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Ecological forecasting and Hindcasting of responses to climate change: development of tools for
GEO-BON (Biodiversity Observation Network)
Table of Contents
Page Limit
Proposal Summary
1
Decision-making Activity – Description and Baseline Performance
2
Earth Science Research Results
1
Technical Approach
12
Transition Approach
1
Performance Measures
1
Anticipated Results
1
Project Management
2
Schedule
1
Statements of Commitment - Co-Is
as needed
Letters from End-User Organizations
4
Budget Justification: Narrative and Details
as needed
Facilities and Equipment
1
Curriculum Vitae:
Principal Investigator
2
Each Co- Investigator
1
Current/Pending Support
as needed
References and citations
as needed
Used
1
2
1
9
1
Proposal Summary (300 Words)
The Biodiversity Observation Network (BON) was established in April 2008 by the Group on
Earth Observations, to integrate biodiversity and climate data with ecological models and
forecasts, and to assess status and trends in biodiversity at levels of organization from genes to
ecosystems. We propose to support this activity in coastal marine habitats with ecological
forecasting, hindcasting and nowcasting tools derived from satellite and in-situ observation
systems. In order to assess and predict long-term trends in biodiversity, it is necessary to
establish observatories where data are regularly collected. The choice of these sites has a strong
influence on our ability to detect change in natural ecosystems. We propose to provide tools for
evaluating sites for inclusion in the BON, and for integrating satellite and in-situ observations
and modeling with biodiversity measurements. If biodiversity is affected by climate, then change
should be most easily detected at locations where change has historically been rapid, or at sites
where organisms live close to their physiological limits (i.e. are near their “tipping points”). We
have developed biogeographic change models for marine populations that integrate climate
change information with physiology and demography to hindcast and forecast changes in the
geographic distribution of species. We have also developed thermal risk assessment tools for
coastal habitats, which integrate the timing of low tides and the degree of solar exposure of
surfaces. We have developed nowcasting and forecasting tools to estimate body temperatures
experienced by organisms in the zone between the tide marks on ocean shores. We propose to
provide assessment tools for coastal habitats worldwide on spatial scales of 10-20 km, using
these hindcasting, nowcasting and forecasting methods. These tools will provide guidance for
establishment of long-term and short-term biodiversity monitoring sites. In addition they will
provide guidance for establishment of marine protected areas and marine reserves.
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Decision Making Activity – Description and Baseline Performance
The Biodiversity Observation Network (GEO BON) was established in April 2008 by the Group
on Earth Observations, to integrate biodiversity and climate data with ecological models and
forecasts, and assess status and trends in biodiversity at levels of organization from genes to
ecosystems. We propose to support this activity in coastal marine habitats with ecological
forecasting, hindcasting and nowcasting tools derived from satellite and in-situ observations.
“GEO BON has as its objective the improved delivery of information to decisionmakers …. The
main users of GEO BON will likely be countries (especially in relation to their obligations under
biodiversity related conventions) and their natural resource and biodiversity conservation
agencies, international organizations and the biodiversity-relevant treaty bodies, nongovernmental organizations (both national and international) in the fields of biodiversity
protection and resource management, and environmental and scientific research organizations
both in and out of academia.” (GEO BON 2008)
The GEO BON Draft Concept Document (GEO BON 2008) outlined goals for “detection of
change, trend analysis, forward projections, range interpolations and model-based estimations of
the supply of ecosystem services” at the levels of ecosystems, species, and genes.
Goals for monitoring ecosystems include
 “Mapping the distribution of terrestrial, freshwater and marine ecosystems”
 “detecting change to these ecosystems”.
Short term goals with regard to trends in distribution and abundance of species are:
 “establish a coordinated and sustained global sampling scheme for a large set of species,
selected to cover many aspects of biodiversity,”
 “implement a process that establishes distributional ranges for a large and representative
set of species.”
Long term goals for species include
 “Improve the early-warning function of GEO BON by developing forecasting scenarios
using a variety of models … that alert stakeholders to impending threats, such as regional
or global extinctions or outbreaks of invasive species, pests, or pathogens.”
Short term goals for monitoring genetics include quantification of
 “fragmentation of habitats to the point where they cannot support viable genetic
populations, which are then lost”, and
 “an observational system … in order to track changes in genetic diversity in a selected set
of species that are important in the supply of provisioning services: staple food crops,
domestic livestock and aquaculture species, and wild-harvested fish populations and
important forestry species.”
Goals for integrated modeling and assessment include
 “an integrative view…which encompasses different trends and their relationships. For
example … shellfish harvest may increase due to increasing seawater temperatures, but
shellfish quality may be compromised by toxic algal blooms stimulated by sewage
discharges from an increasing urban population,” and
 “prediction of future states, and … the use of scenarios for exploring the consequences of
particular hypothesized futures. In support of such efforts, GEO BON will provide the
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initial condition datasets, and trends in driver variables, as well as understanding of how
drivers are related to changes in biodiversity and ecosystems.”
Since GEO BON is still in the concept stage, this solicitation asked for proposals for satellite
observation products with associated models that would support GEO member nations and
participating organizations in their development of a Biodiversity Observation Network.
In order to assess and predict long-term trends in biodiversity, it is necessary to establish
observatories where data are regularly collected. The choice of these sites has a potentially
strong influence on the ability to detect change, just as the choice of meteorological stations
determines the pattern and direction of change in climate. To date, sites used for long term
monitoring have been chosen because of proximity to marine laboratories or coastal power
plants, rather than based on their environmental sensitivity. Some locations have been
historically relatively isolated from global change, and others have been strongly influenced by
it. For example, in France, the national marine observation network SOMLIT is collecting
biodiversity and
environmental data at 5 sites
on the Atlantic and English
Channel coasts. The sites at
Wimereux, Luc s/ Mer, and
Arcachon have seen
increases in sea surface
temperature to record levels
since the 1980s (Fig 1).
Therefore they are likely
sites for detection of
biodiversity change in
Figure 1. Changes in August sea surface temperature (SST)
at SOMLIT biodiversity/environmental observatory sites on
response to climate change.
the coast of France since 1900. Data are from ICOADS.
By contrast, the sites in NW
France at Roscoff and Brest
are slightly cooler than they were in the 1940s, due to upwelling. Thus, despite the 100 year
presence of a marine laboratory at Roscoff, and the location of the headquarters of the French
marine agency IFREMER at Brest, these two sites are not necessarily the best places to look for
the effects of climate change on biodiversity. Comparable situations exist in most other
countries.
For the decision-making process of the GEO BON to be data-based, it is necessary to
have both the data and the appropriate tools for integrating data into decision-making. NASA
and NOAA Earth Science products provide the historical environmental record that is essential
to this process. We propose to integrate NASA and NOAA products with hindcasting and
forecasting tools, to provide GEO BON members with data needed for decision making in
several areas:
 hindcasts and forecasts of sites for inclusion in monitoring networks, identifying locations
with large historical environmental change and those with large predicted future change
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



identification of important marine species to be included in monitoring efforts, based on their
ecosystem engineering effects on ecosystems and sensitivity to environmental change
identification of climatic drivers of range expansion and contraction of key species that
influence biodiversity change (invasives and ecosystem engineers)
quantification of connectivity in coastal habitats, using genetic changes in broad scale hybrid
zones as an indicator system
hindcasts and forecasts of sites for inclusion in marine reserves and protected areas,
identifying locations with small historical environmental change and small predicted future
change.
Earth Science Research Results
This proposal will use NASA and NOAA remote sensing sea surface temperature products and
cloud free high resolution daily products derived from them. For historical reconstructions, we
will use ICOADS sea surface temperature and products derived from it. We will also use
MODIS land surface temperature products. For climate scenario forecasts we will use output
from NASA, NOAA, and Hadley Centre models archived at the WCRP CMIP3 site on the Earth
System Grid. For short-term forecasts and early warning of threats to populations, we will use
NOAA weather and wave forecast data and Xtide (www.flaterco.com/xtide) as input to our
intertidal temperature model. We will use the NASA JPL DE-405 ephemeris in conjunction with
tide models to calculate decadal scale and continental scale risk of intertidal exposure to high
temperature conditions.
Organization
NASA
Data Source
MODIS
Data Type
SST
Archive
JPL
NASA
AMSR-E
SST
JPL
NASA NOAA
AVHRR
SST
JPL
UCAR/NCAR
UK Met
ICOADS
OSTIA
SST
SST
ucar.org
JPL
IFREMER
ODYSSEA
SST
JPL
Danish Met Inst
DMI_OI
SST
JPL
Hadley Ctr
NOAA
HadISST
SST
GFS/NAM
Solar Rad, LW
Forecast models Rad, Rel Humid,
Air T, Wind Spd,
Atm Press,Precip
Wave Watch III Wave height
GISS-Model E
SST
NOAA
NASA
hadobs.org
NCEP
NCEP
ESG
Use
Nowcast
Hindcast
Nowcast
Hindcast
Nowcast
Hindcast
Hindcast
Nowcast
Forecast
Nowcast
Forecast
Forecast
Hindcast
Forecast
Forecast
Climate
Scenario
5
NOAA
GFDL- CM2.X
SST
ESG
UK Met
Hadley CM3
SST
ESG
NASA
DE-405
ephemeris
Xtide
OTPS / Topex
Poseidon
Solar elevation
Solar azimuth
Tide Height
Tide Height
JPL
Flaterco
Oregon State U
Flaterco.com
OSU
Climate
Scenario
Climate
Scenario
Risk
Analysis
Forecast
Risk Anal
Technical Approach
In this project, we propose to provide tools for evaluating sites for inclusion in GEO
BON based on quantitative sensitivities of sites to climate change, and to provide tools for
integrating satellite and in-situ observations and modeling with biodiversity measurements at
established sites. We also propose to provide risk and early-warning assessment tools for key
habitats. We propose to focus on coastal marine habitats, and on ecosystem engineer species,
whose successes or failures control the dynamics of the coastal ecosystem and its biodiversity.
We focus on ecosystem engineers and keystone species, which are species that create habitat or
disturb habitat by their activities, or those that are ecologically dominant. The fates of entire
communities and ecosystems are tied to the success or failure of such species. Examples are
competitively dominant barnacles and mussels, habitat creating tube-building worms, and habitat
disturbers like bioturbating worms and burrowing shrimp. We focus on coastal marine habitats in
the zone between the tides, because these coastal habitats are extensively used for aquaculture in
many parts of the world, and serve reservoirs of biodiversity and as nursery grounds for
commercially important species.
Tools for Integrating Satellite and In-Situ Observations with Biodiversity Measurements
Biodiversity and species distribution modeling has been dominated by methods
developed for museum and collection-based data sets. These sorts of data tend to report only
presence of species, not their absence, because museum records do not include places where
species were not found. The methods developed for these sorts of data include GARP
(Stockwell and Peters 1999), and maximum entropy or MAXENT (Phillips et al. 2006). Such
methods allow modeling of the expected geographic range of a species, based on relationships
with environmental variables. These models do not incorporate physiological, population
dynamic or dispersal information into the predictions, nor do they include consideration of the
effects that environmental variables may have on organisms. For example air temperature may
be correlated to organism distribution, but actual body temperature (which is strongly influenced
by solar radiation) is a better metric of the effect of environmental temperature on organisms.
An alternative approach is to use physiological performance, as influenced by environment, to
predict geographic distribution (e.g. , Kearney and Porter 2004, Buckley 2008).
We have been developing a suite of methods for biogeographic hindcasting and
forecasting which are based on physiological performance measures, historical presence and
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absence data, biophysical models of body temperature, and population dynamics. In the sections
below, we propose the application of these methods to evaluation of sites for inclusion in
monitoring networks and marine reserves, reconstruction and forecasting changes in
biogeographic limits of species in response to climate change, and provision of early-warning
and longer-term warning of risk to natural populations from climate fluctuations and change.
Tools for evaluating coastal sites for inclusion in the GEO BON.
Historical change in climatic conditions can
serve as an index of suitability of sites for studies of
biodiversity response to climate change. If
biodiversity is partially controlled by climate,
change should be most easily detected at locations
where change has historically been rapid. Because
of the differences in oceanography among coasts,
not all sites are good candidates for such studies.
We propose to use the ICOADS ship of opportunity
data set to reconstruct worldwide coastal sea
surface temperatures for the past century in order to
establish the baseline rates of change in ocean
climate. Point data are transformed into monthly
Fig 2. February sea surface temperature on
the coast of Europe 1900-2007 from
ICOADS. Horizontal lines are at latitudes
30°, 35°, 40°, 45°, 50° N, corresponding to
map. Contours are at 2°C increments from
4° to 18°C.
4km resolution maps using a 12-point inverse
distance squared weighting method (Lima et
al 2007). Reconstructions were verified
against 20 years of AVHRR data. We have
generated maps of this sort for the period
1900-2007 on the US east and west coasts,
and the continental Atlantic coast of Europe
from France to Morocco.
Figure 3. Rates of change of sea surface
temperature 1900-2007, plotted by month and
geographic location. Blue/magenta colors
represent cooling.
These data are used to generate
contour plots of climate as a function of
position along the coastline on a centennial
time scale (Fig 2), and measures of the rates
of climate change as a function of geography
(Fig 3). These rates of change provide a
quantitative measure of the suitability of
coastal sites for studies of response of
populations and ecosystems to climate
change. Sites with low rates of climate
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change are unsuitable for such studies. Figure 3 represents the centennial rates of change of sea
surface temperature plotted by month and geographic location in Europe, the US west coast, and
the US east coast. Blue and magenta colors represent cooling, and other colors represent
warming. The coast of Europe is clearly a mosaic of sites with different rates of change
interspersed among one another. Most obvious changes are rapid warming in the Bay of Biscay
in summer, and rapid warming in the English Channel between October and December. Other
locations in Europe show much lower rates of change. Therefore climate effects on biodiversity
would be relatively easily detected in the lower Bay of Biscay and the English Channel, which
should be focal areas for the study of biodiversity responses to climate change in Europe,
especially in species affected by
warming winter conditions. On the
US west coast, there has been
rapid warming in central Oregon in
May and June, and rapid warming
south of Point Conception from
March to July. These areas appear
to be more sensitive to climate
change than other areas of the
coast, and there should be stronger
biodiversity changes expected in
these areas than elsewhere,
especially in species affected by
warming summer conditions. On
the US east coast, there has been
rapid winter warming in the mid
Atlantic states, rapid winter
cooling in winter south of Cape
Hatteras, and little change
elsewhere. On the US east coast,
there should be much stronger
climate-biodiversity effects south
of Cape Hatteras than north. These
Figure 4. Reconstructions and forecasts of geographic
results indicate that all locations
limits of ecosystem engineer species on the coasts of
are clearly not equal in sensitivity
Europe, using ICOADS historical temperatures, and
to climate change, and that studies
GFDL A1B climate forecasts. Time range is 1850-2007
of the effects of climate change on
(ICOADS) and 2008-2100 (GFDL). Vertical axis is
biodiversity need to be targeted to
geographic position on the same scale as the map on
locations where climate is
Fig. 2. Black lines are hindcast models of geographic
changing the most.
limits, red dots are historical records of the geographic
limits. A: Semibalanus (barnacle); B: Chthamalus
We propose to expand our
(barnacle); C: Diopatra (tube-building annelid); D
coverage to the coastlines of GEO
Arenicola (annelid bioturbator). The barnacle population
member countries to examine the
models were limited by winter temperatures, the annelid
sensitivity of all coastal locations
models were limited by summer temperatures.
to global climate change, as a
means to evaluate the suitability of
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coastal sites for inclusion in GEO BON monitoring efforts.
Tools for integrating satellite and in-situ observations and modeling with biodiversity
measurements in GEO BON
We propose to use historical MODIS, AVHRR and derived GHRSST temperatures, and
our ICOADS reconstructions of historical temperature conditions, in conjunction with
demographic models, to reconstruct the historical shifts in geographic range of ecosystem
engineer species on the world’s coastlines, in support of biodiversity assessments in GEO BON.
For nowcasts, we propose to use current MODIS and GHRSST temperatures as input to
demographic models. For future projections, we propose to use climate forecast scenarios from
NASA, NOAA and the Hadley Centre to establish baselines for ocean climate change. Our
approach has a great advantage over traditional niche modeling such as GARP and MAXENT
because it includes both presence and absence data and incorporates known physiological and
population dynamic constraints on biodiversity models. This modeling effort will provide
baseline information for GEO BON evaluation of changes in abundance and geographic range of
target species. The modeling approach is easily modified to accommodate new species with
different physiological constraints and different demographic characteristics.
Our biogeographic modeling approach is to use known physiological or demographic
performance constraints in conjunction with the temperature reconstructions, to hindcast changes
in the geographic distribution of species. For example, the north Atlantic barnacle Semibalanus
balanoides is known to suffer reproductive failure if winter temperatures exceed 10-12°C (Crisp
and Patel 1960, Barnes 1964). An age-structured population model of populations along the
entire European coast, including reproduction limited by temperature, and dispersal to
neighboring localities, was used to examine patterns of temporal change in geographic
distribution over the past century. Each spatial location had multiple year classes, and defined
survival from one year to the next, and a small fraction of each population dispersed each year to
neighboring locations.
Using demographic models in conjunction with our hindcasts of sea surface temperature,
we have made biogeographic hindcasts for keystone and ecosystem engineer species that can be
tested with historical data on geographic limits. Reconstructions of changes in biogeographic
ranges of Semibalanus are shown in Fig 4 A. This is an arctic species, whose southern limit has
moved 300 km north since 1870 (Fig 4 A). The tropical barnacle Chthamalus was expected to
shift much less based on our hindcasts, and the historical record of its known range limits agrees
well with our model results (Fig 4B). The tropical tube building annelid Diopatra has shifted its
northern limit on the French coast 300 km north since 1920, and has a gap in its distribution in
Portugal (Fig 4 C). The arctic bioturbating annelid Arenicola is known to begin anaerobic
metabolism at temperatures above 20°C (Pörtner et al 2006), which is likely to dramatically
reduce energy available for reproduction during hot summers. The model and observations
indicate that, consistent with our model, Arenicola has shifted its southern limit south on the
Portuguese coast since 1903, and has developed a gap in its distribution in the Bay of Biscay (Fig
4 D).
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These reconstructions allow us to hindcast and forecast the changes in key ecosystem
engineer species. Semibalanus is a dominant space-occupier, that out-competes other species in
the intertidal zone in northern areas (Connell 1961). Diopatra creates habitat for other species
by providing a refuge from predators (Woodin 1978). Arenicola excludes other species
including commercially important clams, by rapid sediment turnover and active porewater
pumping (Volkenborn & Reise 2007,
Wethey et al 2008). In addition, it
provides key ecosystem services by
controlling rates of organic matter
decomposition (Volkenborn et al 2007).
The implication of the biogeographic
models is that Diopatra will invade the
North Sea and Arenicola will become
extinct in southern Europe early in the
21st century, causing fundamental shifts
in the dynamics of sedimentary
assemblages over the entire European
coast, with enormous consequences for
biodiversity and human interaction with
coastal systems. For example, clam
Figure 5. Distribution of M. edulis alleles in
aquaculture will become immediately
much more effective in Spain and
Europe.
Portugal, and operators will no longer
have to actively remove Arenicola from
their growout beds. In northern Europe,
recruitment of aquaculture clams will improve because of the refuge from small predators
afforded by Diopatra.
We propose to apply this approach to key ecosystem engineer species, on both hard and
soft substrata, along the coastline of GEO member countries, in support of GEO BON
biodiversity modeling efforts. These methods are superior to MAXENT and GARP models
because they explicitly include physiological performance, dispersal, and demography in
predicting distribution and abundance.
Genetic Assessment Tools for GEO BON JERRY – can you work on this section?
GEO Bon goals for genetic assessment include “an observational system … in order to
track changes in genetic diversity in a selected set of species that are important in the supply of
provisioning services: staple food crops, domestic livestock and aquaculture species, and wildharvested fish populations and important forestry species.” We have been studying the
geographical population genetics of Mytilus edulis and M. galloprovincialis, aquaculture species
of enormous worldwide importance, on the coasts of the USA, Japan, and Europe (Hilbish et al
1996, 200X). These species are grown in raft culture in Mexico, and Spain, in subtidal beds in
the UK, Netherlands, and Germany, and in rack culture in France. Mytilus production in Europe
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exceeds 500,000 tons per year, with a value of $750M. Oyster production in France alone is on
the order of 150,000 tons per year with a value of $250M (Buestel 2005).
The Mytilus edulis/galloprovincialis/trossulus complex of mussels all form hybrids in
natural populations. We have been studying the geography of the hybrid zones in Europe, North
America, and Asia. An example of the complexity of the system can be seen in Europe (Fig 5).
There are nearly pure populations of M. edulis on the Bay of Biscay and the eastern English
Channel in France, with transitions to nearly pure M. galloprovincialis in Iberia and NW France.
In southern France, over a distance of 100km, the M. edulis allele frequency drops from 80% to
5% (Fig X). Throughout the Atlantic coast of Iberia, approximately 2-5% of the alleles in the
populations are M. edulis although the last pure edulis individual was found on the France/Spain
border (Fig. X). In NW France, similar transitions occur, where populations shift from 80%
edulis to 100% galloprovinicalis and back to 100% edulis over 250 km.
The repeated transitions of M.edulis to M. galloprovincialis and back along the coasts of
Europe provide an opportunity to use mussels as indicators of population and genetic
fragmentation. These populations provide one of the best models for population response to
climate change, because they are a replacement series of tropical and temperate species. In all
places where there are mussel beds, we know mussels can survive, and the genetic structure
allows us to quantify the changes in representation of tropical versus temperate species.
We have mapped the genetics of mussel populations on both coasts of the USA, Europe
from Gibraltar to northern Scotland, Japan from Hokkaido to northern Honshu, all at spatial
scales of 50 to 100 km, and on temporal scales of 5 to 30 years. We propose to use these results
in the assessment of the effects of climate change on species of enormous economic importance.
Risk and Early-Warning Assessment Tools for GEO BON
We have developed autonomous biomimetic data loggers for in-situ observation of
thermal stresses on intertidal mussels and limpets (Helmuth et al., Lima et al.), as well as for
observation of stresses in sedimentary habitats. These devices are based on Onset Tidbit and
Dallas Ibutton data loggers. We have used these data logger observations to validate our
biophysical models of intertidal heat transfer and body temperature. Our models for mussels are
as good at representing daily body temperature dynamics as the loggers are at measuring them
(Gilman et al. 2006). The models use a combination of satellite observations and weather data
(Table 1) to hindcast, nowcast, and forecast body temperatures as a function of species, position
on the shore and geographic location. Sea surface temperatures are derived from MODIS,
AMSR-E and other satellite sources.
To generate early warning of catastrophic conditions, we make 7-day forecasts of
intertidal temperature conditions, using data from NOAA weather forecast models as inputs. All
weather forecast models have a land surface component, which simulates the daily temperature
fluctuations in the vegetation and ground. We have developed new ‘vegetation types’ which we
have included in the standard land surface model (NOAH, Miller et al. 2006) used by NOAA in
its forecasts. Our vegetation type, intertidal mussel bed, replaces the deciduous forest or
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grassland vegetation type in the NOAH land surface model, and is used to simulate the thermal
transients experienced by intertidal organisms. The mussel bed model is used as a proxy for
stressful conditions on intertidal shores. We generate 7-day forecasts at 100km spatial scales
over the coasts of the USA, Europe, South Africa, New Zealand, and Hokkaido Japan every day,
using the midnight GMT output from the NOAA weather forecasts. This approach has predicted
two mass mortality events
that we are aware of. A mass
mortality of keystone
predator starfish occurred on
the Oregon coast in the
summer of 2006. Our
forecasts predicted
anomalously high
temperatures in Oregon
during the time of the die-off.
where thermal
extremes were not
predicted and did
not occur.
The second mass
mortality event that we are
aware of took place in New
Zealand in February 2007,
where there was a
catastrophic die-off of the
ecologically dominant
Figure 6. Mass mortality of Pisaster starfish predicted by
burrowing sea urchin
intertidal forecasts model. Unusually high temperatures above
Echinocardium. This species
30°C predicted July 23-24 were coincident with mass
burrows rapidly through
mortality.
sediments, dramatically
altering biogeochemical
fluxes of nutrients, and
altering the dynamics of
other species in the system
(Lohrer et al 2004). On Feb
21 2007, temperatures above
30°C were predicted for the
low intertidal zone in the
Leigh region of the North
Island. One day later, there
Figure 7. Mass mortality of burrowing sea urchins,
were hundreds of dead
Echinocardium, predicted by intertidal forecast model.
Echnocardium per meter
square in the intertidal zone
in this location. This location has been a primary research site for researchers from the New
Zealand Institute for Water and Atmospheres (Lohrer et al 2004), and this event was
unprecedented.
These successful predictions of stressful conditions leading to catastrophic mortality are
indications that our forecasting approach is very powerful as an early-warning tool, on time
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scales of 2 to 7 days. We propose to expand our coverage of intertidal sites to intertidal shores
worldwide, with high density coverage near sites of interest to GEO BON, specifically near
existing biodiversity monitoring locations, and locations under consideration by GEO BON.
Another approach to analysis of
mortality risks is based on our historical
reconstruction methods (see above). An
example of its utility can be seen in
populations of commercially important
oysters, Crassostrea gigas, on the European
coast. This species suffers mass mortality
under hot summer conditions, with the
highest risk at temperatures above 19°C
(Samain and McCombie 2007). Our
historical reconstructions indicate that
mortality risks have been increasing during
the 20th century in the lower Bay of Biscay,
and that now there are beginning to be risks
to commercial grow-out areas in the English
Channel, where temperatures have begun to
Figure 8. Geographic distribution of
exceed 19°C during the past decade (Fig. 8).
temperatures risky for oyster Crassostrea
Upwelling on the Portuguese coast has
gigas survival. Left panel – summer sea
begun to collapse (see also Lima et al 2007),
surface temperatures 1900-2007 on
leading to higher risk in southern
European coast. Contours are at 19°C, the
populations of this species (Fig. 8). The
temperature at which summer mortality of
prognosis for the future is that aquaculture
oysters increases dramatically (Samain &
of Crassostrea on the European coast will
McCombie 2007).
be limited to cool upwelling zones in France
and farther north, with an expected collapse
of the industry in Iberia and southern
France. Since oysters are competitively dominant members of the intertidal zone, and overgrow
nearly everything in areas where they are under cultivation, this is likely to have a dramatic
effect on biodiversity.
These physiologically based approaches to
risk can be applied most readily to aquaculture
species, for which there are large bodies of
physiological data. We propose to provide tools
of this sort which can be adapted to other species
as such data become available.
We have also developed thermal risk
assessment tools for coastal habitats, which
integrate the timing of low tides (OTPS, Egbert
and Erofeeva 2002) and the degree of solar
exposure of surfaces from solar system ephemeris
Figure 9. Intertidal exposure risk
model of southern England and
northern France, derived from a tide
model and solar system ephemeris.
Warm colors indicate high risk of
exposure to hot conditions, cool
colors indicate low risk.
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simulators, (Horizons, http://ssd.jpl.nasa.gov/horizons.html). These analyses indicate sharp
boundaries between areas of high and low thermal risk. Figure 9, for example, indicates that
there is high risk of mid summer low tide exposure in the western English Channel, with a sharp
boundary crossing the channel between Devon in SW England and Cherbourg on the Contentin
Peninsula in France. This boundary is also associated with sharp biodiversity change; to the west
of this line there are more subtropical species than to the east. Under conditions of climate
change we predict more rapid changes with loss of temperate species and incursion of tropical
species into the higher risk regions of this map (warm colors) than into the low risk regions.
Therefore monitoring locations for BOM in Europe should include these identified areas of high
risk in NW France and SW England where rapid biodiversity change is more likely than
elsewhere.
We propose to enhance this risk assessment tool by incorporating our biophysical models of
body temperature, and then to apply these risk assessment tools to the coastlines of GEO member
nations.
Tools for Establishment of Marine Reserves – Brian can you work on this section?
Marine reserves are a key component of worldwide efforts to preserve marine
biodiversity. For this goal to be realized over the long term, it is essential that reserves be
located in regions where conditions are likely to be suitable for at least 50 to 100 years into the
future, rather than being suitable only today. We propose to use our historical hindcasting and
climate scenario forecasting methods to provide tools for identification of sites that have changed
little over the past 100-150 years, and that are predicted to change very little in the future. These
quantitative approaches are critical because climate changes are not linear with latitude or time
(Figs 2, 3), so simple extrapolation of thermal rise from hemisphere averages of climate models
do not work. We propose to carry out this evaluation on the coastlines of the GEO member
nations.
Integration of Results into Decision-Making Activity and Tests of Integrated System
Venkat – can you work on this?
Approach to Quantify Performance, Risks and Uncertainties
Venkat – can you work on this?
Approach to Quantify Socioeconomic Value
Challenges and Risks
Transition Approach / Activities – 1 page
Specific activities to enable end-users to adopt the enhancements. We propose to host
meetings with end users at the beginning, middle, and end of the project, first to determine exact
user needs, to exchange ideas regarding tool structure, and to provide training in tool use. We
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have chosen end users with government and non-governmental organizations in the USA,
France, and Germany for these activities.
Anticipated Results – 1 page
Project Management – 1 page
The principal investigator will be in charge of overall project management.
Schedule – 1 page
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