e c o s y s t e m i n f o r m a t i c s
DESCRIPTION
John J. Kineman Physical Scientist/Ecologist ( National Geophysical Data Center) Research Associate ( University of Colorado) [email protected] www.ngdc.noaa.gov/seg/ecosys.shtml. E C O S Y S T E M I N F O R M A T I C S. Biodiversity and Ecosystem Informatics. - PowerPoint PPT PresentationTRANSCRIPT
John J. KinemanPhysical Scientist/Ecologist
(National Geophysical Data Center)Research Associate
(University of Colorado)
[email protected]/seg/ecosys.shtml
ECOSYSTEM INFORMATICS
Biodiversity and Ecosystem Informatics
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.”
ECOSYSTEM INFORMATICS
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 IndicatorsHeinz - The State of the Nations EcosystemsIPCC - Climate Change 2001UNEP - Global Environmental Outlook - 3WRI - Pilot Assessment of Global EcosystemsWRI - World Resources 2000-2001GOOS - Coastal Ocean Observing SystemWorld Bank - World Development Report 2004WRI - Reefs at RiskCORIS - Coral Reef Action Strategy
Millennium Ecosystem Assessment
Problem: Human demand for ecosystem goods and
services is growing dramatically
We have made, and are making, changes to ecosystems of unprecedented magnitude
"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)
MILLENNIUM ASSESSMENT
Biodiversity underlies all other goods and services and provides “goods” in its own right.
An estimated 10-15% of the world’s species will be committed to extinction over the next 30 years.
MILLENNIUM ASSESSMENT
Ecosystem "Goods and Services"
Ecosystem services: the conditions and processes supported by biodiversity through which ecosystems sustain and fulfil human life…
Biological Goods: e.g. food, water, fibre, fuel, other biological products and biotechnology
Ecological Functions: e.g. biodiversity, pollination, waste treatment, biogeochemical cycling
Human Values: e.g. cultural, aesthetic, social, psychological, and ethical
MILLENNIUM ASSESSMENT
Integrated Ecosystem Assessment “Optimizing” Multi-Sector “Tradeoffs”
Excellent
Good
Fair
Poor
Bad
Not Assessed
AgroCoas
tFores
t
Freshwate
r
Grassla
nds
Food-Fiber Production
Water Quality
Water Quantity
Biodiversity
Carbon StorageIncreasing
Decreasing
Mixed
Condition
ChangingCapacity
KeyMILLENNIUM ASSESSMENT
But what do we know about Ecosystems?
UNEP/GEO-3: “Missing data and data of uncertain quality are seriously hindering integrated environmental assessment at global and regional levels"
WRI: “…the PAGE study faced limitations in the basic data needed to determine the condition of global ecosystems.”
IPCC: "Human activity is significantly affecting the climate system."
A Data "Collaboratory"
High Quality Publication of Case-study Data
Reg
ion
al E
cosy
stem
s A
sses
smen
t D
atab
ase
Regional
Ecosystems
Assessment
Database
Evaluation of Observing System Data
Modeling Support
Pacific Basin Coastal Ecosystems
Ecosystem Decline and Vulnerability Causes and Effects Management Options
Hotspot Detection and Early Warning
Ecological Indicators and Monitoring
R
E
A
D
Regional Ecosystems Assessment Database
D ata Q uality and S truc tu re D ocum en tation / M etadata
S c ien tif ic D es ign / L inkage S patial M odels
P ublication / A rch ive F ield A ss is tance
C om ponen ts
R
E
A
D
Combined probability
M
Niche ModelingWe combine probability distributions to model the niche in “character” space.
0 10 20 30 40 500
0.2
0.4
y
Temperature
Y
0 10 20 30 40 500
0.2
0.4
x
Salinity
XT
S
ECOSYSTEM INFORMATICS
Model Interface
Multi-variate Stratification
Define Classes Define training Define thresholds System defined
User Definition Delineation Method
Cluster analysis Maximum liklihood Baysian probabilities gradient analysis
Sel
ec
td
ata
la
yer
s
temp precip swhc PAR LUI [N] elev Vrel PET LAIPdens
Add data layer / time period
Remove data layer
100100507550504576864525
Wei
gh
tfa
cto
r
Ran
ge
10<23300<700==<10====<2050<
Res
po
ns
e
fun
cti
on
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
Input Output
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
eS
cale
start time
end time
time step(run avg.)
Sp
atia
lS
cale
of
un
it
Coarse------
--Fine
Lin
e fr
acta
ld
imen
sio
n (
FD
)
1.2D---------1.7D
link FD withspatial scale
Preview
Save Model and Execute
Set preview background image
Previous Next
Reset defaults
FD = 1.3420
Niche Model
T
P
S
ECOSYSTEM INFORMATICS
Adaptive Ecological Mapping
PotentialEco-units
improved data, time seriesnew measureshigher resolutionerror correction
validation
Iterationand validation Revisions
Environmental Database
Multi-variate Stratification
Se
lect
da
ta l
ay
ers
temp precip swhc PARÿ LUIÿ [N]ÿ elevÿ Vrelÿ PETÿ LAIÿ Pdens
Add data layer / time period
Remove data layer
100100507550504576864525
We
igh
t
fact
or
Ra
ng
e
10<23300<700==<10====<2050<
Resp
on
se
fun
ctio
n
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
ÿ Define Classesÿ Define trainingÿ Define thresholds System defined
User Definition Delineation Method Cluster analysisÿ Maximum liklihoodÿ Baysian probabilitiesÿ gradient analysis
OutputInput
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
e
Sca
le
start time
end time
time step(run avg.)
Sp
ati
al
Sca
le o
f u
nit
Coarse------
--Fine
Lin
e f
racta
l
dim
en
sio
n (
FD
)
1.2D---------1.7D
link FD withspatial scale
Preview
Save Model and Execute
Set preview background image
Previous Next
Reset defaults
FD = 1.3420
Multi-variate Stratification
Se
lect
da
ta l
ay
ers
temp precip swhc PARÿ LUIÿ [N]ÿ elevÿ Vrelÿ PETÿ LAIÿ Pdens
Add data layer / time period
Remove data layer
Se
lect
da
ta l
ay
ers
temp precip swhc PARÿ LUIÿ [N]ÿ elevÿ Vrelÿ PETÿ LAIÿ Pdens
Se
lect
da
ta l
ay
ers
temp precip swhc PARÿ LUIÿ [N]ÿ elevÿ Vrelÿ PETÿ LAIÿ Pdens
Add data layer / time period
Remove data layer
100100507550504576864525
We
igh
t
fact
or
100100507550504576864525
We
igh
t
fact
or
Ra
ng
e
10<23300<700==<10====<2050<
Ra
ng
e
10<23300<700==<10====<2050<
Resp
on
se
fun
ctio
n
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
Resp
on
se
fun
ctio
n
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
ÿ Define Classesÿ Define trainingÿ Define thresholds System defined
User Definition Delineation Method Cluster analysisÿ Maximum liklihoodÿ Baysian probabilitiesÿ gradient analysis
ÿ Define Classesÿ Define trainingÿ Define thresholds System defined
User Definitionÿ Define Classesÿ Define trainingÿ Define thresholds System defined
User Definition Delineation Method Cluster analysisÿ Maximum liklihoodÿ Baysian probabilitiesÿ gradient analysis
Delineation Method Cluster analysisÿ Maximum liklihoodÿ Baysian probabilitiesÿ gradient analysis
OutputInput
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
e
Sca
le
start time
end time
time step(run avg.)
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
e
Sca
le
start time
end time
time step(run avg.)
Sp
ati
al
Sca
le o
f u
nit
Coarse------
--Fine
Sp
ati
al
Sca
le o
f u
nit
Coarse------
--Fine
Lin
e f
racta
l
dim
en
sio
n (
FD
)
1.2D---------1.7D
link FD withspatial scale
Lin
e f
racta
l
dim
en
sio
n (
FD
)
1.2D---------1.7D
link FD withspatial scale
Preview
Save Model and Execute
Set preview background image
Previous Next
Reset defaults
FD = 1.3420Preview
Save Model and Execute
Set preview background image
Previous Next
Reset defaults
FD = 1.3420
Model
Test Observations (Satellite, in-situ, collections, research, etc.)
24 April 2002
Ecosystem Informatics at NGDC
Define Controlling Variables
Temperature Photosynthetic Radiation
Precipitation Soil Water Holding Capacity
ECOSYSTEM INFORMATICS
Model Test
Eastern Hardwood (T,P,E)
ECOSYSTEM INFORMATICS
MA Goal: “...to increase the amount, quality, and credibility of policy-relevant scientific research findings.”
What is Ecosystem
Health?
What are the Measures?
What aboutComplexity?
GOOS: Phenomena of Interest
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
HEINZ
REPORT
Coastal Ecosystems- fragmentation and pattern
Hotspots (change, genetic)Stratification (diversity)
Available nitrogen?
Runoff, Water column?
dredging? trawling?coastline modification?
Tankers,
HEINZ
REPORT
Core Coastal
Species turnover / extinction
Keystone speciesIndicator species
Zooplankton?
Disease vectors
Ecological Health Indicators
Toxic Pollution Environmental Samples (air, water, sediments) Tissue burdens, bioaccumulation
Biochemical Cycling Nitrogen: "leaky ecosystems" Disolved oxygen / Eutrophication ("dead" zones)
Species Composition & Range Shifts Keystone & indicator species health & population Extinction, invasion, replacement Algal blooms, bacterial compositions Diversity, richness
Ecosystem Structure and Function Spatial extent, fragmentation, disturbance, conversion Feedbacks, rates, stability, resilience, attractors, etc. Productivity, food chain
Disease Vectors
ECOSYSTEM INFORMATICS
Ecological Forcing
Harvesting Fish, shellfish, seaweed Agricultural production and practices
Species and Habitat Changes Introduced and Invasive Species Extinction and replacement rates Habitat conversion
Coastal Development Human population, settlement, and use Infrastructure Hydrologic alteration Toxic pollution, sewage Shoreline change
Climate Change and Variability
ECOSYSTEM INFORMATICS
Integrated Ecosystem Assessment
Status of ecological goods and services Commercial harvest / sustainability Valuation Tradeoffs
Habitat & Niche Status 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
ECOSYSTEM INFORMATICS
Monitoring and Detection
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
ECOSYSTEM INFORMATICS
Synthesis and Decision Support
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
ECOSYSTEM INFORMATICS
Environmental Controls
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.)
ECOSYSTEM INFORMATICS
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 AhmedNational Geophysical Data Center
ECOSYSTEM INFORMATICS
API vs. Soil Moisture (from met. data)
Microwave emissivity and polarization differenceAccounted for 80 % of the observed variability in the soil moisture
Correlation coefficient 0.91 and Standard Error 1.18mm
ECOSYSTEM INFORMATICS