output
Delineation Method
 Cluster analysis
ÿ Maximum liklihood
ÿ Baysian probabilities
ÿ gradient analysis
FD = 1.3420
Previous Next
Time
Scale
User Definition
Spatial
Scale of unit
Range
10<23
300<700
=
=
<10
=
=
=
=
<20
50<
Response
function
Weight
factor
Iteration
and validation
Select
data layers
Input
Decadal
Annual
Seasonal
MonthlyDaily
Hourly
Coarse
Fine
start time
Set preview background image
Add data layer / time period
Save Model and Execute
Remove data layer
Reset defaults
end time
time step
(run avg.)
Line fractal
dimension (FD)
Potential
Eco-units
1.2D
1.7D
Model
improved data, time series
new measures
higher resolution
error correction
Define Controlling Variables
Temperature
Precipitation
Revisions
link FD with
spatial scale
Photosynthetic Radiation
Soil Water Holding Capacity
Ecosystem Informatics at NGDC
24 April 2002
Environmental Database
validation
Test Observations
(Satellite, in-situ, collections, research, etc.)
ECOSYSTEM INFORMATICS
Model Test
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Eastern
Hardwood
(T,P,E)
MA Goal: “...to increase the amount, quality, and credibility of
policy-relevant scientific research findings.”
What are the
Measures?
What is
Ecosystem
Health?
What about
Complexity?
GOOS: Phenomena of Interest
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sea state and surface currents
sea level rise
coastal erosion and flooding
public health risks
habitat modification and loss (e.g., coral reefs,
sea grass beds, tidal wetlands)
loss of biodiversity
oxygen depletion
harmful algal events
fish kills
declining fish stocks
beach and shellfish bed closures
increasing public health risks
Ecological Indicators?
Heinz Report
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Hotspots (change, genetic)
Stratification (diversity)
Coastal Ecosystems
- fragmentation and pattern
Available nitrogen?
Tankers,
Runoff, Water column?
dredging? trawling?
coastline modification?
Core
Coastal
Species turnover / extinction
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Keystone species
Indicator species
Zooplankton?
Disease vectors
Ecological Health Indicators
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 Toxic Pollution
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Environmental Samples (air, water, sediments)
Tissue burdens, bioaccumulation
 Biochemical Cycling
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Nitrogen: "leaky ecosystems"
Disolved oxygen / Eutrophication ("dead" zones)
 Species Composition & Range Shifts
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Keystone & indicator species health & population
Extinction, invasion, replacement
Algal blooms, bacterial compositions
Diversity, richness
 Ecosystem Structure and Function
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Spatial extent, fragmentation, disturbance, conversion
Feedbacks, rates, stability, resilience, attractors, etc.
Productivity, food chain
 Disease Vectors
Ecological Forcing
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 Harvesting
 Fish, shellfish, seaweed
 Agricultural production and practices
 Species and Habitat Changes
 Introduced and Invasive Species
 Extinction and replacement rates
 Habitat conversion
 Coastal Development
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Human population, settlement, and use
Infrastructure
Hydrologic alteration
Toxic pollution, sewage
Shoreline change
 Climate Change and Variability
Integrated Ecosystem Assessment
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 Status of ecological goods and services
 Commercial harvest / sustainability
 Valuation
 Tradeoffs
 Habitat & Niche Status
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Habitat vs. environmental & human induced change
Protection needs (e.g., Gap Analysis)
Migration, invasion, colonization pathways
Biodiversity hotspots and genetic resources
 Ecological Design
 Management & protection areas
 Development and management future scenarios
Monitoring and Detection
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 Hotspot Detection and Delineation
 Sudden/significant ecological events
 Genetic hotspots and resource stratification
 Documenting Ecological Change
 Cumulative/creeping processes and effects
 Macro-ecological changes
 Societal impacts
 Early Warning
 Drivers of ecosystem change
 Molecular scale biological changes
 Societal risks from ecological change
Synthesis and Decision Support
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 Community-based assessment and planning
 Ecological Characterization
 Valuation of Goods and Services
 Regional Planning
 State-Federal collaboration
 National Policy
 Mandated Information
 Data & Information Sharing
 Indexing (metadata, etc.), Publication
 Presentation products
 Mitigation and Restoration Options
Environmental Controls
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Climate (temperature, humidity, rain, etc.)
Weather, waves, tides
Water availability & quality
Atmospheric chemistry
Aerosols, turbidity
Soil/substrate characteristics
Sunlight
Nutrient availability / cycling
Physical and geographical structure
Ocean and atmosphere circulation & mixing
Deposition
Disturbance (natural & human)
Toxins
Biotic controls (competition, disease, allelopaths, etc.)
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A Proposed Soil Moisture Product (time
series), from Single Instrument
(SMMR/SSMI):
API = a – b(Tv + Th)0.5 – c(Tv –Th)d
WHERE,
API = Antecedent Precipitation Index
Tv =Vertical Polarization,
Th = Horizontal Polarization,
a = 139.55; b = 0.21; c = 86.12 and d = -0.017
This model has been tested to derive soil
moisture from the least to the most densely
vegetated areas (NDVI 0.3 to 0.65)
Dr. Nizam Ahmed
National Geophysical Data Center
API vs. Soil Moisture (from met. data)
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Microwave
emissivity and
polarization
difference
Accounted for
80 % of the
observed
variability in the
soil moisture
Correlation
coefficient 0.91
and Standard
Error 1.18mm