john j. kineman physical scientist/ecologist ( national geophysical data center) research associate...

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.”


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.


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


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


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

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temp precip swhc PAR LUI [N] elev Vrel PET LAIPdens

Add data layer / time period

Remove data layer

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linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear

Input Output

Decadal-Annual-Seasonal--Monthly--Daily-Hourly

Tim

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end time

time step(run avg.)

Sp

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link FD withspatial scale

Preview

Save Model and Execute

Set preview background image

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FD = 1.3420

Niche Model

T

P

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Adaptive Ecological Mapping

PotentialEco-units

improved data, time seriesnew measureshigher resolutionerror correction

validation

Iterationand validation Revisions

Environmental Database


Se

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Resp

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ÿ Define Classesÿ Define trainingÿ Define thresholds System defined

User Definition Delineation Method Cluster analysisÿ Maximum liklihoodÿ Baysian probabilitiesÿ gradient analysis

OutputInput


Tim

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Sca

le

start time

end time

time step(run avg.)

Sp

ati

al

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le o

f u

nit

Coarse------

--Fine

Lin

e f

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l

dim

en

sio

n (

FD

)

1.2D---------1.7D


Preview



Previous Next

Reset defaults

FD = 1.3420


Se

lect

da

ta l

ay

ers



Remove data layer

Se

lect

da

ta l

ay

ers


Se

lect

da

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Remove data layer

100100507550504576864525

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Ra

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10<23300<700==<10====<2050<

Resp

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fun

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Resp

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User Definitionÿ Define Classesÿ Define trainingÿ Define thresholds System defined


Delineation Method Cluster analysisÿ Maximum liklihoodÿ Baysian probabilitiesÿ gradient analysis

OutputInput


Tim

e

Sca

le

start time

end time

time step(run avg.)


Tim

e

Sca

le

start time

end time

time step(run avg.)

Sp

ati

al

Sca

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f u

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Coarse------

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Sp

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1.2D---------1.7D


Lin

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racta

l

dim

en

sio

n (

FD

)

1.2D---------1.7D


Preview



Previous Next

Reset defaults

FD = 1.3420Preview



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


Model Test

Eastern Hardwood (T,P,E)


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


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


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


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


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


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.)


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


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


john j. kineman physical scientist/ecologist ( national geophysical data center) research associate...

Documents

ecosystem informaticsreport

ecosystem informaticspcast

ecosystem capacity

ecosystem domain

ecosystem informatics

ecosystem types page

range of goods

human lifebiological