The Dance of Pattern

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The Dance of Pattern Process along the Road to
PredictionChange its Prediction in
BiologyorNASA Biodiversity Research to
Ecological Forecasting

• Woody Turner
• Biodiversity Ecological Forecasting
• Team Meeting
• Westin Grand Hotel
• Washington, DC
• August 29, 2005

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Nothing has changed since the Greeks. (Mary
Helen Lowry, 1978)
On the origin of species over 2300 years ago
Aristotle 384-322 BC
Empedocles c.495-c.435 BC
Another matter which must not be passed over
without consideration is, whether the proper
subject of our exposition is that with which the
ancient writers concerned themselves, namely,
what is the process of formation of each animal
or whether it is not rather, what are the
characters of a given creature when formed.
For the process of evolution is for the sake of
the thing finally evolved, and not this for the
sake of the process. Empedocles, then, was in
error when he said that many of the characters
presented by animals were merely the results of
incidental occurrences during their
development. (Aristotle, De Partibus
Animalium Book I, Chapter 1)
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The concept of pattern or regularity is central
to science. Pattern implies some sort of
repetition. The existence of repetition means
some prediction is possiblehaving witnessed an
event once, we can partially predict its future
course when it repeats itself. Robert MacArthur
in Geographical Ecology 1972
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A Fundamental Challenge for Biology
(http//ccinfo.ims.ac.jp/periodic/periodic.jpg)
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Forecastings Legacy, e.g. Weather

• 340 BC Aristotles Meteorologica
• 1590s Galileos thermometer
• 1643 Torricellis barometer
• 1860s Telegraphs link weather stations
• 1904-1922 Numerical weather forecasts
• 1920s Invention of the radiosonde
• 1960s First meteorological satellites improved
  hurricane warnings
• 1980s-1990s Coupling of atmosphere/ocean/land
  surface models (SiB, BATS?biophysics) early
  climate forecasts (ENSO predictions)
• Mid to Late 1990s Dynamic Global Vegetation
  Models for GCMs (e.g. LPJ, Triffid, IBIS)

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A Plethora of Biodiversity Data
GenBank
Forest Inventory and Analysis National Program
IUCN Red List
World Database on Protected Areas
The Species Analyst
Global Mammal Assessment
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• Models allow
• Assimilation of data from a number of
  sources/platforms
• Translation of our understanding of physical
  ecological processes into numerical form
• Tests of our understanding of these processes in
  the form of predictions

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ESMF A Tool for Model Coupling
(slide from NASA/Don Anderson)
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Two of Sciences Top 25 Questions
Systems approach. Circuit diagrams help clarify
nerve cell functions.


Science, Vol 309, 1 July 2005
gt What Determines Species Diversity?
CREDIT MICHAEL T. SHIPLEY
gt How Will Big Pictures Emerge From a Sea of
Biological Data?
New institutions around the world are gathering
interdisciplinary teams of biologists,
mathematicians, and computer specialists to help
promote systems biology approaches Sounds a
little like ESMF
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Some Other Big Questions

• How does climate variability affect the abundance
  of organisms?
• How does climate change affect the distribution
  of organisms?
• Are the organisms in communities fungible, e.g.
  how stable are trophic webs at retaining their
  function?
• How does biodiversity relate to the functioning
  of ecosystems what are the effects of spatial
  scale?
• From all of the above, what are the hottest of
  the hot spots for retaining maximum species
  diversity?
• What are the best RS proxies for ecosystem
  diversity and ecosystem health (e.g., NDVI, NPP,
  soil moisture, SST/SSH location of fronts,
  vegetation structural complexity, rates of
  disturbance, rates of upwelling, etc.)? And what
  scales are necessary?
• Can we catch evolution in action by first
  correlating environmental changes at
  landscape/broader scales with genomic/proteomic
  changes?

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Outside In Niche Definition Statistics
(with thanks to Robert MacArthur)
Productivity
Patterns of Species Diversity
Environmental Stability (e.g., Climate)
Habitat Structure
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Using Climate (Temperature Moisture) to Predict
Vegetation Holdridge Life Zones
(http//www.icsu-scope.org/downloadpubs/scope56/im
age/Fig16.1.gif)
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The Physical Drivers of Life
Nemani et al. 2003. Science 30015601563.
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Inside Out Physiology/Energetics to Niche
Energy Budget f ( Feeding, Resting, Migratory
Flight)
Challenge Can we link Outside In Inside
Out Approaches?
(Farmer and Wiens, 1998)
Rn - H - lE M 0
(figures courtesy of GSFC/Jim Smith)
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Biological Drivers (e.g., competition
predation) Trophic Models To Understand
Relationships Among Organisms
http//www.chadevans.co.uk/asite/Alevel/u03/ln/ene
rgyflow2_files/image005.gif
Little Rock Lake in Wisconsin produced by Neo D.
Martinez of San Francisco State University,
Romberg Tiburon Center for Environmental Studies
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Big Questions Biodiversity Ecosystem Function
( Scale)
Waide et al. surveyed 200 relationships between
species richness and productivity in different
systems (aquatic and terrestrial) and found 30
unimodal, 26 positive linear, 12 negative
linear, and 32 not significant. (R.B. Waide et
al. in Annu. Rev. Ecol. Syst. 1999, 30257-300)
Species Richness
Species Richness
NPP
NPP
Issue of scale? At smaller scales see more niche
partitioning competition effects while at
larger scales see more environmental effects
(the good life)?Melinda Smith at ESA Annual
Mtg. 2005 Or is it a matter of Experimental
systems vs. Natural systems?Tom Stohlgren at ESA
Annual Mtg. 2005
Non- Native Species Richness
Non- Native Species Richness
Native Species Richness
Native Species Richness
(backdrop http//cedarcreek.umn.edu/research/biod
iversity.html)
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Big Question Climate Variability Abundance
(Source UME/Fei Chai)
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Big Question Disturbance Trophic Web Stability
Food Webs Are Dynamic. The Players Change But The
Plot Remains.
The food web of Tuesday Lake, 1984. The width of
the horizontal bars shows the body mass (log10
kg), number (log10 individuals per m3), and
biomass (log10 kg/m3), respectively, of each
species. The vertical positions of the species
show trophic height (20). Despite a major change
in species composition, following a manipulation,
this energetic setup of the food web remained
roughly the same (19). T. Jonsson, J. E. Cohen,
S. R. Carpenter, Adv. Ecol. Res. 36, 1 (2005)
cited in Science by de Ruiter et al. (2005)
30968-71.
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Big Question Molecular Data Hindcasting Life
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Where To?
Feedbacks to climate us start here.
Helicobacterium pylorii Genome from http//biocrs
.biomed.brown.edu/Books/Chapters/Ch2038/Pylori-Ge
nome.gif
(from USGS Global Visualization Viewer)
A Grand Synthesis for the 21st Century
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As usual, the oceanographers are ahead!
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Grand Challenge Understanding this Variety
BIOGEOCHEMISTRY
BIODIVERSITY Terra Incognita
In understanding lies the road to
prediction (e.g. if we want to understand the
biogeochemical cycling of carbon /or other
elemental cycling, we need to know other half of
ecosystem equation)
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Integrated global analyses
Carbon Cycle and Ecosystems Roadmap
Human-Ecosystems-Climate Interactions (Model-Data
Fusion, Assimilation) Global Air-Sea Flux
Sub-regional sources/sinks

T
Funded
High-Resolution Atmospheric CO2
Unfunded
Process controls errors in sink reduced
Southern Ocean Carbon Program, Air-Sea
CO2 Flux
Partnership
Models w/improved ecosystem functions
T Technology development
Physiology Functional Types
T
Reduced flux uncertainties coastal carbon
dynamics
Coastal Carbon
Field Campaign
Reduced flux uncertainties global carbon
dynamics
Global Ocean Carbon / Particle Abundance
Goals Global productivity and land cover change
at fine resolution biomass and carbon fluxes
quantified useful ecological forecasts and
improved climate change projections
Vegetation 3-D Structure, Biomass, Disturbance
T
Terrestrial carbon stocks species habitat
characterized
CH4 sources characterized and quantified
Global CH4 Wetlands, Flooding Permafrost
Knowledge Base
Global Atmospheric CO2 (OCO)
Regional carbon sources/sinks quantified for
planet
N. American Carbon Program
N. Americas carbon budget quantified
Effects of tropical deforestation quantified
uncertainties in tropical carbon source reduced
Land Use Change in Amazonia
2002 Global productivity and land cover
resolution coarse Large uncertainties in
biomass, fluxes, disturbance, and coastal events
Models Computing Capacity
Process Understanding
Case Studies
Improvements
P
Land Cover (Landsat)
LDCM
Land Cover (OLI)
Systematic Observations
Ocean Color (SeaWiFS, MODIS)
Ocean/Land (VIIRS/NPP)
Ocean/Land (VIIRS/NPOESS)
Vegetation (AVHRR, MODIS)
Vegetation, Fire (AVHRR, MODIS)
IPCC
IPCC
2010
2012
2014
2015
2008
2002
2004
2006
Global C Cycle
Global C Cycle
NA Carbon
NA Carbon
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Ecological Forecasting Roadmap
Integration of remotely-sensed data with various
model types, e.g. ecosystem, ecological niche,
population habitat viability, biogeography,
biogeochemistry, regional ocean atmospheric
models -- as well as the development of new
predictive models
If-Then Scenarios for Ecosystem Responses to
Change/Disturbance
Species Distribution Forecasting System gt
biodiversity/stability/ productivity links
Ongoing global land cover change product global
precipitation data
Soil surface moisture, sea surface salinity,
global river discharge measurements
Species distribution models with improved accuracy
Operational SERVIR, Protected Areas Management
System, Marine Fisheries Forecasting System
DSSs
Vegetation structure disturbance from active
sensors new data on physiology functional
groups (hyperspectral/fluorescence)
Prototype Marine Fisheries Forecasting System DSS
for fisheries management also Protected Areas
Management System DSS incorporating species
habitat demographic data into a planning tool
Regional ocean models coupled to ecosystem
models global land cover change product
Socioeconomic Impact
Initial operation of Regional Monitoring
Visualization System DSS (SERVIR) for
environmental management sustainable
development in Central America
Prototype predictive models linking
remotely-sensed environmental parameters to
changes in terrestrial aquatic ecosystems
Current trajectory
Operational ecological forecasting systems
supporting environmental natural resource
management for sustainable development
Assessment of land cover change/climate impacts
on ecosystems
EOS global land cover observations early
coupling of regional climate ecosystem models
Steady improvement in models linking functional,
structural, spatial, temporal environmental
measurements (ongoing measurements include land
cover, ocean color, primary productivity)
SRTM
TRMM
Aqua
NPP/VIIRS
NPOESS
Landsat 7
Terra
GPM
Aquarius
HYDROS
2009
2003
2013
2005
2011
2007
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Ecological ForecastingObservations Models for
Global Management
EARTH SYSTEM MODELS

• Ecological Niche (GARP)
• Scalable spatio-temporal models a la NREL
• Regional Ocean Models Empirical Atmospheric
  Models coupled with ecosystem trophic models
• Ecosystem (ED, CASA)
• Population Habitat Viability Assessment
  (VORTEX, RAMAS GIS)
• Biogeography (MAPSS, BIOME3, DOLY)
• Biogeochemistry (BIOME-BGC, CENTURY, TEM)

DECISION SUPPORT TOOLS
SERVIR (Spanish acronym for Regional
Visualization Monitoring System)
Predictions
Predictions

• Monitor changes in land cover, weather, fires
  to assist the sustainable management of the
  Mesoamerican Biological Corridor

VALUE BENEFITS

• Species Distributions
• Ecosystem Fluxes
• Ecosystem Productivity
• Population Ecology
• Land Cover Change

• Management of a global hotspot of biodiversity,
  i.e. Mesoamerica, at a regional scale through the
  coordination of the activities of 7 countries - a
  model for other regions.
• Predict the impacts of changing land use patterns
  climate on the ecosystem services that support
  all human enterprises.
• Develop ecological forecasts with reliable
  assessments of error.

(example models)
Protected Area Management (with VISTA TOPS)

• Coordinate multi-NGO effort to pool resources for
  monitoring protected areas per CBD 2010 goal
• Link to Presidents illegal logging initiative
  CBFP

Data

• Land Cover/Land Use Disturbances (e.g., fire)
• Species Composition
• Biomass/Productivity
• Phenology
• Vegetation Structure
• Elevation
• Surface Temperature
• SST, SSH, Circulation, Salinity
• Atmospheric Temp.
• Soil Moisture
• Precipitation
• Winds

EARTH OBSERVATORIES

• Land cover MODIS, AVHRR, Landsat, ASTER, ALI,
  Hyperion, IKONOS/QuickBird
• Topography/Vegetation Structure SRTM, ASTER,
  IKONOS, LVIS, SLICER, Radars
• Primary Productivity/Phenology AVHRR, SeaWiFS,
  MODIS, Landsat, ASTER, ALI, Hyperion, IKONOS,
  QuickBird, AVIRIS
• Atmosphere/Climate AIRS/AMSU/HSB, TRMM (PR,
  LIS, TMI), AVHRR, MODIS, MISR, CERES, QuikScat
• Ocean AVHRR, SeaWiFS, MODIS, TOPEX/Poseidon,
  JASON, AQUARIUS
• Soils AMSR-E, AIRSAR

• Impact of ENSO PDO Events on Fisheries

• Combine physical ocean models ecosystem
  trophic-level models to predict impacts of
  climatological changes on regional fisheries

Observations
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Future Mission
January 25, 2005
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Goals for Meeting

• Bring people together
• Suggest additional questions
• Define technological needs
• Make programmatic connections
• Among research activities
• Between research educational activities
• Between projects Center activities
• Among agencies