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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
7
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.
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 observation systems.
“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
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 response to climate
change. 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
Figure 1. Changes in August sea surface temperature (SST)
of the French marine agency
at SOMLIT biodiversity/environmental observatory sites on
IFREMER at Brest, these two
the coast of France since 1900. Data are from ICOADS.
sites are not necessarily the
best places to look for the effects of climate change on biodiversity.
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
 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
GFS/NAM
hadobs.org
NCEP
Wave Watch III
GISS-Model E
SST
Solar Rad, LW
Rad, Rel Humid,
Air T, Wind Spd,
Atm Press,Precip
Wave height
SST
NOAA
NASA
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
NCEP
ESG
Flaterco.com
OSU
Use
Nowcast
Hindcast
Nowcast
Hindcast
Nowcast
Hindcast
Hindcast
Nowcast
Forecast
Nowcast
Forecast
Forecast
Hindcast
Forecast
Forecast
Climate
Scenario
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.
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 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 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. The
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.
average root mean square error of the
ICOADS reconstructions compared to
AVHRR was 1.18ºC, with a bias of +0.58ºC and maximum RMS error was 1.6ºC. 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.
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 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
Figure 3. Rates of change of sea surface
temperature 1900-2007, plotted by month and
geographic location. Blue/magenta colors
represent cooling.
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 climatebiodiversity effects south of Cape Hatteras
than north. These results indicate that all
locations are clearly not equal in
sensitivity to climate change, and that
studies of the effects of climate change on
biodiversity need to be targeted to
locations where climate is changing the most.
We propose to expand our coverage to the coastlines of GEO member countries to
examine the sensitivity of all coastal locations to global climate change, as a means to evaluate
the suitability of 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 4 year classes, and 10%
survival from one year to the
next (e.g. Wethey 1985). Each
adult produced 10000 larvae,
80% of which stayed in the
parental habitat, 10% dispersed
one location to the north, and
10% dispersed one location to
the south, equivalent to 10 km
dispersal per year (e.g. Crisp
1959). Each geographic
location had a maximum
population density of 10000
individuals.
Using demographic
Figure 4. Reconstructions and forecasts of geographic
models in conjunction with our
limits of ecosystem engineer species on the coasts of
hindcasts of sea surface
Europe, using ICOADS historical temperatures, and
temperature, we have made
GFDL A1B climate forecasts. Time range is 1850-2007
biogeographic hindcasts that
(ICOADS) and 2008-2100 (GFDL). Vertical axis is
can be tested with historical
geographic position on the same scale as the map on
data on geographic limits of
Fig. 2. Black lines are hindcast models of geographic
species. Reconstructions of
limits, red dots are historical records of the geographic
changes in biogeographic
limits. A: Semibalanus (barnacle); B: Chthamalus
ranges of Semibalanus are
(barnacle); C: Diopatra (tube-building annelid); D
shown in Fig 4 A. This is an
Arenicola (annelid bioturbator). The barnacle population
arctic species, whose southern
models were limited by winter temperatures, the annelid
limit has moved 300 km north
models were limited by summer temperatures.
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).
These reconstructions allow us to hindcast and forecast the changes in key
ecosystem engineer species. Semibalanus is a dominant space-occupier, that outcompetes 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). The implication of these 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 aquaculture will become immediately much more effective
in Spain and 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 Fragmentation Assessment
Tools for GEO BON
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 wild-harvested fish populations and
important forestry species.” We have been
studying the geographical population
genetics of Mytilus edulis and M.
galloprovincialis, aquaculture species of
Figure 5. Distribution of M. edulis alleles in
Europe.
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 intertidal rack culture in France. Commerce is so
large that a 60 ft (18 m) tractor trailer filled with mussels leaves Yerseke, Netherlands once per
hour, and that is only one single grow-out site.
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. We have used genetically homogeneous populations as sources of genetically
marked larvae to validate physical oceanographic models of larval dispersal (Gilg and Hilbish
2003a,b). Results of these studies indicate that despite planktonic dispersal stages lasting up to 1
month, populations can be strongly fragmented by oceanographic fronts and other hydrographic
barriers. The probability of larval penetration of barriers like Start Point and Lands End in SW
England is less than 5% (Gilg and Hilbish 2003a,b).
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. We propose to use this information to generate indices of population
fragmentation by hydrography, as a model for fragmentation of other coastal populations.
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
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
Figure 6. Mass mortality of Pisaster starfish predicted by
the ecologically
intertidal forecasts model. Unusually high temperatures above
dominant burrowing sea
urchin Echinocardium.
30°C predicted July 23-24 were coincident with mass
This species burrows
mortality.
rapidly through
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 were
hundreds of dead
Figure 7. Mass mortality of burrowing sea urchins,
Echnocardium per
Echinocardium, predicted by intertidal forecast model.
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 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
Figure 8. Geographic distribution of
beginning to be risks to commercial
temperatures risky for oyster Crassostrea
grow-out areas in the English Channel,
gigas survival. Left panel – summer sea
where temperatures have begun to
surface temperatures 1900-2007 on
exceed 19°C during the past decade
European coast. Contours are at 19°C, the
(Fig. 8). Upwelling on the Portuguese
temperature at which summer mortality of
coast has begun to collapse (see also
oysters increases dramatically (Samain &
Lima et al 2007), leading to higher risk
McCombie 2007).
in southern populations of this species
(Fig. 8). The prognosis for the future is
that aquaculture of Crassostrea on the
European coast will 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 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.
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.
We propose to apply these risk assessment tools to the coastlines of GEO member nations.
Tools for Establishment of Marine Reserves
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.