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Climate change and biodiversity: implications for Bay Area conservation
• Bay Area climate: historical patterns and future changes • Climate impacts on Bay Area vegetation • Climate heterogeneity and biodiversity • Management in the face of change
Bay Area Climate Change and Protected Areas Workshop ‘The Pepperwood Meeting’
July 19-21, 2010
Left to right: Miguel Fernandez, Jim Thorne, Mary Lee Hannah, Alicia Torregrosa, Stu Weiss, Mike Hamilton, Meg Krawchuk, Will Cornwell, Nicole Heller, Al Flint, David Ackerly, Lorrie Flint, Ryan Branciforte, Scott Loarie, Dave Conklin, Jason Kreitler, Sam Veloz, Lisa Micheli, Healy Hamilton, Max Moritz, Morgan Kennedy, Beth Sabo, Jim Johnstone
Missing: Kirk Klausmeyer, Lee Hannah, Diana Stalberg, Phil Duffy, Karen Gaffney, Adina Merenlender
, 3,000 Native Plant Species
Biodiversity hotspots in the United States
from Precious Heritage, 2000, Nature Conservancy and NatureServe
Global CO2 emissions – IPCC 4th assessment
Raupach et al. 2007 PNAS
A2
B1: stabilizing population, rapid technology conversion
growing population, high carbon energy sources
Figure 10.4
IPCC 2007, Fig. 10.4
Projections of future temperature – IPCC 4th assessment
A2
B1
Bay Area climate
summer max temperature
precipitation
water deficit
winter min temperature
PRISM climate layers downscaled to 270 m by Al and Lorrie Flint, USS
Temperature increase, averaged over Bay Area 1.6 – 4°C = 3 – 7 °F
Winter minimum temperatures Summer maximum temperatures
Year (end of 30 year periods)
+1.7 °C
+3.9 °C
+1.6 °C
+3.8 °C
preliminary analyses – please do not distribute without permission
Climatic Water Deficit: excess evaporative demand relative to available water
PET depends on temperature and
insolation Water availability
depends on precipitation, soil storage and runoff
CWD 2001
<775775 - 800800 - 825825 - 850850 - 875875 - 900900 - 925925 - 950950 - 975975 - 1000
!mm/yr"
Climatic Water Deficit
Annual evaporative demand that exceedsavailable water
Potential ‒ Actual Evapotranspiration
courtesy: Al and Lorrie Flint, USGS see Stephenson 1998 J. Biogeog.
Year (end of 30 year periods)
Clim
atic
wat
er d
efic
it (m
m)
Water deficits are projected to increase due to evaporative demand (whether precip goes up or down)
historical
preliminary analyses – please do not distribute without permission
The future is expected to be warmer and drier – the uncertainty is about how fast these changes occur
Summer maximum temperatures (°C)
Clim
atic
wat
er d
efic
it (m
m)
historical
preliminary analyses – please do not distribute without permission
Distance from the ocean is the primary influence on regional temperature patterns
Summer max temperatures
Distance to coast or bay
4°C per 10 km
1°C per 10 km
Winter minima: 0.35°C colder per 10 km
Summer maxima
prel
imin
ary
anal
yses
– p
leas
e do
not
dis
trib
ute
with
out p
erm
issi
on
B
A
Summer and winter temperatures are negatively correlated across the Bay Area
Historical 1971-2000 C
A
B
C
A
Due to the coastal-inland pattern, rising temperatures create novel climates throughout the Bay Area
Historical 1971-2000
GFDL A2 2041-2070
A
B
C
B
A
C
prel
imin
ary
anal
yses
– p
leas
e do
not
dis
trib
ute
with
out p
erm
issi
on
1900 1950 2000 2050 2100
1718
1920
2122
23
!
!
!
!
!
!!!!
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!!
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!
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GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
GFDL SRES A2 GFDL SRES B1
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
1970-2000 vs 2041-2070 1970-2000 vs 2041-2070
1970-2000 vs 2071-2100 1970-2000 vs 2071-2100
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
GFDL SRES A2 GFDL SRES B1
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
1970-2000 vs 2041-2070 1970-2000 vs 2041-2070
1970-2000 vs 2071-2100 1970-2000 vs 2071-2100
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
B1 A2
Mid-century
End-century
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
GFDL SRES A2 GFDL SRES B1
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
1970-2000 vs 2041-2070 1970-2000 vs 2041-2070
1970-2000 vs 2071-2100 1970-2000 vs 2071-2100
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
GFDL SRES A2 GFDL SRES B1
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
1970-2000 vs 2041-2070 1970-2000 vs 2041-2070
1970-2000 vs 2071-2100 1970-2000 vs 2071-2100
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
Future climates will rapidly exceed the range of recent historical variability
analysis and figure: Sam Veloz, PRBO
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
GFDL SRES A2 GFDL SRES B1
GFDL SRES A2 GFDL SRES B1
1970-2000 vs 2011-2040
1970-2000 vs 2041-2070 1970-2000 vs 2041-2070
1970-2000 vs 2071-2100 1970-2000 vs 2071-2100
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
SED0.2 - 2
2.1 - 3.9
4 - 5
5.1 - 9.9
10 - 14
Year
Aver
age
Tmax
(°C
)
1971 – 2000
±1 s.d. interannual variability
Berkeley CA small symbols: annual
large symbols: 30 yr means
preliminary analyses – please do not distribute without permission
figure: Johnstone and Dawson 2010 PNAS
Year
map: Loarie and Johnstone unpubl. please do not distribute without permission
Fog frequency, 2000-2010 Modis satellite imagery
Impacts on biodiversity: observation, experiments, models
Several, independent approaches to vegetation modeling agree: future climates favor shrub and grassland at the expense of forest
High resolution run of the MC1 vegetation model for the Bay Area
Analysis and figures: Dave Conklin, Conservation Biology Institute
present climate MIROC A2, end-century
conifer
hardwood forest
hardwood woodland
grassland
shrubland
please do not distribute without permission
Diana Stalberg et al. 2010 PLoS ONE (PRBO) Will Cornwell et al. in prep. (UC Berkeley)
Several, independent approaches to vegetation modeling agree: future climates favor shrub and grassland at the expense of forest
‘Random forest’ model of CalVeg types 800 m resolution, UCSC regional climate model
Predictive vegetation modeling of Bay Area vegetation 270 m downscaled climate, GFDL mid-century future
forest remaining
forest woodland
forest shrubland
please do not distribute without permission
source: Bay Area Open Space Council, Ryan Branciforte & Stu Weiss
Bay Area Vegetation Map Upland Habitat Goals Project
60 cover types 51 natural/semi-natural
30 m mapping units
Remote imagery + vegetation plots + expert opinion
Altamont Pass, Livermore CA
Vegetation 30m
Spatial downscaling to model vegetation-climate relationships
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PRISM climate 800m
0 2.5 51.25Kilometers
30 Arc Second GridOver 1/9 Arc Second DEM
1/9 sec DEM Mask
Meters
High : 691
Low : 94
Elevation 3m
800 m
Topoclimate influence on vegetation
Modeling Bay Area Vegetation Desired features: 1) small grain model with a realistic representation of
topography (30 m) 2) simultaneous model of all vegetation types 3) comparison with documented vegetation transitions
Predictive layers 1) Seasonal water deficit (270 m) Al and Lorrie Flint (USGS) 2) Potential annual insolation
(annual, 30 m) 3) Min Temp (270 m downscaled
from PRISM) 4) Max Temp (270 m downscaled
from PRISM) 5) Wind (100 m) 6) Soil Depth (STATSGO)
Modeling Bay Area Vegetation
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DED
DEF
DEG
DEH
DEI
JED
Vector of probabilities for each veg type in each
pixel
redwood
Doug fir
Montane hardwood Baccharis California bay live oak
Other
multinomial logistic regression
W. Cornwell et al. in prep. please do not distribute without permission
Relative probability of vegetation transition (GFDL A2, mid-century vs.
present)
The vulnerability of vegetation types is very patchy: high probabilities of change occur where vegetation patches are
near the edge of their climate envelope
W. Cornwell et al. in prep. please do not distribute without permission
Regional and topographic shifts in vegetation types Blue oak example
0 20 40 60 80
0.00
0.05
0.10
0.15
0.20
Distance from coast or bay (km)
Fre
quency o
f B
lue O
ak
05000
10000
15000
Insolation north-facing south-facing
Distance from coast or bay (km)
present
A2 mid-century
present
A2 mid-century
please do not distribute without permission
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GFDL A2 2041-2070 versus present
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Native vegetation transitions vs. alien invasions
vegetation transitions depend on: 1) mortality of existing mature plants 2) propagule sources for new species
source: Larry Workman QIN, Panoramio.com
?
Agents of mortality: Fire
Historical probability of fire 1950-2003
(climate-driven model) 2010-2039 (A2) 2070-2099 (A2)
16 GCM ensemble (A2 scenario): change relative to historical period
Figures: courtesy Meg Krawchuck and Max Mortiz, UC Berkeley Historical: Parisien and Moritz 2009 Ecol. Monogr.
Futures: Moritz et al. in review please do not distribute without permission
Agents of mortality: Disease
source: UC Davis; http://www.sciencedaily.com/releases/2007/08/070815145316.htm
Sudden oak death
source: Center for Invasive Species Research UC Riverside
Agents of mortality: Drought and pests
piñon pine mortality credit: Craig Allen, USGS
Documented vegetation transitions (coastal CA)
Cornwell, Sandel and Ackerly, unpublished please do not distribute without permission
Documented vegetation transitions vs. projected transitions
Cornwell, Sandel and Ackerly, in prep. please do not distribute without permission
Local diversity provides seed sources for vegetation shifts
Heterogeneous landscapes support a greater diversity of vegetation types
analysis and figures: Jason Kreitler, USGS and Nicole Heller, Climate Central
UHG Vegetation Layer # of veg classes/12km cell Climatic water deficit
Legend12km
<VALUE>0 - 150
151 - 400
401 - 500
501 - 600
601 - 700
701 - 800
801 - 900
901 - 1,000
1,001 - 1,100
1,101 - 1,257
LegendVarietyValue
High : 26
Low : 1
N 0 25 5012.5 Kilometers
CWD # veg types per 12km cell Veg map
100 300 500 700
05
1015
2025
RANGE of CWDN
umbe
r of V
eg T
ypes
please do not distribute without permission
Loarie et al. 2009 Nature
Velocity of climate change: how fast will populations need to move to offset rising temperature?
rate of climate change (°C/yr) ÷ spatial climate gradient (°C/km) = velocity (km/yr)
Eradication of invasives is more important than ever in
the face of changing climates!
sources: nps.gov, cal-ipc.org
thermal refugia
Implications for conservation and management
Large, climatically heterogeneous reserves are critical to maintain diverse local species pools as propagule sources for potential vegetation transitions
In restoration and revegetation projects, a diverse pool of species and genotypes may enhance success in the face of uncertain future climate
Implications for strategic acquisition priorities.... Stu and Ryan – next talk!